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RAG Question Answering System
NLP & Semantic Search
Python
LLMs
RAG
LangChain
Vector Databases
NLP
Project Overview
This project is an advanced Retrieval-Augmented Generation (RAG) Question Answering system that allows users to seamlessly extract accurate and context-aware information from custom document collections using state-of-the-art Large Language Models (LLMs).
Key Objectives & Methodology:
- Architecture Design: Designed and built an end-to-end RAG system capable of synthesizing and querying knowledge efficiently, prioritizing high-relevance semantic search over rigid keyword matching.
- Data Ingestion & Embeddings: Built a comprehensive document ingestion pipeline incorporating text chunking, metadata extraction, and vector embedding models, seamlessly integrated with a vector database.
- LLM Integration & Prompt Engineering: Integrated powerful LLMs using LangChain, leveraging context-aware retrieval and advanced prompt engineering techniques to drastically reduce hallucinations and improve factual accuracy.
