resolve conflict

docs/docs/agents/modules/memory/hybrid_memory.md

@@ -57,12 +57,10 @@ from dbgpt_ext.storage.vector_store.chroma_store import ChromaVectorConfig, Chro
 shutil.rmtree("/tmp/tmp_ltm_vector_store", ignore_errors=True)
 vector_store = ChromaStore(
     ChromaVectorConfig(
-        embedding_fn=embeddings,
-        vector_store_config=ChromaVectorConfig(
-            name="ltm_vector_store",
-            persist_path="/tmp/tmp_ltm_vector_store",
-        ),
-    )
+        persist_path="/tmp/tmp_ltm_vector_store",
+    ),
+    name="ltm_vector_store",
+    embedding_fn=embeddings
 )
 ```
 

docs/docs/agents/modules/memory/long_term_memory.md

@@ -47,13 +47,11 @@ from dbgpt_ext.storage.vector_store.chroma_store import ChromaVectorConfig, Chro
 # Delete old vector store directory(/tmp/tmp_ltm_vector_stor)
 shutil.rmtree("/tmp/tmp_ltm_vector_store", ignore_errors=True)
 vector_store = ChromaStore(
-    ChromaVectorConfig(
-        embedding_fn=embeddings,
-        vector_store_config=ChromaVectorConfig(
-            name="ltm_vector_store",
-            persist_path="/tmp/tmp_ltm_vector_store",
-        ),
-    )
+    vector_store_config=ChromaVectorConfig(
+        persist_path="/tmp/tmp_ltm_vector_store",
+    ),
+    name="ltm_vector_store",
+    embedding_fn=embeddings,
 )
 ```
 

docs/docs/awel/cookbook/first_rag_with_awel.md

@@ -11,7 +11,7 @@ In this example, we will load your knowledge from a URL and store it in a vector
 First, you need to install the `dbgpt` library.
 
 ```bash
-pip install "dbgpt[rag]>=0.5.2"
+pip install "dbgpt[agent,simple_framework, client]>=0.7.1" "dbgpt_ext>=0.7.1" -U
 ````
 
 ### Prepare Embedding Model
@@ -84,10 +84,10 @@ shutil.rmtree("/tmp/awel_rag_test_vector_store", ignore_errors=True)
 
 vector_store = ChromaStore(
     vector_store_config=ChromaVectorConfig(
-        name="test_vstore",
-        persist_path="/tmp/awel_rag_test_vector_store",
-        embedding_fn=embeddings
-    )
+        persist_path="/tmp/awel_rag_test_vector_store"
+    ),
+    name="test_vstore",
+    embedding_fn=embeddings
 )
 
 with DAG("load_knowledge_dag") as knowledge_dag:
@@ -274,10 +274,10 @@ shutil.rmtree("/tmp/awel_rag_test_vector_store", ignore_errors=True)
 
 vector_store = ChromaStore(
     vector_store_config=ChromaVectorConfig(
-        name="test_vstore",
         persist_path="/tmp/awel_rag_test_vector_store",
-        embedding_fn=embeddings
     ),
+    name="test_vstore",
+    embedding_fn=embeddings
 )
 
 with DAG("load_knowledge_dag") as knowledge_dag:

docs/docs/awel/cookbook/write_your_chat_database.md

@@ -29,7 +29,8 @@ In this guide, we mainly focus on step 1, 2, and 3.
 First, you need to install the `dbgpt` library.
 
 ```bash
-pip install "dbgpt[rag]>=0.7.0" -U
+pip install "dbgpt[rag, agent, client, simple_framework]>=0.7.0" "dbgpt_ext>=0.7.0" -U
+pip install openai
 ````
 
 ## Build Knowledge Base
@@ -92,9 +93,9 @@ shutil.rmtree("/tmp/awel_with_data_vector_store", ignore_errors=True)
 vector_store = ChromaStore(
     ChromaVectorConfig(
         persist_path="/tmp/tmp_ltm_vector_store",
-        name="ltm_vector_store",
-        embedding_fn=embeddings,
-    )
+    ),
+    name="ltm_vector_store",
+    embedding_fn=embeddings,
 )
 
 with DAG("load_schema_dag") as load_schema_dag:
@@ -102,7 +103,7 @@ with DAG("load_schema_dag") as load_schema_dag:
     # Load database schema to vector store
     assembler_task = DBSchemaAssemblerOperator(
         connector=db_conn,
-        index_store=vector_store,
+        table_vector_store_connector=vector_store,
         chunk_parameters=ChunkParameters(chunk_strategy="CHUNK_BY_SIZE")
     )
     input_task >> assembler_task
@@ -122,7 +123,8 @@ with DAG("retrieve_schema_dag") as retrieve_schema_dag:
     # Retrieve database schema from vector store
     retriever_task = DBSchemaRetrieverOperator(
         top_k=1,
-        index_store=vector_store,
+        table_vector_store_connector=vector_store,
+        field_vector_store_connector=vector_store
     )
     input_task >> retriever_task
 
@@ -487,10 +489,10 @@ db_conn.create_temp_tables(
 
 vector_store = ChromaStore(
     ChromaVectorConfig(
-        embedding_fn=embeddings,
-        name="db_schema_vector_store",
         persist_path="/tmp/awel_with_data_vector_store",
-    )
+    ),
+    embedding_fn=embeddings,
+    name="db_schema_vector_store",
 )
 
 antv_charts = [
@@ -623,7 +625,7 @@ with DAG("load_schema_dag") as load_schema_dag:
     # Load database schema to vector store
     assembler_task = DBSchemaAssemblerOperator(
         connector=db_conn,
-        index_store=vector_store,
+        table_vector_store_connector=vector_store,
         chunk_parameters=ChunkParameters(chunk_strategy="CHUNK_BY_SIZE"),
     )
     input_task >> assembler_task

examples/awel/simple_nl_schema_sql_chart_example.py

@@ -51,16 +51,16 @@ INPUT_PROMPT = "\n###Input:\n{}\n###Response:"
 
 def _create_vector_connector():
     """Create vector connector."""
-    config = ChromaVectorConfig(
-        persist_path=PILOT_PATH,
+    config = ChromaVectorConfig(persist_path=PILOT_PATH)
+
+    return ChromaStore(
+        config,
         name="embedding_rag_test",
         embedding_fn=DefaultEmbeddingFactory(
             default_model_name=os.path.join(MODEL_PATH, "text2vec-large-chinese"),
         ).create(),
     )
 
-    return ChromaStore(config)
-
 
 def _create_temporary_connection():
     """Create a temporary database connection for testing."""

examples/sdk/chat_data_with_awel.py

@@ -60,12 +60,12 @@ db_conn.create_temp_tables(
     }
 )
 
-config = ChromaVectorConfig(
-    persist_path=PILOT_PATH,
+config = ChromaVectorConfig(persist_path=PILOT_PATH)
+vector_store = ChromaStore(
+    config,
     name="db_schema_vector_store",
     embedding_fn=embeddings,
 )
-vector_store = ChromaStore(config)
 
 antv_charts = [
     {"response_line_chart": "used to display comparative trend analysis data"},

packages/dbgpt-app/src/dbgpt_app/openapi/api_v2.py

@@ -326,7 +326,7 @@ def check_chat_request(request: ChatCompletionRequestBody = Body()):
                 status_code=400,
                 detail={
                     "error": {
-                        "message": "chart param is None",
+                        "message": "chat param is None",
                         "type": "invalid_request_error",
                         "param": None,
                         "code": "invalid_chat_param",

packages/dbgpt-core/src/dbgpt/agent/core/memory/hybrid.py

@@ -94,11 +94,9 @@ class HybridMemory(Memory, Generic[T]):
         vstore_path = vstore_path or os.path.join(DATA_DIR, "agent_memory")
 
         vector_store = ChromaStore(
-            ChromaVectorConfig(
-                name=vstore_name,
-                persist_path=vstore_path,
-                embedding_fn=embeddings,
-            )
+            ChromaVectorConfig(persist_path=vstore_path),
+            name=vstore_name,
+            embedding_fn=embeddings,
         )
         return cls.from_vstore(
             vector_store=vector_store,

packages/dbgpt-core/src/dbgpt/vis/tags/vis_thinking.py

@@ -7,12 +7,12 @@ class VisThinking(Vis):
     @classmethod
     async def build_message(cls, message: str) -> str:
         vis = VisThinking()
-        return f"```{vis.vis_tag()}\n{message}\n```"
+        return f"``````{vis.vis_tag()}\n{message}\n``````"
 
     def sync_display(self, **kwargs) -> str:
         """Display the content using the vis protocol."""
         content = kwargs.get("content")
-        return f"```{self.vis_tag()}\n{content}\n```"
+        return f"``````{self.vis_tag()}\n{content}\n``````"
 
     @classmethod
     def vis_tag(cls):

packages/dbgpt-serve/src/dbgpt_serve/conversation/api/endpoints.py

@@ -1,9 +1,12 @@
+import io
+import json
 import uuid
 from functools import cache
-from typing import List, Optional
+from typing import List, Literal, Optional
 
 from fastapi import APIRouter, Depends, HTTPException, Query, Request
 from fastapi.security.http import HTTPAuthorizationCredentials, HTTPBearer
+from starlette.responses import JSONResponse, StreamingResponse
 
 from dbgpt.component import SystemApp
 from dbgpt.util import PaginationResult
@@ -231,6 +234,89 @@ async def get_history_messages(con_uid: str, service: Service = Depends(get_serv
     return Result.succ(service.get_history_messages(ServeRequest(conv_uid=con_uid)))
 
 
+@router.get(
+    "/export_messages",
+    dependencies=[Depends(check_api_key)],
+)
+async def export_all_messages(
+    user_name: Optional[str] = None,
+    user_id: Optional[str] = None,
+    sys_code: Optional[str] = None,
+    format: Literal["file", "json"] = Query(
+        "file", description="response format(file or json)"
+    ),
+    service: Service = Depends(get_service),
+):
+    """Export all conversations and messages for a user
+
+    Args:
+        user_name (str): The user name
+        user_id (str): The user id (alternative to user_name)
+        sys_code (str): The system code
+        format (str): The format of the response, either 'file' or 'json', defaults to
+            'file'
+
+    Returns:
+        A dictionary containing all conversations and their messages
+    """
+    # 1. Get all conversations for the user
+    request = ServeRequest(
+        user_name=user_name or user_id,
+        sys_code=sys_code,
+    )
+
+    # Initialize pagination variables
+    page = 1
+    page_size = 100  # Adjust based on your needs
+    all_conversations = []
+
+    # Paginate through all conversations
+    while True:
+        pagination_result = service.get_list_by_page(request, page, page_size)
+        all_conversations.extend(pagination_result.items)
+
+        if page >= pagination_result.total_pages:
+            break
+        page += 1
+
+    # 2. For each conversation, get all messages
+    result = {
+        "user_name": user_name or user_id,
+        "sys_code": sys_code,
+        "total_conversations": len(all_conversations),
+        "conversations": [],
+    }
+
+    for conv in all_conversations:
+        messages = service.get_history_messages(ServeRequest(conv_uid=conv.conv_uid))
+        conversation_data = {
+            "conv_uid": conv.conv_uid,
+            "chat_mode": conv.chat_mode,
+            "app_code": conv.app_code,
+            "create_time": conv.gmt_created,
+            "update_time": conv.gmt_modified,
+            "total_messages": len(messages),
+            "messages": [msg.dict() for msg in messages],
+        }
+        result["conversations"].append(conversation_data)
+
+    if format == "json":
+        return JSONResponse(content=result)
+    else:
+        file_name = (
+            f"conversation_export_{user_name or user_id or 'dbgpt'}_"
+            f"{sys_code or 'dbgpt'}"
+        )
+        # Return the json file
+        return StreamingResponse(
+            io.BytesIO(
+                json.dumps(result, ensure_ascii=False, indent=4).encode("utf-8")
+            ),
+            media_type="application/file",
+            headers={"Content-Disposition": f"attachment;filename={file_name}.json"},
+        )
+
+
 def init_endpoints(system_app: SystemApp, config: ServeConfig) -> None:
     """Initialize the endpoints"""
     global global_system_app

packages/dbgpt-serve/src/dbgpt_serve/conversation/api/schemas.py

@@ -109,6 +109,20 @@ class ServerResponse(BaseModel):
             "dbgpt",
         ],
     )
+    gmt_created: Optional[str] = Field(
+        default=None,
+        description="The record creation time.",
+        examples=[
+            "2023-01-07 09:00:00",
+        ],
+    )
+    gmt_modified: Optional[str] = Field(
+        default=None,
+        description="The record update time.",
+        examples=[
+            "2023-01-07 09:00:00",
+        ],
+    )
 
     def to_dict(self, **kwargs) -> Dict[str, Any]:
         """Convert the model to a dictionary"""

packages/dbgpt-serve/src/dbgpt_serve/conversation/models/models.py

@@ -60,6 +60,17 @@ class ServeDao(BaseDao[ServeEntity, ServeRequest, ServerResponse]):
             RES: The response
         """
         # TODO implement your own logic here, transfer the entity to a response
+        gmt_created = (
+            entity.gmt_created.strftime("%Y-%m-%d %H:%M:%S")
+            if entity.gmt_created
+            else None
+        )
+        gmt_modified = (
+            entity.gmt_modified.strftime("%Y-%m-%d %H:%M:%S")
+            if entity.gmt_modified
+            else None
+        )
+
         return ServerResponse(
             app_code=entity.app_code,
             conv_uid=entity.conv_uid,
@@ -67,6 +78,8 @@ class ServeDao(BaseDao[ServeEntity, ServeRequest, ServerResponse]):
             chat_mode=entity.chat_mode,
             user_name="",
             sys_code=entity.sys_code,
+            gmt_created=gmt_created,
+            gmt_modified=gmt_modified,
         )
 
     def get_latest_message(self, conv_uid: str) -> Optional[MessageStorageItem]: