Building a Text to SQL Chatbot with RAG, LangChain, FastAPI And Streamlit | Tech Edge AI

Description

In this video, we’ll build a powerful AI chatbot that can convert natural language questions into SQL queries — and fetch real answers directly from a SQLite database! 🚀 Using Retrieval-Augmented Generation (RAG), LangChain, Hugging Face embeddings, and Chroma vector store, we’ll connect unstructured user input to structured data. You’ll learn how to build both the FastAPI backend and the Streamlit frontend, making your very own Text-to-SQL system from scratch. 👉 Perfect for data analysts, AI developers, and anyone who wants to make their database “talk back”! 🔍 What You’ll Learn: What is Text-to-SQL and why it matters How RAG (Retrieval-Augmented Generation) prevents hallucination Embedding and indexing a SQLite schema using Hugging Face & Chroma Building the LangChain pipeline for query generation Creating a FastAPI backend to serve your AI Designing an interactive Streamlit UI for real-time SQL responses 🧩 Tech Stack: Python 🐍 LangChain FastAPI Streamlit SQLite Hugging Face Embeddings #AIChatbot #LangChain #RAG #FastAPI #Streamlit #Python #DataScience #SQL #TextToSQL #OpenAI #LLM #MachineLearning #ArtificialIntelligence #AIDevelopment #ChatbotDevelopment #ChromaDB #HuggingFace #VectorDatabase #GenerativeAI #CodingTutorial #PythonProjects #FullStackAI #AIAutomation #TechTutorial #SQLChatbot