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Python generator works fine with this one 👍 |
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We can merge now, because there's already other changes for 1.16 in our development branch too. |
JojiiOfficial
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Sep 12, 2025
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⚠️ Outside diff range comments (1)
lib/api/src/grpc/proto/collections.proto (1)
557-568: Update docs/OpenAPI artifacts and regenerate clients —vectors_countis still referenced.
- Occurrences found in generated OpenAPI JSONs under docs/redoc/*/openapi.json (e.g. docs/redoc/v0.11.1/openapi.json:2707-2709; docs/redoc/v0.9.1/openapi.json:2402-2404; docs/redoc/v0.8.6/openapi.json:2247-2249).
- Example docs reference: docs/QUICK_START.md:81 (
"vectors_count": 0).- Action: remove
vectors_countfrom these generated artifacts and docs, update examples/changelogs/migrations that reference it, then regenerate client SDKs and verify no clients/examples still referencevectors_count.
🧹 Nitpick comments (2)
lib/api/src/grpc/proto/collections.proto (1)
560-568: Also reserve the removed field name and document versions.To prevent accidental reuse of the identifier and to aid future readers, reserve the name and capture deprecation/removal versions.
message CollectionInfo { CollectionStatus status = 1; // operating condition of the collection OptimizerStatus optimizer_status = 2; // status of collection optimizers - reserved 3; // Deprecated + reserved 3; // Deprecated in 1.9.0, removed in 1.16.0 (vectors_count) + reserved "vectors_count"; // Prevent reuse of the field name uint64 segments_count = 4; // Number of independent segmentsdocs/redoc/master/openapi.json (1)
13812-13821: Add missing description for UpdateVectors.update_filter (source-level).Docs are auto-generated; please add a description to UpdateVectors.update_filter in lib/api/src/rest/schema.rs, then re-generate OpenAPI:
“If specified, only update vectors for points that match this filter; points not matching the filter are left unchanged.”
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📒 Files selected for processing (9)
docs/grpc/docs.md(0 hunks)docs/redoc/master/openapi.json(1 hunks)lib/api/src/grpc/proto/collections.proto(1 hunks)lib/api/src/grpc/qdrant.rs(0 hunks)lib/collection/src/collection/collection_ops.rs(0 hunks)lib/collection/src/operations/conversions.rs(0 hunks)lib/collection/src/operations/types.rs(0 hunks)lib/collection/src/shards/local_shard/mod.rs(0 hunks)openapi/openapi-main.ytt.yaml(0 hunks)
💤 Files with no reviewable changes (7)
- openapi/openapi-main.ytt.yaml
- docs/grpc/docs.md
- lib/collection/src/collection/collection_ops.rs
- lib/api/src/grpc/qdrant.rs
- lib/collection/src/operations/conversions.rs
- lib/collection/src/operations/types.rs
- lib/collection/src/shards/local_shard/mod.rs
🧰 Additional context used
🧠 Learnings (3)
📓 Common learnings
Learnt from: generall
PR: qdrant/qdrant#7006
File: docs/redoc/master/openapi.json:13645-13652
Timestamp: 2025-08-10T18:26:12.443Z
Learning: In qdrant, docs/redoc/master/openapi.json is auto-generated from the REST schemas. To change field docs, edit lib/api/src/rest/schema.rs (e.g., add doc comments or #[schemars(description = ...)]). Specifically, UpdateVectors.update_filter lacked a description and should state: "If specified, only update vectors for points that match this filter; points not matching the filter are left unchanged."
📚 Learning: 2025-08-10T18:26:12.443Z
Learnt from: generall
PR: qdrant/qdrant#7006
File: docs/redoc/master/openapi.json:13645-13652
Timestamp: 2025-08-10T18:26:12.443Z
Learning: In qdrant, docs/redoc/master/openapi.json is auto-generated from the REST schemas. To change field docs, edit lib/api/src/rest/schema.rs (e.g., add doc comments or #[schemars(description = ...)]). Specifically, UpdateVectors.update_filter lacked a description and should state: "If specified, only update vectors for points that match this filter; points not matching the filter are left unchanged."
Applied to files:
docs/redoc/master/openapi.json
📚 Learning: 2025-08-10T18:26:33.017Z
Learnt from: generall
PR: qdrant/qdrant#7006
File: docs/redoc/master/openapi.json:10393-10401
Timestamp: 2025-08-10T18:26:33.017Z
Learning: In qdrant, docs/redoc/master/openapi.json is auto-generated. Any description changes must be made in the source (e.g., lib/api/src/rest/schema.rs and/or lib/api/src/grpc/proto/points.proto), not edited directly in the JSON.
Applied to files:
docs/redoc/master/openapi.json
🔇 Additional comments (3)
lib/api/src/grpc/proto/collections.proto (1)
560-560: Good: reserved field tag keeps wire compatibility.Reserving tag 3 after removing vectors_count is the right move and preserves on-the-wire compatibility for older clients.
docs/redoc/master/openapi.json (2)
6442-6452: Reminder: don’t hand-edit docs/redoc/master/openapi.json.Ensure changes (description/examples and schema removals) come from source templates/schemas (e.g., openapi/openapi-main.ytt.yaml, lib/api/src/rest/schema.rs), then regenerate this file.
6593-6641: vectors_count removed from CollectionInfo — verify no lingering references.
Automated ripgrep returned "No files were searched" so verification failed. Run in repo root:
rg -n -uu '\bvectors_count\b' || echo "No matches found"
| "info": { | ||
| "title": "Qdrant API", | ||
| "description": "API description for Qdrant vector search engine.\n\nThis document describes CRUD and search operations on collections of points (vectors with payload).\n\nQdrant supports any combinations of `should`, `min_should`, `must` and `must_not` conditions, which makes it possible to use in applications when object could not be described solely by vector. It could be location features, availability flags, and other custom properties businesses should take into account.\n## Examples\nThis examples cover the most basic use-cases - collection creation and basic vector search.\n### Create collection\nFirst - let's create a collection with dot-production metric.\n```\ncurl -X PUT 'http://localhost:6333/collections/test_collection' \\\n -H 'Content-Type: application/json' \\\n --data-raw '{\n \"vectors\": {\n \"size\": 4,\n \"distance\": \"Dot\"\n }\n }'\n\n```\nExpected response:\n```\n{\n \"result\": true,\n \"status\": \"ok\",\n \"time\": 0.031095451\n}\n```\nWe can ensure that collection was created:\n```\ncurl 'http://localhost:6333/collections/test_collection'\n```\nExpected response:\n```\n{\n \"result\": {\n \"status\": \"green\",\n \"vectors_count\": 0,\n \"segments_count\": 5,\n \"disk_data_size\": 0,\n \"ram_data_size\": 0,\n \"config\": {\n \"params\": {\n \"vectors\": {\n \"size\": 4,\n \"distance\": \"Dot\"\n }\n },\n \"hnsw_config\": {\n \"m\": 16,\n \"ef_construct\": 100,\n \"full_scan_threshold\": 10000\n },\n \"optimizer_config\": {\n \"deleted_threshold\": 0.2,\n \"vacuum_min_vector_number\": 1000,\n \"default_segment_number\": 2,\n \"max_segment_size\": null,\n \"memmap_threshold\": null,\n \"indexing_threshold\": 20000,\n \"flush_interval_sec\": 5,\n \"max_optimization_threads\": null\n },\n \"wal_config\": {\n \"wal_capacity_mb\": 32,\n \"wal_segments_ahead\": 0\n }\n }\n },\n \"status\": \"ok\",\n \"time\": 2.1199e-05\n}\n```\n\n### Add points\nLet's now add vectors with some payload:\n```\ncurl -L -X PUT 'http://localhost:6333/collections/test_collection/points?wait=true' \\ -H 'Content-Type: application/json' \\ --data-raw '{\n \"points\": [\n {\"id\": 1, \"vector\": [0.05, 0.61, 0.76, 0.74], \"payload\": {\"city\": \"Berlin\"}},\n {\"id\": 2, \"vector\": [0.19, 0.81, 0.75, 0.11], \"payload\": {\"city\": [\"Berlin\", \"London\"] }},\n {\"id\": 3, \"vector\": [0.36, 0.55, 0.47, 0.94], \"payload\": {\"city\": [\"Berlin\", \"Moscow\"] }},\n {\"id\": 4, \"vector\": [0.18, 0.01, 0.85, 0.80], \"payload\": {\"city\": [\"London\", \"Moscow\"] }},\n {\"id\": 5, \"vector\": [0.24, 0.18, 0.22, 0.44], \"payload\": {\"count\": [0]}},\n {\"id\": 6, \"vector\": [0.35, 0.08, 0.11, 0.44]}\n ]\n}'\n```\nExpected response:\n```\n{\n \"result\": {\n \"operation_id\": 0,\n \"status\": \"completed\"\n },\n \"status\": \"ok\",\n \"time\": 0.000206061\n}\n```\n### Search with filtering\nLet's start with a basic request:\n```\ncurl -L -X POST 'http://localhost:6333/collections/test_collection/points/search' \\ -H 'Content-Type: application/json' \\ --data-raw '{\n \"vector\": [0.2,0.1,0.9,0.7],\n \"top\": 3\n}'\n```\nExpected response:\n```\n{\n \"result\": [\n { \"id\": 4, \"score\": 1.362, \"payload\": null, \"version\": 0 },\n { \"id\": 1, \"score\": 1.273, \"payload\": null, \"version\": 0 },\n { \"id\": 3, \"score\": 1.208, \"payload\": null, \"version\": 0 }\n ],\n \"status\": \"ok\",\n \"time\": 0.000055785\n}\n```\nBut result is different if we add a filter:\n```\ncurl -L -X POST 'http://localhost:6333/collections/test_collection/points/search' \\ -H 'Content-Type: application/json' \\ --data-raw '{\n \"filter\": {\n \"should\": [\n {\n \"key\": \"city\",\n \"match\": {\n \"value\": \"London\"\n }\n }\n ]\n },\n \"vector\": [0.2, 0.1, 0.9, 0.7],\n \"top\": 3\n}'\n```\nExpected response:\n```\n{\n \"result\": [\n { \"id\": 4, \"score\": 1.362, \"payload\": null, \"version\": 0 },\n { \"id\": 2, \"score\": 0.871, \"payload\": null, \"version\": 0 }\n ],\n \"status\": \"ok\",\n \"time\": 0.000093972\n}\n```\n", | ||
| "description": "API description for Qdrant vector search engine.\n\nThis document describes CRUD and search operations on collections of points (vectors with payload).\n\nQdrant supports any combinations of `should`, `min_should`, `must` and `must_not` conditions, which makes it possible to use in applications when object could not be described solely by vector. It could be location features, availability flags, and other custom properties businesses should take into account.\n## Examples\nThis examples cover the most basic use-cases - collection creation and basic vector search.\n### Create collection\nFirst - let's create a collection with dot-production metric.\n```\ncurl -X PUT 'http://localhost:6333/collections/test_collection' \\\n -H 'Content-Type: application/json' \\\n --data-raw '{\n \"vectors\": {\n \"size\": 4,\n \"distance\": \"Dot\"\n }\n }'\n\n```\nExpected response:\n```\n{\n \"result\": true,\n \"status\": \"ok\",\n \"time\": 0.031095451\n}\n```\nWe can ensure that collection was created:\n```\ncurl 'http://localhost:6333/collections/test_collection'\n```\nExpected response:\n```\n{\n \"result\": {\n \"status\": \"green\",\n \"segments_count\": 5,\n \"disk_data_size\": 0,\n \"ram_data_size\": 0,\n \"config\": {\n \"params\": {\n \"vectors\": {\n \"size\": 4,\n \"distance\": \"Dot\"\n }\n },\n \"hnsw_config\": {\n \"m\": 16,\n \"ef_construct\": 100,\n \"full_scan_threshold\": 10000\n },\n \"optimizer_config\": {\n \"deleted_threshold\": 0.2,\n \"vacuum_min_vector_number\": 1000,\n \"default_segment_number\": 2,\n \"max_segment_size\": null,\n \"memmap_threshold\": null,\n \"indexing_threshold\": 20000,\n \"flush_interval_sec\": 5,\n \"max_optimization_threads\": null\n },\n \"wal_config\": {\n \"wal_capacity_mb\": 32,\n \"wal_segments_ahead\": 0\n }\n }\n },\n \"status\": \"ok\",\n \"time\": 2.1199e-05\n}\n```\n\n### Add points\nLet's now add vectors with some payload:\n```\ncurl -L -X PUT 'http://localhost:6333/collections/test_collection/points?wait=true' \\ -H 'Content-Type: application/json' \\ --data-raw '{\n \"points\": [\n {\"id\": 1, \"vector\": [0.05, 0.61, 0.76, 0.74], \"payload\": {\"city\": \"Berlin\"}},\n {\"id\": 2, \"vector\": [0.19, 0.81, 0.75, 0.11], \"payload\": {\"city\": [\"Berlin\", \"London\"] }},\n {\"id\": 3, \"vector\": [0.36, 0.55, 0.47, 0.94], \"payload\": {\"city\": [\"Berlin\", \"Moscow\"] }},\n {\"id\": 4, \"vector\": [0.18, 0.01, 0.85, 0.80], \"payload\": {\"city\": [\"London\", \"Moscow\"] }},\n {\"id\": 5, \"vector\": [0.24, 0.18, 0.22, 0.44], \"payload\": {\"count\": [0]}},\n {\"id\": 6, \"vector\": [0.35, 0.08, 0.11, 0.44]}\n ]\n}'\n```\nExpected response:\n```\n{\n \"result\": {\n \"operation_id\": 0,\n \"status\": \"completed\"\n },\n \"status\": \"ok\",\n \"time\": 0.000206061\n}\n```\n### Search with filtering\nLet's start with a basic request:\n```\ncurl -L -X POST 'http://localhost:6333/collections/test_collection/points/search' \\ -H 'Content-Type: application/json' \\ --data-raw '{\n \"vector\": [0.2,0.1,0.9,0.7],\n \"top\": 3\n}'\n```\nExpected response:\n```\n{\n \"result\": [\n { \"id\": 4, \"score\": 1.362, \"payload\": null, \"version\": 0 },\n { \"id\": 1, \"score\": 1.273, \"payload\": null, \"version\": 0 },\n { \"id\": 3, \"score\": 1.208, \"payload\": null, \"version\": 0 }\n ],\n \"status\": \"ok\",\n \"time\": 0.000055785\n}\n```\nBut result is different if we add a filter:\n```\ncurl -L -X POST 'http://localhost:6333/collections/test_collection/points/search' \\ -H 'Content-Type: application/json' \\ --data-raw '{\n \"filter\": {\n \"should\": [\n {\n \"key\": \"city\",\n \"match\": {\n \"value\": \"London\"\n }\n }\n ]\n },\n \"vector\": [0.2, 0.1, 0.9, 0.7],\n \"top\": 3\n}'\n```\nExpected response:\n```\n{\n \"result\": [\n { \"id\": 4, \"score\": 1.362, \"payload\": null, \"version\": 0 },\n { \"id\": 2, \"score\": 0.871, \"payload\": null, \"version\": 0 }\n ],\n \"status\": \"ok\",\n \"time\": 0.000093972\n}\n```\n", |
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🛠️ Refactor suggestion
Fix typos and refresh examples in top-level API description.
- Typo: “dot-production metric” → “dot-product metric”.
- Grammar: “This examples cover” → “These examples cover”.
- Avoid deprecated endpoints in the primary examples: prefer /collections/{collection_name}/points/query over the deprecated /points/search.
- Align the “Collection info” example with the current CollectionInfo schema (e.g., remove fields like disk_data_size/ram_data_size; optionally show points_count/indexed_vectors_count/segments_count).
Apply at least the typo/grammar fixes now:
-This examples cover the most basic use-cases - collection creation and basic vector search.
-First - let's create a collection with dot-production metric.
+These examples cover the most basic use-cases—collection creation and basic vector search.
+First, let's create a collection with the dot-product metric.📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| "description": "API description for Qdrant vector search engine.\n\nThis document describes CRUD and search operations on collections of points (vectors with payload).\n\nQdrant supports any combinations of `should`, `min_should`, `must` and `must_not` conditions, which makes it possible to use in applications when object could not be described solely by vector. It could be location features, availability flags, and other custom properties businesses should take into account.\n## Examples\nThis examples cover the most basic use-cases - collection creation and basic vector search.\n### Create collection\nFirst - let's create a collection with dot-production metric.\n```\ncurl -X PUT 'http://localhost:6333/collections/test_collection' \\\n -H 'Content-Type: application/json' \\\n --data-raw '{\n \"vectors\": {\n \"size\": 4,\n \"distance\": \"Dot\"\n }\n }'\n\n```\nExpected response:\n```\n{\n \"result\": true,\n \"status\": \"ok\",\n \"time\": 0.031095451\n}\n```\nWe can ensure that collection was created:\n```\ncurl 'http://localhost:6333/collections/test_collection'\n```\nExpected response:\n```\n{\n \"result\": {\n \"status\": \"green\",\n \"segments_count\": 5,\n \"disk_data_size\": 0,\n \"ram_data_size\": 0,\n \"config\": {\n \"params\": {\n \"vectors\": {\n \"size\": 4,\n \"distance\": \"Dot\"\n }\n },\n \"hnsw_config\": {\n \"m\": 16,\n \"ef_construct\": 100,\n \"full_scan_threshold\": 10000\n },\n \"optimizer_config\": {\n \"deleted_threshold\": 0.2,\n \"vacuum_min_vector_number\": 1000,\n \"default_segment_number\": 2,\n \"max_segment_size\": null,\n \"memmap_threshold\": null,\n \"indexing_threshold\": 20000,\n \"flush_interval_sec\": 5,\n \"max_optimization_threads\": null\n },\n \"wal_config\": {\n \"wal_capacity_mb\": 32,\n \"wal_segments_ahead\": 0\n }\n }\n },\n \"status\": \"ok\",\n \"time\": 2.1199e-05\n}\n```\n\n### Add points\nLet's now add vectors with some payload:\n```\ncurl -L -X PUT 'http://localhost:6333/collections/test_collection/points?wait=true' \\ -H 'Content-Type: application/json' \\ --data-raw '{\n \"points\": [\n {\"id\": 1, \"vector\": [0.05, 0.61, 0.76, 0.74], \"payload\": {\"city\": \"Berlin\"}},\n {\"id\": 2, \"vector\": [0.19, 0.81, 0.75, 0.11], \"payload\": {\"city\": [\"Berlin\", \"London\"] }},\n {\"id\": 3, \"vector\": [0.36, 0.55, 0.47, 0.94], \"payload\": {\"city\": [\"Berlin\", \"Moscow\"] }},\n {\"id\": 4, \"vector\": [0.18, 0.01, 0.85, 0.80], \"payload\": {\"city\": [\"London\", \"Moscow\"] }},\n {\"id\": 5, \"vector\": [0.24, 0.18, 0.22, 0.44], \"payload\": {\"count\": [0]}},\n {\"id\": 6, \"vector\": [0.35, 0.08, 0.11, 0.44]}\n ]\n}'\n```\nExpected response:\n```\n{\n \"result\": {\n \"operation_id\": 0,\n \"status\": \"completed\"\n },\n \"status\": \"ok\",\n \"time\": 0.000206061\n}\n```\n### Search with filtering\nLet's start with a basic request:\n```\ncurl -L -X POST 'http://localhost:6333/collections/test_collection/points/search' \\ -H 'Content-Type: application/json' \\ --data-raw '{\n \"vector\": [0.2,0.1,0.9,0.7],\n \"top\": 3\n}'\n```\nExpected response:\n```\n{\n \"result\": [\n { \"id\": 4, \"score\": 1.362, \"payload\": null, \"version\": 0 },\n { \"id\": 1, \"score\": 1.273, \"payload\": null, \"version\": 0 },\n { \"id\": 3, \"score\": 1.208, \"payload\": null, \"version\": 0 }\n ],\n \"status\": \"ok\",\n \"time\": 0.000055785\n}\n```\nBut result is different if we add a filter:\n```\ncurl -L -X POST 'http://localhost:6333/collections/test_collection/points/search' \\ -H 'Content-Type: application/json' \\ --data-raw '{\n \"filter\": {\n \"should\": [\n {\n \"key\": \"city\",\n \"match\": {\n \"value\": \"London\"\n }\n }\n ]\n },\n \"vector\": [0.2, 0.1, 0.9, 0.7],\n \"top\": 3\n}'\n```\nExpected response:\n```\n{\n \"result\": [\n { \"id\": 4, \"score\": 1.362, \"payload\": null, \"version\": 0 },\n { \"id\": 2, \"score\": 0.871, \"payload\": null, \"version\": 0 }\n ],\n \"status\": \"ok\",\n \"time\": 0.000093972\n}\n```\n", | |
| "description": "API description for Qdrant vector search engine.\n\nThis document describes CRUD and search operations on collections of points (vectors with payload).\n\nQdrant supports any combinations of `should`, `min_should`, `must` and `must_not` conditions, which makes it possible to use in applications when object could not be described solely by vector. It could be location features, availability flags, and other custom properties businesses should take into account.\n## Examples\nThese examples cover the most basic use-cases—collection creation and basic vector search.\n### Create collection\nFirst, let's create a collection with the dot-product metric.\n |
🤖 Prompt for AI Agents
In docs/redoc/master/openapi.json around line 6443, fix the typos and refresh
examples: change "dot-production metric" to "dot-product metric", change "This
examples cover" to "These examples cover", replace deprecated /points/search
example requests with /collections/{collection_name}/points/query, and update
the "Collection info" example to match current CollectionInfo schema by removing
disk_data_size/ram_data_size and optionally including points_count,
indexed_vectors_count, and segments_count; ensure curl examples' endpoints and
descriptions are consistent with these edits.
ffuugoo
approved these changes
Sep 12, 2025
timvisee
added a commit
that referenced
this pull request
Sep 29, 2025
* Remove deprecated vectors count from collection info * Remove vectors count from shard info * Update OpenAPI and gRPC spec * Remove vectors count from example
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Remove
vectors_countfrom collection info. It got deprecated in Qdrant 1.9.0.We can merge this along with the other changes for Qdrant 1.16.0.
Related: qdrant/landing_page#1905
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