Update app.py

app.py

@@ -457,17 +457,6 @@ def refresh_faiss_index(documents=None):
 
 
 
-def load_user_data_source(user_id):
-    try:
-        docs = db.child("users").child(user_id).child("KnowledgeBase").get().val()
-        if not docs:
-            return []
-        user_documents = [Document(page_content=doc["content"]) for doc in docs.values()]
-        return user_documents
-    except Exception as e:
-        st.error(f"Error loading user data source: {e}")
-        return []
-
 
 def update_message_counter():
     remaining_messages = st.session_state["message_limit"] - st.session_state["used_messages"]
@@ -1083,29 +1072,36 @@ def google_search(query):
         return ["Error occurred during Google search"]
 
 
-
 def rag_response(query):
     """
-    Handle queries by searching both static and dynamically uploaded knowledge base.
+    Handle queries by searching both static and dynamically uploaded knowledge bases.
     """
     try:
-        # Retrieve relevant chunks from the vector store
+        # Initialize results list
         results = []
+
+        # Search FAISS database (static knowledge base)
+        if "faiss_db" in st.session_state:
+            retrieved_docs = search_knowledge_base(query)
+            results.extend(retrieved_docs)
+        
+        # Search vector stores (dynamic knowledge base)
         if "vector_store" in st.session_state:
             for vector_store in st.session_state["vector_store"].values():
-                results.extend(vector_store.similarity_search(query, k=3))  # Adjust `k` for the number of results
+                vector_store_results = vector_store.similarity_search(query, k=3)  # Adjust `k` as needed
+                results.extend(vector_store_results)
 
-        # Combine results into a context
-        context = "\n".join([result.page_content for result in results])
-        if not context:
+        # Combine results into a single context
+        context = "\n".join([doc.page_content for doc in results])
+        if not context.strip():
             return "No relevant information found in the knowledge base."
 
         # Generate AI response with the retrieved context
         prompt = f"Context:\n{context}\n\nQuestion: {query}\nAnswer:"
-        llm = ChatOpenAI(model="gpt-4o", temperature=0.5, api_key=openai_api_key)
+        llm = ChatOpenAI(model="gpt-4o", temperature=0.3, api_key=openai_api_key)
         response = llm.invoke(prompt)
 
-        return response.content
+        return response.content.strip()
     except Exception as e:
         logger.error(f"Error generating RAG response: {e}")
         return "An error occurred during the RAG response generation process."