Retrieval-Augmented Generation (RAG)

A simple RAG pipeline that answers JavaScript questions over your own .txt documents, using LangChain’s in-memory vector store and a locally-hosted Ollama LLM.

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🔍 What It Does

1. Loads your docs/javascript.txt file
2. Splits it into “documents” by sentence (splitting on .)
3. Embeds each chunk with nomic-embed-text
4. Indexes embeddings in memory (no external vector DB)
5. Retrieves the top 2 most-similar chunks for any question
6. Prompts your Ollama model with those chunks + strict instructions
7. Returns a concise, accurate answer (max 3 sentences) or “Insufficient information…”

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⚙️ Prerequisites

• Node.js ≥ 24
• Ollama installed & running locally