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The capability pages said tool results from earlier turns are replaced before every model request. That is now the compaction capability's job, and only when a host registers it, so both pages point at it instead of describing it as automatic. `RAGState` gains its `evidence` field, and the note about per-run resets now says what a resumption keeps.
53 lines
3 KiB
Markdown
53 lines
3 KiB
Markdown
# Analysis Capability
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`AnalysisCapability` adds search, citations, and sandboxed Python computation over the document corpus. Use it for counts, aggregation, comparison, structural traversal, and section-scoped reading.
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It is deferred by default, keeping its substantial instructions and tool schemas out of context until the model chooses to load it.
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The default request limit is 30 model requests per question. Override it with `create_capability(request_limit=...)`, or set `request_limit=None` to disable it. As with the RAG capability, `create_capability(vision=...)` overrides the image-attachment gate, defaulting to the configured analysis model's `vision` flag. At the limit, `analysis_search` and `analysis_execute_code` are removed while `analysis_cite` remains for two further requests that call an analysis tool, so the model can register citations before answering from gathered evidence. Requests spent on other capabilities do not count against that window. Other agent and capability tools remain available, and the budget resets for every agent run.
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When `qa.max_searches` or `analysis.max_executions` runs out, the exhausted tool keeps failing rather than disappearing, and the instructions name it on every following request. Searching from inside `analysis_execute_code` does not count against `qa.max_searches`.
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## Tools
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| Tool | Purpose |
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|---|---|
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| `analysis_search(query, limit?)` | Search the corpus for evidence. |
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| `analysis_execute_code(code)` | Run Python against the virtual document filesystem. |
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| `analysis_cite(chunk_ids)` | Register retrieved or filesystem-derived chunk IDs. |
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The sandbox exposes documents under `/documents/{document_id}/` with `metadata.json`, `content.txt`, `items.jsonl`, and `toc.json`.
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## Compose with RAG
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```python
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from pydantic_ai import Agent
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from haiku.rag.capabilities.analysis import create_capability as analysis
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from haiku.rag.capabilities.rag import create_capability as rag
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agent = Agent(
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"openai:gpt-5",
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capabilities=[
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rag(db_path="my.lancedb"),
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analysis(db_path="my.lancedb"),
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],
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)
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```
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For the high-level convenience API:
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```python
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from haiku.rag.client import HaikuRAG
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async with HaikuRAG("my.lancedb") as client:
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result = await client.analyze("Which quarter had the highest revenue?")
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print(result.answer)
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```
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## State
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When dependencies expose a state dictionary, `AnalysisState` is stored under `"analysis"`. It contains the document filter, code execution log, searches, citations, and the `evidence` record of what was retrieved and cited per question. Searches and executions are cleared when a new question starts, and a resumed question keeps them; the filter, citation index and evidence record persist.
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This capability does not alter the message history either. Register the [compaction capability](index.md#multi-turn-conversations) to compact earlier questions.
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The capability lazily opens both LanceDB and the sandbox only after it is loaded and a tool requires them. Resources close at the end of the agent run.
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