* feat(bm25): add explicit Disabled stemmer; deprecate language hack
Adds a `Disabled` variant to `StemmingAlgorithm` (`stemmer: {"type": "none"}`)
so stemming can be turned off explicitly in both the main engine and Edge,
instead of relying on the undocumented `language: "none"` footgun that
silently disabled both stemming and stopwords.
For language-neutral text processing the supported setup is now:
1. set the stemmer to disabled, and
2. configure an empty stopword set.
The main engine still tolerates unsupported languages (so existing
`language: "none"` configs keep working on upgrade) but now logs a
deprecation warning pointing users to the explicit setup. Edge continues
to reject unsupported languages, and now has a real way to disable stemming.
Refs: https://github.com/qdrant/qdrant/issues/9289
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(edge-py): handle Disabled stemmer in python bindings; fix openapi schema
- Handle the new StemmingAlgorithm::Disabled variant in the qdrant-edge-py
bindings (FromPyObject/IntoPyObject/Repr) and add a DisabledStemmer pyclass
plus its .pyi stub entry.
- Match generator output for the StemmingAlgorithm OpenAPI schema (plain $ref
in anyOf) so docs/redoc/master/openapi.json stays consistent.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(openapi): regenerate StemmingAlgorithm schema with generator output
Ran tools/generate_openapi_models.sh so docs/redoc/master/openapi.json
exactly matches generator output: DisabledStemmerParams/NoStemmer are placed
after SnowballLanguage, and the StemmingAlgorithm anyOf entry is a plain $ref
(the schema2openapi step flattens the allOf+description wrapper).
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(test): avoid wildcard enum match arm in bm25 sparse_len helper
clippy --all-targets flags `other => panic!()` as wildcard_enum_match_arm;
match the Dense/MultiDense variants explicitly instead.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix: issues
* fix: log::warn as call once
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Daniel Boros <dancixx@gmail.com>
* Add routing token structure
* Implement routing token in read operation executor as per design doc
* Add TODO to glue routing token to user requests
* Implement routing header for REST API
* Source routing token from request, not from JWT token
* Implement routing token in gRPC API
* Add test
* Review remarks
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
* Use lower case header name to prevent panic
* Rename header to X-Qdrant-Route-Affinity
* Assert routing consistency in test on all peers
---------
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
`FieldCondition::validate` checked geo polygon *shape* (point count and
closure) but never the *coordinate ranges* of any geo sub-condition. A gRPC
filter with latitude outside [-90, 90] or longitude outside [-180, 180] (in
geo_bounding_box, geo_radius, or geo_polygon) passed validation, reached the
geo index, and panicked during geohash encoding, which expects pre-validated
input.
Reject out-of-range coordinates at the API boundary for all three geo
sub-conditions, mirroring `segment::types::GeoPoint::validate`. The bounds are
duplicated because the `api` crate does not depend on `segment`.
Includes a regression test covering geo_radius, geo_bounding_box, and a
well-formed (shape-valid) geo_polygon with out-of-range coordinates.
* Use assert_matches!
* Add trailing commas
* Use more assert_matches!
Also, drop now redundant `expected blah but got blah` messages because
`assert_matches!` will print these.
* Use debug_assert_matches!
---------
Co-authored-by: xzfc <xzfcpw@gmail.com>
`validate_multi_vector_len(N, &[])` with N > 0 previously returned Ok:
it passes the `vectors_count != 0` check, an empty `flatten_dense_vector`
clears the size check, and `0.is_multiple_of(N)` is true, so it falls into
the Ok branch. The unvalidated value then reaches
`convert_to_plain_multi_vector`, where `dim = data.len() / vectors_count = 0`,
the divisibility check `dim * vectors_count != data.len()` (0 == 0) passes,
and `data.into_iter().chunks(0)` panics (itertools asserts the chunk size is
non-zero). This is reachable from a single malformed gRPC Upsert via the
deprecated `vectors_count` field.
Add an `is_empty()` guard to `validate_multi_vector_len`, mirroring the
sibling `validate_multi_vector_by_length`, so empty flattened data is
rejected with a clear validation error before any conversion. Also add a
defensive `vectors_count == 0 || data.is_empty()` guard at the top of
`convert_to_plain_multi_vector`, which aligns it with the already-guarded
`MultiDenseVectorInternal::try_from_flatten` and closes the same panic on the
internal node-to-node `sync` path (where validation is log-only and
`SyncPoints.points` is not validated).
This completes the empty-vector hardening started in #9070, which covered the
REST side only and did not touch the gRPC flattened `vectors_count` form.
Includes a regression test covering both the rejected (empty) and accepted
(consistent multivector) cases.
`impl Validate for grpc::FieldCondition` only checked that at least one
condition field was set; it never validated the contents of a geo polygon.
A `FieldCondition` carrying a malformed `geo_polygon` (empty exterior, fewer
than 4 points, or an unclosed exterior/interior ring) therefore passed
validation, and the invalid shape later reached the geo index and panicked.
Recurse into the polygon's existing validator (`geo_polygon.validate()?`),
which already rejects these shapes (it is covered by `test_geo_polygon`), so
the request is rejected with a clean validation error instead.
Includes a regression test asserting a FieldCondition with an empty polygon
exterior is rejected while a well-formed polygon still passes.
* Strict mode: add `max_disk_usage_percent`
Mirrors `max_resident_memory_percent`: rejects disk-consuming update ops
(upsert, set/overwrite payload, update vectors) when the filesystem hosting
Qdrant storage is filled above the configured percentage. Delete-style ops
remain allowed so callers can free disk.
Disk usage is sampled via `statvfs` and TTL-cached for 5s (same cadence as
the resident-memory reader) so high-RPS request paths don't hammer the
syscall. Reader is keyed by path in `common::disk_usage` and returns `None`
on stat failure — callers (the strict-mode check) treat `None` as "skip",
matching the memory-check behaviour.
Plumbing follows the existing pattern: field on `StrictModeConfig` (+
output/diff/Hash), gRPC proto field `22`, validation 1..=100, REST/proto
conversions, and the hook into `check_strict_mode_toc_batch` alongside the
memory check (both guarded by `any_consumes_memory`).
Co-authored-by: Cursor <cursoragent@cursor.com>
* Fix CI: Windows disk_usage test + e2e WAL config
- `missing_path_returns_none` panicked on Windows because
`GetDiskFreeSpaceEx` succeeds for non-existent paths (it resolves up to
the containing drive). Relax the assertion to "must not panic; if a
value is returned it must be well-formed". The contract we care about
(None on failure) is platform-defined, not something we can portably
force.
- e2e test failed at batch 0 with "WAL buffer size exceeds available disk
space": Qdrant's existing per-shard `DiskUsageWatcher` enforces
`free >= 2 * wal_capacity_mb` and the default WAL didn't fit in the
50 MB tmpfs. Bump tmpfs to 200 MB and shrink `wal_capacity_mb` to 1 MB
(same pattern as `test_low_disk.py`) so our strict-mode gate is the
one that fires, not the WAL pre-check. Raise the gate threshold to
50% to match the larger headroom.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
* [ai] TQDT in the API
* [ai] unify new sparse error for TQDT
* Rename to `turbo4`
* Add `Turbo4` to comments and doc strings.
* Also validate named sparse vector creation
The previous description was outdated: it claimed that enabling this
option "blocks updates at the request level" until segments are
re-optimized. In practice the implementation uses "deferred points":
new points written to large unoptimized segments are persisted but
excluded from read/search results until the segments are optimized.
Updates are not blocked; only `wait=true` clients are made to wait for
the deferred points to become visible. Update this in the REST schema
(via `OptimizersConfig` / `OptimizersConfigDiff`), in the gRPC proto,
in the edge config docstrings, and regenerate the OpenAPI bundle via
`tools/generate_openapi_models.sh`.
Co-authored-by: Cursor Agent <agent@cursor.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Match the REST API by adding an optional `WriteOrdering` field to
`CreateVectorNameRequest` and `DeleteVectorNameRequest`, and propagate
it through the tonic handlers and remote-shard forwarding paths.
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* [AI] strict mode parameter for limiting update requests if ram usage is over threshold
* opanAPI update
* [AI] end-to-end test
* fmt
* Fix e2e test: memory rejection check broken by string truncation
UnexpectedResponse.__str__() truncates the raw response body, cutting
off the `max_resident_memory_percent` hint at the end of the error
message. Use `resident memory usage` instead, which appears early
enough to survive the truncation.
Made-with: Cursor
* add grpc validation
* test check_resident_memory
---------
Co-authored-by: Cursor Agent <agent@cursor.com>
Co-authored-by: Arnaud Gourlay <arnaud.gourlay@gmail.com>
* Add mincore-based memory stats to MmapFile
Add `resident_bytes()`, `disk_bytes()`, and `probe_memory_stats()` methods
to `MmapFile` for measuring page cache residency via `mincore(2)`. This is
the foundation for per-collection memory usage reporting.
Also extract `page_size()` as a public function in `mmap::advice`, replacing
the internal `PAGE_SIZE_MASK` with a direct page size cache.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* [AI] introduce trait for reporting memory usage per component
* [AI] memory reporter implementation for vector storage
* [AI] implement MemoryReporter for QuantizedVectors
* [AI] implement MemoryReporter for VectorIndexEnum
* Implement MemoryReporter for IdTrackerEnum with RAM estimation
Add ram_usage_bytes() to all ID tracker types and their data structures:
- PointMappings, CompressedPointMappings, CompressedVersions,
CompressedInternalToExternal, CompressedExternalToInternal
- MutableIdTracker, ImmutableIdTracker, InMemoryIdTracker
All ID trackers load their data into RAM (none use mmap for working data).
Files are reported as OnDisk (persistence only), actual RAM footprint
is reported via extra_ram_bytes. Uses struct destructuring to ensure
new fields trigger compile errors if not accounted for.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* [AI] implement MemoryReporter for PayloadStorageEnum and adjust FileStorageIntent
* [AI] implement MemoryReporter for PayloadStorageEnum and adjust FileStorageIntent
* [AI] implement MemoryReporter for payload indexes: in-ram structures memory consumtion computation + caching
* [AI] implement MemoryReporter for payload indexes: in-ram structures memory consumtion computation + caching
* [AI] segment-level memory usage report
* [AI] Block 3: Aggregation Layer and Data Model + internal api for remote shard
* [AI] REST API handler
* fmt
* [AI] clippy fixes
* [AI] macos fix + proxy segment fix
* [AI] make text index estimation a bit more correct
* fix is_on_disk reporting for dense_vector_storage
* fix after rebase
* [AI] deep account for quantized vectors RAM usage + unify chunk size + shring volatile storage after load
* remove debug log
* cache in test
* make manual test easier to run
* rollback chunk size diff, but keep it for test only
* review fixes
* Use exhaustive match
* Use div_ceil on bits everywhere
It does not seem to be strictly necessary because the number of bits
should already be a multiple of the used container size bytes. Still
it's good practice to be careful with this calculation.
* Improve heap size bytes for encoded product quantization vectors
* Include vector stats for binary quantized vectors
* In volatile chunked vectors, include heap allocated vector
* Include rest of heap allocated structures for mutable map index
* In mutable geo index, the hash map is also heap allocated
* Update tests/manual/test_memory_reporting.py
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: timvisee <tim@visee.me>
Co-authored-by: Tim Visée <tim+github@visee.me>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
* Add empty placeholder vector storage types for named vector CRUD
Introduce EmptyDenseVectorStorage and EmptySparseVectorStorage as
placeholder storages for newly created named vectors on immutable
segments. These report all vectors as deleted, consume no disk space,
and are reconstructed from segment config on load via the new
VectorStorageType::Empty and SparseVectorStorageType::Empty variants.
Key design decisions:
- is_on_disk is derived from original user config, not hardcoded
- MultiVectorConfig is preserved for multi-vector support
- Config mismatch optimizer skips Empty storage to avoid false rebuilds
- Quantization delegates normally (handles 0 vectors gracefully)
- get_vector includes debug_assert to catch unexpected access
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* [AI] segment-level operations for creating and deleting anmed vectors
* [AI] implement named vector creation and deleting in proxy segment
* [AI] Step 3: Proxy Segment Handling for Named Vector Operations
* [AI] implement for Edge
* [AI] implement consensus operations for named vector operations
* [AI] refactor VectorNameConfig, remove VectorNameConfigInternal
* [AI] handle vector schema inconsistency in raft snapshot recovery
* [AI] rest + grpc API
* [AI] clippy
* [AI] generate openAPI schema
* fmt
* ci fixes
* [AI] fix jwt access test
* [AI] nop operation for awaiting of consensus-commited update ops
* [AI] move vector name operations into points service
* [AI] implement internal api for vector name operations
* [AI] change collection-level config along with segment level operation
* [AI] vector schema reconceliation instead of error
* fmt
* missing compile-time option
* [AI] integration test
* [AI] fix missing JWT tests
* [AI] remove NOP
* [AI] openapi test
* [AI] fix initialization of mutable segment
* [AI] more simple integration tests
* fmt
* [AI] make cluster test a bit harder
* [AI] make test less flacky
* [AI] rabbit comments
* [AI] check params compatibility before writing vector config
* [AI] make sure to register vector storages in structure payload index
* [AI] vector name validation
* lower vector length validation to 200 chars to account for prefix in filename
* [AI] proxy segment: prevent stale data leak through optimization
* fmt
* [AI] filter out removed vectors from proxy response
* [AI] handle vector name in proxy
* fmt
* adjust proxy info based on dropped vectors
* [AI] proxy segment: update filters to correct has_vector condition
* fmt
* clippy
* Fix consensus snapshot applicaiton for vector schema
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* [ai] Replace manual into mappings with Into::into
* Reformat
* [ai] Use implicit .iter
* Don't iterate over keys too
* [ai] Replace unwrap_or
* Reformat
* [ai] Use as_deref and then_some
* [ai] Use more to_string
* [ai] Use explicitly typed into conversions
* Reformat
* [ai] More explicit into conversions
* Reformat
* feat: add search_max_batchsize to strict mode config
* added test case for search_max_batchsize
* Changes for fixing CI issue dure openapi
* Modify check_strict_mode_batch
---------
Co-authored-by: Arnaud Gourlay <arnaud.gourlay@gmail.com>
* Add REST API for reading audit logs across the cluster
Introduces a new `GET /audit/logs` endpoint that retrieves audit log
entries from all peers in the cluster. The API supports filtering by
time range (time_from/time_to), dynamic key=value field filters, and
a built-in limit parameter to prevent reading too many logs at once.
- Add `audit_reader` module in storage crate for efficient file-based
log retrieval, selecting only files whose date range overlaps the
query window
- Add `GetAuditLog` internal gRPC RPC for cross-peer log retrieval
- Add `GET /audit/logs` REST endpoint restricted to management access
- Aggregate and sort results from all peers by timestamp (newest first)
Made-with: Cursor
* [AI] Update filter_files_by_time_range make it aware of the log rotation granulatity (either hourly or daily)
* [AI] For AuditLogParams, try to use DateTime types natively into serde, instead of manual parsing
* [AI] Refactor API into separate file, propagate timeout, use spawn-blocking
* [AI] introduce cancellation token
* [AI] move timestamp to constant
* small manual fixes
* review fixes part 1
* review: switch to POST instead of GET
* [AI] review: sorting update
* [AI] use strict typing
---------
Co-authored-by: Cursor Agent <agent@cursor.com>
Co-authored-by: Andrey Vasnetsov <andrey@vasnetsov.com>
* Add deferred point count to UpdateQueueInfo
* Hide if prevent_unoptimized is false
* Clippy
* Openapi
* Iterate over appendable segments only
* Only calculate deferred point count if prevent_unoptimized is true
* Rebase fixes
* Codespell
* Move `DenseVector`/`MultiDenseVector` from `api` to `segment`
* Move `OrderByInterface` from `api` to `segment`
Reason: it's used in `edge` which shouldn't depend on `api`.
* Make `shard` -> `api` dependency optional
* Remove `api` from the amalgamation
* Don't install protoc in edge Actions
* Fixups of amalgamator
Fix issues that break `qdrant-edge` build process:
- `use … as segment;` - this causes `ast-grep` rules to replace wrong
paths. So, rename to avoid collisions.
- `#[macro_use]` and `extern crate` required be in the top-level
`lib.rs`.
- `format!("…", crate::something::…)` - `ast-grep` can't fix paths
inside macros. Fixed by moving `crate::something::…` out of the macro.
* Add lib/edge/publish workspace and amalgamation script
* Move `lib/edge/examples` into `lib/edge/publish/` workspace
And fix them to use the generated `qdrant-edge` crate.
* Add github workflow
* Cleanup `qdrant-edge` public API
Removes empty modules. Checked by `cargo doc`.
* [manual] make untagged enum for consistent API for FeedbackStrategy
* add #[validate(nested)]
---------
Co-authored-by: Luis Cossío <luis.cossio@outlook.com>
