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>
Round-1 claude-review findings:
- 🔴 Multi-doc_type search billed N embedding calls as 1. nc_semantic_search
loops search() once per doc_type on one BM25HybridSearchAlgorithm instance,
and each call re-embedded the query, so only the last query_token_count was
recorded. Cache the dense embedding per query on the (per-request) instance
so the query is embedded — and metered — exactly once regardless of how many
doc_types are searched. This also removes the redundant per-type embed work
and avoids billing a user N× for one logical query.
- 🟡 Ollama embed() now delegates to embed_with_usage() so single and batch
embeds use the same /api/embed endpoint (was the legacy /api/embeddings),
keeping _detect_dimension and other embed() callers consistent.
- 🟢 round() instead of truncating int() when coercing provider-reported token
counts (forward-compatible if a provider ever returns a float).
Tests: per-instance query-embedding cache (embedded once across 3 doc_types;
re-embeds on a different query).
Deferred (stated on the PR): mistral/openai single-embed dual path (changes
tested error/request semantics on the cloud-critical path — separate refactor),
bedrock boto3 sync-in-async (pre-existing; no new invoke_model calls per doc).
Deck #67.
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>
- test_registry.py: stub `mistralai.client.Mistral` in
`test_registry_mistral_wins_over_ollama`, mirroring the sibling
picker test, so the test doesn't depend on the SDK accepting
arbitrary keys.
- openai.py: convert remaining f-string `logger.info(...)` calls to
lazy `%s` formatting, aligning with the pattern in mistral.py and
the repo's logging convention.
- test_mistral.py: add four tests covering the defensive RuntimeError
guards in `embed()` and `_embed_batch_request()` — empty
response.data, single null embedding, batch null embedding, and
count-mismatch.
- docs/configuration.md: add `AWS_ACCESS_KEY_ID` and
`AWS_SECRET_ACCESS_KEY` rows to the env-var reference table; they
were already mentioned in prose but missing from the table.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- _retry.py: replace `assert last_error is not None` with explicit
`if last_error is None: raise RuntimeError(...)` so the original
rate-limit error is preserved under `python -O`.
- openai.py: drop the `_retry_factory` alias chain; rename the bound
decorator to `_retry_429` to match the pattern in mistral.py.
- mistral.py: comment the imports so future reviewers understand why
`from mistralai.client import …` is the canonical path on 2.x (no
top-level `__init__.py`; no `mistralai.models` subpackage either).
- docs/configuration.md: add `OPENAI_GENERATION_MODEL` and
`OLLAMA_GENERATION_MODEL` rows to the env-var reference table.
- test_mistral.py: add direct unit test for the `_is_rate_limit`
predicate (429 → True, 500 → False, missing-attr → False).
- test_registry.py: stub `mistralai.client.Mistral` in the registry
picker test, mirroring the Ollama sibling, so the test doesn't
depend on the SDK accepting arbitrary keys.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Addresses the Claude Code review on PR #772 plus the SonarCloud S1192 finding:
- Extract `retry_on_rate_limit` into `nextcloud_mcp_server/providers/_retry.py`
as a parametric decorator. OpenAI and Mistral now share the same backoff
loop; future providers can reuse it without copy-paste.
- New `tests/unit/providers/test_retry.py` covers the decorator: 429 retry +
success, non-429 immediate re-raise, MAX_RETRIES exhaustion, default
predicate, and unrelated exception passthrough.
- Tighten Mistral SDK import to `from mistralai.client.errors import SDKError`
(the canonical sub-path; the reviewer's `from mistralai.models import
SDKError` does not exist in mistralai 2.4.5).
- Replace `MistralProvider.close()`'s direct `__aexit__` call with a no-op +
comment — the Speakeasy-generated client has no public close hook and the
underlying httpx client is closed by GC.
- Extract the duplicated "Embedding not supported" message to a module-level
constant (SonarCloud S1192).
- Align `Settings.get_embedding_model_name()` Bedrock check with the registry
by also considering `bedrock_generation_model`.
- Add the `mock_mistral_client` fixture to
`test_mistral_no_embeddings_disabled` for parity with the rest of the file.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Fixed 8 type checker errors across the codebase:
- vector/scanner.py: Handle None scroll results with null-safe iteration
- search/{bm25_hybrid,semantic}.py: Add None checks for result.payload
- auth/{unified_verifier,webhook_routes}.py: Assert non-None auth credentials
- client/webdav.py: Add None checks before int() conversions
- providers/openai.py: Assert embedding_model is not None
- search/algorithms.py: Explicitly type doc_types set and cast values
- observability/logging_config.py: Match parent class signature (log_data)
Also fixed test_create_tag_creates_system_tag to match WebDAV implementation
(was testing OCS API endpoint, now tests correct WebDAV endpoint with
Content-Location header).
Type checker: 0 errors (down from 8), 20 warnings (ignored)
Tests: All 192 unit tests passing
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Add exponential backoff retry handling for OpenAI API rate limits
(429 errors). This is needed for GitHub Models API which has stricter
rate limits than standard OpenAI API.
- Add retry_on_rate_limit decorator with exponential backoff
- Max 5 retries with delays: 2s → 4s → 8s → 16s → 32s
- Apply to embed(), _embed_batch_request(), and generate() methods
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <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>
