# Search and Question Answering

## Search Settings

Configure search behavior and context expansion:

```yaml
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.

!!! note "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`.

!!! warning "Reranker compatibility with multimodal content"
    Text-only rerankers (mxbai, cohere, jina, vllm, cross-encoder) score documents by their text content. Picture chunks emitted by docling have empty content, so the reranker scores them near zero and drops them from the top results.

    If retrieval relies on visual content (charts, figures, screenshots with text rendered as pixels), disable reranking by leaving `reranking.model` empty so the multimodal embedder's ranking survives.

## Question Answering Configuration

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

```yaml
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](providers.md#model-settings))
- **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](processing.md#pictures-embedder-qa-model-how-the-pieces-compose) for the full matrix.
- **max_searches**: Maximum number of search tool calls the rag skill can make per question (default: 3)

!!! note "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`](providers.md#raw-provider-pass-through) instead.

## Analysis Configuration

Configure the analysis skill:

```yaml
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](providers.md#model-settings)). 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](../skills/analysis.md) for usage details.