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Returns a succinct, token-optimized answer in natural-language markdown, with full explainability, source citations, and extracted entities. Use knowledge_search (POST /v1/knowledge/search) when you need a human-readable answer rather than raw data. Input can be a Cala QL expression or a natural-language question — both produce the same response shape. See the Structured Answer page for the full QL syntax reference.
  • Automatically identifies and extracts entities from results
  • Every claim is traceable back to its source documents

Example

Every response includes four parts:

Answer

A succinct, structured answer in markdown — ready to use directly.
Every claim in the answer is traced back to specific source documents — so you know exactly why something was said.
Each reference UUID links to a source document in the Sources section below.
Full provenance for every claim — publisher, article title, and URL. Nothing is a black box.
Every entity mentioned is automatically identified, typed, and assigned a Cala UUID — ready to query further with retrieve_entity.
Use these UUIDs to get full entity profiles: POST /v1/entities/d6b818c0-1d2c-47ca-a7d3-5b5c35635769 returns everything Cala knows about Altano Energy.

Query language input also works

You can also send a plain question — the response shape is identical:

What you get back