Update app.py

app.py

@@ -3,7 +3,7 @@ import shutil
 import streamlit as st
 from io import BytesIO
 
-# Importing LlamaIndex components
+
 from llama_index.llms.openai import OpenAI
 from qdrant_client.http import models
 from llama_index.embeddings.openai import OpenAIEmbedding
@@ -12,37 +12,30 @@ from llama_index.vector_stores.qdrant import QdrantVectorStore
 from llama_index.core.memory import ChatMemoryBuffer
 import qdrant_client
 
-# =============================================================================
-# Configuration and Global Initialization
-# =============================================================================
 
-# Ensure that the OpenAI API key is available
 openai_api_key = os.getenv("OPENAI_API_KEY")
 if not openai_api_key:
     raise ValueError("Please set your OPENAI_API_KEY environment variable.")
 
-# System prompt for the chat engine
 SYSTEM_PROMPT = (
     "You are an AI assistant who answers the user questions, "
     "use the schema fields to generate appropriate and valid json queries"
 )
 
-# Configure the LLM and embedding models
+
 Settings.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.4)
 Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")
 
-# Load initial documents from a directory called "new_file"
 if os.path.exists("new_file"):
     documents = SimpleDirectoryReader("new_file").load_data()
 else:
     documents = []
 
-# Set up the Qdrant vector store (using an in-memory collection for simplicity)
 
 client = qdrant_client.QdrantClient(location=":memory:")
 
 vector_store = QdrantVectorStore(
-    collection_name="paper",
+    # collection_name="paper",
     client=client,
     vector_field="embedding", 
     enable_hybrid=True,
@@ -50,7 +43,6 @@ vector_store = QdrantVectorStore(
 )
 storage_context = StorageContext.from_defaults(vector_store=vector_store)
 
-# Build the initial index and chat engine
 index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
 chat_memory = ChatMemoryBuffer.from_defaults(token_limit=3000)
 chat_engine = index.as_chat_engine(
@@ -59,42 +51,28 @@ chat_engine = index.as_chat_engine(
     system_prompt=SYSTEM_PROMPT,
 )
 
-# =============================================================================
-# Helper Functions
-# =============================================================================
 
 def process_uploaded_file(uploaded_file: BytesIO) -> str:
-    """
-    Process the uploaded file:
-      1. Save the file to an "uploads" folder.
-      2. Copy it to a temporary folder ("temp_upload") for reading.
-      3. Update the global documents list and rebuild the index and chat engine.
-    """
+
     if uploaded_file is None:
         return "No file uploaded."
 
-    # Ensure the uploads directory exists
     uploads_dir = "uploads"
     os.makedirs(uploads_dir, exist_ok=True)
 
-    # Save the uploaded file locally
     file_name = uploaded_file.name
     dest_path = os.path.join(uploads_dir, file_name)
     with open(dest_path, "wb") as f:
         f.write(uploaded_file.getbuffer())
 
-    # Prepare a temporary directory for processing the file
     temp_dir = "temp_upload"
     os.makedirs(temp_dir, exist_ok=True)
-    # Clear any existing file in temp_upload directory
     for f_name in os.listdir(temp_dir):
         os.remove(os.path.join(temp_dir, f_name))
     shutil.copy(dest_path, temp_dir)
 
-    # Load new document(s) from the temporary folder using SimpleDirectoryReader
     new_docs = SimpleDirectoryReader(temp_dir).load_data()
 
-    # Update global documents and rebuild the index and chat engine
     global documents, index, chat_engine
     documents.extend(new_docs)
     index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
@@ -107,11 +85,8 @@ def process_uploaded_file(uploaded_file: BytesIO) -> str:
     return f"File '{file_name}' processed and added to the index."
 
 def chat_with_ai(user_input: str) -> str:
-    """
-    Send user input to the chat engine and return the response.
-    """
+
     response = chat_engine.chat(user_input)
-    # Extract references from the response (if any)
     references = response.source_nodes
     ref = []
     for node in references:
@@ -122,49 +97,36 @@ def chat_with_ai(user_input: str) -> str:
         complete_response += "\n\nReferences: " + ", ".join(ref)
     return complete_response
 
-# =============================================================================
-# Streamlit App Layout
-# =============================================================================
-
 st.set_page_config(page_title="LlamaIndex Chat & File Upload", layout="wide")
 st.title("Chat Interface for LlamaIndex with File Upload")
 
-# Use Streamlit tabs for separate Chat and Upload functionalities
+
 tab1, tab2 = st.tabs(["Chat", "Upload"])
 
-# -----------------------------------------------------------------------------
-# Chat Tab
-# -----------------------------------------------------------------------------
+
 with tab1:
     st.header("Chat with the AI")
-    # Initialize chat history in session state if it does not exist
     if "chat_history" not in st.session_state:
         st.session_state["chat_history"] = []
 
-    # Display conversation history
     for chat in st.session_state["chat_history"]:
         st.markdown(f"**User:** {chat[0]}")
         st.markdown(f"**AI:** {chat[1]}")
         st.markdown("---")
 
-    # Input text for user query
     user_input = st.text_input("Enter your question:")
 
-    # When the "Send" button is clicked, process the chat
     if st.button("Send") and user_input:
         with st.spinner("Processing..."):
             response = chat_with_ai(user_input)
         st.session_state["chat_history"].append((user_input, response))
         st.experimental_rerun()  # Refresh the page to show updated history
 
-    # Button to clear the conversation history
     if st.button("Clear History"):
         st.session_state["chat_history"] = []
         st.experimental_rerun()
 
-# -----------------------------------------------------------------------------
-# Upload Tab
-# -----------------------------------------------------------------------------
+
 with tab2:
     st.header("Upload a File")
     uploaded_file = st.file_uploader("Choose a file to upload", type=["txt", "pdf", "doc", "docx", "csv", "xlsx"])