Update app.py

app.py

@@ -72,13 +72,15 @@ def chat_query_doc(question, history):
     llm = ChatOpenAI(model = llm_name, temperature = 0.1, api_key = OPENAI_API_KEY)
     #llm = GoogleGenerativeAI(model = "gemini-pro", google_api_key = GEMINI_API_KEY)
     #llm = ChatGoogleGenerativeAI(model = "gemini-1.0-pro", google_api_key = GEMINI_API_KEY, temperature = 0.1, top_k = 1, top_p = 0.95)
+    retriever=vectordb.as_retriever(search_type="mmr")
 
-    def get_relevant_passage(query, db):
-        passage = db.query(query_texts=[query], n_results=1)['documents'][0][0]
+    def get_relevant_passage(query, retriever):
+        
+        passage = retriever.invoke(query)[0]['page_content']
         return passage
 
     # Perform embedding search
-    passage = get_relevant_passage(question, vectordb)
+    passage = get_relevant_passage(question, retriever)
 
 
     def make_prompt(query, relevant_passage):
@@ -100,7 +102,7 @@ def chat_query_doc(question, history):
 
     # Conversation Retrival Chain with Memory
     # memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-    retriever=vectordb.as_retriever()
+    
     qa = ConversationalRetrievalChain.from_llm(llm, retriever=retriever, return_source_documents=True)
 
     # # Replace input() with question variable for Gradio