doc:update deploy doc

pilot/scene/chat_knowledge/v1/chat.py

@@ -55,11 +55,6 @@ class ChatKnowledge(BaseChat):
             "vector_store_name": self.knowledge_space,
             "vector_store_type": CFG.VECTOR_STORE_TYPE,
         }
-        # from pilot.graph_engine.graph_factory import RAGGraphFactory
-        #
-        # self.rag_engine = CFG.SYSTEM_APP.get_component(
-        #     ComponentType.RAG_GRAPH_DEFAULT.value, RAGGraphFactory
-        # ).create()
         embedding_factory = CFG.SYSTEM_APP.get_component(
             "embedding_factory", EmbeddingFactory
         )

pilot/scene/chat_knowledge/v1/prompt.py

@@ -13,13 +13,13 @@ The assistant gives helpful, detailed, professional and polite answers to the us
 
 
 _DEFAULT_TEMPLATE_ZH = """ 基于以下已知的信息, 专业、简要的回答用户的问题,
-            如果无法从提供的内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题" 禁止胡乱编造。 
+            如果无法从提供的内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题" 禁止胡乱编造, 回答的时候最好按照1.2.3.点进行总结。 
             已知内容: 
             {context}
             问题:
             {question}
 """
-_DEFAULT_TEMPLATE_EN = """ Based on the known information below, provide users with professional and concise answers to their questions. If the answer cannot be obtained from the provided content, please say: "The information provided in the knowledge base is not sufficient to answer this question." It is forbidden to make up information randomly. 
+_DEFAULT_TEMPLATE_EN = """ Based on the known information below, provide users with professional and concise answers to their questions. If the answer cannot be obtained from the provided content, please say: "The information provided in the knowledge base is not sufficient to answer this question." It is forbidden to make up information randomly. When answering, it is best to summarize according to points 1.2.3.
             known information: 
             {context}
             question:

pilot/server/component_configs.py

@@ -29,9 +29,9 @@ def initialize_components(
     system_app.register_instance(controller)
 
     # Register global default RAGGraphFactory
-    from pilot.graph_engine.graph_factory import DefaultRAGGraphFactory
+    # from pilot.graph_engine.graph_factory import DefaultRAGGraphFactory
 
-    system_app.register(DefaultRAGGraphFactory)
+    # system_app.register(DefaultRAGGraphFactory)
 
     from pilot.base_modules.agent.controller import module_agent
 

pilot/server/knowledge/service.py

@@ -441,16 +441,7 @@ class KnowledgeService:
         logger.info(
             f"async_document_summary, doc:{doc.doc_name}, chunk_size:{len(texts)}, begin generate summary"
         )
-        # summary = self._llm_extract_summary(texts[0])
         summary = self._mapreduce_extract_summary(texts)
-        # summaries = prompt_helper.repack(prompt=DEFAULT_TREE_SUMMARIZE_PROMPT_SEL, text_chunks=summaries)
-        # if (len(summaries)) > 1:
-        #     outputs, summary = self._refine_extract_summary(summaries[1:], summaries[0])
-        # else:
-        #     summary = self._llm_extract_summary("\n".join(summaries))
-        # print(
-        #     f"refine summary outputs:{summaries}"
-        # )
         print(f"final summary:{summary}")
         doc.summary = summary
         return knowledge_document_dao.update_knowledge_document(doc)
@@ -466,7 +457,6 @@ class KnowledgeService:
             f"async doc sync, doc:{doc.doc_name}, chunk_size:{len(chunk_docs)}, begin embedding to vector store-{CFG.VECTOR_STORE_TYPE}"
         )
         try:
-            self.async_document_summary(chunk_docs, doc)
             vector_ids = client.knowledge_embedding_batch(chunk_docs)
             doc.status = SyncStatus.FINISHED.name
             doc.result = "document embedding success"
@@ -543,37 +533,9 @@ class KnowledgeService:
         )
         return summary_iters[0]
 
-    def _refine_extract_summary(self, docs, summary: str, max_iteration: int = 5):
-        """Extract refine summary by llm"""
-        from pilot.scene.base import ChatScene
-        from pilot.common.chat_util import llm_chat_response_nostream
-        import uuid
-
-        print(f"initialize summary is :{summary}")
-        outputs = [summary]
-        max_iteration = max_iteration if len(docs) > max_iteration else len(docs)
-        for doc in docs[0:max_iteration]:
-            chat_param = {
-                "chat_session_id": uuid.uuid1(),
-                "current_user_input": doc,
-                "select_param": summary,
-                "model_name": self.model_name,
-            }
-            from pilot.utils import utils
-
-            loop = utils.get_or_create_event_loop()
-            summary = loop.run_until_complete(
-                llm_chat_response_nostream(
-                    ChatScene.ExtractRefineSummary.value(), **{"chat_param": chat_param}
-                )
-            )
-            outputs.append(summary)
-            print(f"iterator is {len(outputs)} current summary is :{summary}")
-        return outputs, summary
-
     def _mapreduce_extract_summary(self, docs):
-        """Extract mapreduce summary by llm
-        map -> multi thread generate summary
+        """Extract summary by mapreduce mode
+        map -> multi async thread generate summary
         reduce -> merge the summaries by map process
         Args:
             docs:List[str]