chatbot.py

import google.generativeai as genai
from pinecone import Pinecone, ServerlessSpec
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_pinecone import PineconeVectorStore
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_core.documents import Document
import io
import PyPDF2
import pandas as pd
import logging
import asyncio
from dotenv import load_dotenv
import os
import uuid

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Load environment variables
load_dotenv()

# Configure Gemini API
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
genai.configure(api_key=GEMINI_API_KEY)

# Initialize Pinecone
PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
pc = Pinecone(api_key=PINECONE_API_KEY)
cloud = os.environ.get('PINECONE_CLOUD', 'aws')
region = os.environ.get('PINECONE_REGION', 'us-east-1')
spec = ServerlessSpec(cloud=cloud, region=region)

# Define index name and embedding dimension
index_name = "rag-donor-index"
embedding_dimension = 768  # For text-embedding-004

# Check if index exists, create if not
if index_name not in pc.list_indexes().names():
    logger.info(f"Creating Pinecone index: {index_name}")
    pc.create_index(
        name=index_name,
        dimension=embedding_dimension,
        metric="cosine",
        spec=spec
    )
    # Wait for index to be ready
    while not pc.describe_index(index_name).status['ready']:
        asyncio.sleep(1)

logger.info(f"Pinecone index {index_name} is ready.")

# Initialize embeddings
embeddings = GoogleGenerativeAIEmbeddings(model="models/text-embedding-004", google_api_key=GEMINI_API_KEY)

# Function to process uploaded file (PDF, text, CSV, or XLSX) without saving locally
def process_uploaded_file(file_stream, filename):
    logger.info(f"Processing uploaded file: {filename}")
    try:
        if filename.lower().endswith('.pdf'):
            logger.info("Processing as PDF file.")
            pdf_reader = PyPDF2.PdfReader(file_stream)
            text = ""
            for page in pdf_reader.pages:
                text += page.extract_text() or ""
            
            # Split PDF content into chunks
            text_splitter = RecursiveCharacterTextSplitter(
                chunk_size=500,
                chunk_overlap=100
            )
            chunks = text_splitter.split_text(text)
            documents = [Document(page_content=chunk, metadata={"source": filename, "chunk_id": str(uuid.uuid4())}) for chunk in chunks]
            logger.info(f"Extracted {len(documents)} chunks from PDF.")
            return documents
        
        elif filename.lower().endswith(('.txt', '.md')):
            logger.info("Processing as text file.")
            content = file_stream.read().decode('utf-8', errors='replace')
            text_splitter = RecursiveCharacterTextSplitter(
                chunk_size=500,
                chunk_overlap=100
            )
            chunks = text_splitter.split_text(content)
            documents = [Document(page_content=chunk, metadata={"source": filename, "chunk_id": str(uuid.uuid4())}) for chunk in chunks]
            logger.info(f"Extracted {len(documents)} chunks from text file.")
            return documents
        
        elif filename.lower().endswith('.csv'):
            logger.info("Processing as CSV file.")
            df = pd.read_csv(file_stream)
            # Convert DataFrame to string representation
            text = df.to_string()
            text_splitter = RecursiveCharacterTextSplitter(
                chunk_size=500,
                chunk_overlap=100
            )
            chunks = text_splitter.split_text(text)
            documents = [Document(page_content=chunk, metadata={"source": filename, "chunk_id": str(uuid.uuid4())}) for chunk in chunks]
            logger.info(f"Extracted {len(documents)} chunks from CSV.")
            return documents
        
        elif filename.lower().endswith('.xlsx'):
            logger.info("Processing as XLSX file.")
            df = pd.read_excel(file_stream, engine='openpyxl')
            # Convert DataFrame to string representation
            text = df.to_string()
            text_splitter = RecursiveCharacterTextSplitter(
                chunk_size=500,
                chunk_overlap=100
            )
            chunks = text_splitter.split_text(text)
            documents = [Document(page_content=chunk, metadata={"source": filename, "chunk_id": str(uuid.uuid4())}) for chunk in chunks]
            logger.info(f"Extracted {len(documents)} chunks from XLSX.")
            return documents
        
        else:
            raise ValueError("Unsupported file type. Only PDF, text, CSV, and XLSX files are supported.")
    
    except Exception as e:
        logger.error(f"Error processing file {filename}: {str(e)}")
        raise Exception(f"Error processing file: {str(e)}")

# Function to index documents in Pinecone
def index_documents(documents, namespace="chatbot-knowledge", batch_size=50):
    logger.info(f"Indexing {len(documents)} documents in Pinecone.")
    try:
        vector_store = PineconeVectorStore(
            index_name=index_name,
            embedding=embeddings,
            namespace=namespace
        )
        
        # Batch documents to avoid Pinecone size limits
        for i in range(0, len(documents), batch_size):
            batch = documents[i:i + batch_size]
            batch_size_bytes = sum(len(doc.page_content.encode('utf-8')) for doc in batch)
            if batch_size_bytes > 4_000_000:
                logger.warning(f"Batch size {batch_size_bytes} bytes exceeds Pinecone limit. Reducing batch size.")
                smaller_batch_size = batch_size // 2
                for j in range(0, len(batch), smaller_batch_size):
                    smaller_batch = batch[j:j + smaller_batch_size]
                    vector_store.add_documents(smaller_batch)
                    logger.info(f"Indexed batch {j // smaller_batch_size + 1} of {len(batch) // smaller_batch_size + 1}")
            else:
                vector_store.add_documents(batch)
                logger.info(f"Indexed batch {i // batch_size + 1} of {len(documents) // batch_size + 1}")
        
        logger.info("Document indexing completed.")
        return vector_store
    
    except Exception as e:
        logger.error(f"Error indexing documents: {e}")
        raise Exception(f"Error indexing documents: {e}")

# RAG chatbot function
def rag_chatbot(query, namespace="chatbot-knowledge"):
    logger.info(f"Processing query: {query}")
    try:
        # Initialize vector store
        vector_store = PineconeVectorStore(
            index_name=index_name,
            embedding=embeddings,
            namespace=namespace
        )
        
        # Retrieve relevant documents
        relevant_docs_with_scores = vector_store.similarity_search_with_score(query, k=3)
        for doc, score in relevant_docs_with_scores:
            logger.info(f"Score: {score:.4f} | Document: {doc.page_content}")
        
        # Combine context from retrieved documents
        context = "\n".join([doc.page_content for doc, score in relevant_docs_with_scores])
        
        # Create prompt for Gemini
        prompt = f"""You are a helpful chatbot that answers questions based on provided context.

Context:

{context}



User Query: {query}



Provide a concise and accurate answer based on the context. If the context doesn't contain relevant information, say so and provide a general response if applicable.

"""
        
        # Initialize Gemini model
        model = genai.GenerativeModel('gemini-1.5-flash')
        
        # Generate response
        response = model.generate_content(prompt)
        logger.info("Generated response successfully.")
        return response.text
    
    except Exception as e:
        logger.error(f"Error processing query: {e}")
        return f"Error processing query: {e}"