66 lines
3.7 KiB
Markdown
66 lines
3.7 KiB
Markdown
# Search and Question Answering
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## Search Settings
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Configure search behavior and context expansion:
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```yaml
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search:
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limit: 10 # Default number of results to return
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max_context_chars: 10000 # Maximum characters in expanded context
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```
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- **limit**: Default number of search results to return when no limit is specified. Used by CLI, MCP server, and QA. Default: 10
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- **max_context_chars**: Hard limit on total characters in expanded content. Default: 10000.
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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.
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!!! note "Reranking behavior"
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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`.
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!!! warning "Reranker compatibility with multimodal content"
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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.
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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.
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## Question Answering Configuration
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Configure the rag skill (used by `client.ask`, `haiku-rag ask`, and the MCP `ask_question` tool):
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```yaml
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qa:
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model:
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provider: ollama
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name: gpt-oss
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enable_thinking: true
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temperature: 0.3 # Default: 0.3
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vision: false # Set true for vision-capable models
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max_searches: 3 # Maximum search tool calls per question
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```
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- **model**: LLM configuration (see [Providers](providers.md#model-settings))
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- **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.
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- **max_searches**: Maximum number of search tool calls the rag skill can make per question (default: 3)
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!!! note "Thinking on vLLM"
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`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.
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## Analysis Configuration
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Configure the analysis skill:
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```yaml
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analysis:
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model:
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provider: anthropic
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name: claude-sonnet-4-20250514
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temperature: 0.0 # Default: 0.0 (deterministic for code generation)
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code_timeout: 60.0 # Max seconds for code execution
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max_output_chars: 50000 # Truncate output after this many chars
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```
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- **model**: LLM configuration (see [Providers](providers.md#model-settings)). When unset, falls back to `qa.model`.
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- **code_timeout**: Maximum seconds for each code execution (default: 60)
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- **max_output_chars**: Truncate code output after this many characters (default: 50000)
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See [Analysis skill](../skills/analysis.md) for usage details.
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