Retrieval-Augmented Generation system that lets employees query internal knowledge bases in natural language using LangChain, ChromaDB, and Groq.
The Enterprise RAG Knowledge Assistant enables employees to ask natural language questions about internal company knowledge bases — policy documents, technical guides, meeting notes, and reports — and receive accurate, sourced answers.
The system is built on a Retrieval-Augmented Generation (RAG) architecture using LangChain. Documents are chunked, embedded, and stored in ChromaDB (local) and Pinecone (cloud-scale). Groq's ultra-fast LLM inference ensures sub-second response times.
A Gradio chat interface with source attribution shows users exactly which documents were used to generate each answer, building trust in the system. The REST API allows the RAG engine to be integrated into existing company intranets or Slack/Teams bots.
Supporting PDF, DOCX, TXT, and web pages with LangChain document loaders.
Comparing recursive character, semantic, and markdown-aware chunking for optimal retrieval.
Generating embeddings with OpenAI/HuggingFace and indexing in ChromaDB and Pinecone.
Implementing similarity search, MMR (Maximum Marginal Relevance), and hybrid search for diverse results.
Integrating Groq API (llama-3, mixtral) with custom RAG prompts and source citation formatting.
Building a multi-turn Gradio chat with conversation history, source display, and feedback buttons.