feat(ChatKnowledge): Support Financial Report Analysis

.env.template

@@ -292,4 +292,9 @@ DBGPT_LOG_LEVEL=INFO
 # OTEL_EXPORTER_OTLP_TRACES_CERTIFICATE=
 # OTEL_EXPORTER_OTLP_TRACES_HEADERS=
 # OTEL_EXPORTER_OTLP_TRACES_TIMEOUT=
-# OTEL_EXPORTER_OTLP_TRACES_COMPRESSION=
+# OTEL_EXPORTER_OTLP_TRACES_COMPRESSION=
+
+#*******************************************************************#
+#**                     FINANCIAL CHAT Config                     **#
+#*******************************************************************#
+# FIN_REPORT_MODEL=/app/models/bge-large-zh

assets/schema/dbgpt.sql

@@ -17,6 +17,7 @@ CREATE TABLE IF NOT EXISTS `knowledge_space`
     `id`           int          NOT NULL AUTO_INCREMENT COMMENT 'auto increment id',
     `name`         varchar(100) NOT NULL COMMENT 'knowledge space name',
     `vector_type`  varchar(50)  NOT NULL COMMENT 'vector type',
+    `domain_type`  varchar(50)  NOT NULL COMMENT 'domain type',
     `desc`         varchar(500) NOT NULL COMMENT 'description',
     `owner`        varchar(100) DEFAULT NULL COMMENT 'owner',
     `context`      TEXT         DEFAULT NULL COMMENT 'context argument',

assets/schema/upgrade/v0_5_10/upgrade_to_v0.5.10.sql

@@ -0,0 +1,3 @@
+USE dbgpt;
+ALTER TABLE  knowledge_space
+    ADD COLUMN `domain_type` varchar(50) null comment 'space domain type' after `vector_type`;

dbgpt/_private/config.py

@@ -166,18 +166,18 @@ def __init__(self) -> None:
         self.execute_local_commands = (
             os.getenv("EXECUTE_LOCAL_COMMANDS", "False").lower() == "true"
         )
-        ### message stor file
+        # message stor file
         self.message_dir = os.getenv("MESSAGE_HISTORY_DIR", "../../message")
 
-        ### Native SQL Execution Capability Control Configuration
+        # Native SQL Execution Capability Control Configuration
         self.NATIVE_SQL_CAN_RUN_DDL = (
             os.getenv("NATIVE_SQL_CAN_RUN_DDL", "True").lower() == "true"
         )
         self.NATIVE_SQL_CAN_RUN_WRITE = (
             os.getenv("NATIVE_SQL_CAN_RUN_WRITE", "True").lower() == "true"
         )
 
-        ### dbgpt meta info database connection configuration
+        # dbgpt meta info database connection configuration
         self.LOCAL_DB_HOST = os.getenv("LOCAL_DB_HOST")
         self.LOCAL_DB_PATH = os.getenv("LOCAL_DB_PATH", "data/default_sqlite.db")
         self.LOCAL_DB_TYPE = os.getenv("LOCAL_DB_TYPE", "sqlite")
@@ -193,13 +193,13 @@ def __init__(self) -> None:
 
         self.CHAT_HISTORY_STORE_TYPE = os.getenv("CHAT_HISTORY_STORE_TYPE", "db")
 
-        ### LLM Model Service Configuration
+        # LLM Model Service Configuration
         self.LLM_MODEL = os.getenv("LLM_MODEL", "glm-4-9b-chat")
         self.LLM_MODEL_PATH = os.getenv("LLM_MODEL_PATH")
 
-        ### Proxy llm backend, this configuration is only valid when "LLM_MODEL=proxyllm"
-        ### When we use the rest API provided by deployment frameworks like fastchat as a proxyllm, "PROXYLLM_BACKEND" is the model they actually deploy.
-        ### We need to use "PROXYLLM_BACKEND" to load the prompt of the corresponding scene.
+        # Proxy llm backend, this configuration is only valid when "LLM_MODEL=proxyllm"
+        # When we use the rest API provided by deployment frameworks like fastchat as a proxyllm, "PROXYLLM_BACKEND" is the model they actually deploy.
+        # We need to use "PROXYLLM_BACKEND" to load the prompt of the corresponding scene.
         self.PROXYLLM_BACKEND = None
         if self.LLM_MODEL == "proxyllm":
             self.PROXYLLM_BACKEND = os.getenv("PROXYLLM_BACKEND")
@@ -211,7 +211,7 @@ def __init__(self) -> None:
             "MODEL_SERVER", "http://127.0.0.1" + ":" + str(self.MODEL_PORT)
         )
 
-        ### Vector Store Configuration
+        # Vector Store Configuration
         self.VECTOR_STORE_TYPE = os.getenv("VECTOR_STORE_TYPE", "Chroma")
         self.MILVUS_URL = os.getenv("MILVUS_URL", "127.0.0.1")
         self.MILVUS_PORT = os.getenv("MILVUS_PORT", "19530")
@@ -223,7 +223,7 @@ def __init__(self) -> None:
         self.ELASTICSEARCH_USERNAME = os.getenv("ELASTICSEARCH_USERNAME", None)
         self.ELASTICSEARCH_PASSWORD = os.getenv("ELASTICSEARCH_PASSWORD", None)
 
-        ## OceanBase Configuration
+        # OceanBase Configuration
         self.OB_HOST = os.getenv("OB_HOST", "127.0.0.1")
         self.OB_PORT = int(os.getenv("OB_PORT", "2881"))
         self.OB_USER = os.getenv("OB_USER", "root")
@@ -245,7 +245,7 @@ def __init__(self) -> None:
         os.environ["load_8bit"] = str(self.IS_LOAD_8BIT)
         os.environ["load_4bit"] = str(self.IS_LOAD_4BIT)
 
-        ### EMBEDDING Configuration
+        # EMBEDDING Configuration
         self.EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "text2vec")
         # Rerank model configuration
         self.RERANK_MODEL = os.getenv("RERANK_MODEL")
@@ -276,17 +276,17 @@ def __init__(self) -> None:
             os.getenv("KNOWLEDGE_CHAT_SHOW_RELATIONS", "False").lower() == "true"
         )
 
-        ### SUMMARY_CONFIG Configuration
+        # SUMMARY_CONFIG Configuration
         self.SUMMARY_CONFIG = os.getenv("SUMMARY_CONFIG", "FAST")
 
         self.MAX_GPU_MEMORY = os.getenv("MAX_GPU_MEMORY", None)
 
-        ### Log level
+        # Log level
         self.DBGPT_LOG_LEVEL = os.getenv("DBGPT_LOG_LEVEL", "INFO")
 
         self.SYSTEM_APP: Optional["SystemApp"] = None
 
-        ### Temporary configuration
+        # Temporary configuration
         self.USE_FASTCHAT: bool = os.getenv("USE_FASTCHAT", "True").lower() == "true"
 
         self.MODEL_CACHE_ENABLE: bool = (
@@ -312,6 +312,8 @@ def __init__(self) -> None:
         self.DBGPT_APP_SCENE_NON_STREAMING_PARALLELISM_BASE = int(
             os.getenv("DBGPT_APP_SCENE_NON_STREAMING_PARALLELISM_BASE", 1)
         )
+        # experimental financial report model configuration
+        self.FIN_REPORT_MODEL = os.getenv("FIN_REPORT_MODEL", None)
 
     @property
     def local_db_manager(self) -> "ConnectorManager":

dbgpt/_version.py

@@ -1 +1 @@
-version = "0.5.9"
+version = "0.5.10"

dbgpt/app/knowledge/api.py

@@ -23,19 +23,27 @@
 from dbgpt.app.knowledge.service import KnowledgeService
 from dbgpt.app.openapi.api_v1.api_v1 import no_stream_generator, stream_generator
 from dbgpt.app.openapi.api_view_model import Result
+from dbgpt.configs import TAG_KEY_KNOWLEDGE_FACTORY_DOMAIN_TYPE
 from dbgpt.configs.model_config import (
     EMBEDDING_MODEL_CONFIG,
     KNOWLEDGE_UPLOAD_ROOT_PATH,
 )
+from dbgpt.core.awel.dag.dag_manager import DAGManager
 from dbgpt.rag import ChunkParameters
 from dbgpt.rag.embedding.embedding_factory import EmbeddingFactory
 from dbgpt.rag.knowledge.base import ChunkStrategy
 from dbgpt.rag.knowledge.factory import KnowledgeFactory
 from dbgpt.rag.retriever.embedding import EmbeddingRetriever
-from dbgpt.serve.rag.api.schemas import KnowledgeSyncRequest
+from dbgpt.serve.rag.api.schemas import (
+    KnowledgeConfigResponse,
+    KnowledgeDomainType,
+    KnowledgeStorageType,
+    KnowledgeSyncRequest,
+)
 from dbgpt.serve.rag.connector import VectorStoreConnector
 from dbgpt.serve.rag.service.service import Service
 from dbgpt.storage.vector_store.base import VectorStoreConfig
+from dbgpt.util.i18n_utils import _
 from dbgpt.util.tracer import SpanType, root_tracer
 
 logger = logging.getLogger(__name__)
@@ -52,6 +60,11 @@ def get_rag_service() -> Service:
     return Service.get_instance(CFG.SYSTEM_APP)
 
 
+def get_dag_manager() -> DAGManager:
+    """Get DAG Manager."""
+    return DAGManager.get_instance(CFG.SYSTEM_APP)
+
+
 @router.post("/knowledge/space/add")
 def space_add(request: KnowledgeSpaceRequest):
     print(f"/space/add params: {request}")
@@ -147,6 +160,55 @@ def chunk_strategies():
         return Result.failed(code="E000X", msg=f"chunk strategies error {e}")
 
 
+@router.get("/knowledge/space/config", response_model=Result[KnowledgeConfigResponse])
+async def space_config() -> Result[KnowledgeConfigResponse]:
+    """Get space config"""
+    try:
+        storage_list: List[KnowledgeStorageType] = []
+        dag_manager: DAGManager = get_dag_manager()
+        # Vector Storage
+        vs_domain_types = [KnowledgeDomainType(name="Normal", desc="Normal")]
+        dag_map = dag_manager.get_dags_by_tag_key(TAG_KEY_KNOWLEDGE_FACTORY_DOMAIN_TYPE)
+        for domain_type, dags in dag_map.items():
+            vs_domain_types.append(
+                KnowledgeDomainType(
+                    name=domain_type, desc=dags[0].description or domain_type
+                )
+            )
+
+        storage_list.append(
+            KnowledgeStorageType(
+                name="VectorStore",
+                desc=_("Vector Store"),
+                domain_types=vs_domain_types,
+            )
+        )
+        # Graph Storage
+        storage_list.append(
+            KnowledgeStorageType(
+                name="KnowledgeGraph",
+                desc=_("Knowledge Graph"),
+                domain_types=[KnowledgeDomainType(name="Normal", desc="Normal")],
+            )
+        )
+        # Full Text
+        storage_list.append(
+            KnowledgeStorageType(
+                name="FullText",
+                desc=_("Full Text"),
+                domain_types=[KnowledgeDomainType(name="Normal", desc="Normal")],
+            )
+        )
+
+        return Result.succ(
+            KnowledgeConfigResponse(
+                storage=storage_list,
+            )
+        )
+    except Exception as e:
+        return Result.failed(code="E000X", msg=f"space config error {e}")
+
+
 @router.post("/knowledge/{space_name}/document/list")
 def document_list(space_name: str, query_request: DocumentQueryRequest):
     print(f"/document/list params: {space_name}, {query_request}")
@@ -350,27 +412,3 @@ async def document_summary(request: DocumentSummaryRequest):
             )
     except Exception as e:
         return Result.failed(code="E000X", msg=f"document summary error {e}")
-
-
-@router.post("/knowledge/entity/extract")
-async def entity_extract(request: EntityExtractRequest):
-    logger.info(f"Received params: {request}")
-    try:
-        import uuid
-
-        from dbgpt.app.scene import ChatScene
-        from dbgpt.util.chat_util import llm_chat_response_nostream
-
-        chat_param = {
-            "chat_session_id": uuid.uuid1(),
-            "current_user_input": request.text,
-            "select_param": "entity",
-            "model_name": request.model_name,
-        }
-
-        res = await llm_chat_response_nostream(
-            ChatScene.ExtractEntity.value(), **{"chat_param": chat_param}
-        )
-        return Result.succ(res)
-    except Exception as e:
-        return Result.failed(code="E000X", msg=f"entity extract error {e}")

dbgpt/app/knowledge/request/request.py

@@ -1,3 +1,4 @@
+from enum import Enum
 from typing import List, Optional
 
 from dbgpt._private.pydantic import BaseModel, ConfigDict
@@ -19,12 +20,20 @@ class KnowledgeSpaceRequest(BaseModel):
     name: str = None
     """vector_type: vector type"""
     vector_type: str = None
+    """vector_type: vector type"""
+    domain_type: str = "normal"
     """desc: description"""
     desc: str = None
     """owner: owner"""
     owner: str = None
 
 
+class BusinessFieldType(Enum):
+    """BusinessFieldType"""
+
+    NORMAL = "Normal"
+
+
 class KnowledgeDocumentRequest(BaseModel):
     """doc_name: doc path"""
 

dbgpt/app/knowledge/request/response.py

@@ -33,6 +33,8 @@ class SpaceQueryResponse(BaseModel):
     name: str = None
     """vector_type: vector type"""
     vector_type: str = None
+    """field_type: field type"""
+    domain_type: str = None
     """desc: description"""
     desc: str = None
     """context: context"""

dbgpt/app/knowledge/service.py

@@ -23,6 +23,7 @@
     SpaceQueryResponse,
 )
 from dbgpt.component import ComponentType
+from dbgpt.configs import DOMAIN_TYPE_FINANCIAL_REPORT
 from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG
 from dbgpt.core import LLMClient
 from dbgpt.model import DefaultLLMClient
@@ -133,6 +134,7 @@ def get_knowledge_space(self, request: KnowledgeSpaceRequest):
             res.id = space.id
             res.name = space.name
             res.vector_type = space.vector_type
+            res.domain_type = space.domain_type
             res.desc = space.desc
             res.owner = space.owner
             res.gmt_created = space.gmt_created
@@ -299,6 +301,10 @@ def delete_space(self, space_name: str):
             llm_client=self.llm_client,
             model_name=None,
         )
+        if space.domain_type == DOMAIN_TYPE_FINANCIAL_REPORT:
+            conn_manager = CFG.local_db_manager
+            conn_manager.delete_db(f"{space.name}_fin_report")
+
         vector_store_connector = VectorStoreConnector(
             vector_store_type=space.vector_type, vector_store_config=config
         )