Search and Question Answering

Search Settings

Configure search behavior and context expansion:

search:
  limit: 10                    # Default number of results to return
  max_context_chars: 10000     # Maximum characters in expanded context

limit: Default number of search results to return when no limit is specified. Used by CLI, MCP server, and QA. Default: 10
max_context_chars: Hard limit on total characters in expanded content. Default: 10000.

Context expansion is automatic and section-aware. For structured documents (with section headers), expansion includes the entire section containing the match. For sections that exceed the budget or are too small (e.g., a title+authors area), expansion grows outward item-by-item from the match center, skipping noise labels (footnotes, page headers). This naturally crosses into adjacent sections until the budget is filled. For unstructured documents, expansion grows outward item-by-item. Results without doc_item_refs (e.g., custom chunks passed to import_document) pass through unexpanded.

Reranking behavior

When a reranker is configured, search automatically retrieves 10x the requested limit, then reranks to return the final count. This improves result quality without requiring you to adjust limit.

Question Answering Configuration

Configure the rag skill (used by client.ask, haiku-rag ask, and the MCP ask_question tool):

qa:
  model:
    provider: ollama
    name: gpt-oss
    enable_thinking: true
    temperature: 0.3          # Default: 0.3
    vision: false             # Set true for vision-capable models
  max_searches: 3       # Maximum search tool calls per question

model: LLM configuration (see Providers)
model.vision: Set to true for vision-capable models (qwen2.5vl, qwen3.6, gpt-4o, claude-sonnet, …). The skill's search tool only attaches picture bytes (BinaryContent) to its ToolReturn when this is true, otherwise picture bytes are withheld. See Pictures × embedder × QA model for the full matrix.
max_searches: Maximum number of search tool calls the rag skill can make per question (default: 3)

Thinking on vLLM

enable_thinking only applies to models with a pydantic-ai reasoning profile (o-series, gpt-5, gpt-oss). For other vLLM-served models such as Qwen3 or the Gemma family, the field is a silent no-op — set the chat template switch via extra_body instead.

Analysis Configuration

Configure the analysis skill:

analysis:
  model:
    provider: anthropic
    name: claude-sonnet-4-20250514
    temperature: 0.0        # Default: 0.0 (deterministic for code generation)
  code_timeout: 60.0      # Max seconds for code execution
  max_output_chars: 50000 # Truncate output after this many chars

model: LLM configuration (see Providers). When unset, falls back to qa.model.
code_timeout: Maximum seconds for each code execution (default: 60)
max_output_chars: Truncate code output after this many characters (default: 50000)

See Analysis skill for usage details.