fix(ChatKnowledge): summary support zhipu message_converter (#1085)

dbgpt/app/knowledge/service.py

@@ -400,7 +400,9 @@ class KnowledgeService:
         assembler = SummaryAssembler(
             knowledge=knowledge,
             model_name=request.model_name,
-            llm_client=DefaultLLMClient(worker_manager=worker_manager),
+            llm_client=DefaultLLMClient(
+                worker_manager=worker_manager, auto_convert_message=True
+            ),
             language=CFG.LANGUAGE,
             chunk_parameters=chunk_parameters,
         )

dbgpt/rag/extractor/summary.py

@@ -18,7 +18,7 @@ Write a quick summary of the following context:
 the summary should be as concise as possible and not overly lengthy.Please keep the answer within approximately 200 characters.
 """
 
-REFINE_SUMMARY_TEMPLATE_ZH = """我们已经提供了一个到某一点的现有总结:{context}\n 请根据你之前推理的内容进行最终的总结,总结回答的时候最好按照1.2.3.进行. 注意:请用<中文>来进行总结。"""
+REFINE_SUMMARY_TEMPLATE_ZH = """我们已经提供了一个到某一点的现有总结:{context}\n 请根据你之前推理的内容进行总结,总结回答的时候最好按照1.2.3.进行. 注意:请用<中文>来进行总结。"""
 
 REFINE_SUMMARY_TEMPLATE_EN = """
 We have provided an existing summary up to a certain point: {context}, We have the opportunity to refine the existing summary (only if needed) with some more context below. 
@@ -144,7 +144,7 @@ class SummaryExtractor(Extractor):
             from dbgpt.core import ModelMessage
 
             prompt = prompt_template.format(context=chunk_text)
-            messages = [ModelMessage(role=ModelMessageRoleType.SYSTEM, content=prompt)]
+            messages = [ModelMessage(role=ModelMessageRoleType.HUMAN, content=prompt)]
             request = ModelRequest(model=self._model_name, messages=messages)
             tasks.append(self._llm_client.generate(request))
         summary_results = await run_async_tasks(

examples/awel/simple_rag_summary_example.py

@@ -21,11 +21,9 @@ This example shows how to use AWEL to build a simple rag summary example.
 
     .. code-block:: shell
 
-        DBGPT_SERVER="http://127.0.0.1:5000"
-        FILE_PATH="{your_file_path}"
         curl -X POST http://127.0.0.1:5555/api/v1/awel/trigger/examples/rag/summary \
         -H "Content-Type: application/json" -d '{
-            "file_path": $FILE_PATH
+            "url": "https://docs.dbgpt.site/docs/awel"
         }'
 """
 from typing import Dict
@@ -33,12 +31,13 @@ from typing import Dict
 from dbgpt._private.pydantic import BaseModel, Field
 from dbgpt.core.awel import DAG, HttpTrigger, MapOperator
 from dbgpt.model import OpenAILLMClient
+from dbgpt.rag.knowledge.base import KnowledgeType
 from dbgpt.rag.operator.knowledge import KnowledgeOperator
 from dbgpt.rag.operator.summary import SummaryAssemblerOperator
 
 
 class TriggerReqBody(BaseModel):
-    file_path: str = Field(..., description="file_path")
+    url: str = Field(..., description="url")
 
 
 class RequestHandleOperator(MapOperator[TriggerReqBody, Dict]):
@@ -47,7 +46,7 @@ class RequestHandleOperator(MapOperator[TriggerReqBody, Dict]):
 
     async def map(self, input_value: TriggerReqBody) -> Dict:
         params = {
-            "file_path": input_value.file_path,
+            "url": input_value.url,
         }
         print(f"Receive input value: {input_value}")
         return params
@@ -58,9 +57,9 @@ with DAG("dbgpt_awel_simple_rag_summary_example") as dag:
         "/examples/rag/summary", methods="POST", request_body=TriggerReqBody
     )
     request_handle_task = RequestHandleOperator()
-    path_operator = MapOperator(lambda request: request["file_path"])
+    path_operator = MapOperator(lambda request: request["url"])
     # build knowledge operator
-    knowledge_operator = KnowledgeOperator()
+    knowledge_operator = KnowledgeOperator(knowledge_type=KnowledgeType.URL)
     # build summary assembler operator
     summary_operator = SummaryAssemblerOperator(
         llm_client=OpenAILLMClient(), language="en"