58 lines
2.9 KiB
Markdown
58 lines
2.9 KiB
Markdown
# 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`.
|
||
|
||
## 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)
|
||
|
||
## 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](../agents/analysis.md) for usage details.
|