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""" - x: int + x: "int" baz: Baz qux_schema = CallableSchema( return_schema=UndefinedNode(original_annotation=None), call_schema=ObjectNode( constructor_fn=Qux, - constructor_signature=inspect.signature(Qux), + constructor_signature=inspect.signature(Qux, eval_str=True), name="Qux", hint="I am Qux.", fields=[ diff --git a/wanga/__init__.py b/wanga/__init__.py index 8b13789..a9834a0 100644 --- a/wanga/__init__.py +++ b/wanga/__init__.py @@ -1 +1,5 @@ +from importlib.util import find_spec +_PIL_INSTALLED = find_spec("Pillow") is None +_OPENAI_INSTALLED = find_spec("openai") is None +_ANTHROPIC_INSTALLED = find_spec("anthropic") is None diff --git a/wanga/common.py b/wanga/common.py new file mode 100644 index 0000000..9f52cd0 --- /dev/null +++ b/wanga/common.py @@ -0,0 +1,3 @@ +from typing import TypeAlias + +JSON: TypeAlias = str | float | int | bool | None | dict[str, "JSON"] | list["JSON"] diff --git a/wanga/models/messages.py b/wanga/models/messages.py new file mode 100644 index 0000000..7a2d372 --- /dev/null +++ b/wanga/models/messages.py @@ -0,0 +1,118 @@ +from pprint import pformat +from typing import TYPE_CHECKING, TypeAlias + +from attrs import frozen + +from wanga import _PIL_INSTALLED + +from ..common import JSON + +__all__ = [ + "ImageURL", + "ImageContent", + "Message", + "UserMessage", + "AssistantMessage", + "ToolInvocation", + "ToolMessage", +] + + +@frozen +class ImageURL: + url: str + + +if TYPE_CHECKING or _PIL_INSTALLED: + from PIL.Image import Image + + ImageContent: TypeAlias = Image | ImageURL +else: + ImageContent: TypeAlias = ImageURL + + +_MESSAGE_TAG_OPEN = "[|" +_MESSAGE_TAG_CLOSE = "|]" + +_CONTENT_BLOCK_TAG_OPEN = "<|" +_CONTENT_BLOCK_TAG_CLOSE = "|>" + + +def _pretty_print_message(role: str, name: str | None, content: str | list[str | ImageContent]) -> str: + if name: + role_str = f"{_MESSAGE_TAG_OPEN}{role} {name}{_MESSAGE_TAG_CLOSE}" + else: + role_str = f"{_MESSAGE_TAG_OPEN}{role}{_MESSAGE_TAG_CLOSE}" + + content_strings = [] + if isinstance(content, str): + content = [content] + for content_block in content: + if isinstance(content_block, ImageURL): + content_block = f'{_CONTENT_BLOCK_TAG_OPEN}image url="{content_block.url}"{_CONTENT_BLOCK_TAG_CLOSE}' + if not isinstance(content_block, str): + content_block = f"{_CONTENT_BLOCK_TAG_OPEN}image{_CONTENT_BLOCK_TAG_CLOSE}" + content_strings.append(content_block) + joined_content = "\n".join(content_strings) + + return f"{role_str}\n{joined_content}" + + +@frozen +class Message: + pass + + +class SystemMessage(Message): + name: str | None + content: str + + def __str__(self) -> str: + return _pretty_print_message("system", self.name, self.content) + + +@frozen +class UserMessage(Message): + name: str | None + content: str | list[str | ImageContent] + + def __str__(self) -> str: + return _pretty_print_message("user", self.name, self.content) + + +@frozen +class ToolInvocation: + invocation_id: str + tool_name: str + tool_args: JSON + + def __str__(self) -> str: + pretty_json = pformat(self.tool_args, sort_dicts=True) + call_header = ( + f'{_CONTENT_BLOCK_TAG_OPEN}call {self.tool_name} id="{self.invocation_id}"{_CONTENT_BLOCK_TAG_CLOSE}' + ) + return f"{call_header}\n{pretty_json}" + + +@frozen +class AssistantMessage(Message): + name: str | None + content: str | None + tool_invocations: list[ToolInvocation] + + def __str__(self) -> str: + content_blocks = [] + if self.content: + content_blocks.append(self.content) + for tool_invocation in self.tool_invocations: + content_blocks.append(str(tool_invocation)) + return "\n".join(content_blocks) + + +@frozen +class ToolMessage: + invocation_id: str + tool_result: str | list[str | ImageContent] + + def __str__(self) -> str: + return _pretty_print_message("tool", f"id={self.invocation_id}", self.tool_result) diff --git a/wanga/models/model.py b/wanga/models/model.py index e69de29..5b6433b 100644 --- a/wanga/models/model.py +++ b/wanga/models/model.py @@ -0,0 +1,107 @@ +from enum import Enum + +from attrs import field, frozen + +from ..schema.schema import CallableSchema +from .messages import AssistantMessage, Message + + +class ToolUseMode(Enum): + AUTO = "auto" + FORCE = "force" + NEVER = "never" + + +@frozen +class ToolParams: + tools: list[CallableSchema] = field(factory=list) + tool_use_mode: ToolUseMode | str = ToolUseMode.AUTO + allow_parallel_calls: bool = False + + +@frozen +class GenerationParams: + max_tokens: int | None = None + temperature: float | None = None + top_p: float | None = None + frequency_penalty: float | None = None + presence_penalty: float | None = None + stop_sequences: list[str] = field(factory=list) + random_seed: int | None = None + force_json: bool = False + + +@frozen +class UsageStats: + prompt_tokens: int + response_tokens: int + + @property + def total_tokens(self) -> int: + return self.prompt_tokens + self.response_tokens + + +class FinishReason(Enum): + STOP = "stop" + LENGTH = "length" + TOOL_CALL = "tool_call" + CONTENT_FILTER = "content_filter" + + +@frozen +class ResponseOption: + message: AssistantMessage + finish_reason: FinishReason + + +@frozen +class ModelResponse: + response_options: list[ResponseOption] + usage: UsageStats + + +class PromptTooLongError(ValueError): + pass + + +class ModelTimeoutError(TimeoutError): + pass + + +class Model: + def reply( + self, + messages: list[Message], + tools: ToolParams = ToolParams(), + params: GenerationParams = GenerationParams(), + num_options: int = 1, + ) -> ModelResponse: + raise NotImplementedError + + async def reply_async( + self, + messages: list[Message], + tools: ToolParams = ToolParams(), + params: GenerationParams = GenerationParams(), + num_options: int = 1, + ) -> ModelResponse: + raise NotImplementedError + + @property + def context_length(self) -> int: + raise NotImplementedError + + def estimate_num_tokens(self, messages: list[Message], tools: ToolParams) -> int: + r"""Returns the rough estimate of the total number of tokens in the prompt. + + May return inaccurate results, use `calculate_num_tokens` instead for precise results. + Note that `calculate_num_tokens` may send request to the LLM api, which will count towards your usage bill. + """ + raise NotImplementedError + + def calculate_num_tokens(self, messages: list[Message], tools: ToolParams) -> int: + r"""Returns the precise number of tokens in the prompt. + + This method will send a request to the LLM api, which will count towards your usage bill. + """ + raise NotImplementedError