a69f3a8a98
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.
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Analysis Capability
AnalysisCapability adds search, citations, and sandboxed Python computation over the document corpus. Use it for counts, aggregation, comparison, structural traversal, and section-scoped reading.
It is deferred by default, keeping its substantial instructions and tool schemas out of context until the model chooses to load it.
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.
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.
Tools
| Tool | Purpose |
|---|---|
analysis_search(query, limit?) |
Search the corpus for evidence. |
analysis_execute_code(code) |
Run Python against the virtual document filesystem. |
analysis_cite(chunk_ids) |
Register retrieved or filesystem-derived chunk IDs. |
The sandbox exposes documents under /documents/{document_id}/ with metadata.json, content.txt, items.jsonl, and toc.json.
Compose with RAG
from pydantic_ai import Agent
from haiku.rag.capabilities.analysis import create_capability as analysis
from haiku.rag.capabilities.rag import create_capability as rag
agent = Agent(
"openai:gpt-5",
capabilities=[
rag(db_path="my.lancedb"),
analysis(db_path="my.lancedb"),
],
)
For the high-level convenience API:
from haiku.rag.client import HaikuRAG
async with HaikuRAG("my.lancedb") as client:
result = await client.analyze("Which quarter had the highest revenue?")
print(result.answer)
State
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.
This capability does not alter the message history either. Register the compaction capability to compact earlier questions.
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.