Add read-only support for the Nextcloud Mail app, plus semantic indexing
of mail messages. The MCP server never speaks IMAP/POP3 itself: it calls
the Mail app's CSRF-free OCS API (/ocs/v2.php/apps/mail/api/...) with the
existing Basic-Auth app-password flow and an OCS-APIRequest header, and the
Mail app handles IMAP server-side.
- client/mail.py: MailClient (accounts, mailboxes, messages, message,
attachment), OCS-envelope aware.
- models/mail.py: Pydantic models with the API's camelCase aliases.
- server/mail.py: 5 read-only MCP tools (mail.read scope), registered in
AVAILABLE_APPS.
- Vector pipeline: new "mail_message" doc_type wired into scanner
(scan_mail_messages, newest-N per mailbox), processor (body -> markdown
embedding), per-id verifier, and context expansion.
- Tests: client API, model round-trips, verifier behavior; consent-backstop
test now derives its allowed set from INDEXED_DOC_TYPES.
- README + semantic-search docstrings updated.
Requires Mail 5.x / Nextcloud 32+ and a mail account configured in the
Mail app. Follow-up: astrolabe must advertise "mail_message" in its
enabled_doc_types capability for search under admin doc_type restriction.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round-7 claude-review (no blockers):
- 🟡 query_token_count/query_embedding were class-level defaults on
SearchAlgorithm, relying on each subclass's __init__ to shadow them. Added
SearchAlgorithm.__init__ that sets both as instance attributes and had
BM25HybridSearchAlgorithm + SemanticSearchAlgorithm call super().__init__(),
so per-request concurrency isolation is structural, not by convention.
- 🟡 Documented the v1 search-path billing gap: record_search_usage fires only
on a fully successful search, so if the query embed succeeded (provider billed
+ Prometheus recorded) but a later step (Qdrant/verify) raised, no
tokens_embedded billing row is written. Added a NOTE at the call site.
Left as-is (reasons in PR reply): deployment sequencing (CP METRIC_EVENT_NAMES
already renamed; pipeline inert); Ollama _detect_dimension double dimension-set
(idempotent, same value); SonarQube issues — 1 is the deliberate TODO(#282)
(INFO), 4 are S7503 false positives on async test stubs that must be awaitable
(gate green).
Deck #284.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Billing product model finalized (Deck #281): bill pages externally, record
tokens internally. Rename the data-plane metric literals to match the now-
canonical contract (Deck #284) — the control plane's METRIC_EVENT_NAMES is
already renamed, so the old names would be unmapped and never sync to Stripe.
Rename (values unchanged):
- embeddings_queries → tokens_embedded (value = real token count, already
emitted by this PR; the unit upstream providers bill on).
- pages_chunks → pages_embedded (value kept as len(chunk_texts) interim;
TODO(#282): real normalized "pages indexed" count — real pages for paginated
types, chars/tokens-per-page constant otherwise — is deferred to the
instrumentation card, this only lands the name/contract).
- All literals, log strings, docstrings, comments, the migration comment, and
tests renamed; grep confirms zero old strings remain.
Observability (new): export embedding token cost to Prometheus as
astrolabe_embedding_tokens_total{provider,operation} (operation = index|query)
so the billed cost unit is visible in Grafana, not just the per-tenant billing
DB. Dedicated counter (doesn't inflate the existing chunk/request metrics) and
always-on (independent of USAGE_METERING_ENABLED, so OSS/self-host gets it).
Wired on both the indexing batch embed and the search query embed (query inside
the per-request cache-miss branch, so reused embeddings aren't double-counted).
Note: the rename orphans any pre-existing embeddings_queries/pages_chunks rows
in tenant app DBs (CP no longer maps them) — acceptable; pipeline is inert with
throwaway dev/sandbox data.
Deck #284 (folded into PR #875).
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>
Round 3 review follow-ups:
- Enforce the folder cap (MAX_PATH_PREFIXES=20) inside normalize_path_prefixes
so the REST/viz endpoints are bounded too, not just the MCP tool's Field
and the PHP client. Single server-side enforcement point; the MCP tool's
Field(max_length=...) now references the same constant.
- Widen the SearchAlgorithm ABC and both concrete implementations'
path_prefixes param to Iterable[str] | None, matching the widening of
build_base_filter_conditions from the prior round.
- Add a normalize_path_prefixes cap test.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Extend the ADR-027 Phase 2 path filter from a single path_prefix to a
list of folders. The new normalize_path_prefixes() helper is the single
source of truth for trimming, dropping blanks, and de-duplicating, and
folds the legacy single path_prefix into the list for backward
compatibility.
build_base_filter_conditions() adds one MatchText to the must clause for
a single folder (unchanged shape) and OR-s multiple folders via a nested
Filter(should=[...]) so a file under any selected folder matches while
still AND-ing against the ACL/doc_type/date conditions.
path_prefixes is threaded through every search surface: the
nc_semantic_search MCP tool, the visualization API (JSON body), and the
viz route (CSV query param). The Astrolabe frontend folder picker that
produces these lists ships in a companion astrolabe PR.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add a path_prefix filter to semantic search, honoured on both the MCP tool and
the dense-only visualization/API paths through the shared filter contract.
- build_base_filter_conditions: append FieldCondition(file_path,
MatchText(path_prefix)) when set. file_path is only on doc_type == "file"
points, so a non-empty path_prefix implicitly restricts to files.
- Promote path_prefix to an explicit keyword param on the SearchAlgorithm ABC
and both algorithms; thread it through nc_semantic_search (blank ⇒ no filter),
the /api/v1 search endpoints, and the viz route.
- Add a file_path TEXT payload index to _PAYLOAD_INDEX_FIELDS (no content
re-index; idempotent startup migration). MatchText tokenizes on server Qdrant
and matches by substring on local/embedded qdrant-client — both serve folder
scoping.
- Update ADR-027 (Phase 2 implemented; readiness table; semantics note). Tests.
Refs ADR-027 Phase 2. Deck #177.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add a modified_after/modified_before date-range filter to semantic search,
honoured on both the MCP tool path (BM25HybridSearchAlgorithm) and the
dense-only visualization/API path (SemanticSearchAlgorithm) through one shared
contract.
- Promote modified_after/modified_before to explicit keyword params on the
SearchAlgorithm ABC and both concrete algorithms; factor the shared
placeholder+ownership+doc_type+date filter into
access_filter.build_base_filter_conditions so new filters land in one place.
- nc_semantic_search: accept RFC 3339 / ISO 8601 (or Unix seconds) bounds via
utils.validation.parse_modified_timestamp; Annotated/Field constraints on the
numeric args; explicit McpError guard for after > before. Thread the parsed
bounds through the cross-app and per-doc_type dispatch.
- /api/v1 search endpoints + viz route parse the same formats and 400 on bad or
inverted ranges.
- Add a modified_at INTEGER payload index to _PAYLOAD_INDEX_FIELDS; the
idempotent _ensure_payload_indexes() startup path migrates existing
collections with no content re-index.
- Update ADR-027 to resolve the review feedback (validation placement, shared
algorithm contract, deferral of nc_semantic_search_answer, payload index,
RFC-3339-at-the-boundary rationale). Add unit tests.
Refs ADR-027. Deck #177.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Verify-on-read only checked file *accessibility* (file_accessible_by_id),
never tag membership, so a file removed from the `vector-index` tag (but
still readable) kept surfacing in semantic search, and stale points only
got evicted when they happened to rank in a search's top-K.
Rework `_verify_files` to gate on current `vector-index` tag membership via
a single batch `find_files_by_tag(tag, mime_type_filter="application/pdf")`
REPORT per search (plus a one-shot EXCLUDED_TAGS lookup for exclusion-wins
parity) — exactly what the scanner indexes. A file is kept iff it is in that
set, so untagged / deleted / excluded files drop out immediately and the
existing eviction wiring reclaims their Qdrant points. The gate is strict
for all file results, own and shared. Mirrors the batch-fetch-and-intersect
shape of `_verify_news_items` (one semaphore slot, fail-open on fetch error,
malformed-id keep).
- Promote the tag name to a `vector_sync_pdf_tag` Settings field (dynaconf
env mapping VECTOR_SYNC_PDF_TAG) used by both scanner and verifier;
drop the scanner's direct os.getenv.
- Expose `find_files_by_tag` on NextcloudClientProtocol.
- Rewrite the file-verifier unit tests (tagged/untagged/deleted/excluded/
fail-open/non-numeric); update the ACL + verify-on-read integration tests
to seed tagged PDFs.
- Amend ADR-019 and the configuration.md verify-on-read latency budget.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- get_indexed_doc_types: add optional accessible_owners param and reuse
build_ownership_filter so cross-user doc-type discovery matches the real
search scope (was self-only / ACL-blind); docstring documents the self-only
default. Covered by test_get_indexed_doc_types_is_acl_aware.
- access_filter: build_ownership_filter now omits the owner_id branch entirely
for an empty owner set instead of relying on undocumented MatchAny(any=[])
semantics; updated the empty-list unit test accordingly.
- access_filter: make the uid_owner/owner share-owner extraction explicit
("absent, not empty") to avoid skipping on a falsy-but-present field.
- access_filter: add an operator note that pre-owner_id points need a re-index
to surface to share recipients (ACL search is a no-op for legacy data).
- verification/webdav: lock the file_accessible_by_id(scope="") contract with a
targeted multi-user test (owner + recipient True, non-recipient False).
- viz_routes: comment that verify-on-read eviction runs inline by design (no
lifespan task group available on the Starlette route).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- vector/qdrant_client.py: add owner_id to _PAYLOAD_INDEX_FIELDS (BLOCKING).
Every search applies MatchAny(key="owner_id", ...); without a keyword index
Qdrant full-scans the collection and may 400 on Qdrant Cloud strict mode.
_ensure_payload_indexes is idempotent so existing collections migrate at
startup.
- search/access_filter.py: bound the process-global _owners_cache with an LRU
cap (was one unbounded entry per active user, never evicted); document the
owner-level over-fetch limitation (a prolific sharer floods the recall
buffer with ghost candidates that verify-on-read drops, with no second
Qdrant pass) as a TODO toward per-file filtering.
- search/algorithms.py + semantic.py + bm25_hybrid.py: promote
accessible_owners from **kwargs to an explicit keyword-only parameter on the
SearchAlgorithm ABC and both implementations, so a misspelled keyword is a
type error rather than a silent fall back to self-only scope.
- search/verification.py: document that _verify_files now verifies by global
file id (WebDAV SEARCH), not by path.
- tests/unit/search/test_access_filter.py: add cache-hit, TTL-expiry,
failure-not-cached, and LRU-bound tests.
Bumps the astrolabe submodule with the matching #89 review fixes.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- viz_routes: run verify_search_results before returning results. After the
accessible_owners expansion the viz can surface OTHER users' shared docs, so
it must drop ones the caller can no longer access (revoked share) — same as
the nc_semantic_search tool path. (Blocking review item.)
- access_filter: cache list_accessible_owners per user for 30s to keep the OCS
shares round-trip off the search hot path (failures aren't cached); document
the single-page OCS limitation; add a clear_accessible_owners_cache() test
helper. Comment the empty-accessible_owners MatchAny([]) edge case.
- verification: comment why cross-user eviction is a deliberate no-op (eviction
is scoped to the querying user's id, so a recipient's revoked access never
deletes the owner's points; the recipient self-heals via accessible_owners).
- algorithms: declare SearchResult.original_score (set by the viz route) so the
now-precisely-typed result list type-checks.
- tests: cross-user eviction-no-op safety test; autouse owners-cache reset in
the access_filter + shared-search tests; replace async-no-await qdrant fakes
with AsyncMock (clears SonarCloud S7503).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- _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>
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>
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>
- Cap all_results to limit*2 after sort in the per-doc_types branch of
nc_semantic_search to bound over-verification (was unbounded N-types).
- Switch BatchVerifier from (client, doc_ids, user_id) to (client, results,
semaphore). Verifiers now read file paths and deck board/stack ids from
SearchResult.metadata instead of doing fresh Qdrant scrolls — eliminates
one duplicate round-trip per file/deck-card verification.
- Bound per-id verification concurrency with a shared anyio.Semaphore
(default 20, matching server/semantic.py context-expansion convention).
Prevents httpx pool exhaustion / rate limiting on large search pages.
- Propagate stack_id from Qdrant payload to SearchResult.metadata in both
bm25_hybrid.py and semantic.py (board_id was already propagated).
- Drop now-unused _resolve_file_path / _resolve_deck_metadata helpers.
- Drop redundant int(d) in requested predicate from _verify_news_items.
- Rewrite eviction comment to be honest about inline (not background)
execution and the resulting latency coupling.
- ADR-019 status: Proposed -> Accepted.
- Add news property to NextcloudClientProtocol.
- Widen SearchResult.id and SemanticSearchResult.id to int | str to match
BatchVerifier signature and document support for future string-id types.
- Flip openWorldHint to True on nc_semantic_search_answer (it calls into
Nextcloud via nc_semantic_search).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Changes:
- Add file_path to metadata in semantic and BM25 hybrid search algorithms
for PDF viewer integration (search/semantic.py:161-163, search/bm25_hybrid.py:230-232)
- Include chunk_start_offset, chunk_end_offset, page_number, and page_count
in search results for rich chunk display (api/management.py:981-1004)
- Add point_id field to SearchResult for batch retrieval (models/semantic.py)
- Fix type narrowing for chunk context API parameters (api/management.py:1102-1111)
- Fix None-safety in doc_types discovery (search/algorithms.py:114)
This enables the Astroglobe UI to display PDF pages at the correct
location for matched chunks.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <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>
- 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>
Introduces a placeholder-based state tracking system to prevent duplicate
document processing during the gap between scanner queuing and processor
completion.
**Key Changes:**
1. **Placeholder Helper Functions** (`vector/placeholder.py`):
- `write_placeholder_point()` - Creates zero-vector placeholder when queuing
- `query_document_metadata()` - Queries for existing entry (placeholder or real)
- `delete_placeholder_point()` - Removes placeholder before writing real vectors
- `get_placeholder_filter()` - Filters placeholders from user-facing queries
2. **Scanner Updates** (`vector/scanner.py`):
- Replace `indexed_at` comparison with `modified_at` comparison
- Write placeholder before queuing each document
- Query per-document metadata instead of bulk-querying indexed_at
- Fixes bug where files were resubmitted every scan cycle
3. **Processor Updates** (`vector/processor.py`):
- Delete placeholder before upserting real vectors
- Ensures no duplicate points in Qdrant
4. **Query Filters** (all search files):
- Add `get_placeholder_filter()` to all user-facing queries
- Ensures placeholders never appear in search results or visualizations
- Applied to: bm25_hybrid.py, semantic.py, viz_routes.py, algorithms.py
**Architecture:**
- Placeholders use zero vectors with dimension from embedding service
- Payload includes `is_placeholder: True` flag for filtering
- Status field tracks: "pending", "processing", "completed", "failed"
- Deterministic UUIDs using uuid5 for consistent point IDs
**Impact:**
- Eliminates duplicate processing of same documents
- Fixes race condition where long-running documents get queued multiple times
- Prevents scanner from resubmitting files every scan cycle
- Maintains clean separation between in-flight and indexed documents
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- algorithms.py: Revert SearchResult.id to int (all docs use int IDs now)
- semantic.py: Revert SemanticSearchResult.id to int, remove Union import
- viz_routes.py: Remove str() conversion when querying doc_id from Qdrant
- viz_routes.py: Convert doc_id from query param to int in chunk context
Fixes vector visualization which was collapsing all chunks to a single
point because Qdrant queries were failing to match doc_id (string vs int).
Major improvements to vector visualization page:
- Refactor PCA to display individual chunks instead of averaged documents
- Add context expansion module for fetching surrounding text from notes and PDFs
- Update deduplication to use (doc_id, doc_type, chunk_start, chunk_end) keys
- Fix Alpine.js rendering with chunk-specific keys including offsets
- Refactor authentication helper to return NextcloudClient for better reuse
- Add async context manager support to NextcloudClient
Technical details:
- viz_routes.py: Fetch specific chunk vectors instead of averaging per document
- context.py: New module supporting both notes and PDF text extraction via PyMuPDF
- search algorithms: Extract page_number, chunk_index, total_chunks from Qdrant
- vector-viz.js/html: Use chunk positions in expansion tracking keys
This enables users to see which specific chunks match their query
and view them with surrounding context in the PCA visualization.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Fix false-positive validation error where DBSF (Distribution-Based Score
Fusion) correctly produces scores > 1.0 but SearchResult validation
incorrectly rejected them.
**Root Cause**: SearchResult.__post_init__() enforced scores in [0.0, 1.0]
range, but DBSF sums normalized scores from multiple retrieval systems
(dense semantic + sparse BM25), resulting in scores like 1.55 when both
systems strongly agree a document is relevant.
**Changes**:
- Relaxed validation to allow any score ≥ 0.0 (algorithms.py:147-157)
- Updated SearchResult and SemanticSearchResult documentation to explain
score ranges for RRF ([0.0, 1.0]) vs DBSF (unbounded)
- Added comprehensive test coverage for both fusion methods
- Added DBSF fusion option to vector visualization UI
- Updated viz routes and vizApp() to support fusion parameter selection
**Testing**: All 157 unit tests pass, type checking passes, ruff passes
Fixes error: "Configuration error: Score must be between 0.0 and 1.0, got 1.1528953"
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Skip tracing for /app/vector-sync/status to reduce noise from HTMX polling.
Metrics collection continues for this endpoint.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implements NextcloudClientProtocol for multi-document type search following
user requirement that document types are not 1:1 with apps (e.g., Notes app
specializes in markdown, while Files/WebDAV handles multiple file types).
Key Changes:
- NextcloudClientProtocol: Generic protocol with app-specific client properties
- get_indexed_doc_types(): Query Qdrant for actually-indexed document types
- Document dispatch: All algorithms check Qdrant before attempting access
- Cross-type deduplication: Use (doc_id, doc_type) tuples in hybrid RRF
Search Algorithm Updates:
- Semantic: Added _verify_document_access() with dispatch to appropriate client
- Deduplication by (doc_id, doc_type) tuple
- Only "note" verification implemented, others return None with info log
- Keyword: Added _fetch_documents() dispatch method
- Queries Qdrant for available types before fetching
- Supports cross-app search when doc_type=None
- Fuzzy: Same pattern as keyword search
- Hybrid: Already uses (doc_id, doc_type) for deduplication (no changes needed)
Future-Proof Design:
- File/calendar verification stubs in place
- Clear logging when unsupported types found
- Easy to extend when processor indexes new document types
Currently Supported:
- "note" documents fully implemented and tested
- Other types gracefully handled (logged but skipped)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
