Production-Ready RAG Chatbot using Groq LLM | LangChain | Docker | HuggingFace | FastAPI | Ragas

Description

In this video, we build a complete production-style RAG (Retrieval-Augmented Generation) chatbot using: - Groq LLM - LangChain - FastAPI - Streamlit - Chromadb Vector Database - HuggingFace Embeddings - Docker - RAGAS Evaluation This is not a toy project — we build an enterprise-grade PDF AI assistant capable of answering questions from HR policy manuals and large documents. 🔥 What You’ll Learn: - PDF ingestion pipeline - Text chunking strategies - Embedding models (BGE embeddings) - Vector search with Chromadb - Building a RAG pipeline - Groq integration with LangChain - FastAPI backend setup - Streamlit frontend UI - Dockerizing the entire project - RAGAS evaluation metrics - Fixing common Docker and RAG errors - Production architecture best practices 🛠 Tech Stack: - Python - LangChain - Groq - FastAPI - Streamlit - Docker - Chromadb - HuggingFace - RAGAS 📌 Features: ✔ PDF Upload ✔ AI Chat with Documents ✔ FastAPI Backend ✔ Streamlit Frontend ✔ Docker Support ✔ Enterprise RAG Architecture ✔ Evaluation Pipeline ✔ Low Hallucination Responses #AI #RAG #LangChain #Groq #FastAPI #Streamlit #Docker #Python #LLM #GenerativeAI #MachineLearning #ArtificialIntelligence