PDF Parsing Chatbot with Advanced Retrieval-Augmented Generation (RAG)

Welcome to the PDF Parsing Chatbot repository, developed as a final internship project at Netlink. This project features a custom chatbot that integrates advanced language modeling and retrieval-augmented generation (RAG) to deliver precise responses to user queries based on PDF documents.

Project Overview

The PDF Parsing Chatbot uses OpenAI and LangChain for intelligent data retrieval and query handling from PDF documents. It incorporates Groq (Llama3) for high-performance language modeling, allowing complex, multi-layered queries.

Features

• Advanced Language Modeling: Utilizes Groq (Llama3) for handling complex language queries with contextual understanding.
• Retrieval-Augmented Generation (RAG): Integrates a RAG pipeline, leveraging LangChain for efficient document retrieval and generating precise responses.
• PDF Data Extraction and Parsing: Extracts structured text, tables, and metadata from PDF files.
• Natural Language Query Processing: Allows users to ask specific questions about the PDF, with accurate retrieval of relevant data points.
• Enhanced Data Retrieval: Optimized with refined algorithms for faster and more accurate responses, achieving a 40% improvement in data retrieval efficiency.
• Real-Time Query Interface: Provides a smooth chat interface for querying PDF documents.

Technologies Used

• Python: Core programming language for backend development.
• OpenAI API: Powers language generation and natural language understanding.
• LangChain: Facilitates document processing, indexing, and integration with RAG pipelines.
• Groq (Llama3): Advanced language model for robust query handling and contextual responses.
• Groq Python API: Used to explore model capabilities and enhance RAG.
• Sentence Transformers: Python framework for state-of-the-art sentence, text, and image embeddings.
• Streamlit: Platform for hosting and deploying the chatbot for a user-friendly web interface.
• FastAPI: Web framework to serve chatbot as a REST API.

Usage

1. Upload a PDF: Start by uploading a PDF document via the chatbot interface or API endpoint.
2. Query the Chatbot: Ask the chatbot specific questions about the PDF content. Examples:
   • "What are the main financial statistics?"
   • "List all sections related to employee benefits."
3. Receive Responses: The chatbot parses and retrieves information using RAG, Groq, and LangChain for accurate answers.

Future Improvements

• Extended RAG Capabilities: Expand retrieval algorithms to handle more complex documents.
• Scalable Multi-Document Support: Allow simultaneous parsing of multiple PDFs.
• Improved NLP for Complex Queries: Enhance chatbot’s language model to better interpret nuanced questions.