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qa/corpus_query: single orchestrator over Corpus; cloud-query DRYed

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`arborist/qa/corpus_query.py:run_query(corpus, question, chat_client, ...)`
is now the single retrieval + evidence + LLM + verify + annotate
pipeline. Takes any Corpus adapter (SqliteShardCorpus | SidecarBucketCorpus
| future edge-proxy), returns the same result-dict shape today's
cloud-query emits (audit_mode, sources w/ used + pointer-ids, capacity,
timings, raw_answer, rendered answer).

`_cmd_cloud_query` reduced from ~310 lines of inline pipeline to ~80
lines of corpus construction + run_query call + progress emission +
render. Behavior identical: same audit_mode, same sources, same
capacity/timings tail.

  Before: cli._cmd_cloud_query owned chunk-pull, evidence-build, prompt
          construction, LLM call, verifier call, source annotation,
          spotlight render — 310 lines of duplication with the local
          query() pipeline.
  After : cli._cmd_cloud_query owns ONLY corpus construction +
          progress emission + render layer. The pipeline lives in
          corpus_query.run_query and will be the single source of
          truth once query.py refactor moves local onto the protocol.

Tests (corpus_query unit, 4 passing):
  * verbatim quote → STRICT (n_verified ≥ 1, used annotation correct)
  * capacity + timings dict populated with expected keys
  * empty retrieval → UNGROUNDED + zero LLM calls (StubClient.calls == [])
  * corpus.name leaks into result for bench attribution

Bench parity preserved (cloud_vs_local.py smoke, 5 questions):
  before refactor: 1 regression (Mercury Seven STRICT → HYBRID)
  after refactor : 1 regression (same — LLM-stochastic, primary source
                                  identical both sides)

Behavior-preserving. Foundation laid for the local query() refactor:
when query.py learns to take a Corpus parameter, it'll delegate to
the same run_query() and every quality fix lands once for both.

32 prior wallet + corpus tests still green.
This commit is contained in:
russell@unturf.com 2026-05-30 22:45:55 -04:00
parent 1f8952ab19
commit c3529ad571
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4 changed files with 511 additions and 263 deletions
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349
arborist/cli.py
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@ -7482,38 +7482,28 @@ def _cmd_cloud_snapshot_root(args: argparse.Namespace) -> int:
def _cmd_cloud_query(args: argparse.Namespace) -> int:
"""Bucket-direct end-to-end query: FTS → fetch chunks → LLM (claim-
lattice mode) → verify. Same audited-answer shape as local
`query()` — EVIDENCE blocks with E1/E2 pointer tags, pointer-line
answer format, four-rung audit ladder (POINTER-LINKED →
ANCHOR-WARRANTED → EVIDENCE-WARRANTED) — but every byte goes
through HttpRangeVFS from the bucket.
"""Bucket-direct end-to-end query routed through the Corpus protocol.
Emits stage-level progress to stderr (mirrors `arborist query`),
auto-on at TTY, override via ARBORIST_PROGRESS=0/1.
Builds a SidecarBucketCorpus (or BucketClient single-shard adapter),
hands it to arborist.qa.corpus_query.run_query() — the single
orchestrator shared with the future query.py refactor — and renders
the result via the human/JSON layer.
The shape (audit_mode, sources w/ used, capacity, timings, etc.)
is identical to before; the inline pipeline that used to live here
is now in arborist/qa/corpus_query.py so local + cloud can share it.
"""
import os as _os
import re as _re
import time as _time
from arborist.compress import unpack_chunk
from arborist.qa.client import OpenAICompatibleClient
from arborist.qa.evidence import (
build_evidence_map,
evidence_map_by_pointer_id,
render_claim_lattice,
render_evidence_map,
)
from arborist.qa.corpus import SidecarBucketCorpus
from arborist.qa.corpus_query import run_query
from arborist.qa.progress import from_env as _progress_from_env
from arborist.qa.prompts import (
CLAIM_LATTICE_GROUNDING_REMINDER,
CLAIM_LATTICE_SYSTEM_PROMPT,
)
from arborist.qa.verify import verify_claim_lattice
progress = _progress_from_env()
timings: dict[str, float] = {}
extra_timings: dict[str, float] = {}
# Multi-shard via manifest, or single-shard if --shard-url given.
# Build the corpus (HTTP-bucket via manifest, or single-shard URL).
if getattr(args, "bucket_url", None):
from arborist.wallet.bucket import (
MultiShardBucketCorpus,
@ -7523,30 +7513,30 @@ def _cmd_cloud_query(args: argparse.Namespace) -> int:
progress.emit("manifest.start", url=args.bucket_url)
ts = _time.time()
manifest = load_bucket_manifest(args.bucket_url)
timings["manifest"] = _time.time() - ts
extra_timings["manifest"] = _time.time() - ts
any_sidecar = any(sh.get("sidecar_url") for sh in manifest.shards)
progress.emit(
"manifest.done", shards=len(manifest.shards),
sidecars=sum(1 for sh in manifest.shards if sh.get("sidecar_url")),
ms=int(timings["manifest"] * 1000),
ms=int(extra_timings["manifest"] * 1000),
)
progress.emit(
"corpus.open.start",
mode="sidecar" if any_sidecar else "bucket-direct",
)
# Pick the right corpus class based on whether the manifest
# advertises per-shard sidecars. Mixed (some sidecar, some not)
# also uses the sidecar-aware path — it falls back to bucket-
# direct FTS5 for shards without one.
progress.emit("corpus.open.start", mode="sidecar" if any_sidecar else "bucket-direct")
ts = _time.time()
if any_sidecar:
client = MultiShardSidecarCorpus(
multi = MultiShardSidecarCorpus(
manifest, cache_bytes_per_shard=args.cache_mb * 1024 * 1024,
)
else:
client = MultiShardBucketCorpus(
multi = MultiShardBucketCorpus(
manifest, cache_bytes_per_shard=args.cache_mb * 1024 * 1024,
)
timings["corpus_open"] = _time.time() - ts
progress.emit("corpus.open.done", ms=int(timings["corpus_open"] * 1000))
multi_shard = True
extra_timings["corpus_open"] = _time.time() - ts
progress.emit("corpus.open.done", ms=int(extra_timings["corpus_open"] * 1000))
corpus = SidecarBucketCorpus(multi)
closer = multi.close
else:
if not getattr(args, "shard_url", None):
print(
@ -7556,242 +7546,75 @@ def _cmd_cloud_query(args: argparse.Namespace) -> int:
)
return 2
client = _make_bucket_client(args)
multi_shard = False
t0 = _time.time()
# Wrap single BucketClient in a degenerate "multi" so the
# adapter's chunks_for_doc can use the same path.
class _Single:
manifest = type("M", (), {"shards": [
{"url": client.endpoint.shard_url, "shard_idx": 0,
"label": "single", "sidecar_url": None}
], "blob_base": client.endpoint.blob_base, "snapshot_root": None})()
def fts_search(self, q, *, limit=8, raw=False):
return client.fts_search(q, limit=limit, raw=raw)
def conn_for_shard(self, url):
return client.conn
def stats(self):
return client.stats()
def close(self):
client.close()
multi = _Single()
corpus = SidecarBucketCorpus(multi)
closer = client.close
endpoint = args.endpoint or _os.environ.get(
"ARBORIST_LLM_ENDPOINT", "https://hermes.ai.unturf.com/v1",
)
model = args.model or _os.environ.get(
"ARBORIST_LLM_MODEL",
"adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic",
)
chat = OpenAICompatibleClient(base_url=endpoint)
try:
progress.emit("search.start", top_k=args.top_k)
ts = _time.time()
hits = client.fts_search(args.question, limit=args.top_k)
timings["search"] = _time.time() - ts
progress.emit("search.done", hits=len(hits), ms=int(timings["search"] * 1000))
if not hits:
print(json.dumps({
"answer_text": "",
"audit_mode": "UNGROUNDED",
"verifier_method": "none",
"n_quotes": 0,
"n_verified": 0,
"sources": [],
"stats": client.stats(),
"timing_s": round(_time.time() - t0, 3),
"note": "FTS5 returned no hits — sanitized query may "
"have lost too many tokens; try `cloud search` first.",
}, indent=2, ensure_ascii=False))
return 0
# Pull the first body chunk of each hit via SQL through the
# same VFS; reuses the already-warm page cache. Per-doc budget
# keeps one huge article from monopolizing the context.
progress.emit(
"context.start", chunks=len(hits),
result = run_query(
corpus, args.question, chat,
model_id=model,
top_k=args.top_k,
max_context_chars=args.max_context_chars,
)
ts = _time.time()
chunks_for_evidence: list[dict] = []
total_chars = 0
per_doc_budget = max(
1000, args.max_context_chars // max(1, len(hits))
)
for rank, h in enumerate(hits, 1):
# Multi-shard: route the chunk read back to the shard that
# owned the FTS hit (so we hit a warm page cache and don't
# re-fetch the .db header for every chunk).
if multi_shard:
conn_for_read = client.conn_for_shard(h["_shard_url"])
else:
conn_for_read = client.conn
rows = list(conn_for_read.execute(
"SELECT idx, leaf_hash, content FROM chunks "
"WHERE document_root = ? AND content IS NOT NULL "
"ORDER BY idx ASC LIMIT 1",
(h["document_root"],),
))
if not rows:
continue
idx, leaf_hash, content_blob = rows[0]
span = unpack_chunk(content_blob) or ""
if not span:
continue
if len(span) > per_doc_budget:
span = span[:per_doc_budget]
chunks_for_evidence.append({
"source_root": h["document_root"],
"document_uri": h["document_uri"],
"title": h["title"],
"chunk_idx": idx,
"chunk_root": leaf_hash,
"span": span,
"source_role": (
"primary_answer_source" if rank == 1
else "background_source"
),
})
total_chars += len(span)
if total_chars >= args.max_context_chars:
break
timings["context"] = _time.time() - ts
progress.emit(
"context.done", sources=len(chunks_for_evidence),
assembled_chars=total_chars, ms=int(timings["context"] * 1000),
)
if not chunks_for_evidence:
print(json.dumps({
"answer_text": "",
"audit_mode": "UNGROUNDED",
"sources": hits,
"note": "FTS hit found, but no readable chunk content",
}, indent=2, ensure_ascii=False))
return 0
# Build the evidence map + format the LLM prompt with E1/E2/…
# pointer tags — same shape the local query.py pipeline uses.
evidence_map = build_evidence_map(chunks_for_evidence)
evidence_text = render_evidence_map(evidence_map)
endpoint = args.endpoint or _os.environ.get(
"ARBORIST_LLM_ENDPOINT", "https://hermes.ai.unturf.com/v1",
)
model = args.model or _os.environ.get(
"ARBORIST_LLM_MODEL",
"adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic",
)
chat = OpenAICompatibleClient(base_url=endpoint)
sys_prompt = CLAIM_LATTICE_SYSTEM_PROMPT
user_prompt = (
f"EVIDENCE:\n\n{evidence_text}\n\n"
f"QUESTION: {args.question}\n\n"
f"{CLAIM_LATTICE_GROUNDING_REMINDER}"
)
messages = [
{"role": "system", "content": sys_prompt},
{"role": "user", "content": user_prompt},
]
prompt_chars = len(sys_prompt) + len(user_prompt)
progress.emit(
"llm.start", model=model, endpoint=endpoint, ctx_chars=prompt_chars,
# run_query owns its own timings; merge in the manifest/open
# phases so the timings tail shows the full picture.
result.setdefault("timings", {}).update(
{k: round(v, 3) for k, v in extra_timings.items()}
)
result["endpoint"] = endpoint
# The orchestrator returns model_id as "model"; keep it.
# Add bucket-side stats for the human render footer.
try:
ts = _time.time()
answer = chat.chat_completion(
messages=messages,
model=model,
max_tokens=512,
temperature=0.1,
)
timings["llm"] = _time.time() - ts
progress.emit(
"llm.done", answer_chars=len(answer),
ms=int(timings["llm"] * 1000),
)
except Exception as e:
print(json.dumps({
"answer_text": "",
"audit_mode": "UNGROUNDED",
"sources": [
{"document_root": h["document_root"],
"document_uri": h["document_uri"],
"title": h["title"], "score": h["score"]}
for h in hits
],
"error": f"LLM call failed: {type(e).__name__}: {e}",
"endpoint": endpoint,
"model": model,
}, indent=2, ensure_ascii=False))
return 4
# Claim-lattice verifier: parses the model's pointer-line
# output, resolves each [E#] back through the evidence map,
# runs the textual-coverage check per claim. Warrant chain
# disabled in cloud mode (no derivations table available).
progress.emit("verify.start", mode="claim_lattice")
ts = _time.time()
verdict = verify_claim_lattice(
answer, evidence_map,
question=args.question,
warrant_check_enabled=False,
)
timings["verify"] = _time.time() - ts
progress.emit(
"verify.done", audit_mode=verdict["audit_mode"],
n_verified=verdict["n_verified"], n_quotes=verdict["n_quotes"],
ms=int(timings["verify"] * 1000),
)
result["stats"] = multi.stats()
except Exception:
result["stats"] = {}
# Emit summary progress events so the live trace still shows
# search.done / llm.done / verify.done shapes operators expect.
t = result.get("timings") or {}
progress.emit("search.done", hits=len(result.get("sources") or []),
ms=int((t.get("search") or 0) * 1000))
progress.emit("context.done", sources=len(result.get("sources") or []),
ms=int((t.get("context") or 0) * 1000))
progress.emit("llm.done",
answer_chars=len(result.get("raw_answer") or ""),
ms=int((t.get("llm") or 0) * 1000))
progress.emit("verify.done",
audit_mode=result.get("audit_mode"),
n_verified=result.get("n_verified"),
n_quotes=result.get("n_quotes"),
ms=int((t.get("verify") or 0) * 1000))
# Surface a single timing_s for legacy renderers / bench harnesses.
result["timing_s"] = round(t.get("total") or 0, 3)
finally:
client.close()
try: closer()
except Exception: pass
elapsed = _time.time() - t0
# Annotate sources with used / used_pointer_ids per the model's
# cited pointers (parsed from the answer text). Same render-time
# signal `arborist query` surfaces for lattice modes.
by_pid = evidence_map_by_pointer_id(evidence_map)
pointer_re = _re.compile(r"\[E\d+(?:,\s*E\d+)*\]")
used_doc_roots: set[str] = set()
doc_root_to_pointers: dict[str, list[str]] = {}
for m in pointer_re.finditer(answer):
for pid in (p.strip() for p in m.group(0).strip("[]").split(",")):
ev = by_pid.get(pid)
if ev:
used_doc_roots.add(ev.source_root)
doc_root_to_pointers.setdefault(ev.source_root, []).append(pid)
sources_for_render: list[dict] = []
seen_roots: set[str] = set()
for ev in evidence_map:
if ev.source_root in seen_roots:
continue
seen_roots.add(ev.source_root)
sources_for_render.append({
"document_root": ev.source_root,
"document_uri": ev.document_uri,
"title": ev.title,
"source_role": ev.source_role,
"used": ev.source_root in used_doc_roots,
"used_pointer_ids": sorted(set(
doc_root_to_pointers.get(ev.source_root, [])
)),
})
# Render the answer in lattice form (claim line + spotlight excerpts).
claim_statuses = verdict.get("claim_statuses") or []
rendered_answer = render_claim_lattice(
[
{
"text": cs.get("text", ""),
"pointer_ids": cs.get("pointer_ids") or [],
}
for cs in claim_statuses
],
by_pid,
window=200,
) or answer # fall back to raw output if parser dropped everything
timings["total"] = elapsed
capacity = {
"sys_prompt_chars": len(sys_prompt),
"evidence_chars": len(evidence_text),
"question_chars": len(args.question),
"prompt_chars": prompt_chars,
"answer_chars": len(answer),
}
result = {
"answer_text": rendered_answer,
"raw_answer": answer,
"audit_mode": verdict["audit_mode"],
"verifier_method": verdict["verifier_method"],
"n_quotes": verdict["n_quotes"],
"n_verified": verdict["n_verified"],
"violations": verdict.get("violations") or [],
"sources": sources_for_render,
"stats": client.stats(),
"timing_s": round(elapsed, 3),
"timings": {k: round(v, 3) for k, v in timings.items()},
"capacity": capacity,
"endpoint": endpoint,
"model": model,
}
if getattr(args, "json", False):
print(json.dumps(result, indent=2, ensure_ascii=False))
else:

279
arborist/qa/corpus_query.py Normal file
View file

@ -0,0 +1,279 @@
"""Corpus-direct query orchestrator — one pipeline over any Corpus adapter.
This is the DRY landing for the local/cloud retrieval split: take a
Corpus (SqliteShardCorpus | SidecarBucketCorpus | future edge-proxy),
a question, and a ChatClient; run the same prompt → LLM → verify →
render pipeline regardless of which backend served the bytes.
Today this powers `arborist cloud query`. The local `arborist query`
still uses the older 2000-line arborist.qa.query.query() — that
refactor is a separate multi-day pass. When it lands, query() will
delegate retrieval to a Corpus and call run_query() too.
What lives here:
- run_query(corpus, question, chat_client, **opts) → dict
Same result-dict shape today's _cmd_cloud_query emits (audit_mode,
n_quotes, n_verified, violations, sources, capacity, timings,
raw_answer, answer_text).
What does NOT live here:
- Render to human / JSON (the caller's job; this returns a data dict)
- Progress emission (callers that want progress wrap individual
phases — see _cmd_cloud_query for the pattern)
- Cache lookup / persistence (cloud has no persistent cache; the
local query() will keep its providence_cache lookup pre-call)
"""
from __future__ import annotations
import re as _re
import time as _time
from typing import Optional
from arborist.compress import unpack_chunk
from arborist.qa.client import ChatClient
from arborist.qa.corpus import Corpus, NotSupportedError
from arborist.qa.evidence import (
build_evidence_map,
evidence_map_by_pointer_id,
render_claim_lattice,
render_evidence_map,
)
from arborist.qa.prompts import (
CLAIM_LATTICE_GROUNDING_REMINDER,
CLAIM_LATTICE_SYSTEM_PROMPT,
)
from arborist.qa.verify import verify_claim_lattice
_POINTER_RE = _re.compile(r"\[E\d+(?:,\s*E\d+)*\]")
def run_query(
corpus: Corpus,
question: str,
chat_client: ChatClient,
*,
model_id: str = "stub",
top_k: int = 4,
max_context_chars: int = 24_000,
temperature: float = 0.1,
max_tokens: int = 512,
warrant_check_enabled: bool = False,
) -> dict:
"""End-to-end claim-lattice query over any Corpus.
Returns a dict with:
answer_text, raw_answer, audit_mode, verifier_method,
n_quotes, n_verified, violations, sources, capacity,
timings, model
Sources carry source_role + used + used_pointer_ids derived from
the model's [E#] citations, so the caller's render layer can
surface "used (E1)" annotations like local query() does.
Times each phase (search, context, llm, verify, total) into
timings dict so callers / benchmarks can attribute cost.
"""
timings: dict[str, float] = {}
t_start = _time.time()
# 1. Retrieve — fts_body is the only route currently shared across
# adapters. Future: add fts_title / fts_phrase / core_keyword
# with NotSupported skip-and-merge logic.
ts = _time.time()
try:
hits = corpus.fts_body(question, limit=top_k)
except NotSupportedError:
hits = []
timings["search"] = _time.time() - ts
if not hits:
timings["total"] = _time.time() - t_start
return {
"answer_text": "",
"raw_answer": "",
"audit_mode": "UNGROUNDED",
"verifier_method": "none",
"n_quotes": 0,
"n_verified": 0,
"violations": [],
"sources": [],
"capacity": {},
"timings": {k: round(v, 3) for k, v in timings.items()},
"model": model_id,
"note": "no retrieval hits — empty corpus, off-topic query, or "
"all hits filtered (e.g. title-relevance).",
}
# 2. Pull chunk content per hit, build evidence rows.
ts = _time.time()
per_doc_budget = max(1000, max_context_chars // max(1, len(hits)))
chunks_for_evidence: list[dict] = []
total_chars = 0
for rank, h in enumerate(hits, 1):
rows = corpus.chunks_for_doc(h.document_root, limit=1)
if not rows:
continue
r = rows[0]
span = r.content
if len(span) > per_doc_budget:
span = span[:per_doc_budget]
chunks_for_evidence.append({
"source_root": h.document_root,
"document_uri": h.document_uri,
"title": h.title,
"chunk_idx": r.idx,
"chunk_root": r.leaf_hash or ("0" * 64),
"span": span,
"source_role": (
"primary_answer_source" if rank == 1 else "background_source"
),
})
total_chars += len(span)
if total_chars >= max_context_chars:
break
timings["context"] = _time.time() - ts
if not chunks_for_evidence:
timings["total"] = _time.time() - t_start
return {
"answer_text": "",
"raw_answer": "",
"audit_mode": "UNGROUNDED",
"verifier_method": "none",
"n_quotes": 0,
"n_verified": 0,
"violations": [],
"sources": [
{"document_root": h.document_root,
"document_uri": h.document_uri,
"title": h.title, "score": h.score}
for h in hits
],
"capacity": {},
"timings": {k: round(v, 3) for k, v in timings.items()},
"model": model_id,
"note": "retrieval hits found but no readable chunk content.",
}
# 3. Build evidence map + format prompt.
evidence_map = build_evidence_map(chunks_for_evidence)
evidence_text = render_evidence_map(evidence_map)
sys_prompt = CLAIM_LATTICE_SYSTEM_PROMPT
user_prompt = (
f"EVIDENCE:\n\n{evidence_text}\n\n"
f"QUESTION: {question}\n\n"
f"{CLAIM_LATTICE_GROUNDING_REMINDER}"
)
messages = [
{"role": "system", "content": sys_prompt},
{"role": "user", "content": user_prompt},
]
prompt_chars = len(sys_prompt) + len(user_prompt)
# 4. LLM call.
ts = _time.time()
try:
answer = chat_client.chat_completion(
messages=messages,
model=model_id,
max_tokens=max_tokens,
temperature=temperature,
)
except Exception as e:
timings["total"] = _time.time() - t_start
return {
"answer_text": "",
"raw_answer": "",
"audit_mode": "UNGROUNDED",
"verifier_method": "none",
"n_quotes": 0,
"n_verified": 0,
"violations": [],
"sources": [
{"document_root": h.document_root,
"document_uri": h.document_uri,
"title": h.title, "score": h.score}
for h in hits
],
"capacity": {},
"timings": {k: round(v, 3) for k, v in timings.items()},
"model": model_id,
"error": f"LLM call failed: {type(e).__name__}: {e}",
}
timings["llm"] = _time.time() - ts
# 5. Verify (claim_lattice).
ts = _time.time()
verdict = verify_claim_lattice(
answer, evidence_map,
question=question,
warrant_check_enabled=warrant_check_enabled,
)
timings["verify"] = _time.time() - ts
# 6. Annotate sources with used / used_pointer_ids from the model's
# actual [E#] citations.
by_pid = evidence_map_by_pointer_id(evidence_map)
used_doc_roots: set[str] = set()
doc_root_to_pointers: dict[str, list[str]] = {}
for m in _POINTER_RE.finditer(answer):
for pid in (p.strip() for p in m.group(0).strip("[]").split(",")):
ev = by_pid.get(pid)
if ev:
used_doc_roots.add(ev.source_root)
doc_root_to_pointers.setdefault(ev.source_root, []).append(pid)
sources_for_render: list[dict] = []
seen_roots: set[str] = set()
for ev in evidence_map:
if ev.source_root in seen_roots:
continue
seen_roots.add(ev.source_root)
sources_for_render.append({
"document_root": ev.source_root,
"document_uri": ev.document_uri,
"title": ev.title,
"source_role": ev.source_role,
"used": ev.source_root in used_doc_roots,
"used_pointer_ids": sorted(set(
doc_root_to_pointers.get(ev.source_root, [])
)),
})
# 7. Render lattice answer with spotlight excerpts (fall back to
# raw answer if the parser dropped everything).
claim_statuses = verdict.get("claim_statuses") or []
rendered_answer = render_claim_lattice(
[
{
"text": cs.get("text", ""),
"pointer_ids": cs.get("pointer_ids") or [],
}
for cs in claim_statuses
],
by_pid,
window=200,
) or answer
timings["total"] = _time.time() - t_start
return {
"answer_text": rendered_answer,
"raw_answer": answer,
"audit_mode": verdict["audit_mode"],
"verifier_method": verdict["verifier_method"],
"n_quotes": verdict["n_quotes"],
"n_verified": verdict["n_verified"],
"violations": verdict.get("violations") or [],
"sources": sources_for_render,
"capacity": {
"sys_prompt_chars": len(sys_prompt),
"evidence_chars": len(evidence_text),
"question_chars": len(question),
"prompt_chars": prompt_chars,
"answer_chars": len(answer),
},
"timings": {k: round(v, 3) for k, v in timings.items()},
"model": model_id,
"corpus_name": getattr(corpus, "name", "unknown"),
}

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{"question": "when did the soviet union dissolve?", "local": {"audit_mode": "STRICT", "n_quotes": 1, "n_verified": 1, "primary": {"title": "Soviet Union", "uri": "https://en.wikipedia.org/wiki/Soviet_Union", "used": true}, "elapsed_s": 8.08, "error": null}, "cloud": {"audit_mode": "STRICT", "n_quotes": 2, "n_verified": 2, "primary": {"title": "Soviet Union", "uri": "https://en.wikipedia.org/wiki/Soviet_Union", "used": true}, "elapsed_s": 48.91, "error": null}, "regression": false, "reason": "ok"}
{"question": "where is mount kilimanjaro located?", "local": {"audit_mode": "STRICT", "n_quotes": 1, "n_verified": 1, "primary": {"title": "Mount Kilimanjaro", "uri": "https://en.wikipedia.org/wiki/Mount_Kilimanjaro", "used": true}, "elapsed_s": 9.23, "error": null}, "cloud": {"audit_mode": "STRICT", "n_quotes": 2, "n_verified": 2, "primary": {"title": "Mount Kilimanjaro", "uri": "https://en.wikipedia.org/wiki/Mount_Kilimanjaro", "used": true}, "elapsed_s": 52.0, "error": null}, "regression": false, "reason": "ok"}
{"question": "who painted the mona lisa?", "local": {"audit_mode": "STRICT", "n_quotes": 2, "n_verified": 2, "primary": {"title": "Mona Lisa", "uri": "https://en.wikipedia.org/wiki/Mona_Lisa", "used": true}, "elapsed_s": 6.08, "error": null}, "cloud": {"audit_mode": "STRICT", "n_quotes": 2, "n_verified": 2, "primary": {"title": "Mona Lisa", "uri": "https://en.wikipedia.org/wiki/Mona_Lisa", "used": true}, "elapsed_s": 46.97, "error": null}, "regression": false, "reason": "ok"}
{"question": "who were the original seven mercury astronauts?", "local": {"audit_mode": "STRICT", "n_quotes": 1, "n_verified": 1, "primary": {"title": "Mercury Seven", "uri": "https://en.wikipedia.org/wiki/Mercury_Seven", "used": true}, "elapsed_s": 7.18, "error": null}, "cloud": {"audit_mode": "HYBRID", "n_quotes": 9, "n_verified": 9, "primary": {"title": "Mercury Seven", "uri": "https://en.wikipedia.org/wiki/Mercury_Seven", "used": true}, "elapsed_s": 52.24, "error": null}, "regression": true, "reason": "audit_mode dropped STRICT → HYBRID"}
{"question": "why did the dinosaurs go extinct?", "local": {"audit_mode": "UNGROUNDED", "n_quotes": 1, "n_verified": 0, "primary": {"title": "Edwina, the Dinosaur Who Didn't Know She Was Extinct", "uri": "https://en.wikipedia.org/wiki/Edwina,_the_Dinosaur_Who_Didn't_Know_She_Was_Extinct", "used": false}, "elapsed_s": 6.4, "error": null}, "cloud": {"audit_mode": "HYBRID", "n_quotes": 4, "n_verified": 1, "primary": {"title": "Edwina, the Dinosaur Who Didn't Know She Was Extinct", "uri": "https://en.wikipedia.org/wiki/Edwina,_the_Dinosaur_Who_Didn't_Know_She_Was_Extinct", "used": true}, "elapsed_s": 48.23, "error": null}, "regression": false, "reason": "ok"}

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"""Unit + functional tests for arborist.qa.corpus_query.run_query.
The single orchestrator both `arborist cloud query` and (eventually)
`arborist query` will share. Tests use SqliteShardCorpus + StubClient
to keep the suite offline + deterministic.
Coverage:
* Happy path — verbatim quote in StubClient answer reaches the verifier
on the assembled evidence, lands STRICT.
* Off-topic — no FTS hits → UNGROUNDED, empty sources, fast return.
* Timings dict populated with the expected phase keys.
* Sources annotated with used / used_pointer_ids from [E#] tags in answer.
* Capacity dict reports sys + evidence + question + answer char counts.
"""
from __future__ import annotations
from typing import Iterator
import pytest
from arborist.document import Document
from arborist.ingest import ingest_source
from arborist.qa.client import StubClient
from arborist.qa.corpus import SqliteShardCorpus
from arborist.qa.corpus_query import run_query
from arborist.source import Source
from arborist.store import connect
class _S(Source):
source_type = "test"
def __init__(self, ds): self.ds = ds
def iter_documents(self) -> Iterator[Document]:
yield from self.ds
@pytest.fixture
def corpus(tmp_path):
db = tmp_path / "shard.db"
c = connect(db)
ingest_source(c, _S([
Document(
uri="test://anarchism", source_type="test", title="Anarchism",
content=(
"Anarchism is a political philosophy that promotes a stateless "
"society. " * 6
+ 'The phrase "abolition of authority" describes the core '
'principle of anarchism. ' * 4
),
),
Document(
uri="test://capital", source_type="test", title="Capital",
content=("The eight forms of capital include living and social. " * 6),
),
]))
yield SqliteShardCorpus(c)
c.close()
# ---------------------------------------------------------------------------
# Happy path
# ---------------------------------------------------------------------------
def test_run_query_grounds_verbatim_quote(corpus):
"""Verbatim quote in the stub answer must verify on the assembled
evidence — STRICT (or higher rung)."""
stub = (
'The core principle of anarchism is the "abolition of authority". [E1]'
)
result = run_query(
corpus, "what is the core principle of anarchism?",
StubClient(answer=stub),
model_id="stub", top_k=2,
)
assert result["audit_mode"] == "STRICT"
assert result["n_verified"] >= 1
assert result["sources"]
assert "Anarchism" in result["sources"][0]["title"]
# Cited source must be marked used (E1).
assert result["sources"][0]["used"] is True
assert "E1" in result["sources"][0]["used_pointer_ids"]
def test_run_query_emits_capacity_and_timings(corpus):
"""Result must carry capacity (prompt char breakdown) + timings
(per-phase + total) so callers don't have to reverse-engineer."""
result = run_query(
corpus, "anarchism",
StubClient(answer="Anarchism is a philosophy. [E1]"),
model_id="stub", top_k=1,
)
cap = result["capacity"]
assert cap["sys_prompt_chars"] > 0
assert cap["evidence_chars"] > 0
assert cap["prompt_chars"] == (
cap["sys_prompt_chars"]
+ cap["evidence_chars"]
+ len("EVIDENCE:\n\n\n\nQUESTION: anarchism\n\n")
# The exact prompt scaffolding char count varies with the
# grounding-reminder; assert it's positive + plausible.
) or cap["prompt_chars"] >= cap["sys_prompt_chars"] + cap["evidence_chars"]
t = result["timings"]
assert t["total"] > 0
for k in ("search", "context", "llm", "verify"):
assert k in t, f"missing timing phase: {k}"
# ---------------------------------------------------------------------------
# Off-topic / no hits
# ---------------------------------------------------------------------------
def test_run_query_returns_ungrounded_on_no_hits(corpus):
"""Query that no corpus doc matches must return UNGROUNDED quickly
with empty sources — no LLM call, no verifier-method confusion."""
stub = StubClient(answer="this should never be returned")
result = run_query(
corpus, "what is the price of tea in xylophone-land?",
stub, model_id="stub", top_k=4,
)
assert result["audit_mode"] == "UNGROUNDED"
assert result["sources"] == []
# Critical: LLM must NOT have been called when retrieval is empty.
assert stub.calls == [], "LLM was called despite empty retrieval"
# ---------------------------------------------------------------------------
# Bucket-adapter parity (not run by default; opt-in fixture-heavy)
# ---------------------------------------------------------------------------
def test_run_query_corpus_name_surfaces(corpus):
"""corpus.name leaks into result so bench harnesses can attribute
which backend produced a row."""
result = run_query(
corpus, "anarchism",
StubClient(answer="Anarchism is a philosophy. [E1]"),
model_id="stub", top_k=1,
)
assert result.get("corpus_name") == "sqlite-shard"