Retrieval-Augmented Generation (RAG) System

Developed a sophisticated Retrieval-Augmented Generation (RAG) system that combines the power of large language models with external knowledge bases to provide accurate and contextual responses. The system utilizes advanced vector databases and embedding techniques to retrieve relevant information before generating responses.

Implemented using Python with LangChain framework, integrating multiple LLM providers including OpenAI and local models. The architecture features efficient chunking strategies, semantic search capabilities, and a robust caching mechanism to optimize performance and reduce API costs.

The system has been successfully deployed in production, handling thousands of queries daily with impressive accuracy rates. It serves as the backbone for an intelligent question-answering platform that assists users across various domains including technical documentation, customer support, and knowledge management.