Round 5 review (PR #910), all nits, no blockers:
- _result_from_success: a succeeded job with `pages=[]` (empty list, not just a
missing key) is now a per-document failure ("no pages returned") instead of a
silent 0-chunk success. Test added.
- Comment the deadline-expiry path: the gateway-side job isn't cancelled (no
cancel endpoint at this layer) — it's reaped by the gateway file purge; we just
stop polling it.
- Drop the vestigial status="pending" from the BatchOcrJob test fakes (the column
was removed in round 4; BatchPollResult.status fakes are untouched).
1653 unit tests pass; ruff + ty green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round 4 review (PR #910), no blockers:
- poll(): a 2xx body with no `status` now fails fast (logged) instead of being
treated as perpetually pending until the deadline; defensive page index
(`p.get("index", i)`) so a malformed page degrades rather than KeyError-ing.
- Document on poll() that job_id is namespaced (embeds "/") so the gateway route
must be a path-capture param (GET /v1/ocr/batch/{job_id:path}).
- Drop the vestigial `status` + `updated_at` columns from batch_ocr_jobs: a row
only ever exists while pending (terminal jobs are deleted) and the live status
comes from a fresh poll, so a stored mirror was permanently "pending" /
redundant with submitted_at. Simplifies the migration, store, and dataclass.
- Tests: submit() ValueError on missing job_id; poll() missing-status → failed.
1653 unit tests pass; ruff + ty green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round 3 review (PR #910):
- Guard an unexpected terminal batch status in _process_batch: anything that
isn't succeeded/failed (gateway version skew, a new lifecycle state) now marks
the document parse-failed instead of falling through to _pages_to_text([]) — a
0-chunk "success" that silently indexed empty text and re-submitted forever.
Test added.
- gateway_batch_client.submit: raise an actionable ValueError on a 2xx response
with no job_id (was a bare KeyError deep in the caller).
- Document that a _process_batch transport error intentionally propagates to
procrastinate for retry rather than falling back to sync (opt-in batch wants
the retry).
- Annotate _batch_client as GatewayBatchOcrClient | None (TYPE_CHECKING import
already present); clarify the delete_stale_for_doc first-submit no-op comment.
- Add a parametrized build_gateway_batch_client test (the gateway-only invariant:
mistral/none/no-URL -> None; gateway|auto + URL -> client).
1653 unit tests pass; ruff + ty green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add DOCUMENT_OCR_MODE=sync|batch (default sync). In batch mode the tier-3 OCR
processor submits documents to the embedding gateway's async Batch OCR routes
(POST /v1/ocr/batch + GET /v1/ocr/batch/{job_id}, astrolabe-cloud-website#372)
for ~50% cheaper large-corpus backfill. The direct Mistral OCR path is left
untouched. Tracked on Deck #332.
Batch jobs run minutes-hours, so the OCR tier cannot block (the procrastinate
worker reclaims jobs in `doing` after INGEST_STALLED_JOB_SECONDS). Instead it
submits, records the gateway job id in a new per-tenant `batch_ocr_jobs` table
(procrastinate args are immutable across retries), and raises a BatchPending
signal that TieredEscalationStrategy turns into a same-queue deferred re-poll —
releasing the worker slot between polls. On completion the per-page markdown is
indexed like the sync path; a failure or a job past
DOCUMENT_OCR_BATCH_MAX_WAIT_SECONDS marks the document parse-failed.
Batch is opt-in and gateway-only: with the direct mistral backend, no gateway
URL, or the inline/memory pipeline (which can't defer), it falls back to sync.
One batch job per document (coalescing N docs/job is a follow-up).
- embedding/gateway_batch_client.py: submit/poll client (reuses GatewayTokenProvider).
- vector/batch_ocr_store.py + migration 008: job tracking (portable SQLite+PG).
- document_processors/escalation.py: BatchPending control-flow signal.
- document_processors/ocr.py: batch state machine + sync fallback.
- vector/processor.py: thread doc identity to the OCR tier; raise BatchPending
from the pending sentinel; propagate it as control flow (not a failure).
- vector/queue/procrastinate.py: BatchPending -> same-queue retry_in, exempt
from the transient cap (bounded by the processor's deadline).
- config + docs; tests across client/store/processor/strategy/parse-tier.
1653 unit tests pass; ruff + ty green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round-3 claude-review finding:
- 🟡 Double _ensure_bearer() on gateway.embed_batch(). Round 1 made
OpenAIProvider.embed_batch() delegate to embed_batch_with_usage(); because
GatewayProvider overrode both embed_batch() and embed_batch_with_usage() (each
calling _ensure_bearer), gateway.embed_batch() refreshed the bearer twice
(the second a cache-hit no-op). Remove the now-redundant embed_batch()
override: OpenAI's embed_batch() routes through embed_batch_with_usage(),
which the gateway still overrides, so the bearer refreshes exactly once on
every path. The remaining two overrides (embed + embed_batch_with_usage) cover
all four entrypoints; documented the topology.
- 🟢 Added test_gateway_embed_batch_ensures_bearer_once locking in the single
refresh.
Cohere token-fallback (🟢 nit) is already covered by
test_bedrock_with_usage_estimates_when_token_count_absent.
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>
EMBEDDING_GATEWAY_URL is configured as a bare origin (scheme://host:port) —
the deployment's Service URL. GatewayProvider now appends the gateway's /v1
base path before handing the URL to the OpenAI SDK, so both embed posts
({base}/embeddings) and dimension discovery ({base}/models) land under /v1.
Idempotent: a URL already ending in /v1 is left unchanged.
This lets EMBEDDING_GATEWAY_URL stay a bare domain (matching the gitops
Service URLs) instead of requiring a hand-appended /v1.
Also align the `embedding_gateway_model` field default with _DEFAULTS
("mistral/mistral-embed"). The gateway catalog is provider-namespaced, and
_detect_dimension matches `entry.id == embedding_model`; the stale
un-namespaced default would silently miss the catalog entry and leave the
dimension unresolved (re-triggering the external-mode startup crash).
Tests: bare / trailing-slash / idempotent normalization + a bare-origin
discovery test asserting /v1/models. 16 gateway-provider tests pass;
providers + vector suites green (129 total); ruff clean.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
External-mode tenant pods CrashLoop at startup: Qdrant collection init calls
get_dimension() before any embed(), but GatewayProvider only learns its
dimension lazily after the first embed, and the gateway model isn't an OpenAI
model so the base class can't know it statically.
- Add GatewayProvider._detect_dimension() — the async startup hook the
vector-sync bootstrap already invokes (vector/qdrant_client.py:
hasattr(provider, "_detect_dimension")) for Ollama. It GETs the gateway's
GET /v1/models and sets _dimension from the entry whose id matches the
configured model. Best-effort: any failure (old gateway, model absent,
network) leaves _dimension unset so the inherited lazy detect-on-first-embed
still applies — never fatal. Presents the M2M bearer when configured.
- Switch the default embedding_gateway_model to the gateway's provider-
namespaced id "mistral/mistral-embed" (the gateway routes on the "/"-prefix
and sends "mistral-embed" upstream); collapse a duplicated config field.
Pairs with astrolabe-cloud-website#229 (gateway /v1/models, namespaced ids).
Tests: discovery sets dim w/o embed, sends bearer, non-fatal on
404/absent/error, skips when already known.
SonarCloud's python:S7632 parses the literal ``# NOSONAR`` token wherever it
appears — including inside explanatory comments that *quote* the directive —
and treats the following text as a malformed suppression. The actual bare
``# NOSONAR`` suppression lines are fine; the flagged lines were the prose
comments describing them. Reword those comments to drop the inner ``#`` so the
analyzer no longer sees a directive.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The new decomposition modules used `# NOSONAR: reason` (colon form), which
SonarCloud flags as a malformed suppression comment (python:S7632) and which
fails to suppress the intended issue. Switch to the repo's bare `# NOSONAR`
convention with the rationale in a comment above, matching config.py and
auth/storage.py. This also lets the suppression silence python:S7503 (async
method without await) on the protocol-required no-op aclose stubs.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- gateway_client: guard token cache with a lazy anyio.Lock so concurrent
embed calls share one M2M token request instead of racing
- status subscriber: distinguish idle fetch timeouts from real broker
errors (log + 5s backoff) instead of swallowing all and spinning
- nats: warn when the bus URL uses unencrypted transport (non-tls://)
- collection_metadata: accept an optional shared httpx client, make TLS
verify explicit, document the unauthenticated control-plane contract
- replace python -O-stripped asserts with explicit ValueError in the bus
status builder and the api metadata source
- document why the nil-UUID sentinel point can't collide with content ids
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
SonarCloud:
- Resolve 6 S5332 hotspots (http→https in test fixture URLs).
- S6418: hoist the unauthenticated AsyncOpenAI placeholder to a named constant
+ NOSONAR (genuine non-secret; gateway ignores it when unauthenticated).
- Fix two reliability bugs: None-index guard in the gateway token-cache test
(S2259) and float `> 0.0` instead of `!= 0.0` in the sentinel test (S1244).
- status.py idle path sleeps 0.1s instead of sleep(0) (S7491); NOSONAR on the
protocol-required async no-await aclose() stubs (S7503).
Claude review:
- Remove three leftover debug print() calls in app.py (logger.info already
covers them).
- payload_backfill: drop parsed_at from the backfilled-keys docstring (it is
per-document state, not a deployment scalar); add a clean 404 precondition
for BasicAuth deployments without an OAuth token verifier.
- status.py: task_status typed TaskStatus | None (drop type: ignore).
- nats.py: TODO to thread etags for file/deck/news; note etag default → None.
- factory: warn on unknown INGEST_BUS_URL scheme; raise ValueError instead of
assert for the external-mode preconditions.
- docs/configuration.md: document the decomposition hook-point env vars + that
nats-py ships core (lazy-imported) and external+bus uses two NATS connections.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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>
The ``BM25SparseEmbeddingProvider.__init__`` calls
``fastembed.SparseTextEmbedding(model_name="Qdrant/bm25")`` which
downloads ~50 MB of model weights from HuggingFace and loads them
into memory — observed >5 s wall-clock in production. The inference
methods (``encode_async``, ``encode_batch_async``) already wrap work
in ``anyio.to_thread.run_sync``, so the design intent is clearly to
keep FastEmbed off the event loop. That protection just didn't
cover the constructor.
Symptom in the Astrolabe Cloud per-tenant deploy (deck #102 smoke):
~30–90 s after a user enables semantic search, the pod tips into a
SIGKILL-restart cycle. Loki shows a single log line
Initializing BM25 sparse embedding provider: Qdrant/bm25
followed by nothing else from the event loop until exitCode 137.
Kubernetes ``/health/live`` httpGet probe timeout=5s fires 6 times
in a row, kubelet kills the container, restart, repeat.
Fix: switch ``get_bm25_service()`` to an async accessor that wraps
the first-time construction in ``anyio.to_thread.run_sync``. Two
existing call sites (``vector/processor.py:603``,
``search/bm25_hybrid.py:123``) update to ``await``. Both are
already inside async functions so the await is free.
New unit test pins the invariant by monkey-patching
``BM25SparseEmbeddingProvider.__init__`` with ``time.sleep(1)`` and
asserting a concurrent ``anyio.sleep(0.05)`` finishes promptly —
the test fails if the constructor ever runs back on the event loop.
Same pattern exists in ``OllamaEmbeddingProvider.__init__`` (sync
``httpx.get`` health-check). Ollama isn't enabled in any current
deploy; filed as a follow-up.
Refs:
- Astrolabe Cloud deck card #102 (smoke discovery)
- Sibling fix#799 (NullPool for cross-loop-asyncpg, same class
of "anyio bites you in production" bug)
Verified:
- ``uv run pytest tests/unit/`` — 1027 passed
- ``uv run ruff check`` clean on touched files
- ``uv run ty check`` clean on touched files
- New tests pass
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Replace sequential Qdrant scroll calls with batch retrieve
(50 HTTP requests → 1 request, ~50x faster vector fetch)
- Add point_id to SearchResult to enable batch retrieval by Qdrant point ID
- Reuse query embedding from search algorithm in viz_routes
(eliminates redundant embedding call, saves ~30ms)
- Make BM25 encode() async with thread pool to avoid blocking event loop
(~4.4s was blocking, now properly async)
- Run PCA computation in thread pool to avoid blocking event loop
(~1.2s was blocking, now properly async)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Previously, pymupdf4llm.to_markdown() was called twice - once in
PyMuPDFProcessor during indexing and again in PDFHighlighter during
visualization. Different image path lengths caused different character
offsets, leading to highlighted pages not matching their chunks.
Also fixed issue where all chunks on the same page showed all highlights
instead of just their own highlight. Now restores original page contents
between chunks using xref stream caching.
Changes:
- Add PDFHighlighter class requiring pre-computed page_boundaries and
full_text from document processor (no fallback extraction)
- Pass pre-computed data from processor to highlighter
- Extract page-relative portion of chunk text for cross-page chunks
- Add bounding box highlighting using text anchor search
- Run highlight generation in parallel with embedding/BM25
- Cache and restore page contents to isolate highlights per chunk
Results: Highlighting success rate improved from 51% to 95% (121/128).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit addresses multiple issues with async operations, PDF metadata
extraction, and type safety in document processing and search.
## Async/Await Fixes
- processor.py:259 - Added await for chunker.chunk_text(content)
- processor.py:270 - Added await for bm25_service.encode_batch(chunk_texts)
- tests/unit/test_document_chunker.py - Converted all 12 test methods to async
## PDF Metadata Enhancement
- pymupdf.py:143 - Added file_size metadata extraction
- pymupdf.py:145-206 - Refactored to extract text page-by-page
- Manually loop through pages instead of using page_chunks=True
- Generate page_boundaries metadata for precise page tracking
- Works around pymupdf.layout.activate() breaking page_chunks=True
- processor.py:32-66 - Added assign_page_numbers() helper function
- Assigns page numbers to chunks based on overlap with page boundaries
- Handles chunks spanning multiple pages
- processor.py:298-300 - Call assign_page_numbers() for PDF files
## Type Safety Fixes
- bm25_hybrid.py:184 - Removed int() conversion of doc_id
- semantic.py:131 - Removed int() conversion of doc_id
- viz_routes.py:275 - Removed int() conversion of doc_id
- Added comments documenting that doc_id can be int (notes) or str (file paths)
## Testing
- All 18 tests passing (12 unit + 6 integration)
- No type errors in modified files
- Container logs show successful processing
- Vector viz searches working correctly
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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>
This fixes dimension mismatch errors when using embedding models with
non-standard dimensions (e.g., qwen3-embedding:4b produces 2560-dim
vectors instead of the hardcoded 768).
Changes:
- OllamaEmbeddingProvider: Detect dimensions dynamically by generating
test embedding instead of hardcoding to 768
- qdrant_client: Call dimension detection before collection creation
- app.py: Initialize Qdrant collection before starting background tasks
in streamable-http transport path
- tests: Fix integration tests to properly mock EmbeddingService wrapper
Fixes dimension mismatch error:
"could not broadcast input array from shape (2560,) into shape (768,)"
All integration tests passing (6/6).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Adds comprehensive integration tests for vector database semantic search that
work without external dependencies (Ollama), making them suitable for CI/CD.
Changes:
- Add SimpleEmbeddingProvider: in-process TF-IDF-like embeddings using feature hashing
- Make Ollama optional: embedding service now falls back to SimpleEmbeddingProvider
- Add 6 integration tests covering semantic search, filtering, and batch operations
- Downgrade urllib3 to 1.26.x for qdrant-client compatibility
- Update docker-compose.yml to comment out Ollama configuration (optional)
The SimpleEmbeddingProvider generates deterministic, normalized embeddings
suitable for testing semantic similarity without requiring external services.
Tests validate that similar texts have higher cosine similarity and that
semantic search correctly ranks results by relevance.
Test coverage:
- Deterministic embedding generation
- Semantic similarity between texts
- Full search flow with Qdrant (in-memory)
- Category filtering
- Empty result handling
- Batch embedding generation
All tests pass and can run in GitHub CI without Ollama infrastructure.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implements background vector database synchronization using anyio
TaskGroups for BasicAuth mode with single-user credentials.
Scanner Implementation:
- Periodic document discovery (hourly, configurable)
- Timestamp-based change detection (Nextcloud vs Qdrant)
- Wake event for immediate scanning on-demand
- Supports both initial sync (all docs) and incremental sync (changes only)
- Detects deleted documents and queues for removal
Processor Implementation:
- Concurrent document processing pool (3 workers default)
- I/O-bound embedding generation via Ollama API
- Retry logic with exponential backoff (3 retries)
- Document chunking (512 words, 50-word overlap)
- Handles both index and delete operations
- Upserts vectors to Qdrant with rich metadata
App Lifespan Integration:
- Extended AppContext with background task state
- Modified app_lifespan_basic() to start tasks via anyio TaskGroups
- Graceful shutdown with coordinated task cancellation
- Only activates when VECTOR_SYNC_ENABLED=true
Embedding Service:
- OllamaEmbeddingProvider with TLS support
- Singleton pattern for shared client instances
- Batch embedding support for efficiency
- Auto-detects embedding dimension (768 for nomic-embed-text)
Qdrant Client:
- Async client wrapper with singleton pattern
- Auto-creates collection on first use
- COSINE distance metric for semantic similarity
- Integrates with embedding service for dimension detection
Health Check Enhancement:
- Added Qdrant status check to /health/ready endpoint
- Only checks when VECTOR_SYNC_ENABLED=true
- 2-second timeout for health probe
- Reports connection errors with details
Configuration:
- VECTOR_SYNC_ENABLED: Enable background sync
- VECTOR_SYNC_SCAN_INTERVAL: Scanner frequency (3600s default)
- VECTOR_SYNC_PROCESSOR_WORKERS: Concurrent processors (3 default)
- QDRANT_URL, QDRANT_API_KEY, QDRANT_COLLECTION: Vector DB config
- OLLAMA_BASE_URL, OLLAMA_EMBEDDING_MODEL: Embedding service config
Dependencies Added:
- qdrant-client>=1.7.0: Vector database client
Docker Compose:
- Added Qdrant service with health check
- Exposed ports 6333 (REST) and 6334 (gRPC)
- Configured MCP service with vector sync environment
- Added qdrant-data volume for persistence
Known Issue:
- FastMCP lifespan not triggering for streamable-http transport
- Background tasks will start once lifespan integration is complete
- Lifespan triggers on MCP session establishment, not server startup
Related: ADR-007 Background Vector Database Synchronization
🤖 Generated with Claude Code (https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
