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Documentation Index

Fetch the complete documentation index at: https://docs.jabrod.com/llms.txt

Use this file to discover all available pages before exploring further.

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Querying & Retrieval

Once your data is chunked, embedded, and stored, you can query it via the /api/rag/query endpoint.

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The Retrieval Process

When you send a query:
  1. Query Embedding: Jabrod converts your query string into a vector using the pipeline’s configured embedding model.
  2. Vector Search: Jabrod searches the vector database for chunks whose vectors are closest (most similar) to the query vector.
  3. Filtering: Any results below the pipeline’s configured Similarity Threshold are discarded.
  4. Ranking: The top K results are returned to you, ordered by similarity score.

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Top K

The topK parameter determines how many chunks are returned.
  • A low topK (e.g., 3) returns only the most highly relevant pieces of information, which is cheaper and faster if you are feeding it into an LLM.
  • A high topK (e.g., 10-20) returns more context, which is useful if the answer is spread across multiple documents, but increases token usage if passed to an LLM.
You configure a default retrievalTopK on the pipeline itself, but you can override it on a per-request basis in the API.

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Similarity Threshold

Vectors are compared using Cosine Similarity, which results in a score between 0 and 1.
  • 1.0 means identical meaning.
  • 0.0 means completely unrelated.
If you set a Similarity Threshold of 0.7, Jabrod will ignore any chunks that score below 0.7. This helps prevent “hallucinations” by ensuring that if no relevant data exists in the knowledge base, the API returns an empty array rather than returning vaguely related garbage.