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How to Build a Custom RAG System for Your Business Documents
1. What Is RAG and Why Do You Need It?
- Understand Retrieval-Augmented Generation (RAG) — combining document search with LLM reasoning to ground responses in your own data.
- Identify real business use cases: internal knowledge bases, customer support, compliance lookup, and research assistants.
- Compare RAG vs. fine-tuning: when to retrieve vs. when to retrain for accuracy and cost efficiency.
2. Tools & Prerequisites You’ll Need
- Set up your environment: Python 3.10+, Jupyter or VS Code, and core libraries (LangChain, ChromaDB, OpenAI API or local LLM via Ollama).
- Prepare a small set of sample documents (PDFs, Markdown, or text files) to test your pipeline from day one.
- Choose an embedding model (text-embedding-3-small, BGE, or all-MiniLM-L6-v2) and a generation model (GPT-4o, Claude, or Mistral).
3. Step-by-Step: Chunking & Indexing Your Data
- Load documents with LangChain’s document loaders, then split them into semantic chunks (500–1000 characters with overlap).
- Generate embeddings for every chunk and store them in a local vector database (ChromaDB or FAISS).
- Verify your index with a test query — retrieve the top 3–5 most relevant chunks before
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