修改knowledge_base_chat实现:利用缓存避免每次请求重新加载向量库；增加top_k参数。

server/chat/knowledge_base_chat.py

@@ -12,10 +12,43 @@ from langchain.prompts import PromptTemplate
 from langchain.vectorstores import FAISS
 from langchain.embeddings.huggingface import HuggingFaceEmbeddings
 from server.knowledge_base.utils import get_vs_path
+from functools import lru_cache
+
+
+@lru_cache(1)
+def load_embeddings(model: str, device: str):
+    embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[model],
+                                        model_kwargs={'device': device})
+    return embeddings
+
+
+@lru_cache(1)
+def load_vector_store(
+    knowledge_base_name: str,
+    embedding_model: str,
+    embedding_device: str,
+):
+    embeddings = load_embeddings(embedding_model, embedding_device)
+    vs_path = get_vs_path(knowledge_base_name)
+    search_index = FAISS.load_local(vs_path, embeddings)
+    return search_index
+
+
+def lookup_vs(
+    query: str,
+    knowledge_base_name: str,
+    top_k: int = 3,
+    embedding_model: str = EMBEDDING_MODEL,
+    embedding_device: str = EMBEDDING_DEVICE,
+):
+    search_index = load_vector_store(knowledge_base_name, embedding_model, embedding_device)
+    docs = search_index.similarity_search(query, k=top_k)
+    return docs
 
 
 def knowledge_base_chat(query: str = Body(..., description="用户输入", example="你好"),
                         knowledge_base_name: str = Body(..., description="知识库名称", example="samples"),
+                        top_k: int = Body(3, description="匹配向量数"),
                         ):
     async def knowledge_base_chat_iterator(query: str,
                                            knowledge_base_name: str,
@@ -30,11 +63,7 @@ def knowledge_base_chat(query: str = Body(..., description="用户输入", examp
             model_name=LLM_MODEL
         )
 
-        vs_path = get_vs_path(knowledge_base_name)
-        embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[EMBEDDING_MODEL],
-                                           model_kwargs={'device': EMBEDDING_DEVICE})
-        search_index = FAISS.load_local(vs_path, embeddings)
-        docs = search_index.similarity_search(query, k=4)
+        docs = lookup_vs(query, knowledge_base_name, top_k)
         context = "\n".join([doc.page_content for doc in docs])
         prompt = PromptTemplate(template=PROMPT_TEMPLATE, input_variables=["context", "question"])
 

webui_pages/dialogue/dialogue.py

@@ -52,7 +52,6 @@ def dialogue_page(api: ApiRequest):
 
             cols = st.columns(2)
             chat_list = chat_box.get_chat_names()
-            print(chat_list, chat_box.cur_chat_name)
             try:
                 index = chat_list.index(chat_box.cur_chat_name)
             except:
@@ -74,7 +73,7 @@ def dialogue_page(api: ApiRequest):
                     on_change=on_kb_change,
                     key="selected_kb",
                 )
-                top_k = st.slider("匹配知识条数：", 1, 20, 3, disabled=True)
+                top_k = st.slider("匹配知识条数：", 1, 20, 3)
                 score_threshold = st.slider("知识匹配分数阈值：", 0, 1000, 0, disabled=True)
                 chunk_content = st.checkbox("关联上下文", False, disabled=True)
                 chunk_size = st.slider("关联长度：", 0, 500, 250, disabled=True)
@@ -95,7 +94,7 @@ def dialogue_page(api: ApiRequest):
         elif dialogue_mode == "知识库问答":
             chat_box.ai_say(f"正在查询知识库： `{selected_kb}` ...")
             text = ""
-            for t in api.knowledge_base_chat(prompt, selected_kb):
+            for t in api.knowledge_base_chat(prompt, selected_kb, top_k):
                 text += t
                 chat_box.update_msg(text)
             chat_box.update_msg(text, streaming=False)