解决es的问题

libs/chatchat-server/chatchat/server/chat/kb_chat.py

@@ -91,8 +91,8 @@ async def kb_chat(query: str = Body(..., description="用户输入", examples=["
                                                 metadata={})
 
                 source_documents = format_reference(kb_name, docs, api_address(is_public=True))
-                logger.info(
-                    f"***********************************knowledge_base_chat_iterator:,after format_reference:{docs}")
+                # logger.info(
+                #     f"***********************************knowledge_base_chat_iterator:,after format_reference:{docs}")
                 end_time1 = time.time()
                 execution_time1 = end_time1 - start_time1
                 logger.info(f"kb_chat Execution time检索完成: {execution_time1:.6f} seconds")

libs/chatchat-server/chatchat/server/knowledge_base/kb_doc_api.py

@@ -72,11 +72,15 @@ def search_docs(
     if kb is not None:
         if query:
             docs = kb.search_docs(query, top_k, score_threshold)
-            # logger.info(f"search_docs, query:{query},top_k:{top_k},len(docs):{len(docs)},docs:{docs}")
+            if docs is not None:
+                logger.info(f"search_docs, query:{query},top_k:{top_k},score_threshold:{score_threshold},len(docs):{len(docs)}")
+
             docs_key = kb.search_content_internal(query,2)
-            # logger.info(f"before merge_and_deduplicate docs_key:{docs_key}")
+            if docs_key is not None:
+                logger.info(f"before merge_and_deduplicate ,len(docs_key):{len(docs_key)}")
             docs = merge_and_deduplicate(docs, docs_key)
-            logger.info(f"after merge_and_deduplicate docs:{docs}")
+            if docs is not None:
+                logger.info(f"after merge_and_deduplicate len(docs):{len(docs)}")
             data = [DocumentWithVSId(**{"id": x.metadata.get("id"), **x.dict()}) for x in docs]
         elif file_name or metadata:
             data = kb.list_docs(file_name=file_name, metadata=metadata)
@@ -87,12 +91,15 @@ def search_docs(
 
 def merge_and_deduplicate(list1: List[Document], list2: List[Document]) -> List[Document]:
     # 使用字典存储唯一的 Document
-    merged_dict = {doc.page_content: doc for doc in list1}
+    merged_dict = {}
+    if list1 is not None:
+        merged_dict = {doc.page_content: doc for doc in list1}
 
     # 遍历 list2，将新的 Document 添加到字典
-    for doc in list2:
-        if doc.page_content not in merged_dict:
-            merged_dict[doc.page_content] = doc
+    if list2 is not None:
+        for doc in list2:
+            if doc.page_content not in merged_dict:
+                merged_dict[doc.page_content] = doc
 
     # 返回去重后的列表
     return list(merged_dict.values())

libs/chatchat-server/chatchat/server/knowledge_base/kb_service/es_kb_service.py

@@ -58,7 +58,7 @@ class ESKBService(KBService):
             # ES python客户端连接（仅连接）
             logger.info(f"connection_info:{connection_info}")
             self.es_client_python = Elasticsearch(**connection_info)
-            # logger.info(f"after Elasticsearch connection_info:{connection_info}")
+            logger.info(f"after Elasticsearch connection_info:{connection_info}")
         except ConnectionError:
             logger.error("连接到 Elasticsearch 失败！")
             raise ConnectionError
@@ -89,9 +89,10 @@ class ESKBService(KBService):
                 es_url=f"{self.scheme}://{self.IP}:{self.PORT}",
                 index_name=self.index_name,
                 query_field="context",
+                distance_strategy="COSINE",
                 vector_query_field="dense_vector",
                 embedding=self.embeddings_model,
-                strategy=ApproxRetrievalStrategy(),
+                # strategy=ApproxRetrievalStrategy(),
                 es_params={
                     "timeout": 60,
                 },
@@ -106,6 +107,7 @@ class ESKBService(KBService):
                     params["es_params"].update(client_key=self.client_key)
                     params["es_params"].update(client_cert=self.client_cert)
             self.db = ElasticsearchStore(**params)
+            logger.info(f"after ElasticsearchStore create params:{params}")
         except ConnectionError:
             logger.error("### 初始化 Elasticsearch 失败！")
             raise ConnectionError
@@ -138,14 +140,20 @@ class ESKBService(KBService):
     def vs_type(self) -> str:
         return SupportedVSType.ES
 
-    def do_search(self, query: str, top_k: int, score_threshold: float):
+    def do_search(self, query: str, top_k: int, score_threshold: float)->List[Document]:
+        # 确保 ElasticsearchStore 正确初始化
+        if not hasattr(self, "db") or self.db is None:
+            raise ValueError("ElasticsearchStore (db) not initialized.")
+
         # 文本相似性检索
         retriever = get_Retriever("vectorstore").from_vectorstore(
             self.db,
             top_k=top_k,
             score_threshold=score_threshold,
         )
+
         docs = retriever.get_relevant_documents(query)
+
         return docs
 
     def searchbyContent(self, query:str, top_k: int = 2):

libs/chatchat-server/chatchat/server/knowledge_base/kb_service/faiss_kb_service.py

@@ -78,11 +78,11 @@ class FaissKBService(KBService):
             docs = retriever.get_relevant_documents(query)
         return docs
 
-    def searchbyContent(self):
+    def searchbyContent(self, query:str, top_k: int = 2):
         pass
 
-    def searchbyContentInternal(self):
-        pass
+    def searchbyContentInternal(self, query:str, top_k: int = 2):
+        return None
 
     def do_add_doc(
         self,

libs/chatchat-server/chatchat/server/knowledge_base/kb_service/milvus_kb_service.py

@@ -88,10 +88,10 @@ class MilvusKBService(KBService):
         docs = retriever.get_relevant_documents(query)
         return docs
 
-    def searchbyContent(self):
+    def searchbyContent(self, query:str, top_k: int = 2):
         pass
 
-    def searchbyContentInternal(self):
+    def searchbyContentInternal(self, query:str, top_k: int = 2):
         pass
 
     def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:

libs/chatchat-server/chatchat/server/knowledge_base/kb_service/pg_kb_service.py

@@ -84,10 +84,10 @@ class PGKBService(KBService):
         docs = retriever.get_relevant_documents(query)
         return docs
 
-    def searchbyContent(self):
+    def searchbyContent(self, query:str, top_k: int = 2):
         pass
 
-    def searchbyContentInternal(self):
+    def searchbyContentInternal(self, query:str, top_k: int = 2):
         pass
 
     def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:

libs/chatchat-server/chatchat/server/knowledge_base/kb_service/relyt_kb_service.py

@@ -95,10 +95,10 @@ class RelytKBService(KBService):
         return score_threshold_process(score_threshold, top_k, docs)
 
 
-    def searchbyContent(self):
+    def searchbyContent(self, query:str, top_k: int = 2):
         pass
 
-    def searchbyContentInternal(self):
+    def searchbyContentInternal(self, query:str, top_k: int = 2):
         pass
 
     def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:

libs/chatchat-server/chatchat/server/knowledge_base/kb_service/zilliz_kb_service.py

@@ -79,10 +79,10 @@ class ZillizKBService(KBService):
         docs = retriever.get_relevant_documents(query)
         return docs
 
-    def searchbyContent(self):
+    def searchbyContent(self, query:str, top_k: int = 2):
         pass
 
-    def searchbyContentInternal(self):
+    def searchbyContentInternal(self, query:str, top_k: int = 2):
         pass
 
     def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:

libs/chatchat-server/chatchat/webui.py

@@ -46,7 +46,7 @@ if __name__ == "__main__":
 
     with st.sidebar:
         st.image(
-            get_img_base64("logo-long-chatchat-trans-v2.png"), use_container_width=True
+            get_img_base64("logo-long-chatchat-trans-v2.png"), use_column_width=True
         )
         st.caption(
             f"""<p align="right">当前版本：{__version__}</p>""",