From c794b53c487220d625d3ec3806b6696531e59d7a Mon Sep 17 00:00:00 2001 From: Fangyin Cheng Date: Wed, 10 Apr 2024 03:24:21 +0800 Subject: [PATCH 1/3] feat: Release agent SDK --- .mypy.ini | 12 +- Makefile | 6 +- dbgpt/_private/config.py | 3 +- dbgpt/_private/pydantic.py | 2 + dbgpt/agent/__init__.py | 32 +- dbgpt/agent/actions/__init__.py | 1 + dbgpt/agent/actions/action.py | 95 ++- dbgpt/agent/actions/aim_recognition_action.py | 0 dbgpt/agent/actions/blank_action.py | 38 +- dbgpt/agent/actions/chart_action.py | 56 +- dbgpt/agent/actions/code_action.py | 61 +- dbgpt/agent/actions/dashboard_action.py | 66 +- dbgpt/agent/actions/indicator_action.py | 81 +- dbgpt/agent/actions/plugin_action.py | 89 +- dbgpt/agent/agents/__init__.py | 0 dbgpt/agent/agents/agent.py | 193 ----- dbgpt/agent/agents/agent_new.py | 150 ---- dbgpt/agent/agents/agents_manage.py | 100 --- dbgpt/agent/agents/base_agent.py | 796 ------------------ dbgpt/agent/agents/base_agent_new.py | 623 -------------- .../expand/Indicator_assistant_agent.py | 35 - dbgpt/agent/agents/expand/__init__.py | 0 .../agents/expand/code_assistant_agent.py | 67 -- .../expand/dashboard_assistant_agent.py | 38 - .../agents/expand/data_scientist_agent.py | 87 -- .../agents/expand/plugin_assistant_agent.py | 61 -- .../retrieve_summary_assistant_agent.py | 608 ------------- .../agents/expand/summary_assistant_agent.py | 70 -- dbgpt/agent/agents/llm/__init__.py | 0 dbgpt/agent/agents/llm/llm.py | 109 --- dbgpt/agent/agents/llm/strategy/__init__.py | 0 dbgpt/agent/agents/role.py | 81 -- dbgpt/agent/agents/user_proxy_agent.py | 18 - dbgpt/agent/common/__init__.py | 0 dbgpt/agent/common/schema.py | 142 ---- dbgpt/agent/core/__init__.py | 1 + dbgpt/agent/core/agent.py | 304 +++++++ dbgpt/agent/core/agent_manage.py | 107 +++ dbgpt/agent/core/base_agent.py | 765 +++++++++++++++++ dbgpt/agent/{agents => core}/base_team.py | 108 ++- dbgpt/agent/core/llm/__init__.py | 1 + dbgpt/agent/core/llm/llm.py | 116 +++ .../agent/{agents => core}/llm/llm_client.py | 39 +- dbgpt/agent/core/llm/strategy/__init__.py | 1 + .../{agents => core}/llm/strategy/priority.py | 20 +- dbgpt/agent/core/role.py | 128 +++ dbgpt/agent/core/schema.py | 27 + dbgpt/agent/core/user_proxy_agent.py | 19 + .../agent/expand/Indicator_assistant_agent.py | 42 + dbgpt/agent/expand/__init__.py | 1 + dbgpt/agent/expand/code_assistant_agent.py | 120 +++ .../agent/expand/dashboard_assistant_agent.py | 57 ++ dbgpt/agent/expand/data_scientist_agent.py | 131 +++ dbgpt/agent/expand/plugin_assistant_agent.py | 72 ++ .../retrieve_summary_assistant_agent.py | 596 +++++++++++++ dbgpt/agent/expand/summary_assistant_agent.py | 42 + dbgpt/agent/memory/__init__.py | 1 + dbgpt/agent/memory/base.py | 194 +++-- dbgpt/agent/memory/default_gpts_memory.py | 56 +- dbgpt/agent/memory/gpts_memory.py | 50 +- dbgpt/agent/memory/gpts_memory_storage.py | 443 ---------- dbgpt/agent/plan/__init__.py | 36 + dbgpt/agent/plan/awel/__init__.py | 4 + .../plan/awel}/agent_operator.py | 175 ++-- .../plan/awel}/agent_operator_resource.py | 49 +- dbgpt/agent/plan/awel/team_awel_layout.py | 261 ++++++ .../agent/team => agent}/plan/plan_action.py | 80 +- dbgpt/agent/plan/planner_agent.py | 138 +++ .../team => agent}/plan/team_auto_plan.py | 189 +++-- dbgpt/agent/plugin/__init__.py | 6 + dbgpt/agent/plugin/commands/__init__.py | 1 + .../plugin/commands/built_in/__init__.py | 1 + .../plugin/commands/built_in/audio_text.py | 62 -- .../built_in/display_type/__init__.py | 3 +- .../built_in/display_type/show_chart_gen.py | 129 ++- .../built_in/display_type/show_table_gen.py | 7 +- .../built_in/display_type/show_text_gen.py | 9 +- .../plugin/commands/built_in/image_gen.py | 125 --- dbgpt/agent/plugin/commands/command.py | 43 +- dbgpt/agent/plugin/commands/command_manage.py | 330 ++++---- .../plugin/commands/exception_not_commands.py | 4 - dbgpt/agent/plugin/commands/exceptions.py | 35 + dbgpt/agent/plugin/generator.py | 40 +- dbgpt/agent/plugin/loader.py | 13 +- dbgpt/agent/plugin/plugins_util.py | 92 +- dbgpt/agent/resource/__init__.py | 21 + dbgpt/agent/resource/resource_api.py | 71 +- dbgpt/agent/resource/resource_db_api.py | 63 +- dbgpt/agent/resource/resource_file_api.py | 9 - .../agent/resource/resource_knowledge_api.py | 12 +- dbgpt/agent/resource/resource_loader.py | 29 +- dbgpt/agent/resource/resource_plugin_api.py | 45 +- dbgpt/agent/util/__init__.py | 1 + dbgpt/agent/util/cmp.py | 30 + dbgpt/app/base.py | 5 +- dbgpt/app/scene/base.py | 2 +- dbgpt/client/schema.py | 8 +- dbgpt/core/awel/dag/dag_manager.py | 20 +- dbgpt/core/awel/flow/base.py | 31 +- dbgpt/serve/agent/agents/controller.py | 33 +- dbgpt/serve/agent/agents/db_gpts_memory.py | 10 +- dbgpt/serve/agent/agents/dbgpts.py | 1 - dbgpt/serve/agent/app/controller.py | 4 +- dbgpt/serve/agent/app/gpts_server.py | 2 +- dbgpt/serve/agent/db/gpts_app.py | 20 +- dbgpt/serve/agent/db/gpts_messages_db.py | 13 +- dbgpt/serve/agent/db/gpts_plans_db.py | 2 +- dbgpt/serve/agent/hub/plugin_hub.py | 2 +- dbgpt/serve/agent/model.py | 55 +- .../resource_loader/datasource_load_client.py | 16 +- .../knowledge_space_load_client.py | 12 +- .../resource_loader/plugin_hub_load_client.py | 7 +- dbgpt/serve/agent/team/base.py | 51 -- dbgpt/serve/agent/team/layout/__init__.py | 0 .../agent/team/layout/team_awel_layout.py | 73 -- .../agent/team/layout/team_awel_layout_new.py | 96 --- dbgpt/serve/agent/team/plan/__init__.py | 0 dbgpt/serve/agent/team/plan/planner_agent.py | 93 -- dbgpt/serve/datasource/api/schemas.py | 2 +- dbgpt/serve/flow/service/service.py | 4 +- dbgpt/serve/rag/api/schemas.py | 2 +- dbgpt/util/json_utils.py | 5 +- dbgpt/util/string_utils.py | 3 +- dbgpt/vis/__init__.py | 26 +- dbgpt/vis/base.py | 27 +- dbgpt/vis/client.py | 11 +- dbgpt/vis/tags/__init__.py | 1 + dbgpt/vis/tags/vis_agent_message.py | 8 +- dbgpt/vis/tags/vis_agent_plans.py | 8 +- dbgpt/vis/tags/vis_chart.py | 53 +- dbgpt/vis/tags/vis_code.py | 8 +- dbgpt/vis/tags/vis_dashboard.py | 14 +- dbgpt/vis/tags/vis_gpts_execution.py | 7 +- dbgpt/vis/tags/vis_gpts_result.py | 9 +- dbgpt/vis/tags/vis_plugin.py | 8 +- examples/agents/__init__.py | 0 .../auto_plan_agent_dialogue_example.py | 32 +- .../awel_layout_agents_chat_examples.py | 43 +- .../agents/plugin_agent_dialogue_example.py | 35 +- ...retrieve_summary_agent_dialogue_example.py | 54 +- .../agents/single_agent_dialogue_example.py | 14 +- .../single_summary_agent_dialogue_example.py | 20 +- examples/agents/sql_agent_dialogue_example.py | 25 +- .../simple_nl_schema_sql_chart_example.py | 2 +- .../rag/simple_dbschema_retriever_example.py | 3 +- examples/rag/simple_rag_embedding_example.py | 3 +- examples/rag/simple_rag_retriever_example.py | 3 +- setup.py | 4 + 148 files changed, 4833 insertions(+), 5431 deletions(-) delete mode 100644 dbgpt/agent/actions/aim_recognition_action.py delete mode 100644 dbgpt/agent/agents/__init__.py delete mode 100644 dbgpt/agent/agents/agent.py delete mode 100644 dbgpt/agent/agents/agent_new.py delete mode 100644 dbgpt/agent/agents/agents_manage.py delete mode 100644 dbgpt/agent/agents/base_agent.py delete mode 100644 dbgpt/agent/agents/base_agent_new.py delete mode 100644 dbgpt/agent/agents/expand/Indicator_assistant_agent.py delete mode 100644 dbgpt/agent/agents/expand/__init__.py delete mode 100644 dbgpt/agent/agents/expand/code_assistant_agent.py delete mode 100644 dbgpt/agent/agents/expand/dashboard_assistant_agent.py delete mode 100644 dbgpt/agent/agents/expand/data_scientist_agent.py delete mode 100644 dbgpt/agent/agents/expand/plugin_assistant_agent.py delete mode 100644 dbgpt/agent/agents/expand/retrieve_summary_assistant_agent.py delete mode 100644 dbgpt/agent/agents/expand/summary_assistant_agent.py delete mode 100644 dbgpt/agent/agents/llm/__init__.py delete mode 100644 dbgpt/agent/agents/llm/llm.py delete mode 100644 dbgpt/agent/agents/llm/strategy/__init__.py delete mode 100644 dbgpt/agent/agents/role.py delete mode 100644 dbgpt/agent/agents/user_proxy_agent.py delete mode 100644 dbgpt/agent/common/__init__.py delete mode 100644 dbgpt/agent/common/schema.py create mode 100644 dbgpt/agent/core/__init__.py create mode 100644 dbgpt/agent/core/agent.py create mode 100644 dbgpt/agent/core/agent_manage.py create mode 100644 dbgpt/agent/core/base_agent.py rename dbgpt/agent/{agents => core}/base_team.py (50%) create mode 100644 dbgpt/agent/core/llm/__init__.py create mode 100644 dbgpt/agent/core/llm/llm.py rename dbgpt/agent/{agents => core}/llm/llm_client.py (83%) create mode 100644 dbgpt/agent/core/llm/strategy/__init__.py rename dbgpt/agent/{agents => core}/llm/strategy/priority.py (54%) create mode 100644 dbgpt/agent/core/role.py create mode 100644 dbgpt/agent/core/schema.py create mode 100644 dbgpt/agent/core/user_proxy_agent.py create mode 100644 dbgpt/agent/expand/Indicator_assistant_agent.py create mode 100644 dbgpt/agent/expand/__init__.py create mode 100644 dbgpt/agent/expand/code_assistant_agent.py create mode 100644 dbgpt/agent/expand/dashboard_assistant_agent.py create mode 100644 dbgpt/agent/expand/data_scientist_agent.py create mode 100644 dbgpt/agent/expand/plugin_assistant_agent.py create mode 100644 dbgpt/agent/expand/retrieve_summary_assistant_agent.py create mode 100644 dbgpt/agent/expand/summary_assistant_agent.py delete mode 100644 dbgpt/agent/memory/gpts_memory_storage.py create mode 100644 dbgpt/agent/plan/__init__.py create mode 100644 dbgpt/agent/plan/awel/__init__.py rename dbgpt/{serve/agent/team/layout => agent/plan/awel}/agent_operator.py (56%) rename dbgpt/{serve/agent/team/layout => agent/plan/awel}/agent_operator_resource.py (81%) create mode 100644 dbgpt/agent/plan/awel/team_awel_layout.py rename dbgpt/{serve/agent/team => agent}/plan/plan_action.py (61%) create mode 100644 dbgpt/agent/plan/planner_agent.py rename dbgpt/{serve/agent/team => agent}/plan/team_auto_plan.py (54%) delete mode 100644 dbgpt/agent/plugin/commands/built_in/audio_text.py delete mode 100644 dbgpt/agent/plugin/commands/built_in/image_gen.py delete mode 100644 dbgpt/agent/plugin/commands/exception_not_commands.py create mode 100644 dbgpt/agent/plugin/commands/exceptions.py delete mode 100644 dbgpt/agent/resource/resource_file_api.py create mode 100644 dbgpt/agent/util/__init__.py create mode 100644 dbgpt/agent/util/cmp.py delete mode 100644 dbgpt/serve/agent/team/layout/__init__.py delete mode 100644 dbgpt/serve/agent/team/layout/team_awel_layout.py delete mode 100644 dbgpt/serve/agent/team/layout/team_awel_layout_new.py delete mode 100644 dbgpt/serve/agent/team/plan/__init__.py delete mode 100644 dbgpt/serve/agent/team/plan/planner_agent.py delete mode 100644 examples/agents/__init__.py diff --git a/.mypy.ini b/.mypy.ini index a1a204741..5bfab1fce 100644 --- a/.mypy.ini +++ b/.mypy.ini @@ -5,9 +5,6 @@ exclude = /tests/ [mypy-dbgpt.app.*] follow_imports = skip -[mypy-dbgpt.agent.*] -follow_imports = skip - [mypy-dbgpt.serve.*] follow_imports = skip @@ -89,4 +86,11 @@ ignore_missing_imports = True ignore_missing_imports = True [mypy-fastchat.protocol.api_protocol] -ignore_missing_imports = True \ No newline at end of file +ignore_missing_imports = True + +# Agent +[mypy-seaborn.*] +ignore_missing_imports = True + +[mypy-unstructured.*] +ignore_missing_imports = True diff --git a/Makefile b/Makefile index 58229a709..a6529b0c8 100644 --- a/Makefile +++ b/Makefile @@ -47,7 +47,7 @@ fmt: setup ## Format Python code $(VENV_BIN)/blackdoc examples # TODO: Use flake8 to enforce Python style guide. # https://flake8.pycqa.org/en/latest/ - $(VENV_BIN)/flake8 dbgpt/core/ dbgpt/rag/ dbgpt/storage/ dbgpt/datasource/ dbgpt/client/ + $(VENV_BIN)/flake8 dbgpt/core/ dbgpt/rag/ dbgpt/storage/ dbgpt/datasource/ dbgpt/client/ dbgpt/agent/ dbgpt/vis/ # TODO: More package checks with flake8. .PHONY: fmt-check @@ -56,7 +56,7 @@ fmt-check: setup ## Check Python code formatting and style without making change $(VENV_BIN)/isort --check-only --extend-skip="examples/notebook" examples $(VENV_BIN)/black --check --extend-exclude="examples/notebook" . $(VENV_BIN)/blackdoc --check dbgpt examples - $(VENV_BIN)/flake8 dbgpt/core/ dbgpt/rag/ dbgpt/storage/ dbgpt/datasource/ dbgpt/client/ + $(VENV_BIN)/flake8 dbgpt/core/ dbgpt/rag/ dbgpt/storage/ dbgpt/datasource/ dbgpt/client/ dbgpt/agent/ dbgpt/vis/ .PHONY: pre-commit pre-commit: fmt-check test test-doc mypy ## Run formatting and unit tests before committing @@ -72,7 +72,7 @@ test-doc: $(VENV)/.testenv ## Run doctests .PHONY: mypy mypy: $(VENV)/.testenv ## Run mypy checks # https://github.com/python/mypy - $(VENV_BIN)/mypy --config-file .mypy.ini dbgpt/rag/ dbgpt/datasource/ dbgpt/client/ + $(VENV_BIN)/mypy --config-file .mypy.ini dbgpt/rag/ dbgpt/datasource/ dbgpt/client/ dbgpt/agent/ dbgpt/vis/ # rag depends on core and storage, so we not need to check it again. # $(VENV_BIN)/mypy --config-file .mypy.ini dbgpt/storage/ # $(VENV_BIN)/mypy --config-file .mypy.ini dbgpt/core/ diff --git a/dbgpt/_private/config.py b/dbgpt/_private/config.py index 60fd90b86..280cf873b 100644 --- a/dbgpt/_private/config.py +++ b/dbgpt/_private/config.py @@ -10,6 +10,7 @@ if TYPE_CHECKING: from auto_gpt_plugin_template import AutoGPTPluginTemplate + from dbgpt.agent.plugin import CommandRegistry from dbgpt.component import SystemApp from dbgpt.datasource.manages import ConnectorManager @@ -145,7 +146,7 @@ def __init__(self) -> None: self.prompt_template_registry = PromptTemplateRegistry() ### Related configuration of built-in commands - self.command_registry = [] # type: ignore + self.command_registry: Optional[CommandRegistry] = None disabled_command_categories = os.getenv("DISABLED_COMMAND_CATEGORIES") if disabled_command_categories: diff --git a/dbgpt/_private/pydantic.py b/dbgpt/_private/pydantic.py index 70c53b429..6cc3f9a76 100644 --- a/dbgpt/_private/pydantic.py +++ b/dbgpt/_private/pydantic.py @@ -4,6 +4,7 @@ PYDANTIC_VERSION = 1 from pydantic import ( BaseModel, + ConfigDict, Extra, Field, NonNegativeFloat, @@ -20,6 +21,7 @@ # pydantic 2.x from pydantic.v1 import ( BaseModel, + ConfigDict, Extra, Field, NonNegativeFloat, diff --git a/dbgpt/agent/__init__.py b/dbgpt/agent/__init__.py index 88c7ce02c..0bcc34148 100644 --- a/dbgpt/agent/__init__.py +++ b/dbgpt/agent/__init__.py @@ -1 +1,31 @@ -from .common.schema import PluginStorageType +"""DB-GPT Multi-Agents Module.""" + +from .actions.action import Action, ActionOutput # noqa: F401 +from .core.agent import ( # noqa: F401 + Agent, + AgentContext, + AgentGenerateContext, + AgentMessage, +) +from .core.llm.llm import LLMConfig # noqa: F401 +from .core.schema import PluginStorageType # noqa: F401 +from .core.user_proxy_agent import UserProxyAgent # noqa: F401 +from .memory.gpts_memory import GptsMemory # noqa: F401 +from .resource.resource_api import AgentResource, ResourceType # noqa: F401 +from .resource.resource_loader import ResourceLoader # noqa: F401 + +__ALL__ = [ + "Agent", + "AgentContext", + "AgentGenerateContext", + "AgentMessage", + "Action", + "ActionOutput", + "LLMConfig", + "GptsMemory", + "AgentResource", + "ResourceType", + "ResourceLoader", + "PluginStorageType", + "UserProxyAgent", +] diff --git a/dbgpt/agent/actions/__init__.py b/dbgpt/agent/actions/__init__.py index e69de29bb..c1000aaea 100644 --- a/dbgpt/agent/actions/__init__.py +++ b/dbgpt/agent/actions/__init__.py @@ -0,0 +1 @@ +"""Actions of Agent.""" diff --git a/dbgpt/agent/actions/action.py b/dbgpt/agent/actions/action.py index 69a7adf71..4e598b317 100644 --- a/dbgpt/agent/actions/action.py +++ b/dbgpt/agent/actions/action.py @@ -1,74 +1,91 @@ +"""Base Action class for defining agent actions.""" import json -from abc import ABC +from abc import ABC, abstractmethod from typing import ( Any, Dict, Generic, List, Optional, - Tuple, Type, TypeVar, Union, + cast, get_args, get_origin, ) -from pydantic import BaseModel - -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType -from dbgpt.agent.resource.resource_loader import ResourceLoader +from dbgpt._private.pydantic import BaseModel from dbgpt.util.json_utils import find_json_objects -T = TypeVar("T", None, BaseModel, List[BaseModel]) +from ...vis.base import Vis +from ..resource.resource_api import AgentResource, ResourceType +from ..resource.resource_loader import ResourceLoader + +T = TypeVar("T", bound=Union[BaseModel, List[BaseModel], None]) + +JsonMessageType = Union[Dict[str, Any], List[Dict[str, Any]]] class ActionOutput(BaseModel): + """Action output model.""" + content: str is_exe_success: bool = True - view: str = None + view: Optional[str] = None resource_type: Optional[str] = None resource_value: Optional[Any] = None - @staticmethod - def from_dict(param: Optional[Dict]): + @classmethod + def from_dict( + cls: Type["ActionOutput"], param: Optional[Dict] + ) -> Optional["ActionOutput"]: + """Convert dict to ActionOutput object.""" if not param: return None - return ActionOutput.parse_obj(param) + return cls.parse_obj(param) class Action(ABC, Generic[T]): + """Base Action class for defining agent actions.""" + def __init__(self): - self.resource_loader: ResourceLoader = None + """Create an action.""" + self.resource_loader: Optional[ResourceLoader] = None - def init_resource_loader(self, resource_loader: ResourceLoader): + def init_resource_loader(self, resource_loader: Optional[ResourceLoader]): + """Initialize the resource loader.""" self.resource_loader = resource_loader @property def resource_need(self) -> Optional[ResourceType]: + """Return the resource type needed for the action.""" return None @property - def render_protocal(self): - raise NotImplementedError("The run method should be implemented in a subclass.") + def render_protocol(self) -> Optional[Vis]: + """Return the render protocol.""" + return None - def render_prompt(self): - if self.render_protocal is None: + def render_prompt(self) -> Optional[str]: + """Return the render prompt.""" + if self.render_protocol is None: return None else: - return self.render_protocal.render_prompt() + return self.render_protocol.render_prompt() def _create_example( self, - model_type: Union[Type[BaseModel], Type[List[BaseModel]]], - ) -> Union[Dict[str, Any], List[Dict[str, Any]]]: + model_type: Union[Type[BaseModel], List[Type[BaseModel]]], + ) -> Optional[Union[Dict[str, Any], List[Dict[str, Any]]]]: if model_type is None: return None origin = get_origin(model_type) args = get_args(model_type) if origin is None: example = {} - for field_name, field in model_type.__fields__.items(): + single_model_type = cast(Type[BaseModel], model_type) + for field_name, field in single_model_type.__fields__.items(): field_info = field.field_info if field_info.description: example[field_name] = field_info.description @@ -78,9 +95,11 @@ def _create_example( example[field_name] = "" return example elif origin is list or origin is List: - element_type = args[0] + element_type = cast(Type[BaseModel], args[0]) if issubclass(element_type, BaseModel): - return [self._create_example(element_type)] + list_example = self._create_example(element_type) + typed_list_example = cast(Dict[str, Any], list_example) + return [typed_list_example] else: raise TypeError("List elements must be BaseModel subclasses") else: @@ -89,35 +108,43 @@ def _create_example( ) @property - def out_model_type(self) -> T: + def out_model_type(self) -> Optional[Union[Type[T], List[Type[T]]]]: + """Return the output model type.""" return None @property - def ai_out_schema(self) -> Union[Dict[str, Any], List[Dict[str, Any]]]: + def ai_out_schema(self) -> Optional[str]: + """Return the AI output schema.""" if self.out_model_type is None: return None + json_format_data = json.dumps( + self._create_example(self.out_model_type), indent=2, ensure_ascii=False + ) return f"""Please response in the following json format: - {json.dumps(self._create_example(self.out_model_type), indent=2, ensure_ascii=False)} - Make sure the response is correct json and can be parsed by Python json.loads. + {json_format_data} + Make sure the response is correct json and can be parsed by Python json.loads. """ - def _ai_mesage_2_json(self, ai_message: str) -> json: + def _ai_message_2_json(self, ai_message: str) -> JsonMessageType: json_objects = find_json_objects(ai_message) json_count = len(json_objects) if json_count != 1: raise ValueError("Unable to obtain valid output.") return json_objects[0] - def _input_convert(self, ai_message: str, cls: Type[T]) -> Union[T, List[T]]: - json_result = self._ai_mesage_2_json(ai_message) + def _input_convert(self, ai_message: str, cls: Type[T]) -> T: + json_result = self._ai_message_2_json(ai_message) if get_origin(cls) == list: inner_type = get_args(cls)[0] - return [inner_type.parse_obj(item) for item in json_result] + typed_cls = cast(Type[BaseModel], inner_type) + return [typed_cls.parse_obj(item) for item in json_result] # type: ignore else: - return cls.parse_obj(json_result) + typed_cls = cast(Type[BaseModel], cls) + return typed_cls.parse_obj(json_result) - async def a_run( + @abstractmethod + async def run( self, ai_message: str, resource: Optional[AgentResource] = None, @@ -125,4 +152,4 @@ async def a_run( need_vis_render: bool = True, **kwargs, ) -> ActionOutput: - raise NotImplementedError("The run method should be implemented in a subclass.") + """Perform the action.""" diff --git a/dbgpt/agent/actions/aim_recognition_action.py b/dbgpt/agent/actions/aim_recognition_action.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/dbgpt/agent/actions/blank_action.py b/dbgpt/agent/actions/blank_action.py index 4e7c7d186..04e5757dc 100644 --- a/dbgpt/agent/actions/blank_action.py +++ b/dbgpt/agent/actions/blank_action.py @@ -1,40 +1,36 @@ -import json -import logging -from typing import Any, Dict, List, Optional, Union +"""Blank Action for the Agent.""" -from pydantic import BaseModel, Field +import logging +from typing import Optional -from dbgpt.agent.actions.action import Action, ActionOutput, T -from dbgpt.agent.common.schema import Status -from dbgpt.agent.plugin.generator import PluginPromptGenerator -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType -from dbgpt.agent.resource.resource_plugin_api import ResourcePluginClient -from dbgpt.vis.tags.vis_plugin import Vis, VisPlugin +from ..resource.resource_api import AgentResource +from .action import Action, ActionOutput logger = logging.getLogger(__name__) class BlankAction(Action): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - @property - def resource_need(self) -> Optional[ResourceType]: - return None + """Blank action class.""" - @property - def render_protocal(self) -> Optional[Vis]: - return None + def __init__(self): + """Create a blank action.""" + super().__init__() @property - def ai_out_schema(self) -> Union[Dict[str, Any], List[Dict[str, Any]]]: + def ai_out_schema(self) -> Optional[str]: + """Return the AI output schema.""" return None - async def a_run( + async def run( self, ai_message: str, resource: Optional[AgentResource] = None, rely_action_out: Optional[ActionOutput] = None, need_vis_render: bool = True, + **kwargs, ) -> ActionOutput: + """Perform the action. + + Just return the AI message. + """ return ActionOutput(is_exe_success=True, content=ai_message, view=ai_message) diff --git a/dbgpt/agent/actions/chart_action.py b/dbgpt/agent/actions/chart_action.py index 226201303..5f68fb745 100644 --- a/dbgpt/agent/actions/chart_action.py +++ b/dbgpt/agent/actions/chart_action.py @@ -1,23 +1,25 @@ +"""Chart Action for SQL execution and rendering.""" import json import logging from typing import Optional -from pydantic import BaseModel, Field - -from dbgpt.agent.actions.action import ActionOutput, T -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType -from dbgpt.agent.resource.resource_db_api import ResourceDbClient +from dbgpt._private.pydantic import BaseModel, Field from dbgpt.vis.tags.vis_chart import Vis, VisChart -from .action import Action +from ..resource.resource_api import AgentResource, ResourceType +from ..resource.resource_db_api import ResourceDbClient +from .action import Action, ActionOutput logger = logging.getLogger(__name__) class SqlInput(BaseModel): + """SQL input model.""" + display_type: str = Field( ..., - description="The chart rendering method selected for SQL. If you don’t know what to output, just output 'response_table' uniformly.", + description="The chart rendering method selected for SQL. If you don’t know " + "what to output, just output 'response_table' uniformly.", ) sql: str = Field( ..., description="Executable sql generated for the current target/problem" @@ -26,48 +28,68 @@ class SqlInput(BaseModel): class ChartAction(Action[SqlInput]): + """Chart action class.""" + def __init__(self): - self._render_protocal = VisChart() + """Create a chart action.""" + super().__init__() + self._render_protocol = VisChart() @property def resource_need(self) -> Optional[ResourceType]: + """Return the resource type needed for the action.""" return ResourceType.DB @property - def render_protocal(self) -> Optional[Vis]: - return self._render_protocal + def render_protocol(self) -> Optional[Vis]: + """Return the render protocol.""" + return self._render_protocol @property def out_model_type(self): + """Return the output model type.""" return SqlInput - async def a_run( + async def run( self, ai_message: str, resource: Optional[AgentResource] = None, rely_action_out: Optional[ActionOutput] = None, need_vis_render: bool = True, + **kwargs, ) -> ActionOutput: + """Perform the action.""" try: param: SqlInput = self._input_convert(ai_message, SqlInput) except Exception as e: - logger.exception(f"str(e)! \n {ai_message}") + logger.exception(f"{str(e)}! \n {ai_message}") return ActionOutput( is_exe_success=False, content="The requested correctly structured answer could not be found.", ) try: - resource_db_client: ResourceDbClient = ( - self.resource_loader.get_resesource_api(self.resource_need) + if not self.resource_loader: + raise ValueError("ResourceLoader is not initialized！") + resource_db_client: Optional[ + ResourceDbClient + ] = self.resource_loader.get_resource_api( + self.resource_need, ResourceDbClient ) if not resource_db_client: raise ValueError( - "There is no implementation class bound to database resource execution！" + "There is no implementation class bound to database resource " + "execution！" ) - data_df = await resource_db_client.a_query_to_df(resource.value, param.sql) - view = await self.render_protocal.display( + if not resource: + raise ValueError("The data resource is not found！") + data_df = await resource_db_client.query_to_df(resource.value, param.sql) + if not self.render_protocol: + raise ValueError("The rendering protocol is not initialized！") + view = await self.render_protocol.display( chart=json.loads(param.json()), data_df=data_df ) + if not self.resource_need: + raise ValueError("The resource type is not found！") return ActionOutput( is_exe_success=True, content=param.json(), diff --git a/dbgpt/agent/actions/code_action.py b/dbgpt/agent/actions/code_action.py index 6e8dc77ab..659c23a08 100644 --- a/dbgpt/agent/actions/code_action.py +++ b/dbgpt/agent/actions/code_action.py @@ -1,38 +1,40 @@ -import json +"""Code Action Module.""" import logging -from typing import List, Optional, Union +from typing import Optional, Union -from dbgpt.agent.actions.action import ActionOutput, T -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType from dbgpt.util.code_utils import UNKNOWN, execute_code, extract_code, infer_lang from dbgpt.util.utils import colored from dbgpt.vis.tags.vis_code import Vis, VisCode -from .action import Action +from ..resource.resource_api import AgentResource +from .action import Action, ActionOutput logger = logging.getLogger(__name__) class CodeAction(Action[None]): + """Code Action Module.""" + def __init__(self): - self._render_protocal = VisCode() + """Create a code action.""" + super().__init__() + self._render_protocol = VisCode() self._code_execution_config = {} @property - def resource_need(self) -> Optional[ResourceType]: - return None - - @property - def render_protocal(self) -> Optional[Vis]: - return self._render_protocal + def render_protocol(self) -> Optional[Vis]: + """Return the render protocol.""" + return self._render_protocol - async def a_run( + async def run( self, ai_message: str, resource: Optional[AgentResource] = None, rely_action_out: Optional[ActionOutput] = None, need_vis_render: bool = True, + **kwargs, ) -> ActionOutput: + """Perform the action.""" try: code_blocks = extract_code(ai_message) if len(code_blocks) < 1: @@ -45,14 +47,16 @@ async def a_run( elif len(code_blocks) > 1 and code_blocks[0][0] == UNKNOWN: # found code blocks, execute code and push "last_n_messages" back logger.info( - f"Missing available code block type, unable to execute code,{ai_message}", + f"Missing available code block type, unable to execute code," + f"{ai_message}", ) return ActionOutput( is_exe_success=False, - content="Missing available code block type, unable to execute code.", + content="Missing available code block type, " + "unable to execute code.", ) exitcode, logs = self.execute_code_blocks(code_blocks) - exit_success = True if exitcode == 0 else False + exit_success = exitcode == 0 content = ( logs @@ -60,12 +64,15 @@ async def a_run( else f"exitcode: {exitcode} (execution failed)\n {logs}" ) - param = {} - param["exit_success"] = exit_success - param["language"] = code_blocks[0][0] - param["code"] = code_blocks - param["log"] = logs - view = await self.render_protocal.display(content=param) + param = { + "exit_success": exit_success, + "language": code_blocks[0][0], + "code": code_blocks, + "log": logs, + } + if not self.render_protocol: + raise NotImplementedError("The render_protocol should be implemented.") + view = await self.render_protocol.display(content=param) return ActionOutput(is_exe_success=exit_success, content=content, view=view) except Exception as e: logger.exception("Code Action Run Failed！") @@ -83,7 +90,8 @@ def execute_code_blocks(self, code_blocks): lang = infer_lang(code) print( colored( - f"\n>>>>>>>> EXECUTING CODE BLOCK {i} (inferred language is {lang})...", + f"\n>>>>>>>> EXECUTING CODE BLOCK {i} " + f"(inferred language is {lang})...", "red", ), flush=True, @@ -120,8 +128,11 @@ def execute_code_blocks(self, code_blocks): @property def use_docker(self) -> Union[bool, str, None]: - """Bool value of whether to use docker to execute the code, - or str value of the docker image name to use, or None when code execution is disabled. + """Whether to use docker to execute the code. + + Bool value of whether to use docker to execute the code, + or str value of the docker image name to use, or None when code execution is + disabled. """ return ( None diff --git a/dbgpt/agent/actions/dashboard_action.py b/dbgpt/agent/actions/dashboard_action.py index ae272824a..a28dfcb8d 100644 --- a/dbgpt/agent/actions/dashboard_action.py +++ b/dbgpt/agent/actions/dashboard_action.py @@ -1,25 +1,29 @@ +"""Dashboard Action Module.""" import json import logging from typing import List, Optional -from pydantic import BaseModel, Field - -from dbgpt.agent.actions.action import Action, ActionOutput, T -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType -from dbgpt.agent.resource.resource_db_api import ResourceDbClient +from dbgpt._private.pydantic import BaseModel, Field from dbgpt.vis.tags.vis_dashboard import Vis, VisDashboard +from ..resource.resource_api import AgentResource, ResourceType +from ..resource.resource_db_api import ResourceDbClient +from .action import Action, ActionOutput + logger = logging.getLogger(__name__) class ChartItem(BaseModel): + """Chart item model.""" + title: str = Field( ..., description="The title of the current analysis chart.", ) display_type: str = Field( ..., - description="The chart rendering method selected for SQL. If you don’t know what to output, just output 'response_table' uniformly.", + description="The chart rendering method selected for SQL. If you don’t know " + "what to output, just output 'response_table' uniformly.", ) sql: str = Field( ..., description="Executable sql generated for the current target/problem" @@ -28,61 +32,85 @@ class ChartItem(BaseModel): class DashboardAction(Action[List[ChartItem]]): + """Dashboard action class.""" + def __init__(self): - self._render_protocal = VisDashboard() + """Create a dashboard action.""" + super().__init__() + self._render_protocol = VisDashboard() @property def resource_need(self) -> Optional[ResourceType]: + """Return the resource type needed for the action.""" return ResourceType.DB @property - def render_protocal(self) -> Optional[Vis]: - return self._render_protocal + def render_protocol(self) -> Optional[Vis]: + """Return the render protocol.""" + return self._render_protocol @property def out_model_type(self): + """Return the output model type.""" return List[ChartItem] - async def a_run( + async def run( self, ai_message: str, resource: Optional[AgentResource] = None, rely_action_out: Optional[ActionOutput] = None, need_vis_render: bool = True, + **kwargs, ) -> ActionOutput: + """Perform the action.""" try: - chart_items: List[ChartItem] = self._input_convert( - ai_message, List[ChartItem] - ) + input_param = self._input_convert(ai_message, List[ChartItem]) except Exception as e: logger.exception(str(e)) return ActionOutput( is_exe_success=False, content="The requested correctly structured answer could not be found.", ) + if not isinstance(input_param, list): + return ActionOutput( + is_exe_success=False, + content="The requested correctly structured answer could not be found.", + ) + chart_items: List[ChartItem] = input_param try: - resource_db_client: ResourceDbClient = ( - self.resource_loader.get_resesource_api(self.resource_need) + if not self.resource_loader: + raise ValueError("Resource loader is not initialized!") + resource_db_client: Optional[ + ResourceDbClient + ] = self.resource_loader.get_resource_api( + self.resource_need, ResourceDbClient ) if not resource_db_client: raise ValueError( - "There is no implementation class bound to database resource execution！" + "There is no implementation class bound to database resource " + "execution！" ) + if not resource: + raise ValueError("Resource is not initialized!") + chart_params = [] for chart_item in chart_items: + chart_dict = {} try: - sql_df = await resource_db_client.a_query_to_df( + sql_df = await resource_db_client.query_to_df( resource.value, chart_item.sql ) chart_dict = chart_item.dict() chart_dict["data"] = sql_df except Exception as e: - logger.warn(f"Sql excute Failed！{str(e)}") + logger.warning(f"Sql execute failed！{str(e)}") chart_dict["err_msg"] = str(e) chart_params.append(chart_dict) - view = await self.render_protocal.display(charts=chart_params) + if not self.render_protocol: + raise ValueError("The render protocol is not initialized!") + view = await self.render_protocol.display(charts=chart_params) return ActionOutput( is_exe_success=True, content=json.dumps([chart_item.dict() for chart_item in chart_items]), diff --git a/dbgpt/agent/actions/indicator_action.py b/dbgpt/agent/actions/indicator_action.py index dcc0023d1..1df0e9d39 100644 --- a/dbgpt/agent/actions/indicator_action.py +++ b/dbgpt/agent/actions/indicator_action.py @@ -1,21 +1,22 @@ +"""Indicator Action.""" + import json import logging -from typing import Any, Dict, List, Optional, Union - -import requests -from pydantic import BaseModel, Field -from requests.exceptions import HTTPError +from typing import Optional -from dbgpt.agent.actions.action import Action, ActionOutput, T -from dbgpt.agent.common.schema import Status -from dbgpt.agent.plugin.generator import PluginPromptGenerator -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType +from dbgpt._private.pydantic import BaseModel, Field from dbgpt.vis.tags.vis_plugin import Vis, VisPlugin +from ..core.schema import Status +from ..resource.resource_api import AgentResource, ResourceType +from .action import Action, ActionOutput + logger = logging.getLogger(__name__) class IndicatorInput(BaseModel): + """Indicator input model.""" + indicator_name: str = Field( ..., description="The name of a indicator.", @@ -30,32 +31,41 @@ class IndicatorInput(BaseModel): ) args: dict = Field( default={"arg name1": "", "arg name2": ""}, - description="The tool selected for the current target, the parameter information required for execution", + description="The tool selected for the current target, the parameter " + "information required for execution", ) thought: str = Field(..., description="Summary of thoughts to the user") class IndicatorAction(Action[IndicatorInput]): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._render_protocal = VisPlugin() + """Indicator action class.""" + + def __init__(self): + """Create a indicator action.""" + super().__init__() + self._render_protocol = VisPlugin() @property def resource_need(self) -> Optional[ResourceType]: + """Return the resource type needed for the action.""" return ResourceType.Knowledge @property - def render_protocal(self) -> Optional[Vis]: - return self._render_protocal + def render_protocol(self) -> Optional[Vis]: + """Return the render protocol.""" + return self._render_protocol @property def out_model_type(self): + """Return the output model type.""" return IndicatorInput @property - def ai_out_schema(self) -> Union[Dict[str, Any], List[Dict[str, Any]]]: + def ai_out_schema(self) -> Optional[str]: + """Return the AI output schema.""" out_put_schema = { - "indicator_name": "The name of a tool that can be used to answer the current question or solve the current task.", + "indicator_name": "The name of a tool that can be used to answer the " + "current question or solve the current task.", "api": "", "method": "", "args": { @@ -67,26 +77,37 @@ def ai_out_schema(self) -> Union[Dict[str, Any], List[Dict[str, Any]]]: return f"""Please response in the following json format: {json.dumps(out_put_schema, indent=2, ensure_ascii=False)} - Make sure the response is correct json and can be parsed by Python json.loads. + Make sure the response is correct json and can be parsed by Python json.loads. """ - async def a_run( + async def run( self, ai_message: str, - plugin_generator: PluginPromptGenerator, resource: Optional[AgentResource] = None, rely_action_out: Optional[ActionOutput] = None, need_vis_render: bool = True, + **kwargs, ) -> ActionOutput: + """Perform the action.""" + import requests + from requests.exceptions import HTTPError + try: - param: IndicatorInput = self._input_convert(ai_message, IndicatorInput) + input_param = self._input_convert(ai_message, IndicatorInput) except Exception as e: logger.exception((str(e))) return ActionOutput( is_exe_success=False, content="The requested correctly structured answer could not be found.", ) - + if isinstance(input_param, list): + return ActionOutput( + is_exe_success=False, + content="The requested correctly structured answer could not be found.", + ) + param: IndicatorInput = input_param + response_success = True + result: Optional[str] = None try: status = Status.COMPLETE.value err_msg = None @@ -100,8 +121,12 @@ async def a_run( response = requests.request( param.method.lower(), param.api, data=param.args ) - response.raise_for_status() # 如果请求返回一个错误状态码，则抛出HTTPError异常 + # Raise an HTTPError if the HTTP request returned an unsuccessful + # status code + response.raise_for_status() + result = response.text except HTTPError as http_err: + response_success = False print(f"HTTP error occurred: {http_err}") except Exception as e: response_success = False @@ -110,18 +135,20 @@ async def a_run( err_msg = f"API [{param.api}] request Failed!{str(e)}" plugin_param = { - "name": param.tool_name, + "name": param.indicator_name, "args": param.args, "status": status, "logo": None, - "result": response.text, + "result": result, "err_msg": err_msg, } - view = await self.render_protocal.display(content=plugin_param) + if not self.render_protocol: + raise NotImplementedError("The render_protocol should be implemented.") + view = await self.render_protocol.display(content=plugin_param) return ActionOutput( - is_exe_success=response_success, content=response.text, view=view + is_exe_success=response_success, content=result, view=view ) except Exception as e: logger.exception("Indicator Action Run Failed！") diff --git a/dbgpt/agent/actions/plugin_action.py b/dbgpt/agent/actions/plugin_action.py index 715d7c84c..025f1d52b 100644 --- a/dbgpt/agent/actions/plugin_action.py +++ b/dbgpt/agent/actions/plugin_action.py @@ -1,52 +1,65 @@ +"""Plugin Action Module.""" import json import logging -from typing import Any, Dict, List, Optional, Union +from typing import Optional -from pydantic import BaseModel, Field - -from dbgpt.agent.actions.action import Action, ActionOutput, T -from dbgpt.agent.common.schema import Status -from dbgpt.agent.plugin.generator import PluginPromptGenerator -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType -from dbgpt.agent.resource.resource_plugin_api import ResourcePluginClient +from dbgpt._private.pydantic import BaseModel, Field from dbgpt.vis.tags.vis_plugin import Vis, VisPlugin +from ..core.schema import Status +from ..plugin.generator import PluginPromptGenerator +from ..resource.resource_api import AgentResource, ResourceType +from ..resource.resource_plugin_api import ResourcePluginClient +from .action import Action, ActionOutput + logger = logging.getLogger(__name__) class PluginInput(BaseModel): + """Plugin input model.""" + tool_name: str = Field( ..., - description="The name of a tool that can be used to answer the current question or solve the current task.", + description="The name of a tool that can be used to answer the current question" + " or solve the current task.", ) args: dict = Field( default={"arg name1": "", "arg name2": ""}, - description="The tool selected for the current target, the parameter information required for execution", + description="The tool selected for the current target, the parameter " + "information required for execution", ) thought: str = Field(..., description="Summary of thoughts to the user") class PluginAction(Action[PluginInput]): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._render_protocal = VisPlugin() + """Plugin action class.""" + + def __init__(self): + """Create a plugin action.""" + super().__init__() + self._render_protocol = VisPlugin() @property def resource_need(self) -> Optional[ResourceType]: + """Return the resource type needed for the action.""" return ResourceType.Plugin @property - def render_protocal(self) -> Optional[Vis]: - return self._render_protocal + def render_protocol(self) -> Optional[Vis]: + """Return the render protocol.""" + return self._render_protocol @property def out_model_type(self): + """Return the output model type.""" return PluginInput @property - def ai_out_schema(self) -> Union[Dict[str, Any], List[Dict[str, Any]]]: + def ai_out_schema(self) -> Optional[str]: + """Return the AI output schema.""" out_put_schema = { - "tool_name": "The name of a tool that can be used to answer the current question or solve the current task.", + "tool_name": "The name of a tool that can be used to answer the current " + "question or solve the current task.", "args": { "arg name1": "arg value1", "arg name2": "arg value2", @@ -56,17 +69,33 @@ def ai_out_schema(self) -> Union[Dict[str, Any], List[Dict[str, Any]]]: return f"""Please response in the following json format: {json.dumps(out_put_schema, indent=2, ensure_ascii=False)} - Make sure the response is correct json and can be parsed by Python json.loads. + Make sure the response is correct json and can be parsed by Python json.loads. """ - async def a_run( + async def run( self, ai_message: str, - plugin_generator: PluginPromptGenerator, resource: Optional[AgentResource] = None, rely_action_out: Optional[ActionOutput] = None, need_vis_render: bool = True, + **kwargs, ) -> ActionOutput: + """Perform the plugin action. + + Args: + ai_message (str): The AI message. + resource (Optional[AgentResource], optional): The resource. Defaults to + None. + rely_action_out (Optional[ActionOutput], optional): The rely action output. + Defaults to None. + need_vis_render (bool, optional): Whether need visualization rendering. + Defaults to True. + """ + plugin_generator: Optional[PluginPromptGenerator] = kwargs.get( + "plugin_generator", None + ) + if not plugin_generator: + raise ValueError("No plugin generator found!") try: param: PluginInput = self._input_convert(ai_message, PluginInput) except Exception as e: @@ -77,25 +106,29 @@ async def a_run( ) try: - resource_plugin_client: ResourcePluginClient = ( - self.resource_loader.get_resesource_api(self.resource_need) + if not self.resource_loader: + raise ValueError("No resource_loader found!") + resource_plugin_client: Optional[ + ResourcePluginClient + ] = self.resource_loader.get_resource_api( + self.resource_need, ResourcePluginClient ) if not resource_plugin_client: raise ValueError("No implementation of the use of plug-in resources！") response_success = True status = Status.RUNNING.value - tool_result = "" err_msg = None try: - tool_result = await resource_plugin_client.a_execute_command( + tool_result = await resource_plugin_client.execute_command( param.tool_name, param.args, plugin_generator ) status = Status.COMPLETE.value except Exception as e: response_success = False - logger.exception(f"Tool [{param.tool_name}] excute Failed!") + logger.exception(f"Tool [{param.tool_name}] execute failed!") status = Status.FAILED.value - err_msg = f"Tool [{param.tool_name}] excute Failed!{str(e)}" + err_msg = f"Tool [{param.tool_name}] execute failed! {str(e)}" + tool_result = err_msg plugin_param = { "name": param.tool_name, @@ -105,8 +138,10 @@ async def a_run( "result": tool_result, "err_msg": err_msg, } + if not self.render_protocol: + raise NotImplementedError("The render_protocol should be implemented.") - view = await self.render_protocal.display(content=plugin_param) + view = await self.render_protocol.display(content=plugin_param) return ActionOutput( is_exe_success=response_success, content=tool_result, view=view diff --git a/dbgpt/agent/agents/__init__.py b/dbgpt/agent/agents/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/dbgpt/agent/agents/agent.py b/dbgpt/agent/agents/agent.py deleted file mode 100644 index 1b9e22933..000000000 --- a/dbgpt/agent/agents/agent.py +++ /dev/null @@ -1,193 +0,0 @@ -from __future__ import annotations - -import dataclasses -from typing import Any, Dict, List, Optional, Tuple, Union - -from dbgpt.agent.resource.resource_loader import ResourceLoader -from dbgpt.core import LLMClient -from dbgpt.core.interface.llm import ModelMetadata -from dbgpt.util.annotations import PublicAPI - -from ..memory.gpts_memory import GptsMemory - - -class Agent: - """An interface for AI agent. - An agent can communicate with other agents and perform actions. - """ - - def __init__( - self, - name: str, - memory: GptsMemory, - describe: str, - ): - """ - Args: - name (str): name of the agent. - """ - self._name = name - self._describe = describe - - # the agent's collective memory - self._memory = memory - - @property - def name(self) -> str: - """Get the name of the agent.""" - return self._name - - @property - def memory(self) -> GptsMemory: - return self._memory - - @property - def describe(self) -> str: - """Get the name of the agent.""" - return self._describe - - async def a_notification( - self, - message: Union[Dict, str], - recipient: Agent, - reviewer: Agent, - ): - """Notification a message to recipient agent(Receive a record message from the notification and process it according to your own process. You cannot send the message through send and directly return the current final result.)""" - - async def a_send( - self, - message: Union[Dict, str], - recipient: Agent, - reviewer: Agent, - request_reply: Optional[bool] = True, - is_recovery: Optional[bool] = False, - ) -> None: - """(Abstract async method) Send a message to recipient agent.""" - - async def a_receive( - self, - message: Optional[Dict], - sender: Agent, - reviewer: Agent, - request_reply: Optional[bool] = None, - silent: Optional[bool] = False, - is_recovery: Optional[bool] = False, - ) -> None: - """(Abstract async method) Receive a message from another agent.""" - - async def a_review( - self, message: Union[Dict, str], censored: Agent - ) -> Tuple[bool, Any]: - """ - - Args: - message: - censored: - - Returns: - bool: whether the message is censored - Any: the censored message - """ - - async def a_generate_reply( - self, - message: Optional[Dict], - sender: Agent, - reviewer: Agent, - silent: Optional[bool] = False, - rely_messages: Optional[List[Dict]] = None, - **kwargs, - ) -> Union[str, Dict, None]: - """(Abstract async method) Generate a reply based on the received messages. - - Args: - messages (Optional[Dict]): a dict of messages received from other agents. - sender: sender of an Agent instance. - Returns: - str or dict or None: the generated reply. If None, no reply is generated. - """ - - async def a_reasoning_reply( - self, messages: Optional[List[Dict]] - ) -> Union[str, Dict, None]: - """Based on the requirements of the current agent, reason about the current task goal through LLM - Args: - messages: - - Returns: - str or dict or None: the generated reply. If None, no reply is generated. - """ - - async def a_action_reply( - self, - messages: Optional[str], - sender: Agent, - **kwargs, - ) -> Union[str, Dict, None]: - """ - Parse the inference results for the current target and execute the inference results using the current agent's executor - Args: - messages (list[dict]): a list of messages received. - sender: sender of an Agent instance. - **kwargs: - Returns: - str or dict or None: the agent action reply. If None, no reply is generated. - """ - - async def a_verify_reply( - self, - message: Optional[Dict], - sender: Agent, - reviewer: Agent, - **kwargs, - ) -> Union[str, Dict, None]: - """ - Verify whether the current execution results meet the target expectations - Args: - messages: - sender: - **kwargs: - - Returns: - - """ - - def reset(self) -> None: - """(Abstract method) Reset the agent.""" - - -@dataclasses.dataclass -class AgentContext: - conv_id: str - gpts_app_name: str = None - language: str = None - max_chat_round: Optional[int] = 100 - max_retry_round: Optional[int] = 10 - max_new_tokens: Optional[int] = 1024 - temperature: Optional[float] = 0.5 - allow_format_str_template: Optional[bool] = False - - def to_dict(self) -> Dict[str, Any]: - return dataclasses.asdict(self) - - -@dataclasses.dataclass -@PublicAPI(stability="beta") -class AgentGenerateContext: - """A class to represent the input of a Agent.""" - - message: Optional[Dict] - sender: Agent - reviewer: Agent - silent: Optional[bool] = False - - rely_messages: List[Dict] = dataclasses.field(default_factory=list) - final: Optional[bool] = True - - memory: Optional[GptsMemory] = None - agent_context: Optional[AgentContext] = None - resource_loader: Optional[ResourceLoader] = None - llm_client: Optional[LLMClient] = None - - def to_dict(self) -> Dict: - return dataclasses.asdict(self) diff --git a/dbgpt/agent/agents/agent_new.py b/dbgpt/agent/agents/agent_new.py deleted file mode 100644 index 32259ef74..000000000 --- a/dbgpt/agent/agents/agent_new.py +++ /dev/null @@ -1,150 +0,0 @@ -from __future__ import annotations - -import dataclasses -from abc import ABC, abstractmethod -from typing import Any, Dict, List, Optional, Tuple, Union - -from dbgpt.agent.resource.resource_loader import ResourceLoader -from dbgpt.core import LLMClient -from dbgpt.util.annotations import PublicAPI - -from ..memory.gpts_memory import GptsMemory - - -class Agent(ABC): - async def a_send( - self, - message: Union[Dict, str], - recipient: Agent, - reviewer: Agent, - request_reply: Optional[bool] = True, - is_recovery: Optional[bool] = False, - ) -> None: - """(Abstract async method) Send a message to recipient agent.""" - - async def a_receive( - self, - message: Optional[Dict], - sender: Agent, - reviewer: Agent, - request_reply: Optional[bool] = None, - silent: Optional[bool] = False, - is_recovery: Optional[bool] = False, - ) -> None: - """(Abstract async method) Receive a message from another agent.""" - - async def a_generate_reply( - self, - recive_message: Optional[Dict], - sender: Agent, - reviewer: Agent = None, - rely_messages: Optional[List[Dict]] = None, - **kwargs, - ) -> Union[str, Dict, None]: - """(Abstract async method) Generate a reply based on the received messages. - - Args: - messages (Optional[Dict]): a dict of messages received from other agents. - sender: sender of an Agent instance. - Returns: - str or dict or None: the generated reply. If None, no reply is generated. - """ - - async def a_thinking( - self, messages: Optional[List[Dict]] - ) -> Union[str, Dict, None]: - """Based on the requirements of the current agent, reason about the current task goal through LLM - Args: - messages: - - Returns: - str or dict or None: the generated reply. If None, no reply is generated. - """ - - async def a_review( - self, message: Union[Dict, str], censored: Agent - ) -> Tuple[bool, Any]: - """ - - Args: - message: - censored: - - Returns: - bool: whether the message is censored - Any: the censored message - """ - - async def a_act( - self, - message: Optional[str], - sender: Optional[Agent] = None, - reviewer: Optional[Agent] = None, - **kwargs, - ) -> Union[str, Dict, None]: - """ - Parse the inference results for the current target and execute the inference results using the current agent's executor - Args: - messages (list[dict]): a list of messages received. - sender: sender of an Agent instance. - **kwargs: - Returns: - str or dict or None: the agent action reply. If None, no reply is generated. - """ - - async def a_verify( - self, - message: Optional[Dict], - sender: Agent, - reviewer: Agent, - **kwargs, - ) -> Union[str, Dict, None]: - """ - Verify whether the current execution results meet the target expectations - Args: - messages: - sender: - **kwargs: - - Returns: - - """ - - -@dataclasses.dataclass -class AgentContext: - conv_id: str - gpts_app_name: str = None - language: str = None - max_chat_round: Optional[int] = 100 - max_retry_round: Optional[int] = 10 - max_new_tokens: Optional[int] = 1024 - temperature: Optional[float] = 0.5 - allow_format_str_template: Optional[bool] = False - - def to_dict(self) -> Dict[str, Any]: - return dataclasses.asdict(self) - - -@dataclasses.dataclass -@PublicAPI(stability="beta") -class AgentGenerateContext: - """A class to represent the input of a Agent.""" - - message: Optional[Dict] - sender: Agent - reviewer: Agent - silent: Optional[bool] = False - - rely_messages: List[Dict] = dataclasses.field(default_factory=list) - final: Optional[bool] = True - - memory: Optional[GptsMemory] = None - agent_context: Optional[AgentContext] = None - resource_loader: Optional[ResourceLoader] = None - llm_client: Optional[LLMClient] = None - - round_index: int = None - - def to_dict(self) -> Dict: - return dataclasses.asdict(self) diff --git a/dbgpt/agent/agents/agents_manage.py b/dbgpt/agent/agents/agents_manage.py deleted file mode 100644 index cb91d62b8..000000000 --- a/dbgpt/agent/agents/agents_manage.py +++ /dev/null @@ -1,100 +0,0 @@ -import logging -import re -from collections import defaultdict -from typing import Dict, List, Optional, Type - -from .agent import Agent -from .expand.code_assistant_agent import CodeAssistantAgent -from .expand.dashboard_assistant_agent import DashboardAssistantAgent -from .expand.data_scientist_agent import DataScientistAgent -from .expand.plugin_assistant_agent import PluginAssistantAgent -from .expand.summary_assistant_agent import SummaryAssistantAgent - -logger = logging.getLogger(__name__) - - -def get_all_subclasses(cls): - all_subclasses = [] - direct_subclasses = cls.__subclasses__() - all_subclasses.extend(direct_subclasses) - - for subclass in direct_subclasses: - all_subclasses.extend(get_all_subclasses(subclass)) - return all_subclasses - - -def participant_roles(agents: List[Agent] = None) -> str: - # Default to all agents registered - if agents is None: - agents = agents - - roles = [] - for agent in agents: - if agent.system_message.strip() == "": - logger.warning( - f"The agent '{agent.name}' has an empty system_message, and may not work well with GroupChat." - ) - roles.append(f"{agent.name}: {agent.describe}") - return "\n".join(roles) - - -def mentioned_agents(message_content: str, agents: List[Agent]) -> Dict: - """ - Finds and counts agent mentions in the string message_content, taking word boundaries into account. - - Returns: A dictionary mapping agent names to mention counts (to be included, at least one mention must occur) - """ - mentions = dict() - for agent in agents: - regex = ( - r"(?<=\W)" + re.escape(agent.name) + r"(?=\W)" - ) # Finds agent mentions, taking word boundaries into account - count = len( - re.findall(regex, " " + message_content + " ") - ) # Pad the message to help with matching - if count > 0: - mentions[agent.name] = count - return mentions - - -class AgentsManage: - def __init__(self): - self._agents = defaultdict() - - def register_agent(self, cls): - self._agents[cls().profile] = cls - - def get_by_name(self, name: str) -> Optional[Type[Agent]]: - if name not in self._agents: - raise ValueError(f"Agent:{name} not register!") - return self._agents[name] - - def get_describe_by_name(self, name: str) -> Optional[Type[Agent]]: - return self._agents[name].DEFAULT_DESCRIBE - - def all_agents(self): - result = {} - for name, cls in self._agents.items(): - result[name] = cls.DEFAULT_DESCRIBE - return result - - def list_agents(self): - result = [] - for name, cls in self._agents.items(): - instance = cls() - result.append( - { - "name": instance.profile, - "desc": instance.goal, - } - ) - return result - - -agent_manage = AgentsManage() - -agent_manage.register_agent(CodeAssistantAgent) -agent_manage.register_agent(DashboardAssistantAgent) -agent_manage.register_agent(DataScientistAgent) -agent_manage.register_agent(SummaryAssistantAgent) -agent_manage.register_agent(PluginAssistantAgent) diff --git a/dbgpt/agent/agents/base_agent.py b/dbgpt/agent/agents/base_agent.py deleted file mode 100644 index ea319761f..000000000 --- a/dbgpt/agent/agents/base_agent.py +++ /dev/null @@ -1,796 +0,0 @@ -import asyncio -import copy -import json -import logging -from typing import Any, Callable, Dict, List, Optional, Type, Union - -from dbgpt.agent.agents.llm.llm_client import AIWrapper -from dbgpt.core.interface.message import ModelMessageRoleType -from dbgpt.util.error_types import LLMChatError -from dbgpt.util.utils import colored - -from ..memory.base import GptsMessage -from ..memory.gpts_memory import GptsMemory -from .agent import Agent, AgentContext - -logger = logging.getLogger(__name__) - - -class ConversableAgent(Agent): - DEFAULT_SYSTEM_MESSAGE = "You are a helpful AI Assistant." - MAX_CONSECUTIVE_AUTO_REPLY = ( - 100 # maximum number of consecutive auto replies (subject to future change) - ) - - def __init__( - self, - name: str, - describe: str = DEFAULT_SYSTEM_MESSAGE, - memory: GptsMemory = GptsMemory(), - agent_context: AgentContext = None, - system_message: Optional[str] = DEFAULT_SYSTEM_MESSAGE, - max_consecutive_auto_reply: Optional[int] = None, - human_input_mode: Optional[str] = "TERMINATE", - default_auto_reply: Optional[Union[str, Dict, None]] = "", - ): - super().__init__(name, memory, describe) - - # a dictionary of conversations, default value is list - # self._oai_messages = defaultdict(list) - self._oai_system_message = [ - {"content": system_message, "role": ModelMessageRoleType.SYSTEM} - ] - self._rely_messages = [] - - self.client = AIWrapper(llm_client=agent_context.llm_provider) - - self.human_input_mode = human_input_mode - self._max_consecutive_auto_reply = ( - max_consecutive_auto_reply - if max_consecutive_auto_reply is not None - else self.MAX_CONSECUTIVE_AUTO_REPLY - ) - self.consecutive_auto_reply_counter: int = 0 - self.current_retry_counter: int = 0 - self.max_retry_count: int = 5 - - ## By default, the memory of 4 rounds of dialogue is retained. - self.dialogue_memory_rounds = 5 - self._default_auto_reply = default_auto_reply - self._reply_func_list = [] - self._max_consecutive_auto_reply_dict = {} - - self.agent_context = agent_context - - def register_reply( - self, - trigger: Union[Type[Agent], str, Agent, Callable[[Agent], bool], List], - reply_func: Callable, - position: int = 0, - config: Optional[Any] = None, - reset_config: Optional[Callable] = None, - ): - if not isinstance(trigger, (type, str, Agent, Callable, list)): - raise ValueError( - "trigger must be a class, a string, an agent, a callable or a list." - ) - self._reply_func_list.insert( - position, - { - "trigger": trigger, - "reply_func": reply_func, - "config": copy.copy(config), - "init_config": config, - "reset_config": reset_config, - }, - ) - - def is_termination_msg(self, message: Union[Dict, str, bool]): - if isinstance(message, dict): - if "is_termination" in message: - return message.get("is_termination", False) - else: - return message["content"].find("TERMINATE") >= 0 - elif isinstance(message, bool): - return message - elif isinstance(message, str): - return message.find("TERMINATE") >= 0 - else: - return False - - @property - def system_message(self): - """Return the system message.""" - return self._oai_system_message[0]["content"] - - def update_system_message(self, system_message: str): - """Update the system message. - - Args: - system_message (str): system message for the ChatCompletion inference. - """ - self._oai_system_message[0]["content"] = system_message - - def update_max_consecutive_auto_reply( - self, value: int, sender: Optional[Agent] = None - ): - """Update the maximum number of consecutive auto replies. - - Args: - value (int): the maximum number of consecutive auto replies. - sender (Agent): when the sender is provided, only update the max_consecutive_auto_reply for that sender. - """ - if sender is None: - self._max_consecutive_auto_reply = value - for k in self._max_consecutive_auto_reply_dict: - self._max_consecutive_auto_reply_dict[k] = value - else: - self._max_consecutive_auto_reply_dict[sender] = value - - def max_consecutive_auto_reply(self, sender: Optional[Agent] = None) -> int: - """The maximum number of consecutive auto replies.""" - return ( - self._max_consecutive_auto_reply - if sender is None - else self._max_consecutive_auto_reply_dict[sender] - ) - - @property - # def chat_messages(self) -> Dict[Agent, List[Dict]]: - def chat_messages(self) -> Any: - """A dictionary of conversations from agent to list of messages.""" - all_gpts_messages = self.memory.message_memory.get_by_agent( - self.agent_context.conv_id, self.name - ) - return self._gpts_message_to_ai_message(all_gpts_messages) - - def last_message(self, agent: Optional[Agent] = None) -> Optional[Dict]: - """The last message exchanged with the agent. - - Args: - agent (Agent): The agent in the conversation. - If None and more than one agent's conversations are found, an error will be raised. - If None and only one conversation is found, the last message of the only conversation will be returned. - - Returns: - The last message exchanged with the agent. - """ - - if agent is None: - all_oai_messages = self.chat_messages() - n_conversations = len(all_oai_messages) - if n_conversations == 0: - return None - if n_conversations == 1: - for conversation in all_oai_messages.values(): - return conversation[-1] - raise ValueError( - "More than one conversation is found. Please specify the sender to get the last message." - ) - - agent_messages = self.memory.message_memory.get_between_agents( - self.agent_context.conv_id, self.name, agent.name - ) - if len(agent_messages) <= 0: - raise KeyError( - f"The agent '{agent.name}' is not present in any conversation. No history available for this agent." - ) - return self._gpts_message_to_ai_message(agent_messages)[-1] - - @staticmethod - def _message_to_dict(message: Union[Dict, str]): - """Convert a message to a dictionary. - - The message can be a string or a dictionary. The string will be put in the "content" field of the new dictionary. - """ - if isinstance(message, str): - return {"content": message} - elif isinstance(message, dict): - return message - else: - return dict(message) - - def append_rely_message(self, message: Union[Dict, str], role) -> None: - message = self._message_to_dict(message) - message["role"] = role - # create oai message to be appended to the oai conversation that can be passed to oai directly. - self._rely_messages.append(message) - - def reset_rely_message(self) -> None: - # create oai message to be appended to the oai conversation that can be passed to oai directly. - self._rely_messages = [] - - def append_message(self, message: Optional[Dict], role, sender: Agent) -> bool: - """ - Put the received message content into the collective message memory - Args: - message: - role: - sender: - - Returns: - - """ - oai_message = { - k: message[k] - for k in ( - "content", - "function_call", - "name", - "context", - "action_report", - "review_info", - "current_goal", - "model_name", - ) - if k in message - } - if "content" not in oai_message: - if "function_call" in oai_message: - oai_message[ - "content" - ] = None # if only function_call is provided, content will be set to None. - else: - return False - oai_message["role"] = "function" if message.get("role") == "function" else role - if "function_call" in oai_message: - oai_message[ - "role" - ] = "assistant" # only messages with role 'assistant' can have a function call. - oai_message["function_call"] = dict(oai_message["function_call"]) - - gpts_message: GptsMessage = GptsMessage( - conv_id=self.agent_context.conv_id, - team_mode=self.agent_context.team_mode, - sender=sender.name, - receiver=self.name, - role=role, - rounds=self.consecutive_auto_reply_counter, - current_goal=oai_message.get("current_goal", None), - content=oai_message.get("content", None), - context=json.dumps(oai_message["context"], ensure_ascii=False) - if "context" in oai_message - else None, - review_info=json.dumps(oai_message["review_info"], ensure_ascii=False) - if "review_info" in oai_message - else None, - action_report=json.dumps(oai_message["action_report"], ensure_ascii=False) - if "action_report" in oai_message - else None, - model_name=oai_message.get("model_name", None), - ) - - self.memory.message_memory.append(gpts_message) - return True - - async def a_send( - self, - message: Optional[Dict], - recipient: Agent, - reviewer: "Agent", - request_reply: Optional[bool] = True, - silent: Optional[bool] = False, - is_recovery: Optional[bool] = False, - ): - await recipient.a_receive( - message=message, - sender=self, - reviewer=reviewer, - request_reply=request_reply, - silent=silent, - is_recovery=is_recovery, - ) - - async def a_receive( - self, - message: Optional[Dict], - sender: Agent, - reviewer: "Agent", - request_reply: Optional[bool] = True, - silent: Optional[bool] = False, - is_recovery: Optional[bool] = False, - ): - if not is_recovery: - self.consecutive_auto_reply_counter = ( - sender.consecutive_auto_reply_counter + 1 - ) - self.process_received_message(message, sender, silent) - else: - logger.info("Process received retrying") - self.consecutive_auto_reply_counter = sender.consecutive_auto_reply_counter - if request_reply is False or request_reply is None: - logger.info("Messages that do not require a reply") - return - if self.is_termination_msg(message): - logger.info(f"TERMINATE!") - return - - verify_paas, reply = await self.a_generate_reply( - message=message, sender=sender, reviewer=reviewer, silent=silent - ) - if verify_paas: - await self.a_send( - message=reply, recipient=sender, reviewer=reviewer, silent=silent - ) - else: - # Exit after the maximum number of rounds of self-optimization - if self.current_retry_counter >= self.max_retry_count: - # If the maximum number of retries is exceeded, the abnormal answer will be returned directly. - logger.warning( - f"More than {self.current_retry_counter} times and still no valid answer is output." - ) - reply[ - "content" - ] = f"After trying {self.current_retry_counter} times, I still can't generate a valid answer. The current problem is:{reply['content']}!" - reply["is_termination"] = True - await self.a_send( - message=reply, recipient=sender, reviewer=reviewer, silent=silent - ) - # raise ValueError( - # f"After {self.current_retry_counter} rounds of re-optimization, we still cannot get an effective answer." - # ) - else: - self.current_retry_counter += 1 - logger.info( - "The generated answer failed to verify, so send it to yourself for optimization." - ) - await sender.a_send( - message=reply, recipient=self, reviewer=reviewer, silent=silent - ) - - async def a_notification( - self, - message: Union[Dict, str], - recipient: Agent, - ): - recipient.process_received_message(message, self) - - def _print_received_message(self, message: Union[Dict, str], sender: Agent): - # print the message received - print( - colored(sender.name, "yellow"), - "(to", - f"{self.name})-[{message.get('model_name', '')}]:\n", - flush=True, - ) - message = self._message_to_dict(message) - - if message.get("role") == "function": - func_print = ( - f"***** Response from calling function \"{message['name']}\" *****" - ) - print(colored(func_print, "green"), flush=True) - print(message["content"], flush=True) - print(colored("*" * len(func_print), "green"), flush=True) - else: - content = json.dumps(message.get("content"), ensure_ascii=False) - if content is not None: - if "context" in message: - content = AIWrapper.instantiate( - content, - message["context"], - self.agent_context.allow_format_str_template, - ) - print(content, flush=True) - if "function_call" in message: - function_call = dict(message["function_call"]) - func_print = f"***** Suggested function Call: {function_call.get('name', '(No function name found)')} *****" - print(colored(func_print, "green"), flush=True) - print( - "Arguments: \n", - function_call.get("arguments", "(No arguments found)"), - flush=True, - sep="", - ) - print(colored("*" * len(func_print), "green"), flush=True) - - review_info = message.get("review_info", None) - if review_info: - approve_print = f">>>>>>>>{sender.name} Review info: \n {'Pass' if review_info.get('approve') else 'Reject'}.{review_info.get('comments')}" - print(colored(approve_print, "green"), flush=True) - - action_report = message.get("action_report", None) - if action_report: - action_print = f">>>>>>>>{sender.name} Action report: \n{'execution succeeded' if action_report['is_exe_success'] else 'execution failed'},\n{action_report['content']}" - print(colored(action_print, "blue"), flush=True) - print("\n", "-" * 80, flush=True, sep="") - - def process_received_message(self, message, sender, silent): - message = self._message_to_dict(message) - # When the agent receives a message, the role of the message is "user". (If 'role' exists and is 'function', it will remain unchanged.) - valid = self.append_message(message, None, sender) - if not valid: - raise ValueError( - "Received message can't be converted into a valid ChatCompletion message. Either content or function_call must be provided." - ) - if not silent: - self._print_received_message(message, sender) - - async def a_review(self, message: Union[Dict, str], censored: "Agent"): - return True, None - - def _process_action_reply(self, action_reply: Optional[Union[str, Dict, None]]): - if isinstance(action_reply, str): - return {"is_exe_success": True, "content": action_reply} - elif isinstance(action_reply, dict): - return action_reply - elif action_reply is None: - return None - else: - return dict(action_reply) - - def _gpts_message_to_ai_message( - self, gpts_messages: Optional[List[GptsMessage]] - ) -> List[Dict]: - oai_messages: List[Dict] = [] - # Based on the current agent, all messages received are user, and all messages sent are assistant. - for item in gpts_messages: - role = "" - if item.role: - role = role - else: - if item.receiver == self.name: - role = ModelMessageRoleType.HUMAN - elif item.sender == self.name: - role = ModelMessageRoleType.AI - else: - continue - oai_messages.append( - { - "content": item.content, - "role": role, - "context": json.loads(item.context) - if item.context is not None - else None, - "review_info": json.loads(item.review_info) - if item.review_info is not None - else None, - "action_report": json.loads(item.action_report) - if item.action_report is not None - else None, - } - ) - return oai_messages - - def process_now_message( - self, - current_message: Optional[Dict], - sender, - rely_messages: Optional[List[Dict]] = None, - ): - current_goal = current_message.get("current_goal", None) - ### Convert and tailor the information in collective memory into contextual memory available to the current Agent - current_goal_messages = self._gpts_message_to_ai_message( - self.memory.message_memory.get_between_agents( - self.agent_context.conv_id, self.name, sender.name, current_goal - ) - ) - if current_goal_messages is None or len(current_goal_messages) <= 0: - current_message["role"] = ModelMessageRoleType.HUMAN - current_goal_messages = [current_message] - ### relay messages - cut_messages = [] - if rely_messages: - for rely_message in rely_messages: - action_report = rely_message.get("action_report", None) - if action_report: - rely_message["content"] = action_report["content"] - cut_messages.extend(rely_messages) - else: - cut_messages.extend(self._rely_messages) - - if len(current_goal_messages) < self.dialogue_memory_rounds: - cut_messages.extend(current_goal_messages) - else: - # TODO: allocate historical information based on token budget - cut_messages.extend(current_goal_messages[:2]) - # end_round = self.dialogue_memory_rounds - 2 - cut_messages.extend(current_goal_messages[-3:]) - return cut_messages - - async def a_system_fill_param(self): - self.update_system_message(self.DEFAULT_SYSTEM_MESSAGE) - - async def a_generate_reply( - self, - message: Optional[Dict], - sender: Agent, - reviewer: Agent, - silent: Optional[bool] = False, - rely_messages: Optional[List[Dict]] = None, - ): - ## 0.New message build - new_message = {} - new_message["context"] = message.get("context", None) - new_message["current_goal"] = message.get("current_goal", None) - - ## 1.LLM Reasonging - await self.a_system_fill_param() - current_messages = self.process_now_message(message, sender, rely_messages) - ai_reply, model = await self.a_reasoning_reply(messages=current_messages) - new_message["content"] = ai_reply - new_message["model_name"] = model - ## 2.Review of reasoning results - approve = True - comments = None - if reviewer and ai_reply: - approve, comments = await reviewer.a_review(ai_reply, self) - new_message["review_info"] = {"approve": approve, "comments": comments} - ## 3.reasoning result action - if approve: - excute_reply = await self.a_action_reply( - message=ai_reply, - sender=sender, - reviewer=reviewer, - ) - new_message["action_report"] = self._process_action_reply(excute_reply) - ## 4.verify reply - return await self.a_verify_reply(new_message, sender, reviewer) - - async def a_verify(self, message: Optional[Dict]): - return True, message - - async def _optimization_check(self, message: Optional[Dict]): - need_retry = False - fail_reason = "" - ## Check approval results - if "review_info" in message: - review_info = message.get("review_info") - if review_info and not review_info.get("approve"): - fail_reason = review_info.get("comments") - need_retry = True - ## Check execution results - if "action_report" in message and not need_retry: - action_report = message["action_report"] - if action_report: - if not action_report["is_exe_success"]: - fail_reason = action_report["content"] - need_retry = True - else: - if ( - not action_report["content"] - or len(action_report["content"].strip()) < 1 - ): - fail_reason = f'The code is executed successfully but the output:{action_report["content"]} is invalid or empty. Please reanalyze the target to generate valid code.' - need_retry = True - ## Verify the correctness of the results - if not need_retry: - verify_pass, verfiy_msg = await self.a_verify(message) - if not verify_pass: - need_retry = True - fail_reason = verfiy_msg - return need_retry, fail_reason - - async def a_verify_reply( - self, message: Optional[Dict], sender: "Agent", reviewer: "Agent", **kwargs - ) -> Union[str, Dict, None]: - need_retry, fail_reason = await self._optimization_check(message) - if need_retry: - ## Before optimization, wrong answers are stored in memory - await self.a_send( - message=message, - recipient=sender, - reviewer=reviewer, - request_reply=False, - ) - ## Send error messages to yourself for retrieval optimization and increase the number of retrievals - retry_message = {} - retry_message["context"] = message.get("context", None) - retry_message["current_goal"] = message.get("current_goal", None) - retry_message["model_name"] = message.get("model_name", None) - retry_message["content"] = fail_reason - ## Use the original sender to send the retry message to yourself - return False, retry_message - else: - ## The verification passes, the message is released, and the number of retries returns to 0. - self.current_retry_counter = 0 - return True, message - - async def a_retry_chat( - self, - recipient: "ConversableAgent", - reviewer: "Agent" = None, - clear_history: Optional[bool] = True, - silent: Optional[bool] = False, - **context, - ): - last_message: GptsMessage = self.memory.message_memory.get_last_message( - self.agent_context.conv_id - ) - self.consecutive_auto_reply_counter = last_message.rounds - message = { - "content": last_message.content, - "context": json.loads(last_message.context) - if last_message.context - else None, - "current_goal": last_message.current_goal, - "review_info": json.loads(last_message.review_info) - if last_message.review_info - else None, - "action_report": json.loads(last_message.action_report) - if last_message.action_report - else None, - "model_name": last_message.model_name, - } - await self.a_send( - message, - recipient, - reviewer, - request_reply=True, - silent=silent, - is_recovery=True, - ) - - async def a_initiate_chat( - self, - recipient: "ConversableAgent", - reviewer: "Agent" = None, - clear_history: Optional[bool] = True, - silent: Optional[bool] = False, - **context, - ): - await self.a_send( - { - "content": self.generate_init_message(**context), - }, - recipient, - reviewer, - request_reply=True, - silent=silent, - ) - - def reset(self): - """Reset the agent.""" - self.clear_history() - self.reset_consecutive_auto_reply_counter() - - for reply_func_tuple in self._reply_func_list: - if reply_func_tuple["reset_config"] is not None: - reply_func_tuple["reset_config"](reply_func_tuple["config"]) - else: - reply_func_tuple["config"] = copy.copy(reply_func_tuple["init_config"]) - - def reset_consecutive_auto_reply_counter(self): - """Reset the consecutive_auto_reply_counter of the sender.""" - self.consecutive_auto_reply_counter = 0 - - def clear_history(self, agent: Optional[Agent] = None): - """Clear the chat history of the agent. - - Args: - agent: the agent with whom the chat history to clear. If None, clear the chat history with all agents. - """ - pass - - def _get_model_priority(self): - llm_models_priority = self.agent_context.model_priority - if llm_models_priority: - if self.name in llm_models_priority: - model_priority = llm_models_priority[self.name] - else: - model_priority = llm_models_priority["default"] - return model_priority - else: - return None - - def _filter_health_models(self, need_uses: Optional[list]): - all_modes = self.agent_context.llm_models - can_uses = [] - for item in all_modes: - if item.model in need_uses: - can_uses.append(item) - return can_uses - - def _select_llm_model(self, old_model: str = None): - """ - LLM model selector, currently only supports manual selection, more strategies will be opened in the future - Returns: - """ - all_modes = self.agent_context.llm_models - model_priority = self._get_model_priority() - if model_priority and len(model_priority) > 0: - can_uses = self._filter_health_models(model_priority) - if len(can_uses) > 0: - return can_uses[0].model - - now_model = all_modes[0] - if old_model: - filtered_list = [item for item in all_modes if item.model != old_model] - if filtered_list and len(filtered_list) >= 1: - now_model = filtered_list[0] - return now_model.model - - async def a_reasoning_reply( - self, messages: Optional[List[Dict]] = None - ) -> Union[str, Dict, None]: - """(async) Reply based on the conversation history and the sender. - Args: - messages: a list of messages in the conversation history. - default_reply (str or dict): default reply. - sender: sender of an Agent instance. - exclude: a list of functions to exclude. - - Returns: - str or dict or None: reply. None if no reply is generated. - """ - last_model = None - last_err = None - retry_count = 0 - while retry_count < 3: - llm_model = self._select_llm_model(last_model) - try: - response = await self.client.create( - context=messages[-1].pop("context", None), - messages=self._oai_system_message + messages, - llm_model=llm_model, - max_new_tokens=self.agent_context.max_new_tokens, - temperature=self.agent_context.temperature, - ) - return response, llm_model - except LLMChatError as e: - logger.error(f"model:{llm_model} generate Failed!{str(e)}") - retry_count += 1 - last_model = llm_model - last_err = str(e) - await asyncio.sleep(15) ## TODO，Rate limit reached for gpt-3.5-turbo - - if last_err: - raise ValueError(last_err) - - async def a_action_reply( - self, - message: Optional[str] = None, - sender: Optional[Agent] = None, - reviewer: "Agent" = None, - exclude: Optional[List[Callable]] = None, - **kwargs, - ) -> Union[str, Dict, None]: - for reply_func_tuple in self._reply_func_list: - reply_func = reply_func_tuple["reply_func"] - if exclude and reply_func in exclude: - continue - if self._match_trigger(reply_func_tuple["trigger"], sender): - if asyncio.coroutines.iscoroutinefunction(reply_func): - final, reply = await reply_func( - self, - message=message, - sender=sender, - reviewer=reviewer, - config=reply_func_tuple["config"], - ) - else: - final, reply = reply_func( - self, - message=message, - sender=sender, - reviewer=reviewer, - config=reply_func_tuple["config"], - ) - if final: - return reply - return self._default_auto_reply - - def _match_trigger(self, trigger, sender): - """Check if the sender matches the trigger.""" - if trigger is None: - return sender is None - elif isinstance(trigger, str): - return trigger == sender.name - elif isinstance(trigger, type): - return isinstance(sender, trigger) - elif isinstance(trigger, Agent): - return trigger == sender - elif isinstance(trigger, Callable): - return trigger(sender) - elif isinstance(trigger, list): - return any(self._match_trigger(t, sender) for t in trigger) - else: - raise ValueError(f"Unsupported trigger type: {type(trigger)}") - - def generate_init_message(self, **context) -> Union[str, Dict]: - """Generate the initial message for the agent. - - Override this function to customize the initial message based on user's request. - If not overridden, "message" needs to be provided in the context. - """ - return context["message"] diff --git a/dbgpt/agent/agents/base_agent_new.py b/dbgpt/agent/agents/base_agent_new.py deleted file mode 100644 index f8ac83934..000000000 --- a/dbgpt/agent/agents/base_agent_new.py +++ /dev/null @@ -1,623 +0,0 @@ -from __future__ import annotations - -import asyncio -import json -import logging -from typing import Any, Dict, List, Optional, Tuple, Union - -from pydantic import BaseModel, Field - -from dbgpt.agent.actions.action import Action, ActionOutput -from dbgpt.agent.agents.agent_new import Agent, AgentContext -from dbgpt.agent.agents.llm.llm import LLMConfig, LLMStrategyType -from dbgpt.agent.agents.llm.llm_client import AIWrapper -from dbgpt.agent.agents.role import Role -from dbgpt.agent.memory.base import GptsMessage -from dbgpt.agent.memory.gpts_memory import GptsMemory -from dbgpt.agent.resource.resource_api import AgentResource, ResourceClient -from dbgpt.agent.resource.resource_loader import ResourceLoader -from dbgpt.core.interface.message import ModelMessageRoleType -from dbgpt.util.error_types import LLMChatError -from dbgpt.util.utils import colored - -logger = logging.getLogger(__name__) - - -class ConversableAgent(Role, Agent): - agent_context: Optional[AgentContext] = None - actions: List[Action] = Field(default_factory=list) - resources: List[AgentResource] = Field(default_factory=list) - llm_config: Optional[LLMConfig] = None - memory: GptsMemory = Field(default_factory=GptsMemory) - resource_loader: Optional[ResourceLoader] = None - max_retry_count: int = 3 - consecutive_auto_reply_counter: int = 0 - llm_client: Optional[AIWrapper] = None - oai_system_message: List[Dict] = Field(default_factory=list) - - class Config: - arbitrary_types_allowed = True - - def __init__(self, **kwargs): - Role.__init__(self, **kwargs) - Agent.__init__(self) - - def init_system_message(self): - content = self.prompt_template() - self.oai_system_message = [ - {"content": content, "role": ModelMessageRoleType.SYSTEM} - ] - - def check_available(self): - self.identity_check() - # check run context - if self.agent_context is None: - raise ValueError( - f"{self.name}[{self.profile}] Missing context in which agent is running！" - ) - - # rource check - for resource in self.resources: - if ( - self.resource_loader is None - or self.resource_loader.get_resesource_api(resource.type) is None - ): - raise ValueError( - f"Resource {resource.type}:{resource.value} missing resource loader implementation,unable to read resources!" - ) - - # action check - if self.actions and len(self.actions) > 0: - have_resource_types = [item.type for item in self.resources] - for action in self.actions: - if ( - action.resource_need - and action.resource_need not in have_resource_types - ): - raise ValueError( - f"{self.name}[{self.profile}] Missing resources required for runtime！" - ) - else: - if not self.is_human and not self.is_team: - raise ValueError( - f"This agent {self.name}[{self.profile}] is missing action modules." - ) - # llm check - if not self.is_human: - if self.llm_config is None or self.llm_config.llm_client is None: - raise ValueError( - f"{self.name}[{self.profile}] Model configuration is missing or model service is unavailable！" - ) - - async def a_preload_resource(self): - pass - - async def build(self) -> ConversableAgent: - # Check if agent is available - self.check_available() - - self.language = self.agent_context.language - # Preload resources - await self.a_preload_resource() - # Initialize resource loader - for action in self.actions: - action.init_resource_loader(self.resource_loader) - - # Initialize system messages - self.init_system_message() - - # Initialize LLM Server - if not self.is_human: - self.llm_client = AIWrapper(llm_client=self.llm_config.llm_client) - return self - - def bind(self, target: Any) -> ConversableAgent: - if isinstance(target, LLMConfig): - self.llm_config = target - elif isinstance(target, GptsMemory): - self.memory = target - elif isinstance(target, AgentContext): - self.agent_context = target - elif isinstance(target, ResourceLoader): - self.resource_loader = target - elif isinstance(target, list): - if target and len(target) > 0: - if self._is_list_of_type(target, Action): - self.actions.extend(target) - elif self._is_list_of_type(target, AgentResource): - self.resources = target - return self - - async def a_send( - self, - message: Union[Dict, str], - recipient: Agent, - reviewer: Optional[Agent] = None, - request_reply: Optional[bool] = True, - is_recovery: Optional[bool] = False, - ) -> None: - await recipient.a_receive( - message=message, - sender=self, - reviewer=reviewer, - request_reply=request_reply, - is_recovery=is_recovery, - ) - - async def a_receive( - self, - message: Optional[Dict], - sender: Agent, - reviewer: Optional[Agent] = None, - request_reply: Optional[bool] = None, - silent: Optional[bool] = False, - is_recovery: Optional[bool] = False, - ) -> None: - await self._a_process_received_message(message, sender) - if request_reply is False or request_reply is None: - return - - if not self.is_human: - is_success, reply = await self.a_generate_reply( - recive_message=message, sender=sender, reviewer=reviewer - ) - if reply is not None: - await self.a_send(reply, sender) - - def prepare_act_param(self) -> Optional[Dict]: - return {} - - async def a_generate_reply( - self, - recive_message: Optional[Dict], - sender: Agent, - reviewer: Agent = None, - rely_messages: Optional[List[Dict]] = None, - ): - logger.info( - f"generate agent reply!sender={sender}, rely_messages_len={rely_messages}" - ) - try: - reply_message = self._init_reply_message(recive_message=recive_message) - await self._system_message_assembly( - recive_message["content"], reply_message.get("context", None) - ) - - fail_reason = None - current_retry_counter = 0 - is_sucess = True - while current_retry_counter < self.max_retry_count: - if current_retry_counter > 0: - retry_message = self._init_reply_message( - recive_message=recive_message - ) - retry_message["content"] = fail_reason - retry_message["current_goal"] = recive_message.get( - "current_goal", None - ) - # The current message is a self-optimized message that needs to be recorded. - # It is temporarily set to be initiated by the originating end to facilitate the organization of historical memory context. - await sender.a_send( - retry_message, self, reviewer, request_reply=False - ) - - # 1.Think about how to do things - llm_reply, model_name = await self.a_thinking( - self._load_thinking_messages(recive_message, sender, rely_messages) - ) - reply_message["model_name"] = model_name - reply_message["content"] = llm_reply - - # 2.Review whether what is being done is legal - approve, comments = await self.a_review(llm_reply, self) - reply_message["review_info"] = { - "approve": approve, - "comments": comments, - } - - # 3.Act based on the results of your thinking - act_extent_param = self.prepare_act_param() - act_out: ActionOutput = await self.a_act( - message=llm_reply, - sender=sender, - reviewer=reviewer, - **act_extent_param, - ) - reply_message["action_report"] = act_out.dict() - - # 4.Reply information verification - check_paas, reason = await self.a_verify( - reply_message, sender, reviewer - ) - is_sucess = check_paas - # 5.Optimize wrong answers myself - if not check_paas: - current_retry_counter += 1 - # Send error messages and issue new problem-solving instructions - if current_retry_counter < self.max_retry_count: - await self.a_send( - reply_message, sender, reviewer, request_reply=False - ) - fail_reason = reason - else: - break - return is_sucess, reply_message - - except Exception as e: - logger.exception("Generate reply exception!") - return False, {"content": str(e)} - - async def a_thinking( - self, messages: Optional[List[Dict]], prompt: Optional[str] = None - ) -> Union[str, Dict, None]: - last_model = None - last_err = None - retry_count = 0 - # LLM inference automatically retries 3 times to reduce interruption probability caused by speed limit and network stability - while retry_count < 3: - llm_model = await self._a_select_llm_model(last_model) - try: - if prompt: - messages = self._new_system_message(prompt) + messages - else: - messages = self.oai_system_message + messages - - response = await self.llm_client.create( - context=messages[-1].pop("context", None), - messages=messages, - llm_model=llm_model, - max_new_tokens=self.agent_context.max_new_tokens, - temperature=self.agent_context.temperature, - ) - return response, llm_model - except LLMChatError as e: - logger.error(f"model:{llm_model} generate Failed!{str(e)}") - retry_count += 1 - last_model = llm_model - last_err = str(e) - await asyncio.sleep(10) - - if last_err: - raise ValueError(last_err) - - async def a_review( - self, message: Union[Dict, str], censored: Agent - ) -> Tuple[bool, Any]: - return True, None - - async def a_act( - self, - message: Optional[str], - sender: Optional[ConversableAgent] = None, - reviewer: Optional[ConversableAgent] = None, - **kwargs, - ) -> Optional[ActionOutput]: - last_out = None - for action in self.actions: - # Select the resources required by acton - need_resource = None - if self.resources and len(self.resources) > 0: - for item in self.resources: - if item.type == action.resource_need: - need_resource = item - break - - last_out: ActionOutput = await action.a_run( - ai_message=message, - resource=need_resource, - rely_action_out=last_out, - **kwargs, - ) - return last_out - - async def a_correctness_check(self, message: Optional[Dict]): - ## Verify the correctness of the results - return True, None - - async def a_verify( - self, message: Optional[Dict], sender: Agent, reviewer: Agent, **kwargs - ) -> Union[str, Dict, None]: - ## Check approval results - if "review_info" in message: - review_info = message.get("review_info") - if review_info and not review_info.get("approve"): - return False, review_info.get("comments") - - ## Check action run results - action_output: ActionOutput = ActionOutput.from_dict( - message.get("action_report", None) - ) - if action_output: - if not action_output.is_exe_success: - return False, action_output.content - elif not action_output.content or len(action_output.content.strip()) < 1: - return ( - False, - f"The current execution result is empty. Please rethink the question and background and generate a new answer.. ", - ) - - ## agent output correctness check - return await self.a_correctness_check(message) - - async def a_initiate_chat( - self, - recipient: "ConversableAgent", - reviewer: "Agent" = None, - clear_history: Optional[bool] = True, - **context, - ): - await self.a_send( - { - "content": context["message"], - "current_goal": context["message"], - }, - recipient, - reviewer, - request_reply=True, - ) - - ####################################################################### - ## Private Function Begin - ####################################################################### - - def _init_actions(self, actions: List[Action] = None): - self.actions = [] - for idx, action in enumerate(actions): - if not isinstance(action, Action): - self.actions.append(action()) - - async def _a_append_message( - self, message: Optional[Dict], role, sender: Agent - ) -> bool: - self.consecutive_auto_reply_counter = sender.consecutive_auto_reply_counter + 1 - oai_message = { - k: message[k] - for k in ( - "content", - "function_call", - "name", - "context", - "action_report", - "review_info", - "current_goal", - "model_name", - ) - if k in message - } - - gpts_message: GptsMessage = GptsMessage( - conv_id=self.agent_context.conv_id, - sender=sender.profile, - receiver=self.profile, - role=role, - rounds=self.consecutive_auto_reply_counter, - current_goal=oai_message.get("current_goal", None), - content=oai_message.get("content", None), - context=json.dumps(oai_message["context"], ensure_ascii=False) - if "context" in oai_message - else None, - review_info=json.dumps(oai_message["review_info"], ensure_ascii=False) - if "review_info" in oai_message - else None, - action_report=json.dumps(oai_message["action_report"], ensure_ascii=False) - if "action_report" in oai_message - else None, - model_name=oai_message.get("model_name", None), - ) - - self.memory.message_memory.append(gpts_message) - return True - - def _print_received_message(self, message: Union[Dict], sender: ConversableAgent): - # print the message received - print("\n", "-" * 80, flush=True, sep="") - print( - colored(sender.name if sender.name else sender.profile, "yellow"), - "(to", - f"{self.name if self.name else self.profile})-[{message.get('model_name', '')}]:\n", - flush=True, - ) - - content = json.dumps(message.get("content"), ensure_ascii=False) - if content is not None: - print(content, flush=True) - - review_info = message.get("review_info", None) - if review_info: - approve_print = f">>>>>>>>{sender.name if sender.name else sender.profile} Review info: \n {'Pass' if review_info.get('approve') else 'Reject'}.{review_info.get('comments')}" - print(colored(approve_print, "green"), flush=True) - - action_report = message.get("action_report", None) - if action_report: - action_print = f">>>>>>>>{sender.name if sender.name else sender.profile} Action report: \n{'execution succeeded' if action_report['is_exe_success'] else 'execution failed'},\n{action_report['content']}" - print(colored(action_print, "blue"), flush=True) - - print("\n", "-" * 80, flush=True, sep="") - - async def _a_process_received_message(self, message: Optional[Dict], sender: Agent): - valid = await self._a_append_message(message, None, sender) - if not valid: - raise ValueError( - "Received message can't be converted into a valid ChatCompletion message. Either content or function_call must be provided." - ) - - self._print_received_message(message, sender) - - async def _system_message_assembly( - self, qustion: Optional[str], context: Optional[Dict] = None - ): - ## system message - self.init_system_message() - if len(self.oai_system_message) > 0: - resource_prompt_list = [] - for item in self.resources: - resource_client = self.resource_loader.get_resesource_api(item.type) - resource_prompt_list.append( - await resource_client.get_resource_prompt( - self.agent_context.conv_id, item, qustion - ) - ) - if context is None: - context = {} - - resource_prompt = "" - if len(resource_prompt_list) > 0: - resource_prompt = "RESOURCES:" + "\n".join(resource_prompt_list) - - out_schema = "" - if self.actions and len(self.actions) > 0: - out_schema = self.actions[0].ai_out_schema - for message in self.oai_system_message: - new_content = message["content"].format( - resource_prompt=resource_prompt, - out_schema=out_schema, - **context, - ) - message["content"] = new_content - - def _excluded_models( - self, - all_models: Optional[List[str]], - order_llms: Optional[List[str]] = [], - excluded_models: Optional[List[str]] = [], - ): - can_uses = [] - if order_llms and len(order_llms) > 0: - for llm_name in order_llms: - if llm_name in all_models: - if not excluded_models or llm_name not in excluded_models: - can_uses.append(llm_name) - else: - for llm_name in all_models: - if not excluded_models or llm_name not in excluded_models: - can_uses.append(llm_name) - - return can_uses - - async def _a_select_llm_model( - self, excluded_models: Optional[List[str]] = None - ) -> str: - logger.info(f"_a_select_llm_model:{excluded_models}") - try: - all_models = await self.llm_config.llm_client.models() - all_model_names = [item.model for item in all_models] - # TODO Currently only two strategies, priority and default, are implemented. - if self.llm_config.llm_strategy == LLMStrategyType.Priority: - priority: List[str] = [] - if self.llm_config.strategy_context is not None: - priority: List[str] = json.loads(self.llm_config.strategy_context) - can_uses = self._excluded_models( - all_model_names, priority, excluded_models - ) - else: - can_uses = self._excluded_models(all_model_names, None, excluded_models) - if can_uses and len(can_uses) > 0: - return can_uses[0] - else: - raise ValueError("No model service available!") - except Exception as e: - logger.error(f"{self.profile} get next llm failed!{str(e)}") - raise ValueError(f"Failed to allocate model service,{str(e)}!") - - def _init_reply_message(self, recive_message): - """ - Initialize a new message from the received message - Args: - recive_message: - - Returns: - - """ - new_message = {} - new_message["context"] = recive_message.get("context", None) - new_message["current_goal"] = recive_message.get("current_goal", None) - return new_message - - def _convert_to_ai_message( - self, gpts_messages: Optional[List[GptsMessage]] - ) -> List[Dict]: - oai_messages: List[Dict] = [] - # Based on the current agent, all messages received are user, and all messages sent are assistant. - for item in gpts_messages: - role = "" - if item.role: - role = role - else: - if item.receiver == self.profile: - role = ModelMessageRoleType.HUMAN - elif item.sender == self.profile: - role = ModelMessageRoleType.AI - else: - continue - - # Message conversion, priority is given to converting execution results, and only model output results will be used if not. - content = item.content - if item.action_report: - action_out = ActionOutput.from_dict(json.loads(item.action_report)) - if ( - action_out is not None - and action_out.is_exe_success - and action_out.content is not None - ): - content = action_out.content - oai_messages.append( - { - "content": content, - "role": role, - "context": json.loads(item.context) - if item.context is not None - else None, - } - ) - return oai_messages - - def _load_thinking_messages( - self, - receive_message: Optional[Dict], - sender, - rely_messages: Optional[List[Dict]] = None, - ) -> Optional[List[Dict]]: - current_goal = receive_message.get("current_goal", None) - - ### Convert and tailor the information in collective memory into contextual memory available to the current Agent - current_goal_messages = self._convert_to_ai_message( - self.memory.message_memory.get_between_agents( - self.agent_context.conv_id, self.profile, sender.profile, current_goal - ) - ) - - # When there is no target and context, the current received message is used as the target problem - if current_goal_messages is None or len(current_goal_messages) <= 0: - receive_message["role"] = ModelMessageRoleType.HUMAN - current_goal_messages = [receive_message] - - ### relay messages - cut_messages = [] - if rely_messages: - ## When directly relying on historical messages, use the execution result content as a dependency - for rely_message in rely_messages: - action_report: Optional[ActionOutput] = ActionOutput.from_dict( - rely_message.get("action_report", None) - ) - if action_report: - rely_message["content"] = action_report.content - - cut_messages.extend(rely_messages) - - # TODO: allocate historical information based on token budget - if len(current_goal_messages) < 5: - cut_messages.extend(current_goal_messages) - else: - # For the time being, the smallest size of historical message records will be used by default. - # Use the first two rounds of messages to understand the initial goals - cut_messages.extend(current_goal_messages[:2]) - # Use information from the last three rounds of communication to ensure that current thinking knows what happened and what to do in the last communication - cut_messages.extend(current_goal_messages[-3:]) - return cut_messages - - def _new_system_message(self, content): - """Return the system message.""" - return [{"content": content, "role": ModelMessageRoleType.SYSTEM}] - - def _is_list_of_type(self, lst: List[Any], type_cls: type) -> bool: - return all(isinstance(item, type_cls) for item in lst) diff --git a/dbgpt/agent/agents/expand/Indicator_assistant_agent.py b/dbgpt/agent/agents/expand/Indicator_assistant_agent.py deleted file mode 100644 index 957edc968..000000000 --- a/dbgpt/agent/agents/expand/Indicator_assistant_agent.py +++ /dev/null @@ -1,35 +0,0 @@ -import logging -from typing import Callable, Dict, List, Literal, Optional, Union - -from dbgpt.agent.actions.indicator_action import IndicatorAction -from dbgpt.agent.agents.base_agent_new import ConversableAgent -from dbgpt.core.interface.message import ModelMessageRoleType - -logger = logging.getLogger() - -CHECK_RESULT_SYSTEM_MESSAGE = f""" -You are an expert in analyzing the results of a summary task. -Your responsibility is to check whether the summary results can summarize the input provided by the user, and then make a judgment. You need to answer according to the following rules: - Rule 1: If you think the summary results can summarize the input provided by the user, only return True. - Rule 2: If you think the summary results can NOT summarize the input provided by the user, return False and the reason, splitted by | and ended by TERMINATE. For instance: False|Some important concepts in the input are not summarized. TERMINATE -""" - - -class IndicatorAssistantAgent(ConversableAgent): - name = "Indicator" - profile: str = "Indicator" - goal: str = "Summarize answer summaries based on user questions from provided resource information or from historical conversation memories." - - constraints: List[str] = [ - "Prioritize the summary of answers to user questions from the improved resource text. If no relevant information is found, summarize it from the historical dialogue memory given. It is forbidden to make up your own.", - "You need to first detect user's question that you need to answer with your summarization.", - "Extract the provided text content used for summarization.", - "Then you need to summarize the extracted text content.", - "Output the content of summarization ONLY related to user's question. The output language must be the same to user's question language.", - """If you think the provided text content is not related to user questions at all, ONLY output "Did not find the information you want."!!.""", - ] - desc: str = "You can summarize provided text content according to user's questions and output the summaraization." - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._init_actions([IndicatorAction]) diff --git a/dbgpt/agent/agents/expand/__init__.py b/dbgpt/agent/agents/expand/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/dbgpt/agent/agents/expand/code_assistant_agent.py b/dbgpt/agent/agents/expand/code_assistant_agent.py deleted file mode 100644 index c84e2917f..000000000 --- a/dbgpt/agent/agents/expand/code_assistant_agent.py +++ /dev/null @@ -1,67 +0,0 @@ -from typing import Callable, Dict, List, Literal, Optional, Union - -from dbgpt.agent.actions.code_action import CodeAction -from dbgpt.core.interface.message import ModelMessageRoleType -from dbgpt.util.string_utils import str_to_bool - -from ..base_agent_new import ConversableAgent - -CHECK_RESULT_SYSTEM_MESSAGE = f""" -You are an expert in analyzing the results of task execution. Your responsibility is to analyze the task goals and execution results provided by the user, and then make a judgment. You need to answer according to the following rules: - Rule 1: Determine whether the content of the focused execution results is related to the task target content and whether it can be used as the answer to the target question. For those who do not understand the content, as long as the execution result type is required, it can be judged as correct. - Rule 2: There is no need to pay attention to whether the boundaries, time range, and values of the answer content are correct. - -As long as the task goal and execution result meet the above rules, True will be returned; otherwise, False will be returned and the failure reason will be given. -For example: - If it is determined to be successful, only true will be returned, such as: True - If it is determined to be a failure, return false and the reason, such as: False. There are no numbers in the execution results that answer the computational goals of the mission. -""" - - -class CodeAssistantAgent(ConversableAgent): - name: str = "Turing" - profile: str = "CodeEngineer" - goal: str = """Solve tasks using your coding and language skills. - In the following cases, suggest python code (in a python coding block) or shell script (in a sh coding block) for the user to execute. - 1. When you need to collect info, use the code to output the info you need, for example, browse or search the web, download/read a file, print the content of a webpage or a file, get the current date/time, check the operating system. After sufficient info is printed and the task is ready to be solved based on your language skill, you can solve the task by yourself. - 2. When you need to perform some task with code, use the code to perform the task and output the result. Finish the task smartly.""" - constraints: List[str] = [ - """The user cannot provide any other feedback or perform any other action beyond executing the code you suggest. The user can't modify your code. So do not suggest incomplete code which requires users to modify. Don't use a code block if it's not intended to be executed by the user.Don't ask users to copy and paste results. Instead, the "Print" function must be used for output when relevant.""", - """When using code, you must indicate the script type in the code block. Please don't include multiple code blocks in one response.""", - """If you want the user to save the code in a file before executing it, put # filename: inside the code block as the first line.""", - """If you receive user input that indicates an error in the code execution, fix the error and output the complete code again. It is recommended to use the complete code rather than partial code or code changes. If the error cannot be fixed, or the task is not resolved even after the code executes successfully, analyze the problem, revisit your assumptions, gather additional information you need from historical conversation records, and consider trying a different approach""", - """Unless necessary, give priority to solving problems with python code. If it involves downloading files or storing data locally, please use "Print" to output the full file path of the stored data and a brief introduction to the data.""", - """The output content of the "print" function will be passed to other LLM agents as dependent data. Please control the length of the output content of the "print" function. The "print" function only outputs part of the key data information that is relied on, and is as concise as possible.""", - """The code is executed without user participation. It is forbidden to use methods that will block the process or need to be shut down, such as the plt.show() method of matplotlib.pyplot as plt.""", - """It is prohibited to fabricate non-existent data to achieve goals.""", - ] - desc: str = """Can independently write and execute python/shell code to solve various problems""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._init_actions([CodeAction]) - - async def a_correctness_check(self, message: Optional[Dict]): - task_gogal = message.get("current_goal", None) - action_report = message.get("action_report", None) - task_result = "" - if action_report: - task_result = action_report.get("content", "") - - check_result, model = await self.a_thinking( - messages=[ - { - "role": ModelMessageRoleType.HUMAN, - "content": f"""Please understand the following task objectives and results and give your judgment: - Task Gogal: {task_gogal} - Execution Result: {task_result} - """, - } - ], - prompt=CHECK_RESULT_SYSTEM_MESSAGE, - ) - success = str_to_bool(check_result) - fail_reason = None - if not success: - fail_reason = f"Your answer was successfully executed by the agent, but the goal cannot be completed yet. Please regenerate based on the failure reason:{check_result}" - return success, fail_reason diff --git a/dbgpt/agent/agents/expand/dashboard_assistant_agent.py b/dbgpt/agent/agents/expand/dashboard_assistant_agent.py deleted file mode 100644 index 6f122dbfd..000000000 --- a/dbgpt/agent/agents/expand/dashboard_assistant_agent.py +++ /dev/null @@ -1,38 +0,0 @@ -import json -from typing import Callable, Dict, List, Literal, Optional, Union - -from dbgpt.agent.actions.dashboard_action import DashboardAction -from dbgpt.agent.plugin.commands.command_manage import ApiCall -from dbgpt.util.json_utils import find_json_objects - -from ...memory.gpts_memory import GptsMemory -from ..base_agent_new import ConversableAgent - - -class DashboardAssistantAgent(ConversableAgent): - name: str = "Visionary" # Chartwell - - profile: str = "Reporter" - goal: str = "Read the provided historical messages, collect various analysis SQLs from them, and assemble them into professional reports." - constraints: List[str] = [ - "You are only responsible for collecting and sorting out the analysis SQL that already exists in historical messages, and do not generate any analysis sql yourself.", - "In order to build a report with rich display types, you can appropriately adjust the display type of the charts you collect so that you can build a better report. Of course, you can choose from the following available display types: {display_type}", - "Please read and completely collect all analysis sql in the historical conversation, and do not omit or modify the content of the analysis sql.", - ] - desc: str = "Observe and organize various analysis results and construct professional reports" - - max_retry_count: int = 3 - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._init_actions([DashboardAction]) - - def _init_reply_message(self, recive_message): - reply_message = super()._init_reply_message(recive_message) - reply_message["context"] = { - "display_type": self.actions[0].render_prompt(), - "dialect": self.resource_loader.get_resesource_api( - self.actions[0].resource_need - ).get_data_type(self.resources[0]), - } - return reply_message diff --git a/dbgpt/agent/agents/expand/data_scientist_agent.py b/dbgpt/agent/agents/expand/data_scientist_agent.py deleted file mode 100644 index 4e4818f99..000000000 --- a/dbgpt/agent/agents/expand/data_scientist_agent.py +++ /dev/null @@ -1,87 +0,0 @@ -import json -import logging -from typing import Callable, Dict, List, Literal, Optional, Union - -from dbgpt.agent.actions.action import ActionOutput -from dbgpt.agent.actions.chart_action import ChartAction -from dbgpt.agent.resource.resource_api import ResourceType -from dbgpt.agent.resource.resource_db_api import ResourceDbClient - -from ..base_agent_new import ConversableAgent - -logger = logging.getLogger(__name__) - - -class DataScientistAgent(ConversableAgent): - name = "Edgar" - profile: str = "DataScientist" - goal: str = "Use correct {dialect} SQL to analyze and solve tasks based on the data structure information of the database given in the resource." - constraints: List[str] = [ - "Please check the generated SQL carefully. Please strictly abide by the data structure definition given. It is prohibited to use non-existent fields and data values. Do not use fields from table A to table B. You can perform multi-table related queries.", - "If the data and fields that need to be analyzed in the target are in different tables, it is recommended to use multi-table correlation queries first, and pay attention to the correlation between multiple table structures.", - "It is forbidden to construct data by yourself as a query condition. If you want to query a specific field, if the value of the field is provided, then you can perform a group statistical query on the field.", - "Please select an appropriate one from the supported display methods for data display. If no suitable display type is found, table display is used by default. Supported display types: \n {display_type}", - ] - desc: str = "Use database resources to conduct data analysis, analyze SQL, and provide recommended rendering methods." - max_retry_count: int = 5 - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._init_actions([ChartAction]) - - def _init_reply_message(self, recive_message): - reply_message = super()._init_reply_message(recive_message) - reply_message["context"] = { - "display_type": self.actions[0].render_prompt(), - "dialect": self.resource_loader.get_resesource_api( - self.actions[0].resource_need - ).get_data_type(self.resources[0]), - } - return reply_message - - async def a_correctness_check(self, message: Optional[Dict]): - action_reply = message.get("action_report", None) - if action_reply is None: - return ( - False, - f"No executable analysis SQL is generated,{message['content']}.", - ) - action_out = ActionOutput.from_dict(action_reply) - if action_out.is_exe_success == False: - return ( - False, - f"Please check your answer, {action_out.content}.", - ) - action_reply_obj = json.loads(action_out.content) - sql = action_reply_obj.get("sql", None) - if not sql: - return ( - False, - "Please check your answer, the sql information that needs to be generated is not found.", - ) - try: - resource_db_client: ResourceDbClient = ( - self.resource_loader.get_resesource_api( - ResourceType(action_out.resource_type) - ) - ) - - columns, values = await resource_db_client.a_query( - db=action_out.resource_value, sql=sql - ) - if not values or len(values) <= 0: - return ( - False, - "Please check your answer, the current SQL cannot find the data to determine whether filtered field values or inappropriate filter conditions are used.", - ) - else: - logger.info( - f"reply check success! There are {len(values)} rows of data" - ) - return True, None - except Exception as e: - logger.exception(f"DataScientist check exception！{str(e)}") - return ( - False, - f"SQL execution error, please re-read the historical information to fix this SQL. The error message is as follows:{str(e)}", - ) diff --git a/dbgpt/agent/agents/expand/plugin_assistant_agent.py b/dbgpt/agent/agents/expand/plugin_assistant_agent.py deleted file mode 100644 index aab0cff33..000000000 --- a/dbgpt/agent/agents/expand/plugin_assistant_agent.py +++ /dev/null @@ -1,61 +0,0 @@ -import logging -from pathlib import Path -from typing import Callable, Dict, List, Literal, Optional, Union - -from dbgpt.agent.actions.plugin_action import PluginAction -from dbgpt.agent.plugin.generator import PluginPromptGenerator -from dbgpt.agent.resource.resource_api import ResourceType -from dbgpt.agent.resource.resource_plugin_api import ResourcePluginClient - -from ..base_agent_new import ConversableAgent - -try: - from termcolor import colored -except ImportError: - - def colored(x, *args, **kwargs): - return x - - -logger = logging.getLogger(__name__) - - -class PluginAssistantAgent(ConversableAgent): - plugin_generator: PluginPromptGenerator = None - - name = "LuBan" - profile: str = "ToolExpert" - goal: str = ( - "Read and understand the tool information given in the resources below to understand their " - "capabilities and how to use them,and choosing the right tools to achieve the user's goals." - ) - constraints: List[str] = [ - "Please read the parameter definition of the tool carefully and extract the specific parameters required to execute the tool from the user gogal.", - "Please output the selected tool name and specific parameter information in json format according to the following required format. If there is an example, please refer to the sample format output.", - ] - desc: str = "You can use the following tools to complete the task objectives, tool information: {tool_infos}" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._init_actions([PluginAction]) - - @property - def introduce(self, **kwargs): - return self.desc.format( - tool_infos=self.plugin_generator.generate_commands_string() - ) - - async def a_preload_resource(self): - plugin_loader_client: ResourcePluginClient = ( - self.resource_loader.get_resesource_api(ResourceType.Plugin) - ) - item_list = [] - for item in self.resources: - if item.type == ResourceType.Plugin: - item_list.append(item.value) - self.plugin_generator = await plugin_loader_client.a_load_plugin( - item_list, self.plugin_generator - ) - - def prepare_act_param(self) -> Optional[Dict]: - return {"plugin_generator": self.plugin_generator} diff --git a/dbgpt/agent/agents/expand/retrieve_summary_assistant_agent.py b/dbgpt/agent/agents/expand/retrieve_summary_assistant_agent.py deleted file mode 100644 index 6d492121a..000000000 --- a/dbgpt/agent/agents/expand/retrieve_summary_assistant_agent.py +++ /dev/null @@ -1,608 +0,0 @@ -import asyncio -import glob -import json -import logging -import os -import pdb -from typing import Callable, Dict, List, Literal, Optional, Union -from urllib.parse import urlparse - -import pypdf -import requests -import tiktoken -from bs4 import BeautifulSoup - -from dbgpt.agent.agents.agent import Agent, AgentContext -from dbgpt.agent.agents.base_agent import ConversableAgent -from dbgpt.agent.memory.gpts_memory import GptsMemory -from dbgpt.agent.plugin.commands.command_manage import ApiCall -from dbgpt.configs.model_config import PILOT_PATH -from dbgpt.core.interface.message import ModelMessageRoleType - -try: - from termcolor import colored -except ImportError: - - def colored(x, *args, **kwargs): - return x - - -try: - from unstructured.partition.auto import partition - - HAS_UNSTRUCTURED = True -except ImportError: - HAS_UNSTRUCTURED = False - -logger = logging.getLogger() - -TEXT_FORMATS = [ - "txt", - "json", - "csv", - "tsv", - "md", - "html", - "htm", - "rtf", - "rst", - "jsonl", - "log", - "xml", - "yaml", - "yml", - "pdf", -] -UNSTRUCTURED_FORMATS = [ - "doc", - "docx", - "epub", - "msg", - "odt", - "org", - "pdf", - "ppt", - "pptx", - "rtf", - "rst", - "xlsx", -] # These formats will be parsed by the 'unstructured' library, if installed. -if HAS_UNSTRUCTURED: - TEXT_FORMATS += UNSTRUCTURED_FORMATS - TEXT_FORMATS = list(set(TEXT_FORMATS)) - -VALID_CHUNK_MODES = frozenset({"one_line", "multi_lines"}) - - -class RetrieveSummaryAssistantAgent(ConversableAgent): - """(In preview) Assistant agent, designed to solve a task with LLM. - - AssistantAgent is a subclass of ConversableAgent configured with a default system message. - The default system message is designed to solve a task with LLM, - including suggesting python code blocks and debugging. - `human_input_mode` is default to "NEVER" - and `code_execution_config` is default to False. - This agent doesn't execute code by default, and expects the user to execute the code. - """ - - DEFAULT_SYSTEM_MESSAGE = """You're an expert extrater. You need to extract - Please complete this task step by step following instructions below: - 1. You need to first ONLY extract user's question that you need to answer without ANY file paths and URLs. - 3. Extract the provided file paths and URLs. - 4. Construct the extracted file paths and URLs as a list of strings. - 5. ONLY output the extracted results with the following json format: "{"user_question": user's question, "file_list": file&URL list}". - """ - - PROMPT_QA = """You are a great summary writter to summarize the provided text content according to user questions. - - User's Question is: {input_question} - - Provided text content is: {input_context} - - Please complete this task step by step following instructions below: - 1. You need to first detect user's question that you need to answer with your summarization. - 2. Then you need to summarize the provided text content that ONLY CAN ANSWER user's question and filter useless information as possible as you can. YOU CAN ONLY USE THE PROVIDED TEXT CONTENT!! DO NOT CREATE ANY SUMMARIZATION WITH YOUR OWN KNOWLEGE!!! - 3. Output the content of summarization that ONLY CAN ANSWER user's question and filter useless information as possible as you can. The output language must be the same to user's question language!! You must give as short an summarization as possible!!! DO NOT CREATE ANY SUMMARIZATION WITH YOUR OWN KNOWLEGE!!! - - ####Important Notice#### - If the provided text content CAN NOT ANSWER user's question, ONLY output "NO RELATIONSHIP.UPDATE TEXT CONTENT."!!. - """ - - CHECK_RESULT_SYSTEM_MESSAGE = f""" - You are an expert in analyzing the results of a summary task. - Your responsibility is to check whether the summary results can summarize the input provided by the user, and then make a judgment. You need to answer according to the following rules: - Rule 1: If you think the summary results can summarize the input provided by the user, only return True. - Rule 2: If you think the summary results can NOT summarize the input provided by the user, return False and the reason, splitted by | and ended by TERMINATE. For instance: False|Some important concepts in the input are not summarized. TERMINATE - """ - - DEFAULT_DESCRIBE = """Summarize provided content according to user's questions and the provided file paths.""" - - NAME = "RetrieveSummarizer" - - def __init__( - self, - memory: GptsMemory, - agent_context: AgentContext, - describe: Optional[str] = DEFAULT_DESCRIBE, - is_termination_msg: Optional[Callable[[Dict], bool]] = lambda x: isinstance( - x, dict - ) - and "TERMINATE" in str(x).upper(), - max_consecutive_auto_reply: Optional[int] = None, - human_input_mode: Optional[str] = "NEVER", - retrieve_config: Optional[Dict] = None, - **kwargs, - ): - super().__init__( - name=self.NAME, - memory=memory, - describe=describe, - system_message="", - is_termination_msg=is_termination_msg, - max_consecutive_auto_reply=max_consecutive_auto_reply, - human_input_mode="NEVER", - agent_context=agent_context, - **kwargs, - ) - - self.chunk_token_size = 4000 - self.chunk_mode = "multi_lines" - self._model = "gpt-3.5-turbo-16k" - self._max_tokens = self.get_max_tokens(self._model) - self.context_max_tokens = self._max_tokens * 0.8 - self.search_string = "" # the search string used in the current query - self.chunks = [] - - # Register_reply - self.register_reply(Agent, RetrieveSummaryAssistantAgent.retrieve_summary_reply) - self.agent_context = agent_context - - @staticmethod - def get_max_tokens(model="gpt-3.5-turbo"): - if "32k" in model: - return 32000 - elif "16k" in model: - return 16000 - elif "gpt-4" in model: - return 8000 - else: - return 4000 - - async def a_generate_reply( - self, - message: Optional[Dict], - sender: Agent, - reviewer: Agent, - silent: Optional[bool] = False, - rely_messages: Optional[List[Dict]] = None, - ): - print("HERE IS THE MESSAGE!!!!!") - print(message["content"]) - ## 1.Using LLM to reason user's question and list of file and URLs context - await self.a_system_fill_param() - await asyncio.sleep(5) ##TODO Rate limit reached for gpt-3.5-turbo - current_messages = self.process_now_message(message, sender, rely_messages) - print("HERE IS THE CURRENT MESSAGE!!!!!") - print(current_messages) - ai_reply, model = await self.a_reasoning_reply(messages=current_messages) - ai_reply_dic = json.loads(ai_reply) - user_question = ai_reply_dic["user_question"] - file_list = ai_reply_dic["file_list"] - - ## 2. Split files and URLs in the file list dictionary into chunks - extracted_files = self._get_files_from_dir(file_list) - self.chunks = self._split_files_to_chunks(files=extracted_files) - - ## New message build - new_message = {} - new_message["context"] = current_messages[-1].get("context", None) - new_message["current_goal"] = current_messages[-1].get("current_goal", None) - new_message["role"] = "assistant" - new_message["content"] = user_question - new_message["model_name"] = model - # current_messages.append(new_message) - ## 3. Update system message as a summarizer message for each chunk - print(len(self.chunks)) - ## Summary message build - summary_message = {} - summary_message["context"] = message.get("context", None) - summary_message["current_goal"] = message.get("current_goal", None) - - summaries = "" - count = 0 - for chunk in self.chunks[:]: - count += 1 - print(count) - temp_sys_message = self.PROMPT_QA.format( - input_question=user_question, input_context=chunk - ) - self.update_system_message(system_message=temp_sys_message) - chunk_message = self.process_now_message( - current_message=new_message, sender=sender, rely_messages=None - ) - chunk_message[0]["role"] = "assistant" - chunk_ai_reply, model = await self.a_reasoning_reply(messages=chunk_message) - if chunk_ai_reply != "NO RELATIONSHIP. UPDATE TEXT CONTENT.": - summaries += f"{chunk_ai_reply}\n" - - temp_sys_message = self.PROMPT_QA.format( - input_question=user_question, input_context=summaries - ) - self.update_system_message(system_message=temp_sys_message) - final_summary_message = self.process_now_message( - current_message=new_message, sender=sender, rely_messages=None - ) - final_summary_message[0]["role"] = "assistant" - final_summary_ai_reply, model = await self.a_reasoning_reply( - messages=final_summary_message - ) - summary_message["content"] = final_summary_ai_reply - summary_message["model_name"] = model - print("HERE IS THE FINAL SUMMARY!!!!!") - print(final_summary_ai_reply) - - ## 4.Review of reasoning results - approve = True - comments = None - if reviewer and final_summary_ai_reply: - approve, comments = await reviewer.a_review(final_summary_ai_reply, self) - summary_message["review_info"] = {"approve": approve, "comments": comments} - - ## 3.reasoning result action - if approve: - excute_reply = await self.a_action_reply( - message=final_summary_ai_reply, - sender=sender, - reviewer=reviewer, - ) - summary_message["action_report"] = self._process_action_reply(excute_reply) - - # 4.verify reply - return await self.a_verify_reply(summary_message, sender, reviewer) - - async def a_verify(self, message: Optional[Dict]): - self.update_system_message(self.CHECK_RESULT_SYSTEM_MESSAGE) - current_goal = message.get("current_goal", None) - action_report = message.get("action_report", None) - task_result = "" - if action_report: - task_result = action_report.get("content", "") - - check_result, model = await self.a_reasoning_reply( - [ - { - "role": ModelMessageRoleType.HUMAN, - "content": f"""Please understand the following user input and summary results and give your judgment: - User Input: {current_goal} - Summary Results: {task_result} - """, - } - ] - ) - fail_reason = "" - if "True" in check_result: - success = True - else: - success = False - try: - _, fail_reason = check_result.split("|") - fail_reason = f"The summary results cannot summarize the user input due to: {fail_reason}. Please re-understand and complete the summary task." - except: - logger.warning( - f"The model thought the results are irrelevant but did not give the correct format of results." - ) - fail_reason = "The summary results cannot summarize the user input. Please re-understand and complete the summary task." - return success, fail_reason - - async def retrieve_summary_reply( - self, - message: Optional[str] = None, - sender: Optional[Agent] = None, - reviewer: Optional[Agent] = None, - config: Optional[Union[Dict, Literal[False]]] = None, - ): - """Generate a reply with summary.""" - # TODO: - # 1. Extract User Question from massage - Done with parameteres - # 2. Extract file / webpage list from message - # 3. Summarize each chunk - # 4. Combine summarization of each chunk - - fail_reason = None - response_success = True - view = None - content = None - if message is None: - # Answer failed, turn on automatic repair - fail_reason += f"Nothing is summarized, please check your input." - response_success = False - else: - try: - if "NO RELATIONSHIP." in message: - fail_reason = f"Return summarization error, the provided text content has no relationship to user's question. TERMINATE." - response_success = False - else: - content = message - view = content - except Exception as e: - fail_reason = f"Return summarization error, {str(e)}" - response_success = False - - if not response_success: - content = fail_reason - return True, { - "is_exe_success": response_success, - "content": content, - "view": view, - } - - def _get_files_from_dir( - self, - dir_path: Union[str, List[str]], - types: list = TEXT_FORMATS, - recursive: bool = True, - ): - """Return a list of all the files in a given directory, a url, a file path or a list of them.""" - if len(types) == 0: - raise ValueError("types cannot be empty.") - types = [t[1:].lower() if t.startswith(".") else t.lower() for t in set(types)] - types += [t.upper() for t in types] - - files = [] - # If the path is a list of files or urls, process and return them - if isinstance(dir_path, list): - for item in dir_path: - if os.path.isfile(item): - files.append(item) - elif self._is_url(item): - files.append(self._get_file_from_url(item)) - elif os.path.exists(item): - try: - files.extend(self._get_files_from_dir(item, types, recursive)) - except ValueError: - logger.warning(f"Directory {item} does not exist. Skipping.") - else: - logger.warning(f"File {item} does not exist. Skipping.") - return files - - # If the path is a file, return it - if os.path.isfile(dir_path): - return [dir_path] - - # If the path is a url, download it and return the downloaded file - if self._is_url(dir_path): - return [self._get_file_from_url(dir_path)] - - if os.path.exists(dir_path): - for type in types: - if recursive: - files += glob.glob( - os.path.join(dir_path, f"**/*.{type}"), recursive=True - ) - else: - files += glob.glob( - os.path.join(dir_path, f"*.{type}"), recursive=False - ) - else: - logger.error(f"Directory {dir_path} does not exist.") - raise ValueError(f"Directory {dir_path} does not exist.") - return files - - def _get_file_from_url(self, url: str, save_path: str = None): - """Download a file from a URL.""" - if save_path is None: - target_directory = os.path.join(PILOT_PATH, "data") - os.makedirs(target_directory, exist_ok=True) - save_path = os.path.join(target_directory, os.path.basename(url)) - else: - os.makedirs(os.path.dirname(save_path), exist_ok=True) - with requests.get(url, stream=True) as r: - r.raise_for_status() - with open(save_path, "wb") as f: - for chunk in r.iter_content(chunk_size=8192): - f.write(chunk) - - with open(save_path, "r", encoding="utf-8") as file: - html_content = file.read() - - soup = BeautifulSoup(html_content, "html.parser") - - # 可以根据需要从Beautiful Soup对象中提取数据，例如： - # title = soup.title.string # 获取网页标题 - paragraphs = soup.find_all("p") # 获取所有段落文本 - - # 将解析后的内容重新写入到相同的save_path - with open(save_path, "w", encoding="utf-8") as f: - for paragraph in paragraphs: - f.write(paragraph.get_text() + "\n") # 获取段落文本并写入文件 - - return save_path - - def _is_url(self, string: str): - """Return True if the string is a valid URL.""" - try: - result = urlparse(string) - return all([result.scheme, result.netloc]) - except ValueError: - return False - - def _split_text_to_chunks( - self, - text: str, - max_tokens: int = 4000, - chunk_mode: str = "multi_lines", - must_break_at_empty_line: bool = True, - overlap: int = 10, - ): - """Split a long text into chunks of max_tokens.""" - max_tokens = self.chunk_token_size - if chunk_mode not in VALID_CHUNK_MODES: - raise AssertionError - if chunk_mode == "one_line": - must_break_at_empty_line = False - chunks = [] - lines = text.split("\n") - lines_tokens = [self._count_token(line) for line in lines] - sum_tokens = sum(lines_tokens) - while sum_tokens > max_tokens: - if chunk_mode == "one_line": - estimated_line_cut = 2 - else: - estimated_line_cut = int(max_tokens / sum_tokens * len(lines)) + 1 - cnt = 0 - prev = "" - for cnt in reversed(range(estimated_line_cut)): - if must_break_at_empty_line and lines[cnt].strip() != "": - continue - if sum(lines_tokens[:cnt]) <= max_tokens: - prev = "\n".join(lines[:cnt]) - break - if cnt == 0: - logger.warning( - f"max_tokens is too small to fit a single line of text. Breaking this line:\n\t{lines[0][:100]} ..." - ) - if not must_break_at_empty_line: - split_len = int(max_tokens / lines_tokens[0] * 0.9 * len(lines[0])) - prev = lines[0][:split_len] - lines[0] = lines[0][split_len:] - lines_tokens[0] = self._count_token(lines[0]) - else: - logger.warning( - "Failed to split docs with must_break_at_empty_line being True, set to False." - ) - must_break_at_empty_line = False - chunks.append(prev) if len( - prev - ) > 10 else None # don't add chunks less than 10 characters - lines = lines[cnt:] - lines_tokens = lines_tokens[cnt:] - sum_tokens = sum(lines_tokens) - text_to_chunk = "\n".join(lines) - chunks.append(text_to_chunk) if len( - text_to_chunk - ) > 10 else None # don't add chunks less than 10 characters - return chunks - - def _extract_text_from_pdf(self, file: str) -> str: - """Extract text from PDF files""" - text = "" - with open(file, "rb") as f: - reader = pypdf.PdfReader(f) - if reader.is_encrypted: # Check if the PDF is encrypted - try: - reader.decrypt("") - except pypdf.errors.FileNotDecryptedError as e: - logger.warning(f"Could not decrypt PDF {file}, {e}") - return text # Return empty text if PDF could not be decrypted - - for page_num in range(len(reader.pages)): - page = reader.pages[page_num] - text += page.extract_text() - - if not text.strip(): # Debugging line to check if text is empty - logger.warning(f"Could not decrypt PDF {file}") - - return text - - def _split_files_to_chunks( - self, - files: list, - max_tokens: int = 4000, - chunk_mode: str = "multi_lines", - must_break_at_empty_line: bool = True, - custom_text_split_function: Callable = None, - ): - """Split a list of files into chunks of max_tokens.""" - max_tokens = self.chunk_token_size - chunks = [] - - for file in files: - _, file_extension = os.path.splitext(file) - file_extension = file_extension.lower() - - if HAS_UNSTRUCTURED and file_extension[1:] in UNSTRUCTURED_FORMATS: - text = partition(file) - text = "\n".join([t.text for t in text]) if len(text) > 0 else "" - elif file_extension == ".pdf": - text = self._extract_text_from_pdf(file) - else: # For non-PDF text-based files - with open(file, "r", encoding="utf-8", errors="ignore") as f: - text = f.read() - - if ( - not text.strip() - ): # Debugging line to check if text is empty after reading - logger.warning(f"No text available in file: {file}") - continue # Skip to the next file if no text is available - - if custom_text_split_function is not None: - chunks += custom_text_split_function(text) - else: - chunks += self._split_text_to_chunks( - text, max_tokens, chunk_mode, must_break_at_empty_line - ) - - return chunks - - def _count_token( - self, input: Union[str, List, Dict], model: str = "gpt-3.5-turbo-0613" - ) -> int: - """Count number of tokens used by an OpenAI model. - Args: - input: (str, list, dict): Input to the model. - model: (str): Model name. - - Returns: - int: Number of tokens from the input. - """ - if isinstance(input, str): - return self._num_token_from_text(input, model=model) - elif isinstance(input, list) or isinstance(input, dict): - return self._num_token_from_messages(input, model=model) - else: - raise ValueError("input must be str, list or dict") - - def _num_token_from_text(self, text: str, model: str = "gpt-3.5-turbo-0613"): - """Return the number of tokens used by a string.""" - try: - encoding = tiktoken.encoding_for_model(model) - except KeyError: - logger.warning(f"Model {model} not found. Using cl100k_base encoding.") - encoding = tiktoken.get_encoding("cl100k_base") - return len(encoding.encode(text)) - - -if __name__ == "__main__": - import asyncio - import os - - from dbgpt.agent.agents.agent import AgentContext - from dbgpt.agent.agents.user_proxy_agent import UserProxyAgent - from dbgpt.core.interface.llm import ModelMetadata - from dbgpt.model.proxy import OpenAILLMClient - - llm_client = OpenAILLMClient() - context: AgentContext = AgentContext( - conv_id="retrieve_summarize", llm_provider=llm_client - ) - context.llm_models = [ModelMetadata(model="gpt-3.5-turbo-16k")] - - default_memory = GptsMemory() - summarizer = RetrieveSummaryAssistantAgent( - memory=default_memory, agent_context=context - ) - - user_proxy = UserProxyAgent(memory=default_memory, agent_context=context) - - asyncio.run( - user_proxy.a_initiate_chat( - recipient=summarizer, - reviewer=user_proxy, - message="""I want to summarize advantages of Nuclear Power. - You can refer the following file paths and URLs: ['/home/ubuntu/chenguang-dbgpt/DB-GPT/dbgpt/agent/agents/expand/Nuclear_power.pdf', 'https://en.wikipedia.org/wiki/Modern_Family', '/home/ubuntu/chenguang-dbgpt/DB-GPT/dbgpt/agent/agents/expand/Taylor_Swift.pdf', 'https://en.wikipedia.org/wiki/Chernobyl_disaster'] - """, - ) - ) - - ## dbgpt-vis message infos - print(asyncio.run(default_memory.one_plan_chat_competions("summarize"))) diff --git a/dbgpt/agent/agents/expand/summary_assistant_agent.py b/dbgpt/agent/agents/expand/summary_assistant_agent.py deleted file mode 100644 index a7d6055d0..000000000 --- a/dbgpt/agent/agents/expand/summary_assistant_agent.py +++ /dev/null @@ -1,70 +0,0 @@ -import logging -from typing import Callable, Dict, List, Literal, Optional, Union - -from dbgpt.agent.actions.blank_action import BlankAction -from dbgpt.agent.agents.base_agent_new import ConversableAgent -from dbgpt.core.interface.message import ModelMessageRoleType - -logger = logging.getLogger() - -CHECK_RESULT_SYSTEM_MESSAGE = f""" -You are an expert in analyzing the results of a summary task. -Your responsibility is to check whether the summary results can summarize the input provided by the user, and then make a judgment. You need to answer according to the following rules: - Rule 1: If you think the summary results can summarize the input provided by the user, only return True. - Rule 2: If you think the summary results can NOT summarize the input provided by the user, return False and the reason, splitted by | and ended by TERMINATE. For instance: False|Some important concepts in the input are not summarized. TERMINATE -""" - - -class SummaryAssistantAgent(ConversableAgent): - name = "Aristotle" - profile: str = "Summarizer" - goal: str = "Summarize answer summaries based on user questions from provided resource information or from historical conversation memories." - - constraints: List[str] = [ - "Prioritize the summary of answers to user questions from the improved resource text. If no relevant information is found, summarize it from the historical dialogue memory given. It is forbidden to make up your own.", - "You need to first detect user's question that you need to answer with your summarization.", - "Extract the provided text content used for summarization.", - "Then you need to summarize the extracted text content.", - "Output the content of summarization ONLY related to user's question. The output language must be the same to user's question language.", - """If you think the provided text content is not related to user questions at all, ONLY output "Did not find the information you want."!!.""", - ] - desc: str = "You can summarize provided text content according to user's questions and output the summaraization." - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._init_actions([BlankAction]) - - # async def a_correctness_check(self, message: Optional[Dict]): - # current_goal = message.get("current_goal", None) - # action_report = message.get("action_report", None) - # task_result = "" - # if action_report: - # task_result = action_report.get("content", "") - # - # check_result, model = await self.a_thinking( - # messages=[ - # { - # "role": ModelMessageRoleType.HUMAN, - # "content": f"""Please understand the following user input and summary results and give your judgment: - # User Input: {current_goal} - # Summary Results: {task_result} - # """, - # } - # ], - # prompt=CHECK_RESULT_SYSTEM_MESSAGE, - # ) - # - # fail_reason = "" - # if "True" in check_result: - # success = True - # else: - # success = False - # try: - # _, fail_reason = check_result.split("|") - # fail_reason = f"The summary results cannot summarize the user input due to: {fail_reason}. Please re-understand and complete the summary task." - # except: - # logger.warning( - # f"The model thought the results are irrelevant but did not give the correct format of results." - # ) - # fail_reason = "The summary results cannot summarize the user input. Please re-understand and complete the summary task." - # return success, fail_reason diff --git a/dbgpt/agent/agents/llm/__init__.py b/dbgpt/agent/agents/llm/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/dbgpt/agent/agents/llm/llm.py b/dbgpt/agent/agents/llm/llm.py deleted file mode 100644 index 7daf02ceb..000000000 --- a/dbgpt/agent/agents/llm/llm.py +++ /dev/null @@ -1,109 +0,0 @@ -import json -import logging -from abc import ABC, abstractmethod -from collections import defaultdict -from enum import Enum -from typing import Any, Dict, List, Optional, Type, Union - -from pydantic import BaseModel, Field - -from dbgpt.core import LLMClient -from dbgpt.core.interface.llm import ModelMetadata, ModelRequest - -logger = logging.getLogger(__name__) - - -def _build_model_request( - input_value: Union[Dict, str], model: Optional[str] = None -) -> ModelRequest: - """Build model request from input value. - - Args: - input_value(str or dict): input value - model(Optional[str]): model name - - Returns: - ModelRequest: model request, pass to llm client - """ - if isinstance(input_value, str): - return ModelRequest._build(model, input_value) - elif isinstance(input_value, dict): - parm = { - "model": input_value.get("model"), - "messages": input_value.get("messages"), - "temperature": input_value.get("temperature", None), - "max_new_tokens": input_value.get("max_new_tokens", None), - "stop": input_value.get("stop", None), - "stop_token_ids": input_value.get("stop_token_ids", None), - "context_len": input_value.get("context_len", None), - "echo": input_value.get("echo", None), - "span_id": input_value.get("span_id", None), - } - - return ModelRequest(**parm) - else: - raise ValueError("Build model request input Error!") - - -class LLMStrategyType(Enum): - Priority = "priority" - Auto = "auto" - Default = "default" - - -class LLMStrategy(ABC): - def __init__(self, llm_client: LLMClient, context: Optional[str] = None): - self._llm_client = llm_client - self._context = context - - @property - def type(self) -> LLMStrategyType: - return LLMStrategyType.Default - - def _excluded_models( - self, - all_models: Optional[list], - need_uses: Optional[list] = [], - excluded_models: Optional[list] = [], - ): - can_uses = [] - for item in all_models: - if item.model in need_uses and item.model not in excluded_models: - can_uses.append(item) - return can_uses - - async def next_llm(self, excluded_models: Optional[List[str]] = None): - """ - Args: - excluded_model: - - Returns: - """ - try: - all_models = self._llm_client.models() - priority: List[str] = json.loads(self._context) - available_llms = self._excluded_models(all_models, None, excluded_models) - if available_llms and len(available_llms) > 0: - return available_llms[0].model - else: - raise ValueError("No model service available!") - - except Exception as e: - logger.error(f"{self.type} get next llm failed!{str(e)}") - raise ValueError(f"Failed to allocate model service,{str(e)}!") - - -llm_strategys = defaultdict(list) - - -def register_llm_strategy(type: LLMStrategyType, strategy: Type[LLMStrategy]): - llm_strategys[type] = strategy - - -class LLMConfig(BaseModel): - llm_client: Optional[LLMClient] = Field(default_factory=LLMClient) - llm_strategy: LLMStrategyType = Field(default=LLMStrategyType.Default) - strategy_context: Optional[Any] = None - - class Config: - arbitrary_types_allowed = True diff --git a/dbgpt/agent/agents/llm/strategy/__init__.py b/dbgpt/agent/agents/llm/strategy/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/dbgpt/agent/agents/role.py b/dbgpt/agent/agents/role.py deleted file mode 100644 index f309a9b27..000000000 --- a/dbgpt/agent/agents/role.py +++ /dev/null @@ -1,81 +0,0 @@ -from abc import ABC -from typing import Any, List, Optional - -from pydantic import BaseModel - - -class Role(ABC, BaseModel): - profile: str = "" - name: str = "" - resource_introduction = "" - goal: str = "" - - expand_prompt: str = "" - - fixed_subgoal: Optional[str] = None - - constraints: List[str] = [] - examples: str = "" - desc: str = "" - language: str = "en" - is_human: bool = False - is_team: bool = False - - class Config: - arbitrary_types_allowed = True - - def prompt_template( - self, - specified_prompt: Optional[str] = None, - ): - if specified_prompt: - return specified_prompt - - template = f""" - {self.role_prompt} - Please think step by step to achieve the goal. You can use the resources given below. At the same time, please strictly abide by the constraints and specifications in IMPORTANT REMINDER. - - {{resource_prompt}} - - {self.expand_prompt if len(self.expand_prompt) > 0 else ""} - - *** IMPORTANT REMINDER *** - {self.language_require_prompt} - {self.constraints_prompt} - - {'You can refer to the following examples:' if len(self.examples) > 0 else ""} - {self.examples if len(self.examples) > 0 else ""} - - {{out_schema}} - """ - return template - - @property - def role_prompt(self): - """You are a {self.profile}, named {self.name}, your goal is {self.goal}.""" - profile_prompt = f"You are a {self.profile}," - name_prompt = f"named {self.name}," if len(self.name) > 0 else "" - goal_prompt = f"your goal is {self.goal}" - prompt = f"""{profile_prompt}{name_prompt}{goal_prompt}""" - return prompt - - @property - def constraints_prompt(self): - if len(self.constraints) > 0: - return "\n".join( - f"{i + 1}. {item}" for i, item in enumerate(self.constraints) - ) - - @property - def language_require_prompt(self): - if self.language == "zh": - return "Please answer in simplified Chinese." - else: - return "Please answer in English." - - @property - def introduce(self): - return self.desc - - def identity_check(self): - pass diff --git a/dbgpt/agent/agents/user_proxy_agent.py b/dbgpt/agent/agents/user_proxy_agent.py deleted file mode 100644 index b5b2b6c30..000000000 --- a/dbgpt/agent/agents/user_proxy_agent.py +++ /dev/null @@ -1,18 +0,0 @@ -from typing import Callable, Dict, List, Literal, Optional, Tuple, Union - -from ..memory.gpts_memory import GptsMemory -from .agent import Agent, AgentContext -from .base_agent_new import ConversableAgent - - -class UserProxyAgent(ConversableAgent): - """(In preview) A proxy agent for the user, that can execute code and provide feedback to the other agents.""" - - name = "User" - profile: str = "Human" - - desc: str = ( - "A human admin. Interact with the planner to discuss the plan. Plan execution needs to be approved by this admin.", - ) - - is_human = True diff --git a/dbgpt/agent/common/__init__.py b/dbgpt/agent/common/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/dbgpt/agent/common/schema.py b/dbgpt/agent/common/schema.py deleted file mode 100644 index 35e06ba3d..000000000 --- a/dbgpt/agent/common/schema.py +++ /dev/null @@ -1,142 +0,0 @@ -from __future__ import annotations - -import asyncio -import json -import logging -from asyncio import Queue, QueueEmpty, wait_for -from datetime import datetime -from enum import Enum -from json import JSONDecodeError -from typing import Any, Callable, Dict, List, Optional, Type, TypeVar, Union - -from pydantic import BaseModel, ConfigDict, Field, PrivateAttr - -logger = logging.getLogger(__name__) - - -class PluginStorageType(Enum): - Git = "git" - Oss = "oss" - - -class ApiTagType(Enum): - API_VIEW = "dbgpt_view" - API_CALL = "dbgpt_call" - - -class Status(Enum): - TODO = "todo" - RUNNING = "running" - WAITING = "waiting" - RETRYING = "retrying" - FAILED = "failed" - COMPLETE = "complete" - - -class GptsMessage: - """Gpts message""" - - conv_id: str - sender: str - - receiver: str - role: str - content: str - rounds: Optional[int] - current_goal: str = None - context: Optional[str] = None - review_info: Optional[str] = None - action_report: Optional[str] = None - model_name: Optional[str] = None - created_at: datetime = datetime.utcnow - updated_at: datetime = datetime.utcnow - - @staticmethod - def from_dict(d: Dict[str, Any]) -> GptsMessage: - return GptsMessage( - conv_id=d["conv_id"], - sender=d["sender"], - receiver=d["receiver"], - role=d["role"], - content=d["content"], - rounds=d["rounds"], - model_name=d["model_name"], - current_goal=d["current_goal"], - context=d["context"], - review_info=d["review_info"], - action_report=d["action_report"], - created_at=d["created_at"], - updated_at=d["updated_at"], - ) - - -class MessageQueue(BaseModel): - """Message queue which supports asynchronous updates.""" - - model_config = ConfigDict(arbitrary_types_allowed=True) - - _queue: Queue = PrivateAttr(default_factory=Queue) - - def pop(self) -> GptsMessage | None: - """Pop one message from the queue.""" - try: - item = self._queue.get_nowait() - if item: - self._queue.task_done() - return item - except QueueEmpty: - return None - - def pop_all(self) -> List[GptsMessage]: - """Pop all messages from the queue.""" - ret = [] - while True: - msg = self.pop() - if not msg: - break - ret.append(msg) - return ret - - def push(self, msg: GptsMessage): - """Push a message into the queue.""" - self._queue.put_nowait(msg) - - def empty(self): - """Return true if the queue is empty.""" - return self._queue.empty() - - async def dump(self) -> str: - """Convert the `MessageQueue` object to a json string.""" - if self.empty(): - return "[]" - - lst = [] - msgs = [] - try: - while True: - item = await wait_for(self._queue.get(), timeout=1.0) - if item is None: - break - msgs.append(item) - lst.append(item.dump()) - self._queue.task_done() - except asyncio.TimeoutError: - logger.debug("Queue is empty, exiting...") - finally: - for m in msgs: - self._queue.put_nowait(m) - return json.dumps(lst, ensure_ascii=False) - - @staticmethod - def load(data) -> "MessageQueue": - """Convert the json string to the `MessageQueue` object.""" - queue = MessageQueue() - try: - lst = json.loads(data) - for i in lst: - msg = GptsMessage.load(i) - queue.push(msg) - except JSONDecodeError as e: - logger.warning(f"JSON load failed: {data}, error:{e}") - - return queue diff --git a/dbgpt/agent/core/__init__.py b/dbgpt/agent/core/__init__.py new file mode 100644 index 000000000..67e379d50 --- /dev/null +++ b/dbgpt/agent/core/__init__.py @@ -0,0 +1 @@ +"""Core Module for the Agent.""" diff --git a/dbgpt/agent/core/agent.py b/dbgpt/agent/core/agent.py new file mode 100644 index 000000000..27bb70877 --- /dev/null +++ b/dbgpt/agent/core/agent.py @@ -0,0 +1,304 @@ +"""Agent Interface.""" + +from __future__ import annotations + +import dataclasses +from abc import ABC, abstractmethod +from typing import Any, Dict, List, Optional, Tuple, Union + +from dbgpt.core import LLMClient +from dbgpt.util.annotations import PublicAPI + +from ..actions.action import ActionOutput +from ..memory.gpts_memory import GptsMemory +from ..resource.resource_loader import ResourceLoader + + +class Agent(ABC): + """Agent Interface.""" + + @abstractmethod + async def send( + self, + message: AgentMessage, + recipient: Agent, + reviewer: Optional[Agent] = None, + request_reply: Optional[bool] = True, + is_recovery: Optional[bool] = False, + ) -> None: + """Send a message to recipient agent. + + Args: + message(AgentMessage): the message to be sent. + recipient(Agent): the recipient agent. + reviewer(Agent): the reviewer agent. + request_reply(bool): whether to request a reply. + is_recovery(bool): whether the message is a recovery message. + + Returns: + None + """ + + @abstractmethod + async def receive( + self, + message: AgentMessage, + sender: Agent, + reviewer: Optional[Agent] = None, + request_reply: Optional[bool] = None, + silent: Optional[bool] = False, + is_recovery: Optional[bool] = False, + ) -> None: + """Receive a message from another agent. + + Args: + message(AgentMessage): the received message. + sender(Agent): the sender agent. + reviewer(Agent): the reviewer agent. + request_reply(bool): whether to request a reply. + silent(bool): whether to be silent. + is_recovery(bool): whether the message is a recovery message. + + Returns: + None + """ + + @abstractmethod + async def generate_reply( + self, + received_message: AgentMessage, + sender: Agent, + reviewer: Optional[Agent] = None, + rely_messages: Optional[List[AgentMessage]] = None, + **kwargs, + ) -> AgentMessage: + """Generate a reply based on the received messages. + + Args: + received_message(AgentMessage): the received message. + sender: sender of an Agent instance. + reviewer: reviewer of an Agent instance. + rely_messages: a list of messages received. + + Returns: + AgentMessage: the generated reply. If None, no reply is generated. + """ + + @abstractmethod + async def thinking( + self, messages: List[AgentMessage], prompt: Optional[str] = None + ) -> Tuple[Optional[str], Optional[str]]: + """Think and reason about the current task goal. + + Based on the requirements of the current agent, reason about the current task + goal through LLM + + Args: + messages(List[AgentMessage]): the messages to be reasoned + prompt(str): the prompt to be reasoned + + Returns: + Tuple[Union[str, Dict, None], Optional[str]]: First element is the generated + reply. If None, no reply is generated. The second element is the model + name of current task. + """ + + @abstractmethod + async def review(self, message: Optional[str], censored: Agent) -> Tuple[bool, Any]: + """Review the message based on the censored message. + + Args: + message: + censored: + + Returns: + bool: whether the message is censored + Any: the censored message + """ + + @abstractmethod + async def act( + self, + message: Optional[str], + sender: Optional[Agent] = None, + reviewer: Optional[Agent] = None, + **kwargs, + ) -> Optional[ActionOutput]: + """Act based on the LLM inference results. + + Parse the inference results for the current target and execute the inference + results using the current agent's executor + + Args: + message: the message to be executed + sender: sender of an Agent instance. + reviewer: reviewer of an Agent instance. + **kwargs: + + Returns: + ActionOutput: the action output of the agent. + """ + + @abstractmethod + async def verify( + self, + message: AgentMessage, + sender: Agent, + reviewer: Optional[Agent] = None, + **kwargs, + ) -> Tuple[bool, Optional[str]]: + """Verify whether the current execution results meet the target expectations. + + Args: + message: the message to be verified + sender: sender of an Agent instance. + reviewer: reviewer of an Agent instance. + **kwargs: + + Returns: + Tuple[bool, Optional[str]]: whether the verification is successful and the + verification result. + """ + + @abstractmethod + def get_name(self) -> str: + """Return name of the agent.""" + + @abstractmethod + def get_profile(self) -> str: + """Return profile of the agent.""" + + @abstractmethod + def get_describe(self) -> str: + """Return describe of the agent.""" + + +@dataclasses.dataclass +class AgentContext: + """A class to represent the context of an Agent.""" + + conv_id: str + gpts_app_name: Optional[str] = None + language: Optional[str] = None + max_chat_round: int = 100 + max_retry_round: int = 10 + max_new_tokens: int = 1024 + temperature: float = 0.5 + allow_format_str_template: Optional[bool] = False + + def to_dict(self) -> Dict[str, Any]: + """Return a dictionary representation of the AgentContext.""" + return dataclasses.asdict(self) + + +@dataclasses.dataclass +@PublicAPI(stability="beta") +class AgentGenerateContext: + """A class to represent the input of a Agent.""" + + message: Optional[AgentMessage] + sender: Optional[Agent] = None + reviewer: Optional[Agent] = None + silent: Optional[bool] = False + + rely_messages: List[AgentMessage] = dataclasses.field(default_factory=list) + final: Optional[bool] = True + + memory: Optional[GptsMemory] = None + agent_context: Optional[AgentContext] = None + resource_loader: Optional[ResourceLoader] = None + llm_client: Optional[LLMClient] = None + + round_index: Optional[int] = None + + def to_dict(self) -> Dict: + """Return a dictionary representation of the AgentGenerateContext.""" + return dataclasses.asdict(self) + + +ActionReportType = Dict[str, Any] +MessageContextType = Union[str, Dict[str, Any]] + + +@dataclasses.dataclass +@PublicAPI(stability="beta") +class AgentReviewInfo: + """Message object for agent communication.""" + + approve: bool = False + comments: Optional[str] = None + + def copy(self) -> "AgentReviewInfo": + """Return a copy of the current AgentReviewInfo.""" + return AgentReviewInfo(approve=self.approve, comments=self.comments) + + +@dataclasses.dataclass +@PublicAPI(stability="beta") +class AgentMessage: + """Message object for agent communication.""" + + content: Optional[str] = None + name: Optional[str] = None + context: Optional[MessageContextType] = None + action_report: Optional[ActionReportType] = None + review_info: Optional[AgentReviewInfo] = None + current_goal: Optional[str] = None + model_name: Optional[str] = None + role: Optional[str] = None + success: Optional[bool] = None + + def to_dict(self) -> Dict: + """Return a dictionary representation of the AgentMessage.""" + return dataclasses.asdict(self) + + def to_llm_message(self) -> Dict[str, Any]: + """Return a dictionary representation of the AgentMessage.""" + return { + "content": self.content, + "context": self.context, + "role": self.role, + } + + @classmethod + def from_llm_message(cls, message: Dict[str, Any]) -> AgentMessage: + """Create an AgentMessage object from a dictionary.""" + return cls( + content=message.get("content"), + context=message.get("context"), + role=message.get("role"), + ) + + @classmethod + def from_messages(cls, messages: List[Dict[str, Any]]) -> List[AgentMessage]: + """Create a list of AgentMessage objects from a list of dictionaries.""" + results = [] + field_names = [f.name for f in dataclasses.fields(cls)] + for message in messages: + kwargs = { + key: value for key, value in message.items() if key in field_names + } + results.append(cls(**kwargs)) + return results + + def copy(self) -> "AgentMessage": + """Return a copy of the current AgentMessage.""" + copied_context: Optional[MessageContextType] = None + if self.context: + if isinstance(self.context, dict): + copied_context = self.context.copy() + else: + copied_context = self.context + copied_action_report = self.action_report.copy() if self.action_report else None + copied_review_info = self.review_info.copy() if self.review_info else None + return AgentMessage( + content=self.content, + name=self.name, + context=copied_context, + action_report=copied_action_report, + review_info=copied_review_info, + current_goal=self.current_goal, + model_name=self.model_name, + role=self.role, + success=self.success, + ) diff --git a/dbgpt/agent/core/agent_manage.py b/dbgpt/agent/core/agent_manage.py new file mode 100644 index 000000000..e860b8ec4 --- /dev/null +++ b/dbgpt/agent/core/agent_manage.py @@ -0,0 +1,107 @@ +"""Manages the registration and retrieval of agents.""" + +import logging +import re +from collections import defaultdict +from typing import Dict, List, Type + +from ..expand.code_assistant_agent import CodeAssistantAgent +from ..expand.dashboard_assistant_agent import DashboardAssistantAgent +from ..expand.data_scientist_agent import DataScientistAgent +from ..expand.plugin_assistant_agent import PluginAssistantAgent +from ..expand.summary_assistant_agent import SummaryAssistantAgent +from .agent import Agent + +logger = logging.getLogger(__name__) + + +def participant_roles(agents: List[Agent]) -> str: + """Return a string listing the roles of the agents.""" + # Default to all agents registered + roles = [] + for agent in agents: + roles.append(f"{agent.get_name()}: {agent.get_describe()}") + return "\n".join(roles) + + +def mentioned_agents(message_content: str, agents: List[Agent]) -> Dict: + """Return a dictionary mapping agent names to mention counts. + + Finds and counts agent mentions in the string message_content, taking word + boundaries into account. + + Returns: A dictionary mapping agent names to mention counts (to be included, + at least one mention must occur) + """ + mentions = dict() + for agent in agents: + regex = ( + r"(?<=\W)" + re.escape(agent.get_name()) + r"(?=\W)" + ) # Finds agent mentions, taking word boundaries into account + count = len( + re.findall(regex, " " + message_content + " ") + ) # Pad the message to help with matching + if count > 0: + mentions[agent.get_name()] = count + return mentions + + +class AgentManager: + """Manages the registration and retrieval of agents.""" + + def __init__(self): + """Create a new AgentManager.""" + self._agents = defaultdict() + + def register_agent(self, cls): + """Register an agent.""" + self._agents[cls().profile] = cls + + def get_by_name(self, name: str) -> Type[Agent]: + """Return an agent by name. + + Args: + name (str): The name of the agent to retrieve. + + Returns: + Type[Agent]: The agent with the given name. + + Raises: + ValueError: If the agent with the given name is not registered. + """ + if name not in self._agents: + raise ValueError(f"Agent:{name} not register!") + return self._agents[name] + + def get_describe_by_name(self, name: str) -> str: + """Return the description of an agent by name.""" + return self._agents[name].desc + + def all_agents(self): + """Return a dictionary of all registered agents and their descriptions.""" + result = {} + for name, cls in self._agents.items(): + result[name] = cls.desc + return result + + def list_agents(self): + """Return a list of all registered agents and their descriptions.""" + result = [] + for name, cls in self._agents.items(): + instance = cls() + result.append( + { + "name": instance.profile, + "desc": instance.goal, + } + ) + return result + + +agent_manage = AgentManager() + +agent_manage.register_agent(CodeAssistantAgent) +agent_manage.register_agent(DashboardAssistantAgent) +agent_manage.register_agent(DataScientistAgent) +agent_manage.register_agent(SummaryAssistantAgent) +agent_manage.register_agent(PluginAssistantAgent) diff --git a/dbgpt/agent/core/base_agent.py b/dbgpt/agent/core/base_agent.py new file mode 100644 index 000000000..ac13e6c08 --- /dev/null +++ b/dbgpt/agent/core/base_agent.py @@ -0,0 +1,765 @@ +"""Base agent class for conversable agents.""" + +import asyncio +import json +import logging +from typing import Any, Dict, List, Optional, Tuple, Type, Union, cast + +from dbgpt._private.pydantic import Field +from dbgpt.core import LLMClient, ModelMessageRoleType +from dbgpt.util.error_types import LLMChatError +from dbgpt.util.utils import colored + +from ..actions.action import Action, ActionOutput +from ..memory.base import GptsMessage +from ..memory.gpts_memory import GptsMemory +from ..resource.resource_api import AgentResource, ResourceClient +from ..resource.resource_loader import ResourceLoader +from .agent import Agent, AgentContext, AgentMessage, AgentReviewInfo +from .llm.llm import LLMConfig, LLMStrategyType +from .llm.llm_client import AIWrapper +from .role import Role + +logger = logging.getLogger(__name__) + + +class ConversableAgent(Role, Agent): + """ConversableAgent is a agent that can communicate with other agents.""" + + agent_context: Optional[AgentContext] = Field(None, description="Agent context") + actions: List[Action] = Field(default_factory=list) + resources: List[AgentResource] = Field(default_factory=list) + llm_config: Optional[LLMConfig] = None + memory: GptsMemory = Field(default_factory=GptsMemory) + resource_loader: Optional[ResourceLoader] = None + max_retry_count: int = 3 + consecutive_auto_reply_counter: int = 0 + llm_client: Optional[AIWrapper] = None + oai_system_message: List[Dict] = Field(default_factory=list) + + class Config: + """Pydantic configuration.""" + + arbitrary_types_allowed = True + + def __init__(self, **kwargs): + """Create a new agent.""" + Role.__init__(self, **kwargs) + Agent.__init__(self) + + def init_system_message(self) -> None: + """Initialize the system message.""" + content = self.prompt_template() + # TODO: Don't modify the original data, need to be optimized + self.oai_system_message = [ + {"content": content, "role": ModelMessageRoleType.SYSTEM} + ] + + def check_available(self) -> None: + """Check if the agent is available. + + Raises: + ValueError: If the agent is not available. + """ + self.identity_check() + # check run context + if self.agent_context is None: + raise ValueError( + f"{self.name}[{self.profile}] Missing context in which agent is " + f"running!" + ) + + # resource check + for resource in self.resources: + if ( + self.resource_loader is None + or self.resource_loader.get_resource_api( + resource.type, check_instance=False + ) + is None + ): + raise ValueError( + f"Resource {resource.type}:{resource.value} missing resource loader" + f" implementation,unable to read resources!" + ) + + # action check + if self.actions and len(self.actions) > 0: + have_resource_types = [item.type for item in self.resources] + for action in self.actions: + if ( + action.resource_need + and action.resource_need not in have_resource_types + ): + raise ValueError( + f"{self.name}[{self.profile}] Missing resources required for " + "runtime！" + ) + else: + if not self.is_human and not self.is_team: + raise ValueError( + f"This agent {self.name}[{self.profile}] is missing action modules." + ) + # llm check + if not self.is_human and ( + self.llm_config is None or self.llm_config.llm_client is None + ): + raise ValueError( + f"{self.name}[{self.profile}] Model configuration is missing or model " + "service is unavailable！" + ) + + @property + def not_null_agent_context(self) -> AgentContext: + """Get the agent context. + + Returns: + AgentContext: The agent context. + + Raises: + ValueError: If the agent context is not initialized. + """ + if not self.agent_context: + raise ValueError("Agent context is not initialized！") + return self.agent_context + + @property + def not_null_resource_loader(self) -> ResourceLoader: + """Get the resource loader.""" + if not self.resource_loader: + raise ValueError("Resource loader is not initialized！") + return self.resource_loader + + @property + def not_null_llm_config(self) -> LLMConfig: + """Get the LLM config.""" + if not self.llm_config: + raise ValueError("LLM config is not initialized！") + return self.llm_config + + @property + def not_null_llm_client(self) -> LLMClient: + """Get the LLM client.""" + llm_client = self.not_null_llm_config.llm_client + if not llm_client: + raise ValueError("LLM client is not initialized！") + return llm_client + + async def preload_resource(self) -> None: + """Preload resources before agent initialization.""" + pass + + async def build(self) -> "ConversableAgent": + """Build the agent.""" + # Check if agent is available + self.check_available() + _language = self.not_null_agent_context.language + if _language: + self.language = _language + + # Preload resources + await self.preload_resource() + # Initialize resource loader + for action in self.actions: + action.init_resource_loader(self.resource_loader) + + # Initialize system messages + self.init_system_message() + + # Initialize LLM Server + if not self.is_human: + if not self.llm_config or not self.llm_config.llm_client: + raise ValueError("LLM client is not initialized！") + self.llm_client = AIWrapper(llm_client=self.llm_config.llm_client) + return self + + def bind(self, target: Any) -> "ConversableAgent": + """Bind the resources to the agent.""" + if isinstance(target, LLMConfig): + self.llm_config = target + elif isinstance(target, GptsMemory): + self.memory = target + elif isinstance(target, AgentContext): + self.agent_context = target + elif isinstance(target, ResourceLoader): + self.resource_loader = target + elif isinstance(target, list) and target and len(target) > 0: + if _is_list_of_type(target, Action): + self.actions.extend(target) + elif _is_list_of_type(target, AgentResource): + self.resources = target + return self + + async def send( + self, + message: AgentMessage, + recipient: Agent, + reviewer: Optional[Agent] = None, + request_reply: Optional[bool] = True, + is_recovery: Optional[bool] = False, + ) -> None: + """Send a message to recipient agent.""" + await recipient.receive( + message=message, + sender=self, + reviewer=reviewer, + request_reply=request_reply, + is_recovery=is_recovery, + ) + + async def receive( + self, + message: AgentMessage, + sender: Agent, + reviewer: Optional[Agent] = None, + request_reply: Optional[bool] = None, + silent: Optional[bool] = False, + is_recovery: Optional[bool] = False, + ) -> None: + """Receive a message from another agent.""" + await self._a_process_received_message(message, sender) + if request_reply is False or request_reply is None: + return + + if not self.is_human: + reply = await self.generate_reply( + received_message=message, sender=sender, reviewer=reviewer + ) + if reply is not None: + await self.send(reply, sender) + + def prepare_act_param(self) -> Dict[str, Any]: + """Prepare the parameters for the act method.""" + return {} + + async def generate_reply( + self, + received_message: AgentMessage, + sender: Agent, + reviewer: Optional[Agent] = None, + rely_messages: Optional[List[AgentMessage]] = None, + **kwargs, + ) -> AgentMessage: + """Generate a reply based on the received messages.""" + logger.info( + f"generate agent reply!sender={sender}, rely_messages_len={rely_messages}" + ) + try: + reply_message: AgentMessage = self._init_reply_message( + received_message=received_message + ) + await self._system_message_assembly( + received_message.content, reply_message.context + ) + + fail_reason = None + current_retry_counter = 0 + is_success = True + while current_retry_counter < self.max_retry_count: + if current_retry_counter > 0: + retry_message = self._init_reply_message( + received_message=received_message + ) + retry_message.content = fail_reason + retry_message.current_goal = received_message.current_goal + # The current message is a self-optimized message that needs to be + # recorded. + # It is temporarily set to be initiated by the originating end to + # facilitate the organization of historical memory context. + await sender.send( + retry_message, self, reviewer, request_reply=False + ) + + # 1.Think about how to do things + llm_reply, model_name = await self.thinking( + self._load_thinking_messages( + received_message, sender, rely_messages + ) + ) + reply_message.model_name = model_name + reply_message.content = llm_reply + + # 2.Review whether what is being done is legal + approve, comments = await self.review(llm_reply, self) + reply_message.review_info = AgentReviewInfo( + approve=approve, + comments=comments, + ) + + # 3.Act based on the results of your thinking + act_extent_param = self.prepare_act_param() + act_out: Optional[ActionOutput] = await self.act( + message=llm_reply, + sender=sender, + reviewer=reviewer, + **act_extent_param, + ) + if act_out: + reply_message.action_report = act_out.dict() + + # 4.Reply information verification + check_pass, reason = await self.verify(reply_message, sender, reviewer) + is_success = check_pass + # 5.Optimize wrong answers myself + if not check_pass: + current_retry_counter += 1 + # Send error messages and issue new problem-solving instructions + if current_retry_counter < self.max_retry_count: + await self.send( + reply_message, sender, reviewer, request_reply=False + ) + fail_reason = reason + else: + break + reply_message.success = is_success + return reply_message + + except Exception as e: + logger.exception("Generate reply exception!") + err_message = AgentMessage(content=str(e)) + err_message.success = False + return err_message + + async def thinking( + self, messages: List[AgentMessage], prompt: Optional[str] = None + ) -> Tuple[Optional[str], Optional[str]]: + """Think and reason about the current task goal. + + Args: + messages(List[AgentMessage]): the messages to be reasoned + prompt(str): the prompt to be reasoned + """ + last_model = None + last_err = None + retry_count = 0 + llm_messages = [message.to_llm_message() for message in messages] + # LLM inference automatically retries 3 times to reduce interruption + # probability caused by speed limit and network stability + while retry_count < 3: + llm_model = await self._a_select_llm_model(last_model) + try: + if prompt: + llm_messages = _new_system_message(prompt) + llm_messages + else: + llm_messages = self.oai_system_message + llm_messages + + if not self.llm_client: + raise ValueError("LLM client is not initialized!") + response = await self.llm_client.create( + context=llm_messages[-1].pop("context", None), + messages=llm_messages, + llm_model=llm_model, + max_new_tokens=self.not_null_agent_context.max_new_tokens, + temperature=self.not_null_agent_context.temperature, + ) + return response, llm_model + except LLMChatError as e: + logger.error(f"model:{llm_model} generate Failed!{str(e)}") + retry_count += 1 + last_model = llm_model + last_err = str(e) + await asyncio.sleep(10) + + if last_err: + raise ValueError(last_err) + else: + raise ValueError("LLM model inference failed!") + + async def review(self, message: Optional[str], censored: Agent) -> Tuple[bool, Any]: + """Review the message based on the censored message.""" + return True, None + + async def act( + self, + message: Optional[str], + sender: Optional[Agent] = None, + reviewer: Optional[Agent] = None, + **kwargs, + ) -> Optional[ActionOutput]: + """Perform actions.""" + last_out: Optional[ActionOutput] = None + for action in self.actions: + # Select the resources required by acton + need_resource = None + if self.resources and len(self.resources) > 0: + for item in self.resources: + if item.type == action.resource_need: + need_resource = item + break + + if not message: + raise ValueError("The message content is empty!") + + last_out = await action.run( + ai_message=message, + resource=need_resource, + rely_action_out=last_out, + **kwargs, + ) + return last_out + + async def correctness_check( + self, message: AgentMessage + ) -> Tuple[bool, Optional[str]]: + """Verify the correctness of the results.""" + return True, None + + async def verify( + self, + message: AgentMessage, + sender: Agent, + reviewer: Optional[Agent] = None, + **kwargs, + ) -> Tuple[bool, Optional[str]]: + """Verify the current execution results.""" + # Check approval results + if message.review_info and not message.review_info.approve: + return False, message.review_info.comments + + # Check action run results + action_output: Optional[ActionOutput] = ActionOutput.from_dict( + message.action_report + ) + if action_output: + if not action_output.is_exe_success: + return False, action_output.content + elif not action_output.content or len(action_output.content.strip()) < 1: + return ( + False, + "The current execution result is empty. Please rethink the " + "question and background and generate a new answer.. ", + ) + + # agent output correctness check + return await self.correctness_check(message) + + async def initiate_chat( + self, + recipient: Agent, + reviewer: Optional[Agent] = None, + message: Optional[str] = None, + ): + """Initiate a chat with another agent. + + Args: + recipient (Agent): The recipient agent. + reviewer (Agent): The reviewer agent. + message (str): The message to send. + """ + await self.send( + AgentMessage(content=message, current_goal=message), + recipient, + reviewer, + request_reply=True, + ) + + ####################################################################### + # Private Function Begin + ####################################################################### + + def _init_actions(self, actions: List[Type[Action]]): + self.actions = [] + for idx, action in enumerate(actions): + if issubclass(action, Action): + self.actions.append(action()) + + async def _a_append_message( + self, message: AgentMessage, role, sender: Agent + ) -> bool: + new_sender = cast(ConversableAgent, sender) + self.consecutive_auto_reply_counter = ( + new_sender.consecutive_auto_reply_counter + 1 + ) + message_dict = message.to_dict() + oai_message = { + k: message_dict[k] + for k in ( + "content", + "function_call", + "name", + "context", + "action_report", + "review_info", + "current_goal", + "model_name", + ) + if k in message_dict + } + + gpts_message: GptsMessage = GptsMessage( + conv_id=self.not_null_agent_context.conv_id, + sender=sender.get_profile(), + receiver=self.profile, + role=role, + rounds=self.consecutive_auto_reply_counter, + current_goal=oai_message.get("current_goal", None), + content=oai_message.get("content", None), + context=json.dumps(oai_message["context"], ensure_ascii=False) + if "context" in oai_message + else None, + review_info=json.dumps(oai_message["review_info"], ensure_ascii=False) + if "review_info" in oai_message + else None, + action_report=json.dumps(oai_message["action_report"], ensure_ascii=False) + if "action_report" in oai_message + else None, + model_name=oai_message.get("model_name", None), + ) + + self.memory.message_memory.append(gpts_message) + return True + + def _print_received_message(self, message: AgentMessage, sender: Agent): + # print the message received + print("\n", "-" * 80, flush=True, sep="") + _print_name = self.name if self.name else self.profile + print( + colored( + sender.get_name() if sender.get_name() else sender.get_profile(), + "yellow", + ), + "(to", + f"{_print_name})-[{message.model_name or ''}]:\n", + flush=True, + ) + + content = json.dumps(message.content, ensure_ascii=False) + if content is not None: + print(content, flush=True) + + review_info = message.review_info + if review_info: + name = sender.get_name() if sender.get_name() else sender.get_profile() + pass_msg = "Pass" if review_info.approve else "Reject" + review_msg = f"{pass_msg}({review_info.comments})" + approve_print = f">>>>>>>>{name} Review info: \n{review_msg}" + print(colored(approve_print, "green"), flush=True) + + action_report = message.action_report + if action_report: + name = sender.get_name() if sender.get_name() else sender.get_profile() + action_msg = ( + "execution succeeded" + if action_report["is_exe_success"] + else "execution failed" + ) + action_report_msg = f"{action_msg},\n{action_report['content']}" + action_print = f">>>>>>>>{name} Action report: \n{action_report_msg}" + print(colored(action_print, "blue"), flush=True) + + print("\n", "-" * 80, flush=True, sep="") + + async def _a_process_received_message(self, message: AgentMessage, sender: Agent): + valid = await self._a_append_message(message, None, sender) + if not valid: + raise ValueError( + "Received message can't be converted into a valid ChatCompletion" + " message. Either content or function_call must be provided." + ) + + self._print_received_message(message, sender) + + async def _system_message_assembly( + self, question: Optional[str], context: Optional[Union[str, Dict]] = None + ): + # system message + self.init_system_message() + if len(self.oai_system_message) > 0: + resource_prompt_list = [] + for item in self.resources: + resource_client = self.not_null_resource_loader.get_resource_api( + item.type, ResourceClient + ) + if not resource_client: + raise ValueError( + f"Resource {item.type}:{item.value} missing resource loader" + f" implementation,unable to read resources!" + ) + resource_prompt_list.append( + await resource_client.get_resource_prompt(item, question) + ) + if context is None or not isinstance(context, dict): + context = {} + + resource_prompt = "" + if len(resource_prompt_list) > 0: + resource_prompt = "RESOURCES:" + "\n".join(resource_prompt_list) + + out_schema: Optional[str] = "" + if self.actions and len(self.actions) > 0: + out_schema = self.actions[0].ai_out_schema + for message in self.oai_system_message: + new_content = message["content"].format( + resource_prompt=resource_prompt, + out_schema=out_schema, + **context, + ) + message["content"] = new_content + + def _excluded_models( + self, + all_models: List[str], + order_llms: Optional[List[str]] = None, + excluded_models: Optional[List[str]] = None, + ): + if not order_llms: + order_llms = [] + if not excluded_models: + excluded_models = [] + can_uses = [] + if order_llms and len(order_llms) > 0: + for llm_name in order_llms: + if llm_name in all_models and ( + not excluded_models or llm_name not in excluded_models + ): + can_uses.append(llm_name) + else: + for llm_name in all_models: + if not excluded_models or llm_name not in excluded_models: + can_uses.append(llm_name) + + return can_uses + + async def _a_select_llm_model( + self, excluded_models: Optional[List[str]] = None + ) -> str: + logger.info(f"_a_select_llm_model:{excluded_models}") + try: + all_models = await self.not_null_llm_client.models() + all_model_names = [item.model for item in all_models] + # TODO Currently only two strategies, priority and default, are implemented. + if self.not_null_llm_config.llm_strategy == LLMStrategyType.Priority: + priority: List[str] = [] + strategy_context = self.not_null_llm_config.strategy_context + if strategy_context is not None: + priority = json.loads(strategy_context) # type: ignore + can_uses = self._excluded_models( + all_model_names, priority, excluded_models + ) + else: + can_uses = self._excluded_models(all_model_names, None, excluded_models) + if can_uses and len(can_uses) > 0: + return can_uses[0] + else: + raise ValueError("No model service available!") + except Exception as e: + logger.error(f"{self.profile} get next llm failed!{str(e)}") + raise ValueError(f"Failed to allocate model service,{str(e)}!") + + def _init_reply_message(self, received_message: AgentMessage) -> AgentMessage: + """Create a new message from the received message. + + Initialize a new message from the received message + + Args: + received_message(AgentMessage): The received message + + Returns: + AgentMessage: A new message + """ + return AgentMessage( + content=received_message.content, + current_goal=received_message.current_goal, + ) + + def _convert_to_ai_message( + self, gpts_messages: List[GptsMessage] + ) -> List[AgentMessage]: + oai_messages: List[AgentMessage] = [] + # Based on the current agent, all messages received are user, and all messages + # sent are assistant. + for item in gpts_messages: + if item.role: + role = item.role + else: + if item.receiver == self.profile: + role = ModelMessageRoleType.HUMAN + elif item.sender == self.profile: + role = ModelMessageRoleType.AI + else: + continue + + # Message conversion, priority is given to converting execution results, + # and only model output results will be used if not. + content = item.content + if item.action_report: + action_out = ActionOutput.from_dict(json.loads(item.action_report)) + if ( + action_out is not None + and action_out.is_exe_success + and action_out.content is not None + ): + content = action_out.content + oai_messages.append( + AgentMessage( + content=content, + role=role, + context=json.loads(item.context) + if item.context is not None + else None, + ) + ) + return oai_messages + + def _load_thinking_messages( + self, + received_message: AgentMessage, + sender: Agent, + rely_messages: Optional[List[AgentMessage]] = None, + ) -> List[AgentMessage]: + current_goal = received_message.current_goal + + # Convert and tailor the information in collective memory into contextual + # memory available to the current Agent + current_goal_messages = self._convert_to_ai_message( + self.memory.message_memory.get_between_agents( + self.not_null_agent_context.conv_id, + self.profile, + sender.get_profile(), + current_goal, + ) + ) + + # When there is no target and context, the current received message is used as + # the target problem + if current_goal_messages is None or len(current_goal_messages) <= 0: + received_message.role = ModelMessageRoleType.HUMAN + current_goal_messages = [received_message] + + # relay messages + cut_messages = [] + if rely_messages: + # When directly relying on historical messages, use the execution result + # content as a dependency + for rely_message in rely_messages: + action_report: Optional[ActionOutput] = ActionOutput.from_dict( + rely_message.action_report + ) + if action_report: + # TODO: Modify in-place, need to be optimized + rely_message.content = action_report.content + + cut_messages.extend(rely_messages) + + # TODO: allocate historical information based on token budget + if len(current_goal_messages) < 5: + cut_messages.extend(current_goal_messages) + else: + # For the time being, the smallest size of historical message records will + # be used by default. + # Use the first two rounds of messages to understand the initial goals + cut_messages.extend(current_goal_messages[:2]) + # Use information from the last three rounds of communication to ensure + # that current thinking knows what happened and what to do in the last + # communication + cut_messages.extend(current_goal_messages[-3:]) + return cut_messages + + +def _new_system_message(content): + """Return the system message.""" + return [{"content": content, "role": ModelMessageRoleType.SYSTEM}] + + +def _is_list_of_type(lst: List[Any], type_cls: type) -> bool: + return all(isinstance(item, type_cls) for item in lst) diff --git a/dbgpt/agent/agents/base_team.py b/dbgpt/agent/core/base_team.py similarity index 50% rename from dbgpt/agent/agents/base_team.py rename to dbgpt/agent/core/base_team.py index 8a7d3f03b..d04e0ca64 100644 --- a/dbgpt/agent/agents/base_team.py +++ b/dbgpt/agent/core/base_team.py @@ -1,36 +1,41 @@ +"""Base classes for managing a group of agents in a team chat.""" + import logging -from typing import Dict, List, Optional, Union +from typing import Dict, List, Optional, Tuple, Union -from pydantic import BaseModel, Field +from dbgpt._private.pydantic import BaseModel, Field -from dbgpt.agent.actions.action import Action, ActionOutput, T -from dbgpt.agent.agents.agent_new import Agent -from dbgpt.agent.agents.base_agent_new import ConversableAgent -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType -from dbgpt.vis.tags.vis_agent_plans import Vis, VisAgentPlans +from ..actions.action import ActionOutput +from .agent import Agent, AgentMessage +from .base_agent import ConversableAgent logger = logging.getLogger(__name__) -def content_str(content: Union[str, List, None]) -> str: - """Converts `content` into a string format. +def _content_str(content: Union[str, List, None]) -> str: + """Convert content into a string format. - This function processes content that may be a string, a list of mixed text and image URLs, or None, - and converts it into a string. Text is directly appended to the result string, while image URLs are - represented by a placeholder image token. If the content is None, an empty string is returned. + This function processes content that may be a string, a list of mixed text and + image URLs, or None, and converts it into a string. Text is directly appended to + the result string, while image URLs are represented by a placeholder image token. + If the content is None, an empty string is returned. Args: - - content (Union[str, List, None]): The content to be processed. Can be a string, a list of dictionaries - representing text and image URLs, or None. + content (Union[str, List, None]): The content to be processed. Can be a + string, a list of dictionaries representing text and image URLs, or None. Returns: - str: A string representation of the input content. Image URLs are replaced with an image token. + str: A string representation of the input content. Image URLs are replaced with + an image token. Note: - - The function expects each dictionary in the list to have a "type" key that is either "text" or "image_url". - For "text" type, the "text" key's value is appended to the result. For "image_url", an image token is appended. - - This function is useful for handling content that may include both text and image references, especially - in contexts where images need to be represented as placeholders. + - The function expects each dictionary in the list to have a "type" key that is + either "text" or "image_url". + For "text" type, the "text" key's value is appended to the result. + For "image_url", an image token is appended. + - This function is useful for handling content that may include both text and image + references, especially in contexts where images need to be represented as + placeholders. """ if content is None: return "" @@ -43,7 +48,8 @@ def content_str(content: Union[str, List, None]) -> str: for item in content: if not isinstance(item, dict): raise TypeError( - "Wrong content format: every element should be dict if the content is a list." + "Wrong content format: every element should be dict if the content is " + "a list." ) assert ( "type" in item @@ -60,32 +66,44 @@ def content_str(content: Union[str, List, None]) -> str: class Team(BaseModel): + """Team class for managing a group of agents in a team chat.""" + agents: List[Agent] = Field(default_factory=list) messages: List[Dict] = Field(default_factory=list) - max_round: Optional[int] = 100 + max_round: int = 100 is_team: bool = True class Config: + """Pydantic model configuration.""" + arbitrary_types_allowed = True def __init__(self, **kwargs): + """Create a new Team instance.""" super().__init__(**kwargs) def hire(self, agents: List[Agent]): - """Hire roles to cooperate""" + """Hire roles to cooperate.""" self.agents.extend(agents) @property def agent_names(self) -> List[str]: """Return the names of the agents in the group chat.""" - return [agent.profile for agent in self.agents] + return [agent.get_profile() for agent in self.agents] def agent_by_name(self, name: str) -> Agent: - """Returns the agent with a given name.""" + """Return the agent with a given name.""" return self.agents[self.agent_names.index(name)] - async def a_select_speaker(self, last_speaker: Agent, selector: Agent): - pass + async def select_speaker( + self, + last_speaker: Agent, + selector: Agent, + now_goal_context: Optional[str] = None, + pre_allocated: Optional[str] = None, + ) -> Tuple[Agent, Optional[str]]: + """Select the next speaker in the group chat.""" + raise NotImplementedError def reset(self): """Reset the group chat.""" @@ -93,42 +111,56 @@ def reset(self): def append(self, message: Dict): """Append a message to the group chat. + We cast the content to str here so that it can be managed by text-based model. """ - message["content"] = content_str(message["content"]) + message["content"] = _content_str(message["content"]) self.messages.append(message) class ManagerAgent(ConversableAgent, Team): + """Manager Agent class.""" + profile: str = "TeamManager" goal: str = "manage all hired intelligent agents to complete mission objectives" constraints: List[str] = [] desc: str = goal is_team: bool = True - # The management agent does not need to retry the exception. The actual execution of the agent has already been retried. + # The management agent does not need to retry the exception. The actual execution + # of the agent has already been retried. max_retry_count: int = 1 + class Config: + """Pydantic model configuration.""" + + arbitrary_types_allowed = True + def __init__(self, **kwargs): + """Create a new ManagerAgent instance.""" ConversableAgent.__init__(self, **kwargs) Team.__init__(self, **kwargs) - async def a_thinking( - self, messages: Optional[List[Dict]], prompt: Optional[str] = None - ) -> Union[str, Dict, None]: - # TeamManager, which is based on processes and plans by default, only needs to ensure execution and does not require additional thinking. + async def thinking( + self, messages: List[AgentMessage], prompt: Optional[str] = None + ) -> Tuple[Optional[str], Optional[str]]: + """Think and reason about the current task goal.""" + # TeamManager, which is based on processes and plans by default, only needs to + # ensure execution and does not require additional thinking. if messages is None or len(messages) <= 0: return None, None else: message = messages[-1] - self.messages.append(message) - return message["content"], None + self.messages.append(message.to_llm_message()) + return message.content, None - async def a_act( + async def act( self, message: Optional[str], - sender: Optional[ConversableAgent] = None, - reviewer: Optional[ConversableAgent] = None, + sender: Optional[Agent] = None, + reviewer: Optional[Agent] = None, + **kwargs, ) -> Optional[ActionOutput]: - pass + """Perform actions based on the received message.""" + return None diff --git a/dbgpt/agent/core/llm/__init__.py b/dbgpt/agent/core/llm/__init__.py new file mode 100644 index 000000000..8afd3a9f1 --- /dev/null +++ b/dbgpt/agent/core/llm/__init__.py @@ -0,0 +1 @@ +"""LLM for agents.""" diff --git a/dbgpt/agent/core/llm/llm.py b/dbgpt/agent/core/llm/llm.py new file mode 100644 index 000000000..b0e655dc5 --- /dev/null +++ b/dbgpt/agent/core/llm/llm.py @@ -0,0 +1,116 @@ +"""LLM module.""" +import logging +from collections import defaultdict +from enum import Enum +from typing import Any, Dict, List, Optional, Type + +from dbgpt._private.pydantic import BaseModel, Field +from dbgpt.core import LLMClient, ModelMetadata, ModelRequest + +logger = logging.getLogger(__name__) + + +def _build_model_request(input_value: Dict) -> ModelRequest: + """Build model request from input value. + + Args: + input_value(str or dict): input value + + Returns: + ModelRequest: model request, pass to llm client + """ + parm = { + "model": input_value.get("model"), + "messages": input_value.get("messages"), + "temperature": input_value.get("temperature", None), + "max_new_tokens": input_value.get("max_new_tokens", None), + "stop": input_value.get("stop", None), + "stop_token_ids": input_value.get("stop_token_ids", None), + "context_len": input_value.get("context_len", None), + "echo": input_value.get("echo", None), + "span_id": input_value.get("span_id", None), + } + + return ModelRequest(**parm) + + +class LLMStrategyType(Enum): + """LLM strategy type.""" + + Priority = "priority" + Auto = "auto" + Default = "default" + + +class LLMStrategy: + """LLM strategy base class.""" + + def __init__(self, llm_client: LLMClient, context: Optional[str] = None): + """Create an LLMStrategy instance.""" + self._llm_client = llm_client + self._context = context + + @property + def type(self) -> LLMStrategyType: + """Return the strategy type.""" + return LLMStrategyType.Default + + def _excluded_models( + self, + all_models: List[ModelMetadata], + excluded_models: List[str], + need_uses: Optional[List[str]] = None, + ): + if not need_uses: + need_uses = [] + can_uses = [] + for item in all_models: + if item.model in need_uses and item.model not in excluded_models: + can_uses.append(item) + return can_uses + + async def next_llm(self, excluded_models: Optional[List[str]] = None): + """Return next available llm model name. + + Args: + excluded_models(List[str]): excluded models + + Returns: + str: Next available llm model name + """ + if not excluded_models: + excluded_models = [] + try: + all_models = await self._llm_client.models() + available_llms = self._excluded_models(all_models, excluded_models, None) + if available_llms and len(available_llms) > 0: + return available_llms[0].model + else: + raise ValueError("No model service available!") + + except Exception as e: + logger.error(f"{self.type} get next llm failed!{str(e)}") + raise ValueError(f"Failed to allocate model service,{str(e)}!") + + +llm_strategies: Dict[LLMStrategyType, List[Type[LLMStrategy]]] = defaultdict(list) + + +def register_llm_strategy( + llm_strategy_type: LLMStrategyType, strategy: Type[LLMStrategy] +): + """Register llm strategy.""" + llm_strategies[llm_strategy_type].append(strategy) + + +class LLMConfig(BaseModel): + """LLM configuration.""" + + llm_client: Optional[LLMClient] = Field(default_factory=LLMClient) + llm_strategy: LLMStrategyType = Field(default=LLMStrategyType.Default) + strategy_context: Optional[Any] = None + + class Config: + """Pydantic model config.""" + + arbitrary_types_allowed = True diff --git a/dbgpt/agent/agents/llm/llm_client.py b/dbgpt/agent/core/llm/llm_client.py similarity index 83% rename from dbgpt/agent/agents/llm/llm_client.py rename to dbgpt/agent/core/llm/llm_client.py index fc67e0639..dc84b5124 100644 --- a/dbgpt/agent/agents/llm/llm_client.py +++ b/dbgpt/agent/core/llm/llm_client.py @@ -1,3 +1,4 @@ +"""AIWrapper for LLM.""" import json import logging import traceback @@ -8,12 +9,14 @@ from dbgpt.util.error_types import LLMChatError from dbgpt.util.tracer import root_tracer -from ..llm.llm import _build_model_request +from .llm import _build_model_request logger = logging.getLogger(__name__) class AIWrapper: + """AIWrapper for LLM.""" + cache_path_root: str = ".cache" extra_kwargs = { "cache_seed", @@ -26,6 +29,7 @@ class AIWrapper: def __init__( self, llm_client: LLMClient, output_parser: Optional[BaseOutputParser] = None ): + """Create an AIWrapper instance.""" self.llm_echo = False self.model_cache_enable = False self._llm_client = llm_client @@ -38,6 +42,7 @@ def instantiate( context: Optional[Dict] = None, allow_format_str_template: Optional[bool] = False, ): + """Instantiate the template with the context.""" if not context or template is None: return template if isinstance(template, str): @@ -49,7 +54,7 @@ def _construct_create_params(self, create_config: Dict, extra_kwargs: Dict) -> D # Validate the config prompt = create_config.get("prompt") messages = create_config.get("messages") - if (prompt is None) == (messages is None): + if prompt is None and messages is None: raise ValueError( "Either prompt or messages should be in create config but not both." ) @@ -67,7 +72,7 @@ def _construct_create_params(self, create_config: Dict, extra_kwargs: Dict) -> D params["prompt"] = self.instantiate( prompt, context, allow_format_str_template ) - elif context: + elif context and messages and isinstance(messages, list): # Instantiate the messages params["messages"] = [ { @@ -105,7 +110,8 @@ def _get_key(self, config): config.pop(key) return json.dumps(config, sort_keys=True, ensure_ascii=False) - async def create(self, **config): + async def create(self, **config) -> Optional[str]: + """Create a response from the input config.""" # merge the input config with the i-th config in the config list full_config = {**config} # separate the config into create_config and extra_kwargs @@ -113,28 +119,9 @@ async def create(self, **config): # construct the create params params = self._construct_create_params(create_config, extra_kwargs) - # get the cache_seed, filter_func and context - cache_seed = extra_kwargs.get("cache_seed", 66) filter_func = extra_kwargs.get("filter_func") context = extra_kwargs.get("context") llm_model = extra_kwargs.get("llm_model") - if context: - use_cache = context.get("use_cache", True) - if not use_cache: - cache_seed = None - # # Try to load the response from cache - # if cache_seed is not None: - # with diskcache.Cache(f"{self.cache_path_root}/{cache_seed}") as cache: - # # Try to get the response from cache - # key = self._get_key(params) - # response = cache.get(key, None) - # if response is not None: - # # check the filter - # pass_filter = filter_func is None or filter_func(context=context, response=response) - # if pass_filter : - # # Return the response if it passes the filter - # # TODO: add response.cost - # return response try: response = await self._completions_create(llm_model, params) except LLMChatError as e: @@ -147,6 +134,8 @@ async def create(self, **config): if pass_filter: # Return the response if it passes the filter return response + else: + return None def _get_span_metadata(self, payload: Dict) -> Dict: metadata = {k: v for k, v in payload.items()} @@ -158,11 +147,11 @@ def _get_span_metadata(self, payload: Dict) -> Dict: def _llm_messages_convert(self, params): gpts_messages = params["messages"] - ### TODO + # TODO return gpts_messages - async def _completions_create(self, llm_model, params): + async def _completions_create(self, llm_model, params) -> str: payload = { "model": llm_model, "prompt": params.get("prompt"), diff --git a/dbgpt/agent/core/llm/strategy/__init__.py b/dbgpt/agent/core/llm/strategy/__init__.py new file mode 100644 index 000000000..463f98a7d --- /dev/null +++ b/dbgpt/agent/core/llm/strategy/__init__.py @@ -0,0 +1 @@ +"""LLM strategy module.""" diff --git a/dbgpt/agent/agents/llm/strategy/priority.py b/dbgpt/agent/core/llm/strategy/priority.py similarity index 54% rename from dbgpt/agent/agents/llm/strategy/priority.py rename to dbgpt/agent/core/llm/strategy/priority.py index 2d0c5bdf9..e696b5a36 100644 --- a/dbgpt/agent/agents/llm/strategy/priority.py +++ b/dbgpt/agent/core/llm/strategy/priority.py @@ -1,24 +1,32 @@ +"""Priority strategy for LLM.""" + import json import logging -from typing import Dict, Optional - -from dbgpt.agent.agents.llm.llm import LLMStrategyType +from typing import List, Optional -from ..llm import LLMStrategy +from ..llm import LLMStrategy, LLMStrategyType logger = logging.getLogger(__name__) class LLMStrategyPriority(LLMStrategy): + """Priority strategy for llm model service.""" + @property def type(self) -> LLMStrategyType: + """Return the strategy type.""" return LLMStrategyType.Priority async def next_llm(self, excluded_models: Optional[List[str]] = None) -> str: + """Return next available llm model name.""" try: - all_models = self._llm_client.models() + if not excluded_models: + excluded_models = [] + all_models = await self._llm_client.models() + if not self._context: + raise ValueError("No context provided for priority strategy!") priority: List[str] = json.loads(self._context) - can_uses = self._excluded_models(all_models, priority, excluded_models) + can_uses = self._excluded_models(all_models, excluded_models, priority) if can_uses and len(can_uses) > 0: return can_uses[0].model else: diff --git a/dbgpt/agent/core/role.py b/dbgpt/agent/core/role.py new file mode 100644 index 000000000..1a97af64f --- /dev/null +++ b/dbgpt/agent/core/role.py @@ -0,0 +1,128 @@ +"""Role class for role-based conversation.""" +from abc import ABC +from typing import List, Optional + +from dbgpt._private.pydantic import BaseModel + + +class Role(ABC, BaseModel): + """Role class for role-based conversation.""" + + profile: str = "" + name: str = "" + resource_introduction = "" + goal: str = "" + + expand_prompt: str = "" + + fixed_subgoal: Optional[str] = None + + constraints: List[str] = [] + examples: str = "" + desc: str = "" + language: str = "en" + is_human: bool = False + is_team: bool = False + + class Config: + """Pydantic config.""" + + arbitrary_types_allowed = True + + def prompt_template( + self, + specified_prompt: Optional[str] = None, + ) -> str: + """Return the prompt template for the role. + + Args: + specified_prompt (str, optional): The specified prompt. Defaults to None. + + Returns: + str: The prompt template. + """ + if specified_prompt: + return specified_prompt + + expand_prompt = self.expand_prompt if len(self.expand_prompt) > 0 else "" + examples_prompt = ( + "You can refer to the following examples:\n" + if len(self.examples) > 0 + else "" + ) + examples = self.examples if len(self.examples) > 0 else "" + template = ( + f"{self.role_prompt}\n" + "Please think step by step to achieve the goal. You can use the resources " + "given below. At the same time, please strictly abide by the constraints " + "and specifications in IMPORTANT REMINDER.\n\n" + f"{{resource_prompt}}\n\n" + f"{expand_prompt}\n\n" + "*** IMPORTANT REMINDER ***\n" + f"{self.language_require_prompt}\n" + f"{self.constraints_prompt}\n" + f"{examples_prompt}{examples}\n\n" + f"{{out_schema}}" + ) + return template + + @property + def role_prompt(self) -> str: + """Return the role prompt. + + You are a {self.profile}, named {self.name}, your goal is {self.goal}. + + Returns: + str: The role prompt. + """ + profile_prompt = f"You are a {self.profile}," + name_prompt = f"named {self.name}," if len(self.name) > 0 else "" + goal_prompt = f"your goal is {self.goal}" + prompt = f"""{profile_prompt}{name_prompt}{goal_prompt}""" + return prompt + + @property + def constraints_prompt(self) -> str: + """Return the constraints prompt. + + Return: + str: The constraints prompt. + """ + if len(self.constraints) > 0: + return "\n".join( + f"{i + 1}. {item}" for i, item in enumerate(self.constraints) + ) + return "" + + @property + def language_require_prompt(self) -> str: + """Return the language requirement prompt. + + Returns: + str: The language requirement prompt. + """ + if self.language == "zh": + return "Please answer in simplified Chinese." + else: + return "Please answer in English." + + @property + def introduce(self) -> str: + """Introduce the role.""" + return self.desc + + def identity_check(self) -> None: + """Check the identity of the role.""" + pass + + def get_name(self) -> str: + """Get the name of the role.""" + return self.name + + def get_profile(self) -> str: + """Get the profile of the role.""" + return self.profile + + def get_describe(self) -> str: + """Get the describe of the role.""" + return self.desc diff --git a/dbgpt/agent/core/schema.py b/dbgpt/agent/core/schema.py new file mode 100644 index 000000000..a0df0bef0 --- /dev/null +++ b/dbgpt/agent/core/schema.py @@ -0,0 +1,27 @@ +"""Schema definition for the agent.""" +from enum import Enum + + +class PluginStorageType(Enum): + """Plugin storage type.""" + + Git = "git" + Oss = "oss" + + +class ApiTagType(Enum): + """API tag type.""" + + API_VIEW = "dbgpt_view" + API_CALL = "dbgpt_call" + + +class Status(Enum): + """Status of a task.""" + + TODO = "todo" + RUNNING = "running" + WAITING = "waiting" + RETRYING = "retrying" + FAILED = "failed" + COMPLETE = "complete" diff --git a/dbgpt/agent/core/user_proxy_agent.py b/dbgpt/agent/core/user_proxy_agent.py new file mode 100644 index 000000000..62cb7a112 --- /dev/null +++ b/dbgpt/agent/core/user_proxy_agent.py @@ -0,0 +1,19 @@ +"""A proxy agent for the user.""" +from .base_agent import ConversableAgent + + +class UserProxyAgent(ConversableAgent): + """A proxy agent for the user. + + That can execute code and provide feedback to the other agents. + """ + + name = "User" + profile: str = "Human" + + desc: str = ( + "A human admin. Interact with the planner to discuss the plan. " + "Plan execution needs to be approved by this admin." + ) + + is_human = True diff --git a/dbgpt/agent/expand/Indicator_assistant_agent.py b/dbgpt/agent/expand/Indicator_assistant_agent.py new file mode 100644 index 000000000..91ae4c965 --- /dev/null +++ b/dbgpt/agent/expand/Indicator_assistant_agent.py @@ -0,0 +1,42 @@ +"""Indicator Assistant Agent.""" +import logging +from typing import List + +from ..actions.indicator_action import IndicatorAction +from ..core.base_agent import ConversableAgent + +logger = logging.getLogger(__name__) + + +class IndicatorAssistantAgent(ConversableAgent): + """Indicator Assistant Agent.""" + + name = "Indicator" + profile: str = "Indicator" + goal: str = ( + "Summarize answer summaries based on user questions from provided " + "resource information or from historical conversation memories." + ) + + constraints: List[str] = [ + "Prioritize the summary of answers to user questions from the improved resource" + " text. If no relevant information is found, summarize it from the historical" + " dialogue memory given. It is forbidden to make up your own.", + "You need to first detect user's question that you need to answer with your " + "summarization.", + "Extract the provided text content used for summarization.", + "Then you need to summarize the extracted text content.", + "Output the content of summarization ONLY related to user's question. The " + "output language must be the same to user's question language.", + "If you think the provided text content is not related to user questions at " + "all, ONLY output 'Did not find the information you want.'!!.", + ] + desc: str = ( + "You can summarize provided text content according to user's questions " + "and output the summarization." + ) + + def __init__(self, **kwargs): + """Create a new instance.""" + super().__init__(**kwargs) + self._init_actions([IndicatorAction]) diff --git a/dbgpt/agent/expand/__init__.py b/dbgpt/agent/expand/__init__.py new file mode 100644 index 000000000..2a45c82d3 --- /dev/null +++ b/dbgpt/agent/expand/__init__.py @@ -0,0 +1 @@ +"""Expand your agents.""" diff --git a/dbgpt/agent/expand/code_assistant_agent.py b/dbgpt/agent/expand/code_assistant_agent.py new file mode 100644 index 000000000..285c6e4ec --- /dev/null +++ b/dbgpt/agent/expand/code_assistant_agent.py @@ -0,0 +1,120 @@ +"""Code Assistant Agent.""" +from typing import List, Optional, Tuple + +from dbgpt.core import ModelMessageRoleType +from dbgpt.util.string_utils import str_to_bool + +from ..actions.code_action import CodeAction +from ..core.agent import AgentMessage +from ..core.base_agent import ConversableAgent + +CHECK_RESULT_SYSTEM_MESSAGE = ( + "You are an expert in analyzing the results of task execution. Your responsibility " + "is to analyze the task goals and execution results provided by the user, and " + "then make a judgment. You need to answer according to the following rules:\n" + " Rule 1: Determine whether the content of the focused execution results " + "is related to the task target content and whether it can be used as the answer to " + "the target question. For those who do not understand the content, as long as the " + "execution result type is required, it can be judged as correct.\n" + " Rule 2: There is no need to pay attention to whether the boundaries, " + "time range, and values of the answer content are correct.\n" + "As long as the task goal and execution result meet the above rules, True will be " + "returned; otherwise, False will be returned and the failure reason will be given." + "\nFor example:\n" + " If it is determined to be successful, only true will be returned, " + "such as: True.\n" + " If it is determined to be a failure, return false and the reason, " + "such as: False. There are no numbers in the execution results that answer the " + "computational goals of the mission." +) + + +class CodeAssistantAgent(ConversableAgent): + """Code Assistant Agent.""" + + name: str = "Turing" + profile: str = "CodeEngineer" + goal: str = ( + "Solve tasks using your coding and language skills.\n" + "In the following cases, suggest python code (in a python coding block) or " + "shell script (in a sh coding block) for the user to execute.\n" + " 1. When you need to collect info, use the code to output the info you " + "need, for example, browse or search the web, download/read a file, print the " + "content of a webpage or a file, get the current date/time, check the " + "operating system. After sufficient info is printed and the task is ready to be" + " solved based on your language skill, you can solve the task by yourself.\n" + " 2. When you need to perform some task with code, use the code to perform " + "the task and output the result. Finish the task smartly." + ) + constraints: List[str] = [ + "The user cannot provide any other feedback or perform any other action beyond" + " executing the code you suggest. The user can't modify your code. So do not " + "suggest incomplete code which requires users to modify. Don't use a code block" + " if it's not intended to be executed by the user.Don't ask users to copy and " + "paste results. Instead, the 'Print' function must be used for output when " + "relevant.", + "When using code, you must indicate the script type in the code block. Please " + "don't include multiple code blocks in one response.", + "If you want the user to save the code in a file before executing it, put " + "# filename: inside the code block as the first line.", + "If you receive user input that indicates an error in the code execution, fix " + "the error and output the complete code again. It is recommended to use the " + "complete code rather than partial code or code changes. If the error cannot be" + " fixed, or the task is not resolved even after the code executes successfully," + " analyze the problem, revisit your assumptions, gather additional information" + " you need from historical conversation records, and consider trying a " + "different approach.", + "Unless necessary, give priority to solving problems with python code. If it " + "involves downloading files or storing data locally, please use 'Print' to " + "output the full file path of the stored data and a brief introduction to the " + "data.", + "The output content of the 'print' function will be passed to other LLM agents " + "as dependent data. Please control the length of the output content of the " + "'print' function. The 'print' function only outputs part of the key data " + "information that is relied on, and is as concise as possible.", + "The code is executed without user participation. It is forbidden to use " + "methods that will block the process or need to be shut down, such as the " + "plt.show() method of matplotlib.pyplot as plt.", + "It is prohibited to fabricate non-existent data to achieve goals.", + ] + desc: str = ( + "Can independently write and execute python/shell code to solve various" + " problems" + ) + + def __init__(self, **kwargs): + """Create a new CodeAssistantAgent instance.""" + super().__init__(**kwargs) + self._init_actions([CodeAction]) + + async def correctness_check( + self, message: AgentMessage + ) -> Tuple[bool, Optional[str]]: + """Verify whether the current execution results meet the target expectations.""" + task_goal = message.current_goal + action_report = message.action_report + task_result = "" + if action_report: + task_result = action_report.get("content", "") + + check_result, model = await self.thinking( + messages=[ + AgentMessage( + role=ModelMessageRoleType.HUMAN, + content="Please understand the following task objectives and " + f"results and give your judgment:\n" + f"Task goal: {task_goal}\n" + f"Execution Result: {task_result}", + ) + ], + prompt=CHECK_RESULT_SYSTEM_MESSAGE, + ) + success = str_to_bool(check_result) + fail_reason = None + if not success: + fail_reason = ( + f"Your answer was successfully executed by the agent, but " + f"the goal cannot be completed yet. Please regenerate based on the " + f"failure reason:{check_result}" + ) + return success, fail_reason diff --git a/dbgpt/agent/expand/dashboard_assistant_agent.py b/dbgpt/agent/expand/dashboard_assistant_agent.py new file mode 100644 index 000000000..bdfd2656e --- /dev/null +++ b/dbgpt/agent/expand/dashboard_assistant_agent.py @@ -0,0 +1,57 @@ +"""Dashboard Assistant Agent.""" + +from typing import List + +from ..actions.dashboard_action import DashboardAction +from ..core.agent import AgentMessage +from ..core.base_agent import ConversableAgent +from ..resource.resource_db_api import ResourceDbClient + + +class DashboardAssistantAgent(ConversableAgent): + """Dashboard Assistant Agent.""" + + name: str = "Visionary" + + profile: str = "Reporter" + goal: str = ( + "Read the provided historical messages, collect various analysis SQLs " + "from them, and assemble them into professional reports." + ) + constraints: List[str] = [ + "You are only responsible for collecting and sorting out the analysis SQL that" + " already exists in historical messages, and do not generate any analysis sql " + "yourself.", + "In order to build a report with rich display types, you can appropriately " + "adjust the display type of the charts you collect so that you can build a " + "better report. Of course, you can choose from the following available " + "display types: {display_type}", + "Please read and completely collect all analysis sql in the historical " + "conversation, and do not omit or modify the content of the analysis sql.", + ] + desc: str = ( + "Observe and organize various analysis results and construct " + "professional reports" + ) + + max_retry_count: int = 3 + + def __init__(self, **kwargs): + """Create a new instance of DashboardAssistantAgent.""" + super().__init__(**kwargs) + self._init_actions([DashboardAction]) + + def _init_reply_message(self, received_message: AgentMessage) -> AgentMessage: + reply_message = super()._init_reply_message(received_message) + client = self.not_null_resource_loader.get_resource_api( + self.actions[0].resource_need, ResourceDbClient + ) + if not client: + raise ValueError( + f"Resource type {self.actions[0].resource_need} is not supported." + ) + reply_message.context = { + "display_type": self.actions[0].render_prompt(), + "dialect": client.get_data_type(self.resources[0]), + } + return reply_message diff --git a/dbgpt/agent/expand/data_scientist_agent.py b/dbgpt/agent/expand/data_scientist_agent.py new file mode 100644 index 000000000..55fd73ccb --- /dev/null +++ b/dbgpt/agent/expand/data_scientist_agent.py @@ -0,0 +1,131 @@ +"""Data Scientist Agent.""" + +import json +import logging +from typing import List, Optional, Tuple, cast + +from ..actions.action import ActionOutput +from ..actions.chart_action import ChartAction +from ..core.agent import AgentMessage +from ..core.base_agent import ConversableAgent +from ..resource.resource_api import ResourceType +from ..resource.resource_db_api import ResourceDbClient + +logger = logging.getLogger(__name__) + + +class DataScientistAgent(ConversableAgent): + """Data Scientist Agent.""" + + name = "Edgar" + profile: str = "DataScientist" + goal: str = ( + "Use correct {dialect} SQL to analyze and solve tasks based on the data" + " structure information of the database given in the resource." + ) + constraints: List[str] = [ + "Please check the generated SQL carefully. Please strictly abide by the data " + "structure definition given. It is prohibited to use non-existent fields and " + "data values. Do not use fields from table A to table B. You can perform " + "multi-table related queries.", + "If the data and fields that need to be analyzed in the target are in different" + " tables, it is recommended to use multi-table correlation queries first, and " + "pay attention to the correlation between multiple table structures.", + "It is forbidden to construct data by yourself as a query condition. If you " + "want to query a specific field, if the value of the field is provided, then " + "you can perform a group statistical query on the field.", + "Please select an appropriate one from the supported display methods for data " + "display. If no suitable display type is found, table display is used by " + "default. Supported display types: \n {display_type}", + ] + desc: str = ( + "Use database resources to conduct data analysis, analyze SQL, and " + "provide recommended rendering methods." + ) + max_retry_count: int = 5 + + def __init__(self, **kwargs): + """Create a new DataScientistAgent instance.""" + super().__init__(**kwargs) + self._init_actions([ChartAction]) + + def _init_reply_message(self, received_message: AgentMessage) -> AgentMessage: + reply_message = super()._init_reply_message(received_message) + client = self.not_null_resource_loader.get_resource_api( + self.actions[0].resource_need, ResourceDbClient + ) + if not client: + raise ValueError( + f"Resource type {self.actions[0].resource_need} is not supported." + ) + reply_message.context = { + "display_type": self.actions[0].render_prompt(), + "dialect": client.get_data_type(self.resources[0]), + } + return reply_message + + async def correctness_check( + self, message: AgentMessage + ) -> Tuple[bool, Optional[str]]: + """Verify whether the current execution results meet the target expectations.""" + action_reply = message.action_report + if action_reply is None: + return ( + False, + f"No executable analysis SQL is generated,{message.content}.", + ) + action_out = cast(ActionOutput, ActionOutput.from_dict(action_reply)) + if not action_out.is_exe_success: + return ( + False, + f"Please check your answer, {action_out.content}.", + ) + action_reply_obj = json.loads(action_out.content) + sql = action_reply_obj.get("sql", None) + if not sql: + return ( + False, + "Please check your answer, the sql information that needs to be " + "generated is not found.", + ) + try: + resource_db_client: Optional[ + ResourceDbClient + ] = self.not_null_resource_loader.get_resource_api( + ResourceType(action_out.resource_type), ResourceDbClient + ) + if not resource_db_client: + return ( + False, + "Please check your answer, the data resource type is not " + "supported.", + ) + if not action_out.resource_value: + return ( + False, + "Please check your answer, the data resource information is not " + "found.", + ) + + columns, values = await resource_db_client.query( + db=action_out.resource_value, sql=sql + ) + if not values or len(values) <= 0: + return ( + False, + "Please check your answer, the current SQL cannot find the data to " + "determine whether filtered field values or inappropriate filter " + "conditions are used.", + ) + else: + logger.info( + f"reply check success! There are {len(values)} rows of data" + ) + return True, None + except Exception as e: + logger.exception(f"DataScientist check exception！{str(e)}") + return ( + False, + f"SQL execution error, please re-read the historical information to " + f"fix this SQL. The error message is as follows:{str(e)}", + ) diff --git a/dbgpt/agent/expand/plugin_assistant_agent.py b/dbgpt/agent/expand/plugin_assistant_agent.py new file mode 100644 index 000000000..0829d4c6a --- /dev/null +++ b/dbgpt/agent/expand/plugin_assistant_agent.py @@ -0,0 +1,72 @@ +"""Plugin Assistant Agent.""" +import logging +from typing import Any, Dict, List, Optional + +from ..actions.plugin_action import PluginAction +from ..core.base_agent import ConversableAgent +from ..plugin.generator import PluginPromptGenerator +from ..resource.resource_api import ResourceType +from ..resource.resource_plugin_api import ResourcePluginClient + +logger = logging.getLogger(__name__) + + +class PluginAssistantAgent(ConversableAgent): + """Plugin Assistant Agent.""" + + plugin_generator: Optional[PluginPromptGenerator] = None + + name = "LuBan" + profile: str = "ToolExpert" + goal: str = ( + "Read and understand the tool information given in the resources below to " + "understand their capabilities and how to use them,and choosing the right tools" + " to achieve the user's goals." + ) + constraints: List[str] = [ + "Please read the parameter definition of the tool carefully and extract the " + "specific parameters required to execute the tool from the user goal.", + "Please output the selected tool name and specific parameter information in " + "json format according to the following required format. If there is an " + "example, please refer to the sample format output.", + ] + desc: str = ( + "You can use the following tools to complete the task objectives, tool " + "information: {tool_infos}" + ) + + def __init__(self, **kwargs): + """Create a new instance of PluginAssistantAgent.""" + super().__init__(**kwargs) + self._init_actions([PluginAction]) + + @property + def introduce(self, **kwargs) -> str: + """Introduce the agent.""" + if not self.plugin_generator: + raise ValueError("PluginGenerator is not loaded.") + return self.desc.format( + tool_infos=self.plugin_generator.generate_commands_string() + ) + + async def preload_resource(self): + """Preload the resource.""" + plugin_loader_client: ResourcePluginClient = ( + self.not_null_resource_loader.get_resource_api( + ResourceType.Plugin, ResourcePluginClient + ) + ) + item_list = [] + for item in self.resources: + if item.type == ResourceType.Plugin: + item_list.append(item.value) + plugin_generator = self.plugin_generator + for item in item_list: + plugin_generator = await plugin_loader_client.load_plugin( + item, plugin_generator + ) + self.plugin_generator = plugin_generator + + def prepare_act_param(self) -> Dict[str, Any]: + """Prepare the act parameter.""" + return {"plugin_generator": self.plugin_generator} diff --git a/dbgpt/agent/expand/retrieve_summary_assistant_agent.py b/dbgpt/agent/expand/retrieve_summary_assistant_agent.py new file mode 100644 index 000000000..e71f7fcdc --- /dev/null +++ b/dbgpt/agent/expand/retrieve_summary_assistant_agent.py @@ -0,0 +1,596 @@ +"""Retrieve Summary Assistant Agent.""" +import glob +import json +import logging +import os +from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from urllib.parse import urlparse + +from dbgpt.configs.model_config import PILOT_PATH +from dbgpt.core import ModelMessageRoleType + +from ..actions.action import Action, ActionOutput +from ..core.agent import Agent, AgentMessage, AgentReviewInfo +from ..core.base_agent import ConversableAgent +from ..resource.resource_api import AgentResource +from ..util.cmp import cmp_string_equal + +try: + from unstructured.partition.auto import partition + + HAS_UNSTRUCTURED = True +except ImportError: + HAS_UNSTRUCTURED = False + +logger = logging.getLogger() + +TEXT_FORMATS = [ + "txt", + "json", + "csv", + "tsv", + "md", + "html", + "htm", + "rtf", + "rst", + "jsonl", + "log", + "xml", + "yaml", + "yml", + "pdf", +] +UNSTRUCTURED_FORMATS = [ + "doc", + "docx", + "epub", + "msg", + "odt", + "org", + "pdf", + "ppt", + "pptx", + "rtf", + "rst", + "xlsx", +] # These formats will be parsed by the 'unstructured' library, if installed. +if HAS_UNSTRUCTURED: + TEXT_FORMATS += UNSTRUCTURED_FORMATS + TEXT_FORMATS = list(set(TEXT_FORMATS)) + +VALID_CHUNK_MODES = frozenset({"one_line", "multi_lines"}) + + +def _get_max_tokens(model="gpt-3.5-turbo"): + """Get the maximum number of tokens for a given model.""" + if "32k" in model: + return 32000 + elif "16k" in model: + return 16000 + elif "gpt-4" in model: + return 8000 + else: + return 4000 + + +_NO_RESPONSE = "NO RELATIONSHIP.UPDATE TEXT CONTENT." + + +class RetrieveSummaryAssistantAgent(ConversableAgent): + """Assistant agent, designed to solve a task with LLM. + + AssistantAgent is a subclass of ConversableAgent configured with a default + system message. + The default system message is designed to solve a task with LLM, + including suggesting python code blocks and debugging. + """ + + goal = ( + "You're an extraction expert. You need to extract Please complete this task " + "step by step following instructions below:\n" + " 1. You need to first ONLY extract user's question that you need to answer " + "without ANY file paths and URLs. \n" + " 2. Extract the provided file paths and URLs.\n" + " 3. Construct the extracted file paths and URLs as a list of strings.\n" + " 4. ONLY output the extracted results with the following json format: " + "{response}." + ) + + PROMPT_QA = ( + "You are a great summary writer to summarize the provided text content " + "according to user questions.\n" + "User's Question is: {input_question}\n\n" + "Provided text content is: {input_context}\n\n" + "Please complete this task step by step following instructions below:\n" + " 1. You need to first detect user's question that you need to answer with " + "your summarization.\n" + " 2. Then you need to summarize the provided text content that ONLY CAN " + "ANSWER user's question and filter useless information as possible as you can. " + "YOU CAN ONLY USE THE PROVIDED TEXT CONTENT!! DO NOT CREATE ANY SUMMARIZATION " + "WITH YOUR OWN KNOWLEDGE!!!\n" + " 3. Output the content of summarization that ONLY CAN ANSWER user's question" + " and filter useless information as possible as you can. The output language " + "must be the same to user's question language!! You must give as short an " + "summarization as possible!!! DO NOT CREATE ANY SUMMARIZATION WITH YOUR OWN " + "KNOWLEDGE!!!\n\n" + "####Important Notice####\n" + "If the provided text content CAN NOT ANSWER user's question, ONLY output " + "'NO RELATIONSHIP.UPDATE TEXT CONTENT.'!!." + ) + CHECK_RESULT_SYSTEM_MESSAGE = ( + "You are an expert in analyzing the results of a summary task." + "Your responsibility is to check whether the summary results can summarize the " + "input provided by the user, and then make a judgment. You need to answer " + "according to the following rules:\n" + " Rule 1: If you think the summary results can summarize the input provided" + " by the user, only return True.\n" + " Rule 2: If you think the summary results can NOT summarize the input " + "provided by the user, return False and the reason, split by | and ended " + "by TERMINATE. For instance: False|Some important concepts in the input are " + "not summarized. TERMINATE" + ) + + DEFAULT_DESCRIBE = ( + "Summarize provided content according to user's questions and " + "the provided file paths." + ) + + name = "RetrieveSummarizer" + desc = DEFAULT_DESCRIBE + + chunk_token_size: int = 4000 + chunk_mode: str = "multi_lines" + + _model = "gpt-3.5-turbo-16k" + _max_tokens = _get_max_tokens(_model) + context_max_tokens = _max_tokens * 0.8 + + def __init__( + self, + **kwargs, + ): + """Create a new instance of the agent.""" + super().__init__( + **kwargs, + ) + self._init_actions([SummaryAction]) + + def _init_reply_message(self, received_message: AgentMessage) -> AgentMessage: + reply_message = super()._init_reply_message(received_message) + json_data = {"user_question": "user's question", "file_list": "file&URL list"} + reply_message.context = {"response": json.dumps(json_data, ensure_ascii=False)} + return reply_message + + async def generate_reply( + self, + received_message: AgentMessage, + sender: Agent, + reviewer: Optional[Agent] = None, + rely_messages: Optional[List[AgentMessage]] = None, + **kwargs, + ): + """Generate a reply based on the received messages.""" + reply_message: AgentMessage = self._init_reply_message( + received_message=received_message + ) + await self._system_message_assembly( + received_message.content, reply_message.context + ) + # 1.Think about how to do things + llm_reply, model_name = await self.thinking( + self._load_thinking_messages(received_message, sender, rely_messages) + ) + + if not llm_reply: + raise ValueError("No reply from LLM.") + ai_reply_dic = json.loads(llm_reply) + user_question = ai_reply_dic["user_question"] + file_list = ai_reply_dic["file_list"] + + # 2. Split files and URLs in the file list dictionary into chunks + extracted_files = self._get_files_from_dir(file_list) + chunks = await self._split_files_to_chunks(files=extracted_files) + + summaries = "" + for count, chunk in enumerate(chunks[:]): + print(count) + temp_sys_message = self.PROMPT_QA.format( + input_question=user_question, input_context=chunk + ) + chunk_ai_reply, model = await self.thinking( + messages=[ + AgentMessage(role=ModelMessageRoleType.HUMAN, content=user_question) + ], + prompt=temp_sys_message, + ) + if chunk_ai_reply and not cmp_string_equal( + _NO_RESPONSE, chunk_ai_reply, True, True, True + ): + summaries += f"{chunk_ai_reply}\n" + + temp_sys_message = self.PROMPT_QA.format( + input_question=user_question, input_context=summaries + ) + + final_summary_ai_reply, model = await self.thinking( + messages=[ + AgentMessage(role=ModelMessageRoleType.HUMAN, content=user_question) + ], + prompt=temp_sys_message, + ) + reply_message.model_name = model + reply_message.content = final_summary_ai_reply + + print("HERE IS THE FINAL SUMMARY!!!!!") + print(final_summary_ai_reply) + + approve = True + comments = None + if reviewer and final_summary_ai_reply: + approve, comments = await reviewer.review(final_summary_ai_reply, self) + + reply_message.review_info = AgentReviewInfo( + approve=approve, + comments=comments, + ) + if approve: + # 3.Act based on the results of your thinking + act_extent_param = self.prepare_act_param() + act_out: Optional[ActionOutput] = await self.act( + message=final_summary_ai_reply, + sender=sender, + reviewer=reviewer, + **act_extent_param, + ) + if act_out: + reply_message.action_report = act_out.dict() + # 4.Reply information verification + check_pass, reason = await self.verify(reply_message, sender, reviewer) + is_success = check_pass + # 5.Optimize wrong answers myself + if not check_pass: + reply_message.content = reason + reply_message.success = is_success + return reply_message + + async def verify( + self, + message: AgentMessage, + sender: Agent, + reviewer: Optional[Agent] = None, + **kwargs, + ) -> Tuple[bool, Optional[str]]: + """Verify the correctness of the message.""" + action_report = message.action_report + task_result = "" + if action_report: + task_result = action_report.get("content", "") + + check_result, model = await self.thinking( + messages=[ + AgentMessage( + role=ModelMessageRoleType.HUMAN, + content=( + "Please understand the following user input and summary results" + " and give your judgment:\n" + f"User Input: {message.current_goal}\n" + f"Summary Results: {task_result}" + ), + ) + ], + prompt=self.CHECK_RESULT_SYSTEM_MESSAGE, + ) + fail_reason = "" + if check_result and ( + "true" in check_result.lower() or "yes" in check_result.lower() + ): + success = True + elif not check_result: + success = False + fail_reason = ( + "The summary results cannot summarize the user input. " + "Please re-understand and complete the summary task." + ) + else: + success = False + try: + _, fail_reason = check_result.split("|") + fail_reason = ( + "The summary results cannot summarize the user input due" + f" to: {fail_reason}. Please re-understand and complete the summary" + " task." + ) + except Exception: + logger.warning( + "The model thought the results are irrelevant but did not give the" + " correct format of results." + ) + fail_reason = ( + "The summary results cannot summarize the user input. " + "Please re-understand and complete the summary task." + ) + return success, fail_reason + + def _get_files_from_dir( + self, + dir_path: Union[str, List[str]], + types: list = TEXT_FORMATS, + recursive: bool = True, + ): + """Return a list of all the files in a given directory. + + A url, a file path or a list of them. + """ + if len(types) == 0: + raise ValueError("types cannot be empty.") + types = [t[1:].lower() if t.startswith(".") else t.lower() for t in set(types)] + types += [t.upper() for t in types] + + files = [] + # If the path is a list of files or urls, process and return them + if isinstance(dir_path, list): + for item in dir_path: + if os.path.isfile(item): + files.append(item) + elif self._is_url(item): + files.append(self._get_file_from_url(item)) + elif os.path.exists(item): + try: + files.extend(self._get_files_from_dir(item, types, recursive)) + except ValueError: + logger.warning(f"Directory {item} does not exist. Skipping.") + else: + logger.warning(f"File {item} does not exist. Skipping.") + return files + + # If the path is a file, return it + if os.path.isfile(dir_path): + return [dir_path] + + # If the path is a url, download it and return the downloaded file + if self._is_url(dir_path): + return [self._get_file_from_url(dir_path)] + + if os.path.exists(dir_path): + for type in types: + if recursive: + files += glob.glob( + os.path.join(dir_path, f"**/*.{type}"), recursive=True + ) + else: + files += glob.glob( + os.path.join(dir_path, f"*.{type}"), recursive=False + ) + else: + logger.error(f"Directory {dir_path} does not exist.") + raise ValueError(f"Directory {dir_path} does not exist.") + return files + + def _get_file_from_url(self, url: str, save_path: Optional[str] = None): + """Download a file from a URL.""" + import requests + from bs4 import BeautifulSoup + + if save_path is None: + target_directory = os.path.join(PILOT_PATH, "data") + os.makedirs(target_directory, exist_ok=True) + save_path = os.path.join(target_directory, os.path.basename(url)) + else: + os.makedirs(os.path.dirname(save_path), exist_ok=True) + proxies: Dict[str, Any] = {} + if os.getenv("http_proxy"): + proxies["http"] = os.getenv("http_proxy") + if os.getenv("https_proxy"): + proxies["https"] = os.getenv("https_proxy") + with requests.get(url, proxies=proxies, timeout=10, stream=True) as r: + r.raise_for_status() + with open(save_path, "wb") as f: + for chunk in r.iter_content(chunk_size=8192): + f.write(chunk) + + with open(save_path, "r", encoding="utf-8") as file: + html_content = file.read() + + soup = BeautifulSoup(html_content, "html.parser") + + # 可以根据需要从Beautiful Soup对象中提取数据，例如： + # title = soup.title.string # 获取网页标题 + paragraphs = soup.find_all("p") # 获取所有段落文本 + + # 将解析后的内容重新写入到相同的save_path + with open(save_path, "w", encoding="utf-8") as f: + for paragraph in paragraphs: + f.write(paragraph.get_text() + "\n") # 获取段落文本并写入文件 + + return save_path + + def _is_url(self, string: str): + """Return True if the string is a valid URL.""" + try: + result = urlparse(string) + return all([result.scheme, result.netloc]) + except ValueError: + return False + + async def _split_text_to_chunks( + self, + text: str, + chunk_mode: str = "multi_lines", + must_break_at_empty_line: bool = True, + ): + """Split a long text into chunks of max_tokens.""" + max_tokens = self.chunk_token_size + if chunk_mode not in VALID_CHUNK_MODES: + raise AssertionError + if chunk_mode == "one_line": + must_break_at_empty_line = False + chunks = [] + lines = text.split("\n") + lines_tokens = [await self._count_token(line) for line in lines] + sum_tokens = sum(lines_tokens) + while sum_tokens > max_tokens: + if chunk_mode == "one_line": + estimated_line_cut = 2 + else: + estimated_line_cut = int(max_tokens / sum_tokens * len(lines)) + 1 + cnt = 0 + prev = "" + for cnt in reversed(range(estimated_line_cut)): + if must_break_at_empty_line and lines[cnt].strip() != "": + continue + if sum(lines_tokens[:cnt]) <= max_tokens: + prev = "\n".join(lines[:cnt]) + break + if cnt == 0: + logger.warning( + f"max_tokens is too small to fit a single line of text. Breaking " + f"this line:\n\t{lines[0][:100]} ..." + ) + if not must_break_at_empty_line: + split_len = int(max_tokens / lines_tokens[0] * 0.9 * len(lines[0])) + prev = lines[0][:split_len] + lines[0] = lines[0][split_len:] + lines_tokens[0] = await self._count_token(lines[0]) + else: + logger.warning( + "Failed to split docs with must_break_at_empty_line being True," + " set to False." + ) + must_break_at_empty_line = False + chunks.append(prev) if len( + prev + ) > 10 else None # don't add chunks less than 10 characters + lines = lines[cnt:] + lines_tokens = lines_tokens[cnt:] + sum_tokens = sum(lines_tokens) + text_to_chunk = "\n".join(lines) + chunks.append(text_to_chunk) if len( + text_to_chunk + ) > 10 else None # don't add chunks less than 10 characters + return chunks + + def _extract_text_from_pdf(self, file: str) -> str: + """Extract text from PDF files.""" + text = "" + import pypdf + + with open(file, "rb") as f: + reader = pypdf.PdfReader(f) + if reader.is_encrypted: # Check if the PDF is encrypted + try: + reader.decrypt("") + except pypdf.errors.FileNotDecryptedError as e: + logger.warning(f"Could not decrypt PDF {file}, {e}") + return text # Return empty text if PDF could not be decrypted + + for page_num in range(len(reader.pages)): + page = reader.pages[page_num] + text += page.extract_text() + + if not text.strip(): # Debugging line to check if text is empty + logger.warning(f"Could not decrypt PDF {file}") + + return text + + async def _split_files_to_chunks( + self, + files: list, + chunk_mode: str = "multi_lines", + must_break_at_empty_line: bool = True, + custom_text_split_function: Optional[Callable] = None, + ): + """Split a list of files into chunks of max_tokens.""" + chunks = [] + + for file in files: + _, file_extension = os.path.splitext(file) + file_extension = file_extension.lower() + + if HAS_UNSTRUCTURED and file_extension[1:] in UNSTRUCTURED_FORMATS: + text = partition(file) + text = "\n".join([t.text for t in text]) if len(text) > 0 else "" + elif file_extension == ".pdf": + text = self._extract_text_from_pdf(file) + else: # For non-PDF text-based files + with open(file, "r", encoding="utf-8", errors="ignore") as f: + text = f.read() + + if ( + not text.strip() + ): # Debugging line to check if text is empty after reading + logger.warning(f"No text available in file: {file}") + continue # Skip to the next file if no text is available + + if custom_text_split_function is not None: + chunks += custom_text_split_function(text) + else: + chunks += await self._split_text_to_chunks( + text, chunk_mode, must_break_at_empty_line + ) + + return chunks + + async def _count_token( + self, input: Union[str, List, Dict], model: str = "gpt-3.5-turbo-0613" + ) -> int: + """Count number of tokens used by an OpenAI model. + + Args: + input: (str, list, dict): Input to the model. + model: (str): Model name. + + Returns: + int: Number of tokens from the input. + """ + _llm_client = self.not_null_llm_client + if isinstance(input, str): + return await _llm_client.count_token(model, input) + elif isinstance(input, list): + return sum([await _llm_client.count_token(model, i) for i in input]) + else: + raise ValueError("input must be str or list") + + +class SummaryAction(Action[None]): + """Simple Summary Action.""" + + def __init__(self): + """Create a new instance of the action.""" + super().__init__() + + async def run( + self, + ai_message: str, + resource: Optional[AgentResource] = None, + rely_action_out: Optional[ActionOutput] = None, + need_vis_render: bool = True, + **kwargs, + ) -> ActionOutput: + """Perform the action.""" + fail_reason = None + response_success = True + view = None + content = None + if ai_message is None: + # Answer failed, turn on automatic repair + fail_reason += "Nothing is summarized, please check your input." + response_success = False + else: + try: + if "NO RELATIONSHIP." in ai_message: + fail_reason = ( + "Return summarization error, the provided text " + "content has no relationship to user's question. TERMINATE." + ) + response_success = False + else: + content = ai_message + view = content + except Exception as e: + fail_reason = f"Return summarization error, {str(e)}" + response_success = False + + if not response_success: + content = fail_reason + return ActionOutput(is_exe_success=response_success, content=content, view=view) diff --git a/dbgpt/agent/expand/summary_assistant_agent.py b/dbgpt/agent/expand/summary_assistant_agent.py new file mode 100644 index 000000000..f7dc65ace --- /dev/null +++ b/dbgpt/agent/expand/summary_assistant_agent.py @@ -0,0 +1,42 @@ +"""Summary Assistant Agent.""" +import logging +from typing import List + +from ..actions.blank_action import BlankAction +from ..core.base_agent import ConversableAgent + +logger = logging.getLogger(__name__) + + +class SummaryAssistantAgent(ConversableAgent): + """Summary Assistant Agent.""" + + name = "Aristotle" + profile: str = "Summarizer" + goal: str = ( + "Summarize answer summaries based on user questions from provided " + "resource information or from historical conversation memories." + ) + + constraints: List[str] = [ + "Prioritize the summary of answers to user questions from the improved resource" + " text. If no relevant information is found, summarize it from the historical " + "dialogue memory given. It is forbidden to make up your own.", + "You need to first detect user's question that you need to answer with your" + " summarization.", + "Extract the provided text content used for summarization.", + "Then you need to summarize the extracted text content.", + "Output the content of summarization ONLY related to user's question. The " + "output language must be the same to user's question language.", + "If you think the provided text content is not related to user questions at " + "all, ONLY output 'Did not find the information you want.'!!.", + ] + desc: str = ( + "You can summarize provided text content according to user's questions" + " and output the summarization." + ) + + def __init__(self, **kwargs): + """Create a new SummaryAssistantAgent instance.""" + super().__init__(**kwargs) + self._init_actions([BlankAction]) diff --git a/dbgpt/agent/memory/__init__.py b/dbgpt/agent/memory/__init__.py index e69de29bb..48d1d49b2 100644 --- a/dbgpt/agent/memory/__init__.py +++ b/dbgpt/agent/memory/__init__.py @@ -0,0 +1 @@ +"""Memory module for agents.""" diff --git a/dbgpt/agent/memory/base.py b/dbgpt/agent/memory/base.py index 25c51b754..7d4077e69 100644 --- a/dbgpt/agent/memory/base.py +++ b/dbgpt/agent/memory/base.py @@ -1,17 +1,15 @@ -from __future__ import annotations - +"""Base memory interface for agents.""" import dataclasses -from abc import ABC -from dataclasses import dataclass +from abc import ABC, abstractmethod from datetime import datetime from typing import Any, Dict, List, Optional -from dbgpt.agent.common.schema import Status +from dbgpt.agent.core.schema import Status -@dataclass +@dataclasses.dataclass class GptsPlan: - """Gpts plan""" + """Gpts plan.""" conv_id: str sub_task_num: int @@ -21,15 +19,16 @@ class GptsPlan: resource_name: Optional[str] = None rely: Optional[str] = None agent_model: Optional[str] = None - retry_times: Optional[int] = 0 - max_retry_times: Optional[int] = 5 + retry_times: int = 0 + max_retry_times: int = 5 state: Optional[str] = Status.TODO.value result: Optional[str] = None @staticmethod - def from_dict(d: Dict[str, Any]) -> GptsPlan: + def from_dict(d: Dict[str, Any]) -> "GptsPlan": + """Create a GptsPlan object from a dictionary.""" return GptsPlan( - conv_id=d.get("conv_id"), + conv_id=d["conv_id"], sub_task_num=d["sub_task_num"], sub_task_content=d["sub_task_content"], sub_task_agent=d["sub_task_agent"], @@ -43,12 +42,13 @@ def from_dict(d: Dict[str, Any]) -> GptsPlan: ) def to_dict(self) -> Dict[str, Any]: + """Return a dictionary representation of the GptsPlan object.""" return dataclasses.asdict(self) -@dataclass +@dataclasses.dataclass class GptsMessage: - """Gpts plan""" + """Gpts message.""" conv_id: str sender: str @@ -57,16 +57,17 @@ class GptsMessage: role: str content: str rounds: Optional[int] - current_goal: str = None + current_goal: Optional[str] = None context: Optional[str] = None review_info: Optional[str] = None action_report: Optional[str] = None model_name: Optional[str] = None - created_at: datetime = datetime.utcnow - updated_at: datetime = datetime.utcnow + created_at: datetime = dataclasses.field(default_factory=datetime.utcnow) + updated_at: datetime = dataclasses.field(default_factory=datetime.utcnow) @staticmethod - def from_dict(d: Dict[str, Any]) -> GptsMessage: + def from_dict(d: Dict[str, Any]) -> "GptsMessage": + """Create a GptsMessage object from a dictionary.""" return GptsMessage( conv_id=d["conv_id"], sender=d["sender"], @@ -84,152 +85,165 @@ def from_dict(d: Dict[str, Any]) -> GptsMessage: ) def to_dict(self) -> Dict[str, Any]: + """Return a dictionary representation of the GptsMessage object.""" return dataclasses.asdict(self) class GptsPlansMemory(ABC): - def batch_save(self, plans: list[GptsPlan]): - """ - batch save gpts plan + """Gpts plans memory interface.""" + + @abstractmethod + def batch_save(self, plans: List[GptsPlan]) -> None: + """Save plans in batch. + Args: plans: panner generate plans info - Returns: - None + """ - pass + @abstractmethod def get_by_conv_id(self, conv_id: str) -> List[GptsPlan]: - """ - get plans by conv_id + """Get plans by conv_id. + Args: conv_id: conversation id + Returns: - List of planning steps + List[GptsPlan]: List of planning steps """ + @abstractmethod def get_by_conv_id_and_num( self, conv_id: str, task_nums: List[int] ) -> List[GptsPlan]: - """ - get + """Get plans by conv_id and task number. + Args: - conv_id: conversation id - task_nums: List of sequence numbers of plans in the same conversation + conv_id(str): conversation id + task_nums(List[int]): List of sequence numbers of plans in the same + conversation Returns: - List of planning steps - + List[GptsPlan]: List of planning steps """ + @abstractmethod def get_todo_plans(self, conv_id: str) -> List[GptsPlan]: - """ - Get unfinished planning steps + """Get unfinished planning steps. + Args: - conv_id: conversation id + conv_id(str): Conversation id Returns: - List of planning steps + List[GptsPlan]: List of planning steps """ - def complete_task(self, conv_id: str, task_num: int, result: str): - """ - Complete designated planning step - Args: - conv_id: conversation id - task_num: Planning step num - result: Plan step results + @abstractmethod + def complete_task(self, conv_id: str, task_num: int, result: str) -> None: + """Set the planning step to complete. - Returns: - None + Args: + conv_id(str): conversation id + task_num(int): Planning step num + result(str): Plan step results """ + @abstractmethod def update_task( self, conv_id: str, task_num: int, state: str, retry_times: int, - agent: str = None, - model: str = None, - result: str = None, - ): - """ - Update planning step information - Args: - conv_id: conversation id - task_num: Planning step num - state: the status to update to - retry_times: Latest number of retries - agent: Agent's name + agent: Optional[str] = None, + model: Optional[str] = None, + result: Optional[str] = None, + ) -> None: + """Update planning step information. - Returns: - - """ - - def remove_by_conv_id(self, conv_id: str): - """ - Delete planning Args: - conv_id: + conv_id(str): conversation id + task_num(int): Planning step num + state(str): the status to update to + retry_times(int): Latest number of retries + agent(str): Agent's name + model(str): Model name + result(str): Plan step results + """ - Returns: + @abstractmethod + def remove_by_conv_id(self, conv_id: str) -> None: + """Remove plan by conversation id. + Args: + conv_id(str): conversation id """ class GptsMessageMemory(ABC): - def append(self, message: GptsMessage): - """ - Add a message - Args: - message: + """Gpts message memory interface.""" - Returns: + @abstractmethod + def append(self, message: GptsMessage) -> None: + """Add a message. + Args: + message(GptsMessage): Message object """ + @abstractmethod def get_by_agent(self, conv_id: str, agent: str) -> Optional[List[GptsMessage]]: - """ - Query information related to an agent + """Return all messages of the agent in the conversation. + Args: - agent:agent's name + conv_id(str): Conversation id + agent(str): Agent's name Returns: - messages + List[GptsMessage]: List of messages """ + @abstractmethod def get_between_agents( self, conv_id: str, agent1: str, agent2: str, current_goal: Optional[str] = None, - ) -> Optional[List[GptsMessage]]: - """ + ) -> List[GptsMessage]: + """Get messages between two agents. + Query information related to an agent + Args: - agent:agent's name + conv_id(str): Conversation id + agent1(str): Agent1's name + agent2(str): Agent2's name + current_goal(str): Current goal Returns: - messages + List[GptsMessage]: List of messages """ - def get_by_conv_id(self, conv_id: str) -> Optional[List[GptsMessage]]: - """ - Query messages by conv id - Args: - conv_id: + @abstractmethod + def get_by_conv_id(self, conv_id: str) -> List[GptsMessage]: + """Return all messages in the conversation. - Returns: + Query messages by conv id. + Args: + conv_id(str): Conversation id + Returns: + List[GptsMessage]: List of messages """ + @abstractmethod def get_last_message(self, conv_id: str) -> Optional[GptsMessage]: - """ - Query last message + """Return the last message in the conversation. + Args: - conv_id: + conv_id(str): Conversation id Returns: - + GptsMessage: The last message in the conversation """ diff --git a/dbgpt/agent/memory/default_gpts_memory.py b/dbgpt/agent/memory/default_gpts_memory.py index 1e50346f8..d21eeb35f 100644 --- a/dbgpt/agent/memory/default_gpts_memory.py +++ b/dbgpt/agent/memory/default_gpts_memory.py @@ -1,23 +1,29 @@ +"""Default memory for storing plans and messages.""" + from dataclasses import fields from typing import List, Optional import pandas as pd -from dbgpt.agent.common.schema import Status - +from ..core.schema import Status from .base import GptsMessage, GptsMessageMemory, GptsPlan, GptsPlansMemory class DefaultGptsPlansMemory(GptsPlansMemory): + """Default memory for storing plans.""" + def __init__(self): + """Create a memory to store plans.""" self.df = pd.DataFrame(columns=[field.name for field in fields(GptsPlan)]) def batch_save(self, plans: list[GptsPlan]): + """Save plans in batch.""" new_rows = pd.DataFrame([item.to_dict() for item in plans]) self.df = pd.concat([self.df, new_rows], ignore_index=True) def get_by_conv_id(self, conv_id: str) -> List[GptsPlan]: - result = self.df.query(f"conv_id==@conv_id") + """Get plans by conv_id.""" + result = self.df.query(f"conv_id==@conv_id") # noqa: F541 plans = [] for row in result.itertuples(index=False, name=None): row_dict = dict(zip(self.df.columns, row)) @@ -27,8 +33,11 @@ def get_by_conv_id(self, conv_id: str) -> List[GptsPlan]: def get_by_conv_id_and_num( self, conv_id: str, task_nums: List[int] ) -> List[GptsPlan]: - task_nums_int = [int(num) for num in task_nums] - result = self.df.query(f"conv_id==@conv_id and sub_task_num in @task_nums_int") + """Get plans by conv_id and task number.""" + task_nums_int = [int(num) for num in task_nums] # noqa:F841 + result = self.df.query( # noqa + f"conv_id==@conv_id and sub_task_num in @task_nums_int" # noqa + ) plans = [] for row in result.itertuples(index=False, name=None): row_dict = dict(zip(self.df.columns, row)) @@ -36,8 +45,9 @@ def get_by_conv_id_and_num( return plans def get_todo_plans(self, conv_id: str) -> List[GptsPlan]: - todo_states = [Status.TODO.value, Status.RETRYING.value] - result = self.df.query(f"conv_id==@conv_id and state in @todo_states") + """Get unfinished planning steps.""" + todo_states = [Status.TODO.value, Status.RETRYING.value] # noqa: F841 + result = self.df.query(f"conv_id==@conv_id and state in @todo_states") # noqa plans = [] for row in result.itertuples(index=False, name=None): row_dict = dict(zip(self.df.columns, row)) @@ -45,6 +55,7 @@ def get_todo_plans(self, conv_id: str) -> List[GptsPlan]: return plans def complete_task(self, conv_id: str, task_num: int, result: str): + """Set the planning step to complete.""" condition = (self.df["conv_id"] == conv_id) & ( self.df["sub_task_num"] == task_num ) @@ -57,10 +68,11 @@ def update_task( task_num: int, state: str, retry_times: int, - agent: str = None, - model=None, - result: str = None, + agent: Optional[str] = None, + model: Optional[str] = None, + result: Optional[str] = None, ): + """Update the state of the planning step.""" condition = (self.df["conv_id"] == conv_id) & ( self.df["sub_task_num"] == task_num ) @@ -75,19 +87,25 @@ def update_task( self.df.loc[condition, "agent_model"] = model def remove_by_conv_id(self, conv_id: str): + """Remove all plans in the conversation.""" self.df.drop(self.df[self.df["conv_id"] == conv_id].index, inplace=True) class DefaultGptsMessageMemory(GptsMessageMemory): + """Default memory for storing messages.""" + def __init__(self): + """Create a memory to store messages.""" self.df = pd.DataFrame(columns=[field.name for field in fields(GptsMessage)]) def append(self, message: GptsMessage): + """Append a message to the memory.""" self.df.loc[len(self.df)] = message.to_dict() def get_by_agent(self, conv_id: str, agent: str) -> Optional[List[GptsMessage]]: + """Get all messages sent or received by the agent in the conversation.""" result = self.df.query( - f"conv_id==@conv_id and (sender==@agent or receiver==@agent)" + f"conv_id==@conv_id and (sender==@agent or receiver==@agent)" # noqa: F541 ) messages = [] for row in result.itertuples(index=False, name=None): @@ -101,14 +119,15 @@ def get_between_agents( agent1: str, agent2: str, current_goal: Optional[str] = None, - ) -> Optional[List[GptsMessage]]: + ) -> List[GptsMessage]: + """Get all messages between two agents in the conversation.""" if current_goal: result = self.df.query( - f"conv_id==@conv_id and ((sender==@agent1 and receiver==@agent2) or (sender==@agent2 and receiver==@agent1)) and current_goal==@current_goal" + f"conv_id==@conv_id and ((sender==@agent1 and receiver==@agent2) or (sender==@agent2 and receiver==@agent1)) and current_goal==@current_goal" # noqa ) else: result = self.df.query( - f"conv_id==@conv_id and ((sender==@agent1 and receiver==@agent2) or (sender==@agent2 and receiver==@agent1))" + f"conv_id==@conv_id and ((sender==@agent1 and receiver==@agent2) or (sender==@agent2 and receiver==@agent1))" # noqa ) messages = [] for row in result.itertuples(index=False, name=None): @@ -116,10 +135,15 @@ def get_between_agents( messages.append(GptsMessage.from_dict(row_dict)) return messages - def get_by_conv_id(self, conv_id: str) -> Optional[List[GptsMessage]]: - result = self.df.query(f"conv_id==@conv_id") + def get_by_conv_id(self, conv_id: str) -> List[GptsMessage]: + """Get all messages in the conversation.""" + result = self.df.query(f"conv_id==@conv_id") # noqa: F541 messages = [] for row in result.itertuples(index=False, name=None): row_dict = dict(zip(self.df.columns, row)) messages.append(GptsMessage.from_dict(row_dict)) return messages + + def get_last_message(self, conv_id: str) -> Optional[GptsMessage]: + """Get the last message in the conversation.""" + return None diff --git a/dbgpt/agent/memory/gpts_memory.py b/dbgpt/agent/memory/gpts_memory.py index c66386271..bbad0ad2c 100644 --- a/dbgpt/agent/memory/gpts_memory.py +++ b/dbgpt/agent/memory/gpts_memory.py @@ -1,13 +1,11 @@ -from __future__ import annotations - +"""GPTs memory.""" import json from collections import OrderedDict, defaultdict from typing import Dict, List, Optional -from dbgpt.agent.actions.action import ActionOutput -from dbgpt.util.json_utils import EnhancedJSONEncoder from dbgpt.vis.client import VisAgentMessages, VisAgentPlans, vis_client +from ..actions.action import ActionOutput from .base import GptsMessage, GptsMessageMemory, GptsPlansMemory from .default_gpts_memory import DefaultGptsMessageMemory, DefaultGptsPlansMemory @@ -15,11 +13,14 @@ class GptsMemory: + """GPTs memory.""" + def __init__( self, plans_memory: Optional[GptsPlansMemory] = None, message_memory: Optional[GptsMessageMemory] = None, ): + """Create a memory to store plans and messages.""" self._plans_memory: GptsPlansMemory = ( plans_memory if plans_memory is not None else DefaultGptsPlansMemory() ) @@ -28,11 +29,13 @@ def __init__( ) @property - def plans_memory(self): + def plans_memory(self) -> GptsPlansMemory: + """Return the plans memory.""" return self._plans_memory @property - def message_memory(self): + def message_memory(self) -> GptsMessageMemory: + """Return the message memory.""" return self._message_memory async def _message_group_vis_build(self, message_group): @@ -86,40 +89,42 @@ async def _plan_vis_build(self, plan_group: dict[str, list]): ) return await self._messages_to_plan_vis(plan_items) - async def one_chat_competions_v2(self, conv_id: str): + async def one_chat_completions_v2(self, conv_id: str): + """Generate a visualization of the conversation.""" messages = self.message_memory.get_by_conv_id(conv_id=conv_id) - temp_group = OrderedDict() + temp_group: Dict[str, List[GptsMessage]] = OrderedDict() none_goal_count = 1 count: int = 0 for message in messages: count = count + 1 if count == 1: continue - current_gogal = message.current_goal + current_goal = message.current_goal last_goal = next(reversed(temp_group)) if temp_group else None if last_goal: last_goal_messages = temp_group[last_goal] - if current_gogal: - if current_gogal == last_goal: + if current_goal: + if current_goal == last_goal: last_goal_messages.append(message) else: - temp_group[current_gogal] = [message] + temp_group[current_goal] = [message] else: temp_group[f"{NONE_GOAL_PREFIX}{none_goal_count}"] = [message] none_goal_count += 1 else: - if current_gogal: - temp_group[current_gogal] = [message] + if current_goal: + temp_group[current_goal] = [message] else: temp_group[f"{NONE_GOAL_PREFIX}{none_goal_count}"] = [message] none_goal_count += 1 return await self._message_group_vis_build(temp_group) - async def one_chat_competions(self, conv_id: str): + async def one_chat_completions(self, conv_id: str): + """Generate a visualization of the conversation.""" messages = self.message_memory.get_by_conv_id(conv_id=conv_id) - temp_group = defaultdict(list) + temp_group: Dict[str, List[GptsMessage]] = defaultdict(list) temp_messages = [] vis_items = [] count: int = 0 @@ -138,8 +143,8 @@ async def one_chat_competions(self, conv_id: str): vis_items.append(await self._messages_to_agents_vis(temp_messages)) temp_messages.clear() - last_gogal = message.current_goal - temp_group[last_gogal].append(message) + last_goal = message.current_goal + temp_group[last_goal].append(message) if len(temp_group) > 0: vis_items.append(await self._plan_vis_build(temp_group)) @@ -161,10 +166,11 @@ async def _messages_to_agents_vis( view_info = message.content if action_report_str and len(action_report_str) > 0: action_out = ActionOutput.from_dict(json.loads(action_report_str)) - if action_out is not None: - if action_out.is_exe_success or is_last_message: - view = action_out.view - view_info = view if view else action_out.content + if action_out is not None and ( + action_out.is_exe_success or is_last_message + ): + view = action_out.view + view_info = view if view else action_out.content messages_view.append( { diff --git a/dbgpt/agent/memory/gpts_memory_storage.py b/dbgpt/agent/memory/gpts_memory_storage.py deleted file mode 100644 index 3064277a5..000000000 --- a/dbgpt/agent/memory/gpts_memory_storage.py +++ /dev/null @@ -1,443 +0,0 @@ -import dataclasses -from dataclasses import dataclass -from datetime import datetime -from typing import Any, Dict, List, Optional - -from dbgpt.agent.common.schema import Status -from dbgpt.core.interface.storage import ( - InMemoryStorage, - QuerySpec, - ResourceIdentifier, - StorageInterface, - StorageItem, -) - -from .base import GptsMessage, GptsMessageMemory - - -@dataclass -class GptsPlanIdentifier(ResourceIdentifier): - identifier_split: str = dataclasses.field(default="___$$$$___", init=False) - conv_id: str - sub_task_num: Optional[str] - - def __post_init__(self): - if self.conv_id is None or self.sub_task_num is None: - raise ValueError("conv_id and sub_task_num cannot be None") - - if any( - self.identifier_split in key - for key in [ - self.conv_id, - self.sub_task_num, - ] - if key is not None - ): - raise ValueError( - f"identifier_split {self.identifier_split} is not allowed in conv_id, sub_task_num" - ) - - @property - def str_identifier(self) -> str: - return self.identifier_split.join( - key - for key in [ - self.conv_id, - self.sub_task_num, - ] - if key is not None - ) - - def to_dict(self) -> Dict: - return { - "conv_id": self.conv_id, - "sub_task_num": self.sub_task_num, - } - - -@dataclass -class GptsPlanStorage(StorageItem): - """Gpts plan""" - - conv_id: str - sub_task_num: int - sub_task_content: Optional[str] - sub_task_title: Optional[str] = None - sub_task_agent: Optional[str] = None - resource_name: Optional[str] = None - rely: Optional[str] = None - agent_model: Optional[str] = None - retry_times: Optional[int] = 0 - max_retry_times: Optional[int] = 5 - state: Optional[str] = Status.TODO.value - result: Optional[str] = None - - _identifier: GptsPlanIdentifier = dataclasses.field(init=False) - - @staticmethod - def from_dict(d: Dict[str, Any]): - return GptsPlanStorage( - conv_id=d.get("conv_id"), - sub_task_num=d["sub_task_num"], - sub_task_content=d["sub_task_content"], - sub_task_agent=d["sub_task_agent"], - resource_name=d["resource_name"], - rely=d["rely"], - agent_model=d["agent_model"], - retry_times=d["retry_times"], - max_retry_times=d["max_retry_times"], - state=d["state"], - result=d["result"], - ) - - def to_dict(self) -> Dict[str, Any]: - return dataclasses.asdict(self) - - def _check(self): - if self.conv_id is None: - raise ValueError("conv_id cannot be None") - if self.sub_task_num is None: - raise ValueError("sub_task_num cannot be None") - if self.sub_task_content is None: - raise ValueError("sub_task_content cannot be None") - if self.state is None: - raise ValueError("state cannot be None") - - @property - def identifier(self) -> GptsPlanIdentifier: - return self._identifier - - def merge(self, other: "StorageItem") -> None: - """Merge the other item into the current item. - - Args: - other (StorageItem): The other item to merge - """ - if not isinstance(other, GptsPlanStorage): - raise ValueError( - f"Cannot merge {type(other)} into {type(self)} because they are not the same type." - ) - self.from_object(other) - - -@dataclass -class GptsMessageIdentifier(ResourceIdentifier): - identifier_split: str = dataclasses.field(default="___$$$$___", init=False) - conv_id: str - sender: Optional[str] - receiver: Optional[str] - rounds: Optional[int] - - def __post_init__(self): - if ( - self.conv_id is None - or self.sender is None - or self.receiver is None - or self.rounds is None - ): - raise ValueError("conv_id and sub_task_num cannot be None") - - if any( - self.identifier_split in key - for key in [ - self.conv_id, - self.sender, - self.receiver, - self.rounds, - ] - if key is not None - ): - raise ValueError( - f"identifier_split {self.identifier_split} is not allowed in conv_id, sender, receiver, rounds" - ) - - @property - def str_identifier(self) -> str: - return self.identifier_split.join( - key - for key in [ - self.conv_id, - self.sender, - self.receiver, - self.rounds, - ] - if key is not None - ) - - def to_dict(self) -> Dict: - return { - "conv_id": self.conv_id, - "sender": self.sender, - "receiver": self.receiver, - "rounds": self.rounds, - } - - -@dataclass -class GptsMessageStorage(StorageItem): - """Gpts Message""" - - conv_id: str - sender: str - - receiver: str - role: str - content: str - rounds: Optional[int] - current_goal: str = None - context: Optional[str] = None - review_info: Optional[str] = None - action_report: Optional[str] = None - model_name: Optional[str] = None - created_at: datetime = datetime.utcnow - updated_at: datetime = datetime.utcnow - - _identifier: GptsMessageIdentifier = dataclasses.field(init=False) - - @staticmethod - def from_dict(d: Dict[str, Any]): - return GptsMessageStorage( - conv_id=d["conv_id"], - sender=d["sender"], - receiver=d["receiver"], - role=d["role"], - content=d["content"], - rounds=d["rounds"], - model_name=d["model_name"], - current_goal=d["current_goal"], - context=d["context"], - review_info=d["review_info"], - action_report=d["action_report"], - created_at=d["created_at"], - updated_at=d["updated_at"], - ) - - def to_dict(self) -> Dict[str, Any]: - return dataclasses.asdict(self) - - def _check(self): - if self.conv_id is None: - raise ValueError("conv_id cannot be None") - if self.sub_task_num is None: - raise ValueError("sub_task_num cannot be None") - if self.sub_task_content is None: - raise ValueError("sub_task_content cannot be None") - if self.state is None: - raise ValueError("state cannot be None") - - def to_gpts_message(self) -> GptsMessage: - """Convert the storage to a GptsMessage.""" - input_variables = ( - None - if not self.input_variables - else self.input_variables.strip().split(",") - ) - return GptsMessage( - conv_id=self.conv_id, - sender=self.sender, - receiver=self.receiver, - role=self.role, - content=self.content, - rounds=self.rounds, - current_goal=self.current_goal, - context=self.context, - review_info=self.review_info, - action_report=self.action_report, - model_name=self.model_name, - created_at=self.created_at, - updated_at=self.updated_at, - ) - - @staticmethod - def from_gpts_message(gpts_message: GptsMessage) -> "StoragePromptTemplate": - """Convert a GptsMessage to a storage e.""" - return GptsMessageStorage( - conv_id=gpts_message.conv_id, - sender=gpts_message.sender, - receiver=gpts_message.receiver, - role=gpts_message.role, - content=gpts_message.content, - rounds=gpts_message.rounds, - current_goal=gpts_message.current_goal, - context=gpts_message.context, - review_info=gpts_message.review_info, - action_report=gpts_message.action_report, - model_name=gpts_message.model_name, - created_at=gpts_message.created_at, - updated_at=gpts_message.updated_at, - ) - - @property - def identifier(self) -> GptsMessageIdentifier: - return self._identifier - - def merge(self, other: "StorageItem") -> None: - """Merge the other item into the current item. - - Args: - other (StorageItem): The other item to merge - """ - if not isinstance(other, GptsMessageStorage): - raise ValueError( - f"Cannot merge {type(other)} into {type(self)} because they are not the same type." - ) - self.from_object(other) - - -class GptsMessageManager(GptsMessageMemory): - """The manager class for GptsMessage. - - Simple wrapper for the storage interface. - - TODO: Import gpts storage with storage interface. - """ - - def __init__(self, storage: Optional[StorageInterface[GptsMessage, Any]] = None): - if storage is None: - storage = InMemoryStorage() - self._storage = storage - - @property - def storage(self) -> StorageInterface[GptsMessage, Any]: - """The storage interface for prompt templates.""" - return self._storage - - def append(self, message: GptsMessage): - self.storage.save(GptsMessageStorage.from_gpts_message(message)) - - def get_by_agent(self, conv_id: str, agent: str) -> Optional[List[GptsMessage]]: - query_spec = QuerySpec( - conditions={ - "conv_id": conv_id, - "sys_code": sys_code, - **kwargs, - } - ) - queries: List[GptsMessageStorage] = self.storage.query( - query_spec, GptsMessageStorage - ) - if not queries: - return [] - if prefer_prompt_language: - prefer_prompt_language = prefer_prompt_language.lower() - temp_queries = [ - query - for query in queries - if query.prompt_language - and query.prompt_language.lower() == prefer_prompt_language - ] - if temp_queries: - queries = temp_queries - if prefer_model: - prefer_model = prefer_model.lower() - temp_queries = [ - query - for query in queries - if query.model and query.model.lower() == prefer_model - ] - if temp_queries: - queries = temp_queries - return queries - - def get_between_agents( - self, - conv_id: str, - agent1: str, - agent2: str, - current_goal: Optional[str] = None, - ) -> Optional[List[GptsMessage]]: - return super().get_between_agents(conv_id, agent1, agent2, current_goal) - - def get_by_conv_id(self, conv_id: str) -> Optional[List[GptsMessage]]: - return super().get_by_conv_id(conv_id) - - def get_last_message(self, conv_id: str) -> Optional[GptsMessage]: - return super().get_last_message(conv_id) - - def prefer_query( - self, - prompt_name: str, - sys_code: Optional[str] = None, - prefer_prompt_language: Optional[str] = None, - prefer_model: Optional[str] = None, - **kwargs, - ) -> List[GptsMessage]: - """Query prompt templates from storage with prefer params. - - Sometimes, we want to query prompt templates with prefer params(e.g. some language or some model). - This method will query prompt templates with prefer params first, if not found, will query all prompt templates. - - Examples: - - Query a prompt template. - .. code-block:: python - - prompt_template_list = prompt_manager.prefer_query("hello") - - Query with sys_code and username. - - .. code-block:: python - - prompt_template_list = prompt_manager.prefer_query( - "hello", sys_code="sys_code", user_name="user_name" - ) - - Query with prefer prompt language. - - .. code-block:: python - - # First query with prompt name "hello" exactly. - # Second filter with prompt language "zh-cn", if not found, will return all prompt templates. - prompt_template_list = prompt_manager.prefer_query( - "hello", prefer_prompt_language="zh-cn" - ) - - Query with prefer model. - - .. code-block:: python - - # First query with prompt name "hello" exactly. - # Second filter with model "vicuna-13b-v1.5", if not found, will return all prompt templates. - prompt_template_list = prompt_manager.prefer_query( - "hello", prefer_model="vicuna-13b-v1.5" - ) - - Args: - prompt_name (str): The name of the prompt template. - sys_code (Optional[str], optional): The system code of the prompt template. Defaults to None. - prefer_prompt_language (Optional[str], optional): The language of the prompt template. Defaults to None. - prefer_model (Optional[str], optional): The model of the prompt template. Defaults to None. - kwargs (Dict): Other query params(If some key and value not None, wo we query it exactly). - """ - query_spec = QuerySpec( - conditions={ - "prompt_name": prompt_name, - "sys_code": sys_code, - **kwargs, - } - ) - queries: List[StoragePromptTemplate] = self.storage.query( - query_spec, StoragePromptTemplate - ) - if not queries: - return [] - if prefer_prompt_language: - prefer_prompt_language = prefer_prompt_language.lower() - temp_queries = [ - query - for query in queries - if query.prompt_language - and query.prompt_language.lower() == prefer_prompt_language - ] - if temp_queries: - queries = temp_queries - if prefer_model: - prefer_model = prefer_model.lower() - temp_queries = [ - query - for query in queries - if query.model and query.model.lower() == prefer_model - ] - if temp_queries: - queries = temp_queries - return queries diff --git a/dbgpt/agent/plan/__init__.py b/dbgpt/agent/plan/__init__.py new file mode 100644 index 000000000..95f8bb56f --- /dev/null +++ b/dbgpt/agent/plan/__init__.py @@ -0,0 +1,36 @@ +"""Plan module for the agent.""" + +from .awel.agent_operator import ( # noqa: F401 + AgentDummyTrigger, + AWELAgentOperator, + WrappedAgentOperator, +) +from .awel.agent_operator_resource import ( # noqa: F401 + AWELAgent, + AWELAgentConfig, + AWELAgentResource, +) +from .awel.team_awel_layout import ( # noqa: F401 + AWELTeamContext, + DefaultAWELLayoutManager, + WrappedAWELLayoutManager, +) +from .plan_action import PlanAction, PlanInput # noqa: F401 +from .planner_agent import PlannerAgent # noqa: F401 +from .team_auto_plan import AutoPlanChatManager # noqa: F401 + +__all__ = [ + "PlanAction", + "PlanInput", + "PlannerAgent", + "AutoPlanChatManager", + "AWELAgent", + "AWELAgentConfig", + "AWELAgentResource", + "AWELTeamContext", + "DefaultAWELLayoutManager", + "WrappedAWELLayoutManager", + "AgentDummyTrigger", + "AWELAgentOperator", + "WrappedAgentOperator", +] diff --git a/dbgpt/agent/plan/awel/__init__.py b/dbgpt/agent/plan/awel/__init__.py new file mode 100644 index 000000000..f8f2c1c3f --- /dev/null +++ b/dbgpt/agent/plan/awel/__init__.py @@ -0,0 +1,4 @@ +"""External planner. + +Use AWEL as the external planner. +""" diff --git a/dbgpt/serve/agent/team/layout/agent_operator.py b/dbgpt/agent/plan/awel/agent_operator.py similarity index 56% rename from dbgpt/serve/agent/team/layout/agent_operator.py rename to dbgpt/agent/plan/awel/agent_operator.py index 482f78d3a..6e6c4ad77 100644 --- a/dbgpt/serve/agent/team/layout/agent_operator.py +++ b/dbgpt/agent/plan/awel/agent_operator.py @@ -1,33 +1,36 @@ +"""Agent Operator for AWEL.""" from abc import ABC -from typing import Dict, List, Optional +from typing import List, Optional, Type -from dbgpt.agent.agents.agent_new import Agent, AgentGenerateContext -from dbgpt.agent.agents.agents_manage import agent_manage -from dbgpt.agent.agents.base_agent_new import ConversableAgent -from dbgpt.agent.agents.llm.llm import LLMConfig -from dbgpt.core.awel import BranchFunc, BranchOperator, MapOperator +from dbgpt.core.awel import MapOperator from dbgpt.core.awel.flow import ( IOField, OperatorCategory, OperatorType, Parameter, - ResourceCategory, ViewMetadata, ) from dbgpt.core.awel.trigger.base import Trigger from dbgpt.core.interface.message import ModelMessageRoleType + +# TODO: Don't dependent on MixinLLMOperator from dbgpt.model.operators.llm_operator import MixinLLMOperator -from .agent_operator_resource import AwelAgent +from ...core.agent import Agent, AgentGenerateContext, AgentMessage +from ...core.agent_manage import agent_manage +from ...core.base_agent import ConversableAgent +from ...core.llm.llm import LLMConfig +from .agent_operator_resource import AWELAgent class BaseAgentOperator: - """The abstract operator for a Agent.""" + """The abstract operator for an Agent.""" SHARE_DATA_KEY_MODEL_NAME = "share_data_key_agent_name" def __init__(self, agent: Optional[Agent] = None): - self._agent: ConversableAgent = agent + """Create an AgentOperator.""" + self._agent = agent @property def agent(self) -> Agent: @@ -37,66 +40,90 @@ def agent(self) -> Agent: return self._agent -class AgentOperator( +class WrappedAgentOperator( BaseAgentOperator, MapOperator[AgentGenerateContext, AgentGenerateContext], ABC ): + """The Agent operator. + + Wrap the agent and trigger the agent to generate a reply. + """ + def __init__(self, agent: Agent, **kwargs): + """Create an WrappedAgentOperator.""" super().__init__(agent=agent) MapOperator.__init__(self, **kwargs) async def map(self, input_value: AgentGenerateContext) -> AgentGenerateContext: - now_rely_messages: List[Dict] = [] + """Trigger agent to generate a reply.""" + now_rely_messages: List[AgentMessage] = [] + if not input_value.message: + raise ValueError("The message is empty.") + input_message = input_value.message.copy() # Isolate the message delivery mechanism and pass it to the operator - input_value.message["current_goal"] = ( - f"[{self._agent.name if self._agent.name else self._agent.profile}]:" - + input_value.message["content"] + _goal = ( + self.agent.get_name() if self.agent.get_name() else self.agent.get_profile() ) - ###What was received was the User message - human_message = input_value.message.copy() - human_message["role"] = ModelMessageRoleType.HUMAN + current_goal = f"[{_goal}]:" + + if input_message.content: + current_goal += input_message.content + input_message.current_goal = current_goal + + # What was received was the User message + human_message = input_message.copy() + human_message.role = ModelMessageRoleType.HUMAN now_rely_messages.append(human_message) - ###Send a message (no reply required) and pass the message content - now_message = input_value.message + # Send a message (no reply required) and pass the message content + now_message = input_message if input_value.rely_messages and len(input_value.rely_messages) > 0: now_message = input_value.rely_messages[-1] - await input_value.sender.a_send( - now_message, self._agent, input_value.reviewer, False + if not input_value.sender: + raise ValueError("The sender is empty.") + await input_value.sender.send( + now_message, self.agent, input_value.reviewer, False ) - is_success, reply_message = await self._agent.a_generate_reply( - recive_message=input_value.message, + agent_reply_message = await self.agent.generate_reply( + received_message=input_message, sender=input_value.sender, reviewer=input_value.reviewer, rely_messages=input_value.rely_messages, ) + is_success = agent_reply_message.success if not is_success: raise ValueError( - f"The task failed at step {self._agent.profile} and the attempt to repair it failed. The final reason for failure:{reply_message['content']}!" + f"The task failed at step {self.agent.get_profile()} and the attempt " + f"to repair it failed. The final reason for " + f"failure:{agent_reply_message.content}!" ) - ###What is sent is an AI message - ai_message = reply_message - ai_message["role"] = ModelMessageRoleType.AI + # What is sent is an AI message + ai_message = agent_reply_message.copy() + ai_message.role = ModelMessageRoleType.AI + now_rely_messages.append(ai_message) - ### Handle user goals and outcome dependencies + # Handle user goals and outcome dependencies return AgentGenerateContext( - message=input_value.message, - sender=self._agent, + message=input_message, + sender=self.agent, reviewer=input_value.reviewer, - rely_messages=now_rely_messages, ## Default single step transfer of information + # Default single step transfer of information + rely_messages=now_rely_messages, silent=input_value.silent, ) -class AwelAgentOperator( +class AWELAgentOperator( MixinLLMOperator, MapOperator[AgentGenerateContext, AgentGenerateContext] ): + """The Agent operator for AWEL.""" + metadata = ViewMetadata( - label="Agent Operator", + label="AWEL Agent Operator", name="agent_operator", category=OperatorCategory.AGENT, description="The Agent operator.", @@ -104,7 +131,7 @@ class AwelAgentOperator( Parameter.build_from( "Agent", "awel_agent", - AwelAgent, + AWELAgent, description="The dbgpt agent.", ), ], @@ -126,7 +153,8 @@ class AwelAgentOperator( ], ) - def __init__(self, awel_agent: AwelAgent, **kwargs): + def __init__(self, awel_agent: AWELAgent, **kwargs): + """Create an AgentOperator.""" MixinLLMOperator.__init__(self) MapOperator.__init__(self, **kwargs) self.awel_agent = awel_agent @@ -135,57 +163,73 @@ async def map( self, input_value: AgentGenerateContext, ) -> AgentGenerateContext: - now_message = input_value.message + """Trigger agent to generate a reply.""" + if not input_value.message: + raise ValueError("The message is empty.") + input_message = input_value.message.copy() agent = await self.get_agent(input_value) if agent.fixed_subgoal and len(agent.fixed_subgoal) > 0: # Isolate the message delivery mechanism and pass it to the operator - input_value.message["current_goal"] = ( - f"[{agent.name if agent.name else agent.profile}]:" - + agent.fixed_subgoal + current_goal = ( + f"[{agent.get_name() if agent.get_name() else agent.get_profile()}]:" ) - now_message["content"] = agent.fixed_subgoal + if agent.fixed_subgoal: + current_goal += agent.fixed_subgoal + input_message.current_goal = current_goal + input_message.content = agent.fixed_subgoal else: # Isolate the message delivery mechanism and pass it to the operator - input_value.message["current_goal"] = ( - f"[{agent.name if agent.name else agent.profile}]:" - + input_value.message["content"] + current_goal = ( + f"[{agent.get_name() if agent.get_name() else agent.get_profile()}]:" ) - - now_rely_messages: List[Dict] = [] - ###What was received was the User message - human_message = input_value.message.copy() - human_message["role"] = ModelMessageRoleType.HUMAN + if input_message.content: + current_goal += input_message.content + input_message.current_goal = current_goal + + now_rely_messages: List[AgentMessage] = [] + # What was received was the User message + human_message = input_message.copy() + human_message.role = ModelMessageRoleType.HUMAN now_rely_messages.append(human_message) - ###Send a message (no reply required) and pass the message content + # Send a message (no reply required) and pass the message content + now_message = input_message if input_value.rely_messages and len(input_value.rely_messages) > 0: now_message = input_value.rely_messages[-1] - await input_value.sender.a_send(now_message, agent, input_value.reviewer, False) - - is_success, reply_message = await agent.a_generate_reply( - recive_message=input_value.message, - sender=input_value.sender, + sender = input_value.sender + if not sender: + raise ValueError("The sender is empty.") + await sender.send(now_message, agent, input_value.reviewer, False) + + agent_reply_message = await agent.generate_reply( + received_message=input_message, + sender=sender, reviewer=input_value.reviewer, rely_messages=input_value.rely_messages, ) + is_success = agent_reply_message.success + if not is_success: raise ValueError( - f"The task failed at step {agent.profile} and the attempt to repair it failed. The final reason for failure:{reply_message['content']}!" + f"The task failed at step {agent.get_profile()} and the attempt to " + f"repair it failed. The final reason for " + f"failure:{agent_reply_message.content}!" ) - ###What is sent is an AI message - ai_message = reply_message - ai_message["role"] = ModelMessageRoleType.AI + # What is sent is an AI message + ai_message: AgentMessage = agent_reply_message.copy() + ai_message.role = ModelMessageRoleType.AI now_rely_messages.append(ai_message) - ### Handle user goals and outcome dependencies + # Handle user goals and outcome dependencies return AgentGenerateContext( - message=input_value.message, + message=input_message, sender=agent, reviewer=input_value.reviewer, - rely_messages=now_rely_messages, ## Default single step transfer of information + # Default single step transfer of information + rely_messages=now_rely_messages, silent=input_value.silent, memory=input_value.memory, agent_context=input_value.agent_context, @@ -197,9 +241,10 @@ async def map( async def get_agent( self, input_value: AgentGenerateContext, - ): - ### agent build - agent_cls: ConversableAgent = agent_manage.get_by_name( + ) -> ConversableAgent: + """Build the agent.""" + # agent build + agent_cls: Type[ConversableAgent] = agent_manage.get_by_name( # type: ignore self.awel_agent.agent_profile ) llm_config = self.awel_agent.llm_config diff --git a/dbgpt/serve/agent/team/layout/agent_operator_resource.py b/dbgpt/agent/plan/awel/agent_operator_resource.py similarity index 81% rename from dbgpt/serve/agent/team/layout/agent_operator_resource.py rename to dbgpt/agent/plan/awel/agent_operator_resource.py index 8388f8457..6cacf5d0b 100644 --- a/dbgpt/serve/agent/team/layout/agent_operator_resource.py +++ b/dbgpt/agent/plan/awel/agent_operator_resource.py @@ -1,11 +1,7 @@ -from typing import Any, Dict, List, Optional, Union +"""The AWEL Agent Operator Resource.""" +from typing import Any, Dict, List, Optional -from pydantic import BaseModel, Field - -from dbgpt._private.pydantic import root_validator -from dbgpt.agent.agents.agents_manage import agent_manage -from dbgpt.agent.agents.llm.llm import LLMConfig, LLMStrategyType -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType +from dbgpt._private.pydantic import BaseModel, Field, root_validator from dbgpt.core import LLMClient from dbgpt.core.awel.flow import ( FunctionDynamicOptions, @@ -15,9 +11,13 @@ register_resource, ) +from ...core.agent_manage import agent_manage +from ...core.llm.llm import LLMConfig, LLMStrategyType +from ...resource.resource_api import AgentResource, ResourceType + @register_resource( - label="Awel Agent Resource", + label="AWEL Agent Resource", name="agent_operator_resource", description="The Agent Resource.", category=ResourceCategory.AGENT, @@ -50,12 +50,14 @@ description="The agent resource value.", ), ], + alias=["dbgpt.serve.agent.team.layout.agent_operator_resource.AwelAgentResource"], ) -class AwelAgentResource(AgentResource): +class AWELAgentResource(AgentResource): + """AWEL Agent Resource.""" + @root_validator(pre=True) def pre_fill(cls, values: Dict[str, Any]) -> Dict[str, Any]: - """Pre fill the agent ResourceType""" - + """Pre fill the agent ResourceType.""" name = values.pop("agent_resource_name") type = values.pop("agent_resource_type") value = values.pop("agent_resource_value") @@ -68,7 +70,7 @@ def pre_fill(cls, values: Dict[str, Any]) -> Dict[str, Any]: @register_resource( - label="Awel Agent LLM Config", + label="AWEL Agent LLM Config", name="agent_operator_llm_config", description="The Agent LLM Config.", category=ResourceCategory.AGENT, @@ -102,11 +104,14 @@ def pre_fill(cls, values: Dict[str, Any]) -> Dict[str, Any]: description="The agent LLM Strategy Value.", ), ], + alias=["dbgpt.serve.agent.team.layout.agent_operator_resource.AwelAgentConfig"], ) -class AwelAgentConfig(LLMConfig): +class AWELAgentConfig(LLMConfig): + """AWEL Agent Config.""" + @root_validator(pre=True) def pre_fill(cls, values: Dict[str, Any]) -> Dict[str, Any]: - """Pre fill the agent ResourceType""" + """Pre fill the agent ResourceType.""" return values @@ -118,7 +123,7 @@ def _agent_resource_option_values() -> List[OptionValue]: @register_resource( - label="Awel Layout Agent", + label="AWEL Layout Agent", name="agent_operator_agent", description="The Agent to build the Agent Operator.", category=ResourceCategory.AGENT, @@ -149,7 +154,7 @@ def _agent_resource_option_values() -> List[OptionValue]: Parameter.build_from( label="Agent Resource", name="agent_resource", - type=AwelAgentResource, + type=AWELAgentResource, optional=True, default=None, description="The agent resource.", @@ -157,14 +162,17 @@ def _agent_resource_option_values() -> List[OptionValue]: Parameter.build_from( label="Agent LLM Config", name="agent_llm_Config", - type=AwelAgentConfig, + type=AWELAgentConfig, optional=True, default=None, description="The agent llm config.", ), ], + alias=["dbgpt.serve.agent.team.layout.agent_operator_resource.AwelAgent"], ) -class AwelAgent(BaseModel): +class AWELAgent(BaseModel): + """AWEL Agent.""" + agent_profile: str role_name: Optional[str] = None llm_config: Optional[LLMConfig] = None @@ -172,12 +180,13 @@ class AwelAgent(BaseModel): fixed_subgoal: Optional[str] = None class Config: + """Config for the BaseModel.""" + arbitrary_types_allowed = True @root_validator(pre=True) def pre_fill(cls, values: Dict[str, Any]) -> Dict[str, Any]: - """Pre fill the agent ResourceType""" - + """Pre fill the agent ResourceType.""" resource = values.pop("agent_resource") llm_config = values.pop("agent_llm_Config") diff --git a/dbgpt/agent/plan/awel/team_awel_layout.py b/dbgpt/agent/plan/awel/team_awel_layout.py new file mode 100644 index 000000000..61e6a064e --- /dev/null +++ b/dbgpt/agent/plan/awel/team_awel_layout.py @@ -0,0 +1,261 @@ +"""The manager of the team for the AWEL layout.""" + +import logging +from abc import ABC, abstractmethod +from typing import List, Optional, cast + +from dbgpt._private.config import Config +from dbgpt._private.pydantic import BaseModel, Field, validator +from dbgpt.core.awel import DAG +from dbgpt.core.awel.dag.dag_manager import DAGManager + +from ...actions.action import ActionOutput +from ...core.agent import Agent, AgentGenerateContext, AgentMessage +from ...core.base_team import ManagerAgent +from .agent_operator import AWELAgentOperator, WrappedAgentOperator + +logger = logging.getLogger(__name__) + + +class AWELTeamContext(BaseModel): + """The context of the team for the AWEL layout.""" + + dag_id: str = Field( + ..., + description="The unique id of dag", + examples=["flow_dag_testflow_66d8e9d6-f32e-4540-a5bd-ea0648145d0e"], + ) + uid: str = Field( + default=None, + description="The unique id of flow", + examples=["66d8e9d6-f32e-4540-a5bd-ea0648145d0e"], + ) + name: Optional[str] = Field( + default=None, + description="The name of dag", + ) + label: Optional[str] = Field( + default=None, + description="The label of dag", + ) + version: Optional[str] = Field( + default=None, + description="The version of dag", + ) + description: Optional[str] = Field( + default=None, + description="The description of dag", + ) + editable: bool = Field( + default=False, + description="is the dag is editable", + examples=[True, False], + ) + state: Optional[str] = Field( + default=None, + description="The state of dag", + ) + user_name: Optional[str] = Field( + default=None, + description="The owner of current dag", + ) + sys_code: Optional[str] = Field( + default=None, + description="The system code of current dag", + ) + flow_category: Optional[str] = Field( + default="common", + description="The flow category of current dag", + ) + + def to_dict(self): + """Convert the object to a dictionary.""" + return self.dict() + + +class AWELBaseManager(ManagerAgent, ABC): + """AWEL base manager.""" + + goal: str = ( + "Promote and solve user problems according to the process arranged by AWEL." + ) + constraints: List[str] = [] + desc: str = goal + + class Config: + """Config for the BaseModel.""" + + arbitrary_types_allowed = True + + async def _a_process_received_message(self, message: AgentMessage, sender: Agent): + """Process the received message.""" + pass + + @abstractmethod + def get_dag(self) -> DAG: + """Get the DAG of the manager.""" + + async def act( + self, + message: Optional[str], + sender: Optional[Agent] = None, + reviewer: Optional[Agent] = None, + **kwargs, + ) -> Optional[ActionOutput]: + """Perform the action.""" + try: + agent_dag = self.get_dag() + last_node: AWELAgentOperator = cast( + AWELAgentOperator, agent_dag.leaf_nodes[0] + ) + + start_message_context: AgentGenerateContext = AgentGenerateContext( + message=AgentMessage(content=message, current_goal=message), + sender=sender, + reviewer=reviewer, + memory=self.memory, + agent_context=self.agent_context, + resource_loader=self.resource_loader, + llm_client=self.not_null_llm_config.llm_client, + ) + final_generate_context: AgentGenerateContext = await last_node.call( + call_data=start_message_context + ) + last_message = final_generate_context.rely_messages[-1] + + last_agent = await last_node.get_agent(final_generate_context) + if final_generate_context.round_index is not None: + last_agent.consecutive_auto_reply_counter = ( + final_generate_context.round_index + ) + if not sender: + raise ValueError("sender is required!") + await last_agent.send( + last_message, sender, start_message_context.reviewer, False + ) + + view_message: Optional[str] = None + if last_message.action_report: + view_message = last_message.action_report.get("view", None) + + return ActionOutput( + content=last_message.content, + view=view_message, + ) + except Exception as e: + logger.exception(f"DAG run failed!{str(e)}") + + return ActionOutput( + is_exe_success=False, + content=f"Failed to complete goal! {str(e)}", + ) + + +class WrappedAWELLayoutManager(AWELBaseManager): + """The manager of the team for the AWEL layout. + + Receives a DAG or builds a DAG from the agents. + """ + + profile: str = "WrappedAWELLayoutManager" + + dag: Optional[DAG] = Field(None, description="The DAG of the manager") + + class Config: + """Config for the BaseModel.""" + + arbitrary_types_allowed = True + + def get_dag(self) -> DAG: + """Get the DAG of the manager.""" + if self.dag: + return self.dag + conv_id = self.not_null_agent_context.conv_id + last_node: Optional[WrappedAgentOperator] = None + with DAG( + f"layout_agents_{self.not_null_agent_context.gpts_app_name}_{conv_id}" + ) as dag: + for agent in self.agents: + now_node = WrappedAgentOperator(agent=agent) + if not last_node: + last_node = now_node + else: + last_node >> now_node + last_node = now_node + self.dag = dag + return dag + + async def act( + self, + message: Optional[str], + sender: Optional[Agent] = None, + reviewer: Optional[Agent] = None, + **kwargs, + ) -> Optional[ActionOutput]: + """Perform the action.""" + try: + dag = self.get_dag() + last_node: WrappedAgentOperator = cast( + WrappedAgentOperator, dag.leaf_nodes[0] + ) + start_message_context: AgentGenerateContext = AgentGenerateContext( + message=AgentMessage(content=message, current_goal=message), + sender=self, + reviewer=reviewer, + ) + final_generate_context: AgentGenerateContext = await last_node.call( + call_data=start_message_context + ) + last_message = final_generate_context.rely_messages[-1] + + last_agent = last_node.agent + await last_agent.send( + last_message, + self, + start_message_context.reviewer, + False, + ) + + view_message: Optional[str] = None + if last_message.action_report: + view_message = last_message.action_report.get("view", None) + + return ActionOutput( + content=last_message.content, + view=view_message, + ) + except Exception as e: + logger.exception(f"DAG run failed!{str(e)}") + + return ActionOutput( + is_exe_success=False, + content=f"Failed to complete goal! {str(e)}", + ) + + +class DefaultAWELLayoutManager(AWELBaseManager): + """The manager of the team for the AWEL layout.""" + + profile: str = "DefaultAWELLayoutManager" + + dag: AWELTeamContext = Field(...) + + class Config: + """Config for the BaseModel.""" + + arbitrary_types_allowed = True + + @validator("dag") + def check_dag(cls, value): + """Check the DAG of the manager.""" + assert value is not None and value != "", "dag must not be empty" + return value + + def get_dag(self) -> DAG: + """Get the DAG of the manager.""" + cfg = Config() + _dag_manager = DAGManager.get_instance(cfg.SYSTEM_APP) # type: ignore + agent_dag: Optional[DAG] = _dag_manager.get_dag(alias_name=self.dag.uid) + if agent_dag is None: + raise ValueError(f"The configured flow cannot be found![{self.dag.name}]") + return agent_dag diff --git a/dbgpt/serve/agent/team/plan/plan_action.py b/dbgpt/agent/plan/plan_action.py similarity index 61% rename from dbgpt/serve/agent/team/plan/plan_action.py rename to dbgpt/agent/plan/plan_action.py index 61b221662..a25ab3241 100644 --- a/dbgpt/serve/agent/team/plan/plan_action.py +++ b/dbgpt/agent/plan/plan_action.py @@ -1,56 +1,69 @@ -import json -import logging -from typing import Any, Dict, List, Optional, Union +"""Plan Action.""" -from pydantic import BaseModel, Field +import logging +from typing import List, Optional -from dbgpt.agent.actions.action import Action, ActionOutput, T -from dbgpt.agent.agents.agent import AgentContext -from dbgpt.agent.common.schema import Status -from dbgpt.agent.memory.base import GptsPlan -from dbgpt.agent.memory.gpts_memory import GptsPlansMemory -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType +from dbgpt._private.pydantic import BaseModel, Field from dbgpt.vis.tags.vis_agent_plans import Vis, VisAgentPlans +from ..actions.action import Action, ActionOutput +from ..core.agent import AgentContext +from ..core.schema import Status +from ..memory.base import GptsPlan +from ..memory.gpts_memory import GptsPlansMemory +from ..resource.resource_api import AgentResource + logger = logging.getLogger(__name__) class PlanInput(BaseModel): + """Plan input model.""" + serial_number: int = Field( 0, - description="子任务的步骤编号", + description="Number of sub-tasks", + ) + agent: str = Field(..., description="The agent name to complete current task") + content: str = Field( + ..., + description="The task content of current step, make sure it can by executed by" + " agent", + ) + rely: str = Field( + ..., + description="The rely task number(serial_number), e.g. 1,2,3, empty if no rely", ) - agent: str = Field(..., description="用来完成当前步骤的智能代理") - content: str = Field(..., description="当前步骤的任务内容，确保可以被智能代理执行") - rely: str = Field(..., description="当前任务执行依赖的其他任务serial_number, 如:1,2,3, 无依赖为空") class PlanAction(Action[List[PlanInput]]): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._render_protocal = VisAgentPlans() + """Plan action class.""" - @property - def resource_need(self) -> Optional[ResourceType]: - return None + def __init__(self, **kwargs): + """Create a plan action.""" + super().__init__() + self._render_protocol = VisAgentPlans() @property - def render_protocal(self) -> Optional[Vis]: - return self._render_protocal + def render_protocol(self) -> Optional[Vis]: + """Return the render protocol.""" + return self._render_protocol @property def out_model_type(self): + """Output model type.""" return List[PlanInput] - async def a_run( + async def run( self, ai_message: str, - context: AgentContext, - plans_memory: GptsPlansMemory, resource: Optional[AgentResource] = None, rely_action_out: Optional[ActionOutput] = None, need_vis_render: bool = True, + **kwargs, ) -> ActionOutput: + """Run the plan action.""" + context: AgentContext = kwargs["context"] + plans_memory: GptsPlansMemory = kwargs["plans_memory"] try: param: List[PlanInput] = self._input_convert(ai_message, List[PlanInput]) except Exception as e: @@ -59,9 +72,10 @@ async def a_run( is_exe_success=False, content="The requested correctly structured answer could not be found.", ) + fail_reason = "" try: - rensponse_succ = True + response_success = True plan_objects = [] try: for item in param: @@ -76,7 +90,7 @@ async def a_run( plan.sub_task_title = item.content plan.rely = item.rely plan.retry_times = 0 - plan.status = Status.TODO.value + plan.state = Status.TODO.value plan_objects.append(plan) plans_memory.remove_by_conv_id(context.conv_id) @@ -84,10 +98,15 @@ async def a_run( except Exception as e: logger.exception(str(e)) - fail_reason = f"The generated plan cannot be stored, reason: {str(e)}. Please check whether it is a problem with the plan content. If so, please regenerate the correct plan. If not, please return 'TERMINATE'." - rensponse_succ = False + fail_reason = ( + f"The generated plan cannot be stored, reason: {str(e)}." + f" Please check whether it is a problem with the plan content. " + f"If so, please regenerate the correct plan. If not, please return" + f" 'TERMINATE'." + ) + response_success = False - if rensponse_succ: + if response_success: plan_content = [] mk_plans = [] for item in param: @@ -105,7 +124,6 @@ async def a_run( f"- {item.serial_number}.{item.content}[{item.agent}]" ) - # view = await self.render_protocal.display(content=plan_content) view = "\n".join(mk_plans) return ActionOutput( is_exe_success=True, diff --git a/dbgpt/agent/plan/planner_agent.py b/dbgpt/agent/plan/planner_agent.py new file mode 100644 index 000000000..e8ef08252 --- /dev/null +++ b/dbgpt/agent/plan/planner_agent.py @@ -0,0 +1,138 @@ +"""Planner Agent.""" + +from typing import Any, Dict, List + +from dbgpt._private.pydantic import Field + +from ..core.agent import AgentMessage +from ..core.base_agent import ConversableAgent +from .plan_action import PlanAction + + +class PlannerAgent(ConversableAgent): + """Planner Agent. + + Planner agent, realizing task goal planning decomposition through LLM. + """ + + agents: List[ConversableAgent] = Field(default_factory=list) + + profile: str = "Planner" + goal_zh: str = ( + "理解下面每个智能体(agent)和他们的能力，使用给出的资源，通过协调智能体来解决" + "用户问题。 请发挥你LLM的知识和理解能力，理解用户问题的意图和目标，生成一个可以在没有用户帮助" + "下，由智能体协作完成目标的任务计划。" + ) + goal: str = ( + "Understand each of the following intelligent agents and their " + "capabilities, using the provided resources, solve user problems by " + "coordinating intelligent agents. Please utilize your LLM's knowledge " + "and understanding ability to comprehend the intent and goals of the " + "user's problem, generating a task plan that can be completed through" + " the collaboration of intelligent agents without user assistance." + ) + expand_prompt_zh: str = "可用智能体(agent):\n {agents}" + expand_prompt: str = "Available Intelligent Agents:\n {agents}" + + constraints_zh: List[str] = [ + "任务计划的每个步骤都应该是为了推进解决用户目标而存在，不要生成无意义的任务步骤，确保每个步骤内目标明确内容完整。", + "关注任务计划每个步骤的依赖关系和逻辑，被依赖步骤要考虑被依赖的数据，是否能基于当前目标得到，如果不能请在目标中提示要生成被依赖数据。", + "每个步骤都是一个独立可完成的目标，一定要确保逻辑和信息完整，不要出现类似:" + "'Analyze the retrieved issues data'这样目标不明确，不知道具体要分析啥内容的步骤", + "请确保只使用上面提到的智能体，并且可以只使用其中需要的部分，严格根据描述能力和限制分配给合适的步骤，每个智能体都可以重复使用。", + "根据用户目标的实际需要使用提供的资源来协助生成计划步骤，不要使用不需要的资源。", + "每个步骤最好只使用一种资源完成一个子目标，如果当前目标可以分解为同类型的多个子任务，可以生成相互不依赖的并行任务。", + "数据资源可以被合适的智能体加载使用，不用考虑数据资源的加载链接问题", + "尽量合并有顺序依赖的连续相同步骤,如果用户目标无拆分必要，可以生成内容为用户目标的单步任务。", + "仔细检查计划，确保计划完整的包含了用户问题所涉及的所有信息，并且最终能完成目标，确认每个步骤是否包含了需要用到的资源信息,如URL、资源名等. ", + ] + constraints: List[str] = [ + "Every step of the task plan should exist to advance towards solving the user's" + " goals. Do not generate meaningless task steps; ensure that each step has a " + "clear goal and its content is complete.", + "Pay attention to the dependencies and logic of each step in the task plan. " + "For the steps that are depended upon, consider the data they depend on and " + "whether it can be obtained based on the current goal. If it cannot be obtained" + ", please indicate in the goal that the dependent data needs to be generated.", + "Each step must be an independently achievable goal. Ensure that the logic and" + " information are complete. Avoid steps with unclear objectives, like " + "'Analyze the retrieved issues data,' where it's unclear what specific content" + " needs to be analyzed.", + "Please ensure that only the intelligent agents mentioned above are used, and" + " you may use only the necessary parts of them. Allocate them to appropriate " + "steps strictly based on their described capabilities and limitations. Each " + "intelligent agent can be reused.", + "Utilize the provided resources to assist in generating the plan steps " + "according to the actual needs of the user's goals. Do not use unnecessary " + "resources.", + "Each step should ideally use only one type of resource to accomplish a " + "sub-goal. If the current goal can be broken down into multiple subtasks of the" + " same type, you can create mutually independent parallel tasks.", + "Data resources can be loaded and utilized by the appropriate intelligent " + "agents without the need to consider the issues related to data loading links.", + "Try to merge continuous steps that have sequential dependencies. If the " + "user's goal does not require splitting, you can create a single-step task with" + " content that is the user's goal.", + "Carefully review the plan to ensure it comprehensively covers all information" + " involved in the user's problem and can ultimately achieve the goal. Confirm" + " whether each step includes the necessary resource information, such as URLs," + " resource names, etc.", + ] + desc_zh: str = "你是一个任务规划专家！可以协调智能体，分配资源完成复杂的任务目标。" + desc: str = ( + "You are a task planning expert! You can coordinate intelligent agents" + " and allocate resources to achieve complex task goals." + ) + + examples = """ + user:help me build a sales report summarizing our key metrics and trends + assistants:[ + {{ + "serial_number": "1", + "agent": "DataScientist", + "content": "Retrieve total sales, average sales, and number of transactions grouped by "product_category"'.", + "rely": "" + }}, + {{ + "serial_number": "2", + "agent": "DataScientist", + "content": "Retrieve monthly sales and transaction number trends.", + "rely": "" + }}, + {{ + "serial_number": "3", + "agent": "Reporter", + "content": "Integrate analytical data into the format required to build sales reports.", + "rely": "1,2" + }} + ] + """ # noqa: E501 + + def __init__(self, **kwargs): + """Create a new PlannerAgent instance.""" + super().__init__(**kwargs) + self._init_actions([PlanAction]) + + def _init_reply_message(self, received_message: AgentMessage): + reply_message = super()._init_reply_message(received_message) + reply_message.context = { + "agents": "\n".join( + [f"- {item.profile}:{item.desc}" for item in self.agents] + ), + } + return reply_message + + def bind_agents(self, agents: List[ConversableAgent]) -> ConversableAgent: + """Bind the agents to the planner agent.""" + self.agents = agents + for agent in self.agents: + if agent.resources and len(agent.resources) > 0: + self.resources.extend(agent.resources) + return self + + def prepare_act_param(self) -> Dict[str, Any]: + """Prepare the parameters for the act method.""" + return { + "context": self.not_null_agent_context, + "plans_memory": self.memory.plans_memory, + } diff --git a/dbgpt/serve/agent/team/plan/team_auto_plan.py b/dbgpt/agent/plan/team_auto_plan.py similarity index 54% rename from dbgpt/serve/agent/team/plan/team_auto_plan.py rename to dbgpt/agent/plan/team_auto_plan.py index 455193200..f79621d4a 100644 --- a/dbgpt/serve/agent/team/plan/team_auto_plan.py +++ b/dbgpt/agent/plan/team_auto_plan.py @@ -1,45 +1,56 @@ +"""Auto plan chat manager agent.""" + import logging -import sys -from typing import Any, Dict, List, Optional - -from dbgpt.agent.actions.action import ActionOutput -from dbgpt.agent.agents.agent_new import Agent -from dbgpt.agent.agents.agents_manage import mentioned_agents, participant_roles -from dbgpt.agent.agents.base_agent_new import ConversableAgent -from dbgpt.agent.agents.base_team import ManagerAgent -from dbgpt.agent.common.schema import Status -from dbgpt.agent.memory.base import GptsPlan +from typing import Dict, List, Optional, Tuple + from dbgpt.core.interface.message import ModelMessageRoleType +from ..actions.action import ActionOutput +from ..core.agent import Agent, AgentMessage +from ..core.agent_manage import mentioned_agents, participant_roles +from ..core.base_agent import ConversableAgent +from ..core.base_team import ManagerAgent +from ..core.schema import Status +from ..memory.base import GptsPlan from .planner_agent import PlannerAgent logger = logging.getLogger(__name__) class AutoPlanChatManager(ManagerAgent): - """(In preview) A chat manager agent that can manage a team chat of multiple agents.""" + """A chat manager agent that can manage a team chat of multiple agents.""" profile: str = "PlanManager" - goal: str = "Advance the task plan generated by the planning agent. If the plan does not pre-allocate an agent, it needs to be coordinated with the appropriate agent to complete." + goal: str = ( + "Advance the task plan generated by the planning agent. If the plan " + "does not pre-allocate an agent, it needs to be coordinated with the " + "appropriate agent to complete." + ) constraints: List[str] = [] desc: str = "Advance the task plan generated by the planning agent." def __init__(self, **kwargs): + """Create a new AutoPlanChatManager instance.""" super().__init__(**kwargs) - async def a_process_rely_message( - self, conv_id: str, now_plan: GptsPlan, speaker: ConversableAgent + async def process_rely_message( + self, conv_id: str, now_plan: GptsPlan, speaker: Agent ): + """Process the dependent message.""" rely_prompt = None rely_messages: List[Dict] = [] if now_plan.rely and len(now_plan.rely) > 0: rely_tasks_list = now_plan.rely.split(",") + rely_tasks_list_int = [int(i) for i in rely_tasks_list] rely_tasks = self.memory.plans_memory.get_by_conv_id_and_num( - conv_id, rely_tasks_list + conv_id, rely_tasks_list_int ) if rely_tasks: - rely_prompt = "Read the result data of the dependent steps in the above historical message to complete the current goal:" + rely_prompt = ( + "Read the result data of the dependent steps in the above" + " historical message to complete the current goal:" + ) for rely_task in rely_tasks: rely_messages.append( { @@ -55,22 +66,25 @@ async def a_process_rely_message( ) return rely_prompt, rely_messages - def select_speaker_msg(self, agents: List[Agent]): + def select_speaker_msg(self, agents: List[Agent]) -> str: """Return the message for selecting the next speaker.""" - return f"""You are in a role play game. The following roles are available: - {participant_roles(agents)}. - Read the following conversation. - Then select the next role from {[agent.name for agent in agents]} to play. The role can be selected repeatedly.Only return the role.""" + agent_names = [agent.get_name() for agent in agents] + return ( + "You are in a role play game. The following roles are available:\n" + f" {participant_roles(agents)}.\n" + " Read the following conversation.\n" + f" Then select the next role from {agent_names} to play.\n" + " The role can be selected repeatedly.Only return the role." + ) - async def a_select_speaker( + async def select_speaker( self, last_speaker: Agent, - selector: ConversableAgent, - now_goal_context: str = None, - pre_allocated: str = None, - ): + selector: Agent, + now_goal_context: Optional[str] = None, + pre_allocated: Optional[str] = None, + ) -> Tuple[Agent, Optional[str]]: """Select the next speaker.""" - agents = self.agents if pre_allocated: @@ -81,28 +95,34 @@ async def a_select_speaker( else: # auto speaker selection # TODO selector a_thinking It has been overwritten and cannot be used. - final, name, model = await selector.a_thinking( + agent_names = [agent.get_name() for agent in agents] + fina_name, model = await selector.thinking( messages=[ - { - "role": ModelMessageRoleType.HUMAN, - "content": f"""Read and understand the following task content and assign the appropriate role to complete the task. - Task content: {now_goal_context} - select the role from: {[agent.name for agent in agents]}, - Please only return the role, such as: {agents[0].name}""", - } + AgentMessage( + role=ModelMessageRoleType.HUMAN, + content="Read and understand the following task content and" + " assign the appropriate role to complete the task.\n" + f"Task content: {now_goal_context},\n" + f"Select the role from: {agent_names},\n" + f"Please only return the role, such as: {agents[0].get_name()}", + ) ], prompt=self.select_speaker_msg(agents), ) - if not final: + if not fina_name: raise ValueError("Unable to select next speaker!") + else: + name = fina_name - # If exactly one agent is mentioned, use it. Otherwise, leave the OAI response unmodified + # If exactly one agent is mentioned, use it. Otherwise, leave the OAI response + # unmodified mentions = mentioned_agents(name, agents) if len(mentions) == 1: name = next(iter(mentions)) else: logger.warning( - f"GroupChat select_speaker failed to resolve the next speaker's name. This is because the speaker selection OAI call returned:\n{name}" + "GroupChat select_speaker failed to resolve the next speaker's name. " + f"This is because the speaker selection OAI call returned:\n{name}" ) # Return the result @@ -112,22 +132,37 @@ async def a_select_speaker( logger.exception(f"auto select speaker failed!{str(e)}") raise ValueError("Unable to select next speaker!") - async def a_act( + async def act( self, message: Optional[str], - sender: Optional[ConversableAgent] = None, - reviewer: Optional[ConversableAgent] = None, + sender: Optional[Agent] = None, + reviewer: Optional[Agent] = None, + **kwargs, ) -> Optional[ActionOutput]: - speaker = sender + """Perform an action based on the received message.""" + if not sender: + return ActionOutput( + is_exe_success=False, + content="The sender cannot be empty!", + ) + speaker: Agent = sender final_message = message for i in range(self.max_round): - plans = self.memory.plans_memory.get_by_conv_id(self.agent_context.conv_id) + if not self.memory: + return ActionOutput( + is_exe_success=False, + content="The memory cannot be empty!", + ) + plans = self.memory.plans_memory.get_by_conv_id( + self.not_null_agent_context.conv_id + ) if not plans or len(plans) <= 0: if i > 3: return ActionOutput( is_exe_success=False, - content="Retrying 3 times based on current application resources still fails to build a valid plan！", + content="Retrying 3 times based on current application " + "resources still fails to build a valid plan！", ) planner: ConversableAgent = ( await PlannerAgent() @@ -139,10 +174,14 @@ async def a_act( .build() ) - is_success, plan_message = await planner.a_generate_reply( - recive_message={"content": message}, sender=self, reviewer=reviewer + plan_message = await planner.generate_reply( + received_message=AgentMessage.from_llm_message( + {"content": message} + ), + sender=self, + reviewer=reviewer, ) - await planner.a_send( + await planner.send( message=plan_message, recipient=self, request_reply=False ) else: @@ -152,7 +191,8 @@ async def a_act( if plan.state in [Status.TODO.value, Status.RETRYING.value] ] if not todo_plans or len(todo_plans) <= 0: - ### The plan has been fully executed and a success message is sent to the user. + # The plan has been fully executed and a success message is sent + # to the user. # complete return ActionOutput( is_exe_success=True, @@ -161,72 +201,75 @@ async def a_act( else: try: now_plan: GptsPlan = todo_plans[0] - current_goal_message = { - "content": now_plan.sub_task_content, - "current_goal": now_plan.sub_task_content, - "context": { + current_goal_message = AgentMessage( + content=now_plan.sub_task_content, + current_goal=now_plan.sub_task_content, + context={ "plan_task": now_plan.sub_task_content, "plan_task_num": now_plan.sub_task_num, }, - } + ) # select the next speaker - speaker, model = await self.a_select_speaker( + speaker, model = await self.select_speaker( speaker, self, now_plan.sub_task_content, now_plan.sub_task_agent, ) # Tell the speaker the dependent history information - rely_prompt, rely_messages = await self.a_process_rely_message( - conv_id=self.agent_context.conv_id, + rely_prompt, rely_messages = await self.process_rely_message( + conv_id=self.not_null_agent_context.conv_id, now_plan=now_plan, speaker=speaker, ) if rely_prompt: - current_goal_message["content"] = ( - rely_prompt + current_goal_message["content"] + current_goal_message.content = ( + rely_prompt + current_goal_message.content ) - await self.a_send( + await self.send( message=current_goal_message, recipient=speaker, reviewer=reviewer, request_reply=False, ) - is_success, reply_message = await speaker.a_generate_reply( - recive_message=current_goal_message, + agent_reply_message = await speaker.generate_reply( + received_message=current_goal_message, sender=self, reviewer=reviewer, - rely_messages=rely_messages, + rely_messages=AgentMessage.from_messages(rely_messages), ) - await speaker.a_send( - reply_message, self, reviewer, request_reply=False + is_success = agent_reply_message.success + reply_message = agent_reply_message.to_llm_message() + await speaker.send( + agent_reply_message, self, reviewer, request_reply=False ) plan_result = "" final_message = reply_message["content"] if is_success: if reply_message: - action_report = reply_message.get("action_report", None) + action_report = agent_reply_message.action_report if action_report: plan_result = action_report.get("content", "") final_message = action_report["view"] - ### The current planned Agent generation verification is successful - ##Plan executed successfully + # The current planned Agent generation verification is + # successful + # Plan executed successfully self.memory.plans_memory.complete_task( - self.agent_context.conv_id, + self.not_null_agent_context.conv_id, now_plan.sub_task_num, plan_result, ) else: plan_result = reply_message["content"] self.memory.plans_memory.update_task( - self.agent_context.conv_id, + self.not_null_agent_context.conv_id, now_plan.sub_task_num, Status.FAILED.value, now_plan.retry_times + 1, - speaker.name, + speaker.get_name(), "", plan_result, ) @@ -236,11 +279,13 @@ async def a_act( except Exception as e: logger.exception( - f"An exception was encountered during the execution of the current plan step.{str(e)}" + f"An exception was encountered during the execution of the" + f" current plan step.{str(e)}" ) return ActionOutput( is_exe_success=False, - content=f"An exception was encountered during the execution of the current plan step.{str(e)}", + content=f"An exception was encountered during the execution" + f" of the current plan step.{str(e)}", ) return ActionOutput( is_exe_success=False, diff --git a/dbgpt/agent/plugin/__init__.py b/dbgpt/agent/plugin/__init__.py index e69de29bb..926ffe499 100644 --- a/dbgpt/agent/plugin/__init__.py +++ b/dbgpt/agent/plugin/__init__.py @@ -0,0 +1,6 @@ +"""Plugin module for agent.""" + +from .commands.command_manage import CommandRegistry # noqa: F401 +from .generator import PluginPromptGenerator # noqa: F401 + +__ALL__ = ["PluginPromptGenerator", "CommandRegistry"] diff --git a/dbgpt/agent/plugin/commands/__init__.py b/dbgpt/agent/plugin/commands/__init__.py index e69de29bb..e5c5a5295 100644 --- a/dbgpt/agent/plugin/commands/__init__.py +++ b/dbgpt/agent/plugin/commands/__init__.py @@ -0,0 +1 @@ +"""Commands Module.""" diff --git a/dbgpt/agent/plugin/commands/built_in/__init__.py b/dbgpt/agent/plugin/commands/built_in/__init__.py index e69de29bb..fdd2ea590 100644 --- a/dbgpt/agent/plugin/commands/built_in/__init__.py +++ b/dbgpt/agent/plugin/commands/built_in/__init__.py @@ -0,0 +1 @@ +"""Built-in commands for DB-GPT.""" diff --git a/dbgpt/agent/plugin/commands/built_in/audio_text.py b/dbgpt/agent/plugin/commands/built_in/audio_text.py deleted file mode 100644 index b21ac3253..000000000 --- a/dbgpt/agent/plugin/commands/built_in/audio_text.py +++ /dev/null @@ -1,62 +0,0 @@ -"""Commands for converting audio to text.""" -import json - -import requests - -from dbgpt._private.config import Config - -from ..command_manage import command - -CFG = Config() - - -@command( - "read_audio_from_file", - "Convert Audio to text", - '"filename": ""', - CFG.huggingface_audio_to_text_model, - "Configure huggingface_audio_to_text_model.", -) -def read_audio_from_file(filename: str) -> str: - """ - Convert audio to text. - - Args: - filename (str): The path to the audio file - - Returns: - str: The text from the audio - """ - with open(filename, "rb") as audio_file: - audio = audio_file.read() - return read_audio(audio) - - -def read_audio(audio: bytes) -> str: - """ - Convert audio to text. - - Args: - audio (bytes): The audio to convert - - Returns: - str: The text from the audio - """ - model = CFG.huggingface_audio_to_text_model - api_url = f"https://api-inference.huggingface.co/models/{model}" - api_token = CFG.huggingface_api_token - headers = {"Authorization": f"Bearer {api_token}"} - - if api_token is None: - raise ValueError( - "You need to set your Hugging Face API token in the config file." - ) - - response = requests.post( - api_url, - headers=headers, - data=audio, - ) - - text = json.loads(response.content.decode("utf-8"))["text"] - return f"The audio says: {text}" diff --git a/dbgpt/agent/plugin/commands/built_in/display_type/__init__.py b/dbgpt/agent/plugin/commands/built_in/display_type/__init__.py index a13495925..ca069faba 100644 --- a/dbgpt/agent/plugin/commands/built_in/display_type/__init__.py +++ b/dbgpt/agent/plugin/commands/built_in/display_type/__init__.py @@ -1 +1,2 @@ -from .show_chart_gen import static_message_img_path +"""Visualize Data.""" +from .show_chart_gen import static_message_img_path # noqa: F401 diff --git a/dbgpt/agent/plugin/commands/built_in/display_type/show_chart_gen.py b/dbgpt/agent/plugin/commands/built_in/display_type/show_chart_gen.py index 382ae418a..79528520b 100644 --- a/dbgpt/agent/plugin/commands/built_in/display_type/show_chart_gen.py +++ b/dbgpt/agent/plugin/commands/built_in/display_type/show_chart_gen.py @@ -1,37 +1,33 @@ +"""Chart display command implementation.""" import logging import os import uuid - -import matplotlib -import matplotlib.pyplot as plt -import matplotlib.ticker as mtick -import pandas as pd -import seaborn as sns -from matplotlib.font_manager import FontManager -from pandas import DataFrame +from typing import TYPE_CHECKING from dbgpt.configs.model_config import PILOT_PATH from dbgpt.util.string_utils import is_scientific_notation from ...command_manage import command -matplotlib.use("Agg") - - logger = logging.getLogger(__name__) +if TYPE_CHECKING: + from pandas import DataFrame static_message_img_path = os.path.join(PILOT_PATH, "message/img") -def data_pre_classification(df: DataFrame): - ## Data pre-classification +def data_pre_classification(df: "DataFrame"): + """Return the x and y coordinates of the chart.""" + import pandas as pd + + # Data pre-classification columns = df.columns.tolist() number_columns = [] non_numeric_colums = [] - # 收集数据分类小于10个的列 + # Collect columns with less than 10 unique values non_numeric_colums_value_map = {} numeric_colums_value_map = {} for column_name in columns: @@ -73,6 +69,10 @@ def data_pre_classification(df: DataFrame): def zh_font_set(): + """Set Chinese font.""" + from matplotlib import pyplot as plt + from matplotlib.font_manager import FontManager + font_names = [ "Heiti TC", "Songti SC", @@ -83,7 +83,7 @@ def zh_font_set(): "KaiTi", ] fm = FontManager() - mat_fonts = set(f.name for f in fm.ttflist) + mat_fonts = set(f.name for f in fm.ttflist) # noqa: C401 can_use_fonts = [] for font_name in font_names: if font_name in mat_fonts: @@ -93,10 +93,9 @@ def zh_font_set(): def format_axis(value, pos): - # 判断是否为数字 + """Format axis.""" + # Judge whether scientific counting is needed if is_scientific_notation(value): - # 判断是否需要进行非科学计数法格式化 - return "{:.2f}".format(value) return value @@ -106,8 +105,18 @@ def format_axis(value, pos): "Line chart display, used to display comparative trend analysis data", '"df":""', ) -def response_line_chart(df: DataFrame) -> str: - logger.info(f"response_line_chart") +def response_line_chart(df: "DataFrame") -> str: + """Response line chart.""" + import matplotlib + import matplotlib.pyplot as plt + import matplotlib.ticker as mtick + import pandas as pd + import seaborn as sns + from matplotlib.font_manager import FontManager + + matplotlib.use("Agg") + + logger.info("response_line_chart") if df.size <= 0: raise ValueError("No Data！") try: @@ -123,7 +132,7 @@ def response_line_chart(df: DataFrame) -> str: "KaiTi", ] fm = FontManager() - mat_fonts = set(f.name for f in fm.ttflist) + mat_fonts = set(f.name for f in fm.ttflist) # noqa: C401 can_use_fonts = [] for font_name in font_names: if font_name in mat_fonts: @@ -132,10 +141,10 @@ def response_line_chart(df: DataFrame) -> str: plt.rcParams["font.sans-serif"] = can_use_fonts rc = {"font.sans-serif": can_use_fonts} - plt.rcParams["axes.unicode_minus"] = False # 解决无法显示符号的问题 + plt.rcParams["axes.unicode_minus"] = False - sns.set(font=can_use_fonts[0], font_scale=0.8) # 解决Seaborn中文显示问题 - sns.set_palette("Set3") # 设置颜色主题 + sns.set(font=can_use_fonts[0], font_scale=0.8) + sns.set_palette("Set3") sns.set_style("dark") sns.color_palette("hls", 10) sns.hls_palette(8, l=0.5, s=0.7) @@ -143,7 +152,7 @@ def response_line_chart(df: DataFrame) -> str: fig, ax = plt.subplots(figsize=(8, 5), dpi=100) x, y, non_num_columns, num_colmns = data_pre_classification(df) - # ## 复杂折线图实现 + # Complex line chart implementation if len(num_colmns) > 0: num_colmns.append(y) df_melted = pd.melt( @@ -160,17 +169,19 @@ def response_line_chart(df: DataFrame) -> str: sns.lineplot(data=df, x=x, y=y, ax=ax, palette="Set2") ax.yaxis.set_major_formatter(mtick.FuncFormatter(format_axis)) - # ax.xaxis.set_major_formatter(mtick.FuncFormatter(lambda x, _: "{:,.0f}".format(x))) chart_name = "line_" + str(uuid.uuid1()) + ".png" chart_path = static_message_img_path + "/" + chart_name plt.savefig(chart_path, dpi=100, transparent=True) - html_img = f"""""" + html_img = ( + "' + ) return html_img except Exception as e: - logging.error("Draw Line Chart Faild!" + str(e), e) - raise ValueError("Draw Line Chart Faild!" + str(e)) + logging.error("Draw Line Chart failed!" + str(e)) + raise ValueError("Draw Line Chart failed!" + str(e)) @command( @@ -178,8 +189,18 @@ def response_line_chart(df: DataFrame) -> str: "Histogram, suitable for comparative analysis of multiple target values", '"df":""', ) -def response_bar_chart(df: DataFrame) -> str: - logger.info(f"response_bar_chart") +def response_bar_chart(df: "DataFrame") -> str: + """Response bar chart.""" + import matplotlib + import matplotlib.pyplot as plt + import matplotlib.ticker as mtick + import pandas as pd + import seaborn as sns + from matplotlib.font_manager import FontManager + + matplotlib.use("Agg") + + logger.info("response_bar_chart") if df.size <= 0: raise ValueError("No Data！") @@ -195,7 +216,7 @@ def response_bar_chart(df: DataFrame) -> str: "KaiTi", ] fm = FontManager() - mat_fonts = set(f.name for f in fm.ttflist) + mat_fonts = set(f.name for f in fm.ttflist) # noqa: C401 can_use_fonts = [] for font_name in font_names: if font_name in mat_fonts: @@ -204,9 +225,12 @@ def response_bar_chart(df: DataFrame) -> str: plt.rcParams["font.sans-serif"] = can_use_fonts rc = {"font.sans-serif": can_use_fonts} - plt.rcParams["axes.unicode_minus"] = False # 解决无法显示符号的问题 - sns.set(font=can_use_fonts[0], font_scale=0.8) # 解决Seaborn中文显示问题 - sns.set_palette("Set3") # 设置颜色主题 + # Fix the problem that the symbol cannot be displayed + plt.rcParams["axes.unicode_minus"] = False + # Fix chinese display problem + sns.set(font=can_use_fonts[0], font_scale=0.8) + # Set color theme + sns.set_palette("Set3") sns.set_style("dark") sns.color_palette("hls", 10) sns.hls_palette(8, l=0.5, s=0.7) @@ -250,14 +274,16 @@ def response_bar_chart(df: DataFrame) -> str: else: sns.barplot(data=df, x=x, y=y, hue=hue, palette="Set2", ax=ax) - # 设置 y 轴刻度格式为普通数字格式 + # Set the y-axis scale format to normal number format ax.yaxis.set_major_formatter(mtick.FuncFormatter(format_axis)) - # ax.xaxis.set_major_formatter(mtick.FuncFormatter(lambda x, _: "{:,.0f}".format(x))) chart_name = "bar_" + str(uuid.uuid1()) + ".png" chart_path = static_message_img_path + "/" + chart_name plt.savefig(chart_path, dpi=100, transparent=True) - html_img = f"""""" + html_img = ( + "' + ) return html_img @@ -266,8 +292,16 @@ def response_bar_chart(df: DataFrame) -> str: "Pie chart, suitable for scenarios such as proportion and distribution statistics", '"df":""', ) -def response_pie_chart(df: DataFrame) -> str: - logger.info(f"response_pie_chart") +def response_pie_chart(df: "DataFrame") -> str: + """Response pie chart.""" + import matplotlib + import matplotlib.pyplot as plt + import seaborn as sns + from matplotlib.font_manager import FontManager + + matplotlib.use("Agg") + + logger.info("response_pie_chart") columns = df.columns.tolist() if df.size <= 0: raise ValueError("No Data！") @@ -283,18 +317,18 @@ def response_pie_chart(df: DataFrame) -> str: "KaiTi", ] fm = FontManager() - mat_fonts = set(f.name for f in fm.ttflist) + mat_fonts = set(f.name for f in fm.ttflist) # noqa: C401 can_use_fonts = [] for font_name in font_names: if font_name in mat_fonts: can_use_fonts.append(font_name) if len(can_use_fonts) > 0: plt.rcParams["font.sans-serif"] = can_use_fonts - plt.rcParams["axes.unicode_minus"] = False # 解决无法显示符号的问题 + plt.rcParams["axes.unicode_minus"] = False - sns.set_palette("Set3") # 设置颜色主题 + # Set the font style + sns.set_palette("Set3") - # fig, ax = plt.pie(df[columns[1]], labels=df[columns[0]], autopct='%1.1f%%', startangle=90) fig, ax = plt.subplots(figsize=(8, 5), dpi=100) ax = df.plot( kind="pie", @@ -305,13 +339,16 @@ def response_pie_chart(df: DataFrame) -> str: autopct="%1.1f%%", ) - plt.axis("equal") # 使饼图为正圆形 + plt.axis("equal") # Make the pie chart a perfect circle # plt.title(columns[0]) chart_name = "pie_" + str(uuid.uuid1()) + ".png" chart_path = static_message_img_path + "/" + chart_name plt.savefig(chart_path, bbox_inches="tight", dpi=100, transparent=True) - html_img = f"""""" + html_img = ( + "' + ) return html_img diff --git a/dbgpt/agent/plugin/commands/built_in/display_type/show_table_gen.py b/dbgpt/agent/plugin/commands/built_in/display_type/show_table_gen.py index aa97df665..d3c3eed77 100644 --- a/dbgpt/agent/plugin/commands/built_in/display_type/show_table_gen.py +++ b/dbgpt/agent/plugin/commands/built_in/display_type/show_table_gen.py @@ -1,3 +1,4 @@ +"""Generate a table display for the response.""" import logging from pandas import DataFrame @@ -9,11 +10,13 @@ @command( "response_table", - "Table display, suitable for display with many display columns or non-numeric columns", + "Table display, suitable for display with many display columns or " + "non-numeric columns", '"df":""', ) def response_table(df: DataFrame) -> str: - logger.info(f"response_table") + """Response Table.""" + logger.info("response_table") html_table = df.to_html(index=False, escape=False, sparsify=False) table_str = "".join(html_table.split()) table_str = table_str.replace("\n", " ") diff --git a/dbgpt/agent/plugin/commands/built_in/display_type/show_text_gen.py b/dbgpt/agent/plugin/commands/built_in/display_type/show_text_gen.py index 498ec618e..a67e76036 100644 --- a/dbgpt/agent/plugin/commands/built_in/display_type/show_text_gen.py +++ b/dbgpt/agent/plugin/commands/built_in/display_type/show_text_gen.py @@ -1,3 +1,4 @@ +"""Generate text display content for the data frame.""" import logging from pandas import DataFrame @@ -9,11 +10,13 @@ @command( "response_data_text", - "Text display, the default display method, suitable for single-line or simple content display", + "Text display, the default display method, suitable for single-line or " + "simple content display", '"df":""', ) def response_data_text(df: DataFrame) -> str: - logger.info(f"response_data_text") + """Generate text display content for the data frame.""" + logger.info("response_data_text") data = df.values row_size = data.shape[0] @@ -33,5 +36,5 @@ def response_data_text(df: DataFrame) -> str: value_str = f" ** {value} **" text_info = f" {value_str}" else: - text_info = f"##### _没有找到可用的数据_" + text_info = "##### No data found! #####" return text_info diff --git a/dbgpt/agent/plugin/commands/built_in/image_gen.py b/dbgpt/agent/plugin/commands/built_in/image_gen.py deleted file mode 100644 index ec7252f67..000000000 --- a/dbgpt/agent/plugin/commands/built_in/image_gen.py +++ /dev/null @@ -1,125 +0,0 @@ -""" Image Generation Module for AutoGPT.""" -import io -import logging -import uuid -from base64 import b64decode - -import requests -from PIL import Image - -from dbgpt._private.config import Config - -from ..command_manage import command - -logger = logging.getLogger(__name__) -CFG = Config() - - -@command("generate_image", "Generate Image", '"prompt": ""', CFG.image_provider) -def generate_image(prompt: str, size: int = 256) -> str: - """Generate an image from a prompt. - - Args: - prompt (str): The prompt to use - size (int, optional): The size of the image. Defaults to 256. (Not supported by HuggingFace) - - Returns: - str: The filename of the image - """ - filename = f"{CFG.workspace_path}/{str(uuid.uuid4())}.jpg" - - # HuggingFace - if CFG.image_provider == "huggingface": - return generate_image_with_hf(prompt, filename) - # SD WebUI - elif CFG.image_provider == "sdwebui": - return generate_image_with_sd_webui(prompt, filename, size) - return "No Image Provider Set" - - -def generate_image_with_hf(prompt: str, filename: str) -> str: - """Generate an image with HuggingFace's API. - - Args: - prompt (str): The prompt to use - filename (str): The filename to save the image to - - Returns: - str: The filename of the image - """ - API_URL = ( - f"https://api-inference.huggingface.co/models/{CFG.huggingface_image_model}" - ) - if CFG.huggingface_api_token is None: - raise ValueError( - "You need to set your Hugging Face API token in the config file." - ) - headers = { - "Authorization": f"Bearer {CFG.huggingface_api_token}", - "X-Use-Cache": "false", - } - - response = requests.post( - API_URL, - headers=headers, - json={ - "inputs": prompt, - }, - ) - - image = Image.open(io.BytesIO(response.content)) - logger.info(f"Image Generated for prompt:{prompt}") - - image.save(filename) - - return f"Saved to disk:{filename}" - - -def generate_image_with_sd_webui( - prompt: str, - filename: str, - size: int = 512, - negative_prompt: str = "", - extra: dict = {}, -) -> str: - """Generate an image with Stable Diffusion webui. - Args: - prompt (str): The prompt to use - filename (str): The filename to save the image to - size (int, optional): The size of the image. Defaults to 256. - negative_prompt (str, optional): The negative prompt to use. Defaults to "". - extra (dict, optional): Extra parameters to pass to the API. Defaults to {}. - Returns: - str: The filename of the image - """ - # Create a session and set the basic auth if needed - s = requests.Session() - if CFG.sd_webui_auth: - username, password = CFG.sd_webui_auth.split(":") - s.auth = (username, password or "") - - # Generate the images - response = requests.post( - f"{CFG.sd_webui_url}/sdapi/v1/txt2img", - json={ - "prompt": prompt, - "negative_prompt": negative_prompt, - "sampler_index": "DDIM", - "steps": 20, - "cfg_scale": 7.0, - "width": size, - "height": size, - "n_iter": 1, - **extra, - }, - ) - - logger.info(f"Image Generated for prompt:{prompt}") - - # Save the image to disk - response = response.json() - b64 = b64decode(response["images"][0].split(",", 1)[0]) - image = Image.open(io.BytesIO(b64)) - image.save(filename) - - return f"Saved to disk:{filename}" diff --git a/dbgpt/agent/plugin/commands/command.py b/dbgpt/agent/plugin/commands/command.py index f09c44984..a9fc08498 100644 --- a/dbgpt/agent/plugin/commands/command.py +++ b/dbgpt/agent/plugin/commands/command.py @@ -1,13 +1,15 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - +"""Command module.""" import json -from typing import Dict +from typing import Any, Dict from dbgpt._private.config import Config from dbgpt.agent.plugin.generator import PluginPromptGenerator -from .exception_not_commands import NotCommands +from .exceptions import ( + CreateCommandException, + ExecutionCommandException, + NotCommandException, +) def _resolve_pathlike_command_args(command_args): @@ -25,16 +27,17 @@ def _resolve_pathlike_command_args(command_args): def execute_ai_response_json( prompt: PluginPromptGenerator, ai_response, - user_input: str = None, + user_input: str | None = None, ) -> str: - """ + """Execute the command from the AI response. + Args: - command_registry: - ai_response: - prompt: + prompt(PluginPromptGenerator): The prompt generator + ai_response: The response from the AI + user_input(str): The user input Returns: - + str: The result of the command """ from dbgpt.util.speech.say import say_text @@ -69,8 +72,8 @@ def execute_command( command_name: str, arguments, plugin_generator: PluginPromptGenerator, -): - """Execute the command and return the result +) -> Any: + """Execute the command and return the result. Args: command_name (str): The name of the command to execute @@ -78,6 +81,10 @@ def execute_command( Returns: str: The result of the command + + Raises: + NotCommandException: If the command is not found + ExecutionCommandException: If an error occurs while executing the command """ cmd = None if plugin_generator.command_registry: @@ -88,7 +95,7 @@ def execute_command( try: return cmd(**arguments) except Exception as e: - raise ValueError(f"Error: {str(e)}") + raise CreateCommandException(f"Error: {str(e)}") # return f"Error: {str(e)}" # TODO: Change these to take in a file rather than pasted code, if # non-file is given, return instructions "Input should be a python @@ -109,12 +116,14 @@ def execute_command( print(str(arguments)) return command["function"](**arguments) except Exception as e: - return f"Error: {str(e)}" - raise NotCommands("非可用命令" + command_name) + raise ExecutionCommandException(f"Execution error: {str(e)}") + raise NotCommandException("Invalid command: " + command_name) def get_command(response_json: Dict): - """Parse the response and return the command name and arguments + """Create a command from the response JSON. + + Parse the response and return the command name and arguments Args: response_json (json): The response from the AI diff --git a/dbgpt/agent/plugin/commands/command_manage.py b/dbgpt/agent/plugin/commands/command_manage.py index 492d080b3..959ead090 100644 --- a/dbgpt/agent/plugin/commands/command_manage.py +++ b/dbgpt/agent/plugin/commands/command_manage.py @@ -1,3 +1,5 @@ +"""Module for managing commands and command plugins.""" + import functools import importlib import inspect @@ -5,10 +7,10 @@ import logging import xml.etree.ElementTree as ET from datetime import datetime -from typing import Any, Callable, List, Optional +from typing import Any, Callable, Dict, List, Optional, Union from dbgpt._private.pydantic import BaseModel -from dbgpt.agent.common.schema import Status +from dbgpt.agent.core.schema import Status from dbgpt.util.json_utils import serialize from dbgpt.util.string_utils import extract_content, extract_content_open_ending @@ -25,7 +27,8 @@ class Command: Attributes: name (str): The name of the command. description (str): A brief description of what the command does. - signature (str): The signature of the function that the command executes. Defaults to None. + signature (str): The signature of the function that the command executes. + Defaults to None. """ def __init__( @@ -37,6 +40,7 @@ def __init__( enabled: bool = True, disabled_reason: Optional[str] = None, ): + """Create a new Command object.""" self.name = name self.description = description self.method = method @@ -45,16 +49,19 @@ def __init__( self.disabled_reason = disabled_reason def __call__(self, *args, **kwargs) -> Any: + """Run the command.""" if not self.enabled: return f"Command '{self.name}' is disabled: {self.disabled_reason}" return self.method(*args, **kwargs) def __str__(self) -> str: + """Return a string representation of the Command object.""" return f"{self.name}: {self.description}, args: {self.signature}" class CommandRegistry: - """ + """Command registry class. + The CommandRegistry class is a manager for a collection of Command objects. It allows the registration, modification, and retrieval of Command objects, as well as the scanning and loading of command plugins from a specified @@ -62,6 +69,7 @@ class CommandRegistry: """ def __init__(self): + """Create a new CommandRegistry object.""" self.commands = {} def _import_module(self, module_name: str) -> Any: @@ -71,16 +79,18 @@ def _reload_module(self, module: Any) -> Any: return importlib.reload(module) def register(self, cmd: Command) -> None: + """Register a new Command object with the registry.""" self.commands[cmd.name] = cmd def unregister(self, command_name: str): + """Unregisters a Command object from the registry.""" if command_name in self.commands: del self.commands[command_name] else: raise KeyError(f"Command '{command_name}' not found in registry.") def reload_commands(self) -> None: - """Reloads all loaded command plugins.""" + """Reload all loaded command plugins.""" for cmd_name in self.commands: cmd = self.commands[cmd_name] module = self._import_module(cmd.__module__) @@ -89,32 +99,31 @@ def reload_commands(self) -> None: reloaded_module.register(self) def is_valid_command(self, name: str) -> bool: - if name not in self.commands: - return False - else: - return True + """Check if the specified command name is registered.""" + return name in self.commands def get_command(self, name: str) -> Callable[..., Any]: + """Return the Command object with the specified name.""" return self.commands[name] def call(self, command_name: str, **kwargs) -> Any: + """Run command.""" if command_name not in self.commands: raise KeyError(f"Command '{command_name}' not found in registry.") command = self.commands[command_name] return command(**kwargs) def command_prompt(self) -> str: - """ - Returns a string representation of all registered `Command` objects for use in a prompt - """ + """Return a string representation of all registered `Command` objects.""" commands_list = [ f"{idx + 1}. {str(cmd)}" for idx, cmd in enumerate(self.commands.values()) ] return "\n".join(commands_list) def import_commands(self, module_name: str) -> None: - """ - Imports the specified Python module containing command plugins. + """Import module. + + Import the specified Python module containing command plugins. This method imports the associated module and registers any functions or classes that are decorated with the `AUTO_GPT_COMMAND_IDENTIFIER` attribute @@ -124,7 +133,6 @@ def import_commands(self, module_name: str) -> None: Args: module_name (str): The name of the module to import for command plugins. """ - module = importlib.import_module(module_name) for attr_name in dir(module): @@ -138,7 +146,7 @@ def import_commands(self, module_name: str) -> None: elif ( inspect.isclass(attr) and issubclass(attr, Command) and attr != Command ): - cmd_instance = attr() + cmd_instance = attr() # type: ignore self.register(cmd_instance) @@ -149,7 +157,7 @@ def command( enabled: bool = True, disabled_reason: Optional[str] = None, ) -> Callable[..., Any]: - """The command decorator is used to create Command objects from ordinary functions.""" + """Register a function as a command.""" def decorator(func: Callable[..., Any]) -> Command: cmd = Command( @@ -165,30 +173,34 @@ def decorator(func: Callable[..., Any]) -> Command: def wrapper(*args, **kwargs) -> Any: return func(*args, **kwargs) - wrapper.command = cmd + wrapper.command = cmd # type: ignore setattr(wrapper, AUTO_GPT_COMMAND_IDENTIFIER, True) - return wrapper + return wrapper # type: ignore return decorator class PluginStatus(BaseModel): + """A class representing the status of a plugin.""" + name: str location: List[int] args: dict - status: Status = Status.TODO.value - logo_url: str = None - api_result: str = None - err_msg: str = None + status: Union[Status, str] = Status.TODO.value + logo_url: Optional[str] = None + api_result: Optional[str] = None + err_msg: Optional[str] = None start_time = datetime.now().timestamp() * 1000 - end_time: int = None + end_time: Optional[str] = None df: Any = None class ApiCall: + """A class representing an API call.""" + agent_prefix = "" agent_end = "" name_prefix = "" @@ -200,31 +212,21 @@ def __init__( display_registry: Any = None, backend_rendering: bool = False, ): - # self.name: str = "" - # self.status: Status = Status.TODO.value - # self.logo_url: str = None - # self.args = {} - # self.api_result: str = None - # self.err_msg: str = None - - self.plugin_status_map = {} + """Create a new ApiCall object.""" + self.plugin_status_map: Dict[str, PluginStatus] = {} self.plugin_generator = plugin_generator self.display_registry = display_registry self.start_time = datetime.now().timestamp() * 1000 - self.backend_rendering: bool = False - - def __repr__(self): - return f"ApiCall(name={self.name}, status={self.status}, args={self.args})" + self.backend_rendering: bool = backend_rendering - def __is_need_wait_plugin_call(self, api_call_context): + def _is_need_wait_plugin_call(self, api_call_context): start_agent_count = api_call_context.count(self.agent_prefix) - end_agent_count = api_call_context.count(self.agent_end) if start_agent_count > 0: return True else: - # 末尾新出字符检测 + # Check the new character at the end check_len = len(self.agent_prefix) last_text = api_call_context[-check_len:] for i in range(check_len): @@ -237,6 +239,7 @@ def __is_need_wait_plugin_call(self, api_call_context): return False def check_last_plugin_call_ready(self, all_context): + """Check if the last plugin call is ready.""" start_agent_count = all_context.count(self.agent_prefix) end_agent_count = all_context.count(self.agent_end) @@ -244,7 +247,7 @@ def check_last_plugin_call_ready(self, all_context): return True return False - def __deal_error_md_tags(self, all_context, api_context, include_end: bool = True): + def _deal_error_md_tags(self, all_context, api_context, include_end: bool = True): error_md_tags = [ "```", "```python", @@ -253,7 +256,7 @@ def __deal_error_md_tags(self, all_context, api_context, include_end: bool = Tru "```markdown", "```sql", ] - if include_end == False: + if not include_end: md_tag_end = "" else: md_tag_end = "```" @@ -271,6 +274,7 @@ def __deal_error_md_tags(self, all_context, api_context, include_end: bool = Tru return all_context def api_view_context(self, all_context: str, display_mode: bool = False): + """Return the view content.""" call_context_map = extract_content_open_ending( all_context, self.agent_prefix, self.agent_end, True ) @@ -278,11 +282,12 @@ def api_view_context(self, all_context: str, display_mode: bool = False): api_status = self.plugin_status_map.get(api_context) if api_status is not None: if display_mode: - all_context = self.__deal_error_md_tags(all_context, api_context) + all_context = self._deal_error_md_tags(all_context, api_context) if Status.FAILED.value == api_status.status: + err_msg = api_status.err_msg all_context = all_context.replace( api_context, - f'\nError:{api_status.err_msg}\n' + f'\nError:{err_msg}\n' + self.to_view_antv_vis(api_status), ) else: @@ -291,7 +296,7 @@ def api_view_context(self, all_context: str, display_mode: bool = False): ) else: - all_context = self.__deal_error_md_tags( + all_context = self._deal_error_md_tags( all_context, api_context, False ) all_context = all_context.replace( @@ -303,7 +308,7 @@ def api_view_context(self, all_context: str, display_mode: bool = False): now_time = datetime.now().timestamp() * 1000 cost = (now_time - self.start_time) / 1000 cost_str = "{:.2f}".format(cost) - all_context = self.__deal_error_md_tags(all_context, api_context) + all_context = self._deal_error_md_tags(all_context, api_context) all_context = all_context.replace( api_context, @@ -313,7 +318,8 @@ def api_view_context(self, all_context: str, display_mode: bool = False): return all_context def update_from_context(self, all_context): - api_context_map = extract_content( + """Modify the plugin status map based on the context.""" + api_context_map: Dict[int, str] = extract_content( all_context, self.agent_prefix, self.agent_end, True ) for api_index, api_context in api_context_map.items(): @@ -336,7 +342,7 @@ def update_from_context(self, all_context): else: api_status.location.append(api_index) - def __to_view_param_str(self, api_status): + def _to_view_param_str(self, api_status): param = {} if api_status.name: param["name"] = api_status.name @@ -353,101 +359,105 @@ def __to_view_param_str(self, api_status): return json.dumps(param, default=serialize, ensure_ascii=False) def to_view_text(self, api_status: PluginStatus): + """Return the view content.""" api_call_element = ET.Element("dbgpt-view") - api_call_element.text = self.__to_view_param_str(api_status) + api_call_element.text = self._to_view_param_str(api_status) result = ET.tostring(api_call_element, encoding="utf-8") return result.decode("utf-8") def to_view_antv_vis(self, api_status: PluginStatus): + """Return the vis content.""" if self.backend_rendering: html_table = api_status.df.to_html( index=False, escape=False, sparsify=False ) table_str = "".join(html_table.split()) table_str = table_str.replace("\n", " ") - html = f""" \n
[SQL]{api_status.args["sql"]}
{table_str}
\n """ + sql = api_status.args["sql"] + html = ( + f' \n
[SQL]{sql}
' + f"{table_str}
\n " + ) return html else: api_call_element = ET.Element("chart-view") - api_call_element.attrib["content"] = self.__to_antv_vis_param(api_status) + api_call_element.attrib["content"] = self._to_antv_vis_param(api_status) api_call_element.text = "\n" - # api_call_element.set("content", self.__to_antv_vis_param(api_status)) - # api_call_element.text = self.__to_antv_vis_param(api_status) result = ET.tostring(api_call_element, encoding="utf-8") return result.decode("utf-8") - # return f'' - - def __to_antv_vis_param(self, api_status: PluginStatus): + def _to_antv_vis_param(self, api_status: PluginStatus): param = {} if api_status.name: param["type"] = api_status.name if api_status.args: param["sql"] = api_status.args["sql"] - # if api_status.err_msg: - # param["err_msg"] = api_status.err_msg + data: Any = [] if api_status.api_result: - param["data"] = api_status.api_result - else: - param["data"] = [] + data = api_status.api_result + param["data"] = data return json.dumps(param, ensure_ascii=False) def run(self, llm_text): - if self.__is_need_wait_plugin_call(llm_text): + """Run the API calls.""" + if self._is_need_wait_plugin_call( + llm_text + ) and self.check_last_plugin_call_ready(llm_text): # wait api call generate complete - if self.check_last_plugin_call_ready(llm_text): - self.update_from_context(llm_text) - for key, value in self.plugin_status_map.items(): - if value.status == Status.TODO.value: - value.status = Status.RUNNING.value - logging.info(f"插件执行:{value.name},{value.args}") - try: - value.api_result = execute_command( - value.name, value.args, self.plugin_generator - ) - value.status = Status.COMPLETE.value - except Exception as e: - value.status = Status.FAILED.value - value.err_msg = str(e) - value.end_time = datetime.now().timestamp() * 1000 + self.update_from_context(llm_text) + for key, value in self.plugin_status_map.items(): + if value.status == Status.TODO.value: + value.status = Status.RUNNING.value + logger.info(f"Plugin execution:{value.name},{value.args}") + try: + value.api_result = execute_command( + value.name, value.args, self.plugin_generator + ) + value.status = Status.COMPLETE.value + except Exception as e: + value.status = Status.FAILED.value + value.err_msg = str(e) + value.end_time = datetime.now().timestamp() * 1000 return self.api_view_context(llm_text) def run_display_sql(self, llm_text, sql_run_func): - if self.__is_need_wait_plugin_call(llm_text): + """Run the API calls for displaying SQL data.""" + if self._is_need_wait_plugin_call( + llm_text + ) and self.check_last_plugin_call_ready(llm_text): # wait api call generate complete - if self.check_last_plugin_call_ready(llm_text): - self.update_from_context(llm_text) - for key, value in self.plugin_status_map.items(): - if value.status == Status.TODO.value: - value.status = Status.RUNNING.value - logging.info(f"sql展示执行:{value.name},{value.args}") - try: - sql = value.args["sql"] - if sql: - param = { - "df": sql_run_func(sql), - } - value.df = param["df"] - if self.display_registry.is_valid_command(value.name): - value.api_result = self.display_registry.call( - value.name, **param - ) - else: - value.api_result = self.display_registry.call( - "response_table", **param - ) - - value.status = Status.COMPLETE.value - except Exception as e: - value.status = Status.FAILED.value - value.err_msg = str(e) - value.end_time = datetime.now().timestamp() * 1000 + self.update_from_context(llm_text) + for key, value in self.plugin_status_map.items(): + if value.status == Status.TODO.value: + value.status = Status.RUNNING.value + logger.info(f"sql display execution:{value.name},{value.args}") + try: + sql = value.args["sql"] + if sql: + param = { + "df": sql_run_func(sql), + } + value.df = param["df"] + if self.display_registry.is_valid_command(value.name): + value.api_result = self.display_registry.call( + value.name, **param + ) + else: + value.api_result = self.display_registry.call( + "response_table", **param + ) + + value.status = Status.COMPLETE.value + except Exception as e: + value.status = Status.FAILED.value + value.err_msg = str(e) + value.end_time = datetime.now().timestamp() * 1000 return self.api_view_context(llm_text, True) def display_sql_llmvis(self, llm_text, sql_run_func): - """ - Render charts using the Antv standard protocol + """Render charts using the Antv standard protocol. + Args: llm_text: LLM response text sql_run_func: sql run function @@ -456,46 +466,48 @@ def display_sql_llmvis(self, llm_text, sql_run_func): ChartView protocol text """ try: - if self.__is_need_wait_plugin_call(llm_text): + if self._is_need_wait_plugin_call( + llm_text + ) and self.check_last_plugin_call_ready(llm_text): # wait api call generate complete - if self.check_last_plugin_call_ready(llm_text): - self.update_from_context(llm_text) - for key, value in self.plugin_status_map.items(): - if value.status == Status.TODO.value: - value.status = Status.RUNNING.value - logging.info(f"sql展示执行:{value.name},{value.args}") - try: - sql = value.args["sql"] - if sql is not None and len(sql) > 0: - data_df = sql_run_func(sql) - value.df = data_df - value.api_result = json.loads( - data_df.to_json( - orient="records", - date_format="iso", - date_unit="s", - ) + self.update_from_context(llm_text) + for key, value in self.plugin_status_map.items(): + if value.status == Status.TODO.value: + value.status = Status.RUNNING.value + logger.info(f"SQL execution:{value.name},{value.args}") + try: + sql = value.args["sql"] + if sql is not None and len(sql) > 0: + data_df = sql_run_func(sql) + value.df = data_df + value.api_result = json.loads( + data_df.to_json( + orient="records", + date_format="iso", + date_unit="s", ) - value.status = Status.COMPLETE.value - else: - value.status = Status.FAILED.value - value.err_msg = "No executable sql！" - - except Exception as e: - logging.error(f"data prepare exception！{str(e)}") + ) + value.status = Status.COMPLETE.value + else: value.status = Status.FAILED.value - value.err_msg = str(e) - value.end_time = datetime.now().timestamp() * 1000 + value.err_msg = "No executable sql！" + + except Exception as e: + logger.error(f"data prepare exception！{str(e)}") + value.status = Status.FAILED.value + value.err_msg = str(e) + value.end_time = datetime.now().timestamp() * 1000 except Exception as e: - logging.error("Api parsing exception", e) + logger.error("Api parsing exception", e) raise ValueError("Api parsing exception," + str(e)) return self.api_view_context(llm_text, True) def display_only_sql_vis(self, chart: dict, sql_2_df_func): + """Display the chart using the vis standard protocol.""" err_msg = None + sql = chart.get("sql", None) try: - sql = chart.get("sql", None) param = {} df = sql_2_df_func(sql) if not sql or len(sql) <= 0: @@ -512,11 +524,7 @@ def display_only_sql_vis(self, chart: dict, sql_2_df_func): view_json_str = json.dumps(param, default=serialize, ensure_ascii=False) except Exception as e: logger.error("parse_view_response error!" + str(e)) - err_param = {} - err_param["sql"] = f"{sql}" - err_param["type"] = "response_table" - # err_param["err_msg"] = str(e) - err_param["data"] = [] + err_param = {"sql": f"{sql}", "type": "response_table", "data": []} err_msg = str(e) view_json_str = json.dumps(err_param, default=serialize, ensure_ascii=False) @@ -528,15 +536,16 @@ def display_only_sql_vis(self, chart: dict, sql_2_df_func): return result def display_dashboard_vis( - self, charts: List[dict], sql_2_df_func, title: str = None + self, charts: List[dict], sql_2_df_func, title: Optional[str] = None ): + """Display the dashboard using the vis standard protocol.""" err_msg = None view_json_str = None chart_items = [] try: if not charts or len(charts) <= 0: - return f"""Have no chart data!""" + return "Have no chart data!" for chart in charts: param = {} sql = chart.get("sql", "") @@ -576,40 +585,3 @@ def display_dashboard_vis( ) else: return result - - @staticmethod - def default_chart_type_promot() -> str: - """this function is moved from excel_analyze/chat.py,and used by subclass. - Returns: - - """ - antv_charts = [ - {"response_line_chart": "used to display comparative trend analysis data"}, - { - "response_pie_chart": "suitable for scenarios such as proportion and distribution statistics" - }, - { - "response_table": "suitable for display with many display columns or non-numeric columns" - }, - # {"response_data_text":" the default display method, suitable for single-line or simple content display"}, - { - "response_scatter_plot": "Suitable for exploring relationships between variables, detecting outliers, etc." - }, - { - "response_bubble_chart": "Suitable for relationships between multiple variables, highlighting outliers or special situations, etc." - }, - { - "response_donut_chart": "Suitable for hierarchical structure representation, category proportion display and highlighting key categories, etc." - }, - { - "response_area_chart": "Suitable for visualization of time series data, comparison of multiple groups of data, analysis of data change trends, etc." - }, - { - "response_heatmap": "Suitable for visual analysis of time series data, large-scale data sets, distribution of classified data, etc." - }, - ] - return "\n".join( - f"{key}:{value}" - for dict_item in antv_charts - for key, value in dict_item.items() - ) diff --git a/dbgpt/agent/plugin/commands/exception_not_commands.py b/dbgpt/agent/plugin/commands/exception_not_commands.py deleted file mode 100644 index 7d92f05c0..000000000 --- a/dbgpt/agent/plugin/commands/exception_not_commands.py +++ /dev/null @@ -1,4 +0,0 @@ -class NotCommands(Exception): - def __init__(self, message): - super().__init__(message) - self.message = message diff --git a/dbgpt/agent/plugin/commands/exceptions.py b/dbgpt/agent/plugin/commands/exceptions.py new file mode 100644 index 000000000..738c53122 --- /dev/null +++ b/dbgpt/agent/plugin/commands/exceptions.py @@ -0,0 +1,35 @@ +"""Exceptions for the commands plugin.""" + + +class CommandException(Exception): + """Common command error exception.""" + + def __init__(self, message: str, error_type: str = "Common Error"): + """Create a new CommandException instance.""" + super().__init__(message) + self.message = message + self.error_type = error_type + + +class CreateCommandException(CommandException): + """Create command error exception.""" + + def __init__(self, message: str, error_type="Create Command Error"): + """Create a new CreateCommandException instance.""" + super().__init__(message, error_type) + + +class NotCommandException(CommandException): + """Command not found exception.""" + + def __init__(self, message: str, error_type="Not Command Error"): + """Create a new NotCommandException instance.""" + super().__init__(message, error_type) + + +class ExecutionCommandException(CommandException): + """Command execution error exception.""" + + def __init__(self, message: str, error_type="Execution Command Error"): + """Create a new ExecutionCommandException instance.""" + super().__init__(message, error_type) diff --git a/dbgpt/agent/plugin/generator.py b/dbgpt/agent/plugin/generator.py index 9911d0f6e..ebd6e1562 100644 --- a/dbgpt/agent/plugin/generator.py +++ b/dbgpt/agent/plugin/generator.py @@ -1,38 +1,30 @@ -""" A module for generating custom prompt strings.""" +"""A module for generating custom prompt strings.""" from typing import Any, Callable, Dict, List, Optional class PluginPromptGenerator: - """ + """PluginPromptGenerator class. + A class for generating custom prompt strings based on constraints, commands, resources, and performance evaluations. """ def __init__(self) -> None: - """ + """Create a new PromptGenerator object. + Initialize the PromptGenerator object with empty lists of constraints, - commands, resources, and performance evaluations. + commands, resources, and performance evaluations. """ - self.constraints = [] - self.commands = [] - self.resources = [] - self.performance_evaluation = [] - self.goals = [] - self.command_registry = None - self.response_format = { - "thoughts": { - "text": "thought", - "reasoning": "reasoning", - "plan": "- short bulleted\n- list that conveys\n- long-term plan", - "criticism": "constructive self-criticism", - "speak": "thoughts summary to say to user", - }, - "command": {"name": "command name", "args": {"arg name": "value"}}, - } + from .commands.command_manage import CommandRegistry + + self.constraints: List[str] = [] + self.commands: List[Dict[str, Any]] = [] + self.resources: List[str] = [] + self.performance_evaluation: List[str] = [] + self.command_registry: CommandRegistry = CommandRegistry() def add_constraint(self, constraint: str) -> None: - """ - Add a constraint to the constraints list. + """Add a constraint to the constraints list. Args: constraint (str): The constraint to be added. @@ -46,7 +38,8 @@ def add_command( args=None, function: Optional[Callable] = None, ) -> None: - """ + """Add a command to the commands. + Add a command to the commands list with a label, name, and optional arguments. Args: @@ -131,4 +124,5 @@ def _generate_numbered_list(self, items: List[Any], item_type="list") -> str: return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items)) def generate_commands_string(self) -> str: + """Return a formatted string representation of the commands list.""" return f"{self._generate_numbered_list(self.commands, item_type='command')}" diff --git a/dbgpt/agent/plugin/loader.py b/dbgpt/agent/plugin/loader.py index df9b71338..c1ce99389 100644 --- a/dbgpt/agent/plugin/loader.py +++ b/dbgpt/agent/plugin/loader.py @@ -1,5 +1,6 @@ +"""Plugin loader module.""" + import logging -from pathlib import Path from typing import List, Optional from .generator import PluginPromptGenerator @@ -9,12 +10,14 @@ class PluginLoader: + """Plugin Loader Class.""" + def load_plugins( - self, plugin_path: Optional[str], available_plugins: Optional[List[str]] = None + self, plugin_path: str, available_plugins: Optional[List[str]] = None ) -> PluginPromptGenerator: - logger.info( - f"load_plugin path:{plugin_path}, available:{available_plugins if available_plugins else ''}" - ) + """Load plugins from plugin path.""" + available = available_plugins if available_plugins else "" + logger.info(f"load_plugin path:{plugin_path}, available:{available}") plugins = scan_plugins(plugin_path) generator: PluginPromptGenerator = PluginPromptGenerator() diff --git a/dbgpt/agent/plugin/plugins_util.py b/dbgpt/agent/plugin/plugins_util.py index fbd903e64..6e97dc703 100644 --- a/dbgpt/agent/plugin/plugins_util.py +++ b/dbgpt/agent/plugin/plugins_util.py @@ -1,28 +1,24 @@ -"""加载组件""" +"""Load plugins from a directory or a zip file.""" import datetime import glob import json import logging import os -import threading -import zipfile from pathlib import Path -from typing import List -from zipimport import zipimporter - -import git -import requests -from auto_gpt_plugin_template import AutoGPTPluginTemplate +from typing import TYPE_CHECKING, List, Optional from dbgpt._private.config import Config -from dbgpt.configs.model_config import PLUGINS_DIR + +if TYPE_CHECKING: + from auto_gpt_plugin_template import AutoGPTPluginTemplate logger = logging.getLogger(__name__) def inspect_zip_for_modules(zip_path: str, debug: bool = False) -> list[str]: - """ + """Load the AutoGPTPluginTemplate from a zip file. + Loader zip plugin file. Native support Auto_gpt_plugin Args: @@ -32,6 +28,8 @@ def inspect_zip_for_modules(zip_path: str, debug: bool = False) -> list[str]: Returns: list[str]: The list of module names found or empty list if none were found. """ + import zipfile + result = [] with zipfile.ZipFile(zip_path, "r") as zfile: for name in zfile.namelist(): @@ -44,8 +42,8 @@ def inspect_zip_for_modules(zip_path: str, debug: bool = False) -> list[str]: def write_dict_to_json_file(data: dict, file_path: str) -> None: - """ - Write a dictionary to a JSON file. + """Write a dictionary to a JSON file. + Args: data (dict): Dictionary to write. file_path (str): Path to the file. @@ -55,8 +53,8 @@ def write_dict_to_json_file(data: dict, file_path: str) -> None: def create_directory_if_not_exists(directory_path: str) -> bool: - """ - Create a directory if it does not exist. + """Create a directory if it does not exist. + Args: directory_path (str): Path to the directory. Returns: @@ -75,57 +73,21 @@ def create_directory_if_not_exists(directory_path: str) -> bool: return True -def load_native_plugins(cfg: Config): - if not cfg.plugins_auto_load: - print("not auto load_native_plugins") - return - - def load_from_git(cfg: Config): - print("async load_native_plugins") - branch_name = cfg.plugins_git_branch - native_plugin_repo = "DB-GPT-Plugins" - url = "https://github.com/csunny/{repo}/archive/{branch}.zip" - try: - session = requests.Session() - response = session.get( - url.format(repo=native_plugin_repo, branch=branch_name), - headers={"Authorization": "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"}, - ) +def scan_plugin_file(file_path, debug: bool = False) -> List["AutoGPTPluginTemplate"]: + """Scan a plugin file and load the plugins.""" + from zipimport import zipimporter - if response.status_code == 200: - plugins_path_path = Path(PLUGINS_DIR) - files = glob.glob( - os.path.join(plugins_path_path, f"{native_plugin_repo}*") - ) - for file in files: - os.remove(file) - now = datetime.datetime.now() - time_str = now.strftime("%Y%m%d%H%M%S") - file_name = f"{plugins_path_path}/{native_plugin_repo}-{branch_name}-{time_str}.zip" - print(file_name) - with open(file_name, "wb") as f: - f.write(response.content) - print("save file") - cfg.set_plugins(scan_plugins(cfg.debug_mode)) - else: - print("get file failed，response code：", response.status_code) - except Exception as e: - print("load plugin from git exception!" + str(e)) - - t = threading.Thread(target=load_from_git, args=(cfg,)) - t.start() - - -def scan_plugin_file(file_path, debug: bool = False) -> List[AutoGPTPluginTemplate]: logger.info(f"__scan_plugin_file:{file_path},{debug}") loaded_plugins = [] if moduleList := inspect_zip_for_modules(str(file_path), debug): for module in moduleList: plugin = Path(file_path) - module = Path(module) + module = Path(module) # type: ignore logger.debug(f"Plugin: {plugin} Module: {module}") zipped_package = zipimporter(str(plugin)) - zipped_module = zipped_package.load_module(str(module.parent)) + zipped_module = zipped_package.load_module( + str(module.parent) # type: ignore + ) for key in dir(zipped_module): if key.startswith("__"): continue @@ -142,7 +104,7 @@ def scan_plugin_file(file_path, debug: bool = False) -> List[AutoGPTPluginTempla def scan_plugins( plugins_file_path: str, file_name: str = "", debug: bool = False -) -> List[AutoGPTPluginTemplate]: +) -> List["AutoGPTPluginTemplate"]: """Scan the plugins directory for plugins and loads them. Args: @@ -152,7 +114,6 @@ def scan_plugins( Returns: List[Tuple[str, Path]]: List of plugins. """ - loaded_plugins = [] # Generic plugins plugins_path = Path(plugins_file_path) @@ -198,8 +159,11 @@ def update_from_git( download_path: str, github_repo: str = "", branch_name: str = "main", - authorization: str = None, + authorization: Optional[str] = None, ): + """Update plugins from a git repository.""" + import requests + os.makedirs(download_path, exist_ok=True) if github_repo: if github_repo.index("github.com") <= 0: @@ -237,14 +201,16 @@ def update_from_git( f.write(response.content) return plugin_repo_name else: - logger.error("update plugins faild，response code：", response.status_code) - raise ValueError("download plugin faild!" + response.status_code) + logger.error(f"Update plugins failed，response code：{response.status_code}") + raise ValueError(f"Download plugin failed: {response.status_code}") except Exception as e: logger.error("update plugins from git exception!" + str(e)) raise ValueError("download plugin exception!", e) def __fetch_from_git(local_path, git_url): + import git + logger.info("fetch plugins from git to local path:{}", local_path) os.makedirs(local_path, exist_ok=True) repo = git.Repo(local_path) diff --git a/dbgpt/agent/resource/__init__.py b/dbgpt/agent/resource/__init__.py index e69de29bb..dfc7a8d46 100644 --- a/dbgpt/agent/resource/__init__.py +++ b/dbgpt/agent/resource/__init__.py @@ -0,0 +1,21 @@ +"""Resource module for Agent.""" +from .resource_api import AgentResource, ResourceClient, ResourceType # noqa: F401 +from .resource_db_api import ResourceDbClient, SqliteLoadClient # noqa: F401 +from .resource_knowledge_api import ResourceKnowledgeClient # noqa: F401 +from .resource_loader import ResourceLoader # noqa: F401 +from .resource_plugin_api import ( # noqa: F401 + PluginFileLoadClient, + ResourcePluginClient, +) + +__all__ = [ + "AgentResource", + "ResourceClient", + "ResourceType", + "ResourceDbClient", + "SqliteLoadClient", + "ResourceKnowledgeClient", + "ResourceLoader", + "PluginFileLoadClient", + "ResourcePluginClient", +] diff --git a/dbgpt/agent/resource/resource_api.py b/dbgpt/agent/resource/resource_api.py index 4486845b0..85293cbb8 100644 --- a/dbgpt/agent/resource/resource_api.py +++ b/dbgpt/agent/resource/resource_api.py @@ -1,15 +1,15 @@ -from __future__ import annotations - -import dataclasses +"""Resource API for the agent.""" import json -from abc import ABC +from abc import ABC, abstractmethod from enum import Enum -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Union -from pydantic import BaseModel +from dbgpt._private.pydantic import BaseModel class ResourceType(Enum): + """Resource type enumeration.""" + DB = "database" Knowledge = "knowledge" Internet = "internet" @@ -17,10 +17,12 @@ class ResourceType(Enum): TextFile = "text_file" ExcelFile = "excel_file" ImageFile = "image_file" - AwelFlow = "awel_flow" + AWELFlow = "awel_flow" class AgentResource(BaseModel): + """Agent resource class.""" + type: ResourceType name: str value: str @@ -29,10 +31,12 @@ class AgentResource(BaseModel): ) def resource_prompt_template(self, **kwargs) -> str: - return f"""{{data_type}} --{{data_introduce}}""" + """Get the resource prompt template.""" + return "{data_type} --{data_introduce}" @staticmethod - def from_dict(d: Dict[str, Any]) -> Optional[AgentResource]: + def from_dict(d: Dict[str, Any]) -> Optional["AgentResource"]: + """Create an AgentResource object from a dictionary.""" if d is None: return None return AgentResource( @@ -44,16 +48,25 @@ def from_dict(d: Dict[str, Any]) -> Optional[AgentResource]: ) @staticmethod - def from_josn_list_str(d: Optional[str]) -> Optional[List[AgentResource]]: + def from_json_list_str(d: Optional[str]) -> Optional[List["AgentResource"]]: + """Create a list of AgentResource objects from a json string.""" if d is None: return None try: json_array = json.loads(d) - except Exception as e: + except Exception: raise ValueError(f"Illegal AgentResource json string！{d}") - return [AgentResource.from_dict(item) for item in json_array] + if not isinstance(json_array, list): + raise ValueError(f"Illegal AgentResource json string！{d}") + json_list = [] + for item in json_array: + r = AgentResource.from_dict(item) + if r: + json_list.append(r) + return json_list def to_dict(self) -> Dict[str, Any]: + """Convert the AgentResource object to a dictionary.""" temp = self.dict() for field, value in temp.items(): if isinstance(value, Enum): @@ -62,29 +75,51 @@ def to_dict(self) -> Dict[str, Any]: class ResourceClient(ABC): + """Resource client interface.""" + @property + @abstractmethod def type(self) -> ResourceType: - pass + """Return the resource type.""" async def get_data_introduce( self, resource: AgentResource, question: Optional[str] = None - ) -> str: + ) -> Union[str, List[str]]: """ - Get the content introduction prompt of the specified resource + Get the content introduction prompt of the specified resource. + Args: - value: + resource(AgentResource): The specified resource. + question(str): The question to be asked. Returns: - + str: The introduction content. """ return "" def get_data_type(self, resource: AgentResource) -> str: + """Return the data type of the specified resource. + + Args: + resource(AgentResource): The specified resource. + + Returns: + str: The data type. + """ return "" async def get_resource_prompt( - self, conv_uid, resource: AgentResource, question: Optional[str] = None + self, resource: AgentResource, question: Optional[str] = None ) -> str: + """Get the resource prompt. + + Args: + resource(AgentResource): The specified resource. + question(str): The question to be asked. + + Returns: + str: The resource prompt. + """ return resource.resource_prompt_template().format( data_type=self.get_data_type(resource), data_introduce=await self.get_data_introduce(resource, question), diff --git a/dbgpt/agent/resource/resource_db_api.py b/dbgpt/agent/resource/resource_db_api.py index e5f1cac08..71193d188 100644 --- a/dbgpt/agent/resource/resource_db_api.py +++ b/dbgpt/agent/resource/resource_db_api.py @@ -1,52 +1,67 @@ +"""Database resource client API.""" import logging from contextlib import contextmanager -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Iterator, List, Optional, Union -from dbgpt.agent.resource.resource_api import AgentResource - -from .resource_api import ResourceClient, ResourceType +from .resource_api import AgentResource, ResourceClient, ResourceType logger = logging.getLogger(__name__) class ResourceDbClient(ResourceClient): + """Database resource client API.""" + @property def type(self): + """Return the resource type.""" return ResourceType.DB def get_data_type(self, resource: AgentResource) -> str: + """Return the data type of the resource.""" return super().get_data_type(resource) async def get_data_introduce( self, resource: AgentResource, question: Optional[str] = None - ) -> str: - return await self.a_get_schema_link(resource.value, question) - - async def a_get_schema_link(self, db: str, question: Optional[str] = None) -> str: + ) -> Union[str, List[str]]: + """Return the data introduce of the resource.""" + return await self.get_schema_link(resource.value, question) + + async def get_schema_link( + self, db: str, question: Optional[str] = None + ) -> Union[str, List[str]]: + """Return the schema link of the database.""" raise NotImplementedError("The run method should be implemented in a subclass.") - async def a_query_to_df(self, dbe: str, sql: str): + async def query_to_df(self, dbe: str, sql: str): + """Return the query result as a DataFrame.""" raise NotImplementedError("The run method should be implemented in a subclass.") - async def a_query(self, db: str, sql: str): + async def query(self, db: str, sql: str): + """Return the query result.""" raise NotImplementedError("The run method should be implemented in a subclass.") - async def a_run_sql(self, db: str, sql: str): + async def run_sql(self, db: str, sql: str): + """Run the SQL.""" raise NotImplementedError("The run method should be implemented in a subclass.") class SqliteLoadClient(ResourceDbClient): + """SQLite resource client.""" + from sqlalchemy.orm.session import Session def __init__(self): + """Create a SQLite resource client.""" super(SqliteLoadClient, self).__init__() def get_data_type(self, resource: AgentResource) -> str: + """Return the data type of the resource.""" return "sqlite" @contextmanager - def connect(self, db) -> Session: - from sqlalchemy import create_engine, text + def connect(self, db) -> Iterator[Session]: + """Connect to the database.""" + from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker engine = create_engine("sqlite:///" + db, echo=True) @@ -55,17 +70,20 @@ def connect(self, db) -> Session: try: yield session session.commit() - except: + except Exception: session.rollback() raise finally: session.close() - async def a_get_schema_link(self, db: str, question: Optional[str] = None) -> str: + async def get_schema_link( + self, db: str, question: Optional[str] = None + ) -> Union[str, List[str]]: + """Return the schema link of the database.""" from sqlalchemy import text with self.connect(db) as connect: - _tables_sql = f""" + _tables_sql = """ SELECT name FROM sqlite_master WHERE type='table' """ cursor = connect.execute(text(_tables_sql)) @@ -86,13 +104,15 @@ async def a_get_schema_link(self, db: str, question: Optional[str] = None) -> st results.append(f"{table_name}({','.join(table_colums)});") return results - async def a_query_to_df(self, db: str, sql: str): + async def query_to_df(self, db: str, sql: str): + """Return the query result as a DataFrame.""" import pandas as pd - field_names, result = await self.a_query(db, sql) + field_names, result = await self.query(db, sql) return pd.DataFrame(result, columns=field_names) - async def a_query(self, db: str, sql: str): + async def query(self, db: str, sql: str): + """Return the query result.""" from sqlalchemy import text with self.connect(db) as connect: @@ -100,10 +120,7 @@ async def a_query(self, db: str, sql: str): if not sql: return [] cursor = connect.execute(text(sql)) - if cursor.returns_rows: + if cursor.returns_rows: # type: ignore result = cursor.fetchall() field_names = tuple(i[0:] for i in cursor.keys()) return field_names, result - - async def a_run_sql(self, db: str, sql: str): - pass diff --git a/dbgpt/agent/resource/resource_file_api.py b/dbgpt/agent/resource/resource_file_api.py deleted file mode 100644 index 5c92200fb..000000000 --- a/dbgpt/agent/resource/resource_file_api.py +++ /dev/null @@ -1,9 +0,0 @@ -from typing import Any, Dict, List, Optional, Tuple, Union - -from .resource_api import ResourceClient, ResourceType - - -class ResourceFileClient(ResourceClient): - @property - def type(self) -> ResourceType: - return ResourceType.File diff --git a/dbgpt/agent/resource/resource_knowledge_api.py b/dbgpt/agent/resource/resource_knowledge_api.py index 88cb57de7..722fe127b 100644 --- a/dbgpt/agent/resource/resource_knowledge_api.py +++ b/dbgpt/agent/resource/resource_knowledge_api.py @@ -1,19 +1,23 @@ -from typing import Any, Dict, List, Optional, Tuple, Union - -from dbgpt.rag.retriever.base import BaseRetriever +"""Knowledge resource API for the agent.""" +from typing import Any, Optional from .resource_api import ResourceClient, ResourceType class ResourceKnowledgeClient(ResourceClient): + """Knowledge resource client.""" + @property def type(self): + """Return the resource type.""" return ResourceType.Knowledge - async def a_get_kn(self, space_name: str, question: Optional[str] = None) -> str: + async def get_kn(self, space_name: str, question: Optional[str] = None) -> Any: + """Get the knowledge content.""" raise NotImplementedError("The run method should be implemented in a subclass.") async def add_kn( self, space_name: str, kn_name: str, type: str, content: Optional[Any] ): + """Add knowledge content.""" raise NotImplementedError("The run method should be implemented in a subclass.") diff --git a/dbgpt/agent/resource/resource_loader.py b/dbgpt/agent/resource/resource_loader.py index a6e9560ce..4018ab3f2 100644 --- a/dbgpt/agent/resource/resource_loader.py +++ b/dbgpt/agent/resource/resource_loader.py @@ -1,16 +1,26 @@ +"""Resource loader module.""" from collections import defaultdict -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Optional, Type, TypeVar from .resource_api import ResourceClient, ResourceType +T = TypeVar("T", bound=ResourceClient) + class ResourceLoader: + """Resource loader.""" + def __init__(self): + """Create a new resource loader.""" self._resource_api_instance = defaultdict(ResourceClient) - def get_resesource_api( - self, resource_type: ResourceType - ) -> Optional[ResourceClient]: + def get_resource_api( + self, + resource_type: Optional[ResourceType], + cls: Optional[Type[T]] = None, + check_instance: bool = True, + ) -> Optional[T]: + """Get the resource loader for the given resource type.""" if not resource_type: return None @@ -18,8 +28,13 @@ def get_resesource_api( raise ValueError( f"No loader available for resource of type {resource_type.value}" ) + inst = self._resource_api_instance[resource_type] + if check_instance and cls and not isinstance(inst, cls): + raise ValueError( + f"Resource loader for {resource_type.value} is not an instance of {cls}" + ) + return inst - return self._resource_api_instance[resource_type] - - def register_resesource_api(self, api_instance: ResourceClient): + def register_resource_api(self, api_instance: ResourceClient): + """Register the resource API instance.""" self._resource_api_instance[api_instance.type] = api_instance diff --git a/dbgpt/agent/resource/resource_plugin_api.py b/dbgpt/agent/resource/resource_plugin_api.py index 1f5b4458d..e373fb156 100644 --- a/dbgpt/agent/resource/resource_plugin_api.py +++ b/dbgpt/agent/resource/resource_plugin_api.py @@ -1,58 +1,72 @@ +"""Resource plugin client API.""" import logging import os -from typing import Optional - -from dbgpt.agent.plugin.commands.command_manage import execute_command -from dbgpt.agent.plugin.generator import PluginPromptGenerator -from dbgpt.agent.plugin.plugins_util import scan_plugin_file, scan_plugins -from dbgpt.agent.resource.resource_api import AgentResource +from typing import List, Optional, Union, cast +from ..plugin.commands.command_manage import execute_command +from ..plugin.generator import PluginPromptGenerator +from ..plugin.plugins_util import scan_plugin_file, scan_plugins +from ..resource.resource_api import AgentResource from .resource_api import ResourceClient, ResourceType logger = logging.getLogger(__name__) class ResourcePluginClient(ResourceClient): + """Resource plugin client.""" + @property def type(self): + """Return the resource type.""" return ResourceType.Plugin def get_data_type(self, resource: AgentResource) -> str: + """Return the data type of the specified resource.""" return "Tools" async def get_data_introduce( self, resource: AgentResource, question: Optional[str] = None - ) -> str: - return await self.a_plugins_prompt(resource.value) + ) -> Union[str, List[str]]: + """Get the content introduction prompt of the specified resource.""" + return await self.plugins_prompt(resource.value) - async def a_load_plugin( + async def load_plugin( self, value: str, plugin_generator: Optional[PluginPromptGenerator] = None, ) -> PluginPromptGenerator: + """Load the plugin.""" raise NotImplementedError("The run method should be implemented in a subclass.") - async def a_plugins_prompt( + async def plugins_prompt( self, value: str, plugin_generator: Optional[PluginPromptGenerator] = None ) -> str: - plugin_generator = await self.a_load_plugin(value) + """Get the plugin commands prompt.""" + plugin_generator = await self.load_plugin(value) return plugin_generator.generate_commands_string() - async def a_execute_command( + async def execute_command( self, command_name: str, arguments: Optional[dict], plugin_generator: Optional[PluginPromptGenerator], ): + """Execute the command.""" if plugin_generator is None: raise ValueError("No plugin commands loaded into the executable！") return execute_command(command_name, arguments, plugin_generator) class PluginFileLoadClient(ResourcePluginClient): - async def a_load_plugin( + """File plugin load client. + + Load the plugin from the local file. + """ + + async def load_plugin( self, value: str, plugin_generator: Optional[PluginPromptGenerator] = None ) -> PluginPromptGenerator: + """Load the plugin.""" logger.info(f"PluginFileLoadClient load plugin:{value}") if plugin_generator is None: plugin_generator = PluginPromptGenerator() @@ -66,10 +80,11 @@ async def a_load_plugin( plugins.extend(scan_plugins(value)) else: raise ValueError( - f"The current mode cannot support plug-in loading with relative paths.{value}" + f"The current mode cannot support plug-in loading with relative " + f"paths: {value}" ) for plugin in plugins: if not plugin.can_handle_post_prompt(): continue plugin_generator = plugin.post_prompt(plugin_generator) - return plugin_generator + return cast(PluginPromptGenerator, plugin_generator) diff --git a/dbgpt/agent/util/__init__.py b/dbgpt/agent/util/__init__.py new file mode 100644 index 000000000..6e872b745 --- /dev/null +++ b/dbgpt/agent/util/__init__.py @@ -0,0 +1 @@ +"""Utils for the agent module.""" diff --git a/dbgpt/agent/util/cmp.py b/dbgpt/agent/util/cmp.py new file mode 100644 index 000000000..525dbaaed --- /dev/null +++ b/dbgpt/agent/util/cmp.py @@ -0,0 +1,30 @@ +"""Compare string utility functions.""" +import string + + +def cmp_string_equal( + a: str, + b: str, + ignore_case: bool = False, + ignore_punctuation: bool = False, + ignore_whitespace: bool = False, +) -> bool: + """Compare two strings are equal or not. + + Args: + a(str): The first string. + b(str): The second string. + ignore_case(bool): Ignore case or not. + ignore_punctuation(bool): Ignore punctuation or not. + ignore_whitespace(bool): Ignore whitespace or not. + """ + if ignore_case: + a = a.lower() + b = b.lower() + if ignore_punctuation: + a = a.translate(str.maketrans("", "", string.punctuation)) + b = b.translate(str.maketrans("", "", string.punctuation)) + if ignore_whitespace: + a = "".join(a.split()) + b = "".join(b.split()) + return a == b diff --git a/dbgpt/app/base.py b/dbgpt/app/base.py index bbdff3198..426f38af9 100644 --- a/dbgpt/app/base.py +++ b/dbgpt/app/base.py @@ -44,10 +44,7 @@ def server_init(param: "WebServerParameters", system_app: SystemApp): signal.signal(signal.SIGINT, signal_handler) # Loader plugins and commands - command_categories = [ - "dbgpt.agent.plugin.commands.built_in.audio_text", - "dbgpt.agent.plugin.commands.built_in.image_gen", - ] + command_categories = [] # exclude commands command_categories = [ x for x in command_categories if x not in cfg.disabled_command_categories diff --git a/dbgpt/app/scene/base.py b/dbgpt/app/scene/base.py index 7f18e410c..c36501662 100644 --- a/dbgpt/app/scene/base.py +++ b/dbgpt/app/scene/base.py @@ -70,7 +70,7 @@ class ChatScene(Enum): ChatFlow = Scene( code="chat_flow", name="Flow Chat", - describe="Have conversations with conversational Awel flow.", + describe="Have conversations with conversational AWEL flow.", param_types=["Flow Select"], ) diff --git a/dbgpt/client/schema.py b/dbgpt/client/schema.py index 7043dc427..d89033203 100644 --- a/dbgpt/client/schema.py +++ b/dbgpt/client/schema.py @@ -75,8 +75,8 @@ class ChatMode(Enum): CHAT_DATA = "chat_data" -class AwelTeamModel(BaseModel): - """Awel team model.""" +class AWELTeamModel(BaseModel): + """AWEL team model.""" dag_id: str = Field( ..., @@ -137,7 +137,7 @@ class AgentResourceType(Enum): TextFile = "text_file" ExcelFile = "excel_file" ImageFile = "image_file" - AwelFlow = "awel_flow" + AWELFlow = "awel_flow" class AgentResourceModel(BaseModel): @@ -206,7 +206,7 @@ class AppModel(BaseModel): app_describe: Optional[str] = Field(None, title="app describe") team_mode: Optional[str] = Field(None, title="team mode") language: Optional[str] = Field("en", title="language") - team_context: Optional[Union[str, AwelTeamModel]] = Field( + team_context: Optional[Union[str, AWELTeamModel]] = Field( None, title="team context" ) user_code: Optional[str] = Field(None, title="user code") diff --git a/dbgpt/core/awel/dag/dag_manager.py b/dbgpt/core/awel/dag/dag_manager.py index b9d0c1f59..83b9e8946 100644 --- a/dbgpt/core/awel/dag/dag_manager.py +++ b/dbgpt/core/awel/dag/dag_manager.py @@ -32,6 +32,7 @@ def __init__(self, system_app: SystemApp, dag_dirs: List[str]): self.dag_loader = LocalFileDAGLoader(dag_dirs) self.system_app = system_app self.dag_map: Dict[str, DAG] = {} + self.dag_alias_map: Dict[str, str] = {} self._trigger_manager: Optional["DefaultTriggerManager"] = None def init_app(self, system_app: SystemApp): @@ -58,12 +59,14 @@ def after_start(self): """Execute after the application starts.""" self.load_dags() - def register_dag(self, dag: DAG): + def register_dag(self, dag: DAG, alias_name: Optional[str] = None): """Register a DAG.""" dag_id = dag.dag_id if dag_id in self.dag_map: raise ValueError(f"Register DAG error, DAG ID {dag_id} has already exist") self.dag_map[dag_id] = dag + if alias_name: + self.dag_alias_map[alias_name] = dag_id if self._trigger_manager: for trigger in dag.trigger_nodes: @@ -77,7 +80,22 @@ def unregister_dag(self, dag_id: str): if dag_id not in self.dag_map: raise ValueError(f"Unregister DAG error, DAG ID {dag_id} does not exist") dag = self.dag_map[dag_id] + # Clear the alias map + for alias_name, _dag_id in self.dag_alias_map.items(): + if _dag_id == dag_id: + del self.dag_alias_map[alias_name] + if self._trigger_manager: for trigger in dag.trigger_nodes: self._trigger_manager.unregister_trigger(trigger, self.system_app) del self.dag_map[dag_id] + + def get_dag( + self, dag_id: Optional[str] = None, alias_name: Optional[str] = None + ) -> Optional[DAG]: + """Get a DAG by dag_id or alias_name.""" + if dag_id and dag_id in self.dag_map: + return self.dag_map[dag_id] + if alias_name in self.dag_alias_map: + return self.dag_map.get(self.dag_alias_map[alias_name]) + return None diff --git a/dbgpt/core/awel/flow/base.py b/dbgpt/core/awel/flow/base.py index c07c390e4..ac9145a14 100644 --- a/dbgpt/core/awel/flow/base.py +++ b/dbgpt/core/awel/flow/base.py @@ -734,6 +734,12 @@ def pre_fill(cls, values: Dict[str, Any]) -> Dict[str, Any]: values["id"] = values["flow_type"] + "_" + values["type_cls"] return values + def new_alias(self, alias: Optional[List[str]] = None) -> List[str]: + """Get the new alias id.""" + if not alias: + return [] + return [f"{self.flow_type}_{a}" for a in alias] + def register_resource( label: str, @@ -742,6 +748,7 @@ def register_resource( parameters: Optional[List[Parameter]] = None, description: Optional[str] = None, resource_type: ResourceType = ResourceType.INSTANCE, + alias: Optional[List[str]] = None, **kwargs, ): """Register the resource. @@ -755,6 +762,9 @@ def register_resource( description (Optional[str], optional): The description of the resource. Defaults to None. resource_type (ResourceType, optional): The type of the resource. + alias (Optional[List[str]], optional): The alias of the resource. Defaults to + None. For compatibility, we can use the alias to register the resource. + """ if resource_type == ResourceType.CLASS and parameters: raise ValueError("Class resource can't have parameters.") @@ -784,7 +794,8 @@ def decorator(cls): resource_type=resource_type, **kwargs, ) - _register_resource(cls, resource_metadata) + alias_ids = resource_metadata.new_alias(alias) + _register_resource(cls, resource_metadata, alias_ids) # Attach the metadata to the class cls._resource_metadata = resource_metadata return cls @@ -949,11 +960,19 @@ def __init__(self): self._registry: Dict[str, _RegistryItem] = {} def register_flow( - self, view_cls: Type, metadata: Union[ViewMetadata, ResourceMetadata] + self, + view_cls: Type, + metadata: Union[ViewMetadata, ResourceMetadata], + alias_ids: Optional[List[str]] = None, ): """Register the operator.""" key = metadata.id self._registry[key] = _RegistryItem(key=key, cls=view_cls, metadata=metadata) + if alias_ids: + for alias_id in alias_ids: + self._registry[alias_id] = _RegistryItem( + key=alias_id, cls=view_cls, metadata=metadata + ) def get_registry_item(self, key: str) -> Optional[_RegistryItem]: """Get the registry item by the key.""" @@ -998,6 +1017,10 @@ def _get_resource_class(type_key: str) -> _RegistryItem: return item -def _register_resource(cls: Type, resource_metadata: ResourceMetadata): +def _register_resource( + cls: Type, + resource_metadata: ResourceMetadata, + alias_ids: Optional[List[str]] = None, +): """Register the operator.""" - _OPERATOR_REGISTRY.register_flow(cls, resource_metadata) + _OPERATOR_REGISTRY.register_flow(cls, resource_metadata, alias_ids) diff --git a/dbgpt/serve/agent/agents/controller.py b/dbgpt/serve/agent/agents/controller.py index bad2f4cb0..928b06bea 100644 --- a/dbgpt/serve/agent/agents/controller.py +++ b/dbgpt/serve/agent/agents/controller.py @@ -3,20 +3,21 @@ import logging import uuid from abc import ABC -from collections import defaultdict from typing import Any, Dict, List, Optional, Type from fastapi import APIRouter, Body from fastapi.responses import StreamingResponse from dbgpt._private.config import Config -from dbgpt.agent.agents.agent_new import Agent, AgentContext -from dbgpt.agent.agents.agents_manage import agent_manage -from dbgpt.agent.agents.base_agent_new import ConversableAgent -from dbgpt.agent.agents.llm.llm import LLMConfig, LLMStrategyType -from dbgpt.agent.agents.user_proxy_agent import UserProxyAgent -from dbgpt.agent.common.schema import Status +from dbgpt.agent.core.agent import Agent, AgentContext +from dbgpt.agent.core.agent_manage import agent_manage +from dbgpt.agent.core.base_agent import ConversableAgent +from dbgpt.agent.core.llm.llm import LLMConfig, LLMStrategyType +from dbgpt.agent.core.schema import Status +from dbgpt.agent.core.user_proxy_agent import UserProxyAgent from dbgpt.agent.memory.gpts_memory import GptsMemory +from dbgpt.agent.plan.awel.team_awel_layout import DefaultAWELLayoutManager +from dbgpt.agent.plan.team_auto_plan import AutoPlanChatManager from dbgpt.agent.resource.resource_loader import ResourceLoader from dbgpt.app.openapi.api_view_model import Result from dbgpt.app.scene.base import ChatScene @@ -25,18 +26,16 @@ from dbgpt.model.cluster import WorkerManagerFactory from dbgpt.model.cluster.client import DefaultLLMClient from dbgpt.serve.agent.model import PagenationFilter, PluginHubFilter -from dbgpt.serve.agent.team.plan.team_auto_plan import AutoPlanChatManager from dbgpt.serve.conversation.serve import Serve as ConversationServe from dbgpt.util.json_utils import serialize from ..db.gpts_app import GptsApp, GptsAppDao, GptsAppQuery from ..db.gpts_conversations_db import GptsConversationsDao, GptsConversationsEntity -from ..db.gpts_manage_db import GptsInstanceDao, GptsInstanceEntity +from ..db.gpts_manage_db import GptsInstanceEntity from ..resource_loader.datasource_load_client import DatasourceLoadClient from ..resource_loader.knowledge_space_load_client import KnowledgeSpaceLoadClient from ..resource_loader.plugin_hub_load_client import PluginHubLoadClient from ..team.base import TeamMode -from ..team.layout.team_awel_layout_new import AwelLayoutChatNewManager from .db_gpts_memory import MetaDbGptsMessageMemory, MetaDbGptsPlansMemory CFG = Config() @@ -204,11 +203,11 @@ async def agent_team_chat_new( # Prepare resource loader resource_loader = ResourceLoader() plugin_hub_loader = PluginHubLoadClient() - resource_loader.register_resesource_api(plugin_hub_loader) + resource_loader.register_resource_api(plugin_hub_loader) datasource_loader = DatasourceLoadClient() - resource_loader.register_resesource_api(datasource_loader) + resource_loader.register_resource_api(datasource_loader) knowledge_space_loader = KnowledgeSpaceLoadClient() - resource_loader.register_resesource_api(knowledge_space_loader) + resource_loader.register_resource_api(knowledge_space_loader) context: AgentContext = AgentContext( conv_id=conv_uid, gpts_app_name=gpts_app.app_name, @@ -248,7 +247,7 @@ async def agent_team_chat_new( if TeamMode.AUTO_PLAN == team_mode: manager = AutoPlanChatManager() elif TeamMode.AWEL_LAYOUT == team_mode: - manager = AwelLayoutChatNewManager(dag=gpts_app.team_context) + manager = DefaultAWELLayoutManager(dag=gpts_app.team_context) else: raise ValueError(f"Unknown Agent Team Mode!{team_mode}") manager = ( @@ -273,7 +272,7 @@ async def agent_team_chat_new( self.gpts_conversations.update(conv_uid, Status.RUNNING.value) try: - await user_proxy.a_initiate_chat( + await user_proxy.initiate_chat( recipient=recipient, message=user_query, ) @@ -301,7 +300,7 @@ async def chat_messages( ] else False ) - message = await self.memory.one_chat_competions_v2(conv_id) + message = await self.memory.one_chat_completions_v2(conv_id) yield message if is_complete: @@ -321,7 +320,7 @@ async def stable_message( else False ) if is_complete: - return await self.memory.one_chat_competions_v2(conv_id) + return await self.memory.one_chat_completions_v2(conv_id) else: pass # raise ValueError( diff --git a/dbgpt/serve/agent/agents/db_gpts_memory.py b/dbgpt/serve/agent/agents/db_gpts_memory.py index 14f7f28f1..58af325ce 100644 --- a/dbgpt/serve/agent/agents/db_gpts_memory.py +++ b/dbgpt/serve/agent/agents/db_gpts_memory.py @@ -56,9 +56,9 @@ def update_task( task_num: int, state: str, retry_times: int, - agent: str = None, - model: str = None, - result: str = None, + agent: Optional[str] = None, + model: Optional[str] = None, + result: Optional[str] = None, ): self.gpts_plan.update_task( conv_id=conv_id, @@ -95,7 +95,7 @@ def get_between_agents( agent1: str, agent2: str, current_goal: Optional[str] = None, - ) -> Optional[List[GptsMessage]]: + ) -> List[GptsMessage]: db_results = self.gpts_message.get_between_agents( conv_id, agent1, agent2, current_goal ) @@ -105,7 +105,7 @@ def get_between_agents( results.append(GptsMessage.from_dict(item.__dict__)) return results - def get_by_conv_id(self, conv_id: str) -> Optional[List[GptsMessage]]: + def get_by_conv_id(self, conv_id: str) -> List[GptsMessage]: db_results = self.gpts_message.get_by_conv_id(conv_id) results = [] diff --git a/dbgpt/serve/agent/agents/dbgpts.py b/dbgpt/serve/agent/agents/dbgpts.py index bedc30c6e..36e187477 100644 --- a/dbgpt/serve/agent/agents/dbgpts.py +++ b/dbgpt/serve/agent/agents/dbgpts.py @@ -2,7 +2,6 @@ import dataclasses from dataclasses import dataclass -from enum import Enum from typing import Any, Dict, List, Optional diff --git a/dbgpt/serve/agent/app/controller.py b/dbgpt/serve/agent/app/controller.py index eae45f973..b4848b4d5 100644 --- a/dbgpt/serve/agent/app/controller.py +++ b/dbgpt/serve/agent/app/controller.py @@ -3,8 +3,8 @@ from fastapi import APIRouter from dbgpt._private.config import Config -from dbgpt.agent.agents.agents_manage import agent_manage -from dbgpt.agent.agents.llm.llm import LLMStrategyType +from dbgpt.agent.core.agent_manage import agent_manage +from dbgpt.agent.core.llm.llm import LLMStrategyType from dbgpt.agent.resource.resource_api import ResourceType from dbgpt.app.knowledge.api import knowledge_space_service from dbgpt.app.knowledge.request.request import KnowledgeSpaceRequest diff --git a/dbgpt/serve/agent/app/gpts_server.py b/dbgpt/serve/agent/app/gpts_server.py index f77f345c0..755c83595 100644 --- a/dbgpt/serve/agent/app/gpts_server.py +++ b/dbgpt/serve/agent/app/gpts_server.py @@ -1,7 +1,7 @@ from dbgpt._private.config import Config from dbgpt.component import ComponentType from dbgpt.model.cluster import BaseModelController -from dbgpt.serve.agent.db.gpts_app import GptsApp, GptsAppCollectionDao, GptsAppDao +from dbgpt.serve.agent.db.gpts_app import GptsAppCollectionDao, GptsAppDao CFG = Config() diff --git a/dbgpt/serve/agent/db/gpts_app.py b/dbgpt/serve/agent/db/gpts_app.py index fd0b27081..7396f4e20 100644 --- a/dbgpt/serve/agent/db/gpts_app.py +++ b/dbgpt/serve/agent/db/gpts_app.py @@ -5,11 +5,11 @@ from itertools import groupby from typing import Any, Dict, List, Optional, Union -from pydantic import BaseModel from sqlalchemy import Column, DateTime, Integer, String, Text, UniqueConstraint +from dbgpt._private.pydantic import BaseModel +from dbgpt.agent.plan.awel.team_awel_layout import AWELTeamContext from dbgpt.agent.resource.resource_api import AgentResource -from dbgpt.serve.agent.model import AwelTeamContext from dbgpt.serve.agent.team.base import TeamMode from dbgpt.storage.metadata import BaseDao, Model @@ -60,7 +60,7 @@ def from_dict(cls, d: Dict[str, Any], parse_llm_strategy: bool = False): app_name=d["app_name"], agent_name=d["agent_name"], node_id=d["node_id"], - resources=AgentResource.from_josn_list_str(d.get("resources", None)), + resources=AgentResource.from_json_list_str(d.get("resources", None)), prompt_template=d.get("prompt_template", None), llm_strategy=d.get("llm_strategy", None), llm_strategy_value=llm_strategy_value, @@ -70,7 +70,7 @@ def from_dict(cls, d: Dict[str, Any], parse_llm_strategy: bool = False): @classmethod def from_entity(cls, entity): - resources = AgentResource.from_josn_list_str(entity.resources) + resources = AgentResource.from_json_list_str(entity.resources) return cls( app_code=entity.app_code, app_name=entity.app_name, @@ -91,7 +91,7 @@ class GptsApp(BaseModel): app_describe: Optional[str] = None team_mode: Optional[str] = None language: Optional[str] = None - team_context: Optional[Union[str, AwelTeamContext]] = None + team_context: Optional[Union[str, AWELTeamContext]] = None user_code: Optional[str] = None sys_code: Optional[str] = None is_collected: Optional[str] = None @@ -560,26 +560,26 @@ def edit(self, gpts_app: GptsApp): return True -def _parse_team_context(team_context: Optional[Union[str, AwelTeamContext]] = None): +def _parse_team_context(team_context: Optional[Union[str, AWELTeamContext]] = None): """ parse team_context to str """ - if isinstance(team_context, AwelTeamContext): + if isinstance(team_context, AWELTeamContext): return team_context.json() return team_context def _load_team_context( team_mode: str = None, team_context: str = None -) -> Union[str, AwelTeamContext]: +) -> Union[str, AWELTeamContext]: """ - load team_context to str or AwelTeamContext + load team_context to str or AWELTeamContext """ if team_mode is not None: match team_mode: case TeamMode.AWEL_LAYOUT.value: try: - awel_team_ctx = AwelTeamContext(**json.loads(team_context)) + awel_team_ctx = AWELTeamContext(**json.loads(team_context)) return awel_team_ctx except Exception as ex: logger.info( diff --git a/dbgpt/serve/agent/db/gpts_messages_db.py b/dbgpt/serve/agent/db/gpts_messages_db.py index ab033dc3a..b165e746e 100644 --- a/dbgpt/serve/agent/db/gpts_messages_db.py +++ b/dbgpt/serve/agent/db/gpts_messages_db.py @@ -1,18 +1,7 @@ from datetime import datetime from typing import List, Optional -from sqlalchemy import ( - Column, - DateTime, - Index, - Integer, - String, - Text, - and_, - desc, - func, - or_, -) +from sqlalchemy import Column, DateTime, Index, Integer, String, Text, and_, desc, or_ from dbgpt.storage.metadata import BaseDao, Model diff --git a/dbgpt/serve/agent/db/gpts_plans_db.py b/dbgpt/serve/agent/db/gpts_plans_db.py index 57fe94197..7183ef16e 100644 --- a/dbgpt/serve/agent/db/gpts_plans_db.py +++ b/dbgpt/serve/agent/db/gpts_plans_db.py @@ -2,7 +2,7 @@ from sqlalchemy import Column, DateTime, Integer, String, Text, UniqueConstraint -from dbgpt.agent.common.schema import Status +from dbgpt.agent.core.schema import Status from dbgpt.storage.metadata import BaseDao, Model diff --git a/dbgpt/serve/agent/hub/plugin_hub.py b/dbgpt/serve/agent/hub/plugin_hub.py index b7b7f2a42..2b02fb91f 100644 --- a/dbgpt/serve/agent/hub/plugin_hub.py +++ b/dbgpt/serve/agent/hub/plugin_hub.py @@ -8,7 +8,7 @@ from fastapi import UploadFile -from dbgpt.agent.common.schema import PluginStorageType +from dbgpt.agent.core.schema import PluginStorageType from dbgpt.agent.plugin.plugins_util import scan_plugins, update_from_git from dbgpt.configs.model_config import PLUGINS_DIR diff --git a/dbgpt/serve/agent/model.py b/dbgpt/serve/agent/model.py index fcc1a9f06..6984f835f 100644 --- a/dbgpt/serve/agent/model.py +++ b/dbgpt/serve/agent/model.py @@ -1,5 +1,5 @@ from dataclasses import dataclass -from typing import Any, Dict, Generic, List, Optional, TypedDict, TypeVar +from typing import Generic, List, Optional, TypeVar from dbgpt._private.pydantic import BaseModel, Field @@ -32,59 +32,6 @@ def to_dic(self): } -class AwelTeamContext(BaseModel): - dag_id: str = Field( - ..., - description="The unique id of dag", - examples=["flow_dag_testflow_66d8e9d6-f32e-4540-a5bd-ea0648145d0e"], - ) - uid: str = Field( - default=None, - description="The unique id of flow", - examples=["66d8e9d6-f32e-4540-a5bd-ea0648145d0e"], - ) - name: Optional[str] = Field( - default=None, - description="The name of dag", - ) - label: Optional[str] = Field( - default=None, - description="The label of dag", - ) - version: Optional[str] = Field( - default=None, - description="The version of dag", - ) - description: Optional[str] = Field( - default=None, - description="The description of dag", - ) - editable: bool = Field( - default=False, - description="is the dag is editable", - examples=[True, False], - ) - state: Optional[str] = Field( - default=None, - description="The state of dag", - ) - user_name: Optional[str] = Field( - default=None, - description="The owner of current dag", - ) - sys_code: Optional[str] = Field( - default=None, - description="The system code of current dag", - ) - flow_category: Optional[str] = Field( - default="common", - description="The flow category of current dag", - ) - - def to_dict(self): - return self.dict() - - @dataclass class PluginHubFilter(BaseModel): name: str diff --git a/dbgpt/serve/agent/resource_loader/datasource_load_client.py b/dbgpt/serve/agent/resource_loader/datasource_load_client.py index a32210ab0..1a5dafa25 100644 --- a/dbgpt/serve/agent/resource_loader/datasource_load_client.py +++ b/dbgpt/serve/agent/resource_loader/datasource_load_client.py @@ -1,12 +1,12 @@ import logging -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import List, Optional, Union from dbgpt._private.config import Config -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType +from dbgpt.agent.resource.resource_api import AgentResource from dbgpt.agent.resource.resource_db_api import ResourceDbClient from dbgpt.component import ComponentType from dbgpt.util.executor_utils import ExecutorFactory, blocking_func_to_async -from dbgpt.util.tracer import root_tracer, trace +from dbgpt.util.tracer import root_tracer CFG = Config() @@ -25,7 +25,9 @@ def get_data_type(self, resource: AgentResource) -> str: conn = CFG.local_db_manager.get_connector(resource.value) return conn.db_type - async def a_get_schema_link(self, db: str, question: Optional[str] = None) -> str: + async def get_schema_link( + self, db: str, question: Optional[str] = None + ) -> Union[str, List[str]]: try: from dbgpt.rag.summary.db_summary_client import DBSummaryClient except ImportError: @@ -51,14 +53,14 @@ async def a_get_schema_link(self, db: str, question: Optional[str] = None) -> st return table_infos - async def a_query_to_df(self, db: str, sql: str): + async def query_to_df(self, db: str, sql: str): conn = CFG.local_db_manager.get_connector(db) return conn.run_to_df(sql) - async def a_query(self, db: str, sql: str): + async def query(self, db: str, sql: str): conn = CFG.local_db_manager.get_connector(db) return conn.query_ex(sql) - async def a_run_sql(self, db: str, sql: str): + async def run_sql(self, db: str, sql: str): conn = CFG.local_db_manager.get_connector(db) return conn.run(sql) diff --git a/dbgpt/serve/agent/resource_loader/knowledge_space_load_client.py b/dbgpt/serve/agent/resource_loader/knowledge_space_load_client.py index b0f216147..7fa2ca7ae 100644 --- a/dbgpt/serve/agent/resource_loader/knowledge_space_load_client.py +++ b/dbgpt/serve/agent/resource_loader/knowledge_space_load_client.py @@ -1,5 +1,5 @@ import logging -from typing import Any, List, Optional +from typing import Any, List, Optional, Union from dbgpt._private.config import Config from dbgpt.agent.resource.resource_api import AgentResource @@ -13,10 +13,10 @@ class KnowledgeSpaceLoadClient(ResourceKnowledgeClient): - async def a_get_space_desc(self, space_name) -> str: + async def get_space_desc(self, space_name) -> str: pass - async def a_get_kn( + async def get_kn( self, space_name: str, question: Optional[str] = None ) -> List[Chunk]: kn_retriver = KnowledgeSpaceRetriever(space_name=space_name) @@ -28,10 +28,8 @@ async def add_kn( ): kn_retriver = KnowledgeSpaceRetriever(space_name=space_name) - pass - async def get_data_introduce( self, resource: AgentResource, question: Optional[str] = None - ) -> str: - docs = await self.a_get_kn(resource.value, question) + ) -> Union[str, List[str]]: + docs = await self.get_kn(resource.value, question) return "\n".join([doc.content for doc in docs]) diff --git a/dbgpt/serve/agent/resource_loader/plugin_hub_load_client.py b/dbgpt/serve/agent/resource_loader/plugin_hub_load_client.py index c2923c165..f98650896 100644 --- a/dbgpt/serve/agent/resource_loader/plugin_hub_load_client.py +++ b/dbgpt/serve/agent/resource_loader/plugin_hub_load_client.py @@ -1,14 +1,13 @@ import json import logging -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Optional from dbgpt._private.config import Config from dbgpt.agent.plugin.generator import PluginPromptGenerator from dbgpt.agent.resource.resource_plugin_api import ResourcePluginClient from dbgpt.component import ComponentType from dbgpt.serve.agent.hub.controller import ModulePlugin -from dbgpt.util.executor_utils import ExecutorFactory, blocking_func_to_async -from dbgpt.util.tracer import root_tracer, trace +from dbgpt.util.executor_utils import ExecutorFactory CFG = Config() @@ -23,7 +22,7 @@ def __init__(self): ComponentType.EXECUTOR_DEFAULT, ExecutorFactory ).create() - async def a_load_plugin( + async def load_plugin( self, value: str, plugin_generator: Optional[PluginPromptGenerator] = None ) -> PluginPromptGenerator: logger.info(f"PluginHubLoadClient load plugin:{value}") diff --git a/dbgpt/serve/agent/team/base.py b/dbgpt/serve/agent/team/base.py index 0f75a0396..fa0fc98e4 100644 --- a/dbgpt/serve/agent/team/base.py +++ b/dbgpt/serve/agent/team/base.py @@ -1,58 +1,7 @@ -import logging from enum import Enum -from typing import List, Union - -logger = logging.getLogger(__name__) class TeamMode(Enum): AUTO_PLAN = "auto_plan" AWEL_LAYOUT = "awel_layout" SINGLE_AGENT = "singe_agent" - - -def content_str(content: Union[str, List, None]) -> str: - """Converts `content` into a string format. - - This function processes content that may be a string, a list of mixed text and image URLs, or None, - and converts it into a string. Text is directly appended to the result string, while image URLs are - represented by a placeholder image token. If the content is None, an empty string is returned. - - Args: - - content (Union[str, List, None]): The content to be processed. Can be a string, a list of dictionaries - representing text and image URLs, or None. - - Returns: - str: A string representation of the input content. Image URLs are replaced with an image token. - - Note: - - The function expects each dictionary in the list to have a "type" key that is either "text" or "image_url". - For "text" type, the "text" key's value is appended to the result. For "image_url", an image token is appended. - - This function is useful for handling content that may include both text and image references, especially - in contexts where images need to be represented as placeholders. - """ - if content is None: - return "" - if isinstance(content, str): - return content - if not isinstance(content, list): - raise TypeError(f"content must be None, str, or list, but got {type(content)}") - - rst = "" - for item in content: - if not isinstance(item, dict): - raise TypeError( - "Wrong content format: every element should be dict if the content is a list." - ) - assert ( - "type" in item - ), "Wrong content format. Missing 'type' key in content's dict." - if item["type"] == "text": - rst += item["text"] - elif item["type"] == "image_url": - rst += "" - else: - raise ValueError( - f"Wrong content format: unknown type {item['type']} within the content" - ) - return rst diff --git a/dbgpt/serve/agent/team/layout/__init__.py b/dbgpt/serve/agent/team/layout/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/dbgpt/serve/agent/team/layout/team_awel_layout.py b/dbgpt/serve/agent/team/layout/team_awel_layout.py deleted file mode 100644 index e49127744..000000000 --- a/dbgpt/serve/agent/team/layout/team_awel_layout.py +++ /dev/null @@ -1,73 +0,0 @@ -import logging -from typing import Any, List, Optional - -from dbgpt.agent.actions.action import ActionOutput, T -from dbgpt.agent.agents.agent import Agent, AgentContext, AgentGenerateContext -from dbgpt.agent.agents.base_agent_new import ConversableAgent -from dbgpt.agent.agents.base_team import ManagerAgent -from dbgpt.core.awel import DAG -from dbgpt.serve.agent.team.layout.agent_operator import AgentOperator - -logger = logging.getLogger(__name__) - - -class AwelLayoutChatManager(ManagerAgent): - profile: str = "AwelManager" - goal: str = ( - "Promote and solve user problems according to the process arranged by Awel." - ) - constraints: List[str] = [] - desc: str = goal - - def __init__(self, **kwargs): - super().__init__(**kwargs) - - async def a_act( - self, - message: Optional[str], - sender: Optional[ConversableAgent] = None, - reviewer: Optional[ConversableAgent] = None, - ) -> Optional[ActionOutput]: - try: - # TODO Use programmed DAG - last_node: AgentOperator = None - with DAG( - f"layout_agents_{self.agent_context.gpts_app_name}_{self.agent_context.conv_id}" - ) as dag: - for agent in self.agents: - now_node = AgentOperator(agent=agent) - if not last_node: - last_node = now_node - else: - last_node >> now_node - last_node = now_node - - start_message_context: AgentGenerateContext = AgentGenerateContext( - message={ - "content": message, - "current_goal": message, - }, - sender=self, - reviewer=reviewer, - ) - final_generate_context: AgentGenerateContext = await last_node.call( - call_data=start_message_context - ) - last_message = final_generate_context.rely_messages[-1] - - last_agent = last_node.agent - await last_agent.a_send( - last_message, self, start_message_context.reviewer, False - ) - - return ActionOutput( - content=last_message.get("content", None), - view=last_message.get("view", None), - ) - except Exception as e: - logger.exception(f"DAG run failed!{str(e)}") - - return ActionOutput( - is_exe_success=False, - content=f"Failed to complete goal! {str(e)}", - ) diff --git a/dbgpt/serve/agent/team/layout/team_awel_layout_new.py b/dbgpt/serve/agent/team/layout/team_awel_layout_new.py deleted file mode 100644 index d8533af3f..000000000 --- a/dbgpt/serve/agent/team/layout/team_awel_layout_new.py +++ /dev/null @@ -1,96 +0,0 @@ -import json -import logging -from typing import Any, Dict, List, Optional - -from pydantic import BaseModel, Field, validator - -from dbgpt._private.config import Config -from dbgpt.agent.actions.action import ActionOutput, T -from dbgpt.agent.agents.agent_new import Agent, AgentContext, AgentGenerateContext -from dbgpt.agent.agents.base_agent_new import ConversableAgent -from dbgpt.agent.agents.base_team import ManagerAgent -from dbgpt.core.awel import DAG -from dbgpt.core.awel.dag.dag_manager import DAGManager -from dbgpt.serve.agent.model import AwelTeamContext -from dbgpt.serve.agent.team.layout.agent_operator import AwelAgentOperator -from dbgpt.serve.flow.api.endpoints import get_service as get_flow_service -from dbgpt.serve.flow.service.service import Service as FlowService - -logger = logging.getLogger(__name__) - -CFG = Config() - - -class AwelLayoutChatNewManager(ManagerAgent): - dag: AwelTeamContext = Field(...) - profile: str = "AwelNewManager" - goal: str = ( - "Promote and solve user problems according to the process arranged by Awel." - ) - constraints: List[str] = [] - desc: str = goal - - @validator("dag") - def check_dag(cls, value): - assert value is not None and value != "", "dag must not be empty" - return value - - async def _a_process_received_message(self, message: Optional[Dict], sender: Agent): - pass - - async def a_act( - self, - message: Optional[str], - sender: Optional[ConversableAgent] = None, - reviewer: Optional[ConversableAgent] = None, - ) -> Optional[ActionOutput]: - try: - flow_service: FlowService = get_flow_service() - flow = flow_service.get({"uid": self.dag.uid}) - _dag_manager = DAGManager.get_instance(CFG.SYSTEM_APP) - - dag_id = flow.dag_id - - agent_dag = _dag_manager.dag_map[dag_id] - if agent_dag is None: - raise ValueError( - f"The configured flow cannot be found![{self.dag.name}]" - ) - last_node: AwelAgentOperator = agent_dag.leaf_nodes[0] - - start_message_context: AgentGenerateContext = AgentGenerateContext( - message={ - "content": message, - "current_goal": message, - }, - sender=sender, - reviewer=reviewer, - memory=self.memory, - agent_context=self.agent_context, - resource_loader=self.resource_loader, - llm_client=self.llm_config.llm_client, - ) - final_generate_context: AgentGenerateContext = await last_node.call( - call_data=start_message_context - ) - last_message = final_generate_context.rely_messages[-1] - - last_agent = await last_node.get_agent(final_generate_context) - last_agent.consecutive_auto_reply_counter = ( - final_generate_context.round_index - ) - await last_agent.a_send( - last_message, sender, start_message_context.reviewer, False - ) - - return ActionOutput( - content=last_message.get("content", None), - view=last_message.get("view", None), - ) - except Exception as e: - logger.exception(f"DAG run failed!{str(e)}") - - return ActionOutput( - is_exe_success=False, - content=f"Failed to complete goal! {str(e)}", - ) diff --git a/dbgpt/serve/agent/team/plan/__init__.py b/dbgpt/serve/agent/team/plan/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/dbgpt/serve/agent/team/plan/planner_agent.py b/dbgpt/serve/agent/team/plan/planner_agent.py deleted file mode 100644 index 65b19eb1d..000000000 --- a/dbgpt/serve/agent/team/plan/planner_agent.py +++ /dev/null @@ -1,93 +0,0 @@ -from typing import Any, Callable, Dict, List, Optional, Tuple, Union - -from pydantic import Field - -from dbgpt._private.config import Config -from dbgpt.agent.agents.base_agent_new import ConversableAgent -from dbgpt.agent.resource.resource_api import AgentResource - -from .plan_action import PlanAction - -CFG = Config() - - -class PlannerAgent(ConversableAgent): - """Planner agent, realizing task goal planning decomposition through LLM""" - - agents: List[ConversableAgent] = Field(default_factory=list) - - profile: str = "Planner" - goal: str = "理解下面每个智能代理和他们的能力，使用给出的资源，通过协调智能代理来解决用户问题。 请发挥你LLM的知识和理解能力，理解用户问题的意图和目标，生成一个可以在没有用户帮助下，由智能代理协作完成目标的任务计划。" - expand_prompt: str = """可用智能代理: - {agents} - """ - constraints: List[str] = [ - "任务计划的每个步骤都应该是为了推进解决用户目标而存在，不要生成无意义的任务步骤，确保每个步骤内目标明确内容完整。", - "关注任务计划每个步骤的依赖关系和逻辑，被依赖步骤要考虑被依赖的数据，是否能基于当前目标得到，如果不能请在目标中提示要生成被依赖数据。", - "每个步骤都是一个独立可完成的目标，一定要确保逻辑和信息完整，不要出现类似:'Analyze the retrieved issues data'这样目标不明确，不知道具体要分析啥内容的步骤", - "请确保只使用上面提到的智能代理，并且可以只使用其中需要的部分，严格根据描述能力和限制分配给合适的步骤，每个智能代理都可以重复使用。", - "根据用户目标的实际需要使用提供的资源来协助生成计划步骤，不要使用不需要的资源。", - "每个步骤最好只使用一种资源完成一个子目标，如果当前目标可以分解为同类型的多个子任务，可以生成相互不依赖的并行任务。", - "数据资源可以被合适的智能代理加载使用，不用考虑数据资源的加载链接问题", - "尽量合并有顺序依赖的连续相同步骤,如果用户目标无拆分必要，可以生成内容为用户目标的单步任务。", - "仔细检查计划，确保计划完整的包含了用户问题所涉及的所有信息，并且最终能完成目标，确认每个步骤是否包含了需要用到的资源信息,如URL、资源名等. ", - ] - desc: str = "你是一个任务规划专家！可以协调智能代理，分配资源完成复杂的任务目标。" - - examples = """ - user:help me build a sales report summarizing our key metrics and trends - assisant:[ - {{ - "serial_number": "1", - "agent": "DataScientist", - "content": "Retrieve total sales, average sales, and number of transactions grouped by "product_category"'.", - "rely": "" - }}, - {{ - "serial_number": "2", - "agent": "DataScientist", - "content": "Retrieve monthly sales and transaction number trends.", - "rely": "" - }}, - {{ - "serial_number": "3", - "agent": "Reporter", - "content": "Integrate analytical data into the format required to build sales reports.", - "rely": "1,2" - }} - ] - """ - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._init_actions([PlanAction]) - - def _init_reply_message(self, recive_message): - reply_message = super()._init_reply_message(recive_message) - reply_message["context"] = { - "agents": "\n".join( - [f"- {item.profile}:{item.desc}" for item in self.agents] - ), - } - return reply_message - - @staticmethod - def get_unique_resources_codes(resource: AgentResource) -> str: - return resource.name + "_" + resource.type.value + "_" + resource.value - - def bind_agents(self, agents: List[ConversableAgent]) -> ConversableAgent: - self.agents = agents - unique_resources = set() - for agent in self.agents: - if agent.resources and len(agent.resources) > 0: - for resource in agent.resources: - if ( - self.get_unique_resources_codes(resource) - not in unique_resources - ): - unique_resources.add(self.get_unique_resources_codes(resource)) - self.resources.append(resource) - return self - - def prepare_act_param(self) -> Optional[Dict]: - return {"context": self.agent_context, "plans_memory": self.memory.plans_memory} diff --git a/dbgpt/serve/datasource/api/schemas.py b/dbgpt/serve/datasource/api/schemas.py index 907ed4f6e..f3d28554a 100644 --- a/dbgpt/serve/datasource/api/schemas.py +++ b/dbgpt/serve/datasource/api/schemas.py @@ -1,6 +1,6 @@ from typing import Optional -from pydantic import BaseModel, Field +from dbgpt._private.pydantic import BaseModel, Field from ..config import SERVE_APP_NAME_HUMP diff --git a/dbgpt/serve/flow/service/service.py b/dbgpt/serve/flow/service/service.py index 07d7f5191..ed2547f2f 100644 --- a/dbgpt/serve/flow/service/service.py +++ b/dbgpt/serve/flow/service/service.py @@ -157,7 +157,7 @@ def create_and_save_dag( try: if state == State.DEPLOYED: # Register the DAG - self.dag_manager.register_dag(dag) + self.dag_manager.register_dag(dag, request.uid) # Update state to RUNNING request.state = State.RUNNING request.error_message = "" @@ -199,7 +199,7 @@ def load_dag_from_db(self): entity.version == "0.1.0" and entity.state == State.INITIALIZING ): # Register the DAG - self.dag_manager.register_dag(dag) + self.dag_manager.register_dag(dag, entity.uid) # Update state to RUNNING entity.state = State.RUNNING entity.error_message = "" diff --git a/dbgpt/serve/rag/api/schemas.py b/dbgpt/serve/rag/api/schemas.py index 1b9a91357..c648080a3 100644 --- a/dbgpt/serve/rag/api/schemas.py +++ b/dbgpt/serve/rag/api/schemas.py @@ -1,8 +1,8 @@ from typing import Optional from fastapi import File, UploadFile -from pydantic import BaseModel, Field +from dbgpt._private.pydantic import BaseModel, Field from dbgpt.rag.chunk_manager import ChunkParameters from ..config import SERVE_APP_NAME_HUMP diff --git a/dbgpt/util/json_utils.py b/dbgpt/util/json_utils.py index 9e12e880a..10a040a12 100644 --- a/dbgpt/util/json_utils.py +++ b/dbgpt/util/json_utils.py @@ -1,13 +1,10 @@ """Utilities for the json_fixes package.""" import json import logging -import os.path import re -from dataclasses import asdict, dataclass, is_dataclass +from dataclasses import asdict, is_dataclass from datetime import date, datetime -from jsonschema import Draft7Validator - logger = logging.getLogger(__name__) LLM_DEFAULT_RESPONSE_FORMAT = "llm_response_format_1" diff --git a/dbgpt/util/string_utils.py b/dbgpt/util/string_utils.py index 8970252b3..e32eb1288 100644 --- a/dbgpt/util/string_utils.py +++ b/dbgpt/util/string_utils.py @@ -1,4 +1,5 @@ import re +from typing import Dict def is_all_chinese(text): @@ -34,7 +35,7 @@ def is_scientific_notation(string): return False -def extract_content(long_string, s1, s2, is_include: bool = False): +def extract_content(long_string, s1, s2, is_include: bool = False) -> Dict[int, str]: # extract text match_map = {} start_index = long_string.find(s1) diff --git a/dbgpt/vis/__init__.py b/dbgpt/vis/__init__.py index dab2559a8..6dcb78328 100644 --- a/dbgpt/vis/__init__.py +++ b/dbgpt/vis/__init__.py @@ -1,7 +1,19 @@ -from .client import vis_client -from .tags.vis_agent_message import VisAgentMessages -from .tags.vis_agent_plans import VisAgentPlans -from .tags.vis_chart import VisChart -from .tags.vis_code import VisCode -from .tags.vis_dashboard import VisDashboard -from .tags.vis_plugin import VisPlugin +"""GPT-Vis Module.""" + +from .client import vis_client # noqa: F401 +from .tags.vis_agent_message import VisAgentMessages # noqa: F401 +from .tags.vis_agent_plans import VisAgentPlans # noqa: F401 +from .tags.vis_chart import VisChart # noqa: F401 +from .tags.vis_code import VisCode # noqa: F401 +from .tags.vis_dashboard import VisDashboard # noqa: F401 +from .tags.vis_plugin import VisPlugin # noqa: F401 + +__ALL__ = [ + "vis_client", + "VisAgentMessages", + "VisAgentPlans", + "VisChart", + "VisCode", + "VisDashboard", + "VisPlugin", +] diff --git a/dbgpt/vis/base.py b/dbgpt/vis/base.py index 1c1851044..8286cb782 100644 --- a/dbgpt/vis/base.py +++ b/dbgpt/vis/base.py @@ -1,16 +1,20 @@ +"""Base class for vis protocol module.""" import json -from abc import ABC, abstractmethod -from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Type, Union +from typing import Any, Dict, Optional from dbgpt.util.json_utils import serialize class Vis: - def render_prompt(self): + """Vis protocol base class.""" + + def render_prompt(self) -> Optional[str]: + """Return the prompt for the vis protocol.""" return None - async def generate_param(self, **kwargs) -> Optional[str]: - """ + async def generate_param(self, **kwargs) -> Optional[Dict[str, Any]]: + """Generate the parameters required by the vis protocol. + Display corresponding content using vis protocol Args: **kwargs: @@ -21,12 +25,13 @@ async def generate_param(self, **kwargs) -> Optional[str]: return kwargs["content"] async def display(self, **kwargs) -> Optional[str]: - return f"```{self.vis_tag()}\n{json.dumps(await self.generate_param(**kwargs), default=serialize, ensure_ascii=False)}\n```" + """Display the content using the vis protocol.""" + content = json.dumps( + await self.generate_param(**kwargs), default=serialize, ensure_ascii=False + ) + return f"```{self.vis_tag()}\n{content}\n```" @classmethod def vis_tag(cls) -> str: - """ - Current vis protocol module tag name - Returns: - - """ + """Return current vis protocol module tag name.""" + return "" diff --git a/dbgpt/vis/client.py b/dbgpt/vis/client.py index 4f62fd452..315dd23e5 100644 --- a/dbgpt/vis/client.py +++ b/dbgpt/vis/client.py @@ -1,4 +1,5 @@ -from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Type, Union +"""Client for vis protocol.""" +from typing import Dict, Type from .base import Vis from .tags.vis_agent_message import VisAgentMessages @@ -10,18 +11,24 @@ class VisClient: + """Client for vis protocol.""" + def __init__(self): + """Client for vis protocol.""" self._vis_tag: Dict[str, Vis] = {} - def register(self, vis_cls: Vis): + def register(self, vis_cls: Type[Vis]): + """Register the vis protocol.""" self._vis_tag[vis_cls.vis_tag()] = vis_cls() def get(self, tag_name): + """Get the vis protocol by tag name.""" if tag_name not in self._vis_tag: raise ValueError(f"Vis protocol tags not yet supported！[{tag_name}]") return self._vis_tag[tag_name] def tag_names(self): + """Return the tag names of the vis protocol.""" self._vis_tag.keys() diff --git a/dbgpt/vis/tags/__init__.py b/dbgpt/vis/tags/__init__.py index e69de29bb..cb846887f 100644 --- a/dbgpt/vis/tags/__init__.py +++ b/dbgpt/vis/tags/__init__.py @@ -0,0 +1 @@ +"""Vis Tags.""" diff --git a/dbgpt/vis/tags/vis_agent_message.py b/dbgpt/vis/tags/vis_agent_message.py index 9a1952f8c..a76a306a6 100644 --- a/dbgpt/vis/tags/vis_agent_message.py +++ b/dbgpt/vis/tags/vis_agent_message.py @@ -1,9 +1,11 @@ -from typing import Optional - +"""Protocol module for agent messages vis tag.""" from ..base import Vis class VisAgentMessages(Vis): + """Agent Messages Vis Protocol.""" + @classmethod - def vis_tag(cls): + def vis_tag(cls) -> str: + """Return the tag name of the vis protocol module.""" return "agent-messages" diff --git a/dbgpt/vis/tags/vis_agent_plans.py b/dbgpt/vis/tags/vis_agent_plans.py index 254c17b90..f79464c65 100644 --- a/dbgpt/vis/tags/vis_agent_plans.py +++ b/dbgpt/vis/tags/vis_agent_plans.py @@ -1,9 +1,11 @@ -from typing import Optional - +"""Agent Plans Vis Protocol.""" from ..base import Vis class VisAgentPlans(Vis): + """Agent Plans Vis Protocol.""" + @classmethod - def vis_tag(cls): + def vis_tag(cls) -> str: + """Return the tag name of the vis protocol module.""" return "agent-plans" diff --git a/dbgpt/vis/tags/vis_chart.py b/dbgpt/vis/tags/vis_chart.py index 7e018733e..c7557e8b5 100644 --- a/dbgpt/vis/tags/vis_chart.py +++ b/dbgpt/vis/tags/vis_chart.py @@ -1,39 +1,48 @@ +"""Chart visualization protocol conversion class.""" import json -from typing import Optional - -import yaml +from typing import Any, Dict, Optional from ..base import Vis -def default_chart_type_promot() -> str: - """this function is moved from excel_analyze/chat.py,and used by subclass. - Returns: +def default_chart_type_prompt() -> str: + """Return prompt information for the default chart type. + + This function is moved from excel_analyze/chat.py,and used by subclass. + Returns: + str: prompt information for the default chart type. """ antv_charts = [ {"response_line_chart": "used to display comparative trend analysis data"}, { - "response_pie_chart": "suitable for scenarios such as proportion and distribution statistics" + "response_pie_chart": "suitable for scenarios such as proportion and " + "distribution statistics" }, { - "response_table": "suitable for display with many display columns or non-numeric columns" + "response_table": "suitable for display with many display columns or " + "non-numeric columns" }, - # {"response_data_text":" the default display method, suitable for single-line or simple content display"}, { - "response_scatter_plot": "Suitable for exploring relationships between variables, detecting outliers, etc." + "response_scatter_plot": "Suitable for exploring relationships between " + "variables, detecting outliers, etc." }, { - "response_bubble_chart": "Suitable for relationships between multiple variables, highlighting outliers or special situations, etc." + "response_bubble_chart": "Suitable for relationships between multiple " + "variables, highlighting outliers or special situations, etc." }, { - "response_donut_chart": "Suitable for hierarchical structure representation, category proportion display and highlighting key categories, etc." + "response_donut_chart": "Suitable for hierarchical structure representation" + ", category proportion display and highlighting key categories, etc." }, { - "response_area_chart": "Suitable for visualization of time series data, comparison of multiple groups of data, analysis of data change trends, etc." + "response_area_chart": "Suitable for visualization of time series data, " + "comparison of multiple groups of data, analysis of data change trends, " + "etc." }, { - "response_heatmap": "Suitable for visual analysis of time series data, large-scale data sets, distribution of classified data, etc." + "response_heatmap": "Suitable for visual analysis of time series data, " + "large-scale data sets, distribution of classified data, etc." }, ] return "\n".join( @@ -44,16 +53,21 @@ def default_chart_type_promot() -> str: class VisChart(Vis): - def render_prompt(self): - return default_chart_type_promot() + """Chart visualization protocol conversion class.""" + + def render_prompt(self) -> Optional[str]: + """Return the prompt for the vis protocol.""" + return default_chart_type_prompt() - async def generate_param(self, **kwargs) -> Optional[str]: + async def generate_param(self, **kwargs) -> Optional[Dict[str, Any]]: + """Generate the parameters required by the vis protocol.""" chart = kwargs.get("chart", None) data_df = kwargs.get("data_df", None) if not chart: raise ValueError( - f"Parameter information is missing and {self.vis_tag} protocol conversion cannot be performed." + f"Parameter information is missing and {self.vis_tag} protocol " + "conversion cannot be performed." ) sql = chart.get("sql", None) @@ -72,5 +86,6 @@ async def generate_param(self, **kwargs) -> Optional[str]: return param @classmethod - def vis_tag(cls): + def vis_tag(cls) -> str: + """Return the tag name of the vis protocol.""" return "vis-chart" diff --git a/dbgpt/vis/tags/vis_code.py b/dbgpt/vis/tags/vis_code.py index 6ab8b328e..af663a72b 100644 --- a/dbgpt/vis/tags/vis_code.py +++ b/dbgpt/vis/tags/vis_code.py @@ -1,9 +1,11 @@ -from typing import Optional - +"""Code visualization protocol.""" from ..base import Vis class VisCode(Vis): + """Protocol for visualizing code.""" + @classmethod - def vis_tag(cls): + def vis_tag(cls) -> str: + """Return the tag name of the vis protocol module.""" return "vis-code" diff --git a/dbgpt/vis/tags/vis_dashboard.py b/dbgpt/vis/tags/vis_dashboard.py index eab3ef068..c5c4ecec7 100644 --- a/dbgpt/vis/tags/vis_dashboard.py +++ b/dbgpt/vis/tags/vis_dashboard.py @@ -1,6 +1,7 @@ +"""Protocol for the dashboard vis.""" import json import logging -from typing import Optional +from typing import Any, Dict, Optional from ..base import Vis @@ -8,12 +9,16 @@ class VisDashboard(Vis): - async def generate_param(self, **kwargs) -> Optional[str]: + """Dashboard Vis Protocol.""" + + async def generate_param(self, **kwargs) -> Optional[Dict[str, Any]]: + """Generate the parameters required by the vis protocol.""" charts = kwargs.get("charts", None) title = kwargs.get("title", None) if not charts: raise ValueError( - f"Parameter information is missing and {self.vis_tag} protocol conversion cannot be performed." + f"Parameter information is missing and {self.vis_tag} protocol " + "conversion cannot be performed." ) chart_items = [] @@ -50,5 +55,6 @@ async def generate_param(self, **kwargs) -> Optional[str]: return dashboard_param @classmethod - def vis_tag(cls): + def vis_tag(cls) -> str: + """Return the tag name of the vis protocol.""" return "vis-dashboard" diff --git a/dbgpt/vis/tags/vis_gpts_execution.py b/dbgpt/vis/tags/vis_gpts_execution.py index 2e6711afb..f85f0e3d0 100644 --- a/dbgpt/vis/tags/vis_gpts_execution.py +++ b/dbgpt/vis/tags/vis_gpts_execution.py @@ -1,9 +1,12 @@ -from typing import Optional +"""Agent Plans Vis Protocol.""" from ..base import Vis class VisDbgptsFlow(Vis): + """DBGPts Flow Vis Protocol.""" + @classmethod - def vis_tag(cls): + def vis_tag(cls) -> str: + """Return the tag name of the vis protocol module.""" return "dbgpts-flow" diff --git a/dbgpt/vis/tags/vis_gpts_result.py b/dbgpt/vis/tags/vis_gpts_result.py index af0bd901d..ae7581d89 100644 --- a/dbgpt/vis/tags/vis_gpts_result.py +++ b/dbgpt/vis/tags/vis_gpts_result.py @@ -1,11 +1,12 @@ -from typing import Optional - -from webdriver_manager.chrome import ChromeDriverManager +"""Visualize the result of the DBGPts flow.""" from ..base import Vis class VisDbgptsFlowResult(Vis): + """Protocol for visualizing the result of the DBGPts flow.""" + @classmethod - def vis_tag(cls): + def vis_tag(cls) -> str: + """Return the tag name of the vis protocol module.""" return "dbgpts-result" diff --git a/dbgpt/vis/tags/vis_plugin.py b/dbgpt/vis/tags/vis_plugin.py index d9e24cad9..c09b03abf 100644 --- a/dbgpt/vis/tags/vis_plugin.py +++ b/dbgpt/vis/tags/vis_plugin.py @@ -1,9 +1,11 @@ -from typing import Optional - +"""Visualize plugins.""" from ..base import Vis class VisPlugin(Vis): + """Protocol for visualizing plugins.""" + @classmethod - def vis_tag(cls): + def vis_tag(cls) -> str: + """Return the tag name of the vis protocol module.""" return "vis-plugin" diff --git a/examples/agents/__init__.py b/examples/agents/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/agents/auto_plan_agent_dialogue_example.py b/examples/agents/auto_plan_agent_dialogue_example.py index ed460b0c2..10de22111 100644 --- a/examples/agents/auto_plan_agent_dialogue_example.py +++ b/examples/agents/auto_plan_agent_dialogue_example.py @@ -16,27 +16,23 @@ import asyncio -import os -from dbgpt.agent.agents.agent import AgentContext -from dbgpt.agent.agents.expand.code_assistant_agent import CodeAssistantAgent -from dbgpt.agent.agents.llm.llm import LLMConfig -from dbgpt.agent.agents.user_proxy_agent import UserProxyAgent -from dbgpt.agent.memory.gpts_memory import GptsMemory -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType -from dbgpt.agent.resource.resource_loader import ResourceLoader -from dbgpt.agent.resource.resource_plugin_api import PluginFileLoadClient -from dbgpt.core.interface.llm import ModelMetadata -from dbgpt.serve.agent.team.plan.team_auto_plan import AutoPlanChatManager +from dbgpt.agent import ( + AgentContext, + GptsMemory, + LLMConfig, + ResourceLoader, + UserProxyAgent, +) +from dbgpt.agent.expand.code_assistant_agent import CodeAssistantAgent +from dbgpt.agent.plan import AutoPlanChatManager async def main(): - from dbgpt.model import OpenAILLMClient + from dbgpt.model.proxy import OpenAILLMClient - llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") - context: AgentContext = AgentContext( - conv_id="test456", team_mode=Team, gpts_app_name="代码分析助手" - ) + llm_client = OpenAILLMClient(model_alias="gpt-4") + context: AgentContext = AgentContext(conv_id="test456", gpts_app_name="代码分析助手") default_memory = GptsMemory() @@ -62,7 +58,7 @@ async def main(): user_proxy = await UserProxyAgent().bind(context).bind(default_memory).build() - await user_proxy.a_initiate_chat( + await user_proxy.initiate_chat( recipient=manager, reviewer=user_proxy, message="Obtain simple information about issues in the repository 'eosphoros-ai/DB-GPT' in the past three days and analyze the data. Create a Markdown table grouped by day and status.", @@ -70,7 +66,7 @@ async def main(): # message="find papers on LLM applications from arxiv in the last month, create a markdown table of different domains.", ) - print(await default_memory.one_chat_competions("test456")) + print(await default_memory.one_chat_completions("test456")) if __name__ == "__main__": diff --git a/examples/agents/awel_layout_agents_chat_examples.py b/examples/agents/awel_layout_agents_chat_examples.py index 0ca440a8b..1496d047a 100644 --- a/examples/agents/awel_layout_agents_chat_examples.py +++ b/examples/agents/awel_layout_agents_chat_examples.py @@ -11,31 +11,34 @@ run example. ..code-block:: shell - python examples/agents/auto_plan_agent_dialogue_example.py + python examples/agents/awel_layout_agents_chat_examples.py """ import asyncio import os -from dbgpt.agent.agents.agent import AgentContext -from dbgpt.agent.agents.expand.plugin_assistant_agent import PluginAssistantAgent -from dbgpt.agent.agents.expand.summary_assistant_agent import SummaryAssistantAgent -from dbgpt.agent.agents.llm.llm import LLMConfig -from dbgpt.agent.agents.user_proxy_agent import UserProxyAgent -from dbgpt.agent.memory.gpts_memory import GptsMemory -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType -from dbgpt.agent.resource.resource_loader import ResourceLoader -from dbgpt.agent.resource.resource_plugin_api import PluginFileLoadClient -from dbgpt.core.interface.llm import ModelMetadata -from dbgpt.serve.agent.team.layout.team_awel_layout import AwelLayoutChatManager +from dbgpt.agent import ( + AgentContext, + AgentResource, + GptsMemory, + LLMConfig, + ResourceLoader, + ResourceType, + UserProxyAgent, +) +from dbgpt.agent.expand.plugin_assistant_agent import PluginAssistantAgent +from dbgpt.agent.expand.summary_assistant_agent import SummaryAssistantAgent +from dbgpt.agent.plan import WrappedAWELLayoutManager +from dbgpt.agent.resource import PluginFileLoadClient +from dbgpt.configs.model_config import ROOT_PATH current_dir = os.getcwd() parent_dir = os.path.dirname(current_dir) -test_plugin_dir = os.path.join(parent_dir, "test_files/plugins") +test_plugin_dir = os.path.join(ROOT_PATH, "examples/test_files/plugins") async def main(): - from dbgpt.model import OpenAILLMClient + from dbgpt.model.proxy import OpenAILLMClient llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="test456", gpts_app_name="信息析助手") @@ -44,7 +47,7 @@ async def main(): resource_loader = ResourceLoader() plugin_file_loader = PluginFileLoadClient() - resource_loader.register_resesource_api(plugin_file_loader) + resource_loader.register_resource_api(plugin_file_loader) plugin_resource = AgentResource( type=ResourceType.Plugin, @@ -52,7 +55,7 @@ async def main(): value=test_plugin_dir, ) - tool_enginer = ( + tool_engineer = ( await PluginAssistantAgent() .bind(context) .bind(LLMConfig(llm_client=llm_client)) @@ -70,17 +73,17 @@ async def main(): ) manager = ( - await AwelLayoutChatManager() + await WrappedAWELLayoutManager() .bind(context) .bind(default_memory) .bind(LLMConfig(llm_client=llm_client)) .build() ) - manager.hire([tool_enginer, summarizer]) + manager.hire([tool_engineer, summarizer]) user_proxy = await UserProxyAgent().bind(context).bind(default_memory).build() - await user_proxy.a_initiate_chat( + await user_proxy.initiate_chat( recipient=manager, reviewer=user_proxy, message="查询成都今天天气", @@ -89,7 +92,7 @@ async def main(): # message="find papers on LLM applications from arxiv in the last month, create a markdown table of different domains.", ) - print(await default_memory.one_chat_competions("test456")) + print(await default_memory.one_chat_completions("test456")) if __name__ == "__main__": diff --git a/examples/agents/plugin_agent_dialogue_example.py b/examples/agents/plugin_agent_dialogue_example.py index f8881913f..4138341ae 100644 --- a/examples/agents/plugin_agent_dialogue_example.py +++ b/examples/agents/plugin_agent_dialogue_example.py @@ -11,22 +11,23 @@ run example. ..code-block:: shell - python examples/agents/single_agent_dialogue_example.py + python examples/agents/plugin_agent_dialogue_example.py """ import asyncio import os -from dbgpt.agent.actions.plugin_action import PluginAction -from dbgpt.agent.agents.agent import AgentContext -from dbgpt.agent.agents.expand.plugin_assistant_agent import PluginAssistantAgent -from dbgpt.agent.agents.llm.llm import LLMConfig -from dbgpt.agent.agents.user_proxy_agent import UserProxyAgent -from dbgpt.agent.memory.gpts_memory import GptsMemory -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType -from dbgpt.agent.resource.resource_loader import ResourceLoader -from dbgpt.agent.resource.resource_plugin_api import PluginFileLoadClient -from dbgpt.core.interface.llm import ModelMetadata +from dbgpt.agent import ( + AgentContext, + AgentResource, + GptsMemory, + LLMConfig, + ResourceLoader, + ResourceType, + UserProxyAgent, +) +from dbgpt.agent.expand.plugin_assistant_agent import PluginAssistantAgent +from dbgpt.agent.resource import PluginFileLoadClient current_dir = os.getcwd() parent_dir = os.path.dirname(current_dir) @@ -34,7 +35,7 @@ async def main(): - from dbgpt.model import OpenAILLMClient + from dbgpt.model.proxy import OpenAILLMClient llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="test456") @@ -49,11 +50,11 @@ async def main(): resource_loader = ResourceLoader() plugin_file_loader = PluginFileLoadClient() - resource_loader.register_resesource_api(plugin_file_loader) + resource_loader.register_resource_api(plugin_file_loader) user_proxy = await UserProxyAgent().bind(default_memory).bind(context).build() - tool_enginer = ( + tool_engineer = ( await PluginAssistantAgent() .bind(context) .bind(LLMConfig(llm_client=llm_client)) @@ -63,14 +64,14 @@ async def main(): .build() ) - await user_proxy.a_initiate_chat( - recipient=tool_enginer, + await user_proxy.initiate_chat( + recipient=tool_engineer, reviewer=user_proxy, message="查询今天成都的天气", ) ## dbgpt-vis message infos - print(await default_memory.one_chat_competions("test456")) + print(await default_memory.one_chat_completions("test456")) if __name__ == "__main__": diff --git a/examples/agents/retrieve_summary_agent_dialogue_example.py b/examples/agents/retrieve_summary_agent_dialogue_example.py index f5be9e92b..5e9f706dd 100644 --- a/examples/agents/retrieve_summary_agent_dialogue_example.py +++ b/examples/agents/retrieve_summary_agent_dialogue_example.py @@ -17,50 +17,50 @@ import asyncio import os -from dbgpt.agent.agents.agent import AgentContext -from dbgpt.agent.agents.expand.retrieve_summary_assistant_agent import ( +from dbgpt.agent import AgentContext, GptsMemory, LLMConfig, UserProxyAgent +from dbgpt.agent.expand.retrieve_summary_assistant_agent import ( RetrieveSummaryAssistantAgent, ) -from dbgpt.agent.agents.user_proxy_agent import UserProxyAgent -from dbgpt.agent.memory.gpts_memory import GptsMemory -from dbgpt.core.interface.llm import ModelMetadata +from dbgpt.configs.model_config import ROOT_PATH -def summary_example_with_success(): +async def summary_example_with_success(): from dbgpt.model.proxy import OpenAILLMClient - llm_client = OpenAILLMClient() - context: AgentContext = AgentContext( - conv_id="retrieve_summarize", llm_provider=llm_client - ) - context.llm_models = [ModelMetadata(model="gpt-3.5-turbo-16k")] + llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo-16k") + context: AgentContext = AgentContext(conv_id="retrieve_summarize") default_memory = GptsMemory() - summarizer = RetrieveSummaryAssistantAgent( - memory=default_memory, agent_context=context + summarizer = ( + await RetrieveSummaryAssistantAgent() + .bind(context) + .bind(LLMConfig(llm_client=llm_client)) + .bind(default_memory) + .build() ) user_proxy = UserProxyAgent(memory=default_memory, agent_context=context) - asyncio.run( - user_proxy.a_initiate_chat( - recipient=summarizer, - reviewer=user_proxy, - message="""I want to summarize advantages of Nuclear Power. - You can refer the following file pathes and URLs: ['/home/ubuntu/DB-GPT/examples/Nuclear_power.pdf', 'https://en.wikipedia.org/wiki/Modern_Family', '/home/ubuntu/DB-GPT/examples/Taylor_Swift.pdf', 'https://en.wikipedia.org/wiki/Chernobyl_disaster'] - """, - ) + paths_urls = [ + os.path.join(ROOT_PATH, "examples/agents/example_files/Nuclear_power.pdf"), + os.path.join(ROOT_PATH, "examples/agents/example_files/Taylor_Swift.pdf"), + "https://en.wikipedia.org/wiki/Modern_Family", + "https://en.wikipedia.org/wiki/Chernobyl_disaster", + ] + + await user_proxy.initiate_chat( + recipient=summarizer, + reviewer=user_proxy, + message=f"I want to summarize advantages of Nuclear Power. You can refer the " + f"following file paths and URLs: {paths_urls}", ) - ## dbgpt-vis message infos - print(asyncio.run(default_memory.one_plan_chat_competions("retrieve_summarize"))) + # dbgpt-vis message infos + print(await default_memory.one_chat_completions("retrieve_summarize")) if __name__ == "__main__": - print( - "\033[92m=======================Start The Summary Assistant with Successful Results==================\033[0m" - ) - summary_example_with_success() + asyncio.run(summary_example_with_success()) print( "\033[92m=======================The Summary Assistant with Successful Results Ended==================\n\n\033[91m" ) diff --git a/examples/agents/single_agent_dialogue_example.py b/examples/agents/single_agent_dialogue_example.py index d7cf870c7..2050e7ab9 100644 --- a/examples/agents/single_agent_dialogue_example.py +++ b/examples/agents/single_agent_dialogue_example.py @@ -15,17 +15,13 @@ """ import asyncio -import os -from dbgpt.agent.agents.agent import AgentContext -from dbgpt.agent.agents.expand.code_assistant_agent import CodeAssistantAgent -from dbgpt.agent.agents.llm.llm import LLMConfig -from dbgpt.agent.agents.user_proxy_agent import UserProxyAgent -from dbgpt.agent.memory.gpts_memory import GptsMemory +from dbgpt.agent import AgentContext, GptsMemory, LLMConfig, UserProxyAgent +from dbgpt.agent.expand.code_assistant_agent import CodeAssistantAgent async def main(): - from dbgpt.model import OpenAILLMClient + from dbgpt.model.proxy import OpenAILLMClient llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="test123") @@ -41,14 +37,14 @@ async def main(): user_proxy = await UserProxyAgent().bind(context).bind(default_memory).build() - await user_proxy.a_initiate_chat( + await user_proxy.initiate_chat( recipient=coder, reviewer=user_proxy, message="式计算下321 * 123等于多少", # 用python代码的方式计算下321 * 123等于多少 # message="download data from https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv and plot a visualization that tells us about the relationship between weight and horsepower. Save the plot to a file. Print the fields in a dataset before visualizing it.", ) ## dbgpt-vis message infos - print(await default_memory.one_chat_competions("test123")) + print(await default_memory.one_chat_completions("test123")) if __name__ == "__main__": diff --git a/examples/agents/single_summary_agent_dialogue_example.py b/examples/agents/single_summary_agent_dialogue_example.py index b8cfed027..8a7859b1d 100644 --- a/examples/agents/single_summary_agent_dialogue_example.py +++ b/examples/agents/single_summary_agent_dialogue_example.py @@ -15,17 +15,13 @@ """ import asyncio -import os -from dbgpt.agent.agents.agent import AgentContext -from dbgpt.agent.agents.expand.summary_assistant_agent import SummaryAssistantAgent -from dbgpt.agent.agents.llm.llm import LLMConfig -from dbgpt.agent.agents.user_proxy_agent import UserProxyAgent -from dbgpt.agent.memory.gpts_memory import GptsMemory +from dbgpt.agent import AgentContext, GptsMemory, LLMConfig, UserProxyAgent +from dbgpt.agent.expand.summary_assistant_agent import SummaryAssistantAgent async def summary_example_with_success(): - from dbgpt.model import OpenAILLMClient + from dbgpt.model.proxy import OpenAILLMClient llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="summarize") @@ -42,7 +38,7 @@ async def summary_example_with_success(): user_proxy = await UserProxyAgent().bind(default_memory).bind(context).build() - await user_proxy.a_initiate_chat( + await user_proxy.initiate_chat( recipient=summarizer, reviewer=user_proxy, message="""I want to summarize advantages of Nuclear Power according to the following content. @@ -76,11 +72,11 @@ async def summary_example_with_success(): ) ## dbgpt-vis message infos - print(await default_memory.one_chat_competions("summarize")) + print(await default_memory.one_chat_completions("summarize")) async def summary_example_with_faliure(): - from dbgpt.model import OpenAILLMClient + from dbgpt.model.proxy import OpenAILLMClient llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="summarize") @@ -99,7 +95,7 @@ async def summary_example_with_faliure(): # Test the failure example - await user_proxy.a_initiate_chat( + await user_proxy.initiate_chat( recipient=summarizer, reviewer=user_proxy, message="""I want to summarize advantages of Nuclear Power according to the following content. @@ -116,7 +112,7 @@ async def summary_example_with_faliure(): """, ) - print(await default_memory.one_chat_competions("summarize")) + print(await default_memory.one_chat_completions("summarize")) if __name__ == "__main__": diff --git a/examples/agents/sql_agent_dialogue_example.py b/examples/agents/sql_agent_dialogue_example.py index 1ae060cf5..e006d9fb5 100644 --- a/examples/agents/sql_agent_dialogue_example.py +++ b/examples/agents/sql_agent_dialogue_example.py @@ -17,14 +17,17 @@ import asyncio import os -from dbgpt.agent.agents.agent import AgentContext -from dbgpt.agent.agents.expand.data_scientist_agent import DataScientistAgent -from dbgpt.agent.agents.llm.llm import LLMConfig -from dbgpt.agent.agents.user_proxy_agent import UserProxyAgent -from dbgpt.agent.memory.gpts_memory import GptsMemory -from dbgpt.agent.resource.resource_api import AgentResource, ResourceType -from dbgpt.agent.resource.resource_db_api import SqliteLoadClient -from dbgpt.agent.resource.resource_loader import ResourceLoader +from dbgpt.agent import ( + AgentContext, + AgentResource, + GptsMemory, + LLMConfig, + ResourceLoader, + ResourceType, + UserProxyAgent, +) +from dbgpt.agent.expand.data_scientist_agent import DataScientistAgent +from dbgpt.agent.resource import SqliteLoadClient current_dir = os.getcwd() parent_dir = os.path.dirname(current_dir) @@ -47,7 +50,7 @@ async def main(): resource_loader = ResourceLoader() sqlite_file_loader = SqliteLoadClient() - resource_loader.register_resesource_api(sqlite_file_loader) + resource_loader.register_resource_api(sqlite_file_loader) user_proxy = await UserProxyAgent().bind(default_memory).bind(context).build() @@ -61,14 +64,14 @@ async def main(): .build() ) - await user_proxy.a_initiate_chat( + await user_proxy.initiate_chat( recipient=sql_boy, reviewer=user_proxy, message="当前库有那些表", ) ## dbgpt-vis message infos - print(await default_memory.one_chat_competions("test456")) + print(await default_memory.one_chat_completions("test456")) if __name__ == "__main__": diff --git a/examples/awel/simple_nl_schema_sql_chart_example.py b/examples/awel/simple_nl_schema_sql_chart_example.py index 8466cda2d..46d05af9b 100644 --- a/examples/awel/simple_nl_schema_sql_chart_example.py +++ b/examples/awel/simple_nl_schema_sql_chart_example.py @@ -2,8 +2,8 @@ from typing import Any, Dict, Optional from pandas import DataFrame -from pydantic import BaseModel, Field +from dbgpt._private.pydantic import BaseModel, Field from dbgpt.configs.model_config import MODEL_PATH, PILOT_PATH from dbgpt.core import LLMClient, ModelMessage, ModelMessageRoleType, ModelRequest from dbgpt.core.awel import DAG, HttpTrigger, JoinOperator, MapOperator diff --git a/examples/rag/simple_dbschema_retriever_example.py b/examples/rag/simple_dbschema_retriever_example.py index 476e44863..0b524cab5 100644 --- a/examples/rag/simple_dbschema_retriever_example.py +++ b/examples/rag/simple_dbschema_retriever_example.py @@ -25,9 +25,8 @@ import os from typing import Dict, List -from pydantic import BaseModel, Field - from dbgpt._private.config import Config +from dbgpt._private.pydantic import BaseModel, Field from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG, PILOT_PATH from dbgpt.core import Chunk from dbgpt.core.awel import DAG, HttpTrigger, InputOperator, JoinOperator, MapOperator diff --git a/examples/rag/simple_rag_embedding_example.py b/examples/rag/simple_rag_embedding_example.py index 103894d45..56d9dcc7e 100644 --- a/examples/rag/simple_rag_embedding_example.py +++ b/examples/rag/simple_rag_embedding_example.py @@ -14,9 +14,8 @@ import os from typing import Dict, List -from pydantic import BaseModel, Field - from dbgpt._private.config import Config +from dbgpt._private.pydantic import BaseModel, Field from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG, PILOT_PATH from dbgpt.core.awel import DAG, HttpTrigger, MapOperator from dbgpt.rag.embedding import DefaultEmbeddingFactory diff --git a/examples/rag/simple_rag_retriever_example.py b/examples/rag/simple_rag_retriever_example.py index 798f53468..80786bf9e 100644 --- a/examples/rag/simple_rag_retriever_example.py +++ b/examples/rag/simple_rag_retriever_example.py @@ -29,9 +29,8 @@ import os from typing import Dict, List -from pydantic import BaseModel, Field - from dbgpt._private.config import Config +from dbgpt._private.pydantic import BaseModel, Field from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG, PILOT_PATH from dbgpt.core import Chunk from dbgpt.core.awel import DAG, HttpTrigger, JoinOperator, MapOperator diff --git a/setup.py b/setup.py index a5d738658..4016d9034 100644 --- a/setup.py +++ b/setup.py @@ -681,6 +681,8 @@ def init_install_requires(): "dbgpt", "dbgpt._private", "dbgpt._private.*", + "dbgpt.agent", + "dbgpt.agent.*", "dbgpt.cli", "dbgpt.cli.*", "dbgpt.client", @@ -705,6 +707,8 @@ def init_install_requires(): "dbgpt.storage.*", "dbgpt.util", "dbgpt.util.*", + "dbgpt.vis", + "dbgpt.vis.*", ], ) From 9a91ed4f63bd81233d6166c7821a20a42814525b Mon Sep 17 00:00:00 2001 From: Fangyin Cheng Date: Wed, 10 Apr 2024 04:47:13 +0800 Subject: [PATCH 2/3] feat: Compatible with old AWEL Flow --- dbgpt/core/awel/flow/base.py | 18 +++++++++++-- dbgpt/core/awel/flow/compat.py | 40 ++++++++++++++++++++++++++++ dbgpt/core/awel/flow/flow_factory.py | 24 ++++++++++++++--- 3 files changed, 77 insertions(+), 5 deletions(-) create mode 100644 dbgpt/core/awel/flow/compat.py diff --git a/dbgpt/core/awel/flow/base.py b/dbgpt/core/awel/flow/base.py index ac9145a14..215679e05 100644 --- a/dbgpt/core/awel/flow/base.py +++ b/dbgpt/core/awel/flow/base.py @@ -46,6 +46,13 @@ def _get_type_name(type_: Type[Any]) -> str: return type_name +def _register_alias_types(type_: Type[Any], alias_ids: Optional[List[str]] = None): + if alias_ids: + for alias_id in alias_ids: + if alias_id not in _TYPE_REGISTRY: + _TYPE_REGISTRY[alias_id] = type_ + + def _get_type_cls(type_name: str) -> Type[Any]: """Get the type class by the type name. @@ -58,9 +65,15 @@ def _get_type_cls(type_name: str) -> Type[Any]: Raises: ValueError: If the type is not registered. """ - if type_name not in _TYPE_REGISTRY: + from .compat import get_new_class_name + + new_cls = get_new_class_name(type_name) + if type_name in _TYPE_REGISTRY: + return _TYPE_REGISTRY[type_name] + elif new_cls and new_cls in _TYPE_REGISTRY: + return _TYPE_REGISTRY[new_cls] + else: raise ValueError(f"Type {type_name} not registered.") - return _TYPE_REGISTRY[type_name] # Register the basic types. @@ -795,6 +808,7 @@ def decorator(cls): **kwargs, ) alias_ids = resource_metadata.new_alias(alias) + _register_alias_types(cls, alias_ids) _register_resource(cls, resource_metadata, alias_ids) # Attach the metadata to the class cls._resource_metadata = resource_metadata diff --git a/dbgpt/core/awel/flow/compat.py b/dbgpt/core/awel/flow/compat.py new file mode 100644 index 000000000..1fa7df10b --- /dev/null +++ b/dbgpt/core/awel/flow/compat.py @@ -0,0 +1,40 @@ +"""Compatibility mapping for flow classes.""" + +from typing import Dict, Optional + +_COMPAT_FLOW_MAPPING: Dict[str, str] = {} + + +_OLD_AGENT_RESOURCE_MODULE = "dbgpt.serve.agent.team.layout.agent_operator_resource" +_NEW_AGENT_RESOURCE_MODULE = "dbgpt.agent.plan.awel.agent_operator_resource" + + +def _register( + old_module: str, new_module: str, old_name: str, new_name: Optional[str] = None +): + if not new_name: + new_name = old_name + _COMPAT_FLOW_MAPPING[f"{old_module}.{old_name}"] = f"{new_module}.{new_name}" + + +def get_new_class_name(old_class_name: str) -> Optional[str]: + """Get the new class name for the old class name.""" + new_cls_name = _COMPAT_FLOW_MAPPING.get(old_class_name, None) + return new_cls_name + + +_register( + _OLD_AGENT_RESOURCE_MODULE, + _NEW_AGENT_RESOURCE_MODULE, + "AwelAgentResource", + "AWELAgentResource", +) +_register( + _OLD_AGENT_RESOURCE_MODULE, + _NEW_AGENT_RESOURCE_MODULE, + "AwelAgentConfig", + "AWELAgentConfig", +) +_register( + _OLD_AGENT_RESOURCE_MODULE, _NEW_AGENT_RESOURCE_MODULE, "AwelAgent", "AWELAgent" +) diff --git a/dbgpt/core/awel/flow/flow_factory.py b/dbgpt/core/awel/flow/flow_factory.py index fbbd9bca6..4eebda2c6 100644 --- a/dbgpt/core/awel/flow/flow_factory.py +++ b/dbgpt/core/awel/flow/flow_factory.py @@ -17,6 +17,7 @@ _get_operator_class, _get_resource_class, ) +from .compat import get_new_class_name from .exceptions import ( FlowClassMetadataException, FlowDAGMetadataException, @@ -607,9 +608,26 @@ def pre_load_requirements(self, flow_panel: FlowPanel): f"{metadata_cls}" ) except ImportError as e: - raise FlowClassMetadataException( - f"Import {node_data.type_cls} failed: {e}" - ) + raise_error = True + new_type_cls: Optional[str] = None + try: + new_type_cls = get_new_class_name(node_data.type_cls) + if new_type_cls: + metadata_cls = import_from_string(new_type_cls) + logger.info( + f"Import {new_type_cls} successfully, metadata_cls is : " + f"{metadata_cls}" + ) + raise_error = False + except ImportError as ex: + raise FlowClassMetadataException( + f"Import {node_data.type_cls} with new type {new_type_cls} " + f"failed: {ex}" + ) + if raise_error: + raise FlowClassMetadataException( + f"Import {node_data.type_cls} failed: {e}" + ) def _topological_sort( From 5188b322b87b7eb859a466358bdaec938d8c52a6 Mon Sep 17 00:00:00 2001 From: Fangyin Cheng Date: Wed, 10 Apr 2024 12:40:16 +0800 Subject: [PATCH 3/3] docs: Add agents notebook --- dbgpt/agent/__init__.py | 2 + dbgpt/agent/core/agent_manage.py | 12 +- dbgpt/agent/core/llm/llm_client.py | 2 +- dbgpt/agent/plan/awel/agent_operator.py | 4 +- .../plan/awel/agent_operator_resource.py | 4 +- dbgpt/serve/agent/agents/controller.py | 6 +- dbgpt/serve/agent/app/controller.py | 4 +- .../awel_layout_agents_chat_examples.py | 2 - .../agent_auto_plan_dialogue_example.ipynb | 1022 +++++++++-------- .../agent_awel_layout_dialogue_example.ipynb | 226 ++-- 10 files changed, 688 insertions(+), 596 deletions(-) diff --git a/dbgpt/agent/__init__.py b/dbgpt/agent/__init__.py index 0bcc34148..7e2927a7b 100644 --- a/dbgpt/agent/__init__.py +++ b/dbgpt/agent/__init__.py @@ -7,6 +7,7 @@ AgentGenerateContext, AgentMessage, ) +from .core.base_agent import ConversableAgent # noqa: F401 from .core.llm.llm import LLMConfig # noqa: F401 from .core.schema import PluginStorageType # noqa: F401 from .core.user_proxy_agent import UserProxyAgent # noqa: F401 @@ -19,6 +20,7 @@ "AgentContext", "AgentGenerateContext", "AgentMessage", + "ConversableAgent", "Action", "ActionOutput", "LLMConfig", diff --git a/dbgpt/agent/core/agent_manage.py b/dbgpt/agent/core/agent_manage.py index e860b8ec4..5496b58f5 100644 --- a/dbgpt/agent/core/agent_manage.py +++ b/dbgpt/agent/core/agent_manage.py @@ -98,10 +98,10 @@ def list_agents(self): return result -agent_manage = AgentManager() +agent_manager = AgentManager() -agent_manage.register_agent(CodeAssistantAgent) -agent_manage.register_agent(DashboardAssistantAgent) -agent_manage.register_agent(DataScientistAgent) -agent_manage.register_agent(SummaryAssistantAgent) -agent_manage.register_agent(PluginAssistantAgent) +agent_manager.register_agent(CodeAssistantAgent) +agent_manager.register_agent(DashboardAssistantAgent) +agent_manager.register_agent(DataScientistAgent) +agent_manager.register_agent(SummaryAssistantAgent) +agent_manager.register_agent(PluginAssistantAgent) diff --git a/dbgpt/agent/core/llm/llm_client.py b/dbgpt/agent/core/llm/llm_client.py index dc84b5124..a17f15c49 100644 --- a/dbgpt/agent/core/llm/llm_client.py +++ b/dbgpt/agent/core/llm/llm_client.py @@ -171,7 +171,7 @@ async def _completions_create(self, llm_model, params) -> str: model_request = _build_model_request(payload) model_output = await self._llm_client.generate(model_request.copy()) parsed_output = self._output_parser.parse_model_nostream_resp( - model_output, "###" + model_output, "#########################" ) return parsed_output except Exception as e: diff --git a/dbgpt/agent/plan/awel/agent_operator.py b/dbgpt/agent/plan/awel/agent_operator.py index 6e6c4ad77..643900bb2 100644 --- a/dbgpt/agent/plan/awel/agent_operator.py +++ b/dbgpt/agent/plan/awel/agent_operator.py @@ -17,7 +17,7 @@ from dbgpt.model.operators.llm_operator import MixinLLMOperator from ...core.agent import Agent, AgentGenerateContext, AgentMessage -from ...core.agent_manage import agent_manage +from ...core.agent_manage import agent_manager from ...core.base_agent import ConversableAgent from ...core.llm.llm import LLMConfig from .agent_operator_resource import AWELAgent @@ -244,7 +244,7 @@ async def get_agent( ) -> ConversableAgent: """Build the agent.""" # agent build - agent_cls: Type[ConversableAgent] = agent_manage.get_by_name( # type: ignore + agent_cls: Type[ConversableAgent] = agent_manager.get_by_name( # type: ignore self.awel_agent.agent_profile ) llm_config = self.awel_agent.llm_config diff --git a/dbgpt/agent/plan/awel/agent_operator_resource.py b/dbgpt/agent/plan/awel/agent_operator_resource.py index 6cacf5d0b..0aa4d20c3 100644 --- a/dbgpt/agent/plan/awel/agent_operator_resource.py +++ b/dbgpt/agent/plan/awel/agent_operator_resource.py @@ -11,7 +11,7 @@ register_resource, ) -from ...core.agent_manage import agent_manage +from ...core.agent_manage import agent_manager from ...core.llm.llm import LLMConfig, LLMStrategyType from ...resource.resource_api import AgentResource, ResourceType @@ -118,7 +118,7 @@ def pre_fill(cls, values: Dict[str, Any]) -> Dict[str, Any]: def _agent_resource_option_values() -> List[OptionValue]: return [ OptionValue(label=item["name"], name=item["name"], value=item["name"]) - for item in agent_manage.list_agents() + for item in agent_manager.list_agents() ] diff --git a/dbgpt/serve/agent/agents/controller.py b/dbgpt/serve/agent/agents/controller.py index 928b06bea..5e524f51b 100644 --- a/dbgpt/serve/agent/agents/controller.py +++ b/dbgpt/serve/agent/agents/controller.py @@ -10,7 +10,7 @@ from dbgpt._private.config import Config from dbgpt.agent.core.agent import Agent, AgentContext -from dbgpt.agent.core.agent_manage import agent_manage +from dbgpt.agent.core.agent_manage import agent_manager from dbgpt.agent.core.base_agent import ConversableAgent from dbgpt.agent.core.llm.llm import LLMConfig, LLMStrategyType from dbgpt.agent.core.schema import Status @@ -222,7 +222,7 @@ async def agent_team_chat_new( self.llm_provider = DefaultLLMClient(worker_manager, auto_convert_message=True) for record in gpts_app.details: - cls: Type[ConversableAgent] = agent_manage.get_by_name(record.agent_name) + cls: Type[ConversableAgent] = agent_manager.get_by_name(record.agent_name) llm_config = LLMConfig( llm_client=self.llm_provider, llm_strategy=LLMStrategyType(record.llm_strategy), @@ -340,7 +340,7 @@ def gpts_conv_list(self, user_code: str = None, system_app: str = None): async def agents_list(): logger.info("agents_list!") try: - agents = agent_manage.all_agents() + agents = agent_manager.all_agents() return Result.succ(agents) except Exception as e: return Result.failed(code="E30001", msg=str(e)) diff --git a/dbgpt/serve/agent/app/controller.py b/dbgpt/serve/agent/app/controller.py index b4848b4d5..18d0cf2b5 100644 --- a/dbgpt/serve/agent/app/controller.py +++ b/dbgpt/serve/agent/app/controller.py @@ -3,7 +3,7 @@ from fastapi import APIRouter from dbgpt._private.config import Config -from dbgpt.agent.core.agent_manage import agent_manage +from dbgpt.agent.core.agent_manage import agent_manager from dbgpt.agent.core.llm.llm import LLMStrategyType from dbgpt.agent.resource.resource_api import ResourceType from dbgpt.app.knowledge.api import knowledge_space_service @@ -63,7 +63,7 @@ async def edit(gpts_app: GptsApp): @router.get("/v1/agents/list") async def all_agents(): try: - return Result.succ(agent_manage.list_agents()) + return Result.succ(agent_manager.list_agents()) except Exception as ex: return Result.failed(code="E000X", msg=f"query agents error: {ex}") diff --git a/examples/agents/awel_layout_agents_chat_examples.py b/examples/agents/awel_layout_agents_chat_examples.py index 1496d047a..69812b8ee 100644 --- a/examples/agents/awel_layout_agents_chat_examples.py +++ b/examples/agents/awel_layout_agents_chat_examples.py @@ -32,8 +32,6 @@ from dbgpt.agent.resource import PluginFileLoadClient from dbgpt.configs.model_config import ROOT_PATH -current_dir = os.getcwd() -parent_dir = os.path.dirname(current_dir) test_plugin_dir = os.path.join(ROOT_PATH, "examples/test_files/plugins") diff --git a/examples/notebook/agent_auto_plan_dialogue_example.ipynb b/examples/notebook/agent_auto_plan_dialogue_example.ipynb index 201980a99..550cbbd91 100644 --- a/examples/notebook/agent_auto_plan_dialogue_example.ipynb +++ b/examples/notebook/agent_auto_plan_dialogue_example.ipynb @@ -2,34 +2,29 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T04:38:14.228948Z", + "start_time": "2024-04-10T04:38:14.224972Z" + } + }, "source": [ - "from dbgpt.agent.agents.planner_agent import PlannerAgent\n", - "from dbgpt.agent.agents.user_proxy_agent import UserProxyAgent\n", - "from dbgpt.agent.agents.plan_group_chat import PlanChat, PlanChatManager\n", - "\n", - "from dbgpt.agent.agents.expand.code_assistant_agent import CodeAssistantAgent\n", - "from dbgpt.agent.agents.expand.plugin_assistant_agent import PluginAgent\n", - "from dbgpt.agent.agents.agents_mange import agent_mange\n", - "\n", - "from dbgpt.agent.agents.agent import AgentContext\n", - "from dbgpt.agent.memory.gpts_memory import GptsMemory\n", - "from dbgpt.core.interface.llm import ModelMetadata\n", + "import nest_asyncio\n", + "from dbgpt.agent import (\n", + " AgentContext,\n", + " GptsMemory,\n", + " LLMConfig,\n", + " ResourceLoader,\n", + " UserProxyAgent,\n", + ")\n", + "from dbgpt.agent.expand.code_assistant_agent import CodeAssistantAgent\n", + "from dbgpt.agent.plan import AutoPlanChatManager\n", + "from dbgpt.model.proxy import OpenAILLMClient\n", "\n", - "import asyncio\n", - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "nest_asyncio.apply()" + ], "outputs": [], - "source": [ - "from dbgpt.model import OpenAILLMClient" - ] + "execution_count": 7 }, { "cell_type": "code", @@ -46,44 +41,122 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T04:19:47.838081Z", + "start_time": "2024-04-10T04:17:54.465616Z" + } + }, + "source": [ + "llm_client = OpenAILLMClient(model_alias=\"gpt-4\")\n", + "context: AgentContext = AgentContext(conv_id=\"test456\", gpts_app_name=\"代码分析助手\")\n", + "\n", + "default_memory = GptsMemory()\n", + "\n", + "resource_loader = ResourceLoader()\n", + "\n", + "coder = (\n", + " await CodeAssistantAgent()\n", + " .bind(context)\n", + " .bind(LLMConfig(llm_client=llm_client))\n", + " .bind(default_memory)\n", + " .bind(resource_loader)\n", + " .build()\n", + ")\n", + "\n", + "manager = (\n", + " await AutoPlanChatManager()\n", + " .bind(context)\n", + " .bind(default_memory)\n", + " .bind(LLMConfig(llm_client=llm_client))\n", + " .build()\n", + ")\n", + "manager.hire([coder])\n", + "\n", + "user_proxy = await UserProxyAgent().bind(context).bind(default_memory).build()\n", + "\n", + "\n", + "await user_proxy.initiate_chat(\n", + " recipient=manager,\n", + " reviewer=user_proxy,\n", + " message=\"Obtain simple information about issues in the repository 'eosphoros-ai/DB-GPT' in the past three days and analyze the data. Create a Markdown table grouped by day and status.\",\n", + " # message=\"Find papers on gpt-4 in the past three weeks on arxiv, and organize their titles, authors, and links into a markdown table\",\n", + " # message=\"find papers on LLM applications from arxiv in the last month, create a markdown table of different domains.\",\n", + ")" + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001B[33mUser\u001B[0m (to plan_manager)-[]:\n", - "\n", - "\"Obtain simple information about issues in the repository 'eosphoros-ai/DB-GPT' in the past three days and analyze the data. Create a Markdown table grouped by day and status.\"\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33mplan_manager\u001B[0m (to Planner)-[]:\n", + "\u001B[33mUser\u001B[0m (to PlanManager)-[]:\n", "\n", "\"Obtain simple information about issues in the repository 'eosphoros-ai/DB-GPT' in the past three days and analyze the data. Create a Markdown table grouped by day and status.\"\n", "\n", "--------------------------------------------------------------------------------\n", - "un_stream ai response: [{\n", + "un_stream ai response: [\n", + " {\n", " \"serial_number\": \"1\",\n", " \"agent\": \"CodeEngineer\",\n", - " \"content\": \"Write a Python script to use the GitHub API to retrieve issues from the 'eosphoros-ai/DB-GPT' repository that were created in the past three days. The script should extract the issue's creation date and status.\",\n", + " \"content\": \"Use Github API to retrieve issues from the repository 'eosphoros-ai/DB-GPT' in the past three days.\",\n", " \"rely\": \"\"\n", - "},\n", - "{\n", + " },\n", + " {\n", " \"serial_number\": \"2\",\n", " \"agent\": \"CodeEngineer\",\n", - " \"content\": \"Write a Python script to process the retrieved issues data to group them by creation date and status. Then, format the grouped data into a Markdown table.\",\n", + " \"content\": \"Analyze the retrieved issues data, extract the necessary information such as issue title, status, and created date.\",\n", " \"rely\": \"1\"\n", - "}]\n", - "\u001B[33mPlanner\u001B[0m (to plan_manager)-[gpt-4-vision-preview]:\n", + " },\n", + " {\n", + " \"serial_number\": \"3\",\n", + " \"agent\": \"CodeEngineer\",\n", + " \"content\": \"Group the extracted data by day and status.\",\n", + " \"rely\": \"2\"\n", + " },\n", + " {\n", + " \"serial_number\": \"4\",\n", + " \"agent\": \"CodeEngineer\",\n", + " \"content\": \"Create a Markdown table with the grouped data.\",\n", + " \"rely\": \"3\"\n", + " }\n", + "]\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001B[33mPlanner\u001B[0m (to PlanManager)-[gpt-4]:\n", "\n", - "\"[{\\n \\\"serial_number\\\": \\\"1\\\",\\n \\\"agent\\\": \\\"CodeEngineer\\\",\\n \\\"content\\\": \\\"Write a Python script to use the GitHub API to retrieve issues from the 'eosphoros-ai/DB-GPT' repository that were created in the past three days. The script should extract the issue's creation date and status.\\\",\\n \\\"rely\\\": \\\"\\\"\\n},\\n{\\n \\\"serial_number\\\": \\\"2\\\",\\n \\\"agent\\\": \\\"CodeEngineer\\\",\\n \\\"content\\\": \\\"Write a Python script to process the retrieved issues data to group them by creation date and status. Then, format the grouped data into a Markdown table.\\\",\\n \\\"rely\\\": \\\"1\\\"\\n}]\"\n", + "\"[\\n {\\n \\\"serial_number\\\": \\\"1\\\",\\n \\\"agent\\\": \\\"CodeEngineer\\\",\\n \\\"content\\\": \\\"Use Github API to retrieve issues from the repository 'eosphoros-ai/DB-GPT' in the past three days.\\\",\\n \\\"rely\\\": \\\"\\\"\\n },\\n {\\n \\\"serial_number\\\": \\\"2\\\",\\n \\\"agent\\\": \\\"CodeEngineer\\\",\\n \\\"content\\\": \\\"Analyze the retrieved issues data, extract the necessary information such as issue title, status, and created date.\\\",\\n \\\"rely\\\": \\\"1\\\"\\n },\\n {\\n \\\"serial_number\\\": \\\"3\\\",\\n \\\"agent\\\": \\\"CodeEngineer\\\",\\n \\\"content\\\": \\\"Group the extracted data by day and status.\\\",\\n \\\"rely\\\": \\\"2\\\"\\n },\\n {\\n \\\"serial_number\\\": \\\"4\\\",\\n \\\"agent\\\": \\\"CodeEngineer\\\",\\n \\\"content\\\": \\\"Create a Markdown table with the grouped data.\\\",\\n \\\"rely\\\": \\\"3\\\"\\n }\\n]\"\n", "\u001B[32m>>>>>>>>Planner Review info: \n", - " Pass.None\u001B[0m\n", + "Pass(None)\u001B[0m\n", "\u001B[34m>>>>>>>>Planner Action report: \n", "execution succeeded,\n", - "1,Write a Python script to use the GitHub API to retrieve issues from the 'eosphoros-ai/DB-GPT' repository that were created in the past three days. The script should extract the issue's creation date and status.\n", - "2,Write a Python script to process the retrieved issues data to group them by creation date and status. Then, format the grouped data into a Markdown table.\u001B[0m\n", + "[\n", + " {\n", + " \"serial_number\": \"1\",\n", + " \"agent\": \"CodeEngineer\",\n", + " \"content\": \"Use Github API to retrieve issues from the repository 'eosphoros-ai/DB-GPT' in the past three days.\",\n", + " \"rely\": \"\"\n", + " },\n", + " {\n", + " \"serial_number\": \"2\",\n", + " \"agent\": \"CodeEngineer\",\n", + " \"content\": \"Analyze the retrieved issues data, extract the necessary information such as issue title, status, and created date.\",\n", + " \"rely\": \"1\"\n", + " },\n", + " {\n", + " \"serial_number\": \"3\",\n", + " \"agent\": \"CodeEngineer\",\n", + " \"content\": \"Group the extracted data by day and status.\",\n", + " \"rely\": \"2\"\n", + " },\n", + " {\n", + " \"serial_number\": \"4\",\n", + " \"agent\": \"CodeEngineer\",\n", + " \"content\": \"Create a Markdown table with the grouped data.\",\n", + " \"rely\": \"3\"\n", + " }\n", + "]\u001B[0m\n", "\n", "--------------------------------------------------------------------------------\n" ] @@ -92,73 +165,132 @@ "name": "stderr", "output_type": "stream", "text": [ - "GroupChat is underpopulated with 1 agents. It is recommended to set speaker_selection_method to 'round_robin' or allow_repeat_speaker to False.Or, use direct communication instead.\n" + "GroupChat select_speaker failed to resolve the next speaker's name. This is because the speaker selection OAI call returned:\n", + "CodeEngineer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001B[33mplan_manager\u001B[0m (to CodeEngineer)-[]:\n", "\n", - "\"Write a Python script to use the GitHub API to retrieve issues from the 'eosphoros-ai/DB-GPT' repository that were created in the past three days. The script should extract the issue's creation date and status.\"\n", + "--------------------------------------------------------------------------------\n", + "\u001B[33mPlanManager\u001B[0m (to Turing)-[]:\n", + "\n", + "\"Use Github API to retrieve issues from the repository 'eosphoros-ai/DB-GPT' in the past three days.\"\n", "\n", "--------------------------------------------------------------------------------\n", - "un_stream ai response: ```python\n", - "# filename: github_issues.py\n", + "un_stream ai response: To retrieve issues from the GitHub repository 'eosphoros-ai/DB-GPT' in the past three days, we can use the GitHub REST API. Here is a Python script that accomplishes this. This script uses the 'requests' library to send a GET request to the GitHub API. The script then parses the JSON response to filter out issues created in the last three days.\n", + "\n", + "```python\n", + "# python\n", "import requests\n", "from datetime import datetime, timedelta\n", + "import pytz\n", + "\n", + "# Get the current date and time\n", + "now = datetime.now()\n", + "\n", + "# Calculate the date 3 days ago\n", + "three_days_ago = now - timedelta(days=3)\n", + "\n", + "# Convert the date to ISO 8601 format\n", + "three_days_ago_iso = three_days_ago.isoformat()\n", "\n", - "# Function to get issues created in the past three days from a GitHub repository\n", - "def get_recent_issues(owner, repo):\n", - " # Calculate the date for three days ago\n", - " three_days_ago = datetime.utcnow() - timedelta(days=3)\n", - " # Format the date in ISO 8601 format\n", - " since = three_days_ago.isoformat()\n", - "\n", - " # GitHub API URL for issues of the given repository\n", - " issues_url = f\"https://api.github.com/repos/{owner}/{repo}/issues\"\n", - " \n", - " # Parameters to get issues created in the past three days\n", - " params = {\n", - " 'since': since,\n", - " 'state': 'all', # Get both open and closed issues\n", - " }\n", - " \n", - " # Send a request to the GitHub API\n", - " response = requests.get(issues_url, params=params)\n", - " # Raise an exception if the request was unsuccessful\n", - " response.raise_for_status()\n", - " \n", + "# Specify the URL of the GitHub API\n", + "url = \"https://api.github.com/repos/eosphoros-ai/DB-GPT/issues\"\n", + "\n", + "# Send a GET request to the GitHub API\n", + "response = requests.get(url)\n", + "\n", + "# Check if the request was successful\n", + "if response.status_code == 200:\n", " # Parse the JSON response\n", " issues = response.json()\n", - " \n", - " # Extract and print the creation date and status of each issue\n", - " for issue in issues:\n", - " created_at = issue['created_at']\n", - " state = issue['state']\n", - " print(f\"Issue created at: {created_at}, Status: {state}\")\n", - "\n", - "# Replace 'owner' and 'repo' with the actual owner and repository name\n", - "get_recent_issues('eosphoros-ai', 'DB-GPT')\n", + "\n", + " # Filter out issues created in the last three days\n", + " recent_issues = [issue for issue in issues if issue['created_at'] > three_days_ago_iso]\n", + "\n", + " # Print the recent issues\n", + " for issue in recent_issues:\n", + " print(\"Issue ID: \", issue['id'])\n", + " print(\"Issue Title: \", issue['title'])\n", + " print(\"Issue URL: \", issue['html_url'])\n", + " print(\"Issue Created At: \", issue['created_at'])\n", + " print(\"\\n\")\n", + "else:\n", + " print(\"Failed to retrieve issues. HTTP Status Code: \", response.status_code)\n", "```\n", "\n", - "When you run this script, it will print out the creation date and status of issues from the 'eosphoros-ai/DB-GPT' repository that were created in the past three days. Make sure to have internet access while running the script as it needs to make requests to the GitHub API.\n", + "Please note that this script doesn't handle pagination. The GitHub API returns a maximum of 30 items per page. If there are more than 30 issues, you will need to send multiple requests to retrieve all issues. Also, the GitHub API has a rate limit. For unauthenticated requests, the rate limit is 60 requests per hour. For authenticated requests, the rate limit is 5000 requests per hour.\n", "\u001B[31m\n", - ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001B[0m\n", - "\u001B[33mCodeEngineer\u001B[0m (to plan_manager)-[gpt-4-vision-preview]:\n", + ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001B[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "execute_code was called without specifying a value for use_docker. Since the python docker package is not available, code will be run natively. Note: this fallback behavior is subject to change\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "un_stream ai response: True\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001B[33mTuring\u001B[0m (to PlanManager)-[gpt-4]:\n", + "\n", + "\"To retrieve issues from the GitHub repository 'eosphoros-ai/DB-GPT' in the past three days, we can use the GitHub REST API. Here is a Python script that accomplishes this. This script uses the 'requests' library to send a GET request to the GitHub API. The script then parses the JSON response to filter out issues created in the last three days.\\n\\n```python\\n# python\\nimport requests\\nfrom datetime import datetime, timedelta\\nimport pytz\\n\\n# Get the current date and time\\nnow = datetime.now()\\n\\n# Calculate the date 3 days ago\\nthree_days_ago = now - timedelta(days=3)\\n\\n# Convert the date to ISO 8601 format\\nthree_days_ago_iso = three_days_ago.isoformat()\\n\\n# Specify the URL of the GitHub API\\nurl = \\\"https://api.github.com/repos/eosphoros-ai/DB-GPT/issues\\\"\\n\\n# Send a GET request to the GitHub API\\nresponse = requests.get(url)\\n\\n# Check if the request was successful\\nif response.status_code == 200:\\n # Parse the JSON response\\n issues = response.json()\\n\\n # Filter out issues created in the last three days\\n recent_issues = [issue for issue in issues if issue['created_at'] > three_days_ago_iso]\\n\\n # Print the recent issues\\n for issue in recent_issues:\\n print(\\\"Issue ID: \\\", issue['id'])\\n print(\\\"Issue Title: \\\", issue['title'])\\n print(\\\"Issue URL: \\\", issue['html_url'])\\n print(\\\"Issue Created At: \\\", issue['created_at'])\\n print(\\\"\\\\n\\\")\\nelse:\\n print(\\\"Failed to retrieve issues. HTTP Status Code: \\\", response.status_code)\\n```\\n\\nPlease note that this script doesn't handle pagination. The GitHub API returns a maximum of 30 items per page. If there are more than 30 issues, you will need to send multiple requests to retrieve all issues. Also, the GitHub API has a rate limit. For unauthenticated requests, the rate limit is 60 requests per hour. For authenticated requests, the rate limit is 5000 requests per hour.\"\n", + "\u001B[32m>>>>>>>>Turing Review info: \n", + "Pass(None)\u001B[0m\n", + "\u001B[34m>>>>>>>>Turing Action report: \n", + "execution succeeded,\n", + "\n", + "Issue ID: 2234707865\n", + "Issue Title: [Bug] [remote_worker] 使用model-controller作为集群模式时，通过web进行知识库问答，会将所有模型请求一遍\n", + "Issue URL: https://github.com/eosphoros-ai/DB-GPT/issues/1398\n", + "Issue Created At: 2024-04-10T03:57:27Z\n", + "\n", + "\n", + "Issue ID: 2234675367\n", + "Issue Title: [Bug] LLMServer Generate Error, Please CheckErrorInfo.: CUDA error\n", + "Issue URL: https://github.com/eosphoros-ai/DB-GPT/issues/1397\n", + "Issue Created At: 2024-04-10T03:12:34Z\n", + "\n", + "\n", + "Issue ID: 2234172353\n", + "Issue Title: feat(agent): Release agent SDK\n", + "Issue URL: https://github.com/eosphoros-ai/DB-GPT/pull/1396\n", + "Issue Created At: 2024-04-09T19:35:04Z\n", + "\n", + "\n", + "Issue ID: 2232992034\n", + "Issue Title: feat(RAG):add metadata properties filters\n", + "Issue URL: https://github.com/eosphoros-ai/DB-GPT/pull/1395\n", + "Issue Created At: 2024-04-09T09:19:53Z\n", + "\n", + "\n", + "Issue ID: 2232526474\n", + "Issue Title: [Bug] [datasource] Unable to modify database table structure after executing query\n", + "Issue URL: https://github.com/eosphoros-ai/DB-GPT/issues/1389\n", + "Issue Created At: 2024-04-09T03:18:41Z\n", + "\n", + "\n", + "Issue ID: 2230167238\n", + "Issue Title: [Feature][RAG] Metadata Properties Filter\n", + "Issue URL: https://github.com/eosphoros-ai/DB-GPT/issues/1387\n", + "Issue Created At: 2024-04-08T03:15:31Z\n", + "\n", + "\n", + "Issue ID: 2229746443\n", + "Issue Title: Feat: support oceanbase monaco plugin\n", + "Issue URL: https://github.com/eosphoros-ai/DB-GPT/pull/1386\n", + "Issue Created At: 2024-04-07T12:29:13Z\n", + "\n", "\n", - "\"```python\\n# filename: github_issues.py\\nimport requests\\nfrom datetime import datetime, timedelta\\n\\n# Function to get issues created in the past three days from a GitHub repository\\ndef get_recent_issues(owner, repo):\\n # Calculate the date for three days ago\\n three_days_ago = datetime.utcnow() - timedelta(days=3)\\n # Format the date in ISO 8601 format\\n since = three_days_ago.isoformat()\\n\\n # GitHub API URL for issues of the given repository\\n issues_url = f\\\"https://api.github.com/repos/{owner}/{repo}/issues\\\"\\n \\n # Parameters to get issues created in the past three days\\n params = {\\n 'since': since,\\n 'state': 'all', # Get both open and closed issues\\n }\\n \\n # Send a request to the GitHub API\\n response = requests.get(issues_url, params=params)\\n # Raise an exception if the request was unsuccessful\\n response.raise_for_status()\\n \\n # Parse the JSON response\\n issues = response.json()\\n \\n # Extract and print the creation date and status of each issue\\n for issue in issues:\\n created_at = issue['created_at']\\n state = issue['state']\\n print(f\\\"Issue created at: {created_at}, Status: {state}\\\")\\n\\n# Replace 'owner' and 'repo' with the actual owner and repository name\\nget_recent_issues('eosphoros-ai', 'DB-GPT')\\n```\\n\\nWhen you run this script, it will print out the creation date and status of issues from the 'eosphoros-ai/DB-GPT' repository that were created in the past three days. Make sure to have internet access while running the script as it needs to make requests to the GitHub API.\"\n", - "\u001B[32m>>>>>>>>CodeEngineer Review info: \n", - " Pass.None\u001B[0m\n", - "\u001B[34m>>>>>>>>CodeEngineer Action report: \n", - "execution failed,\n", - "exitcode: 1 (execution failed)\n", - " \n", - "Traceback (most recent call last):\n", - " File \"github_issues.py\", line 2, in \n", - " import requests\n", - "ModuleNotFoundError: No module named 'requests'\n", "\u001B[0m\n", "\n", "--------------------------------------------------------------------------------\n" @@ -168,262 +300,206 @@ "name": "stderr", "output_type": "stream", "text": [ - "GroupChat is underpopulated with 1 agents. It is recommended to set speaker_selection_method to 'round_robin' or allow_repeat_speaker to False.Or, use direct communication instead.\n" + "GroupChat select_speaker failed to resolve the next speaker's name. This is because the speaker selection OAI call returned:\n", + "CodeEngineer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001B[33mplan_manager\u001B[0m (to CodeEngineer)-[]:\n", "\n", - "\"exitcode: 1 (execution failed)\\n \\nTraceback (most recent call last):\\n File \\\"github_issues.py\\\", line 2, in \\n import requests\\nModuleNotFoundError: No module named 'requests'\\n\"\n", + "--------------------------------------------------------------------------------\n", + "\u001B[33mPlanManager\u001B[0m (to Turing)-[]:\n", + "\n", + "\"Read the result data of the dependent steps in the above historical message to complete the current goal:Analyze the retrieved issues data, extract the necessary information such as issue title, status, and created date.\"\n", "\n", "--------------------------------------------------------------------------------\n", - "un_stream ai response: It appears that the `requests` module is not installed on your system. You can install it using `pip`, the Python package manager. Here's the command to install the `requests` module:\n", + "un_stream ai response: From the historical message, we have the following issues data:\n", "\n", - "```sh\n", - "pip install requests\n", - "```\n", + "1. Issue Title: [Bug] [remote_worker] 使用model-controller作为集群模式时，通过web进行知识库问答，会将所有模型请求一遍\n", + " Status: Not specified\n", + " Created Date: 2024-04-10\n", "\n", - "After installing the `requests` module, you can try running the script again. Here is the complete script including the installation of the `requests` module:\n", + "2. Issue Title: [Bug] LLMServer Generate Error, Please CheckErrorInfo.: CUDA error\n", + " Status: Not specified\n", + " Created Date: 2024-04-10\n", "\n", - "```python\n", - "# filename: github_issues.py\n", - "import requests\n", - "from datetime import datetime, timedelta\n", + "3. Issue Title: feat(agent): Release agent SDK\n", + " Status: Not specified\n", + " Created Date: 2024-04-09\n", "\n", - "# Function to get issues created in the past three days from a GitHub repository\n", - "def get_recent_issues(owner, repo):\n", - " # Calculate the date for three days ago\n", - " three_days_ago = datetime.utcnow() - timedelta(days=3)\n", - " # Format the date in ISO 8601 format\n", - " since = three_days_ago.isoformat()\n", - "\n", - " # GitHub API URL for issues of the given repository\n", - " issues_url = f\"https://api.github.com/repos/{owner}/{repo}/issues\"\n", - " \n", - " # Parameters to get issues created in the past three days\n", - " params = {\n", - " 'since': since,\n", - " 'state': 'all', # Get both open and closed issues\n", - " }\n", - " \n", - " # Send a request to the GitHub API\n", - " response = requests.get(issues_url, params=params)\n", - " # Raise an exception if the request was unsuccessful\n", - " response.raise_for_status()\n", - " \n", - " # Parse the JSON response\n", - " issues = response.json()\n", - " \n", - " # Extract and print the creation date and status of each issue\n", - " for issue in issues:\n", - " created_at = issue['created_at']\n", - " state = issue['state']\n", - " print(f\"Issue created at: {created_at}, Status: {state}\")\n", - "\n", - "# Replace 'owner' and 'repo' with the actual owner and repository name\n", - "get_recent_issues('eosphoros-ai', 'DB-GPT')\n", - "```\n", + "4. Issue Title: feat(RAG):add metadata properties filters\n", + " Status: Not specified\n", + " Created Date: 2024-04-09\n", + "\n", + "5. Issue Title: [Bug] [datasource] Unable to modify database table structure after executing query\n", + " Status: Not specified\n", + " Created Date: 2024-04-09\n", "\n", - "Please run the above Python script after you have installed the `requests` module.\n", + "6. Issue Title: [Feature][RAG] Metadata Properties Filter\n", + " Status: Not specified\n", + " Created Date: 2024-04-08\n", + "\n", + "7. Issue Title: Feat: support oceanbase monaco plugin\n", + " Status: Not specified\n", + " Created Date: 2024-04-07\n", + "\n", + "Note: The status of the issues is not provided in the historical message. It could be open, closed, or in review. Further API calls would be needed to retrieve the current status of each issue.\n", "\u001B[31m\n", - ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is sh)...\u001B[0m\n", + ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is unknown)...\u001B[0m\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001B[33mTuring\u001B[0m (to PlanManager)-[gpt-4]:\n", + "\n", + "\"From the historical message, we have the following issues data:\\n\\n1. Issue Title: [Bug] [remote_worker] 使用model-controller作为集群模式时，通过web进行知识库问答，会将所有模型请求一遍\\n Status: Not specified\\n Created Date: 2024-04-10\\n\\n2. Issue Title: [Bug] LLMServer Generate Error, Please CheckErrorInfo.: CUDA error\\n Status: Not specified\\n Created Date: 2024-04-10\\n\\n3. Issue Title: feat(agent): Release agent SDK\\n Status: Not specified\\n Created Date: 2024-04-09\\n\\n4. Issue Title: feat(RAG):add metadata properties filters\\n Status: Not specified\\n Created Date: 2024-04-09\\n\\n5. Issue Title: [Bug] [datasource] Unable to modify database table structure after executing query\\n Status: Not specified\\n Created Date: 2024-04-09\\n\\n6. Issue Title: [Feature][RAG] Metadata Properties Filter\\n Status: Not specified\\n Created Date: 2024-04-08\\n\\n7. Issue Title: Feat: support oceanbase monaco plugin\\n Status: Not specified\\n Created Date: 2024-04-07\\n\\nNote: The status of the issues is not provided in the historical message. It could be open, closed, or in review. Further API calls would be needed to retrieve the current status of each issue.\"\n", + "\u001B[32m>>>>>>>>Turing Review info: \n", + "Pass(None)\u001B[0m\n", + "\u001B[34m>>>>>>>>Turing Action report: \n", + "execution failed,\n", + "exitcode: 1 (execution failed)\n", + " \n", + "unknown language unknown\u001B[0m\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001B[33mPlanManager\u001B[0m (to Turing)-[]:\n", + "\n", + "\"exitcode: 1 (execution failed)\\n \\nunknown language unknown\"\n", + "\n", + "--------------------------------------------------------------------------------\n", + "un_stream ai response: I apologize for the confusion. The information provided was not meant to be executed as a code but was an analysis of the issue data from the historical message. \n", + "\n", + "Here's a summary again:\n", + "\n", + "1. Issue Title: [Bug] [remote_worker] 使用model-controller作为集群模式时，通过web进行知识库问答，会将所有模型请求一遍\n", + " Status: Not specified\n", + " Created Date: 2024-04-10\n", + "\n", + "2. Issue Title: [Bug] LLMServer Generate Error, Please CheckErrorInfo.: CUDA error\n", + " Status: Not specified\n", + " Created Date: 2024-04-10\n", + "\n", + "3. Issue Title: feat(agent): Release agent SDK\n", + " Status: Not specified\n", + " Created Date: 2024-04-09\n", + "\n", + "4. Issue Title: feat(RAG):add metadata properties filters\n", + " Status: Not specified\n", + " Created Date: 2024-04-09\n", + "\n", + "5. Issue Title: [Bug] [datasource] Unable to modify database table structure after executing query\n", + " Status: Not specified\n", + " Created Date: 2024-04-09\n", + "\n", + "6. Issue Title: [Feature][RAG] Metadata Properties Filter\n", + " Status: Not specified\n", + " Created Date: 2024-04-08\n", + "\n", + "7. Issue Title: Feat: support oceanbase monaco plugin\n", + " Status: Not specified\n", + " Created Date: 2024-04-07\n", + "\n", + "Please note that this information is a summary and analysis of the issue data retrieved from the historical message and does not need to be executed as code.\n", "\u001B[31m\n", - ">>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is python)...\u001B[0m\n", - "un_stream ai response: False\n", - "\u001B[33mCodeEngineer\u001B[0m (to plan_manager)-[gpt-4-vision-preview]:\n", - "\n", - "\"It appears that the `requests` module is not installed on your system. You can install it using `pip`, the Python package manager. Here's the command to install the `requests` module:\\n\\n```sh\\npip install requests\\n```\\n\\nAfter installing the `requests` module, you can try running the script again. Here is the complete script including the installation of the `requests` module:\\n\\n```python\\n# filename: github_issues.py\\nimport requests\\nfrom datetime import datetime, timedelta\\n\\n# Function to get issues created in the past three days from a GitHub repository\\ndef get_recent_issues(owner, repo):\\n # Calculate the date for three days ago\\n three_days_ago = datetime.utcnow() - timedelta(days=3)\\n # Format the date in ISO 8601 format\\n since = three_days_ago.isoformat()\\n\\n # GitHub API URL for issues of the given repository\\n issues_url = f\\\"https://api.github.com/repos/{owner}/{repo}/issues\\\"\\n \\n # Parameters to get issues created in the past three days\\n params = {\\n 'since': since,\\n 'state': 'all', # Get both open and closed issues\\n }\\n \\n # Send a request to the GitHub API\\n response = requests.get(issues_url, params=params)\\n # Raise an exception if the request was unsuccessful\\n response.raise_for_status()\\n \\n # Parse the JSON response\\n issues = response.json()\\n \\n # Extract and print the creation date and status of each issue\\n for issue in issues:\\n created_at = issue['created_at']\\n state = issue['state']\\n print(f\\\"Issue created at: {created_at}, Status: {state}\\\")\\n\\n# Replace 'owner' and 'repo' with the actual owner and repository name\\nget_recent_issues('eosphoros-ai', 'DB-GPT')\\n```\\n\\nPlease run the above Python script after you have installed the `requests` module.\"\n", - "\u001B[32m>>>>>>>>CodeEngineer Review info: \n", - " Pass.None\u001B[0m\n", - "\u001B[34m>>>>>>>>CodeEngineer Action report: \n", - "execution succeeded,\n", + ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is unknown)...\u001B[0m\n", "\n", - "Collecting requests\n", - " Obtaining dependency information for requests from https://files.pythonhosted.org/packages/70/8e/0e2d847013cb52cd35b38c009bb167a1a26b2ce6cd6965bf26b47bc0bf44/requests-2.31.0-py3-none-any.whl.metadata\n", - " Downloading requests-2.31.0-py3-none-any.whl.metadata (4.6 kB)\n", - "Collecting charset-normalizer<4,>=2 (from requests)\n", - " Obtaining dependency information for charset-normalizer<4,>=2 from https://files.pythonhosted.org/packages/45/59/3d27019d3b447a88fe7e7d004a1e04be220227760264cc41b405e863891b/charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_aarch64.whl.metadata\n", - " Downloading charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_aarch64.whl.metadata (33 kB)\n", - "Collecting idna<4,>=2.5 (from requests)\n", - " Obtaining dependency information for idna<4,>=2.5 from https://files.pythonhosted.org/packages/c2/e7/a82b05cf63a603df6e68d59ae6a68bf5064484a0718ea5033660af4b54a9/idna-3.6-py3-none-any.whl.metadata\n", - " Downloading idna-3.6-py3-none-any.whl.metadata (9.9 kB)\n", - "Collecting urllib3<3,>=1.21.1 (from requests)\n", - " Obtaining dependency information for urllib3<3,>=1.21.1 from https://files.pythonhosted.org/packages/96/94/c31f58c7a7f470d5665935262ebd7455c7e4c7782eb525658d3dbf4b9403/urllib3-2.1.0-py3-none-any.whl.metadata\n", - " Downloading urllib3-2.1.0-py3-none-any.whl.metadata (6.4 kB)\n", - "Collecting certifi>=2017.4.17 (from requests)\n", - " Obtaining dependency information for certifi>=2017.4.17 from https://files.pythonhosted.org/packages/64/62/428ef076be88fa93716b576e4a01f919d25968913e817077a386fcbe4f42/certifi-2023.11.17-py3-none-any.whl.metadata\n", - " Downloading certifi-2023.11.17-py3-none-any.whl.metadata (2.2 kB)\n", - "Downloading requests-2.31.0-py3-none-any.whl (62 kB)\n", - " ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 62.6/62.6 kB 1.9 MB/s eta 0:00:00\n", - "Downloading certifi-2023.11.17-py3-none-any.whl (162 kB)\n", - " ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 162.5/162.5 kB 1.9 MB/s eta 0:00:00\n", - "Downloading charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_aarch64.whl (138 kB)\n", - " ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 138.3/138.3 kB 4.6 MB/s eta 0:00:00\n", - "Downloading idna-3.6-py3-none-any.whl (61 kB)\n", - " ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 61.6/61.6 kB 3.2 MB/s eta 0:00:00\n", - "Downloading urllib3-2.1.0-py3-none-any.whl (104 kB)\n", - " ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 104.6/104.6 kB 4.9 MB/s eta 0:00:00\n", - "Installing collected packages: urllib3, idna, charset-normalizer, certifi, requests\n", - "Successfully installed certifi-2023.11.17 charset-normalizer-3.3.2 idna-3.6 requests-2.31.0 urllib3-2.1.0\n", - "WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n", - "\n", - "[notice] A new release of pip is available: 23.2.1 -> 23.3.2\n", - "[notice] To update, run: pip install --upgrade pip\n", - "\n", - "/workspace/github_issues.py:8: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).\n", - " three_days_ago = datetime.utcnow() - timedelta(days=3)\n", - "Issue created at: 2023-12-27T03:20:12Z, Status: open\n", - "Issue created at: 2023-12-26T16:13:42Z, Status: open\n", - "Issue created at: 2023-12-26T10:23:43Z, Status: open\n", - "Issue created at: 2023-12-26T07:25:37Z, Status: closed\n", - "Issue created at: 2023-12-26T05:01:26Z, Status: closed\n", - "Issue created at: 2023-12-26T03:54:15Z, Status: open\n", - "Issue created at: 2023-12-26T02:19:48Z, Status: open\n", - "Issue created at: 2023-12-25T12:26:40Z, Status: open\n", - "Issue created at: 2023-12-25T12:00:52Z, Status: open\n", - "Issue created at: 2023-12-25T08:36:56Z, Status: open\n", - "Issue created at: 2023-12-25T08:35:48Z, Status: open\n", - "Issue created at: 2023-12-25T08:34:13Z, Status: closed\n", - "Issue created at: 2023-12-25T06:33:37Z, Status: closed\n", - "Issue created at: 2023-12-23T11:47:40Z, Status: closed\n", - "Issue created at: 2023-12-23T03:01:23Z, Status: open\n", - "Issue created at: 2023-12-19T06:38:57Z, Status: open\n", - "Issue created at: 2023-12-11T08:34:08Z, Status: open\n", - "Issue created at: 2023-11-26T03:55:22Z, Status: open\n", - "Issue created at: 2023-11-25T12:38:23Z, Status: open\n", - "Issue created at: 2023-11-25T02:25:13Z, Status: open\n", - "Issue created at: 2023-11-25T02:09:58Z, Status: open\n", - "Issue created at: 2023-11-24T08:48:24Z, Status: open\n", - "Issue created at: 2023-11-24T06:03:56Z, Status: open\n", - "Issue created at: 2023-11-23T16:25:33Z, Status: open\n", - "Issue created at: 2023-11-23T08:54:36Z, Status: open\n", - "Issue created at: 2023-11-23T08:26:27Z, Status: open\n", - "Issue created at: 2023-11-22T06:42:34Z, Status: open\n", - "Issue created at: 2023-10-26T01:45:37Z, Status: open\n", - "Issue created at: 2023-08-30T06:02:32Z, Status: closed\n", - "Issue created at: 2023-06-18T01:11:22Z, Status: open\n", - "\u001B[0m\n", + "--------------------------------------------------------------------------------\n", + "\u001B[33mTuring\u001B[0m (to PlanManager)-[gpt-4]:\n", "\n", - "--------------------------------------------------------------------------------\n" + "\"I apologize for the confusion. The information provided was not meant to be executed as a code but was an analysis of the issue data from the historical message. \\n\\nHere's a summary again:\\n\\n1. Issue Title: [Bug] [remote_worker] 使用model-controller作为集群模式时，通过web进行知识库问答，会将所有模型请求一遍\\n Status: Not specified\\n Created Date: 2024-04-10\\n\\n2. Issue Title: [Bug] LLMServer Generate Error, Please CheckErrorInfo.: CUDA error\\n Status: Not specified\\n Created Date: 2024-04-10\\n\\n3. Issue Title: feat(agent): Release agent SDK\\n Status: Not specified\\n Created Date: 2024-04-09\\n\\n4. Issue Title: feat(RAG):add metadata properties filters\\n Status: Not specified\\n Created Date: 2024-04-09\\n\\n5. Issue Title: [Bug] [datasource] Unable to modify database table structure after executing query\\n Status: Not specified\\n Created Date: 2024-04-09\\n\\n6. Issue Title: [Feature][RAG] Metadata Properties Filter\\n Status: Not specified\\n Created Date: 2024-04-08\\n\\n7. Issue Title: Feat: support oceanbase monaco plugin\\n Status: Not specified\\n Created Date: 2024-04-07\\n\\nPlease note that this information is a summary and analysis of the issue data retrieved from the historical message and does not need to be executed as code.\"\n", + "\u001B[32m>>>>>>>>Turing Review info: \n", + "Pass(None)\u001B[0m\n", + "\u001B[34m>>>>>>>>Turing Action report: \n", + "execution failed,\n", + "exitcode: 1 (execution failed)\n", + " \n", + "unknown language unknown\u001B[0m\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001B[33mPlanManager\u001B[0m (to Turing)-[]:\n", + "\n", + "\"exitcode: 1 (execution failed)\\n \\nunknown language unknown\"\n", + "\n", + "--------------------------------------------------------------------------------\n", + "un_stream ai response: I apologize for the confusion, it seems like there was a misunderstanding. The text provided was an analysis and not a code to be executed. \n", + "\n", + "However, if you want to format this information in a more structured way using Python code, you can use the following code:\n", + "\n", + "```python\n", + "# python code\n", + "issues_data = [\n", + " {\"title\": \"[Bug] [remote_worker] 使用model-controller作为集群模式时，通过web进行知识库问答，会将所有模型请求一遍\", \"status\": \"Not specified\", \"created_date\": \"2024-04-10\"},\n", + " {\"title\": \"[Bug] LLMServer Generate Error, Please CheckErrorInfo.: CUDA error\", \"status\": \"Not specified\", \"created_date\": \"2024-04-10\"},\n", + " {\"title\": \"feat(agent): Release agent SDK\", \"status\": \"Not specified\", \"created_date\": \"2024-04-09\"},\n", + " {\"title\": \"feat(RAG):add metadata properties filters\", \"status\": \"Not specified\", \"created_date\": \"2024-04-09\"},\n", + " {\"title\": \"[Bug] [datasource] Unable to modify database table structure after executing query\", \"status\": \"Not specified\", \"created_date\": \"2024-04-09\"},\n", + " {\"title\": \"[Feature][RAG] Metadata Properties Filter\", \"status\": \"Not specified\", \"created_date\": \"2024-04-08\"},\n", + " {\"title\": \"Feat: support oceanbase monaco plugin\", \"status\": \"Not specified\", \"created_date\": \"2024-04-07\"}\n", + "]\n", + "\n", + "for issue in issues_data:\n", + " print(f\"Issue Title: {issue['title']}\\nStatus: {issue['status']}\\nCreated Date: {issue['created_date']}\\n\")\n", + "```\n", + "\n", + "This code creates a list of dictionaries where each dictionary represents an issue with its title, status, and created date. The code then iterates over this list and prints the information for each issue.\n", + "\u001B[31m\n", + ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001B[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "GroupChat is underpopulated with 1 agents. It is recommended to set speaker_selection_method to 'round_robin' or allow_repeat_speaker to False.Or, use direct communication instead.\n" + "execute_code was called without specifying a value for use_docker. Since the python docker package is not available, code will be run natively. Note: this fallback behavior is subject to change\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001B[33mplan_manager\u001B[0m (to CodeEngineer)-[]:\n", - "\n", - "\"The execution result of the code you wrote is judged as not answering the task question. Please re-understand and complete the task.\"\n", + "un_stream ai response: True\n", "\n", "--------------------------------------------------------------------------------\n", - "un_stream ai response: I apologize for the inconvenience. Let me provide you with a Python script that includes a check to ensure the `requests` module is installed. If it's not installed, the script will attempt to install it using `pip`. Please run the following Python script:\n", + "\u001B[33mTuring\u001B[0m (to PlanManager)-[gpt-4]:\n", "\n", - "```python\n", - "# filename: github_issues.py\n", - "import subprocess\n", - "import pkg_resources\n", - "from datetime import datetime, timedelta\n", + "\"I apologize for the confusion, it seems like there was a misunderstanding. The text provided was an analysis and not a code to be executed. \\n\\nHowever, if you want to format this information in a more structured way using Python code, you can use the following code:\\n\\n```python\\n# python code\\nissues_data = [\\n {\\\"title\\\": \\\"[Bug] [remote_worker] 使用model-controller作为集群模式时，通过web进行知识库问答，会将所有模型请求一遍\\\", \\\"status\\\": \\\"Not specified\\\", \\\"created_date\\\": \\\"2024-04-10\\\"},\\n {\\\"title\\\": \\\"[Bug] LLMServer Generate Error, Please CheckErrorInfo.: CUDA error\\\", \\\"status\\\": \\\"Not specified\\\", \\\"created_date\\\": \\\"2024-04-10\\\"},\\n {\\\"title\\\": \\\"feat(agent): Release agent SDK\\\", \\\"status\\\": \\\"Not specified\\\", \\\"created_date\\\": \\\"2024-04-09\\\"},\\n {\\\"title\\\": \\\"feat(RAG):add metadata properties filters\\\", \\\"status\\\": \\\"Not specified\\\", \\\"created_date\\\": \\\"2024-04-09\\\"},\\n {\\\"title\\\": \\\"[Bug] [datasource] Unable to modify database table structure after executing query\\\", \\\"status\\\": \\\"Not specified\\\", \\\"created_date\\\": \\\"2024-04-09\\\"},\\n {\\\"title\\\": \\\"[Feature][RAG] Metadata Properties Filter\\\", \\\"status\\\": \\\"Not specified\\\", \\\"created_date\\\": \\\"2024-04-08\\\"},\\n {\\\"title\\\": \\\"Feat: support oceanbase monaco plugin\\\", \\\"status\\\": \\\"Not specified\\\", \\\"created_date\\\": \\\"2024-04-07\\\"}\\n]\\n\\nfor issue in issues_data:\\n print(f\\\"Issue Title: {issue['title']}\\\\nStatus: {issue['status']}\\\\nCreated Date: {issue['created_date']}\\\\n\\\")\\n```\\n\\nThis code creates a list of dictionaries where each dictionary represents an issue with its title, status, and created date. The code then iterates over this list and prints the information for each issue.\"\n", + "\u001B[32m>>>>>>>>Turing Review info: \n", + "Pass(None)\u001B[0m\n", + "\u001B[34m>>>>>>>>Turing Action report: \n", + "execution succeeded,\n", "\n", - "# Ensure the 'requests' package is installed\n", - "required_packages = {'requests'}\n", - "installed_packages = {pkg.key for pkg in pkg_resources.working_set}\n", - "missing_packages = required_packages - installed_packages\n", + "Issue Title: [Bug] [remote_worker] 使用model-controller作为集群模式时，通过web进行知识库问答，会将所有模型请求一遍\n", + "Status: Not specified\n", + "Created Date: 2024-04-10\n", "\n", - "if missing_packages:\n", - " subprocess.check_call(['pip', 'install', *missing_packages])\n", + "Issue Title: [Bug] LLMServer Generate Error, Please CheckErrorInfo.: CUDA error\n", + "Status: Not specified\n", + "Created Date: 2024-04-10\n", "\n", - "import requests\n", + "Issue Title: feat(agent): Release agent SDK\n", + "Status: Not specified\n", + "Created Date: 2024-04-09\n", "\n", - "# Function to get issues created in the past three days from a GitHub repository\n", - "def get_recent_issues(owner, repo):\n", - " # Calculate the date for three days ago\n", - " three_days_ago = datetime.utcnow() - timedelta(days=3)\n", - " # Format the date in ISO 8601 format\n", - " since = three_days_ago.isoformat()\n", - "\n", - " # GitHub API URL for issues of the given repository\n", - " issues_url = f\"https://api.github.com/repos/{owner}/{repo}/issues\"\n", - " \n", - " # Parameters to get issues created in the past three days\n", - " params = {\n", - " 'since': since,\n", - " 'state': 'all', # Get both open and closed issues\n", - " }\n", - " \n", - " # Send a request to the GitHub API\n", - " response = requests.get(issues_url, params=params)\n", - " # Raise an exception if the request was unsuccessful\n", - " response.raise_for_status()\n", - " \n", - " # Parse the JSON response\n", - " issues = response.json()\n", - " \n", - " # Extract and print the creation date and status of each issue\n", - " for issue in issues:\n", - " created_at = issue['created_at']\n", - " state = issue['state']\n", - " print(f\"Issue created at: {created_at}, Status: {state}\")\n", - "\n", - "# Replace 'owner' and 'repo' with the actual owner and repository name\n", - "get_recent_issues('eosphoros-ai', 'DB-GPT')\n", - "```\n", + "Issue Title: feat(RAG):add metadata properties filters\n", + "Status: Not specified\n", + "Created Date: 2024-04-09\n", "\n", - "When you run this script, it will check for the `requests` module and install it if necessary. Then it will retrieve the issues from the 'eosphoros-ai/DB-GPT' repository created in the past three days and print their creation date and status.\n", - "\u001B[31m\n", - ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001B[0m\n", - "un_stream ai response: False\n", - "\u001B[33mCodeEngineer\u001B[0m (to plan_manager)-[gpt-4-vision-preview]:\n", - "\n", - "\"I apologize for the inconvenience. Let me provide you with a Python script that includes a check to ensure the `requests` module is installed. If it's not installed, the script will attempt to install it using `pip`. Please run the following Python script:\\n\\n```python\\n# filename: github_issues.py\\nimport subprocess\\nimport pkg_resources\\nfrom datetime import datetime, timedelta\\n\\n# Ensure the 'requests' package is installed\\nrequired_packages = {'requests'}\\ninstalled_packages = {pkg.key for pkg in pkg_resources.working_set}\\nmissing_packages = required_packages - installed_packages\\n\\nif missing_packages:\\n subprocess.check_call(['pip', 'install', *missing_packages])\\n\\nimport requests\\n\\n# Function to get issues created in the past three days from a GitHub repository\\ndef get_recent_issues(owner, repo):\\n # Calculate the date for three days ago\\n three_days_ago = datetime.utcnow() - timedelta(days=3)\\n # Format the date in ISO 8601 format\\n since = three_days_ago.isoformat()\\n\\n # GitHub API URL for issues of the given repository\\n issues_url = f\\\"https://api.github.com/repos/{owner}/{repo}/issues\\\"\\n \\n # Parameters to get issues created in the past three days\\n params = {\\n 'since': since,\\n 'state': 'all', # Get both open and closed issues\\n }\\n \\n # Send a request to the GitHub API\\n response = requests.get(issues_url, params=params)\\n # Raise an exception if the request was unsuccessful\\n response.raise_for_status()\\n \\n # Parse the JSON response\\n issues = response.json()\\n \\n # Extract and print the creation date and status of each issue\\n for issue in issues:\\n created_at = issue['created_at']\\n state = issue['state']\\n print(f\\\"Issue created at: {created_at}, Status: {state}\\\")\\n\\n# Replace 'owner' and 'repo' with the actual owner and repository name\\nget_recent_issues('eosphoros-ai', 'DB-GPT')\\n```\\n\\nWhen you run this script, it will check for the `requests` module and install it if necessary. Then it will retrieve the issues from the 'eosphoros-ai/DB-GPT' repository created in the past three days and print their creation date and status.\"\n", - "\u001B[32m>>>>>>>>CodeEngineer Review info: \n", - " Pass.None\u001B[0m\n", - "\u001B[34m>>>>>>>>CodeEngineer Action report: \n", - "execution succeeded,\n", + "Issue Title: [Bug] [datasource] Unable to modify database table structure after executing query\n", + "Status: Not specified\n", + "Created Date: 2024-04-09\n", + "\n", + "Issue Title: [Feature][RAG] Metadata Properties Filter\n", + "Status: Not specified\n", + "Created Date: 2024-04-08\n", + "\n", + "Issue Title: Feat: support oceanbase monaco plugin\n", + "Status: Not specified\n", + "Created Date: 2024-04-07\n", "\n", - "/workspace/github_issues.py:3: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", - " import pkg_resources\n", - "/workspace/github_issues.py:19: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).\n", - " three_days_ago = datetime.utcnow() - timedelta(days=3)\n", - "Issue created at: 2023-12-27T03:20:12Z, Status: open\n", - "Issue created at: 2023-12-26T16:13:42Z, Status: open\n", - "Issue created at: 2023-12-26T10:23:43Z, Status: open\n", - "Issue created at: 2023-12-26T07:25:37Z, Status: closed\n", - "Issue created at: 2023-12-26T05:01:26Z, Status: closed\n", - "Issue created at: 2023-12-26T03:54:15Z, Status: open\n", - "Issue created at: 2023-12-26T02:19:48Z, Status: open\n", - "Issue created at: 2023-12-25T12:26:40Z, Status: open\n", - "Issue created at: 2023-12-25T12:00:52Z, Status: open\n", - "Issue created at: 2023-12-25T08:36:56Z, Status: open\n", - "Issue created at: 2023-12-25T08:35:48Z, Status: open\n", - "Issue created at: 2023-12-25T08:34:13Z, Status: closed\n", - "Issue created at: 2023-12-25T06:33:37Z, Status: closed\n", - "Issue created at: 2023-12-23T11:47:40Z, Status: closed\n", - "Issue created at: 2023-12-23T03:01:23Z, Status: open\n", - "Issue created at: 2023-12-19T06:38:57Z, Status: open\n", - "Issue created at: 2023-12-11T08:34:08Z, Status: open\n", - "Issue created at: 2023-11-26T03:55:22Z, Status: open\n", - "Issue created at: 2023-11-25T12:38:23Z, Status: open\n", - "Issue created at: 2023-11-25T02:25:13Z, Status: open\n", - "Issue created at: 2023-11-25T02:09:58Z, Status: open\n", - "Issue created at: 2023-11-24T08:48:24Z, Status: open\n", - "Issue created at: 2023-11-24T06:03:56Z, Status: open\n", - "Issue created at: 2023-11-23T16:25:33Z, Status: open\n", - "Issue created at: 2023-11-23T08:54:36Z, Status: open\n", - "Issue created at: 2023-11-23T08:26:27Z, Status: open\n", - "Issue created at: 2023-11-22T06:42:34Z, Status: open\n", - "Issue created at: 2023-10-26T01:45:37Z, Status: open\n", - "Issue created at: 2023-08-30T06:02:32Z, Status: closed\n", - "Issue created at: 2023-06-18T01:11:22Z, Status: open\n", "\u001B[0m\n", "\n", "--------------------------------------------------------------------------------\n" @@ -433,86 +509,84 @@ "name": "stderr", "output_type": "stream", "text": [ - "GroupChat is underpopulated with 1 agents. It is recommended to set speaker_selection_method to 'round_robin' or allow_repeat_speaker to False.Or, use direct communication instead.\n" + "GroupChat select_speaker failed to resolve the next speaker's name. This is because the speaker selection OAI call returned:\n", + "CodeEngineer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001B[33mplan_manager\u001B[0m (to CodeEngineer)-[]:\n", - "\n", - "\"The execution result of the code you wrote is judged as not answering the task question. Please re-understand and complete the task.\"\n", "\n", "--------------------------------------------------------------------------------\n", - "un_stream ai response: I apologize for the oversight. Let's try a different approach to ensure we get the issues created in the past three days from the 'eosphoros-ai/DB-GPT' repository. I will provide you with an updated Python script that correctly retrieves and filters the issues based on their creation date.\n", - "\n", - "```python\n", - "# filename: github_issues.py\n", - "import requests\n", - "from datetime import datetime, timedelta\n", + "\u001B[33mPlanManager\u001B[0m (to Turing)-[]:\n", "\n", - "# Function to get issues created in the past three days from a GitHub repository\n", - "def get_recent_issues(owner, repo):\n", - " # Calculate the date for three days ago\n", - " three_days_ago = datetime.utcnow() - timedelta(days=3)\n", - " # Format the date in ISO 8601 format\n", - " since = three_days_ago.isoformat()\n", + "\"Read the result data of the dependent steps in the above historical message to complete the current goal:Group the extracted data by day and status.\"\n", "\n", - " # GitHub API URL for issues of the given repository\n", - " issues_url = f\"https://api.github.com/repos/{owner}/{repo}/issues\"\n", - "\n", - " # Parameters to get all issues, GitHub API does not allow filtering by creation date directly\n", - " params = {\n", - " 'state': 'all', # Get both open and closed issues\n", - " }\n", + "--------------------------------------------------------------------------------\n", + "un_stream ai response: ```python\n", + "# filename: group_issues.py\n", + "\n", + "import pandas as pd\n", + "from datetime import datetime\n", + "\n", + "# The issue data from the historical message\n", + "issue_data = [\n", + " {\"title\": \"[Bug] [remote_worker] 使用model-controller作为集群模式时，通过web进行知识库问答，会将所有模型请求一遍\", \"status\": \"Not specified\", \"created_date\": \"2024-04-10\"},\n", + " {\"title\": \"[Bug] LLMServer Generate Error, Please CheckErrorInfo.: CUDA error\", \"status\": \"Not specified\", \"created_date\": \"2024-04-10\"},\n", + " {\"title\": \"feat(agent): Release agent SDK\", \"status\": \"Not specified\", \"created_date\": \"2024-04-09\"},\n", + " {\"title\": \"feat(RAG):add metadata properties filters\", \"status\": \"Not specified\", \"created_date\": \"2024-04-09\"},\n", + " {\"title\": \"[Bug] [datasource] Unable to modify database table structure after executing query\", \"status\": \"Not specified\", \"created_date\": \"2024-04-09\"},\n", + " {\"title\": \"[Feature][RAG] Metadata Properties Filter\", \"status\": \"Not specified\", \"created_date\": \"2024-04-08\"},\n", + " {\"title\": \"Feat: support oceanbase monaco plugin\", \"status\": \"Not specified\", \"created_date\": \"2024-04-07\"},\n", + "]\n", "\n", - " # Send a request to the GitHub API\n", - " response = requests.get(issues_url, params=params)\n", - " # Raise an exception if the request was unsuccessful\n", - " response.raise_for_status()\n", + "# Convert the issue data into a DataFrame\n", + "df = pd.DataFrame(issue_data)\n", "\n", - " # Parse the JSON response\n", - " issues = response.json()\n", + "# Convert the 'created_date' column to datetime\n", + "df['created_date'] = pd.to_datetime(df['created_date'])\n", "\n", - " # Filter and print the creation date and status of each issue created in the past three days\n", - " for issue in issues:\n", - " created_at = datetime.strptime(issue['created_at'], \"%Y-%m-%dT%H:%M:%SZ\")\n", - " if created_at > three_days_ago:\n", - " state = issue['state']\n", - " print(f\"Issue created at: {created_at}, Status: {state}\")\n", + "# Group the data by date and status\n", + "grouped = df.groupby([df['created_date'].dt.date, 'status']).size()\n", "\n", - "# Replace 'owner' and 'repo' with the actual owner and repository name\n", - "get_recent_issues('eosphoros-ai', 'DB-GPT')\n", + "# Print the grouped data\n", + "print(grouped)\n", "```\n", "\n", - "This script retrieves all issues from the specified repository and then filters them based on the creation date to find the ones that were created in the past three days. It then prints the creation date and status of each issue that meets the criteria. Please execute this updated script.\n", + "Please save this Python script as `group_issues.py` and execute it. It will group the extracted issue data by day and status and print the result.\n", "\u001B[31m\n", - ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001B[0m\n", + ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001B[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "execute_code was called without specifying a value for use_docker. Since the python docker package is not available, code will be run natively. Note: this fallback behavior is subject to change\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "un_stream ai response: True\n", - "\u001B[33mCodeEngineer\u001B[0m (to plan_manager)-[gpt-4-vision-preview]:\n", "\n", - "\"I apologize for the oversight. Let's try a different approach to ensure we get the issues created in the past three days from the 'eosphoros-ai/DB-GPT' repository. I will provide you with an updated Python script that correctly retrieves and filters the issues based on their creation date.\\n\\n```python\\n# filename: github_issues.py\\nimport requests\\nfrom datetime import datetime, timedelta\\n\\n# Function to get issues created in the past three days from a GitHub repository\\ndef get_recent_issues(owner, repo):\\n # Calculate the date for three days ago\\n three_days_ago = datetime.utcnow() - timedelta(days=3)\\n # Format the date in ISO 8601 format\\n since = three_days_ago.isoformat()\\n\\n # GitHub API URL for issues of the given repository\\n issues_url = f\\\"https://api.github.com/repos/{owner}/{repo}/issues\\\"\\n\\n # Parameters to get all issues, GitHub API does not allow filtering by creation date directly\\n params = {\\n 'state': 'all', # Get both open and closed issues\\n }\\n\\n # Send a request to the GitHub API\\n response = requests.get(issues_url, params=params)\\n # Raise an exception if the request was unsuccessful\\n response.raise_for_status()\\n\\n # Parse the JSON response\\n issues = response.json()\\n\\n # Filter and print the creation date and status of each issue created in the past three days\\n for issue in issues:\\n created_at = datetime.strptime(issue['created_at'], \\\"%Y-%m-%dT%H:%M:%SZ\\\")\\n if created_at > three_days_ago:\\n state = issue['state']\\n print(f\\\"Issue created at: {created_at}, Status: {state}\\\")\\n\\n# Replace 'owner' and 'repo' with the actual owner and repository name\\nget_recent_issues('eosphoros-ai', 'DB-GPT')\\n```\\n\\nThis script retrieves all issues from the specified repository and then filters them based on the creation date to find the ones that were created in the past three days. It then prints the creation date and status of each issue that meets the criteria. Please execute this updated script.\"\n", - "\u001B[32m>>>>>>>>CodeEngineer Review info: \n", - " Pass.None\u001B[0m\n", - "\u001B[34m>>>>>>>>CodeEngineer Action report: \n", + "--------------------------------------------------------------------------------\n", + "\u001B[33mTuring\u001B[0m (to PlanManager)-[gpt-4]:\n", + "\n", + "\"```python\\n# filename: group_issues.py\\n\\nimport pandas as pd\\nfrom datetime import datetime\\n\\n# The issue data from the historical message\\nissue_data = [\\n {\\\"title\\\": \\\"[Bug] [remote_worker] 使用model-controller作为集群模式时，通过web进行知识库问答，会将所有模型请求一遍\\\", \\\"status\\\": \\\"Not specified\\\", \\\"created_date\\\": \\\"2024-04-10\\\"},\\n {\\\"title\\\": \\\"[Bug] LLMServer Generate Error, Please CheckErrorInfo.: CUDA error\\\", \\\"status\\\": \\\"Not specified\\\", \\\"created_date\\\": \\\"2024-04-10\\\"},\\n {\\\"title\\\": \\\"feat(agent): Release agent SDK\\\", \\\"status\\\": \\\"Not specified\\\", \\\"created_date\\\": \\\"2024-04-09\\\"},\\n {\\\"title\\\": \\\"feat(RAG):add metadata properties filters\\\", \\\"status\\\": \\\"Not specified\\\", \\\"created_date\\\": \\\"2024-04-09\\\"},\\n {\\\"title\\\": \\\"[Bug] [datasource] Unable to modify database table structure after executing query\\\", \\\"status\\\": \\\"Not specified\\\", \\\"created_date\\\": \\\"2024-04-09\\\"},\\n {\\\"title\\\": \\\"[Feature][RAG] Metadata Properties Filter\\\", \\\"status\\\": \\\"Not specified\\\", \\\"created_date\\\": \\\"2024-04-08\\\"},\\n {\\\"title\\\": \\\"Feat: support oceanbase monaco plugin\\\", \\\"status\\\": \\\"Not specified\\\", \\\"created_date\\\": \\\"2024-04-07\\\"},\\n]\\n\\n# Convert the issue data into a DataFrame\\ndf = pd.DataFrame(issue_data)\\n\\n# Convert the 'created_date' column to datetime\\ndf['created_date'] = pd.to_datetime(df['created_date'])\\n\\n# Group the data by date and status\\ngrouped = df.groupby([df['created_date'].dt.date, 'status']).size()\\n\\n# Print the grouped data\\nprint(grouped)\\n```\\n\\nPlease save this Python script as `group_issues.py` and execute it. It will group the extracted issue data by day and status and print the result.\"\n", + "\u001B[32m>>>>>>>>Turing Review info: \n", + "Pass(None)\u001B[0m\n", + "\u001B[34m>>>>>>>>Turing Action report: \n", "execution succeeded,\n", "\n", - "/workspace/github_issues.py:8: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).\n", - " three_days_ago = datetime.utcnow() - timedelta(days=3)\n", - "Issue created at: 2023-12-27 03:20:12, Status: open\n", - "Issue created at: 2023-12-26 16:13:42, Status: open\n", - "Issue created at: 2023-12-26 10:23:43, Status: open\n", - "Issue created at: 2023-12-26 07:25:37, Status: closed\n", - "Issue created at: 2023-12-26 05:01:26, Status: closed\n", - "Issue created at: 2023-12-26 03:54:15, Status: open\n", - "Issue created at: 2023-12-26 02:19:48, Status: open\n", - "Issue created at: 2023-12-25 12:26:40, Status: open\n", - "Issue created at: 2023-12-25 12:00:52, Status: open\n", - "Issue created at: 2023-12-25 08:36:56, Status: open\n", - "Issue created at: 2023-12-25 08:35:48, Status: open\n", - "Issue created at: 2023-12-25 08:34:13, Status: closed\n", - "Issue created at: 2023-12-25 06:33:37, Status: closed\n", + "created_date status \n", + "2024-04-07 Not specified 1\n", + "2024-04-08 Not specified 1\n", + "2024-04-09 Not specified 3\n", + "2024-04-10 Not specified 2\n", + "dtype: int64\n", "\u001B[0m\n", "\n", "--------------------------------------------------------------------------------\n" @@ -522,136 +596,154 @@ "name": "stderr", "output_type": "stream", "text": [ - "GroupChat is underpopulated with 1 agents. It is recommended to set speaker_selection_method to 'round_robin' or allow_repeat_speaker to False.Or, use direct communication instead.\n" + "GroupChat select_speaker failed to resolve the next speaker's name. This is because the speaker selection OAI call returned:\n", + "CodeEngineer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001B[33mplan_manager\u001B[0m (to CodeEngineer)-[]:\n", "\n", - "\"Read the result data of the dependent steps in the above historical message to complete the current goal:Write a Python script to process the retrieved issues data to group them by creation date and status. Then, format the grouped data into a Markdown table.\"\n", + "--------------------------------------------------------------------------------\n", + "\u001B[33mPlanManager\u001B[0m (to Turing)-[]:\n", + "\n", + "\"Read the result data of the dependent steps in the above historical message to complete the current goal:Create a Markdown table with the grouped data.\"\n", "\n", "--------------------------------------------------------------------------------\n", - "un_stream ai response: ```python\n", - "# Let's process the retrieved issues data to group them by creation date and status.\n", - "# Then, we will format the grouped data into a Markdown table.\n", + "un_stream ai response: To create a Markdown table with the grouped data, we can write a Python function that converts the data into a Markdown table format.\n", "\n", - "# The data from the previous step\n", - "issues_data = [\n", - " {\"created_at\": \"2023-12-27 03:20:12\", \"status\": \"open\"},\n", - " {\"created_at\": \"2023-12-26 16:13:42\", \"status\": \"open\"},\n", - " {\"created_at\": \"2023-12-26 10:23:43\", \"status\": \"open\"},\n", - " {\"created_at\": \"2023-12-26 07:25:37\", \"status\": \"closed\"},\n", - " {\"created_at\": \"2023-12-26 05:01:26\", \"status\": \"closed\"},\n", - " {\"created_at\": \"2023-12-26 03:54:15\", \"status\": \"open\"},\n", - " {\"created_at\": \"2023-12-26 02:19:48\", \"status\": \"open\"},\n", - " {\"created_at\": \"2023-12-25 12:26:40\", \"status\": \"open\"},\n", - " {\"created_at\": \"2023-12-25 12:00:52\", \"status\": \"open\"},\n", - " {\"created_at\": \"2023-12-25 08:36:56\", \"status\": \"open\"},\n", - " {\"created_at\": \"2023-12-25 08:35:48\", \"status\": \"open\"},\n", - " {\"created_at\": \"2023-12-25 08:34:13\", \"status\": \"closed\"},\n", - " {\"created_at\": \"2023-12-25 06:33:37\", \"status\": \"closed\"},\n", - "]\n", + "```python\n", + "# python code\n", + "def create_markdown_table(data):\n", + " header = \"| \" + \" | \".join(data.columns) + \" |\"\n", + " separator = \"| \" + \" | \".join(\"---\" for _ in data.columns) + \" |\"\n", + " rows = [\"| \" + \" | \".join(str(x) for x in row) + \" |\" for row in data.values]\n", + " return \"\\n\".join([header, separator] + rows)\n", + "\n", + "# Assuming the data is stored in a pandas DataFrame named 'df'\n", + "print(create_markdown_table(df))\n", + "```\n", "\n", - "# Grouping the issues by creation date and status\n", - "from collections import defaultdict\n", - "grouped_issues = defaultdict(lambda: defaultdict(list))\n", + "Please replace the `df` with the actual DataFrame that contains your data. This function will output a Markdown table as a string. The table can be copied and pasted into any Markdown file or Markdown-supported text field.\n", + "\u001B[31m\n", + ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001B[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "execute_code was called without specifying a value for use_docker. Since the python docker package is not available, code will be run natively. Note: this fallback behavior is subject to change\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "for issue in issues_data:\n", - " date = issue[\"created_at\"].split(' ')[0] # Extract just the date part\n", - " grouped_issues[date][issue[\"status\"]].append(issue)\n", + "--------------------------------------------------------------------------------\n", + "\u001B[33mTuring\u001B[0m (to PlanManager)-[gpt-4]:\n", + "\n", + "\"To create a Markdown table with the grouped data, we can write a Python function that converts the data into a Markdown table format.\\n\\n```python\\n# python code\\ndef create_markdown_table(data):\\n header = \\\"| \\\" + \\\" | \\\".join(data.columns) + \\\" |\\\"\\n separator = \\\"| \\\" + \\\" | \\\".join(\\\"---\\\" for _ in data.columns) + \\\" |\\\"\\n rows = [\\\"| \\\" + \\\" | \\\".join(str(x) for x in row) + \\\" |\\\" for row in data.values]\\n return \\\"\\\\n\\\".join([header, separator] + rows)\\n\\n# Assuming the data is stored in a pandas DataFrame named 'df'\\nprint(create_markdown_table(df))\\n```\\n\\nPlease replace the `df` with the actual DataFrame that contains your data. This function will output a Markdown table as a string. The table can be copied and pasted into any Markdown file or Markdown-supported text field.\"\n", + "\u001B[32m>>>>>>>>Turing Review info: \n", + "Pass(None)\u001B[0m\n", + "\u001B[34m>>>>>>>>Turing Action report: \n", + "execution failed,\n", + "exitcode: 1 (execution failed)\n", + " \n", + "Traceback (most recent call last):\n", + " File \"\", line 9, in \n", + " print(create_markdown_table(df))\n", + "NameError: name 'df' is not defined\n", + "\u001B[0m\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001B[33mPlanManager\u001B[0m (to Turing)-[]:\n", + "\n", + "\"exitcode: 1 (execution failed)\\n \\nTraceback (most recent call last):\\n File \\\"\\\", line 9, in \\n print(create_markdown_table(df))\\nNameError: name 'df' is not defined\\n\"\n", + "\n", + "--------------------------------------------------------------------------------\n", + "un_stream ai response: Apologies for the confusion. It seems there was a misunderstanding. The 'df' was a placeholder for your actual data. However, since there's no data provided in the context, I'll assume the data based on the previous responses and create a markdown table. \n", + "\n", + "```python\n", + "# python code\n", + "import pandas as pd\n", + "\n", + "# Assuming the data is like this:\n", + "data = {\n", + " 'created_date': ['2024-04-07', '2024-04-08', '2024-04-09', '2024-04-10'],\n", + " 'status': ['Not specified', 'Not specified', 'Not specified', 'Not specified'],\n", + " 'count': [1, 1, 3, 2]\n", + "}\n", "\n", - "# Format the grouped data into a Markdown table\n", - "markdown_table = \"Date | Open Issues | Closed Issues\\n\"\n", - "markdown_table += \"--- | --- | ---\\n\"\n", + "df = pd.DataFrame(data)\n", "\n", - "for date, statuses in sorted(grouped_issues.items()):\n", - " open_issues = len(statuses[\"open\"]) if \"open\" in statuses else 0\n", - " closed_issues = len(statuses[\"closed\"]) if \"closed\" in statuses else 0\n", - " markdown_table += f\"{date} | {open_issues} | {closed_issues}\\n\"\n", + "def create_markdown_table(data):\n", + " header = \"| \" + \" | \".join(data.columns) + \" |\"\n", + " separator = \"| \" + \" | \".join(\"---\" for _ in data.columns) + \" |\"\n", + " rows = [\"| \" + \" | \".join(str(x) for x in row) + \" |\" for row in data.values]\n", + " return \"\\n\".join([header, separator] + rows)\n", "\n", - "print(markdown_table)\n", + "print(create_markdown_table(df))\n", "```\n", "\n", - "When you run this script, it will print out a Markdown table with the number of open and closed issues grouped by their creation dates.\n", + "Please replace the 'data' dictionary with your actual data in the same format. This script will create a markdown table from the given data.\n", "\u001B[31m\n", - ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001B[0m\n", + ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001B[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "execute_code was called without specifying a value for use_docker. Since the python docker package is not available, code will be run natively. Note: this fallback behavior is subject to change\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "un_stream ai response: True\n", - "\u001B[33mCodeEngineer\u001B[0m (to plan_manager)-[gpt-4-vision-preview]:\n", "\n", - "\"```python\\n# Let's process the retrieved issues data to group them by creation date and status.\\n# Then, we will format the grouped data into a Markdown table.\\n\\n# The data from the previous step\\nissues_data = [\\n {\\\"created_at\\\": \\\"2023-12-27 03:20:12\\\", \\\"status\\\": \\\"open\\\"},\\n {\\\"created_at\\\": \\\"2023-12-26 16:13:42\\\", \\\"status\\\": \\\"open\\\"},\\n {\\\"created_at\\\": \\\"2023-12-26 10:23:43\\\", \\\"status\\\": \\\"open\\\"},\\n {\\\"created_at\\\": \\\"2023-12-26 07:25:37\\\", \\\"status\\\": \\\"closed\\\"},\\n {\\\"created_at\\\": \\\"2023-12-26 05:01:26\\\", \\\"status\\\": \\\"closed\\\"},\\n {\\\"created_at\\\": \\\"2023-12-26 03:54:15\\\", \\\"status\\\": \\\"open\\\"},\\n {\\\"created_at\\\": \\\"2023-12-26 02:19:48\\\", \\\"status\\\": \\\"open\\\"},\\n {\\\"created_at\\\": \\\"2023-12-25 12:26:40\\\", \\\"status\\\": \\\"open\\\"},\\n {\\\"created_at\\\": \\\"2023-12-25 12:00:52\\\", \\\"status\\\": \\\"open\\\"},\\n {\\\"created_at\\\": \\\"2023-12-25 08:36:56\\\", \\\"status\\\": \\\"open\\\"},\\n {\\\"created_at\\\": \\\"2023-12-25 08:35:48\\\", \\\"status\\\": \\\"open\\\"},\\n {\\\"created_at\\\": \\\"2023-12-25 08:34:13\\\", \\\"status\\\": \\\"closed\\\"},\\n {\\\"created_at\\\": \\\"2023-12-25 06:33:37\\\", \\\"status\\\": \\\"closed\\\"},\\n]\\n\\n# Grouping the issues by creation date and status\\nfrom collections import defaultdict\\ngrouped_issues = defaultdict(lambda: defaultdict(list))\\n\\nfor issue in issues_data:\\n date = issue[\\\"created_at\\\"].split(' ')[0] # Extract just the date part\\n grouped_issues[date][issue[\\\"status\\\"]].append(issue)\\n\\n# Format the grouped data into a Markdown table\\nmarkdown_table = \\\"Date | Open Issues | Closed Issues\\\\n\\\"\\nmarkdown_table += \\\"--- | --- | ---\\\\n\\\"\\n\\nfor date, statuses in sorted(grouped_issues.items()):\\n open_issues = len(statuses[\\\"open\\\"]) if \\\"open\\\" in statuses else 0\\n closed_issues = len(statuses[\\\"closed\\\"]) if \\\"closed\\\" in statuses else 0\\n markdown_table += f\\\"{date} | {open_issues} | {closed_issues}\\\\n\\\"\\n\\nprint(markdown_table)\\n```\\n\\nWhen you run this script, it will print out a Markdown table with the number of open and closed issues grouped by their creation dates.\"\n", - "\u001B[32m>>>>>>>>CodeEngineer Review info: \n", - " Pass.None\u001B[0m\n", - "\u001B[34m>>>>>>>>CodeEngineer Action report: \n", - "execution succeeded,\n", + "--------------------------------------------------------------------------------\n", + "\u001B[33mTuring\u001B[0m (to PlanManager)-[gpt-4]:\n", "\n", - "Date | Open Issues | Closed Issues\n", - "--- | --- | ---\n", - "2023-12-25 | 4 | 2\n", - "2023-12-26 | 4 | 2\n", - "2023-12-27 | 1 | 0\n", + "\"Apologies for the confusion. It seems there was a misunderstanding. The 'df' was a placeholder for your actual data. However, since there's no data provided in the context, I'll assume the data based on the previous responses and create a markdown table. \\n\\n```python\\n# python code\\nimport pandas as pd\\n\\n# Assuming the data is like this:\\ndata = {\\n 'created_date': ['2024-04-07', '2024-04-08', '2024-04-09', '2024-04-10'],\\n 'status': ['Not specified', 'Not specified', 'Not specified', 'Not specified'],\\n 'count': [1, 1, 3, 2]\\n}\\n\\ndf = pd.DataFrame(data)\\n\\ndef create_markdown_table(data):\\n header = \\\"| \\\" + \\\" | \\\".join(data.columns) + \\\" |\\\"\\n separator = \\\"| \\\" + \\\" | \\\".join(\\\"---\\\" for _ in data.columns) + \\\" |\\\"\\n rows = [\\\"| \\\" + \\\" | \\\".join(str(x) for x in row) + \\\" |\\\" for row in data.values]\\n return \\\"\\\\n\\\".join([header, separator] + rows)\\n\\nprint(create_markdown_table(df))\\n```\\n\\nPlease replace the 'data' dictionary with your actual data in the same format. This script will create a markdown table from the given data.\"\n", + "\u001B[32m>>>>>>>>Turing Review info: \n", + "Pass(None)\u001B[0m\n", + "\u001B[34m>>>>>>>>Turing Action report: \n", + "execution succeeded,\n", "\n", + "| created_date | status | count |\n", + "| --- | --- | --- |\n", + "| 2024-04-07 | Not specified | 1 |\n", + "| 2024-04-08 | Not specified | 1 |\n", + "| 2024-04-09 | Not specified | 3 |\n", + "| 2024-04-10 | Not specified | 2 |\n", "\u001B[0m\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33mplan_manager\u001B[0m (to User)-[None]:\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001B[33mPlanManager\u001B[0m (to User)-[]:\n", "\n", "\"Obtain simple information about issues in the repository 'eosphoros-ai/DB-GPT' in the past three days and analyze the data. Create a Markdown table grouped by day and status.\"\n", - "\u001B[32m>>>>>>>>plan_manager Review info: \n", - " Pass.None\u001B[0m\n", - "\u001B[34m>>>>>>>>plan_manager Action report: \n", + "\u001B[32m>>>>>>>>PlanManager Review info: \n", + "Pass(None)\u001B[0m\n", + "\u001B[34m>>>>>>>>PlanManager Action report: \n", "execution succeeded,\n", - "TERMINATE\u001B[0m\n", + "```vis-code\n", + "{\"exit_success\": true, \"language\": \"python\", \"code\": [[\"python\", \"# python code\\nimport pandas as pd\\n\\n# Assuming the data is like this:\\ndata = {\\n 'created_date': ['2024-04-07', '2024-04-08', '2024-04-09', '2024-04-10'],\\n 'status': ['Not specified', 'Not specified', 'Not specified', 'Not specified'],\\n 'count': [1, 1, 3, 2]\\n}\\n\\ndf = pd.DataFrame(data)\\n\\ndef create_markdown_table(data):\\n header = \\\"| \\\" + \\\" | \\\".join(data.columns) + \\\" |\\\"\\n separator = \\\"| \\\" + \\\" | \\\".join(\\\"---\\\" for _ in data.columns) + \\\" |\\\"\\n rows = [\\\"| \\\" + \\\" | \\\".join(str(x) for x in row) + \\\" |\\\" for row in data.values]\\n return \\\"\\\\n\\\".join([header, separator] + rows)\\n\\nprint(create_markdown_table(df))\"]], \"log\": \"\\n| created_date | status | count |\\n| --- | --- | --- |\\n| 2024-04-07 | Not specified | 1 |\\n| 2024-04-08 | Not specified | 1 |\\n| 2024-04-09 | Not specified | 3 |\\n| 2024-04-10 | Not specified | 2 |\\n\"}\n", + "```\u001B[0m\n", "\n", - "--------------------------------------------------------------------------------\n", - "```agent-messages\n", - "[{\"sender\": \"User\", \"receiver\": \"plan_manager\", \"model\": null, \"markdown\": \"Obtain simple information about issues in the repository 'eosphoros-ai/DB-GPT' in the past three days and analyze the data. Create a Markdown table grouped by day and status.\"}, {\"sender\": \"plan_manager\", \"receiver\": \"Planner\", \"model\": null, \"markdown\": \"Obtain simple information about issues in the repository 'eosphoros-ai/DB-GPT' in the past three days and analyze the data. Create a Markdown table grouped by day and status.\"}, {\"sender\": \"Planner\", \"receiver\": \"plan_manager\", \"model\": \"gpt-4-vision-preview\", \"markdown\": \"1,Write a Python script to use the GitHub API to retrieve issues from the 'eosphoros-ai/DB-GPT' repository that were created in the past three days. The script should extract the issue's creation date and status.\\n2,Write a Python script to process the retrieved issues data to group them by creation date and status. Then, format the grouped data into a Markdown table.\"}, {\"sender\": \"plan_manager\", \"receiver\": \"User\", \"model\": null, \"markdown\": \"TERMINATE\"}]\n", - "```\n", - "```agent-plans\n", - "[{\"name\": \"Write a Python script to use the GitHub API to retrieve issues from the 'eosphoros-ai/DB-GPT' repository that were created in the past three days. The script should extract the issue's creation date and status.\", \"num\": \"1\", \"status\": \"complete\", \"agent\": \"CodeEngineer\", \"markdown\": \"```agent-messages\\n[{\\\"sender\\\": \\\"plan_manager\\\", \\\"receiver\\\": \\\"CodeEngineer\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"Write a Python script to use the GitHub API to retrieve issues from the 'eosphoros-ai/DB-GPT' repository that were created in the past three days. The script should extract the issue's creation date and status.\\\"}, {\\\"sender\\\": \\\"CodeEngineer\\\", \\\"receiver\\\": \\\"plan_manager\\\", \\\"model\\\": \\\"gpt-4-vision-preview\\\", \\\"markdown\\\": \\\"```vis-code\\\\n{\\\\\\\"exit_success\\\\\\\": false, \\\\\\\"language\\\\\\\": \\\\\\\"python\\\\\\\", \\\\\\\"code\\\\\\\": [[\\\\\\\"python\\\\\\\", \\\\\\\"# filename: github_issues.py\\\\\\\\nimport requests\\\\\\\\nfrom datetime import datetime, timedelta\\\\\\\\n\\\\\\\\n# Function to get issues created in the past three days from a GitHub repository\\\\\\\\ndef get_recent_issues(owner, repo):\\\\\\\\n # Calculate the date for three days ago\\\\\\\\n three_days_ago = datetime.utcnow() - timedelta(days=3)\\\\\\\\n # Format the date in ISO 8601 format\\\\\\\\n since = three_days_ago.isoformat()\\\\\\\\n\\\\\\\\n # GitHub API URL for issues of the given repository\\\\\\\\n issues_url = f\\\\\\\\\\\\\\\"https://api.github.com/repos/{owner}/{repo}/issues\\\\\\\\\\\\\\\"\\\\\\\\n \\\\\\\\n # Parameters to get issues created in the past three days\\\\\\\\n params = {\\\\\\\\n 'since': since,\\\\\\\\n 'state': 'all', # Get both open and closed issues\\\\\\\\n }\\\\\\\\n \\\\\\\\n # Send a request to the GitHub API\\\\\\\\n response = requests.get(issues_url, params=params)\\\\\\\\n # Raise an exception if the request was unsuccessful\\\\\\\\n response.raise_for_status()\\\\\\\\n \\\\\\\\n # Parse the JSON response\\\\\\\\n issues = response.json()\\\\\\\\n \\\\\\\\n # Extract and print the creation date and status of each issue\\\\\\\\n for issue in issues:\\\\\\\\n created_at = issue['created_at']\\\\\\\\n state = issue['state']\\\\\\\\n print(f\\\\\\\\\\\\\\\"Issue created at: {created_at}, Status: {state}\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\\n# Replace 'owner' and 'repo' with the actual owner and repository name\\\\\\\\nget_recent_issues('eosphoros-ai', 'DB-GPT')\\\\\\\"]], \\\\\\\"log\\\\\\\": \\\\\\\"\\\\\\\\nTraceback (most recent call last):\\\\\\\\n File \\\\\\\\\\\\\\\"github_issues.py\\\\\\\\\\\\\\\", line 2, in \\\\\\\\n import requests\\\\\\\\nModuleNotFoundError: No module named 'requests'\\\\\\\\n\\\\\\\"}\\\\n```\\\"}, {\\\"sender\\\": \\\"plan_manager\\\", \\\"receiver\\\": \\\"CodeEngineer\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"exitcode: 1 (execution failed)\\\\n \\\\nTraceback (most recent call last):\\\\n File \\\\\\\"github_issues.py\\\\\\\", line 2, in \\\\n import requests\\\\nModuleNotFoundError: No module named 'requests'\\\\n\\\"}, {\\\"sender\\\": \\\"CodeEngineer\\\", \\\"receiver\\\": \\\"plan_manager\\\", \\\"model\\\": \\\"gpt-4-vision-preview\\\", \\\"markdown\\\": \\\"```vis-code\\\\n{\\\\\\\"exit_success\\\\\\\": true, \\\\\\\"language\\\\\\\": \\\\\\\"sh\\\\\\\", \\\\\\\"code\\\\\\\": [[\\\\\\\"sh\\\\\\\", \\\\\\\"pip install requests\\\\\\\"], [\\\\\\\"python\\\\\\\", \\\\\\\"# filename: github_issues.py\\\\\\\\nimport requests\\\\\\\\nfrom datetime import datetime, timedelta\\\\\\\\n\\\\\\\\n# Function to get issues created in the past three days from a GitHub repository\\\\\\\\ndef get_recent_issues(owner, repo):\\\\\\\\n # Calculate the date for three days ago\\\\\\\\n three_days_ago = datetime.utcnow() - timedelta(days=3)\\\\\\\\n # Format the date in ISO 8601 format\\\\\\\\n since = three_days_ago.isoformat()\\\\\\\\n\\\\\\\\n # GitHub API URL for issues of the given repository\\\\\\\\n issues_url = f\\\\\\\\\\\\\\\"https://api.github.com/repos/{owner}/{repo}/issues\\\\\\\\\\\\\\\"\\\\\\\\n \\\\\\\\n # Parameters to get issues created in the past three days\\\\\\\\n params = {\\\\\\\\n 'since': since,\\\\\\\\n 'state': 'all', # Get both open and closed issues\\\\\\\\n }\\\\\\\\n \\\\\\\\n # Send a request to the GitHub API\\\\\\\\n response = requests.get(issues_url, params=params)\\\\\\\\n # Raise an exception if the request was unsuccessful\\\\\\\\n response.raise_for_status()\\\\\\\\n \\\\\\\\n # Parse the JSON response\\\\\\\\n issues = response.json()\\\\\\\\n \\\\\\\\n # Extract and print the creation date and status of each issue\\\\\\\\n for issue in issues:\\\\\\\\n created_at = issue['created_at']\\\\\\\\n state = issue['state']\\\\\\\\n print(f\\\\\\\\\\\\\\\"Issue created at: {created_at}, Status: {state}\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\\n# Replace 'owner' and 'repo' with the actual owner and repository name\\\\\\\\nget_recent_issues('eosphoros-ai', 'DB-GPT')\\\\\\\"]], \\\\\\\"log\\\\\\\": \\\\\\\"\\\\\\\\nCollecting requests\\\\\\\\n Obtaining dependency information for requests from https://files.pythonhosted.org/packages/70/8e/0e2d847013cb52cd35b38c009bb167a1a26b2ce6cd6965bf26b47bc0bf44/requests-2.31.0-py3-none-any.whl.metadata\\\\\\\\n Downloading requests-2.31.0-py3-none-any.whl.metadata (4.6 kB)\\\\\\\\nCollecting charset-normalizer<4,>=2 (from requests)\\\\\\\\n Obtaining dependency information for charset-normalizer<4,>=2 from https://files.pythonhosted.org/packages/45/59/3d27019d3b447a88fe7e7d004a1e04be220227760264cc41b405e863891b/charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_aarch64.whl.metadata\\\\\\\\n Downloading charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_aarch64.whl.metadata (33 kB)\\\\\\\\nCollecting idna<4,>=2.5 (from requests)\\\\\\\\n Obtaining dependency information for idna<4,>=2.5 from https://files.pythonhosted.org/packages/c2/e7/a82b05cf63a603df6e68d59ae6a68bf5064484a0718ea5033660af4b54a9/idna-3.6-py3-none-any.whl.metadata\\\\\\\\n Downloading idna-3.6-py3-none-any.whl.metadata (9.9 kB)\\\\\\\\nCollecting urllib3<3,>=1.21.1 (from requests)\\\\\\\\n Obtaining dependency information for urllib3<3,>=1.21.1 from https://files.pythonhosted.org/packages/96/94/c31f58c7a7f470d5665935262ebd7455c7e4c7782eb525658d3dbf4b9403/urllib3-2.1.0-py3-none-any.whl.metadata\\\\\\\\n Downloading urllib3-2.1.0-py3-none-any.whl.metadata (6.4 kB)\\\\\\\\nCollecting certifi>=2017.4.17 (from requests)\\\\\\\\n Obtaining dependency information for certifi>=2017.4.17 from https://files.pythonhosted.org/packages/64/62/428ef076be88fa93716b576e4a01f919d25968913e817077a386fcbe4f42/certifi-2023.11.17-py3-none-any.whl.metadata\\\\\\\\n Downloading certifi-2023.11.17-py3-none-any.whl.metadata (2.2 kB)\\\\\\\\nDownloading requests-2.31.0-py3-none-any.whl (62 kB)\\\\\\\\n \\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501 62.6/62.6 kB 1.9 MB/s eta 0:00:00\\\\\\\\nDownloading certifi-2023.11.17-py3-none-any.whl (162 kB)\\\\\\\\n \\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501 162.5/162.5 kB 1.9 MB/s eta 0:00:00\\\\\\\\nDownloading charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_aarch64.whl (138 kB)\\\\\\\\n 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\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501 61.6/61.6 kB 3.2 MB/s eta 0:00:00\\\\\\\\nDownloading urllib3-2.1.0-py3-none-any.whl (104 kB)\\\\\\\\n \\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501\\\\\\\\u2501 104.6/104.6 kB 4.9 MB/s eta 0:00:00\\\\\\\\nInstalling collected packages: urllib3, idna, charset-normalizer, certifi, requests\\\\\\\\nSuccessfully installed certifi-2023.11.17 charset-normalizer-3.3.2 idna-3.6 requests-2.31.0 urllib3-2.1.0\\\\\\\\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\\\\\\\\n\\\\\\\\n[notice] A new release of pip is available: 23.2.1 -> 23.3.2\\\\\\\\n[notice] To update, run: pip install --upgrade pip\\\\\\\\n\\\\\\\\n/workspace/github_issues.py:8: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).\\\\\\\\n three_days_ago = datetime.utcnow() - timedelta(days=3)\\\\\\\\nIssue created at: 2023-12-27T03:20:12Z, Status: open\\\\\\\\nIssue created at: 2023-12-26T16:13:42Z, Status: open\\\\\\\\nIssue created at: 2023-12-26T10:23:43Z, Status: open\\\\\\\\nIssue created at: 2023-12-26T07:25:37Z, Status: closed\\\\\\\\nIssue created at: 2023-12-26T05:01:26Z, Status: closed\\\\\\\\nIssue created at: 2023-12-26T03:54:15Z, Status: open\\\\\\\\nIssue created at: 2023-12-26T02:19:48Z, Status: open\\\\\\\\nIssue created at: 2023-12-25T12:26:40Z, Status: open\\\\\\\\nIssue created at: 2023-12-25T12:00:52Z, Status: open\\\\\\\\nIssue created at: 2023-12-25T08:36:56Z, Status: open\\\\\\\\nIssue created at: 2023-12-25T08:35:48Z, Status: open\\\\\\\\nIssue created at: 2023-12-25T08:34:13Z, Status: closed\\\\\\\\nIssue created at: 2023-12-25T06:33:37Z, Status: closed\\\\\\\\nIssue created at: 2023-12-23T11:47:40Z, Status: closed\\\\\\\\nIssue created at: 2023-12-23T03:01:23Z, Status: open\\\\\\\\nIssue created at: 2023-12-19T06:38:57Z, Status: open\\\\\\\\nIssue created at: 2023-12-11T08:34:08Z, Status: open\\\\\\\\nIssue created at: 2023-11-26T03:55:22Z, Status: open\\\\\\\\nIssue created at: 2023-11-25T12:38:23Z, Status: open\\\\\\\\nIssue created at: 2023-11-25T02:25:13Z, Status: open\\\\\\\\nIssue created at: 2023-11-25T02:09:58Z, Status: open\\\\\\\\nIssue created at: 2023-11-24T08:48:24Z, Status: open\\\\\\\\nIssue created at: 2023-11-24T06:03:56Z, Status: open\\\\\\\\nIssue created at: 2023-11-23T16:25:33Z, Status: open\\\\\\\\nIssue created at: 2023-11-23T08:54:36Z, Status: open\\\\\\\\nIssue created at: 2023-11-23T08:26:27Z, Status: open\\\\\\\\nIssue created at: 2023-11-22T06:42:34Z, Status: open\\\\\\\\nIssue created at: 2023-10-26T01:45:37Z, Status: open\\\\\\\\nIssue created at: 2023-08-30T06:02:32Z, Status: closed\\\\\\\\nIssue created at: 2023-06-18T01:11:22Z, Status: open\\\\\\\\n\\\\\\\"}\\\\n```\\\"}, {\\\"sender\\\": \\\"plan_manager\\\", \\\"receiver\\\": \\\"CodeEngineer\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"The execution result of the code you wrote is judged as not answering the task question. Please re-understand and complete the task.\\\"}, {\\\"sender\\\": \\\"CodeEngineer\\\", \\\"receiver\\\": \\\"plan_manager\\\", \\\"model\\\": \\\"gpt-4-vision-preview\\\", \\\"markdown\\\": \\\"```vis-code\\\\n{\\\\\\\"exit_success\\\\\\\": true, \\\\\\\"language\\\\\\\": \\\\\\\"python\\\\\\\", \\\\\\\"code\\\\\\\": [[\\\\\\\"python\\\\\\\", \\\\\\\"# filename: github_issues.py\\\\\\\\nimport subprocess\\\\\\\\nimport pkg_resources\\\\\\\\nfrom datetime import datetime, timedelta\\\\\\\\n\\\\\\\\n# Ensure the 'requests' package is installed\\\\\\\\nrequired_packages = {'requests'}\\\\\\\\ninstalled_packages = {pkg.key for pkg in pkg_resources.working_set}\\\\\\\\nmissing_packages = required_packages - installed_packages\\\\\\\\n\\\\\\\\nif missing_packages:\\\\\\\\n subprocess.check_call(['pip', 'install', *missing_packages])\\\\\\\\n\\\\\\\\nimport requests\\\\\\\\n\\\\\\\\n# Function to get issues created in the past three days from a GitHub repository\\\\\\\\ndef get_recent_issues(owner, repo):\\\\\\\\n # Calculate the date for three days ago\\\\\\\\n three_days_ago = datetime.utcnow() - timedelta(days=3)\\\\\\\\n # Format the date in ISO 8601 format\\\\\\\\n since = three_days_ago.isoformat()\\\\\\\\n\\\\\\\\n # GitHub API URL for issues of the given repository\\\\\\\\n issues_url = f\\\\\\\\\\\\\\\"https://api.github.com/repos/{owner}/{repo}/issues\\\\\\\\\\\\\\\"\\\\\\\\n \\\\\\\\n # Parameters to get issues created in the past three days\\\\\\\\n params = {\\\\\\\\n 'since': since,\\\\\\\\n 'state': 'all', # Get both open and closed issues\\\\\\\\n }\\\\\\\\n \\\\\\\\n # Send a request to the GitHub API\\\\\\\\n response = requests.get(issues_url, params=params)\\\\\\\\n # Raise an exception if the request was unsuccessful\\\\\\\\n response.raise_for_status()\\\\\\\\n \\\\\\\\n # Parse the JSON response\\\\\\\\n issues = response.json()\\\\\\\\n \\\\\\\\n # Extract and print the creation date and status of each issue\\\\\\\\n for issue in issues:\\\\\\\\n created_at = issue['created_at']\\\\\\\\n state = issue['state']\\\\\\\\n print(f\\\\\\\\\\\\\\\"Issue created at: {created_at}, Status: {state}\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\\n# Replace 'owner' and 'repo' with the actual owner and repository name\\\\\\\\nget_recent_issues('eosphoros-ai', 'DB-GPT')\\\\\\\"]], \\\\\\\"log\\\\\\\": \\\\\\\"\\\\\\\\n/workspace/github_issues.py:3: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\\\\\\\\n import pkg_resources\\\\\\\\n/workspace/github_issues.py:19: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).\\\\\\\\n three_days_ago = datetime.utcnow() - timedelta(days=3)\\\\\\\\nIssue created at: 2023-12-27T03:20:12Z, Status: open\\\\\\\\nIssue created at: 2023-12-26T16:13:42Z, Status: open\\\\\\\\nIssue created at: 2023-12-26T10:23:43Z, Status: open\\\\\\\\nIssue created at: 2023-12-26T07:25:37Z, Status: closed\\\\\\\\nIssue created at: 2023-12-26T05:01:26Z, Status: closed\\\\\\\\nIssue created at: 2023-12-26T03:54:15Z, Status: open\\\\\\\\nIssue created at: 2023-12-26T02:19:48Z, Status: open\\\\\\\\nIssue created at: 2023-12-25T12:26:40Z, Status: open\\\\\\\\nIssue created at: 2023-12-25T12:00:52Z, Status: open\\\\\\\\nIssue created at: 2023-12-25T08:36:56Z, Status: open\\\\\\\\nIssue created at: 2023-12-25T08:35:48Z, Status: open\\\\\\\\nIssue created at: 2023-12-25T08:34:13Z, Status: closed\\\\\\\\nIssue created at: 2023-12-25T06:33:37Z, Status: closed\\\\\\\\nIssue created at: 2023-12-23T11:47:40Z, Status: closed\\\\\\\\nIssue created at: 2023-12-23T03:01:23Z, Status: open\\\\\\\\nIssue created at: 2023-12-19T06:38:57Z, Status: open\\\\\\\\nIssue created at: 2023-12-11T08:34:08Z, Status: open\\\\\\\\nIssue created at: 2023-11-26T03:55:22Z, Status: open\\\\\\\\nIssue created at: 2023-11-25T12:38:23Z, Status: open\\\\\\\\nIssue created at: 2023-11-25T02:25:13Z, Status: open\\\\\\\\nIssue created at: 2023-11-25T02:09:58Z, Status: open\\\\\\\\nIssue created at: 2023-11-24T08:48:24Z, Status: open\\\\\\\\nIssue created at: 2023-11-24T06:03:56Z, Status: open\\\\\\\\nIssue created at: 2023-11-23T16:25:33Z, Status: open\\\\\\\\nIssue created at: 2023-11-23T08:54:36Z, Status: open\\\\\\\\nIssue created at: 2023-11-23T08:26:27Z, Status: open\\\\\\\\nIssue created at: 2023-11-22T06:42:34Z, Status: open\\\\\\\\nIssue created at: 2023-10-26T01:45:37Z, Status: open\\\\\\\\nIssue created at: 2023-08-30T06:02:32Z, Status: closed\\\\\\\\nIssue created at: 2023-06-18T01:11:22Z, Status: open\\\\\\\\n\\\\\\\"}\\\\n```\\\"}, {\\\"sender\\\": \\\"plan_manager\\\", \\\"receiver\\\": \\\"CodeEngineer\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"The execution result of the code you wrote is judged as not answering the task question. Please re-understand and complete the task.\\\"}, {\\\"sender\\\": \\\"CodeEngineer\\\", \\\"receiver\\\": \\\"plan_manager\\\", \\\"model\\\": \\\"gpt-4-vision-preview\\\", \\\"markdown\\\": \\\"```vis-code\\\\n{\\\\\\\"exit_success\\\\\\\": true, \\\\\\\"language\\\\\\\": \\\\\\\"python\\\\\\\", \\\\\\\"code\\\\\\\": [[\\\\\\\"python\\\\\\\", \\\\\\\"# filename: github_issues.py\\\\\\\\nimport requests\\\\\\\\nfrom datetime import datetime, timedelta\\\\\\\\n\\\\\\\\n# Function to get issues created in the past three days from a GitHub repository\\\\\\\\ndef get_recent_issues(owner, repo):\\\\\\\\n # Calculate the date for three days ago\\\\\\\\n three_days_ago = datetime.utcnow() - timedelta(days=3)\\\\\\\\n # Format the date in ISO 8601 format\\\\\\\\n since = three_days_ago.isoformat()\\\\\\\\n\\\\\\\\n # GitHub API URL for issues of the given repository\\\\\\\\n issues_url = f\\\\\\\\\\\\\\\"https://api.github.com/repos/{owner}/{repo}/issues\\\\\\\\\\\\\\\"\\\\\\\\n\\\\\\\\n # Parameters to get all issues, GitHub API does not allow filtering by creation date directly\\\\\\\\n params = {\\\\\\\\n 'state': 'all', # Get both open and closed issues\\\\\\\\n }\\\\\\\\n\\\\\\\\n # Send a request to the GitHub API\\\\\\\\n response = requests.get(issues_url, params=params)\\\\\\\\n # Raise an exception if the request was unsuccessful\\\\\\\\n response.raise_for_status()\\\\\\\\n\\\\\\\\n # Parse the JSON response\\\\\\\\n issues = response.json()\\\\\\\\n\\\\\\\\n # Filter and print the creation date and status of each issue created in the past three days\\\\\\\\n for issue in issues:\\\\\\\\n created_at = datetime.strptime(issue['created_at'], \\\\\\\\\\\\\\\"%Y-%m-%dT%H:%M:%SZ\\\\\\\\\\\\\\\")\\\\\\\\n if created_at > three_days_ago:\\\\\\\\n state = issue['state']\\\\\\\\n print(f\\\\\\\\\\\\\\\"Issue created at: {created_at}, Status: {state}\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\\n# Replace 'owner' and 'repo' with the actual owner and repository name\\\\\\\\nget_recent_issues('eosphoros-ai', 'DB-GPT')\\\\\\\"]], \\\\\\\"log\\\\\\\": \\\\\\\"\\\\\\\\n/workspace/github_issues.py:8: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).\\\\\\\\n three_days_ago = datetime.utcnow() - timedelta(days=3)\\\\\\\\nIssue created at: 2023-12-27 03:20:12, Status: open\\\\\\\\nIssue created at: 2023-12-26 16:13:42, Status: open\\\\\\\\nIssue created at: 2023-12-26 10:23:43, Status: open\\\\\\\\nIssue created at: 2023-12-26 07:25:37, Status: closed\\\\\\\\nIssue created at: 2023-12-26 05:01:26, Status: closed\\\\\\\\nIssue created at: 2023-12-26 03:54:15, Status: open\\\\\\\\nIssue created at: 2023-12-26 02:19:48, Status: open\\\\\\\\nIssue created at: 2023-12-25 12:26:40, Status: open\\\\\\\\nIssue created at: 2023-12-25 12:00:52, Status: open\\\\\\\\nIssue created at: 2023-12-25 08:36:56, Status: open\\\\\\\\nIssue created at: 2023-12-25 08:35:48, Status: open\\\\\\\\nIssue created at: 2023-12-25 08:34:13, Status: closed\\\\\\\\nIssue created at: 2023-12-25 06:33:37, Status: closed\\\\\\\\n\\\\\\\"}\\\\n```\\\"}]\\n```\"}, {\"name\": \"Write a Python script to process the retrieved issues data to group them by creation date and status. Then, format the grouped data into a Markdown table.\", \"num\": \"2\", \"status\": \"complete\", \"agent\": \"CodeEngineer\", \"markdown\": \"```agent-messages\\n[{\\\"sender\\\": \\\"plan_manager\\\", \\\"receiver\\\": \\\"CodeEngineer\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"Read the result data of the dependent steps in the above historical message to complete the current goal:Write a Python script to process the retrieved issues data to group them by creation date and status. Then, format the grouped data into a Markdown table.\\\"}, {\\\"sender\\\": \\\"CodeEngineer\\\", \\\"receiver\\\": \\\"plan_manager\\\", \\\"model\\\": \\\"gpt-4-vision-preview\\\", \\\"markdown\\\": \\\"```vis-code\\\\n{\\\\\\\"exit_success\\\\\\\": true, \\\\\\\"language\\\\\\\": \\\\\\\"python\\\\\\\", \\\\\\\"code\\\\\\\": [[\\\\\\\"python\\\\\\\", \\\\\\\"# Let's process the retrieved issues data to group them by creation date and status.\\\\\\\\n# Then, we will format the grouped data into a Markdown table.\\\\\\\\n\\\\\\\\n# The data from the previous step\\\\\\\\nissues_data = [\\\\\\\\n {\\\\\\\\\\\\\\\"created_at\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2023-12-27 03:20:12\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"status\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"open\\\\\\\\\\\\\\\"},\\\\\\\\n {\\\\\\\\\\\\\\\"created_at\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2023-12-26 16:13:42\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"status\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"open\\\\\\\\\\\\\\\"},\\\\\\\\n {\\\\\\\\\\\\\\\"created_at\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2023-12-26 10:23:43\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"status\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"open\\\\\\\\\\\\\\\"},\\\\\\\\n {\\\\\\\\\\\\\\\"created_at\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2023-12-26 07:25:37\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"status\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"closed\\\\\\\\\\\\\\\"},\\\\\\\\n {\\\\\\\\\\\\\\\"created_at\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2023-12-26 05:01:26\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"status\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"closed\\\\\\\\\\\\\\\"},\\\\\\\\n {\\\\\\\\\\\\\\\"created_at\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2023-12-26 03:54:15\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"status\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"open\\\\\\\\\\\\\\\"},\\\\\\\\n {\\\\\\\\\\\\\\\"created_at\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2023-12-26 02:19:48\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"status\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"open\\\\\\\\\\\\\\\"},\\\\\\\\n {\\\\\\\\\\\\\\\"created_at\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2023-12-25 12:26:40\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"status\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"open\\\\\\\\\\\\\\\"},\\\\\\\\n {\\\\\\\\\\\\\\\"created_at\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2023-12-25 12:00:52\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"status\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"open\\\\\\\\\\\\\\\"},\\\\\\\\n {\\\\\\\\\\\\\\\"created_at\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2023-12-25 08:36:56\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"status\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"open\\\\\\\\\\\\\\\"},\\\\\\\\n {\\\\\\\\\\\\\\\"created_at\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2023-12-25 08:35:48\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"status\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"open\\\\\\\\\\\\\\\"},\\\\\\\\n {\\\\\\\\\\\\\\\"created_at\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2023-12-25 08:34:13\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"status\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"closed\\\\\\\\\\\\\\\"},\\\\\\\\n {\\\\\\\\\\\\\\\"created_at\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2023-12-25 06:33:37\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"status\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"closed\\\\\\\\\\\\\\\"},\\\\\\\\n]\\\\\\\\n\\\\\\\\n# Grouping the issues by creation date and status\\\\\\\\nfrom collections import defaultdict\\\\\\\\ngrouped_issues = defaultdict(lambda: defaultdict(list))\\\\\\\\n\\\\\\\\nfor issue in issues_data:\\\\\\\\n date = issue[\\\\\\\\\\\\\\\"created_at\\\\\\\\\\\\\\\"].split(' ')[0] # Extract just the date part\\\\\\\\n grouped_issues[date][issue[\\\\\\\\\\\\\\\"status\\\\\\\\\\\\\\\"]].append(issue)\\\\\\\\n\\\\\\\\n# Format the grouped data into a Markdown table\\\\\\\\nmarkdown_table = \\\\\\\\\\\\\\\"Date | Open Issues | Closed Issues\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\"\\\\\\\\nmarkdown_table += \\\\\\\\\\\\\\\"--- | --- | ---\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\"\\\\\\\\n\\\\\\\\nfor date, statuses in sorted(grouped_issues.items()):\\\\\\\\n open_issues = len(statuses[\\\\\\\\\\\\\\\"open\\\\\\\\\\\\\\\"]) if \\\\\\\\\\\\\\\"open\\\\\\\\\\\\\\\" in statuses else 0\\\\\\\\n closed_issues = len(statuses[\\\\\\\\\\\\\\\"closed\\\\\\\\\\\\\\\"]) if \\\\\\\\\\\\\\\"closed\\\\\\\\\\\\\\\" in statuses else 0\\\\\\\\n markdown_table += f\\\\\\\\\\\\\\\"{date} | {open_issues} | {closed_issues}\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\"\\\\\\\\n\\\\\\\\nprint(markdown_table)\\\\\\\"]], \\\\\\\"log\\\\\\\": \\\\\\\"\\\\\\\\nDate | Open Issues | Closed Issues\\\\\\\\n--- | --- | ---\\\\\\\\n2023-12-25 | 4 | 2\\\\\\\\n2023-12-26 | 4 | 2\\\\\\\\n2023-12-27 | 1 | 0\\\\\\\\n\\\\\\\\n\\\\\\\"}\\\\n```\\\"}]\\n```\"}]\n", - "```\n" + "--------------------------------------------------------------------------------\n" ] } ], - "source": [ - "llm_client = OpenAILLMClient()\n", - "context: AgentContext = AgentContext(conv_id=\"test\", llm_provider=llm_client)\n", - "# context.llm_models = [ModelMetadata(model=\"gpt-3.5-turbo\")]\n", - "context.llm_models = [ModelMetadata(model=\"gpt-4-vision-preview\")]\n", - "context.gpts_name = \"代码分析助手\"\n", - "\n", - "default_memory = GptsMemory()\n", - "coder = CodeAssistantAgent(memory=default_memory, agent_context=context)\n", - "## TODO add other agent\n", - "\n", - "groupchat = PlanChat(agents=[coder], messages=[], max_round=50)\n", - "planner = PlannerAgent(\n", - " agent_context=context,\n", - " memory=default_memory,\n", - " plan_chat=groupchat,\n", - ")\n", - "\n", - "manager = PlanChatManager(\n", - " plan_chat=groupchat,\n", - " planner=planner,\n", - " agent_context=context,\n", - " memory=default_memory,\n", - ")\n", - "\n", - "user_proxy = UserProxyAgent(memory=default_memory, agent_context=context)\n", - "\n", - "\n", - "await user_proxy.a_initiate_chat(\n", - " recipient=manager,\n", - " reviewer=user_proxy,\n", - " message=\"Obtain simple information about issues in the repository 'eosphoros-ai/DB-GPT' in the past three days and analyze the data. Create a Markdown table grouped by day and status.\",\n", - " # message=\"Find papers on gpt-4 in the past three weeks on arxiv, and organize their titles, authors, and links into a markdown table\",\n", - " # message=\"find papers on LLM applications from arxiv in the last month, create a markdown table of different domains.\",\n", - " )\n", - "\n" - ] + "execution_count": 4 } ], "metadata": { diff --git a/examples/notebook/agent_awel_layout_dialogue_example.ipynb b/examples/notebook/agent_awel_layout_dialogue_example.ipynb index 1e262d0ff..267d3a19a 100644 --- a/examples/notebook/agent_awel_layout_dialogue_example.ipynb +++ b/examples/notebook/agent_awel_layout_dialogue_example.ipynb @@ -2,162 +2,162 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, "id": "6de2e0bb", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T04:37:21.832993Z", + "start_time": "2024-04-10T04:37:21.828221Z" + } + }, + "source": [ + "import os\n", + "import nest_asyncio\n", + "from dbgpt.agent import (\n", + " AgentContext,\n", + " AgentResource,\n", + " GptsMemory,\n", + " LLMConfig,\n", + " ResourceLoader,\n", + " ResourceType,\n", + " UserProxyAgent,\n", + ")\n", + "from dbgpt.agent.expand.plugin_assistant_agent import PluginAssistantAgent\n", + "from dbgpt.agent.expand.summary_assistant_agent import SummaryAssistantAgent\n", + "from dbgpt.agent.plan import WrappedAWELLayoutManager\n", + "from dbgpt.agent.resource import PluginFileLoadClient\n", + "from dbgpt.configs.model_config import ROOT_PATH\n", + "from dbgpt.model.proxy import OpenAILLMClient\n", + "\n", + "nest_asyncio.apply()\n", + "test_plugin_dir = os.path.join(ROOT_PATH, \"examples/test_files/plugins\")" + ], "outputs": [], + "execution_count": 11 + }, + { + "cell_type": "code", + "id": "437b9c40", + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-10T04:37:27.592117Z", + "start_time": "2024-04-10T04:37:23.569538Z" + } + }, "source": [ - "\"\"\"Agents: auto plan agents example?\n", + "# os.environ['OPENAI_API_KEY']=\"sk-x\"\n", + "# os.environ['OPENAI_API_BASE']=\"https://proxy_url/v1\"\n", + "# os.environ['SEARCH_ENGINE']=\"baidu\"\n", + "# os.environ['BAIDU_COOKIE']=\"\"\"your baidu cookie\"\"\"\n", "\n", - " Examples:\n", + "llm_client = OpenAILLMClient(model_alias=\"gpt-3.5-turbo\")\n", + "context: AgentContext = AgentContext(conv_id=\"test456\", gpts_app_name=\"信息析助手\")\n", "\n", - " Execute the following command in the terminal:\n", - " Set env params.\n", - " .. code-block:: shell\n", + "default_memory = GptsMemory()\n", "\n", - " export OPENAI_API_KEY=sk-xx\n", - " export OPENAI_API_BASE=https://xx:80/v1\n", + "resource_loader = ResourceLoader()\n", + "plugin_file_loader = PluginFileLoadClient()\n", + "resource_loader.register_resource_api(plugin_file_loader)\n", "\n", - " run example.\n", - " ..code-block:: shell\n", - " python examples/agents/auto_plan_agent_dialogue_example.py\n", - "\"\"\"\n", + "plugin_resource = AgentResource(\n", + " type=ResourceType.Plugin,\n", + " name=\"test\",\n", + " value=test_plugin_dir,\n", + ")\n", "\n", - "import os\n", - "from dbgpt.agent.agents.user_proxy_agent import UserProxyAgent\n", - "from dbgpt.serve.agent.team.layout.team_awel_layout import AwelLayoutChatManger\n", - "from dbgpt.agent.agents.expand.plugin_assistant_agent import PluginAssistantAgent\n", - "from dbgpt.agent.agents.expand.summary_assistant_agent import SummaryAssistantAgent\n", + "tool_engineer = (\n", + " await PluginAssistantAgent()\n", + " .bind(context)\n", + " .bind(LLMConfig(llm_client=llm_client))\n", + " .bind(default_memory)\n", + " .bind([plugin_resource])\n", + " .bind(resource_loader)\n", + " .build()\n", + ")\n", + "summarizer = (\n", + " await SummaryAssistantAgent()\n", + " .bind(context)\n", + " .bind(default_memory)\n", + " .bind(LLMConfig(llm_client=llm_client))\n", + " .build()\n", + ")\n", "\n", - "from dbgpt.agent.agents.agent import AgentContext\n", - "from dbgpt.agent.memory.gpts_memory import GptsMemory\n", - "from dbgpt.core.interface.llm import ModelMetadata\n", + "manager = (\n", + " await WrappedAWELLayoutManager()\n", + " .bind(context)\n", + " .bind(default_memory)\n", + " .bind(LLMConfig(llm_client=llm_client))\n", + " .build()\n", + ")\n", + "manager.hire([tool_engineer, summarizer])\n", "\n", - "import asyncio\n", + "user_proxy = await UserProxyAgent().bind(context).bind(default_memory).build()\n", "\n", - "from dbgpt.model import OpenAILLMClient" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "153c9e0e", - "metadata": {}, - "outputs": [], - "source": [ - "current_dir = os.getcwd()\n", - "parent_dir = os.path.dirname(current_dir)\n", - "test_plugin_dir = os.path.join(parent_dir, \"test_files\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "437b9c40", - "metadata": {}, + "await user_proxy.initiate_chat(\n", + " recipient=manager,\n", + " reviewer=user_proxy,\n", + " message=\"查询成都今天天气\",\n", + " # message=\"查询今天的最新热点财经新闻\",\n", + ")" + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mUser\u001b[0m (to layout_manager)-[]:\n", - "\n", - "\"查询成都今天天气\"\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001b[33mlayout_manager\u001b[0m (to ToolScientist)-[]:\n", + "\u001B[33mWrappedAWELLayoutManager\u001B[0m (to LuBan)-[]:\n", "\n", "\"查询成都今天天气\"\n", "\n", "--------------------------------------------------------------------------------\n", "un_stream ai response: {\n", - " \"tool_name\": \"google_search\",\n", + " \"tool_name\": \"baidu_search\",\n", " \"args\": {\n", " \"query\": \"成都今天天气\"\n", " },\n", - " \"thought\": \"I will use the google-search tool to search for the weather in Chengdu today.\"\n", + " \"thought\": \"I have selected the 'baidu_search' tool with the query parameter set to '成都今天天气' to search for the weather in Chengdu today.\"\n", "}\n", "{'query': '成都今天天气'}\n", - "_google_search:成都今天天气\n", - "\u001b[33mToolScientist\u001b[0m (to Summarizer)-[gpt-3.5-turbo]:\n", - "\n", - "\"{\\n \\\"tool_name\\\": \\\"google_search\\\",\\n \\\"args\\\": {\\n \\\"query\\\": \\\"成都今天天气\\\"\\n },\\n \\\"thought\\\": \\\"I will use the google-search tool to search for the weather in Chengdu today.\\\"\\n}\"\n", - "\u001b[32m>>>>>>>>ToolScientist Review info: \n", - " Pass.None\u001b[0m\n", - "\u001b[34m>>>>>>>>ToolScientist Action report: \n", - "execution succeeded,\n", - "Error: Please configure GOOGLE_API_KEY and GOOGLE_API_CX in .env first!\u001b[0m\n", "\n", "--------------------------------------------------------------------------------\n", - "un_stream ai response: The User's Question: 查询成都今天天气\n", + "\u001B[33mLuBan\u001B[0m (to Aristotle)-[gpt-3.5-turbo]:\n", "\n", - "今天成都的天气预报是晴天，最高温度约为28摄氏度，最低温度约为16摄氏度。\n", - "\u001b[33mSummarizer\u001b[0m (to layout_manager)-[gpt-3.5-turbo]:\n", - "\n", - "\"The User's Question: 查询成都今天天气\\n\\n今天成都的天气预报是晴天，最高温度约为28摄氏度，最低温度约为16摄氏度。\"\n", - "\u001b[32m>>>>>>>>Summarizer Review info: \n", - " Pass.None\u001b[0m\n", - "\u001b[34m>>>>>>>>Summarizer Action report: \n", + "\"{\\n \\\"tool_name\\\": \\\"baidu_search\\\",\\n \\\"args\\\": {\\n \\\"query\\\": \\\"成都今天天气\\\"\\n },\\n \\\"thought\\\": \\\"I have selected the 'baidu_search' tool with the query parameter set to '成都今天天气' to search for the weather in Chengdu today.\\\"\\n}\"\n", + "\u001B[32m>>>>>>>>LuBan Review info: \n", + "Pass(None)\u001B[0m\n", + "\u001B[34m>>>>>>>>LuBan Action report: \n", "execution succeeded,\n", - "The User's Question: 查询成都今天天气\n", + "### [...天气预报一周_成都天气预报7天、15天、40天天查询_中国...](http://www.baidu.com/link?url=nSNTTnrxEUFL7oMRAYqg98BfeXkWtwHUaYN7WrTjaxBpSy0blKc4jIZ9m34mP97fFARfXJStjbRoBN6U0s0BDq) \n", + " \n", + "### [成都天气_成都天气预报一周_成都天气预报15天](http://www.baidu.com/link?url=Fmp4cnf8Cqqd8N06PpAe3Mn6Esp5q39Scfsnfr7ALxqB5XfoWu9-wY5UjS4n-95Y) \n", + " \n", + "### [【成都天气】成都天气预报,蓝天,蓝天预报,雾霾,雾霾消散,...](http://www.baidu.com/link?url=BQF3cexr1Z6hqkdOjRO2pq8YnOuruBV8nBFY0LE7FJJl8_TCcO806skK-aWkmC8UAZ23K-v3SvoXO58Ayze7Da) \n", + " \n", + "### [...天气预报一周_成都天气预报7天、15天、40天天查询_中国...](http://www.baidu.com/link?url=rt26_NNSBBWHLr0rAX2RPUbBhVjfr4m3Cd21RG7MOe4gsirRquQyp5fMLbSfeU1iC2b1ZhNVjUzlex39iYN_wq) \n", + " \n", + "### [【成都天气预报15天_成都天气预报15天查询】-中国天气网](http://www.baidu.com/link?url=vnZ3GlUxqllZ7Lenc94cImrur2AixgD6dkSOxfNc63PTewisg-RXg3sKzLpBEuPgCWXLr9VnR9gsSZetfPA_94HdTG0It_uAvZpLdUiGmY_) \n", + " \n", + "\u001B[0m\n", "\n", - "今天成都的天气预报是晴天，最高温度约为28摄氏度，最低温度约为16摄氏度。\u001b[0m\n", + "--------------------------------------------------------------------------------\n", + "un_stream ai response: Did not find the information you want.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001b[33mlayout_manager\u001b[0m (to User)-[None]:\n", + "\u001B[33mWrappedAWELLayoutManager\u001B[0m (to User)-[]:\n", "\n", "\"查询成都今天天气\"\n", - "\u001b[32m>>>>>>>>layout_manager Review info: \n", - " Pass.None\u001b[0m\n", - "\u001b[34m>>>>>>>>layout_manager Action report: \n", + "\u001B[32m>>>>>>>>WrappedAWELLayoutManager Review info: \n", + "Pass(None)\u001B[0m\n", + "\u001B[34m>>>>>>>>WrappedAWELLayoutManager Action report: \n", "execution succeeded,\n", - "The User's Question: 查询成都今天天气\n", - "\n", - "今天成都的天气预报是晴天，最高温度约为28摄氏度，最低温度约为16摄氏度。\u001b[0m\n", + "Did not find the information you want.\u001B[0m\n", "\n", "--------------------------------------------------------------------------------\n" ] } ], - "source": [ - "os.environ['OPENAI_API_KEY']=\"sk-x\"\n", - "os.environ['OPENAI_API_BASE']=\"https://proxy_url/v1\"\n", - "os.environ['BAIDU_COOKIE']=\"\"\"your baidu cookie\"\"\"\n", - "\n", - "llm_client = OpenAILLMClient()\n", - "context: AgentContext = AgentContext(conv_id=\"test456\", llm_provider=llm_client)\n", - "context.llm_models = [ModelMetadata(model=\"gpt-3.5-turbo\")]\n", - "context.gpts_name = \"信息析助手\"\n", - "\n", - "default_memory = GptsMemory()\n", - "manager = AwelLayoutChatManger(\n", - " agent_context=context,\n", - " memory=default_memory,\n", - ")\n", - "\n", - "### agents\n", - "tool_enginer = PluginAssistantAgent(\n", - " agent_context=context,\n", - " memory=default_memory,\n", - " plugin_path=test_plugin_dir,\n", - ")\n", - "summarizer = SummaryAssistantAgent(\n", - " agent_context=context,\n", - " memory=default_memory,\n", - ")\n", - "\n", - "manager.hire([tool_enginer, summarizer])\n", - "\n", - "user_proxy = UserProxyAgent(memory=default_memory, agent_context=context)\n", - "\n", - "\n", - "await user_proxy.a_initiate_chat(\n", - " recipient=manager,\n", - " reviewer=user_proxy,\n", - " message=\"查询成都今天天气\",\n", - ")\n", - "\n" - ] + "execution_count": 12 }, { "cell_type": "code",