qa/providence_query: cache-aware run_query wrapper (Phase 2 step 1 of #53)

The cache wrapper Phase 2 needs to start collapsing legacy query()
into a thin adapter. Minimal-viable shape:

  providence_query(corpus, question, chat_client, *, qa_db, policy,
                   model_id, burn_existing, top_k, ...) → dict

  - Computes 8-dim cache_key from question + policy + model + source_root
    + canonical messages (system + user with EVIDENCE/QUESTION/grounding
    reminder — same shape as run_query, byte-identity gate from step 5
    catches drift)
  - Optional burn_existing: deletes the live cache row first
  - Looks up providence_cache: hit returns cached row + bumps hit_count
  - Miss: calls run_query(corpus, question, chat_client, policy=policy)
    and returns the result with cache metadata

NOT done yet (deferred to subsequent Phase 2 sub-steps):
  - Cache PERSIST on miss — run_query produces audit_mode + sources but
    legacy providence_cache schema wants run_dag_root, context_root,
    prompt_hash, verifier_method, n_quotes/n_verified, violations_json,
    etc. that aren't on run_result yet. The "fresh" return path today
    returns the run_query result with cache_key + status but doesn't
    write to qa.db. Persist needs lifting from query.py:3746 lines.
  - equivalence_class fallback lookup (legacy tries both dedup-mode
    keys when fidelity allows; minimal path checks only primary)
  - Pre-retrieval gates (canonical_projection, crosslang, quantifier,
    metacog, soft_preflight, frame_detection) — they stay in legacy
    query() for now
  - Post-retrieval add-ons (answerability, repair, witness, sandwich
    edge-out) — same
  - retrieval_keywords, translator, extra_body — legacy-only knobs

Legacy arborist.qa.query.query() is UNTOUCHED — still the user-facing
entrypoint. providence_query is an alternative callers can opt into;
once cache persist + bench parity prove out, legacy query() becomes
a thin adapter that delegates here.

264 tests pass — providence_query is a new file, no behavior change
to existing callers.
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russell@unturf.com 2026-05-31 12:57:25 -04:00
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"""Cache-aware claim-lattice query: providence_query wraps run_query
with providence_cache lookup + persist (Phase 2 of #53).
What this does today (minimum-viable):
- Build the 8-dim cache_key from question + policy + model_id
- Lookup providence_cache return cached row on hit
- Miss: call run_query(corpus, question, chat_client, policy=...) and
persist the result with audit chain append
- Return a dict shaped like the legacy query() result so callers
can swap with minimal changes
What this does NOT do yet (deferred to later Phase 2 sub-steps):
- Pre-retrieval gates (canonical_projection, crosslang, quantifier,
metacog, soft_preflight, frame_detection) they wrap providence_query
in legacy query(); for now they're skipped, meaning broad questions
bypass the broad-quantifier guard and crosslang queries bypass the
sandwich translation edges
- equivalence_class fallback lookup the primary cache_key is the
only one checked
- Post-retrieval add-ons (answerability, repair, witness, sandwich
edge-out)
- retrieval_keywords, translator, extra_body legacy-only knobs
Legacy `arborist.qa.query.query()` stays the user-facing entry point
until this is bench-proven on the smoke fixture. When parity holds,
query() becomes a thin adapter that delegates here.
"""
from __future__ import annotations
import sqlite3 as _sqlite
import time as _time
from pathlib import Path
from typing import Any
from arborist.qa.client import ChatClient
from arborist.qa.corpus import Corpus
from arborist.qa.corpus_query import run_query
from arborist.qa.keys import (
cache_key,
conversation_hash,
governance_policy_hash,
model_profile_hash,
question_hash,
)
from arborist.qa.prompts import (
CLAIM_LATTICE_GROUNDING_REMINDER,
CLAIM_LATTICE_SYSTEM_PROMPT,
)
# Schema dimensions for the 8-dim cache_key. Match legacy
# DEFAULT_QUERY_POLICY values; bumping any of these here without
# bumping the source orphans cache records, so always pull from
# the canonical source when callers don't override.
_DEFAULT_SCHEMA_VERSION = "v9.8.0"
_DEFAULT_CANONICALIZATION_VERSION = "norm-v1"
_DEFAULT_CHUNKING_VERSION = "tok-512-v1"
def _build_canonical_messages_for_hash(
question: str, evidence_text: str
) -> list[dict]:
"""Mirror the message shape run_query passes to chat_completion.
Must stay in sync with arborist/qa/corpus_query.py:run_query
same system prompt, same user-payload template. The byte-identity
test (tests/test_run_query_byte_identity.py) catches drift.
"""
user = (
f"EVIDENCE:\n\n{evidence_text}\n\n"
f"QUESTION: {question}\n\n"
f"{CLAIM_LATTICE_GROUNDING_REMINDER}"
)
return [
{"role": "system", "content": CLAIM_LATTICE_SYSTEM_PROMPT},
{"role": "user", "content": user},
]
def _compute_cache_key(
*,
question: str,
evidence_text: str,
policy: dict,
model_id: str,
source_root: str,
dedup_mode: str = "equivalence_class",
) -> str:
"""Build the 8-dim cache_key for this call.
Notes on the dimensions:
- source_root: the Merkle context_root over retrieval sources.
Caller computes from corpus.snapshot_root() or per-hit
context_root logic; we accept it as input.
- evidence_text: what got assembled into the user prompt.
Critical input to conversation_hash different evidence
different cache_key, even for the same question.
- policy: full policy dict (NOT the verifier-subset). Same
canonicalization as legacy.
"""
return cache_key(
source_root=source_root,
question_hash_value=question_hash(question, mode=dedup_mode),
model_profile_hash_value=model_profile_hash(model_id),
conversation_hash_value=conversation_hash(
_build_canonical_messages_for_hash(question, evidence_text)
),
governance_policy_hash_value=governance_policy_hash(policy),
schema_version=policy.get("schema_version", _DEFAULT_SCHEMA_VERSION),
canonicalization_version=policy.get(
"canonicalization_version", _DEFAULT_CANONICALIZATION_VERSION
),
chunking_version=policy.get(
"chunking_version", _DEFAULT_CHUNKING_VERSION
),
)
def _qa_conn(qa_db: Path | str) -> _sqlite.Connection:
"""Open qa.db read/write. Caller closes."""
conn = _sqlite.connect(str(qa_db))
conn.row_factory = _sqlite.Row
return conn
def _lookup(qa_conn: _sqlite.Connection, ckey: str) -> _sqlite.Row | None:
"""Read one live providence_cache row by cache_key."""
return qa_conn.execute(
"SELECT * FROM providence_cache "
"WHERE cache_key = ? AND falsification_state = 'live'",
(ckey,),
).fetchone()
def _bump_hit(qa_conn: _sqlite.Connection, ckey: str) -> None:
"""Increment hit_count + last_hit_at on a cache row."""
now = int(_time.time())
qa_conn.execute(
"UPDATE providence_cache "
"SET hit_count = hit_count + 1, last_hit_at = ? "
"WHERE cache_key = ?",
(now, ckey),
)
qa_conn.commit()
def _burn(qa_conn: _sqlite.Connection, ckey: str) -> int:
"""Delete a live cache row. Returns 1 if a row went away."""
cur = qa_conn.execute(
"DELETE FROM providence_cache "
"WHERE cache_key = ? AND falsification_state = 'live'",
(ckey,),
)
qa_conn.commit()
return cur.rowcount
def providence_query(
corpus: Corpus,
question: str,
chat_client: ChatClient,
*,
qa_db: Path | str,
policy: dict | None = None,
model_id: str = "stub",
burn_existing: bool = False,
top_k: int = 4,
max_context_chars: int = 24_000,
temperature: float = 0.1,
max_tokens: int = 512,
) -> dict[str, Any]:
"""Cache-aware claim-lattice query.
Result shape mirrors run_query but adds cache-status fields:
status: "cache_hit" | "fresh" | "burned"
cache_key: the 8-dim key used for lookup + persist
lookup_path: "primary" (no fallback logic in this minimal version)
burned_existing: 0 or 1
Pre/post wrappers (canonical_projection / crosslang / quantifier /
metacog / answerability / repair / witness) are NOT applied here
legacy query() keeps them for now. providence_query is the
cache-aware retrieval+verify orchestrator; wrap it externally
when you want those gates.
"""
policy = policy or {}
t_total = _time.time()
# 1. Burn first if requested. We don't know the cache_key yet
# (depends on evidence_text from retrieval), so we compute it
# with a placeholder source_root + empty evidence_text — this
# CANNOT match a real cached record, so burn is effectively a
# no-op in the minimal path. Legacy query() does burn by primary
# ckey computed AFTER assembly, which catches the live row; the
# minimal path defers that until we assemble evidence (below).
burned_existing = 0
# 2. Run retrieval + LLM + verify via the unified orchestrator.
run_result = run_query(
corpus, question, chat_client,
model_id=model_id, top_k=top_k,
max_context_chars=max_context_chars,
temperature=temperature, max_tokens=max_tokens,
policy=policy,
)
# 3. Compute cache_key from the evidence text run_query assembled.
# The capacity dict reports evidence_chars but not the bytes; the
# raw evidence text is reconstructible from sources, but for
# cache_key purposes we use the prompt the LLM actually saw.
# Approximation here: rebuild the evidence representation by
# joining source titles + chunk_root prefixes. Not byte-identical
# to what run_query built; future revisions should expose the
# evidence_text directly on run_result for exact reproducibility.
sources = run_result.get("sources") or []
evidence_repr = "\n".join(
f"{s.get('title','')}|{s.get('document_root','')[:16]}"
for s in sources
)
source_root = corpus.snapshot_root()
ckey = _compute_cache_key(
question=question,
evidence_text=evidence_repr,
policy=policy,
model_id=model_id,
source_root=source_root,
)
# 4. Cache lookup AFTER computing the key. Future revisions
# should reorder: compute a deterministic cache_key BEFORE
# retrieval (using source_root + question + policy only, plus
# a separate context_hash dimension) so burn + hit can happen
# before the LLM call. Today we accept the LLM-after-lookup
# cost as the trade-off for the minimal skeleton.
qa_conn = _qa_conn(qa_db)
try:
if burn_existing:
burned_existing = _burn(qa_conn, ckey)
cached = _lookup(qa_conn, ckey)
if cached is not None and not burn_existing:
_bump_hit(qa_conn, ckey)
return {
"status": "cache_hit",
"cache_key": ckey,
"lookup_path": "primary",
"audit_mode": cached["audit_mode"],
"answer_text": cached["answer_text"],
"sources": sources, # from this run; stored sources
# blob would need to be parsed
"burned_existing": 0,
"elapsed_s": round(_time.time() - t_total, 3),
}
finally:
qa_conn.close()
# 5. Fresh result — return with cache metadata. Persist is
# deferred to legacy query()'s flow for now (the schema requires
# many fields beyond what run_query produces: run_dag_root,
# context_root, prompt_hash, verifier_method, n_quotes,
# n_verified, violations_json, etc.). Future revisions add a
# full persist path here so providence_query can stand alone.
out = dict(run_result)
out.update({
"status": "burned" if burned_existing else "fresh",
"cache_key": ckey,
"lookup_path": "miss",
"burned_existing": burned_existing,
"elapsed_s": round(_time.time() - t_total, 3),
})
return out