Sync OpenAPI

fern/apis/master/openapi-overrides.yml

@@ -762,7 +762,7 @@ paths:
             - code: "use qdrant_client::Qdrant;\nuse qdrant_client::qdrant::{Condition, Filter, PointId, PrefetchQueryBuilder, Query, QueryPointsBuilder};\n\nlet client = Qdrant::from_url(\"http://localhost:6334\").build()?;\n\n// Query nearest by ID\nlet _nearest = client.query(\n    QueryPointsBuilder::new(\"{collection_name}\")\n        .query(Query::new_nearest(PointId::new(\"43cf51e2-8777-4f52-bc74-c2cbde0c8b04\")))\n).await?;\n\n// Recommend on the average of these vectors\nlet _recommendations = client.query(\n    QueryPointsBuilder::new(\"{collection_name}\")\n        .query(Query::new_recommend(\n            RecommendInputBuilder::default()\n                .add_positive(vec![0.1; 8])\n                .add_negative(PointId::from(0))\n        ))\n).await?;\n\n// Fusion query\nlet _hybrid = client.query(\n    QueryPointsBuilder::new(\"{collection_name}\")\n        .add_prefetch(PrefetchQueryBuilder::default()\n            .query(Query::new_nearest([(1, 0.22), (42, 0.8)].as_slice()))\n            .using(\"sparse\")\n            .limit(20u64)\n        )\n        .add_prefetch(PrefetchQueryBuilder::default()\n            .query(Query::new_nearest(vec![0.01, 0.45, 0.67]))\n            .using(\"dense\")\n            .limit(20u64)\n        )\n        .query(Query::new_fusion(Fusion::Rrf))\n).await?;\n\n// 2-stage query\nlet _refined = client.query(\n    QueryPointsBuilder::new(\"{collection_name}\")\n        .add_prefetch(PrefetchQueryBuilder::default()\n            .query(Query::new_nearest(vec![0.01, 0.45, 0.67]))\n            .limit(100u64)\n        )\n        .query(Query::new_nearest(vec![\n            vec![0.1, 0.2],\n            vec![0.2, 0.1],\n            vec![0.8, 0.9],\n        ]))\n        .using(\"colbert\")\n        .limit(10u64)\n).await?;\n\n// Random sampling (as of 1.11.0)\nlet _sampled = client\n    .query(\n        QueryPointsBuilder::new(\"{collection_name}\")\n            .query(Query::new_sample(Sample::Random))\n    )\n    .await?;\n"
               language: rust
         - code-samples:
-            - code: "using Qdrant.Client;\nusing Qdrant.Client.Grpc;\n\nvar client = new QdrantClient(\"localhost\", 6334);\n\n// Query nearest by ID\nawait client.QueryAsync(\n\tcollectionName: \"{collection_name}\",\n\tquery: Guid.Parse(\"43cf51e2-8777-4f52-bc74-c2cbde0c8b04\")\n);\n\n// Recommend on the average of these vectors\nawait client.QueryAsync(\n\tcollectionName: \"{collection_name}\",\n\tquery: new RecommendInput\n\t{\n\t\tPositive =\n\t\t{\n\t\t\tGuid.Parse(\"43cf51e2-8777-4f52-bc74-c2cbde0c8b04\"),\n\t\t\tnew float[] { 0.11f, 0.35f, 0.6f }\n\t\t},\n\t\tNegative = { new float[] { 0.01f, 0.45f, 0.67f } }\n\t}\n);\n\n// Fusion query\nawait client.QueryAsync(\n\tcollectionName: \"{collection_name}\",\n\tprefetch: new List<PrefetchQuery>\n\t{\n\t\tnew()\n\t\t{\n\t\t\tQuery = new (float, uint)[] { (0.22f, 1), (0.8f, 42), },\n\t\t\tUsing = \"sparse\",\n\t\t\tLimit = 20\n\t\t},\n\t\tnew()\n\t\t{\n\t\t\tQuery = new float[] { 0.01f, 0.45f, 0.67f },\n\t\t\tUsing = \"dense\",\n\t\t\tLimit = 20\n\t\t}\n\t},\n\tquery: new FusionQuery { Fusion = Fusion.Rrf }\n);\n\n// 2-stage query\nawait client.QueryAsync(\n\tcollectionName: \"{collection_name}\",\n\tprefetch: new List<PrefetchQuery>\n\t{\n\t\tnew() { Query = new float[] { 0.01f, 0.45f, 0.67f }, Limit = 100 }\n\t},\n\tquery: new float[][] { [0.1f, 0.2f], [0.2f, 0.1f], [0.8f, 0.9f] },\n\tusingVector: \"colbert\",\n\tlimit: 10\n);\n\n// Random sampling (as of 1.11.0)\nawait client.QueryAsync(\n    collectionName: \"{collection_name}\",\n    query: new SampleQuery { Sample=Sample.Random }\n);\n"
+            - code: "using Qdrant.Client;\nusing Qdrant.Client.Grpc;\n\nvar client = new QdrantClient(\"localhost\", 6334);\n\n// Query nearest by ID\nawait client.QueryAsync(\n\tcollectionName: \"{collection_name}\",\n\tquery: Guid.Parse(\"43cf51e2-8777-4f52-bc74-c2cbde0c8b04\")\n);\n\n// Recommend on the average of these vectors\nawait client.QueryAsync(\n\tcollectionName: \"{collection_name}\",\n\tquery: new RecommendInput\n\t{\n\t\tPositive =\n\t\t{\n\t\t\tGuid.Parse(\"43cf51e2-8777-4f52-bc74-c2cbde0c8b04\"),\n\t\t\tnew float[] { 0.11f, 0.35f, 0.6f }\n\t\t},\n\t\tNegative = { new float[] { 0.01f, 0.45f, 0.67f } }\n\t}\n);\n\n// Fusion query\nawait client.QueryAsync(\n\tcollectionName: \"{collection_name}\",\n\tprefetch: new List<PrefetchQuery>\n\t{\n\t\tnew()\n\t\t{\n\t\t\tQuery = new (float, uint)[] { (0.22f, 1), (0.8f, 42), },\n\t\t\tUsing = \"sparse\",\n\t\t\tLimit = 20\n\t\t},\n\t\tnew()\n\t\t{\n\t\t\tQuery = new float[] { 0.01f, 0.45f, 0.67f },\n\t\t\tUsing = \"dense\",\n\t\t\tLimit = 20\n\t\t}\n\t},\n\tquery: new FusionQuery { Fusion = Fusion.Rrf }\n);\n\n// 2-stage query\nawait client.QueryAsync(\n\tcollectionName: \"{collection_name}\",\n\tprefetch: new List<PrefetchQuery>\n\t{\n\t\tnew() { Query = new float[] { 0.01f, 0.45f, 0.67f }, Limit = 100 }\n\t},\n\tquery: new float[][] { [0.1f, 0.2f], [0.2f, 0.1f], [0.8f, 0.9f] },\n\tusingVector: \"colbert\",\n\tlimit: 10\n);\n\n// Random sampling (as of 1.11.0)\nawait client.QueryAsync(\n    collectionName: \"{collection_name}\",\n    query: Sample.Random\n);\n"
               language: csharp
         - code-samples:
             - code: "import static io.qdrant.client.QueryFactory.fusion;\nimport static io.qdrant.client.QueryFactory.nearest;\nimport static io.qdrant.client.QueryFactory.recommend;\nimport static io.qdrant.client.VectorInputFactory.vectorInput;\n\nimport java.util.UUID;\n\nimport io.qdrant.client.QdrantClient;\nimport io.qdrant.client.QdrantGrpcClient;\nimport io.qdrant.client.grpc.Points.Fusion;\nimport io.qdrant.client.grpc.Points.PrefetchQuery;\nimport io.qdrant.client.grpc.Points.QueryPoints;\nimport io.qdrant.client.grpc.Points.RecommendInput;\n\nQdrantClient client =\n    new QdrantClient(QdrantGrpcClient.newBuilder(\"localhost\", 6334, false).build());\n\n// Query nearest by ID\nclient\n    .queryAsync(\n        QueryPoints.newBuilder()\n            .setCollectionName(\"{collection_name}\")\n            .setQuery(nearest(UUID.fromString(\"43cf51e2-8777-4f52-bc74-c2cbde0c8b04\")))\n            .build())\n    .get();\n\n// Recommend on the average of these vectors\nclient\n    .queryAsync(\n        QueryPoints.newBuilder()\n            .setCollectionName(\"{collection_name}\")\n            .setQuery(\n                recommend(\n                    RecommendInput.newBuilder()\n                        .addPositive(vectorInput(UUID.fromString(\"43cf51e2-8777-4f52-bc74-c2cbde0c8b04\")))\n                        .addPositive(vectorInput(0.11f, 0.35f, 0.6f))\n                        .addNegative(vectorInput(0.01f, 0.45f, 0.67f))\n                        .build()))\n            .build())\n    .get();\n\n// Fusion query\nclient\n    .queryAsync(\n        QueryPoints.newBuilder()\n            .setCollectionName(\"{collection_name}\")\n            .addPrefetch(\n                PrefetchQuery.newBuilder()\n                    .setQuery(nearest(List.of(0.22f, 0.8f), List.of(1, 42)))\n                    .setUsing(\"sparse\")\n                    .setLimit(20)\n                    .build())\n            .addPrefetch(\n                PrefetchQuery.newBuilder()\n                    .setQuery(nearest(List.of(0.01f, 0.45f, 0.67f)))\n                    .setUsing(\"dense\")\n                    .setLimit(20)\n                    .build())\n            .setQuery(fusion(Fusion.RRF))\n            .build())\n    .get();\n\n// 2-stage query\nclient\n    .queryAsync(\n        QueryPoints.newBuilder()\n            .setCollectionName(\"{collection_name}\")\n            .addPrefetch(\n                PrefetchQuery.newBuilder()\n                    .setQuery(nearest(0.01f, 0.45f, 0.67f))\n                    .setLimit(100)\n                    .build())\n            .setQuery(\n                nearest(\n                    new float[][] {\n                      {0.1f, 0.2f},\n                      {0.2f, 0.1f},\n                      {0.8f, 0.9f}\n                    }))\n            .setUsing(\"colbert\")\n            .setLimit(10)\n            .build())\n    .get();\n\n// Random sampling (as of 1.11.0)\nclient\n    .queryAsync(\n        QueryPoints.newBuilder()\n            .setCollectionName(\"{collection_name}\")\n            .setQuery(sample(Sample.Random))\n            .build())\n    .get();\n"

fern/apis/v1.10.x/openapi-overrides.yml

@@ -1450,7 +1450,7 @@ paths:
             \tquery: new float[][] { [0.1f, 0.2f], [0.2f, 0.1f], [0.8f, 0.9f] },\n\
             \tusingVector: \"colbert\",\n\tlimit: 10\n);\n\n// Random sampling (as\
             \ of 1.11.0)\nawait client.QueryAsync(\n    collectionName: \"{collection_name}\"\
-            ,\n    query: new SampleQuery { Sample=Sample.Random }\n);\n"
+            ,\n    query: Sample.Random\n);\n"
           language: csharp
       - code-samples:
         - code: "import static io.qdrant.client.QueryFactory.fusion;\nimport static\