Update app.py

app.py

@@ -1,6 +1,5 @@
-from helper import download_hugging_face_embeddings
 from url import md_files_url
-from get_data import extract_repo_details, fetch_md_file_via_api, data_loader, chunk_text
+from get_data import extract_repo_details, fetch_md_file_via_api, data_loader, chunk_text, download_hugging_face_embeddings
 from langchain_community.llms import Ollama
 from langchain.chains import create_retrieval_chain
 from langchain.chains.combine_documents import create_stuff_documents_chain
@@ -28,7 +27,7 @@ logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 base = {}
-last_messages = 4
+last_messages = 10
 documents = []
 HF_ORG_NAME = 'HumbleBeeAI'
 DATASET_NAME = 'faiss_index'
@@ -118,7 +117,11 @@ else:
 # ---- Database part ----- #
 # Database Connection
 def connect_db():
-    return sqlite3.connect(DB_PATH)
+    try:
+        return sqlite3.connect(DB_PATH)
+    except sqlite3.Error as e:
+        logger.error(f"Database connection failed: {e}")
+        return None  # Or raise a custom exception
 
 def create_tables():
     with connect_db() as conn:
@@ -239,11 +242,11 @@ docsearch = PineconeVectorStore.from_existing_index(
     embedding=download_hugging_face_embeddings()
 )
     
-retriever = docsearch.as_retriever(search_type='mmr', search_kwargs={'k':5})
+retriever = docsearch.as_retriever(search_type='mmr', search_kwargs={'k': 10, 'lambda_mult': 0.5})
 
 llm = Ollama(model='llama3.2', base_url=BASE_URL)
 
-prompt = ChatPromptTemplate.from_messages(
+= ChatPromptTemplate.from_messages(
     [
         ('system', system_prompt),
         ('human', '{input}'),
@@ -260,6 +263,7 @@ context_prompt = ChatPromptTemplate.from_messages(
         ('human', '{input}'),
     ]
 )
+
 history_aware_retriever = create_history_aware_retriever(llm, retriever, context_prompt)
 
 qa_prompt = ChatPromptTemplate.from_messages(
@@ -269,6 +273,7 @@ qa_prompt = ChatPromptTemplate.from_messages(
         ('human', '{input}'),
     ]
 )
+
 question_answer_chain  = create_stuff_documents_chain(llm, prompt)
 rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
 
@@ -291,15 +296,36 @@ chat_with_msg_history = RunnableWithMessageHistory(
     input_messages_key='input',
     history_messages_key='chat_history'
 )
+from langchain.schema import AIMessage, HumanMessage
+
+def get_combined_history(session_id, user_id):
+    persistent_history = get_chat_history(user_id)  # Retrieve long-term memory
+
+    # Convert stored chat history into messages
+    long_term_messages = [
+        HumanMessage(content=msg) if i % 2 == 0 else AIMessage(content=resp)
+        for i, (msg, resp) in enumerate(persistent_history)
+    ]
+
+    # Collect session-based recent messages
+    recent_messages = base[session_id].messages if session_id in base else []
+
+    # Merge and return the combined context
+    combined_history = long_term_messages + recent_messages
+    print("Combined History for Debugging:", combined_history)  # 🔹 Debugging step
+    return combined_history[-last_messages:]  # Optionally limit
 
 def get_response(message, chat_history, session_id, user_id):
     if not session_id or not user_id:
         return "Session expired. Please log in again.", []
 
-    chat_history = get_chat_history(user_id)  # Load user's previous chat history
+    combined_history = get_combined_history(session_id, user_id)
 
     response = chat_with_msg_history.invoke(
-        {'input': message},
+        {
+            'input': message, 
+            'chat_history': combined_history  # This should be a list of LangChain message objects
+        },
         {'configurable': {'session_id': session_id}},
     )
 
@@ -323,7 +349,6 @@ def logout(session_id):
         del base[session_id]  # Clear session history
     return None, "Logged out successfully.", None
 
-## ... [Keep all previous code up to the Gradio UI section] ...
 # Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("## HumblebeeAI Customer Support Chatbot")
@@ -358,7 +383,7 @@ with gr.Blocks() as demo:
         if session_id:
             return session_id, user_id, message, "", "", get_chat_history(user_id), gr.update(visible=True)
         return None, None, message, username, password, [], gr.update(visible=False)
-
+    
     login_button.click(
         login_user, 
         [username, password], 
@@ -390,18 +415,12 @@ with gr.Blocks() as demo:
     )
 
     # 🔹 Logout Logic (Clears Chat and Resets UI)
-    def logout_user():
-        return None, "", "", [], gr.update(visible=False)
-
-    # 🔹 Logout Function (Clears Status, Session, and Chat History)
     def logout_user():
         return None, "", "", "", [], gr.update(visible=False)
-    
+
     logout_button.click(
         logout_user, 
         None, 
         [session_state, username, password, login_status, chatbot, chat_interface]
     )
-
-
-demo.launch(share=True)
+demo.launch(share=True)