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RAG Knowledge Base

Use when building, indexing, or querying vector databases for Retrieval-Augmented Generation (RAG).

Drvivek34 d4d2c85 2 files · 1.1 KB Updated
AI & ML

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Semantic Search & RAG Instructions

  1. Load document strings and clean HTML/markdown syntax.
  2. Chunk text using recursive character splitting (target chunk size: 500, overlap: 50).
  3. Compute embeddings using model API.
  4. Insert chunks and embeddings into local vector store (e.g. Chroma, FAISS).
  5. For queries, embed query string and retrieve top 3 nearest chunks.
  6. Format prompt template: Context + Query -> Answer.

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