Adds the seven §10.2 hook-point modules + five env vars so Astrolabe Cloud can
offload document processing to the external document-processor / embedding
gateway. Purely additive: with every setting unset the server behaves exactly
as today, so self-hosters are unaffected (Deck #92).
Hook points (all default to current monolith behavior):
- config: EMBEDDING_PROVIDER, INGEST_MODE, STATUS_BACKEND,
COLLECTION_METADATA_SOURCE, FACT_EVENT_EMITTER (+ supporting settings),
validated in Settings.__post_init__ (fail-fast STATUS_BACKEND=local with
INGEST_MODE=external); shared canonical.py.
- vector/payload_keys.py + acl_hash.py: cross-impl NAMESPACE/point_id (§2.2)
and BLAKE2b-128 ACL hash (§11), pinned by fixtures shared with the
document-processor repo.
- embedding/gateway_client.py: OpenAI-compatible GatewayProvider authenticating
via M2M OIDC client-credentials (separate realm); manual-only registry entry.
- vector/collection_metadata.py: sentinel-point / API metadata source with env
fallback.
- vector/queue/: hexagonal ingest producer ports + memory/NATS adapters
(Postgres seam); INGEST_MODE=external publishes mcp.ingest.requested.{tenant}
instead of the in-memory stream and skips the in-process processor pool. The
lifespan becomes a composition root across both deployment branches.
- vector/queue/status.py: STATUS_BACKEND=bus subscriber feeding a StatusStore
the vector-sync status endpoint reads.
- admin/payload_backfill.py: POST /api/v1/admin/payload-backfill (admin scope);
processor writes the new payload keys; query-side ACL pre-filter gated behind
ACL_PREFILTER_ENABLED (default off).
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>
Adds a hosted Mistral embedding option (mistral-embed, 1024-dim) alongside
the existing Bedrock / OpenAI / Ollama / Simple providers. Implementation
mirrors OpenAIProvider: lazy dimension detection with a known-models lookup,
chunked batch requests, defensive index sort, and a 429-aware retry decorator.
In the same change, ProviderRegistry switches from os.getenv to the
dynaconf-backed Settings dataclass so all five providers share a single
configuration path. config.py gains the previously-uncovered Bedrock keys,
the new Mistral keys, the missing OPENAI_GENERATION_MODEL /
OLLAMA_GENERATION_MODEL, and SIMPLE_EMBEDDING_DIMENSION.
Auto-detection priority: Bedrock → OpenAI → Mistral → Ollama → Simple.
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>
- Increase sampling timeout from 30s to 300s in semantic.py to accommodate
slower local LLMs like Ollama
- Refactor RAG integration tests to support multiple providers (ollama,
openai, anthropic, bedrock)
- Remove unnecessary embedding_provider fixture since MCP server handles
embeddings internally
- Add --provider flag via tests/integration/conftest.py
- Add provider_fixtures.py with factory functions for generation providers
🤖 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>
- Switch from sequential loop to /api/embed batch endpoint
- Use 'input' array parameter instead of individual 'prompt' requests
- Process in chunks of 32 to avoid quality degradation (issue #6262)
- Reduces HTTP overhead: 128 texts = 4 requests instead of 128
- Maintains backward compatibility with embed() for single embeddings
Ref: ollama/ollama#6262
Refactored LLM provider infrastructure to support sustainable additions of new providers with both embedding and text generation capabilities.
## Major Changes
### Unified Provider Architecture (ADR-015)
- Created `nextcloud_mcp_server/providers/` with unified Provider ABC
- Providers now support optional capabilities (embeddings and/or generation)
- Auto-detection registry with priority: Bedrock → Ollama → Simple
- Backward compatible - existing code continues to work
### New Providers
- **BedrockProvider**: Full Amazon Bedrock integration
- Embeddings: Titan Embed, Cohere Embed models
- Generation: Claude, Llama, Titan Text, Mistral models
- Model-specific request/response handling
- AWS credential chain integration
- **OllamaProvider**: Migrated with both capabilities support
- **AnthropicProvider**: Moved from test code to production providers
- **SimpleProvider**: Migrated in-memory fallback provider
### Breaking Changes
None - full backward compatibility maintained:
- `embedding.get_embedding_service()` still works
- RAG evaluation tests updated to use unified providers
- All existing tests pass (127 unit tests)
### Testing
- Added 9 comprehensive Bedrock unit tests with mocked boto3
- All existing unit tests pass
- Type checking (ty) and linting (ruff) pass
- Verified backward compatibility
### Documentation
- `docs/ADR-015-unified-provider-architecture.md`: Comprehensive ADR
- `docs/bedrock-setup.md`: AWS setup guide with IAM permissions
- `CLAUDE.md`: Updated with provider architecture section
### Dependencies
- Added `boto3>=1.35.0` to dev dependencies (optional)
## Environment Variables
### Bedrock
- `AWS_REGION`: AWS region (e.g., "us-east-1")
- `BEDROCK_EMBEDDING_MODEL`: Model ID for embeddings
- `BEDROCK_GENERATION_MODEL`: Model ID for generation
- `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`: Optional credentials
### Ollama
- `OLLAMA_BASE_URL`: API URL
- `OLLAMA_EMBEDDING_MODEL`: Embedding model (default: "nomic-embed-text")
- `OLLAMA_GENERATION_MODEL`: Generation model
## AWS Bedrock Permissions Required
Minimal IAM policy:
```json
{
"Effect": "Allow",
"Action": ["bedrock:InvokeModel"],
"Resource": ["arn:aws:bedrock:*::foundation-model/*"]
}
```
See `docs/bedrock-setup.md` for detailed setup instructions.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
