RAG-Based Document Query Application

Background & Objective

Development of a Retrieval-Augmented Generation (RAG) system for context-aware question answering on private documents. The objective was to integrate vector database retrieval with large language models to provide accurate, source-backed answers while maintaining full data privacy for sensitive documents.

Technical Stack

Core: Python, LangChain
AI/ML: OpenAI API (embeddings & generation)
Database: Pinecone Vector Database
Interface: Gradio
Dependencies: PyPDF2, tiktoken

Implementation

Built end-to-end PDF processing pipeline featuring text extraction, intelligent chunking with overlap for context preservation, and vector embedding generation using OpenAI models. Implemented efficient storage and indexing in Pinecone for similarity search. Query processing involves embedding user queries, retrieving top-k relevant chunks from the vector database, constructing context-augmented prompts, and generating LLM-powered answers with source attribution for verification.

Results & Impact

Deployed production RAG system with interactive Gradio web interface for real-time document querying. The system enables corporate knowledge base search, research paper exploration, legal document review, and technical documentation retrieval with source references. Successfully demonstrated practical application of retrieval-augmented generation for private document collections.