Built a highly scalable Retrieval-Augmented Generation

Built a highly scalable Retrieval-Augmented Generation (RAG) chatbot designed to interact with private datasets/PDFs. Unlike standard LLMs, this system minimizes hallucinations by retrieving real-time context from a local knowledge base before generating responses.

Key Features:

Semantic Search: Implemented Vector Embeddings to perform high-speed similarity searches across thousands of document chunks.

Smart Retrieval: Integrated a retrieval pipeline using LangChain to fetch the most relevant context for user queries.

Source Citation: Configured the bot to provide source references from documents, ensuring data transparency and accuracy.

Optimized Performance: Used FAISS/Chromadb for efficient vector storage and retrieval.