64f08429778ddc426d715cf75e91f07407ec1332
19
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Chris CoutinhoandClaude Opus 4.7
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64f0842977 |
fix(vector): guard _group_int_doc_ids against non-int doc_id values
Skip and warn instead of stringifying floats / unexpected types in the backfill helper. A stray doc_id=3.0 would otherwise be rewritten to "3.0", which producers (str(int)) and the keyword index would never match, and which int() on the verification side would reject. Also add a doc_id=0 case to the backfill test to guard against a future falsy-skip regression. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Chris CoutinhoandClaude Opus 4.7
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d390b3a4b8 |
fix(vector): address PR review round 8 — anyio convention + cosine-safe sentinel + dedup get_collection
Reviewer findings (1 blocking + 2 important): - 🔴 Replace `import asyncio` / `await asyncio.sleep(0)` with `import anyio` / `await anyio.sleep(0)` in the four async side-effect helpers (_scroll_raises, _upsert_raises, _get_collection_raises, _create_index). CLAUDE.md mandates anyio for all async operations; conftest pins the backend to asyncio so the asyncio.sleep call worked today, but the inconsistency would surface the moment that pin moves. - 🟡 Replace the sentinel's zero dense vector with a single non-zero element (`[1e-9] + [0.0] * (dimension - 1)`). Cosine distance is mathematically undefined for the zero vector and Qdrant Cloud strict mode rejects zero-vector upserts. The exact value doesn't matter (sentinel never participates in a search — no user_id/doc_id/doc_type payload) but the upsert itself must be valid. - 🟡 Avoid the duplicate `get_collection` round-trip on every restart. `_ensure_payload_indexes` now accepts an optional `existing_schema: dict | None` parameter; when None it fetches collection_info itself (and the get_collection-failure swallow still applies), but `get_qdrant_client` already fetches collection_info for dimension validation in the existing-collection branch — pass `collection_info.payload_schema or {}` through to skip the second call. The new-collection branch passes `existing_schema={}` explicitly since a freshly created collection has no payload schema. The 🟡 deck_card iteration-fallback finding doesn't apply: the `isdigit()` guard at context.py:612 returns early before either the fast-path or the iteration fallback runs, so non-numeric doc_ids cannot reach the inner `c.id == int(doc_id)` comparison. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Chris CoutinhoandClaude Opus 4.7
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d00779ce79 |
fix(vector): add BOOL index for is_placeholder + correct wait=True docstring
Reviewer feedback (2 items):
- Add a BOOL payload index for `is_placeholder` alongside the three
KEYWORD fields. Strict-mode index-required filtering on Qdrant Cloud
enforces a payload index on any field used in a `FieldCondition`
regardless of value type, so `get_placeholder_filter` and
`delete_placeholder_point` would have produced HTTP 400 on Cloud
instances even after this PR's KEYWORD fix.
Implementation: replace `_KEYWORD_PAYLOAD_FIELDS: tuple` with
`_PAYLOAD_INDEX_FIELDS: dict[str, PayloadSchemaType]` so each
field carries its own schema type. Rename
`_ensure_keyword_payload_indexes` to `_ensure_payload_indexes` since
the function now creates more than just KEYWORD indexes. The
per-field log line now includes the schema type
("Created KEYWORD payload index on 'doc_id'", "Created BOOL payload
index on 'is_placeholder'") so operators can tell which type was
created without checking the source.
- Correct the misleading `wait=True` docstring in
`_apply_backfill_writes`. The previous wording said
`_ensure_payload_indexes` runs "immediately after this function",
but `_apply_backfill_writes` is called in a loop inside
`_backfill_doc_id_to_string` — the index creation runs after the
backfill function *returns*, not after each write. Rewrote the
docstring to capture both load-bearing reasons:
(1) per-batch commit ordering for crash-recovery safety, and
(2) ensuring the keyword index built later covers committed
payloads only.
Adds `test_ensure_payload_indexes_includes_is_placeholder_as_bool`
asserting the schema type is BOOL specifically. Existing tests
updated to use the new dict-based registry (side_effect lists now
extend to all four entries; field-set assertions derive from the
registry instead of hardcoding 3 KEYWORD names).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Chris CoutinhoandClaude Opus 4.7
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60a9882c92 |
fix(vector): address PR review round 6 + SonarCloud findings
Reviewer feedback (3 important + 3 nits):
- Wrap _ensure_keyword_payload_indexes' get_collection() call in
try/except. The qdrant_client singleton is already assigned by the
time this function runs, so a transient timeout/DNS failure
propagating out left the process holding a usable client with the
migration silently skipped on every subsequent call. Now logs ERROR
with exc_info and returns; next process restart retries.
- Add `and "doc_id" in point.payload` guard to the four set
comprehensions in scanner.py (indexed_doc_ids, indexed_file_ids,
indexed_item_ids, indexed_card_ids). Previously a payload missing
the doc_id key would raise KeyError and crash the entire scan.
- Tighten test_ensure_keyword_payload_indexes_logs_400_as_warning to
match the per-field warning prefix exactly (`startswith("Schema
conflict on payload index")`), so a future change adding 400s to
the partial-failure summary surfaces here as a count mismatch.
- Add new-collection vs existing-collection context to the
_backfill_doc_id_to_string docstring's `dimension` parameter.
- Replace the misleading "rewrote 0/N from int to str" wording when
no rewriting was needed with "N points scanned, none required
rewriting (collection already in str form)".
- Add test_ensure_keyword_payload_indexes_logs_and_returns_when_
get_collection_raises mirroring the scroll-failure test.
SonarCloud (1 CRITICAL + 1 MINOR):
- Refactor _backfill_doc_id_to_string to bring cognitive complexity
under 15 (was 19). Extracted two pure helpers: _group_int_doc_ids
(group point IDs by stringified doc_id) and _apply_backfill_writes
(apply set_payload calls and return rewritten count). The main
function's scroll/loop/sentinel structure is unchanged.
- Add `await asyncio.sleep(0)` to the three async test side_effect
helpers (_scroll_raises, _upsert_raises, _create_index) so they use
an actual async feature (S7503). The async-callable shape is still
required to avoid the AsyncMock unawaited-coroutine warning when
side_effect raises.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Chris CoutinhoandClaude Opus 4.7
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c27556c332 |
fix(vector): address PR review round 5 — progress logging, summary visibility, sentinel split
Addresses three important findings from the latest reviewer comment: - Add progress INFO log every 20 scroll batches (≈5120 points at batch_size=256) in _backfill_doc_id_to_string so a long-running migration on a large collection (50k+ points) doesn't look like a startup hang. The line carries collection name, scanned count, and rewritten count so it doubles as a heartbeat. - Track non-400 failures in _ensure_keyword_payload_indexes and emit a WARNING summary line listing every field that failed to get an index. Per-field ERROR lines are easy to miss in startup noise; the summary makes the partial-failure state visible at a glance. - Split the sentinel upsert out of the data-scroll try/except in _backfill_doc_id_to_string. A scroll-time failure still logs ERROR with the new "scroll failed" wording (data is incomplete). A sentinel-write failure now logs WARNING with "data succeeded but sentinel write failed" wording — data is correct, only the short-circuit marker is missing, and the next restart re-scrolls an already-clean collection (idempotent zero-write) before retrying the upsert. Also fix the RuntimeWarning emitted by test_backfill_logs_and_returns_when_scroll_raises: replace the bare `RuntimeError` side_effect with an async-callable side_effect so AsyncMock awaits the coroutine before the exception propagates. Three new unit tests cover the new branches: test_backfill_emits_progress_log_every_20_batches, test_backfill_logs_warning_when_sentinel_upsert_fails, test_ensure_keyword_payload_indexes_summarises_failed_fields. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Chris CoutinhoandClaude Opus 4.7
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b97ac23228 |
fix(vector): address PR review round 4 — backfill resilience + degraded-mode docs
- Remove three stale `# Use numeric file ID` / `# Pass file path` comments in scanner.py. file_id is already normalized to str() above each call site, so the inline comments mislead readers. - Wrap `_backfill_doc_id_to_string` scroll loop + sentinel upsert in try/except Exception. The qdrant_client singleton is assigned before this migration runs, so a transient scroll failure was leaving the process holding a usable client with int payloads permanently unbackfilled until the next restart. Catch broadly, log ERROR with exc_info, and return without writing the sentinel — next process restart retries from scratch. - Note `:memory:` mode behavior near the sentinel constants so future readers don't read the every-start scroll as a bug. - Document the two degraded-migration ERROR log signals in docs/configuration.md so operators know when a clean restart is required to recover indexing. - Add unit test asserting scroll-time exceptions are logged and swallowed without writing the sentinel. Closes round-4 review feedback on PR #773. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Chris CoutinhoandClaude Opus 4.7
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92b2d50cd7 |
fix(vector): address PR review round 3 — sentinel guard, skip indexed fields, narrow types
- Add a fixed-UUID sentinel point written after a successful doc_id backfill so subsequent restarts retrieve it and short-circuit the O(N) scroll. Sentinel has no user_id/doc_id/doc_type payload so production search filters never see it. - Pre-fetch payload_schema in _ensure_keyword_payload_indexes and silently skip fields that are already indexed; the "Created KEYWORD payload index" INFO log fires only on actual creation. - Narrow stale `int | str` doc_id annotations to `str` across search/verification.py (BatchVerifier return type, per-verifier accessible sets, by_type / accessible_by_type / inaccessible collections); drop the now-redundant `type(d).__name__` prefix in the dropped-docs log. - Align the backfill log message with the PR description's "Running doc_id backfill" promise; add a caller cross-reference to the wait=True comment. - Fix _get_file_path_from_qdrant docstring (file_id is str, not numeric). - Convert legacy `id=1` to `id="1"` in test_search_result.py to match the SearchResult.id: str annotation. Three new unit tests cover sentinel-found, sentinel-written, and skip-existing-index branches; existing backfill tests pass dimension and explicit retrieve.return_value=[] for the no-sentinel path. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Chris CoutinhoandClaude Opus 4.7
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b5b4025bb4 |
fix(vector): address PR review round 2 — status branching, doc_id guard, doc restore
- _ensure_keyword_payload_indexes: distinguish 400 (schema conflict, warning)
from other status codes (5xx/network, error) so a transient outage doesn't
silently leave the collection unindexed.
- build_search_result_from_point: use .get("doc_id") + return None on missing
instead of KeyError-crashing the search; reverse metadata merge order so
payload-derived chunk_index/total_chunks win over caller-supplied extras.
- docs/configuration.md: restore the OpenAI/Mistral/Bedrock/Simple provider
sections + reference-table rows that were dropped in the rebase. Reword
the "Startup migrations" bullet to describe what the code actually does
(no sampling — full scroll, zero writes when clean). Add operator note
about the SemanticSearchResult.id TypeError path.
- tests: pytest.approx for float equality (Sonar python:S1244); coverage
for non-400 → ERROR, payload={doc_id: None}, and missing doc_id key.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Chris CoutinhoandClaude Opus 4.7
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6aba589a6e |
fix(vector): address PR review — wait=True backfill, batched writes, search helper
Addresses reviewer feedback on PR #773: - Backfill set_payload now uses wait=True to avoid a race where _ensure_keyword_payload_indexes builds the KEYWORD index before fire-and-forget writes have committed, leaving int payloads invisible to filters. - Batch points sharing the same int doc_id into a single set_payload call (one document → many chunks → one round-trip instead of N). - Drop _has_int_doc_id_sample short-circuit. The sample's false-negative window (clean first 256 results, ints further in) is gone; full scroll is the dominant cost on first run anyway. - Simplify _ensure_keyword_payload_indexes: the "already exists" 400 branch was dead code (Qdrant returns 200 on identical re-create); any 400 now logs a warning and continues. - search/context.py: comment the broadened file-type guard. Add explicit not doc_id.isdigit() checks at the top of note/news_item/deck_card branches in _fetch_document_text so malformed payloads surface as warnings instead of being swallowed by the broad except. Also extracts build_search_result_from_point into search/algorithms.py to deduplicate the 71-line payload-extraction loop shared by SemanticSearchAlgorithm and BM25HybridSearchAlgorithm. This fixes SonarQube's quality-gate failure (4.0% new-code duplication, max 3%). Test coverage: - 7 new unit tests for build_search_result_from_point covering missing payload, note/file/deck_card metadata, int doc_id coercion, and metadata_extras merging. - Replace _has_int_doc_id_sample tests with clean-collection no-op and per-batch grouping tests. - Update set_payload assertions from wait=False to wait=True. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Chris CoutinhoandClaude Opus 4.7
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719b3b5034 |
fix(vector): normalize doc_id to str + add Qdrant keyword payload indexes
Production was logging two cascading classes of Qdrant errors against the
welcomed-malamute deployment:
1. HTTP 400 — "Bad request: Index required but not found for \"doc_id\" of
one of the following types: [keyword]". The collection was created via
create_collection() with no payload indexes, so any FieldCondition
filter on doc_id failed at the Qdrant layer (placeholder writes/reads,
eviction, search context lookups).
2. Compounding the missing index, producers wrote a mix of int and str
doc_ids: webhook_parser stringified node_id, scanner stringified note
IDs, news IDs, and deck card IDs — but the file scanner passed the
numeric file_id through unchanged. A keyword index would not have
covered both kinds even if it had existed.
This change:
- Normalizes doc_id to str at every producer site (scanner.py:459,
DocumentTask.doc_id, indexed_*_ids reads from Qdrant).
- Tightens str|int annotations to str across placeholder.py,
eviction.py, search/verification.py, search/context.py,
SearchResult.id, and the auth/api visualization endpoints.
- Defensive str() coercion on doc_id reads in semantic.py /
bm25_hybrid.py / vector/visualization.py for the transition window
before the backfill runs.
- Adds an idempotent startup migration in get_qdrant_client():
- _ensure_keyword_payload_indexes creates KEYWORD indexes for
doc_id, user_id, and doc_type (tolerates "already exists" 400s).
- _backfill_doc_id_to_string scrolls the collection once and rewrites
int doc_ids to str. Skipped after a quick sample shows no legacy
int payloads.
- Public API preserved: SemanticSearchResult.id stays int via explicit
int(r.id) narrowing in server/semantic.py — surfaces a TypeError with
actionable context if a future doc_type ships non-numeric ids.
- Documents the startup migration in docs/configuration.md.
Tests: 11 new unit tests in tests/unit/vector/test_qdrant_client.py
covering happy path / already-exists / unrelated-400 for the index
helpers, and sample-skip / mixed-batch rewrite / payload=None edge cases
for the backfill. 889 unit tests pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Chris CoutinhoandClaude Opus 4.6
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a11ae9c027 |
refactor: enforce PLC0415 (import-outside-top-level) for source code
Enable ruff PLC0415 rule for all source files (tests excluded via per-file-ignores). Move 136 inline imports to top-level across 33 files. 8 imports suppressed with noqa for legitimate reasons: circular dependencies (client/__init__.py, context.py), optional dependency guards (app.py document processors, auth/userinfo_routes.py), and post-env-setup imports (smithery_main.py). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> |
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Chris CoutinhoandClaude Sonnet 4.5
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e4f3beee01 |
fix: resolve type checking warnings for CI
- Add type casts for Starlette app state access - Add assertions for cipher, card, board, stack after initialization - Add None checks for XML element text attributes - Handle __package__ being None in tracing setup - Fix TokenBrokerService initialization to use storage credentials Resolves 42 type warnings from ty-check, enabling CI linting to pass. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com> |
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Chris CoutinhoandClaude
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208365cd3d |
feat: Add OpenAI provider support for embeddings and generation
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> |
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Chris CoutinhoandClaude
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6fe5596c13 |
feat: Implement BM25 hybrid search with native Qdrant RRF fusion
Replace custom keyword/fuzzy search algorithms with industry-standard BM25 sparse vectors, combined with dense semantic vectors using Qdrant's native Reciprocal Rank Fusion (RRF). This consolidates search architecture and improves relevance for both semantic and keyword queries. Key changes: - Add fastembed dependency for BM25 sparse vector generation - Update Qdrant collection schema to support named vectors (dense + sparse) - Create BM25SparseEmbeddingProvider using FastEmbed's Qdrant/bm25 model - Implement BM25HybridSearchAlgorithm with native Qdrant RRF prefetch - Update document processor to generate both dense and sparse embeddings - Simplify nc_semantic_search() tool to use BM25 hybrid only - Remove legacy keyword.py, fuzzy.py, and custom hybrid.py (736 lines) - Update ADR-014 with implementation notes and test results Benefits: - Consolidated architecture (single Qdrant database) - Native database-level RRF fusion (more efficient) - Industry-standard BM25 (replaces brittle custom keyword search) - Better relevance across semantic and keyword queries - Simplified codebase (-285 net lines) Tests: All 125 tests passing (118 unit, 7 integration) Implements ADR-014: Replace Custom Keyword Search with BM25 Hybrid Search 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> |
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Chris CoutinhoandClaude
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6812e1aca7 |
fix: add dynamic dimension detection for Ollama embedding models
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> |
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Chris CoutinhoandClaude
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157e433d65 |
fix: Support in-memory Qdrant for CI testing
Changes to make tests work without external qdrant/ollama dependencies: 1. docker-compose.yml (mcp service): - Switch from QDRANT_URL (network mode) to QDRANT_LOCATION=":memory:" - Comment out QDRANT_URL and QDRANT_API_KEY (not needed for in-memory) - Keep OLLAMA_BASE_URL commented out (use SimpleEmbeddingProvider fallback) 2. nextcloud_mcp_server/vector/qdrant_client.py: - Fix collection creation bug in in-memory mode - Previously: All ValueError exceptions were re-raised - Now: Only dimension mismatch ValueError is re-raised - Allows "Collection not found" ValueError to trigger auto-creation 3. tests/integration/test_sampling.py: - Update test to handle all sampling unsupported cases - Check for multiple fallback search_method values - Skip test gracefully when sampling unavailable This configuration enables: - CI testing without external services (qdrant, ollama) - In-memory vector database (ephemeral but sufficient for tests) - SimpleEmbeddingProvider for embeddings (feature hashing, 384 dims) - Automatic collection creation on first use Test result: test_semantic_search_answer_successful_sampling now passes (skipped with appropriate message when sampling unsupported) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> |
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Chris CoutinhoandClaude
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e575c8e57b |
feat(vector): Support multiple embedding models with auto-generated collection names
This PR enables safe switching between embedding models and multi-server
deployments by implementing auto-generated Qdrant collection names based on
deployment ID and model name.
## Problem
Previously, all deployments used a single hardcoded collection name
"nextcloud_content", which caused two critical issues:
1. **Dimension mismatches when switching models**: Changing
OLLAMA_EMBEDDING_MODEL (e.g., nomic-embed-text at 768D → all-minilm at
384D) would cause runtime errors as vectors couldn't be inserted into a
collection with incompatible dimensions.
2. **Collection collisions in multi-server setups**: Multiple MCP servers
sharing a single Qdrant instance would overwrite each other's data,
making horizontal scaling impossible.
## Solution
### Auto-Generated Collection Naming
Collections are now automatically named using the pattern:
\`{deployment-id}-{model-name}\`
**Deployment ID**: Uses \`OTEL_SERVICE_NAME\` if configured (and not default
value), otherwise falls back to \`hostname\` for simple Docker deployments.
**Model Name**: From \`OLLAMA_EMBEDDING_MODEL\` with path separators sanitized.
**Examples**:
- \`my-mcp-server-nomic-embed-text\` (with OTEL_SERVICE_NAME=my-mcp-server)
- \`mcp-container-all-minilm\` (simple Docker, hostname=mcp-container)
**Override**: Users can still set \`QDRANT_COLLECTION\` explicitly to bypass
auto-generation for backward compatibility.
### Dimension Validation
Added startup validation that checks collection dimensions match the
embedding service. If a mismatch is detected, the server fails fast with a
clear error message explaining:
- Expected vs actual dimensions
- Likely cause (model change)
- Solutions (delete collection, use different name, or revert model)
### Improved Sampling Error Handling
Enhanced MCP sampling rejection handling to treat user rejections as normal
behavior rather than errors:
- **User rejections** ("rejected", "denied") → INFO log, no traceback
- **Unsupported clients** → INFO log, no traceback
- **Other MCP errors** → WARNING log, no traceback
- **Unexpected errors** → ERROR log WITH traceback
This aligns with the MCP specification where clients SHOULD prompt users for
approval/denial of sampling requests.
## Changes
### Core Implementation
- **nextcloud_mcp_server/config.py**: Added \`get_collection_name()\` method
with deployment ID detection and model name sanitization
- **nextcloud_mcp_server/vector/qdrant_client.py**: Dimension validation on
collection open with helpful error messages
- **nextcloud_mcp_server/vector/{scanner,processor}.py**: Updated to use
\`get_collection_name()\`
- **nextcloud_mcp_server/auth/userinfo_routes.py**: Vector sync status uses
\`get_collection_name()\`
- **nextcloud_mcp_server/server/semantic.py**:
- Updated semantic search tools to use \`get_collection_name()\`
- Improved sampling rejection error handling (McpError vs Exception)
### Documentation
- **docs/semantic-search-architecture.md**: New comprehensive architecture
document (557 lines) covering background sync, semantic search flow, RAG
implementation, and deployment modes
- **docs/configuration.md**: Added detailed "Qdrant Collection Naming"
section with examples and multi-server deployment guidance
- **docker-compose.yml**: Added comments explaining collection naming behavior
- **README.md**: Updated semantic search descriptions to clarify
experimental status, Notes-only support, and infrastructure requirements
## Migration Guide
**For existing single-server deployments:**
Option 1 (Recommended): Use explicit collection name for continuity
\`\`\`bash
QDRANT_COLLECTION=nextcloud_content # Keep existing collection
\`\`\`
Option 2: Allow auto-generation and re-embed
\`\`\`bash
# Remove QDRANT_COLLECTION override
# New collection will be created based on deployment ID + model
# Requires re-embedding all documents (may take time)
\`\`\`
**For new multi-server deployments:**
Set unique OTEL service names per server:
\`\`\`bash
# Server 1
OTEL_SERVICE_NAME=mcp-prod
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# → Collection: "mcp-prod-nomic-embed-text"
# Server 2
OTEL_SERVICE_NAME=mcp-staging
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# → Collection: "mcp-staging-nomic-embed-text"
\`\`\`
## Benefits
✅ **Safe model switching**: Each model gets its own collection, preventing
dimension mismatch errors
✅ **Multi-server support**: Multiple MCP servers can share one Qdrant
instance without conflicts
✅ **Clear ownership**: Collection names show which deployment and model owns
the data
✅ **Better error messages**: Dimension validation provides actionable
guidance
✅ **Backward compatible**: Existing deployments can continue using
\`QDRANT_COLLECTION\` override
## Testing
Validated with:
- Single-server deployments (default hostname-based naming)
- Multi-server deployments (OTEL service name-based naming)
- Model switching scenarios (dimension validation)
- Collection override scenarios (backward compatibility)
Next steps: Testing various Ollama embedding models to investigate optimal
chunk sizes and performance characteristics.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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Chris CoutinhoandClaude
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857d8f2152 |
feat: add Qdrant local mode support with in-memory and persistent storage
Adds flexible Qdrant deployment modes to reduce infrastructure requirements
for local development and smaller deployments:
**Configuration Changes:**
- Add QDRANT_LOCATION environment variable (mutually exclusive with QDRANT_URL)
- Three modes: network (URL), in-memory (:memory:, default), persistent (file path)
- Settings dataclass validation via __post_init__ ensures mutual exclusivity
- API key warning when set in local mode (ignored, only for network mode)
**Client Initialization:**
- Auto-detect mode: network (url + api_key) vs local (:memory: or path=)
- In-memory: AsyncQdrantClient(":memory:") - zero config default
- Persistent: AsyncQdrantClient(path="/app/data/qdrant") - file storage
- Network: AsyncQdrantClient(url, api_key) - production mode
**Docker Compose Updates:**
- Qdrant service moved to optional profile (--profile qdrant)
- MCP service uses QDRANT_LOCATION=:memory: by default
- Added mcp-data volume for persistent storage (/app/data)
- No hard dependency on qdrant service
**Documentation:**
- Comprehensive configuration guide in docs/configuration.md
- All three modes documented with pros/cons
- Docker Compose examples for each mode
- Environment variable reference table
**Tests:**
- 13 new config validation tests (mutual exclusivity, defaults, warnings)
- Persistent mode integration test (create, close, reopen, verify persistence)
- All 82 unit tests + 5 smoke tests pass
**Breaking Change:**
- Default changed from QDRANT_URL=http://qdrant:6333 to QDRANT_LOCATION=:memory:
- Simplifies local development (no external service needed)
- Production deployments: explicitly set QDRANT_URL or QDRANT_LOCATION
Related: ADR-007 background vector sync implementation
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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Chris CoutinhoandClaude
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8f45e996e8 |
feat: implement vector sync scanner and processor (ADR-007 Phase 2)
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> |