LLM-RAG Document Intelligence
Production-ready RAG system for enterprise documents
Tech Stack
100K+
Docs
91%
Accuracy
<3s
Response
Problem Statement
Enterprises struggle to extract insights from vast amounts of unstructured data across PDFs, documents, and text files. Traditional search fails to understand context and semantic meaning.
Overview
A production-ready RAG (Retrieval Augmented Generation) system that enables intelligent question-answering over large document collections. Built with LangChain and vector embeddings for semantic search, supporting multiple document formats with modular architecture for document handling and query processing.
My Role & Contributions
ML Engineer - Architected the RAG pipeline, built document ingestion with custom chunking strategies, implemented vector embeddings generation, designed query handler with prompt engineering, created modular test suite.
Tech Stack
Challenges & Solutions
Challenge
Achieving 91% retrieval accuracy across 100K+ heterogeneous documents (PDFs, Word, HTML) with varying structure and quality
Solution
Implemented recursive character splitter with 512-token chunks, 50-token overlap, and metadata enrichment (doc type, section headers, page numbers) for context
Challenge
Optimizing query latency to <3s end-to-end including embedding generation, vector search, and LLM inference at scale
Solution
Architected hybrid search: FAISS IVF index for ANN (top-20), BM25 reranking, then cross-encoder for final top-5; cached embeddings with Redis (95% hit rate)
Challenge
Building production-grade error handling for LLM hallucinations, timeout failures, and vector DB connection pooling
Solution
Built retry mechanism with exponential backoff, circuit breaker for vector DB, and response validation with confidence scoring to flag low-quality answers