Round-1 review on PR #893:
- _drop_reason now descends through nested ExceptionGroups to the first leaf
(was single-level), so a doubly-wrapped cause isn't mislabelled "other";
added a nested-group test. Commented why both the httpx and openai isinstance
branches exist (raw Nextcloud-API errors vs SDK-wrapped variants).
- Documented that generate() intentionally shares the broadened transient retry
(RAG sampling path), with the worst-case latency note.
- Added a docstring note to process_document on how the provider-level retry
(5x) layers over the outer loop (3x in-process / 1x procrastinate).
- Added test_embed_batch_retries_on_connection_error for the batch path.
- Renamed test_retry_reraises_non_rate_limit_immediately ->
test_retry_reraises_when_predicate_returns_false (it tests the predicate, not
a specific status).
SonarCloud:
- S5708 (BLOCKER) on the helper's dynamic `except exception_type`: the type is
constrained to BaseException/tuple by the signature; suppressed with a
justified NOSONAR.
- S7503 (async without await) in the embed-retry test: use AsyncMock side_effect
instead of a hand-rolled async function.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
From card 309 (OHR-Bench smoke-test triage): during a backend-pod rollover the
embedding endpoint was briefly unreachable, and openai.APIConnectionError /
ConnectError propagated unretried (the provider only retried 429). Documents
exhausted the 3 in-process retries and were dropped for that scan cycle.
Broaden the provider-level retry to the transient set -- APIConnectionError,
APITimeoutError, 429, and 5xx -- on the existing exponential backoff (2s->60s,
5 attempts), so a few seconds of retry rides through the rollover. Permanent
4xx (auth, bad request) still re-raise immediately. Generalize the shared
_retry helper (retry_on_rate_limit -> retry_on_transient, predicate renamed to
should_retry, accurate log label) with a back-compat alias; Mistral gets 429+5xx
for parity. The production gateway path inherits this via GatewayProvider, which
delegates to the decorated OpenAIProvider methods.
Add astrolabe_vector_ingest_dropped_total{reason}, incremented when a document
exhausts retries, classified (connection|timeout|rate_limit|server|qdrant|other)
by _drop_reason so the embed-drop rate is alertable per cause. Dropped docs are
NOT marked failed, so the next full scan re-picks them (re-queue via scan loop).
Refs: Deck board 12 card 309 (AC #1 no permanently-dropped docs; embed-drop
metric for AC #5).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
embeddings_queries now records the embedding request's token count (the unit
upstream providers bill on) instead of an operation count, and fires on the
indexing path too. Previously only semantic search recorded it (value=1), so a
re-indexing run produced no embeddings_queries events at all — only pages_chunks.
- Provider layer: additive embed_with_usage / embed_batch_with_usage surface the
per-request token count (Mistral/OpenAI usage.total_tokens, Bedrock Titan
inputTextTokenCount, Ollama prompt_eval_count); a char-based estimate is the
fallback (Simple, and any provider/response without a token field). Gateway and
EmbeddingService forward through. The count travels as a return value / a
per-request SearchAlgorithm attribute — never on the singleton — so concurrent
indexing + search can't mis-attribute bills.
- Indexing (vector/processor.py): records embeddings_queries (value=batch tokens)
alongside the existing pages_chunks event.
- Search (server/semantic.py): value is now the query embedding's token count,
relayed from BM25HybridSearchAlgorithm via query_token_count.
The astrolabe_embeddings_queries Stripe meter (sum aggregation) now sums tokens
with no CP/Terraform change. The meter "queries"->tokens naming/unit
clarification (homelab-terraform #254) + CP rollup/portal copy is a follow-up.
Deck #67.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds OpenAI provider to the unified provider architecture (ADR-015),
supporting:
- OpenAI API (api.openai.com)
- GitHub Models API (models.github.ai/inference)
- OpenAI-compatible endpoints (Fireworks, Together, etc.)
Features:
- Embedding support with text-embedding-3-small/large models
- Text generation via chat completions API
- Automatic retry with exponential backoff for rate limits
- Provider auto-detection in registry (priority after Bedrock)
Environment variables:
- OPENAI_API_KEY: API key (required)
- OPENAI_BASE_URL: Base URL override (optional)
- OPENAI_EMBEDDING_MODEL: Embedding model (default: text-embedding-3-small)
- OPENAI_GENERATION_MODEL: Generation model (default: gpt-4o-mini)
Also adds:
- Integration tests for RAG pipeline with MCP sampling
- MCP client sampling support for integration tests
- Ground truth Q&A pairs for Nextcloud User Manual
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
