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Built-in processors let you enrich or validate data without maintaining custom code. Combine them to prepare content for semantic search or orchestrate human review.

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Vectorizer

Transforms text into numerical embeddings so you can run hybrid search. Configuration:
  • inputProperty — Path to the text field or the output of a previous processor.
  • propertyKey — Destination sub-property that stores the resulting vector.
  • provider — Embedding provider that hosts the model.
  • modelId — Embedding model to use.
  • dimensions — Number of dimensions in the output vector.
Use a Schema Validator ahead of the vectorizer when you want to avoid expensive work on malformed data.

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Actionable

Design actionable steps for scenarios where automated processors cannot make the final decision.

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Schema Validator

Adds an explicit validation checkpoint mid-pipeline. This reuses the rules defined in your profiles and complements the implicit validation that occurs at the end. Use it to:
  • Stop the pipeline before an expensive processor when data is incomplete.
  • Re-validate after a mutation step to ensure enriched data stays compliant.

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Mutating vs. enriching processors

  • Mutating processors (Clinia Function) replace the input property with enriched data. Update your schema first so the new shape passes validation.
  • Enriching processors (Vectorizer) add derived properties under enrichedProperties. They keep the original field intact while making extra data available to partitions.
Plan processor order accordingly and consult pipeline basics for execution semantics.