Merge pull request #893 from cbcoutinho/fix/309-embed-resilience

fix(vector): retry transient embed errors so a pod rollover drops 0 docs

nextcloud_mcp_server/observability/metrics.py

@@ -272,6 +272,18 @@ document_parse_failed_total = Counter(
     ["reason"],  # reason: timeout | oom | error
 )
 
+# Documents dropped after exhausting in-process indexing retries (the scanner
+# re-picks them on a later full scan, so this is "dropped for this cycle", not
+# "lost forever"). Labelled by classified cause so the embed-drop rate from a
+# transient backend-pod rollover (connection/timeout) is alertable distinctly
+# from a persistent fault (card 309).
+vector_ingest_dropped_total = Counter(
+    "astrolabe_vector_ingest_dropped_total",
+    "Documents dropped after exhausting indexing retries, by cause",
+    # reason: connection | timeout | rate_limit | server | qdrant | other
+    ["reason"],
+)
+
 # --- Tier-0 classifier (shadow mode) -----------------------------------------
 #
 # The classifier runs a cheap pre-pass per PDF and recommends a starting tier.
@@ -692,6 +704,16 @@ def record_document_parse_failed(reason: str) -> None:
     document_parse_failed_total.labels(reason=reason).inc()
 
 
+def record_ingest_dropped(reason: str) -> None:
+    """Record a document dropped after exhausting in-process indexing retries.
+
+    Args:
+        reason: ``connection`` | ``timeout`` | ``rate_limit`` | ``server`` |
+            ``qdrant`` | ``other`` (classified from the terminal exception).
+    """
+    vector_ingest_dropped_total.labels(reason=reason).inc()
+
+
 def record_document_classification(
     recommended_tier: str,
     flags: set[str],

nextcloud_mcp_server/providers/_retry.py

@@ -1,8 +1,10 @@
-"""Shared rate-limit retry helper for provider modules.
+"""Shared transient-error retry helper for provider modules.
 
-OpenAI and Mistral both retry on 429 with the same exponential-backoff curve;
+OpenAI and Mistral retry transient failures (429 rate limits, plus connection
-extracting the loop here keeps the two provider modules thin and lets future
+drops / timeouts / 5xx for the embedding path) on the same exponential-backoff
-providers (Bedrock throttling, etc.) reuse the same primitive.
+curve; extracting the loop here keeps the two provider modules thin and lets
+future providers (Bedrock throttling, etc.) reuse the same primitive. The
+``should_retry`` predicate decides which caught exceptions are transient.
 """
 
 from __future__ import annotations
@@ -23,23 +25,26 @@ MAX_RETRY_DELAY = 60.0
 T = TypeVar("T")
 
 
-def retry_on_rate_limit(
+def retry_on_transient(
-    exception_type: type[BaseException],
+    exception_type: type[BaseException] | tuple[type[BaseException], ...],
-    is_rate_limit: Callable[[BaseException], bool] = lambda _exc: True,
+    should_retry: Callable[[BaseException], bool] = lambda _exc: True,
     *,
     provider_name: str = "provider",
+    label: str = "rate limit",
 ) -> Callable[[Callable[..., Awaitable[T]]], Callable[..., Awaitable[T]]]:
-    """Build a decorator that retries on rate-limit exceptions.
+    """Build a decorator that retries transient exceptions with backoff.
 
     Args:
-        exception_type: Catch this exception class (e.g. ``openai.RateLimitError``,
+        exception_type: Catch this exception class (or tuple of classes), e.g.
-            ``mistralai.client.errors.SDKError``).
+            ``openai.APIError`` or ``mistralai.client.errors.SDKError``.
-        is_rate_limit: Predicate that decides whether a caught exception is
+        should_retry: Predicate that decides whether a caught exception is
-            actually a rate-limit (vs. some other error of the same class).
+            transient (and so retryable) vs. a permanent error of the same
-            Defaults to "always True" — appropriate when ``exception_type`` is
+            class. Defaults to "always True" — appropriate when
-            already a rate-limit-specific class.
+            ``exception_type`` is already transient-specific (e.g. a 429 class).
         provider_name: Used in log messages so operators can tell which
             provider exhausted retries.
+        label: Short noun for the log message ("rate limit", "transient error")
+            so the line accurately names what was retried.
     """
 
     def decorator(func: Callable[..., Awaitable[T]]) -> Callable[..., Awaitable[T]]:
@@ -51,23 +56,32 @@ def retry_on_rate_limit(
             for attempt in range(1, MAX_RETRIES + 1):
                 try:
                     return await func(*args, **kwargs)
-                except exception_type as e:
+                # exception_type is constrained by the signature to a
-                    if not is_rate_limit(e):
+                # BaseException subclass or a tuple of them; the dynamic catch is
+                # the whole point of this reusable helper.
+                except exception_type as e:  # NOSONAR(S5708)
+                    if not should_retry(e):
                         raise
                     last_error = e
                     if attempt < MAX_RETRIES:
                         logger.warning(
-                            "%s rate limit hit (attempt %d/%d), retrying in %.1fs...",
+                            "%s %s (attempt %d/%d): %r; retrying in %.1fs...",
                             provider_name,
+                            label,
                             attempt,
                             MAX_RETRIES,
+                            e,
                             retry_delay,
                         )
                         await anyio.sleep(retry_delay)
                         retry_delay = min(retry_delay * 2, MAX_RETRY_DELAY)
 
             logger.error(
-                "%s rate limit exceeded after %d attempts", provider_name, MAX_RETRIES
+                "%s %s not resolved after %d attempts: %r",
+                provider_name,
+                label,
+                MAX_RETRIES,
+                last_error,
             )
             if last_error is None:  # pragma: no cover — loop above always sets this
                 raise RuntimeError("retry loop exited without capturing an error")
@@ -76,3 +90,7 @@ def retry_on_rate_limit(
         return wrapper
 
     return decorator
+
+
+# Back-compat alias: the helper was originally rate-limit-specific.
+retry_on_rate_limit = retry_on_transient

nextcloud_mcp_server/providers/mistral.py

@@ -13,7 +13,7 @@ import logging
 from mistralai.client import Mistral
 from mistralai.client.errors import SDKError
 
-from ._retry import retry_on_rate_limit
+from ._retry import retry_on_transient
 from .base import Provider
 
 logger = logging.getLogger(__name__)
@@ -30,13 +30,25 @@ BATCH_SIZE = 64
 _NO_EMBEDDING_MODEL_MSG = "Embedding not supported - no embedding_model configured"
 
 
-def _is_rate_limit(exc: BaseException) -> bool:
+def _is_transient(exc: BaseException) -> bool:
-    """True only for HTTP 429 SDKErrors."""
+    """Retry HTTP 429 (rate limit) and 5xx (server/transient) SDKErrors.
-    return getattr(exc, "status_code", None) == 429
+
+    Scope is deliberately SDK-level: only ``SDKError`` (an HTTP-status error) is
+    caught by the decorator, so a pure connection drop that the Mistral SDK
+    surfaces as a bare ``httpx``/``ConnectionError`` is NOT retried here. The
+    primary pod-rollover resilience target (card 309) is the gateway path via
+    the OpenAI-compatible client, which does cover connection errors; direct
+    Mistral is a self-hoster fallback where 429/5xx is the common transient.
+    """
+    status = getattr(exc, "status_code", None)
+    return status == 429 or (isinstance(status, int) and status >= 500)
 
 
-_retry_429 = retry_on_rate_limit(
+_retry_transient = retry_on_transient(
-    SDKError, is_rate_limit=_is_rate_limit, provider_name="Mistral"
+    SDKError,
+    should_retry=_is_transient,
+    provider_name="Mistral",
+    label="transient error",
 )
 
 
@@ -90,7 +102,7 @@ class MistralProvider(Provider):
     def supports_generation(self) -> bool:
         return False
 
-    @_retry_429
+    @_retry_transient
     async def embed(self, text: str) -> list[float]:
         """Generate an embedding for a single text."""
         if not self.supports_embeddings:
@@ -169,7 +181,7 @@ class MistralProvider(Provider):
 
         return all_embeddings, total_tokens
 
-    @_retry_429
+    @_retry_transient
     async def _embed_batch_request(
         self, batch: list[str]
     ) -> tuple[list[list[float]], int]:

nextcloud_mcp_server/providers/openai.py

@@ -8,16 +8,37 @@ Supports:
 
 import logging
 
-from openai import AsyncOpenAI, RateLimitError
+from openai import APIConnectionError, APIError, APIStatusError, AsyncOpenAI
 
-from ._retry import retry_on_rate_limit
+from ._retry import retry_on_transient
 from .base import Provider
 
 logger = logging.getLogger(__name__)
 
-# OpenAI's RateLimitError is itself a 429-specific class, so the default
+
-# is_rate_limit predicate ("always True") matches the previous behavior.
+def _is_transient(exc: BaseException) -> bool:
-_retry_429 = retry_on_rate_limit(RateLimitError, provider_name="OpenAI")
+    """Whether an OpenAI APIError is transient and worth retrying.
+
+    Covers the failures seen dropping documents during a backend-pod rollover
+    (card 309): ``APIConnectionError`` / ``APITimeoutError`` (brief gateway
+    unreachability) and 429 / 5xx status errors. Permanent 4xx (auth, bad
+    request) are NOT retried — they would fail identically every attempt.
+    """
+    if isinstance(exc, APIConnectionError):  # incl. APITimeoutError
+        return True
+    if isinstance(exc, APIStatusError):
+        return exc.status_code == 429 or exc.status_code >= 500
+    return False
+
+
+# Catch the APIError base (parent of both APIConnectionError and APIStatusError)
+# and let the predicate decide; non-transient errors re-raise immediately.
+_retry_transient = retry_on_transient(
+    APIError,
+    should_retry=_is_transient,
+    provider_name="OpenAI",
+    label="transient error",
+)
 
 
 # Well-known embedding dimensions for OpenAI models
@@ -95,7 +116,7 @@ class OpenAIProvider(Provider):
         """Whether this provider supports text generation."""
         return self.generation_model is not None
 
-    @_retry_429
+    @_retry_transient
     async def embed(self, text: str) -> list[float]:
         """
         Generate embedding vector for text.
@@ -208,7 +229,7 @@ class OpenAIProvider(Provider):
 
         return all_embeddings, total_tokens
 
-    @_retry_429
+    @_retry_transient
     async def _embed_batch_request(
         self, batch: list[str]
     ) -> tuple[list[list[float]], int]:
@@ -262,7 +283,11 @@ class OpenAIProvider(Provider):
             )
         return self._dimension
 
-    @_retry_429
+    # Transient retry intentionally covers generation too (RAG sampling path):
+    # a pod rollover breaks generation as readily as embedding. Worst case adds
+    # ~30s (5 attempts, 2s→60s backoff) to an interactive call hitting a
+    # sustained connection issue, which is preferable to a hard failure.
+    @_retry_transient
     async def generate(self, prompt: str, max_tokens: int = 500) -> str:
         """
         Generate text from a prompt.

nextcloud_mcp_server/vector/processor.py

@@ -9,6 +9,7 @@ import uuid
 from typing import Any, cast
 
 import anyio
+import httpx
 from anyio.abc import TaskStatus
 from anyio.streams.memory import MemoryObjectReceiveStream
 from qdrant_client.models import PointStruct
@@ -23,6 +24,7 @@ from nextcloud_mcp_server.observability.metrics import (
     record_document_parse_failed,
     record_embedding,
     record_embedding_tokens,
+    record_ingest_dropped,
     record_qdrant_operation,
     record_vector_sync_processing,
     update_vector_sync_queue_size,
@@ -57,6 +59,53 @@ logger = logging.getLogger(__name__)
 _ATTR_CHUNK_COUNT = "vector_sync.chunk_count"
 
 
+def _drop_reason(exc: BaseException) -> str:
+    """Classify a terminal indexing failure into a metric label.
+
+    Distinguishes the transient backend-pod-rollover causes (connection /
+    timeout — the ones provider-level retry should now ride through, card 309)
+    from persistent faults, so ``astrolabe_vector_ingest_dropped_total`` is
+    alertable per cause. Descends through nested ExceptionGroups to the first
+    leaf so a doubly-wrapped cause isn't mislabelled ``other``. Best-effort:
+    unknown causes fall back to ``other``.
+    """
+    # An anyio task group can wrap the real cause (and nest groups when sub-tasks
+    # use their own groups); descend to the first concrete leaf. Best-effort: a
+    # group bundling several distinct failures is labelled by whichever leaf
+    # sorts first, not by a "mixed" bucket.
+    while isinstance(exc, BaseExceptionGroup) and exc.exceptions:
+        exc = exc.exceptions[0]
+
+    # Raw httpx transport errors from direct Nextcloud API calls (the nc_client
+    # uses httpx directly); the openai checks below catch the SDK-wrapped
+    # variants of the same failure modes.
+    if isinstance(exc, httpx.TimeoutException):
+        return "timeout"
+    if isinstance(exc, httpx.ConnectError):
+        return "connection"
+
+    # openai.* is always installed (provider dep) but import lazily to keep this
+    # helper cheap and decoupled from a specific SDK version's surface.
+    try:
+        import openai  # noqa: PLC0415
+
+        if isinstance(exc, openai.APITimeoutError):
+            return "timeout"
+        if isinstance(exc, openai.APIConnectionError):
+            return "connection"
+        if isinstance(exc, openai.RateLimitError):
+            return "rate_limit"
+        if isinstance(exc, openai.APIStatusError):
+            return "server" if exc.status_code >= 500 else "other"
+    except ImportError:  # pragma: no cover — openai is a hard dependency
+        pass
+
+    # Qdrant client errors surface from its own module namespace.
+    if type(exc).__module__.startswith("qdrant_client"):
+        return "qdrant"
+    return "other"
+
+
 def assign_page_numbers(chunks, page_boundaries):
     """Assign page numbers to chunks based on page boundaries.
 
@@ -358,6 +407,13 @@ async def process_document(
             (3) suits the in-process SQLite pool, which has no durable retry. The
             procrastinate worker passes ``1`` so durable retry is owned by the
             queue (and survives worker crashes), avoiding compounding 3×N retries.
+
+    Retry layering: the embedding provider adds its own transient retry (5
+    attempts, 2s→60s backoff — card 309) *inside* each of these attempts. On the
+    in-process path (max_retries=3) a sustained outage therefore costs up to
+    5×3=15 provider calls (~90s wall-clock) before the document is dropped and
+    re-picked on the next scan; the procrastinate path (max_retries=1) caps it at
+    one outer attempt (~30s) and defers. Don't stack a third retry layer here.
     """
     start_time = time.time()
 
@@ -469,11 +525,13 @@ async def process_document(
                         await anyio.sleep(retry_delay)
                         retry_delay *= 2  # Exponential backoff
                     else:
+                        reason = _drop_reason(e)
                         logger.error(
-                            "Failed to index %s_%s after %s retries: %s",
+                            "Failed to index %s_%s after %s retries (%s): %s",
                             doc_task.doc_type,
                             doc_task.doc_id,
                             max_retries,
+                            reason,
                             format_exception_group(e),
                             extra={
                                 "doc_id": doc_task.doc_id,
@@ -481,12 +539,21 @@ async def process_document(
                                 "attempt": max_retries,
                                 "max_retries": max_retries,
                                 "status": "error",
+                                "drop_reason": reason,
                             },
                         )
-                        # Record the failed Qdrant upsert. The processing-error
+                        # Count a failed Qdrant upsert ONLY when Qdrant was the
-                        # metric is recorded once by the outer handler below, so
+                        # failing component; an embed/connection failure exhausts
-                        # exhausted-retry failures aren't double-counted.
+                        # retries before Qdrant is ever called, so attributing it
+                        # to mcp_qdrant_operations_total{error} would inflate that
+                        # signal. The cause is captured by record_ingest_dropped
+                        # instead, and the processing-error metric is recorded
+                        # once by the outer handler below (no double-count). The
+                        # document is NOT marked failed, so the next scan re-picks
+                        # it (re-queue via the scan loop, card 309).
+                        if reason == "qdrant":
                             record_qdrant_operation("upsert", "error")
+                        record_ingest_dropped(reason)
                         raise
 
         except Exception:

tests/unit/providers/test_mistral.py

@@ -9,7 +9,7 @@ from nextcloud_mcp_server.providers.mistral import (
     BATCH_SIZE,
     MISTRAL_EMBEDDING_DIMENSIONS,
     MistralProvider,
-    _is_rate_limit,
+    _is_transient,
 )
 
 
@@ -318,14 +318,58 @@ async def test_mistral_embed_with_usage_single(mock_mistral_client):
 
 
 @pytest.mark.unit
-def test_mistral_is_rate_limit_predicate():
+def test_mistral_is_transient_predicate():
-    """_is_rate_limit returns True only for SDKErrors with status_code == 429."""
+    """_is_transient retries 429 (rate limit) and 5xx (server/transient) SDKErrors."""
     err_429 = MagicMock(spec=SDKError)
     err_429.status_code = 429
     err_500 = MagicMock(spec=SDKError)
     err_500.status_code = 500
+    err_400 = MagicMock(spec=SDKError)
+    err_400.status_code = 400
 
-    assert _is_rate_limit(err_429) is True
+    assert _is_transient(err_429) is True
-    assert _is_rate_limit(err_500) is False
+    assert _is_transient(err_500) is True  # broadened to 5xx (card 309)
+    assert _is_transient(err_400) is False  # permanent client error
     # ValueError has no status_code attr → getattr returns None → False.
-    assert _is_rate_limit(ValueError()) is False
+    assert _is_transient(ValueError()) is False
+
+
+@pytest.mark.unit
+async def test_mistral_embed_retries_on_5xx(mock_mistral_client, monkeypatch):
+    """A 5xx SDKError is retried end-to-end (not just classified by the predicate)."""
+    from nextcloud_mcp_server.providers import _retry
+
+    monkeypatch.setattr(_retry.anyio, "sleep", AsyncMock(return_value=None))
+
+    # Real SDKError instance (so `except SDKError` catches it) with a 5xx status.
+    err = SDKError.__new__(SDKError)
+    err.status_code = 500
+
+    mock_mistral_client.embeddings.create_async = AsyncMock(
+        side_effect=[err, _make_response([[0.1, 0.2, 0.3]])]
+    )
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+
+    embedding = await provider.embed("hello")
+    assert embedding == [0.1, 0.2, 0.3]
+    assert mock_mistral_client.embeddings.create_async.await_count == 2
+
+
+@pytest.mark.unit
+async def test_mistral_embed_batch_retries_on_5xx(mock_mistral_client, monkeypatch):
+    """The batch path (_embed_batch_request) shares the transient retry too."""
+    from nextcloud_mcp_server.providers import _retry
+
+    monkeypatch.setattr(_retry.anyio, "sleep", AsyncMock(return_value=None))
+
+    err = SDKError.__new__(SDKError)
+    err.status_code = 503
+
+    mock_mistral_client.embeddings.create_async = AsyncMock(
+        side_effect=[err, _make_response([[0.1, 0.2], [0.3, 0.4]])]
+    )
+    provider = MistralProvider(api_key="test-key", embedding_model="mistral-embed")
+
+    embeddings = await provider.embed_batch(["a", "b"])
+    assert embeddings == [[0.1, 0.2], [0.3, 0.4]]
+    assert mock_mistral_client.embeddings.create_async.await_count == 2

tests/unit/providers/test_openai.py

@@ -330,3 +330,152 @@ async def test_openai_close(mock_openai_client):
 
     await provider.close()
     mock_openai_client.close.assert_called_once()
+
+
+# --- transient-error retry (card 309) ----------------------------------------
+
+
+def _req():
+    import httpx
+
+    return httpx.Request("POST", "https://gw/v1/embeddings")
+
+
+@pytest.mark.unit
+def test_is_transient_classifies_retryable_errors():
+    """Connection / timeout / 429 / 5xx are transient; 4xx and others are not."""
+    import httpx
+    from openai import (
+        APIConnectionError,
+        APITimeoutError,
+        BadRequestError,
+        InternalServerError,
+        RateLimitError,
+    )
+
+    from nextcloud_mcp_server.providers.openai import _is_transient
+
+    req = _req()
+    assert _is_transient(APIConnectionError(request=req)) is True
+    assert _is_transient(APITimeoutError(request=req)) is True
+    assert (
+        _is_transient(
+            RateLimitError("rl", response=httpx.Response(429, request=req), body=None)
+        )
+        is True
+    )
+    assert (
+        _is_transient(
+            InternalServerError(
+                "boom", response=httpx.Response(500, request=req), body=None
+            )
+        )
+        is True
+    )
+    # Permanent client errors must NOT be retried.
+    assert (
+        _is_transient(
+            BadRequestError("bad", response=httpx.Response(400, request=req), body=None)
+        )
+        is False
+    )
+    assert _is_transient(ValueError("unrelated")) is False
+
+
+@pytest.mark.unit
+async def test_embed_retries_on_connection_error(mock_openai_client, monkeypatch):
+    """A transient APIConnectionError (pod rollover) is retried, not dropped."""
+    from openai import APIConnectionError
+
+    from nextcloud_mcp_server.providers import _retry
+
+    monkeypatch.setattr(_retry.anyio, "sleep", AsyncMock(return_value=None))
+
+    mock_embedding_data = MagicMock()
+    mock_embedding_data.embedding = [0.1, 0.2, 0.3]
+    mock_response = MagicMock()
+    mock_response.data = [mock_embedding_data]
+
+    # First call raises a transient connection error, second succeeds.
+    create = AsyncMock(side_effect=[APIConnectionError(request=_req()), mock_response])
+    mock_openai_client.embeddings.create = create
+    provider = OpenAIProvider(
+        api_key="test-key", embedding_model="text-embedding-3-small"
+    )
+
+    result = await provider.embed("hello")
+    assert result == [0.1, 0.2, 0.3]
+    assert create.await_count == 2  # one failure, one success
+
+
+@pytest.mark.unit
+async def test_embed_batch_retries_on_connection_error(mock_openai_client, monkeypatch):
+    """The batch path (`_embed_batch_request`) shares the transient retry too."""
+    from openai import APIConnectionError
+
+    from nextcloud_mcp_server.providers import _retry
+
+    monkeypatch.setattr(_retry.anyio, "sleep", AsyncMock(return_value=None))
+
+    data = MagicMock()
+    data.embedding = [0.4, 0.5, 0.6]
+    data.index = 0
+    mock_response = MagicMock()
+    mock_response.data = [data]
+    mock_response.usage.total_tokens = 7
+
+    create = AsyncMock(side_effect=[APIConnectionError(request=_req()), mock_response])
+    mock_openai_client.embeddings.create = create
+    provider = OpenAIProvider(
+        api_key="test-key", embedding_model="text-embedding-3-small"
+    )
+
+    embeddings, tokens = await provider.embed_batch_with_usage(["text"])
+    assert embeddings == [[0.4, 0.5, 0.6]]
+    assert tokens == 7
+    assert create.await_count == 2
+
+
+@pytest.mark.unit
+async def test_generate_retries_on_connection_error(mock_openai_client, monkeypatch):
+    """generate() shares the transient retry (RAG sampling survives a rollover)."""
+    from openai import APIConnectionError
+
+    from nextcloud_mcp_server.providers import _retry
+
+    monkeypatch.setattr(_retry.anyio, "sleep", AsyncMock(return_value=None))
+
+    choice = MagicMock()
+    choice.message.content = "Generated response"
+    mock_response = MagicMock()
+    mock_response.choices = [choice]
+
+    create = AsyncMock(side_effect=[APIConnectionError(request=_req()), mock_response])
+    mock_openai_client.chat.completions.create = create
+    provider = OpenAIProvider(api_key="test-key", generation_model="gpt-4o-mini")
+
+    text = await provider.generate("prompt")
+    assert text == "Generated response"
+    assert create.await_count == 2
+
+
+@pytest.mark.unit
+async def test_generate_does_not_retry_on_bad_request(mock_openai_client, monkeypatch):
+    """generate() fast-fails (no retry) on a permanent 4xx."""
+    import httpx
+    from openai import BadRequestError
+
+    from nextcloud_mcp_server.providers import _retry
+
+    monkeypatch.setattr(_retry.anyio, "sleep", AsyncMock(return_value=None))
+
+    err = BadRequestError(
+        "bad", response=httpx.Response(400, request=_req()), body=None
+    )
+    create = AsyncMock(side_effect=err)
+    mock_openai_client.chat.completions.create = create
+    provider = OpenAIProvider(api_key="test-key", generation_model="gpt-4o-mini")
+
+    with pytest.raises(BadRequestError):
+        await provider.generate("prompt")
+    assert create.await_count == 1  # no retry on a permanent 4xx

tests/unit/providers/test_retry.py

@@ -1,4 +1,4 @@
-"""Unit tests for the shared rate-limit retry decorator."""
+"""Unit tests for the shared transient-error retry decorator."""
 
 from unittest.mock import AsyncMock
 
@@ -26,9 +26,7 @@ async def test_retry_succeeds_after_429():
     """A 429 followed by success returns the success value."""
     calls = {"n": 0}
 
-    @_retry.retry_on_rate_limit(
+    @_retry.retry_on_transient(_FakeError, should_retry=lambda e: e.status_code == 429)
-        _FakeError, is_rate_limit=lambda e: e.status_code == 429
-    )
     async def flaky():
         calls["n"] += 1
         if calls["n"] < 3:
@@ -41,13 +39,11 @@ async def test_retry_succeeds_after_429():
 
 
 @pytest.mark.unit
-async def test_retry_reraises_non_rate_limit_immediately():
+async def test_retry_reraises_when_predicate_returns_false():
-    """A non-rate-limit error of the same class is re-raised on first hit."""
+    """An error the predicate rejects is re-raised on first hit (no retry)."""
     calls = {"n": 0}
 
-    @_retry.retry_on_rate_limit(
+    @_retry.retry_on_transient(_FakeError, should_retry=lambda e: e.status_code == 429)
-        _FakeError, is_rate_limit=lambda e: e.status_code == 429
-    )
     async def boom():
         calls["n"] += 1
         raise _FakeError(500)
@@ -62,9 +58,7 @@ async def test_retry_gives_up_after_max_retries():
     """After MAX_RETRIES failed attempts the last error is re-raised."""
     calls = {"n": 0}
 
-    @_retry.retry_on_rate_limit(
+    @_retry.retry_on_transient(_FakeError, should_retry=lambda e: e.status_code == 429)
-        _FakeError, is_rate_limit=lambda e: e.status_code == 429
-    )
     async def always_429():
         calls["n"] += 1
         raise _FakeError(429)
@@ -79,7 +73,7 @@ async def test_retry_default_predicate_treats_all_as_rate_limit():
     """Default predicate (`lambda _: True`) retries every caught exception."""
     calls = {"n": 0}
 
-    @_retry.retry_on_rate_limit(_FakeError)
+    @_retry.retry_on_transient(_FakeError)
     async def fail_once():
         calls["n"] += 1
         if calls["n"] < 2:
@@ -95,9 +89,37 @@ async def test_retry_default_predicate_treats_all_as_rate_limit():
 async def test_retry_does_not_catch_unrelated_exceptions():
     """Exceptions of a different class bypass the decorator entirely."""
 
-    @_retry.retry_on_rate_limit(_FakeError)
+    @_retry.retry_on_transient(_FakeError)
     async def value_error():
         raise ValueError("nope")
 
     with pytest.raises(ValueError, match="nope"):
         await value_error()
+
+
+class _ConnError(Exception):
+    pass
+
+
+@pytest.mark.unit
+async def test_retry_accepts_tuple_of_exception_types():
+    """A tuple of exception classes is caught (the OpenAI transient set shape)."""
+    calls = {"n": 0}
+
+    @_retry.retry_on_transient((_FakeError, _ConnError))
+    async def flaky():
+        calls["n"] += 1
+        if calls["n"] == 1:
+            raise _ConnError("dropped")
+        if calls["n"] == 2:
+            raise _FakeError(503)
+        return "ok"
+
+    assert await flaky() == "ok"
+    assert calls["n"] == 3
+
+
+@pytest.mark.unit
+async def test_retry_on_rate_limit_is_backcompat_alias():
+    """The old name still resolves to the generalized helper."""
+    assert _retry.retry_on_rate_limit is _retry.retry_on_transient

tests/unit/test_processor_drop_reason.py

@@ -0,0 +1,111 @@
+"""Unit tests for the embed-drop classifier (card 309).
+
+``processor._drop_reason`` maps a terminal indexing failure to a metric label
+so the transient backend-pod-rollover causes (connection / timeout) are
+alertable on ``astrolabe_vector_ingest_dropped_total`` distinctly from
+persistent faults.
+"""
+
+import httpx
+import pytest
+
+from nextcloud_mcp_server.vector import processor
+
+
+def _req() -> httpx.Request:
+    return httpx.Request("POST", "https://gw/v1/embeddings")
+
+
+@pytest.mark.unit
+def test_httpx_connect_and_timeout_classified():
+    assert processor._drop_reason(httpx.ConnectError("refused")) == "connection"
+    assert processor._drop_reason(httpx.ReadTimeout("slow")) == "timeout"
+    assert processor._drop_reason(httpx.ConnectTimeout("slow")) == "timeout"
+
+
+@pytest.mark.unit
+def test_openai_errors_classified():
+    from openai import (
+        APIConnectionError,
+        APITimeoutError,
+        InternalServerError,
+        RateLimitError,
+    )
+
+    req = _req()
+    assert processor._drop_reason(APIConnectionError(request=req)) == "connection"
+    assert processor._drop_reason(APITimeoutError(request=req)) == "timeout"
+    assert (
+        processor._drop_reason(
+            RateLimitError("rl", response=httpx.Response(429, request=req), body=None)
+        )
+        == "rate_limit"
+    )
+    assert (
+        processor._drop_reason(
+            InternalServerError(
+                "boom", response=httpx.Response(503, request=req), body=None
+            )
+        )
+        == "server"
+    )
+
+
+@pytest.mark.unit
+def test_exception_group_unwraps_to_leaf():
+    group = BaseExceptionGroup(
+        "unhandled errors in a TaskGroup", [httpx.ConnectError("refused")]
+    )
+    assert processor._drop_reason(group) == "connection"
+
+
+@pytest.mark.unit
+def test_nested_exception_group_descends_to_leaf():
+    """A doubly-wrapped group must still classify by its leaf, not 'other'."""
+    nested = BaseExceptionGroup(
+        "outer", [BaseExceptionGroup("inner", [httpx.ReadTimeout("slow")])]
+    )
+    assert processor._drop_reason(nested) == "timeout"
+
+
+@pytest.mark.unit
+def test_qdrant_namespace_classified():
+    from qdrant_client.http.exceptions import UnexpectedResponse
+
+    exc = UnexpectedResponse(500, "err", b"", headers=None)
+    assert processor._drop_reason(exc) == "qdrant"
+
+
+@pytest.mark.unit
+def test_unknown_error_falls_back_to_other():
+    assert processor._drop_reason(ValueError("nope")) == "other"
+
+
+@pytest.mark.unit
+async def test_process_document_records_drop_on_exhausted_retries(mocker):
+    """Exhausting retries in process_document increments the drop counter with
+    the classified reason (and re-raises so the outer handler counts the error)."""
+    from nextcloud_mcp_server.vector.scanner import DocumentTask
+
+    doc_task = DocumentTask(
+        user_id="alice",
+        doc_id="42",
+        doc_type="note",
+        operation="index",
+        modified_at=0,
+    )
+
+    mocker.patch.object(
+        processor,
+        "get_qdrant_client",
+        mocker.AsyncMock(return_value=mocker.MagicMock()),
+    )
+    mocker.patch.object(
+        processor, "_index_document", side_effect=httpx.ConnectError("refused")
+    )
+    rec = mocker.patch.object(processor, "record_ingest_dropped")
+
+    with pytest.raises(httpx.ConnectError):
+        await processor.process_document(doc_task, mocker.MagicMock(), max_retries=1)
+
+    rec.assert_called_once_with("connection")