refactor(usage): extract indexing metering helper; address review round 2

Round-2 claude-review findings:

- 🟡 Base-class recursion invariant: documented on embed_with_usage /
  embed_batch_with_usage that a provider overriding embed()/embed_batch() to
  delegate to the *_with_usage variant MUST also override that variant, or the
  two recurse. (No recursion today; the shipped providers pair the overrides.)
- 🟡 Processor metering had no unit test: extracted the two-event recording
  into a module-level record_indexing_usage() helper and added
  tests/unit/test_processor_metering.py (value mapping, flag/zero-chunk no-ops,
  best-effort failure swallowed).
- 🟡 SonarQube hotspots (python:S5332) were 3 http:// URLs in the new test
  fixtures (mock hosts, never contacted) blocking the quality gate
  (new_security_hotspots_reviewed). Switched them to https:// so no hotspot is
  raised.
- 🟢 Zero-chunk guard: record_indexing_usage() no-ops when chunk_count == 0, so
  an empty document no longer writes zero-value billing rows.

Deferred (stated on the PR): Mistral x.index-or-0 sort key (pre-existing,
equivalent), CHANGELOG note for the Ollama /api/embed switch (CHANGELOG is
commitizen-generated from commit bodies, which document it), class-var
query_token_count (safe under the per-request instance pattern).

Deck #67.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Chris Coutinho
2026-06-08 01:22:03 +02:00
co-authored by Claude Opus 4.8
parent a0bb5642cb
commit d15ce627ab
5 changed files with 196 additions and 53 deletions
+74 -49
View File
@@ -112,6 +112,65 @@ def should_use_page_aware(
return page_aware_enabled and doc_type == "file" and bool(page_boundaries)
async def record_indexing_usage(
*,
enabled: bool,
provider: str,
model: str,
doc_type: str,
user_id: str,
chunk_count: int,
token_count: int,
total_chars: int,
) -> None:
"""Record the two billable usage events for one embedded document.
``pages_chunks`` is the volume (chunks embedded); ``embeddings_queries`` is
the embedding request's token count — the same metric search records, so the
meter bills embedding tokens whether they were incurred indexing a document
or embedding a query (Deck #67).
Best-effort and flag-gated: a metering failure is logged and never breaks
indexing. No-op when metering is disabled or the document produced no chunks
(an empty batch embeds nothing and would only write zero-value rows).
Privacy note: ``user_id`` stays tenant-local — the CP rollup aggregates
GROUP BY (day, metric) into ``usage_daily`` (no metadata column), so nothing
here reaches Stripe; it is retained only to keep Deck #67's future per-user
attribution derivable from the app DB without a re-migration.
"""
if not enabled or chunk_count == 0:
return
metadata = {
"provider": provider,
"model": model,
"doc_type": doc_type,
"user_id": user_id,
"total_chars": total_chars,
}
try:
store = await UsageEventStore.shared()
# enabled=True: the guard above already confirmed the flag, so the store
# skips a second uncached Settings build per record (ADR-024).
await store.record_usage_event(
metric="pages_chunks", value=chunk_count, metadata=metadata, enabled=True
)
await store.record_usage_event(
metric="embeddings_queries",
value=token_count,
metadata=metadata,
enabled=True,
)
except Exception:
# Reached only when shared()/store construction itself raises
# (record_usage_event swallows its own write failures). Metering is on,
# so warn rather than hide the "enabled but no billing data" case.
logger.warning(
"usage metering hook (indexing embeddings) skipped", exc_info=True
)
async def processor_task(
worker_id: int,
receive_stream: MemoryObjectReceiveStream[DocumentTask],
@@ -825,55 +884,21 @@ async def _index_document(
chunks=len(chunk_texts),
chars=total_chars,
)
# Usage metering (Deck #67): record chunks embedded as a billable
# 'pages_chunks' event. Best-effort and gated on the flag so the
# off-path (OSS default) touches no storage; placed after the
# embedding succeeds so it can never affect the indexing path.
#
# Privacy note: user_id stays tenant-local — the CP rollup
# aggregates GROUP BY (day, metric) into usage_daily (no metadata
# column), so nothing here reaches Stripe; it is retained only to
# keep Deck #67's future per-user attribution derivable from the
# app DB without a re-migration.
if settings.usage_metering_enabled:
# Two billable events per indexed document: 'pages_chunks' is
# the volume (chunks embedded); 'embeddings_queries' is the
# token count of the embedding request — the same metric search
# records, so the meter bills embedding tokens whether they were
# incurred indexing a document or embedding a query (Deck #67).
metering_metadata = {
"provider": provider,
"model": settings.get_embedding_model_name(),
"doc_type": doc_task.doc_type,
"user_id": doc_task.user_id,
"total_chars": total_chars,
}
try:
store = await UsageEventStore.shared()
await store.record_usage_event(
metric="pages_chunks",
value=len(chunk_texts),
metadata=metering_metadata,
# The outer guard already confirmed the flag, so pass
# enabled=True directly — the store then skips a second
# uncached Settings build here (ADR-024).
enabled=True,
)
await store.record_usage_event(
metric="embeddings_queries",
value=embed_tokens,
metadata=metering_metadata,
enabled=True,
)
except Exception:
# Reached only when shared()/store construction itself
# raises (record_usage_event swallows its own write
# failures). Metering is on, so warn rather than hide the
# "enabled but no billing data" case in DEBUG logs.
logger.warning(
"usage metering hook (indexing embeddings) skipped",
exc_info=True,
)
# Usage metering (Deck #67): record the chunk volume +
# embedding-token count for this document. Best-effort and
# flag-gated; placed after the embedding succeeds so it can never
# affect the indexing path. See record_indexing_usage for the
# metric/privacy details.
await record_indexing_usage(
enabled=settings.usage_metering_enabled,
provider=provider,
model=settings.get_embedding_model_name(),
doc_type=doc_task.doc_type,
user_id=doc_task.user_id,
chunk_count=len(chunk_texts),
token_count=embed_tokens,
total_chars=total_chars,
)
async def generate_sparse_embeddings():
"""Generate sparse embeddings (BM25 for keyword matching)."""