fix: knowledge_base_chat and search_engine_chat should stream json text

instead of dict. client should use json.loads.

server/chat/knowledge_base_chat.py

@@ -13,6 +13,7 @@ from typing import AsyncIterable
 import asyncio
 from langchain.prompts import PromptTemplate
 from server.knowledge_base.utils import lookup_vs
+import json
 
 
 def knowledge_base_chat(query: str = Body(..., description="用户输入", example="你好"),
@@ -55,8 +56,8 @@ def knowledge_base_chat(query: str = Body(..., description="用户输入", examp
 
         async for token in callback.aiter():
             # Use server-sent-events to stream the response
-            yield {"answer": token,
-                   "docs": source_documents}
+            yield json.dumps({"answer": token,
+                   "docs": source_documents})
         await task
 
     return StreamingResponse(knowledge_base_chat_iterator(query, knowledge_base_name, top_k),

server/chat/search_engine_chat.py

@@ -12,6 +12,7 @@ from typing import AsyncIterable
 import asyncio
 from langchain.prompts import PromptTemplate
 from langchain.docstore.document import Document
+import json
 
 
 def bing_search(text, result_len=SEARCH_ENGINE_TOP_K):
@@ -94,8 +95,8 @@ def search_engine_chat(query: str = Body(..., description="用户输入", exampl
 
         async for token in callback.aiter():
             # Use server-sent-events to stream the response
-            yield {"answer": token,
-                   "docs": source_documents}
+            yield json.dumps({"answer": token,
+                   "docs": source_documents})
         await task
 
     return StreamingResponse(search_engine_chat_iterator(query, search_engine_name, top_k),

server/knowledge_base/utils.py

@@ -72,7 +72,7 @@ def lookup_vs(
         embedding_model: str = EMBEDDING_MODEL,
         embedding_device: str = EMBEDDING_DEVICE,
 ):
-    search_index = load_vector_store(knowledge_base_name, embedding_model, embedding_device)
+    search_index = load_vector_store(knowledge_base_name, embedding_model, embedding_device, _VECTOR_STORE_TICKS.get(knowledge_base_name))
     docs = search_index.similarity_search(query, k=top_k)
     return docs