Document custom RRF (#1985)

* make dedicated fusion strategy sections

* docs: Java, Go, C# snippets

Signed-off-by: Anush008 <anushshetty90@gmail.com>

* add formula for rrf

* add rust and python snippets

* Use consistent formatting

---------

Signed-off-by: Anush008 <anushshetty90@gmail.com>
Co-authored-by: Anush008 <anushshetty90@gmail.com>
Co-authored-by: Tim Visée <tim@visee.me>

qdrant-landing/content/documentation/headless/snippets/query-points/hybrid-rrf/_description.md

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+This code snippet demonstrates a hybrid search functionality where you can query points from a collection using both sparse and dense vectors. The code showcases how to combine the results of multiple queries with different vector representations using Reciprocal Rank Fusion (RRF). RRF boosts results that are closer to the top in multiple queries. This hybrid search approach can be useful for obtaining semantic and specific word matching results simultaneously.

qdrant-landing/content/documentation/headless/snippets/query-points/hybrid-rrf/csharp.md

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+```csharp
+using Qdrant.Client;
+using Qdrant.Client.Grpc;
+
+var client = new QdrantClient("localhost", 6334);
+
+await client.QueryAsync(
+  collectionName: "{collection_name}",
+  prefetch: new List < PrefetchQuery > {
+    new() {
+      Query = new(float, uint)[] {
+          (0.22f, 1), (0.8f, 42),
+        },
+        Using = "sparse",
+        Limit = 20
+    },
+    new() {
+      Query = new float[] {
+          0.01f, 0.45f, 0.67f
+        },
+        Using = "dense",
+        Limit = 20
+    }
+  },
+  query: Fusion.Rrf
+);
+```

qdrant-landing/content/documentation/headless/snippets/query-points/hybrid-rrf/go.md

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+```go
+import (
+	"context"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+client, err := qdrant.NewClient(&qdrant.Config{
+	Host: "localhost",
+	Port: 6334,
+})
+
+client.Query(context.Background(), &qdrant.QueryPoints{
+	CollectionName: "{collection_name}",
+	Prefetch: []*qdrant.PrefetchQuery{
+		{
+			Query: qdrant.NewQuerySparse([]uint32{1, 42}, []float32{0.22, 0.8}),
+			Using: qdrant.PtrOf("sparse"),
+		},
+		{
+			Query: qdrant.NewQueryDense([]float32{0.01, 0.45, 0.67}),
+			Using: qdrant.PtrOf("dense"),
+		},
+	},
+	Query: qdrant.NewQueryFusion(qdrant.Fusion_RRF),
+})
+```

qdrant-landing/content/documentation/headless/snippets/query-points/hybrid-rrf/http.md

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+```http
+POST /collections/{collection_name}/points/query
+{
+    "prefetch": [
+        {
+            "query": { 
+                "indices": [1, 42],    // <┐
+                "values": [0.22, 0.8]  // <┴─sparse vector
+             },
+            "using": "sparse",
+            "limit": 20
+        },
+        {
+            "query": [0.01, 0.45, 0.67, ...], // <-- dense vector
+            "using": "dense",
+            "limit": 20
+        }
+    ],
+    "query": { "fusion": "rrf" }, // <--- reciprocal rank fusion
+    "limit": 10
+}
+```

qdrant-landing/content/documentation/headless/snippets/query-points/hybrid-rrf/java.md

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+```java
+import java.util.List;
+
+import static io.qdrant.client.QueryFactory.fusion;
+import static io.qdrant.client.QueryFactory.nearest;
+
+import io.qdrant.client.QdrantClient;
+import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.grpc.Points.Fusion;
+import io.qdrant.client.grpc.Points.PrefetchQuery;
+import io.qdrant.client.grpc.Points.QueryPoints;
+
+QdrantClient client = new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
+
+client.queryAsync(
+    QueryPoints.newBuilder()
+    .setCollectionName("{collection_name}")
+    .addPrefetch(PrefetchQuery.newBuilder()
+      .setQuery(nearest(List.of(0.22f, 0.8f), List.of(1, 42)))
+      .setUsing("sparse")
+      .setLimit(20)
+      .build())
+    .addPrefetch(PrefetchQuery.newBuilder()
+      .setQuery(nearest(List.of(0.01f, 0.45f, 0.67f)))
+      .setUsing("dense")
+      .setLimit(20)
+      .build())
+    .setQuery(fusion(Fusion.RRF))
+    .build())
+  .get();
+```

qdrant-landing/content/documentation/headless/snippets/query-points/hybrid-rrf/python.md

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+```python
+from qdrant_client import QdrantClient, models
+
+client = QdrantClient(url="http://localhost:6333")
+
+client.query_points(
+    collection_name="{collection_name}",
+    prefetch=[
+        models.Prefetch(
+            query=models.SparseVector(indices=[1, 42], values=[0.22, 0.8]),
+            using="sparse",
+            limit=20,
+        ),
+        models.Prefetch(
+            query=[0.01, 0.45, 0.67],  # <-- dense vector
+            using="dense",
+            limit=20,
+        ),
+    ],
+    query=models.FusionQuery(fusion=models.Fusion.RRF),
+)
+```

qdrant-landing/content/documentation/headless/snippets/query-points/hybrid-rrf/rust.md

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+```rust
+use qdrant_client::Qdrant;
+use qdrant_client::qdrant::{Fusion, PrefetchQueryBuilder, Query, QueryPointsBuilder};
+
+let client = Qdrant::from_url("http://localhost:6334").build()?;
+
+client.query(
+    QueryPointsBuilder::new("{collection_name}")
+        .add_prefetch(PrefetchQueryBuilder::default()
+            .query(Query::new_nearest([(1, 0.22), (42, 0.8)].as_slice()))
+            .using("sparse")
+            .limit(20u64)
+        )
+        .add_prefetch(PrefetchQueryBuilder::default()
+            .query(Query::new_nearest(vec![0.01, 0.45, 0.67]))
+            .using("dense")
+            .limit(20u64)
+        )
+        .query(Query::new_fusion(Fusion::Rrf))
+).await?;
+```

qdrant-landing/content/documentation/headless/snippets/query-points/hybrid-rrf/typescript.md

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+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 });
+
+client.query("{collection_name}", {
+    prefetch: [
+        {
+            query: {
+                values: [0.22, 0.8],
+                indices: [1, 42],
+            },
+            using: 'sparse',
+            limit: 20,
+        },
+        {
+            query: [0.01, 0.45, 0.67],
+            using: 'dense',
+            limit: 20,
+        },
+    ],
+    query: {
+        fusion: 'rrf',
+    },
+});
+```