Use score function rather than $score variable

fern/apis/master/openapi-overrides.yml

@@ -916,7 +916,7 @@ paths:
             - 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: 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\n// Score boost depending on payload conditions (as of 1.14.0)\nawait client.QueryAsync(\n\tcollectionName: \"{collection_name}\",\n\tprefetch:\n\t[\n\t\tnew PrefetchQuery { Query = new float[] { 0.01f, 0.45f, 0.67f }, Limit = 100 },\n\t],\n\tquery: new Formula\n\t{\n\t\tExpression = new SumExpression\n\t\t{\n\t\t\tSum =\n\t\t\t{\n\t\t\t\t\"$score\",\n\t\t\t\tnew MultExpression\n\t\t\t\t{\n\t\t\t\t\tMult = { 0.5f, Match(\"tag\", [\"h1\", \"h2\", \"h3\", \"h4\"]) },\n\t\t\t\t},\n\t\t\t\tnew MultExpression { Mult = { 0.25f, Match(\"tag\", [\"p\", \"li\"]) } },\n\t\t\t},\n\t\t},\n\t},\n\tlimit: 10\n);\n\n// Score boost geographically closer points (as of 1.14.0)\nawait client.QueryAsync(\n\tcollectionName: \"{collection_name}\",\n\tprefetch:\n\t[\n\t\tnew PrefetchQuery { Query = new float[] { 0.01f, 0.45f, 0.67f }, Limit = 100 },\n\t],\n\tquery: new Formula\n\t{\n\t\tExpression = new SumExpression\n\t\t{\n\t\t\tSum =\n\t\t\t{\n\t\t\t\t\"$score\",\n\t\t\t\tWithExpDecay(\n\t\t\t\t\tnew()\n\t\t\t\t\t{\n\t\t\t\t\t\tX = new GeoDistance\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tOrigin = new GeoPoint { Lat = 52.504043, Lon = 13.393236 },\n\t\t\t\t\t\t\tTo = \"geo.location\",\n\t\t\t\t\t\t},\n\t\t\t\t\t\tScale = 5000,\n\t\t\t\t\t}\n\t\t\t\t),\n\t\t\t},\n\t\t},\n\t\tDefaults =\n\t\t{\n\t\t\t[\"geo.location\"] = new Dictionary<string, Value>\n\t\t\t{\n\t\t\t\t[\"lat\"] = 48.137154,\n\t\t\t\t[\"lon\"] = 11.576124,\n\t\t\t},\n\t\t},\n\t}\n);\n"
               language: csharp
         - code-samples:
-            - code: "use qdrant_client::qdrant::{\n    Condition, DecayParamsExpressionBuilder, Expression, FormulaBuilder, Fusion, GeoPoint,\n    PointId, PrefetchQueryBuilder, Query, QueryPointsBuilder, RecommendInputBuilder,\n    Sample,\n};\nuse qdrant_client::Qdrant;\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(PointId::from(\"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(vec![(1, 0.22), (42, 0.8)])\n            .using(\"sparse\")\n            .limit(20u64)\n        )\n        .add_prefetch(PrefetchQueryBuilder::default()\n            .query(vec![0.01, 0.45, 0.67])\n            .using(\"dense\")\n            .limit(20u64)\n        )\n        .query(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(vec![0.01, 0.45, 0.67])\n            .limit(100u64)\n        )\n        .query(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\n// Score boost depending on payload conditions (as of 1.14.0)\nlet _tag_boosted = client.query(\n    QueryPointsBuilder::new(\"{collection_name}\")\n        .add_prefetch(PrefetchQueryBuilder::default()\n            .query(vec![0.01, 0.45, 0.67])\n            .limit(100u64)\n        )\n        .query(FormulaBuilder::new(Expression::sum_with([\n            Expression::variable(\"$score\"),\n            Expression::mult_with([\n                Expression::constant(0.5),\n                Expression::condition(Condition::matches(\"tag\", [\"h1\", \"h2\", \"h3\", \"h4\"])),\n            ]),\n            Expression::mult_with([\n                Expression::constant(0.25),\n                Expression::condition(Condition::matches(\"tag\", [\"p\", \"li\"])),\n            ]),\n        ])))\n        .limit(10)\n    ).await?;\n\n// Score boost geographically closer points (as of 1.14.0)\nlet _geo_boosted = client.query(\n    QueryPointsBuilder::new(\"{collection_name}\")\n            .add_prefetch(\n                PrefetchQueryBuilder::default()\n                    .query(vec![0.01, 0.45, 0.67])\n                    .limit(100u64),\n            )\n            .query(\n                FormulaBuilder::new(Expression::sum_with([\n                    Expression::variable(\"$score\"),\n                    Expression::exp_decay(\n                        DecayParamsExpressionBuilder::new(Expression::geo_distance_with(\n                            // Berlin\n                            GeoPoint { lat: 52.504043, lon: 13.393236 },\n                            \"geo.location\",\n                        ))\n                        .scale(5_000.0),\n                    ),\n                ]))\n                // Munich\n                .add_default(\"geo.location\", GeoPoint { lat: 48.137154, lon: 11.576124 }),\n            )\n            .limit(10),\n    )\n    .await?;\n"
+            - code: "use qdrant_client::qdrant::{\n    Condition, DecayParamsExpressionBuilder, Expression, FormulaBuilder, Fusion, GeoPoint,\n    PointId, PrefetchQueryBuilder, Query, QueryPointsBuilder, RecommendInputBuilder,\n    Sample,\n};\nuse qdrant_client::Qdrant;\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(PointId::from(\"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(vec![(1, 0.22), (42, 0.8)])\n            .using(\"sparse\")\n            .limit(20u64)\n        )\n        .add_prefetch(PrefetchQueryBuilder::default()\n            .query(vec![0.01, 0.45, 0.67])\n            .using(\"dense\")\n            .limit(20u64)\n        )\n        .query(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(vec![0.01, 0.45, 0.67])\n            .limit(100u64)\n        )\n        .query(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\n// Score boost depending on payload conditions (as of 1.14.0)\nlet _tag_boosted = client.query(\n    QueryPointsBuilder::new(\"{collection_name}\")\n        .add_prefetch(PrefetchQueryBuilder::default()\n            .query(vec![0.01, 0.45, 0.67])\n            .limit(100u64)\n        )\n        .query(FormulaBuilder::new(Expression::sum_with([\n            Expression::score(),\n            Expression::mult_with([\n                Expression::constant(0.5),\n                Expression::condition(Condition::matches(\"tag\", [\"h1\", \"h2\", \"h3\", \"h4\"])),\n            ]),\n            Expression::mult_with([\n                Expression::constant(0.25),\n                Expression::condition(Condition::matches(\"tag\", [\"p\", \"li\"])),\n            ]),\n        ])))\n        .limit(10)\n    ).await?;\n\n// Score boost geographically closer points (as of 1.14.0)\nlet _geo_boosted = client.query(\n    QueryPointsBuilder::new(\"{collection_name}\")\n            .add_prefetch(\n                PrefetchQueryBuilder::default()\n                    .query(vec![0.01, 0.45, 0.67])\n                    .limit(100u64),\n            )\n            .query(\n                FormulaBuilder::new(Expression::sum_with([\n                    Expression::score(),\n                    Expression::exp_decay(\n                        DecayParamsExpressionBuilder::new(Expression::geo_distance_with(\n                            // Berlin\n                            GeoPoint { lat: 52.504043, lon: 13.393236 },\n                            \"geo.location\",\n                        ))\n                        .scale(5_000.0),\n                    ),\n                ]))\n                // Munich\n                .add_default(\"geo.location\", GeoPoint { lat: 48.137154, lon: 11.576124 }),\n            )\n            .limit(10),\n    )\n    .await?;\n"
               language: rust
         - code-samples:
             - code: "# Query nearest by ID\ncurl -X POST \\\n  'http://localhost:6333/collections/collection_name/points/query' \\\n  --header 'api-key: <api-key-value>' \\\n  --header 'Content-Type: application/json' \\\n  --data-raw '{\n  \"query\": \"43cf51e2-8777-4f52-bc74-c2cbde0c8b04\"\n}'\n\n# Recommend on the average of these vectors\ncurl -X POST \\\n  'http://localhost:6333/collections/collection_name/points/query' \\\n  --header 'api-key: <api-key-value>' \\\n  --header 'Content-Type: application/json' \\\n  --data-raw '{\n  \"query\": {\n    \"recommend\": {\n      \"positive\": [\n        [\n          0.11,\n          0.35,\n          0.6\n        ]\n      ],\n      \"negative\": [\n        \"43cf51e2-8777-4f52-bc74-c2cbde0c8b04\",\n        [\n          0.01,\n          0.45,\n          0.67\n        ]\n      ]\n    }\n  }\n}'\n\n# Fusion query\ncurl -X POST \\\n  'http://localhost:6333/collections/collection_name/points/query' \\\n  --header 'api-key: <api-key-value>' \\\n  --header 'Content-Type: application/json' \\\n  --data-raw '{\n  \"prefetch\": [\n    {\n      \"query\": {\n        \"values\": [\n          0.22,\n          0.8\n        ],\n        \"indices\": [\n          1,\n          42\n        ]\n      },\n      \"using\": \"sparse\",\n      \"limit\": 20\n    },\n    {\n      \"query\": [\n        0.01,\n        0.45,\n        0.67\n      ],\n      \"using\": \"dense\",\n      \"limit\": 20\n    }\n  ],\n  \"query\": {\n    \"fusion\": \"rrf\"\n  }\n}'\n\n# 2-stage query\ncurl -X POST \\\n  'http://localhost:6333/collections/collection_name/points/query' \\\n  --header 'api-key: <api-key-value>' \\\n  --header 'Content-Type: application/json' \\\n  --data-raw '{\n  \"prefetch\": {\n    \"query\": [\n      1,\n      23,\n      45,\n      67\n    ],\n    \"limit\": 100\n  },\n  \"query\": [\n    [\n      0.1,\n      0.2,\n      0.3\n    ],\n    [\n      0.2,\n      0.1,\n      0.35\n    ],\n    [\n      0.8,\n      0.9,\n      0.53\n    ]\n  ],\n  \"using\": \"colbert\",\n  \"limit\": 10\n}'\n\n# Random sampling (as of 1.11.0)\ncurl -X POST \\\n  'http://localhost:6333/collections/collection_name/points/query' \\\n  --header 'api-key: <api-key-value>' \\\n  --header 'Content-Type: application/json' \\\n  --data-raw '{\n  \"query\": {\n    \"sample\": \"random\"\n  }\n}'\n\n# Score boost depending on payload conditions (as of 1.14.0)\ncurl -X POST \\\n  'http://localhost:6333/collections/collection_name/points/query' \\\n  --header 'api-key: <api-key-value>' \\\n  --header 'Content-Type: application/json' \\\n  --data-raw '{\n    \"prefetch\": {\n        \"query\": [0.2, 0.8, ...],  // <-- dense vector\n        \"limit\": 50\n    }\n    \"query\": {\n        \"formula\": {\n            \"sum\": [\n                \"$score,\n                { \"mult\": [ 0.5, { \"key\": \"tag\", \"match\": { \"any\": [\"h1\", \"h2\", \"h3\", \"h4\"] } } ] },\n                { \"mult\": [ 0.25, { \"key\": \"tag\", \"match\": { \"any\": [\"p\", \"li\"] } } ] }\n            ]\n        }\n    }\n}'\n\n# Score boost geographically closer points (as of 1.14.0)\ncurl -X POST \\\n  'http://localhost:6333/collections/collection_name/points/query' \\\n  --header 'api-key: <api-key-value>' \\\n  --header 'Content-Type: application/json' \\\n  --data-raw '{\n    \"prefetch\": { \"query\": [0.2, 0.8, ...], \"limit\": 50 },\n    \"query\": {\n        \"formula\": {\n            \"sum\": [\n                \"$score\",\n                {\n                    \"gauss_decay\": {\n                        \"x\": {\n                            \"geo_distance\": {\n                                \"origin\": { \"lat\": 52.504043, \"lon\": 13.393236 } // Berlin\n                                \"to\": \"geo.location\"\n                            }\n                        },\n                        \"scale\": 5000 // 5km\n                    }\n                }\n            ]\n        },\n        \"defaults\": { \"geo.location\": {\"lat\": 48.137154, \"lon\": 11.576124} } // Munich\n    }\n}'\n"

fern/apis/v1.14.x/openapi-overrides.yml

@@ -1964,7 +1964,7 @@ paths:
             {collection_name}\")\n        .add_prefetch(PrefetchQueryBuilder::default()\n\
             \            .query(vec![0.01, 0.45, 0.67])\n            .limit(100u64)\n\
             \        )\n        .query(FormulaBuilder::new(Expression::sum_with([\n\
-            \            Expression::variable(\"$score\"),\n            Expression::mult_with([\n\
+            \            Expression::score(),\n            Expression::mult_with([\n\
             \                Expression::constant(0.5),\n                Expression::condition(Condition::matches(\"\
             tag\", [\"h1\", \"h2\", \"h3\", \"h4\"])),\n            ]),\n        \
             \    Expression::mult_with([\n                Expression::constant(0.25),\n\
@@ -1976,8 +1976,8 @@ paths:
             \                    .query(vec![0.01, 0.45, 0.67])\n                \
             \    .limit(100u64),\n            )\n            .query(\n           \
             \     FormulaBuilder::new(Expression::sum_with([\n                   \
-            \ Expression::variable(\"$score\"),\n                    Expression::exp_decay(\n\
-            \                        DecayParamsExpressionBuilder::new(Expression::geo_distance_with(\n\
+            \ Expression::score(),\n                    Expression::exp_decay(\n \
+            \                       DecayParamsExpressionBuilder::new(Expression::geo_distance_with(\n\
             \                            // Berlin\n                            GeoPoint\
             \ { lat: 52.504043, lon: 13.393236 },\n                            \"\
             geo.location\",\n                        ))\n                        .scale(5_000.0),\n\

snippets/rust/query_points.rs

@@ -71,7 +71,7 @@ let _tag_boosted = client.query(
             .limit(100u64)
         )
         .query(FormulaBuilder::new(Expression::sum_with([
-            Expression::variable("$score"),
+            Expression::score(),
             Expression::mult_with([
                 Expression::constant(0.5),
                 Expression::condition(Condition::matches("tag", ["h1", "h2", "h3", "h4"])),
@@ -94,7 +94,7 @@ let _geo_boosted = client.query(
             )
             .query(
                 FormulaBuilder::new(Expression::sum_with([
-                    Expression::variable("$score"),
+                    Expression::score(),
                     Expression::exp_decay(
                         DecayParamsExpressionBuilder::new(Expression::geo_distance_with(
                             // Berlin