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How to Build a RAG-Powered AI Chatbot Using Your Own Data
1. What Is RAG and Why It’s a Game-Changer for Custom AI
- RAG (Retrieval-Augmented Generation) grounds LLM responses in your own documents, reducing hallucinations and improving factual accuracy.
- It combines a retrieval step (vector search) with a generation step (LLM) so the model answers based on what you’ve fed it — not just its training data.
- Real-world use cases: internal knowledge-base Q&A, customer support bots, research assistants, and compliance-heavy documentation lookups.
2. Prerequisites: What You’ll Need Before You Start
- Python 3.9+ installed, plus core libraries: LangChain, OpenAI, ChromaDB, and `python-dotenv` for managing API keys.
- An OpenAI API key (or any LLM provider of your choice) and a vector database — we’ll use ChromaDB (local, free, and fast).
- A small set of your own documents: PDFs, text files, or markdown notes. Aim for 3–5 files to test the pipeline end‑to‑end.
3. Step 1 – Ingest & Chunk Your Documents
- Use LangChain’s document loaders (`PyPDFLoader`, `TextLoader`, `DirectoryLoader`) to bring your files into a standard format.
- Split text into overlapping chunks with `Recursive
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