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Version: 2.0

Configure queries

Configuring your query parameters enables you to get the most relevant and accurate results. This section covers the key configuration parameters that control search behavior, result retrieval, reranking, context handling, and AI-generated responses.

Corpora search configuration​

The search object controls which corpora to search and how to filter and retrieve results:

  • corpus_key (required): Unique identifier for the corpus to search.
  • metadata_filter: SQL-like filter to narrow results (doc.year = '2024').
  • lexical_interpolation: Balance between semantic (0.0) and keyword (1.0) search. Default: 0.025.
  • limit: Maximum results to retrieve before reranking. Default: 10.
  • offset: Number of results to skip for pagination.
  • semantics: Query interpretation mode ("query", "response", or "default").

SEARCH CONFIGURATION EXAMPLE

Code example with json syntax.
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Context configuration​

The context_configuration object controls how much surrounding text is included with each search result:

  • sentences_before/sentences_after: Number of sentences to include before/after matching text.
  • characters_before/characters_after: Alternative character-based boundaries for precise control.
  • start_tag/end_tag: HTML tags for highlighting matching text in results.
note

You can only use sentences before/after or characters before/after, but not both.

CONTEXT CONFIGURATION EXAMPLE

Code example with json syntax.
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Reranker configuration​

Rerankers improve result quality by reordering search results to place the most relevant content first:

  • type: Reranker type
    • customer_reranker: Default multilingual reranker (recommended).
    • mmr: Maximal Marginal Relevance to reduce redundancy.
    • none: Disables reranking (not recommended).
  • reranker_name: Specific reranker model (Rerank_Multilingual_v1).
  • limit: Maximum results after reranking.
  • cutoff: Minimum relevance score (0.0-1.0) for result inclusion. Typically 0.3-0.7.
  • include_context: Use surrounding context text for more accurate scoring.

RERANKER CONFIGURATION EXAMPLE

Code example with json syntax.
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Generation configuration​

The generation object controls how the agent creates natural language responses:

  • enabled: Enable or disable generative summarization.
  • generation_preset_name: Pre-configured prompt and model bundle (mockingbird-2.0).
  • max_used_search_results: Number of top results to send to the LLM.. Default: 5
  • max_response_characters: Soft limit for response length.
  • response_language: Response language code (auto, eng, spa, etc.).
  • citations: Citation formatting.
    • style: Citation format (numeric, html, markdown, or none).
    • url_pattern: URL template using metadata variables (https://docs.example.com/{doc.id}).
    • text_pattern: Display text template ([{doc.title}]).
  • prompt_template: Override default prompt using Apache Velocity syntax.
  • model_parameters: LLM settings (temperature, max_tokens, etc.).
  • enable_factual_consistency_score: Validate factual consistency of responses.

GENERATION CONFIGURATION EXAMPLE

Code example with json syntax.
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