app.py

import os
import shutil
import time
import gradio as gr
import qdrant_client
from getpass import getpass

# Set your OpenAI API key from environment variables.
openai_api_key = os.getenv('OPENAI_API_KEY')

# -------------------------------------------------------
# Configure LlamaIndex with OpenAI LLM and Embeddings
# -------------------------------------------------------
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings

Settings.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.4)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")

# -------------------------------------------------------
# Import document readers, index, vector store, memory, etc.
# -------------------------------------------------------
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, StorageContext
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core.memory import ChatMemoryBuffer

# Global variables to hold the index and chat engine.
chat_engine = None
index = None
query_engine = None
memory = None
client = None
vector_store = None
storage_context = None

# Define a persistent collection name.
collection_name = "paper"

# Use a persistent folder to store uploaded files.
upload_dir = "uploaded_files"
if not os.path.exists(upload_dir):
    os.makedirs(upload_dir)
# We do not clear the folder to keep previously uploaded files.

# -------------------------------------------------------
# Function to process uploaded files and update the index.
# -------------------------------------------------------
def process_upload(files):
    """
    Accepts a list of uploaded file paths, saves them to a persistent folder,
    loads new documents, and builds or updates the vector index and chat engine.
    """
    global client, vector_store, storage_context, index, query_engine, memory, chat_engine

    # Copy files into the upload directory if not already present.
    new_file_paths = []
    for file_path in files:
        file_name = os.path.basename(file_path)
        dest = os.path.join(upload_dir, file_name)
        if not os.path.exists(dest):
            shutil.copy(file_path, dest)
            new_file_paths.append(dest)
    
    # If no new files are uploaded, notify the user.
    if not new_file_paths:
        return "No new documents to add."

    # Load only the new documents.
    new_documents = SimpleDirectoryReader(input_files=new_file_paths).load_data()

    # Initialize a persistent Qdrant client.
    client = qdrant_client.QdrantClient(
        path="./qdrant_db",  
        prefer_grpc=True
    )
    
    # Ensure the collection exists.
    from qdrant_client.http import models
    existing_collections = {col.name for col in client.get_collections().collections}
    if collection_name not in existing_collections:
        client.create_collection(
            collection_name=collection_name,
            vectors_config={
                "text-dense": models.VectorParams(
                    size=1536,  # text-embedding-ada-002 produces 1536-dimensional vectors.
                    distance=models.Distance.COSINE
                )
            }
        )
        # Wait briefly for the collection creation to complete.
        time.sleep(1)
    
    # Initialize (or re-use) the vector store.
    vector_store = QdrantVectorStore(
        collection_name=collection_name,
        client=client,
        enable_hybrid=True,
        batch_size=20,
    )
    
    storage_context = StorageContext.from_defaults(vector_store=vector_store)
    
    # Build the index if it doesn't exist; otherwise, update it.
    if index is None:
        # Load all documents from the persistent folder.
        index = VectorStoreIndex.from_documents(
            SimpleDirectoryReader(upload_dir).load_data(),
            storage_context=storage_context
        )
    else:
        index.insert_documents(new_documents)
    
    # Reinitialize query and chat engines to reflect updates.
    query_engine = index.as_query_engine(vector_store_query_mode="hybrid")
    memory = ChatMemoryBuffer.from_defaults(token_limit=3000)
    chat_engine = index.as_chat_engine(
        chat_mode="context",
        memory=memory,
        system_prompt=(
            "You are an AI assistant who answers the user questions, "
            "use the schema fields to generate appropriate and valid json queries"
        ),
    )
    
    return "Documents uploaded and index updated successfully!"

# -------------------------------------------------------
# Chat function that uses the built chat engine.
# -------------------------------------------------------
def chat_with_ai(user_input, chat_history):
    global chat_engine
    if chat_engine is None:
        return chat_history, "Please upload documents first."
    
    response = chat_engine.chat(user_input)
    references = response.source_nodes
    ref = []
    for node in references:
        file_name = node.metadata.get('file_name')
        if file_name and file_name not in ref:
            ref.append(file_name)
    
    complete_response = str(response) + "\n\n"
    chat_history.append((user_input, complete_response))
    return chat_history, ""

# -------------------------------------------------------
# Function to clear the chat history.
# -------------------------------------------------------
def clear_history():
    return [], ""

# -------------------------------------------------------
# Build the Gradio interface.
# -------------------------------------------------------
def gradio_interface():
    with gr.Blocks() as demo:
        gr.Markdown("# Chat Interface for LlamaIndex with File Upload")
        
        # Use Tabs to separate the file upload and chat interfaces.
        with gr.Tab("Upload Documents"):
            gr.Markdown("Upload PDF, Excel, CSV, DOC/DOCX, or TXT files below:")
            file_upload = gr.File(
                label="Upload Files",
                file_count="multiple",
                file_types=[".pdf", ".csv", ".txt", ".xlsx", ".xls", ".doc", ".docx"],
                type="filepath"  # returns file paths
            )
            upload_status = gr.Textbox(label="Upload Status", interactive=False)
            upload_button = gr.Button("Process Upload")
            
            upload_button.click(process_upload, inputs=file_upload, outputs=upload_status)
        
        with gr.Tab("Chat"):
            chatbot = gr.Chatbot(label="LlamaIndex Chatbot")
            user_input = gr.Textbox(
                placeholder="Ask a question...", label="Enter your question"
            )
            submit_button = gr.Button("Send")
            btn_clear = gr.Button("Clear History")
            
            # A State to hold the chat history.
            chat_history = gr.State([])
            
            submit_button.click(chat_with_ai, inputs=[user_input, chat_history], outputs=[chatbot, user_input])
            user_input.submit(chat_with_ai, inputs=[user_input, chat_history], outputs=[chatbot, user_input])
            btn_clear.click(clear_history, outputs=[chatbot, user_input])
    
    return demo

# Launch the Gradio app.
gradio_interface().launch(debug=True)