Update local_doc_qa.py

chains/local_doc_qa.py

@@ -117,26 +117,23 @@ class LocalDocQA:
     
     问题:
     {question}"""
-        if  vs_path is None or vs_path =="":# or (not os.path.exists(vs_path))
-            result = self.llm.chat(query)
-        else:
-            prompt = PromptTemplate(
-                template=prompt_template,
-                input_variables=["context", "question"]
-            )
-            self.llm.history = chat_history
-            vector_store = FAISS.load_local(vs_path, self.embeddings)
-            knowledge_chain = RetrievalQA.from_llm(
-                llm=self.llm,
-                retriever=vector_store.as_retriever(search_kwargs={"k": self.top_k}),
-                prompt=prompt
-            )
-            knowledge_chain.combine_documents_chain.document_prompt = PromptTemplate(
-                input_variables=["page_content"], template="{page_content}"
-            )
-
-            knowledge_chain.return_source_documents = True
+        prompt = PromptTemplate(
+            template=prompt_template,
+            input_variables=["context", "question"]
+        )
+        self.llm.history = chat_history
+        vector_store = FAISS.load_local(vs_path, self.embeddings)
+        knowledge_chain = RetrievalQA.from_llm(
+            llm=self.llm,
+            retriever=vector_store.as_retriever(search_kwargs={"k": self.top_k}),
+            prompt=prompt
+        )
+        knowledge_chain.combine_documents_chain.document_prompt = PromptTemplate(
+            input_variables=["page_content"], template="{page_content}"
+        )
 
+        knowledge_chain.return_source_documents = True
         result = knowledge_chain({"query": query})
+            
         self.llm.history[-1][0] = query
         return result, self.llm.history