`_fetch_limit` had no test: a text query over-fetches `limit * 10` only with a
reranker, and an image query keeps its vector ranking either way. The sandbox
test asserted `KeyError`, which `UnknownDatabaseError` subclasses, so it could
not tell the contract from a bare one.
`uses_configured_databases` documents a mapping of one as covered; the test
named for it passed no mapping at all. Its `config` parameter is an `AppConfig`.
`test_an_analysis_capability_mounts_the_configured_set` carried a VCR marker and
no cassette, making no HTTP calls.
`covers_a_set` is true for a mapping of one, which is a configured database
like any other; `uses_configured_databases` says that. Its population guard
said "several" for the same reason. Document evaluating the configured set
with `--skip-db`, against population, which writes one database and needs a
path.
`get_document_by_id`, `get_document_by_uri` and `get_chunk_by_id` read
through repositories a client covering a set does not have, so a lookup by
identifier raised AttributeError and `resolve_document` with it. An
identifier says nothing about which database holds it, so every database is
asked at once and the first that has it, in configured order, answers.
On the evaluation side, `--db` overrides the configured set as the CLI
documents, and population refuses a set rather than ingesting into a
database the run would not read. A case filter matching nothing raises
instead of reporting 0.0000 as though it were a score.
A run over several databases could report which documents were cited but
not which database grounded the answer: `_result_from_run` walked the
citation index for `document_uri` and dropped `Citation.source`. The
distribution is not recoverable from the report afterwards, so a sharded
run would have measured everything except attribution.
`cited_sources` is one entry per cited chunk, in citation order, empty
where the database is unnamed.
The runner passed the dataset's path to every arm, which opens one database
and is what makes `--db` meaningful. A run over `lancedb.databases` has to
pass none instead, so the client resolves the set, and `DatasetSpec.covers_a_set`
is the one place that decides which of the two a run is.
benchmark.py was 1220 lines holding six unrelated jobs: populating a
database, running retrieval, running QA, resolving datasets, moving
databases to and from HuggingFace, and wiring the Typer CLI.
qa.py takes both QA runners with their live summary, refusal metrics and
target resolution. population.py takes populate_db and the batched ingest.
retrieval.py takes run_retrieval_benchmark. artifacts.py takes HF_REPO_ID and
the download/upload bodies. experiment.py takes DEFAULT_JUDGE_MODEL and
build_experiment_metadata, which retrieval and QA both record.
benchmark.py keeps the CLI at 258 lines: the Typer app, config and case-id
loading, dataset resolution, evaluate_dataset, and three commands whose
bodies are now a loop over specs. The module-level side effects stay with it
— load_dotenv before configure_telemetry, so credentials and LOGFIRE_TOKEN
are in the environment before telemetry and model setup read them — so no
importable module carries one.
Test patch targets follow the code. get_model, run_capability_question,
run_capability_conversation, set_eval_attribute and HaikuRAG are patched
inside moved code, so they move with it; run_qa_benchmark,
run_retrieval_benchmark and find_config_file stay patchable on
evaluations.benchmark because evaluate_dataset and _load_config still look
them up there.
One assertion got stronger: a QA test patched benchmark.HaikuRAG to prove the
QA path does not open its own client. qa.py has no HaikuRAG reference at all
now, so the test asserts that instead.
IBM's MTRAG benchmark (ClapNQ domain, pinned repo SHA): retrieval with
Recall@k/nDCG@k against binary qrels, gold-prefix QA replaying reference
conversation prefixes as message history, and live-session replay
carrying the model's own answers and tool history across turns.
Corpus population gains a bounded, resumable batched ingest path.
ConversationInput case type with transcript rendering for the judge,
eligibility-aware citation scoring, refusal precision/recall via a
label-aware RefusalJudge, per-turn verdicts with judged-turn coverage,
and per-turn tool-traffic attributes counted from each turn's new
messages so the arrays survive prior-turn compaction.
No `DatasetSpec` declared `search_filter`, so `resolve_search_filter`
and the `--filter ""` clearing rule reconciled the flag against a
default that never existed. The flag alone covers the case. An empty
clause reaches `ChunkRepository.search`, which already treats it as
unfiltered.
Rename to `document_filter` throughout, matching
`run_capability_question`'s parameter and the metadata key that lands
in Logfire.
`_stub_spec` merges its overrides, so a test can override a loader
instead of rebuilding the whole spec.
The `--filter` examples used `uri LIKE '%arxiv%'`, which matches no
`orb_text` document: its URIs are bare arXiv ids such as `2407.01528v3`.
A clause that matches nothing scores MAP 0 rather than erroring, so the
example failed silently.
`await_args` is typed `_Call | None`, so subscripting it fails
`ty check`; `call_args` carries the same call for an AsyncMock.
The request-limit notice said only the cite tool remained available, but
chat registers rag and analysis in one agent, so exhausting analysis
claimed rag_search was gone too. Scoped to the capability's own tools.
The cite window was counted over every model request once loaded, so
turns spent on another capability expired it before the model was ever
placed where citing was the obvious move. Count only requests whose
preceding response called one of this capability's tools; engagement is
also the only thing that can loop, which is all the bound guards against.
Also: _count_tool_traffic returns a named tuple rather than four bare
ints, and counts failures only for this capability's tools, so host-tool
retries and output-validation retries no longer read as its failures.
The notice told the model to answer from what it had the moment
qa.max_searches ran out, while up to 15 code executions remained and
in-code search() does not count against that budget. It now names the
spent tool and points at whichever evidence tool still has budget,
falling back to answer-and-cite only when none do.
Also count RetryPromptPart in n_failed_tools: _cite rejects with
ModelRetry, so a run whose every cite attempt was refused reported zero
failures. And note that n_requests is the run's request count, which
tracks a capability's own budget only while it stays loaded.
- _budget_notice no longer names the cite tool after prepare_tools has
withdrawn it; the post-grace state gets the plain no-tools text back.
- Split search-budget rejections from any failed tool call: the code tool
raises ToolFailed for every error in model-written Python, so
budget_spent was true for a ZeroDivisionError.
- docs/capabilities/rag.md described the old single-turn removal.
- Drop the rationale clause from the CHANGELOG entry.
Migrate off two APIs slated for removal in pydantic-ai 2.0:
- Agent(tool_retries=, output_retries=) -> Agent(retries={"tools": ...,
"output": ...}) in the LLM-as-judge evaluator.
- Evaluator.evaluation_name class attribute -> overriding
get_default_evaluation_name() on the citation MRR / MAP evaluators.
haiku.skills 0.17.0 already migrated its internal AGUIAdapter,
MCPToolset and ProcessEventStream usage; no further changes needed on
our side beyond the pin bumps.
