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Pinecone for RAG (Retrieval-Augmented Generation)

Determines optimal text chunk size and overlap parameters based on document characteristics, embedding model constraints, and retrieval accuracy requirements

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Optimal chunk parameters

Run the tool to see output.

Examples

Medium document with high recall

{
  "document_length": "15000",
  "embedding_model": "text-embedding-3-small",
  "desired_recall": "85"
}

Expected output

{"chunk_size": 1024, "overlap": 256}

Large document with balanced metrics

{
  "document_length": "50000",
  "embedding_model": "text-embedding-3-large",
  "desired_recall": "75",
  "desired_precision": "80"
}

Expected output

{"chunk_size": 2048, "overlap": 512}

How it works

Calculates optimal chunk size using document length and embedding model context limits. Determines overlap based on inverse relationship between chunk size and retrieval precision requirements. Prioritizes model-specific constraints from the selected embedding model.

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