search example

qdrant-landing/content/documentation/concepts/search.md

@@ -254,7 +254,74 @@ Search is processing only among vectors with the same name.
 
 If the collection was created with sparse vectors, the name of the sparse vector to use for searching should be provided:
 
-TODO: add examples
+```http
+POST /collections/{collection_name}/points/search
+{
+    "vector": {
+        "name": "text",
+        "vector": {
+            "indices": [6, 7],
+            "values": [1.0, 2.0]
+        }    
+    },
+    "limit": 3
+}
+```
+
+```python
+from qdrant_client import QdrantClient
+from qdrant_client.http import models
+
+client = QdrantClient("localhost", port=6333)
+
+client.search(
+    collection_name="{collection_name}",
+    query_vector=models.NamedSparseVector(
+        name="text",
+        vector=models.SparseVector(
+            indices=[1, 7],
+            values=[2.0, 1.0],
+        ),
+    ),
+    limit=3,
+)
+```
+
+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 });
+
+client.search("{collection_name}", {
+  vector: {
+    name: "text",
+    vector: {
+        indices: [1, 7],
+        values: [2.0, 1.0]
+    },
+  },
+  limit: 3,
+});
+```
+
+```rust
+use qdrant_client::{client::QdrantClient, client::Vector, qdrant::SearchPoints};
+
+let client = QdrantClient::from_url("http://localhost:6334").build()?;
+
+let sparse_vector: Vector = vec![(1, 2.0), (7, 1.0)].into();
+
+client
+    .search_points(&SearchPoints {
+        collection_name: "{collection_name}".to_string(),
+        vector_name: Some("text".to_string()),
+        sparse_indices: sparse_vector.indices,
+        vector: sparse_vector.data,
+        limit: 3,
+        ..Default::default()
+    })
+    .await?;
+```
 
 ### Filtering results by score