Merge remote-tracking branch 'origin/inference' into inference

qdrant-landing/content/documentation/headless/snippets/cloud-inference/image/csharp.md

@@ -16,7 +16,7 @@ await client.UpsertAsync(
     new() {
       Id = 1,
         Vectors = new Image() {
-          Image = "https://qdrant.tech/example.png",
+          Image_ = "https://qdrant.tech/example.png",
           Model = "qdrant/clip-vit-b-32-vision",
         },
         Payload = {

qdrant-landing/content/documentation/headless/snippets/cloud-inference/image/go.md

@@ -24,14 +24,14 @@ func main() {
     }
     defer client.Close()
 
-    _, err = client.GetPointsClient().Upsert(ctx, &qdrant.UpsertPoints{
+    _, err = client.Upsert(ctx, &qdrant.UpsertPoints{
         CollectionName: "<your-collection>",
         Points: []*qdrant.PointStruct{
             {
-                Id: qdrant.NewIDNum(uint64(1)),
+                Id: qdrant.NewIDNum(1),
                 Vectors: qdrant.NewVectorsImage(&qdrant.Image{
-                    Image: "https://qdrant.tech/example.png",
                     Model: "qdrant/clip-vit-b-32-vision",
+                    Image: qdrant.NewValueString("https://qdrant.tech/example.png"),
                 }),
                 Payload: qdrant.NewValueMap(map[string]any{
                     "title": "Example image",

qdrant-landing/content/documentation/headless/snippets/cloud-inference/image/java.md

@@ -32,7 +32,7 @@ public class Main {
           .setVectors(
             vectors(
               Image.newBuilder()
-              .setImage("https://qdrant.tech/example.png")
+              .setImage(value("https://qdrant.tech/example.png"))
               .setModel("qdrant/clip-vit-b-32-vision")
               .build()))
           .putAllPayload(Map.of("title", value("Example Image")))

qdrant-landing/content/documentation/headless/snippets/cloud-inference/simple/go.md

@@ -24,11 +24,11 @@ func main() {
     }
     defer client.Close()
 
-    _, err = client.GetPointsClient().Upsert(ctx, &qdrant.UpsertPoints{
+    _, err = client.Upsert(ctx, &qdrant.UpsertPoints{
         CollectionName: "<your-collection>",
         Points: []*qdrant.PointStruct{
             {
-                Id: qdrant.NewIDNum(uint64(1)),
+                Id: qdrant.NewIDNum(1),
                 Vectors: qdrant.NewVectorsDocument(&qdrant.Document{
                     Text:  "Recipe for baking chocolate chip cookies",
                     Model: "<the-model-to-use>",

qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/csharp.md

@@ -0,0 +1,21 @@
+```csharp
+using Qdrant.Client;
+using Qdrant.Client.Grpc;
+
+var client = new QdrantClient(
+  host: "xyz-example.qdrant.io",
+  port: 6334,
+  https: true,
+  apiKey: "<your-api-key>"
+);
+
+await client.QueryAsync(
+    collectionName: "{collection_name}",
+    query: new Document()
+    {
+        Model = "cohere/embed-v4.0",
+        Text = "a green square",
+        Options = { ["cohere-api-key"] = "<YOUR_COHERE_API_KEY>", ["output_dimension"] = 512 },
+    }
+);
+```

qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/go.md

@@ -0,0 +1,29 @@
+```go
+import (
+	"context"
+	"time"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+client, err := qdrant.NewClient(&qdrant.Config{
+    Host:   "xyz-example.qdrant.io",
+    Port:   6334,
+    APIKey: "<paste-your-api-key-here>",
+    UseTLS: true,
+})
+
+client.Query(ctx, &qdrant.QueryPoints{
+	CollectionName: "{collection_name}",
+	Query: qdrant.NewQueryNearest(
+		qdrant.NewVectorInputDocument(&qdrant.Document{
+			Text:  "a green square",
+			Model: "cohere/embed-v4.0",
+			Options: qdrant.NewValueMap(map[string]any{
+				"cohere-api-key":   "<YOUR_COHERE_API_KEY>",
+				"output_dimension": 512,
+			}),
+		}),
+	),
+})
+```

qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/http.md

@@ -1,5 +1,5 @@
 ```http
-POST /collections/<your-collection_name>/points/query
+POST /collections/{collection_name}/points/query
 {
   "query": {
     "text": "a green square",

qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/java.md

@@ -0,0 +1,34 @@
+```java
+import static io.qdrant.client.QueryFactory.nearest;
+import static io.qdrant.client.ValueFactory.value;
+
+import io.qdrant.client.QdrantClient;
+import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.grpc.Points.Document;
+import java.util.Map;
+
+QdrantClient client =
+    new QdrantClient(
+        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+            .withApiKey("<your-api-key")
+            .build());
+
+client
+    .queryAsync(
+        Points.QueryPoints.newBuilder()
+            .setCollectionName("{collection_name}")
+            .setQuery(
+                nearest(
+                    Document.newBuilder()
+                        .setModel("cohere/embed-v4.0")
+                        .setText("a green square")
+                        .putAllOptions(
+                            Map.of(
+                                "cohere-api-key",
+                                value("<YOUR_COHERE_API_KEY>"),
+                                "output_dimension",
+                                value(512)))
+                        .build()))
+            .build())
+    .get();
+```

qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/python.md

@@ -0,0 +1,21 @@
+```python
+from qdrant_client import QdrantClient, models
+
+client = QdrantClient(
+    url="https://xyz-example.qdrant.io:6333", 
+    api_key="<your-api-key>", 
+    cloud_inference=True
+)
+
+client.query_points(
+    collection_name="{collection_name}",
+    query=models.Document(
+        text="a green square", 
+        model="cohere/embed-v4.0", 
+        options={
+            "cohere-api-key": "<your_cohere_api_key>", 
+            "output_dimension": 512
+        }
+    )
+)
+```

qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/rust.md

@@ -0,0 +1,25 @@
+```rust
+use qdrant_client::{
+    Qdrant, QdrantError,
+    qdrant::{Document, Query, QueryPointsBuilder, Value},
+};
+use std::collections::HashMap;
+
+let client = Qdrant::from_url("http://localhost:6333").build().unwrap();
+
+let mut options = HashMap::<String, Value>::new();
+options.insert("cohere-api-key".to_string(), "<YOUR_COHERE_API_KEY>".into());
+options.insert("output_dimension".to_string(), 512.into());
+
+client
+    .query(
+        QueryPointsBuilder::new("{collection_name}")
+            .query(Query::new_nearest(Document {
+                text: "a green square".into(),
+                model: "cohere/embed-v4.0".into(),
+                options,
+            }))
+            .build(),
+    )
+    .await?;
+```

qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/typescript.md

@@ -0,0 +1,16 @@
+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 });
+
+client.query("{collection_name}", {
+    query: {
+        text: 'a green square',
+        model: 'cohere/embed-v4.0',
+        options: {
+            'cohere-api-key': '<your_cohere_api_key>',
+            output_dimension: 512,
+        },
+    },
+});
+```

qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/csharp.md

@@ -0,0 +1,29 @@
+```csharp
+using Qdrant.Client;
+using Qdrant.Client.Grpc;
+
+var client = new QdrantClient(
+host: "xyz-example.qdrant.io", port: 6334, https: true, apiKey: "<your-api-key>");
+
+await client.UpsertAsync(
+    collectionName: "{collection_name}",
+    points: new List<PointStruct>
+    {
+        new()
+        {
+            Id = 1,
+            Vectors = new Image()
+            {
+                Model = "cohere/embed-v4.0",
+                Image_ =
+                    "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC",
+                Options =
+                {
+                    ["cohere-api-key"] = "<YOUR_COHERE_API_KEY>",
+                    ["output_dimension"] = 512,
+                },
+            },
+        },
+    }
+);
+```

qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/go.md

@@ -0,0 +1,32 @@
+```go
+import (
+	"context"
+	"time"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+client, err := qdrant.NewClient(&qdrant.Config{
+    Host:   "xyz-example.qdrant.io",
+    Port:   6334,
+    APIKey: "<paste-your-api-key-here>",
+    UseTLS: true,
+})
+
+client.Upsert(ctx, &qdrant.UpsertPoints{
+	CollectionName: "{collection_name}",
+	Points: []*qdrant.PointStruct{
+		{
+			Id: qdrant.NewIDNum(uint64(1)),
+			Vectors: qdrant.NewVectorsImage(&qdrant.Image{
+				Model: "cohere/embed-v4.0",
+				Image: qdrant.NewValueString("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC"),
+				Options: qdrant.NewValueMap(map[string]any{
+					"cohere-api-key":   "<YOUR_COHERE_API_KEY>",
+					"output_dimension": 512,
+				}),
+			}),
+		},
+	},
+})
+```

qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/http.md

@@ -1,5 +1,5 @@
 ```http
-PUT /collections/<your-collection_name>/points?wait=true
+PUT /collections/{collection_name}/points?wait=true
 {
   "points": [
     {

qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/java.md

@@ -0,0 +1,41 @@
+```java
+import static io.qdrant.client.PointIdFactory.id;
+import static io.qdrant.client.ValueFactory.value;
+import static io.qdrant.client.VectorsFactory.vectors;
+
+import io.qdrant.client.QdrantClient;
+import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.grpc.Points.Image;
+import io.qdrant.client.grpc.Points.PointStruct;
+import java.util.List;
+import java.util.Map;
+
+QdrantClient client =
+    new QdrantClient(
+        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+            .withApiKey("<your-api-key")
+            .build());
+
+client
+    .upsertAsync(
+        "{collection_name}",
+        List.of(
+            PointStruct.newBuilder()
+                .setId(id(1))
+                .setVectors(
+                    vectors(
+                        Image.newBuilder()
+                            .setModel("cohere/embed-v4.0")
+                            .setImage(
+                                value(
+                                    "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC"))
+                            .putAllOptions(
+                                Map.of(
+                                    "cohere-api-key",
+                                    value("<YOUR_COHERE_API_KEY>"),
+                                    "output_dimension",
+                                    value(512)))
+                            .build()))
+                .build()))
+    .get();
+```

qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/python.md

@@ -0,0 +1,26 @@
+```python
+from qdrant_client import QdrantClient, models
+
+client = QdrantClient(
+    url="https://xyz-example.qdrant.io:6333", 
+    api_key="<your-api-key>", 
+    cloud_inference=True
+)
+
+client.upsert(
+    collection_name="{collection_name}",
+    points=[
+        models.PointStruct(
+            id=1,
+            vector=models.Document(
+                text="a green square",
+                model="cohere/embed-v4.0",
+                options={
+                    "cohere-api-key": "<your_cohere_api_key>",
+                    "output_dimension": 512
+                }
+            )
+        )
+    ]
+)
+```

qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/rust.md

@@ -0,0 +1,25 @@
+```rust
+use qdrant_client::{
+    Payload, Qdrant, QdrantError,
+    qdrant::{Document, PointStruct, UpsertPointsBuilder},
+};
+use std::collections::HashMap;
+
+let client = Qdrant::from_url("<your-qdrant-url>").build()?;
+let mut options = HashMap::new();
+options.insert("cohere-api-key".to_string(), "<YOUR_COHERE_API_KEY>".into());
+options.insert("output_dimension".to_string(), 512.into());
+
+client
+    .upsert_points(UpsertPointsBuilder::new("{collection_name}",
+        vec![
+            PointStruct::new(1,
+                Document {
+                  text: "Recipe for baking chocolate chip cookies requires flour, sugar, eggs, and chocolate chips.".into(),
+                  model: "openai/text-embedding-3-small".into(),
+                  options,
+                  },
+                Payload::default())
+            ]).wait(true))
+        .await?;
+```

qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/typescript.md

@@ -0,0 +1,21 @@
+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 });
+
+client.upsert("{collection_name}", {
+    points: [
+        {
+            id: 1,
+            vector: {
+                text: 'a green square',
+                model: 'cohere/embed-v4.0',
+                options: {
+                    'cohere-api-key': '<your_cohere_api_key>',
+                    output_dimension: 512,
+                },
+            },
+        },
+    ],
+});
+```

qdrant-landing/content/documentation/headless/snippets/inference/ingest/csharp.md

@@ -0,0 +1,26 @@
+```csharp
+using Qdrant.Client;
+using Qdrant.Client.Grpc;
+
+var client = new QdrantClient(
+host: "xyz-example.qdrant.io", port: 6334, https: true, apiKey: "<your-api-key>");
+
+await client.UpsertAsync(
+    collectionName: "{collection_name}",
+    points: new List<PointStruct>
+    {
+        new()
+        {
+            Id = 1,
+            Vectors = new Dictionary<string, Vector>
+            {
+                ["my-bm25-vector"] = new Document()
+                {
+                    Model = "qdrant/bm25",
+                    Text = "Recipe for baking chocolate chip cookies",
+                },
+            },
+        },
+    }
+);
+```

qdrant-landing/content/documentation/headless/snippets/inference/ingest/go.md

@@ -0,0 +1,30 @@
+```go
+import (
+	"context"
+	"time"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+client, err := qdrant.NewClient(&qdrant.Config{
+    Host:   "xyz-example.qdrant.io",
+    Port:   6334,
+    APIKey: "<paste-your-api-key-here>",
+    UseTLS: true,
+})
+
+client.Upsert(ctx, &qdrant.UpsertPoints{
+	CollectionName: "{collection_name}",
+	Points: []*qdrant.PointStruct{
+		{
+			Id: qdrant.NewIDNum(uint64(1)),
+			Vectors: qdrant.NewVectorsMap(map[string]*qdrant.Vector{
+				"my-bm25-vector": qdrant.NewVectorDocument(&qdrant.Document{
+					Model: "qdrant/bm25",
+					Text:  "Recipe for baking chocolate chip cookies",
+				}),
+			}),
+		},
+	},
+})
+```

qdrant-landing/content/documentation/headless/snippets/inference/ingest/http.md

@@ -1,5 +1,5 @@
 ```http
-PUT /collections/<your-collection_name>/points
+PUT /collections/{collection_name}/points
 {
   "points": [
     {

qdrant-landing/content/documentation/headless/snippets/inference/ingest/java.md

@@ -0,0 +1,38 @@
+```java
+import static io.qdrant.client.PointIdFactory.id;
+import static io.qdrant.client.ValueFactory.value;
+import static io.qdrant.client.VectorFactory.vector;
+import static io.qdrant.client.VectorsFactory.namedVectors;
+
+
+import io.qdrant.client.QdrantClient;
+import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.grpc.Points.Image;
+import io.qdrant.client.grpc.Points.PointStruct;
+import java.util.List;
+import java.util.Map;
+
+QdrantClient client =
+    new QdrantClient(
+        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+            .withApiKey("<your-api-key")
+            .build());
+
+client
+    .upsertAsync(
+        "{collection_name}",
+        List.of(
+            PointStruct.newBuilder()
+                .setId(id(1))
+                .setVectors(
+                    namedVectors(
+                        Map.of(
+                            "my-bm25-vector",
+                            vector(
+                                Document.newBuilder()
+                                    .setModel("qdrant/bm25")
+                                    .setText("Recipe for baking chocolate chip cookies")
+                                    .build()))))
+                .build()))
+    .get();
+```

qdrant-landing/content/documentation/headless/snippets/inference/ingest/python.md

@@ -0,0 +1,24 @@
+```python
+from qdrant_client import QdrantClient, models
+
+client = QdrantClient(
+    url="https://xyz-example.qdrant.io:6333", 
+    api_key="<your-api-key>", 
+    cloud_inference=True
+)
+
+client.upsert(
+    collection_name="{collection_name}",
+    points=[
+        models.PointStruct(
+            id=1,
+            vector={
+                "my-bm25-vector": models.Document(
+                    text="Recipe for baking chocolate chip cookies",
+                    model="Qdrant/bm25",
+                )
+            },
+        )
+    ],
+)
+```

qdrant-landing/content/documentation/headless/snippets/inference/ingest/rust.md

@@ -0,0 +1,22 @@
+```rust
+use qdrant_client::{
+    Payload, Qdrant, QdrantError,
+    qdrant::{Document, PointStruct, UpsertPointsBuilder},
+};
+
+let client = Qdrant::from_url("<your-qdrant-url>").build()?;
+
+client
+    .upsert_points(UpsertPointsBuilder::new("{collection_name}",
+        vec![
+            PointStruct::new(1,
+                HashMap::from([("my-bm25-vector".to_string(),
+                    Document {
+                      text: "Recipe for baking chocolate chip cookies".into(),
+                      model: "qdrant/bm25".into(),
+                      ..Default::default()
+                      }.into())]),
+                Payload::default())
+            ]))
+        .await?;
+```

qdrant-landing/content/documentation/headless/snippets/inference/ingest/typescript.md

@@ -0,0 +1,19 @@
+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 });
+
+client.upsert("{collection_name}", {
+    points: [
+        {
+            id: 1,
+            vector: {
+                'my-bm25-vector': {
+                    text: 'Recipe for baking chocolate chip cookies',
+                    model: 'Qdrant/bm25',
+                },
+            },
+        },
+    ],
+});
+```

qdrant-landing/content/documentation/headless/snippets/inference/jinaai-query/csharp.md

@@ -0,0 +1,21 @@
+```csharp
+using Qdrant.Client;
+using Qdrant.Client.Grpc;
+
+var client = new QdrantClient(
+  host: "xyz-example.qdrant.io",
+  port: 6334,
+  https: true,
+  apiKey: "<your-api-key>"
+);
+
+await client.QueryAsync(
+    collectionName: "{collection_name}",
+    query: new Document()
+    {
+        Model = "jinaai/jina-clip-v2",
+        Text = "Mission to Mars",
+        Options = { ["jina-api-key"] = "<YOUR_JINAAI_API_KEY>", ["dimensions"] = 512 },
+    }
+);
+```

qdrant-landing/content/documentation/headless/snippets/inference/jinaai-query/go.md

@@ -0,0 +1,29 @@
+```go
+import (
+	"context"
+	"time"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+client, err := qdrant.NewClient(&qdrant.Config{
+    Host:   "xyz-example.qdrant.io",
+    Port:   6334,
+    APIKey: "<paste-your-api-key-here>",
+    UseTLS: true,
+})
+
+client.Query(ctx, &qdrant.QueryPoints{
+	CollectionName: "{collection_name}",
+	Query: qdrant.NewQueryNearest(
+		qdrant.NewVectorInputDocument(&qdrant.Document{
+			Text:  "Mission to Mars",
+			Model: "jinaai/jina-clip-v2",
+			Options: qdrant.NewValueMap(map[string]any{
+				"jina-api-key": "<YOUR_JINAAI_API_KEY>",
+				"dimensions":   512,
+			}),
+		}),
+	),
+})
+```

qdrant-landing/content/documentation/headless/snippets/inference/jinaai-query/http.md

@@ -1,5 +1,5 @@
 ```http
-POST /collections/<your-collection_name>/points/query
+POST /collections/{collection_name}/points/query
 {
   "query": {
     "text": "Mission to Mars",

qdrant-landing/content/documentation/headless/snippets/inference/jinaai-query/java.md

@@ -0,0 +1,33 @@
+```java
+import static io.qdrant.client.QueryFactory.nearest;
+import static io.qdrant.client.ValueFactory.value;
+
+import io.qdrant.client.QdrantClient;
+import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.grpc.Points.Document;
+import java.util.Map;
+
+QdrantClient client =
+    new QdrantClient(
+        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+            .withApiKey("<your-api-key")
+            .build());
+client
+    .queryAsync(
+        Points.QueryPoints.newBuilder()
+            .setCollectionName("{collection_name}")
+            .setQuery(
+                nearest(
+                    Document.newBuilder()
+                        .setModel("jinaai/jina-clip-v2")
+                        .setText("Mission to Mars")
+                        .putAllOptions(
+                            Map.of(
+                                "jina-api-key",
+                                value("<YOUR_JINAAI_API_KEY>"),
+                                "dimensions",
+                                value(512)))
+                        .build()))
+            .build())
+    .get();
+```

qdrant-landing/content/documentation/headless/snippets/inference/jinaai-query/python.md

@@ -0,0 +1,21 @@
+```python
+from qdrant_client import QdrantClient, models
+
+client = QdrantClient(
+    url="https://xyz-example.qdrant.io:6333", 
+    api_key="<your-api-key>", 
+    cloud_inference=True
+)
+
+client.query_points(
+    collection_name="{collection_name}",
+    query=models.Document(
+        text="Mission to Mars", 
+        model="jinaai/jina-clip-v2", 
+        options={
+            "jina-api-key": "<your_jinaai_api_key>", 
+            "dimensions": 512
+        }
+    )
+)
+```

qdrant-landing/content/documentation/headless/snippets/inference/jinaai-query/rust.md

@@ -0,0 +1,25 @@
+```rust
+use qdrant_client::{
+    Qdrant, QdrantError,
+    qdrant::{Document, Query, QueryPointsBuilder, Value},
+};
+use std::collections::HashMap;
+
+let client = Qdrant::from_url("<your-qdrant-url>").build().unwrap();
+
+let mut options = HashMap::<String, Value>::new();
+options.insert("jina-api-key".to_string(), "<YOUR_JINAAI_API_KEY>".into());
+options.insert("dimensions".to_string(), 512.into());
+
+client
+    .query(
+        QueryPointsBuilder::new("{collection_name}")
+            .query(Query::new_nearest(Document {
+                text: "Mission to Mars".into(),
+                model: "jinaai/jina-clip-v2".into(),
+                options,
+            }))
+            .build(),
+    )
+    .await?;
+```

qdrant-landing/content/documentation/headless/snippets/inference/jinaai-query/typescript.md

@@ -0,0 +1,16 @@
+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 });
+
+client.query("{collection_name}", {
+    query: {
+        text: 'Mission to Mars',
+        model: 'jinaai/jina-clip-v2',
+        options: {
+            'jina-api-key': '<your_jinaai_api_key>',
+            dimensions: 512,
+        },
+    },
+});
+```

qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/csharp.md

@@ -0,0 +1,28 @@
+```csharp
+using Qdrant.Client;
+using Qdrant.Client.Grpc;
+
+var client = new QdrantClient(
+  host: "xyz-example.qdrant.io",
+  port: 6334,
+  https: true,
+  apiKey: "<your-api-key>"
+);
+
+await client.UpsertAsync(
+    collectionName: "{collection_name}",
+    points: new List<PointStruct>
+    {
+        new()
+        {
+            Id = 1,
+            Vectors = new Document()
+            {
+                Model = "jinaai/jina-clip-v2",
+                Text = "Mission to Mars",
+                Options = { ["jina-api-key"] = "<YOUR_JINAAI_API_KEY>", ["dimensions"] = 512 },
+            },
+        },
+    }
+);
+```

qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/go.md

@@ -0,0 +1,32 @@
+```go
+import (
+	"context"
+	"time"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+client, err := qdrant.NewClient(&qdrant.Config{
+	Host:   "xyz-example.qdrant.io",
+	Port:   6334,
+	APIKey: "<paste-your-api-key-here>",
+	UseTLS: true,
+})
+
+client.Upsert(ctx, &qdrant.UpsertPoints{
+	CollectionName: "{collection_name}",
+	Points: []*qdrant.PointStruct{
+		{
+			Id: qdrant.NewIDNum(uint64(1)),
+			Vectors: qdrant.NewVectorsImage(&qdrant.Image{
+				Model: "jinaai/jina-clip-v2",
+				Image: qdrant.NewValueString("https://qdrant.tech/example.png"),
+				Options: qdrant.NewValueMap(map[string]any{
+					"jina-api-key": "<YOUR_JINAAI_API_KEY>",
+					"dimensions":   512,
+				}),
+			}),
+		},
+	},
+})
+```

qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/http.md

@@ -1,5 +1,5 @@
 ```http
-PUT /collections/<your-collection_name>/points?wait=true
+PUT /collections/{collection_name}/points?wait=true
 {
   "points": [
     {

qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/java.md

@@ -0,0 +1,39 @@
+```java
+import static io.qdrant.client.PointIdFactory.id;
+import static io.qdrant.client.ValueFactory.value;
+import static io.qdrant.client.VectorsFactory.vectors;
+
+import io.qdrant.client.QdrantClient;
+import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.grpc.Points.Image;
+import io.qdrant.client.grpc.Points.PointStruct;
+import java.util.List;
+import java.util.Map;
+
+QdrantClient client =
+    new QdrantClient(
+        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+            .withApiKey("<your-api-key")
+            .build());
+
+client
+    .upsertAsync(
+        "{collection_name}",
+        List.of(
+            PointStruct.newBuilder()
+                .setId(id(1))
+                .setVectors(
+                    vectors(
+                        Image.newBuilder()
+                            .setModel("jinaai/jina-clip-v2")
+                            .setImage(value("https://qdrant.tech/example.png"))
+                            .putAllOptions(
+                                Map.of(
+                                    "jina-api-key",
+                                    value("<YOUR_JINAAI_API_KEY>"),
+                                    "dimensions",
+                                    value(512)))
+                            .build()))
+                .build()))
+    .get();
+```

qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/python.md

@@ -0,0 +1,26 @@
+```python
+from qdrant_client import QdrantClient, models
+
+client = QdrantClient(
+    url="https://xyz-example.qdrant.io:6333", 
+    api_key="<your-api-key>", 
+    cloud_inference=True
+)
+
+client.upsert(
+    collection_name="{collection_name}",
+    points=[
+        models.PointStruct(
+            id=1,
+            vector=models.Image(
+                image="https://qdrant.tech/example.png",
+                model="jinaai/jina-clip-v2",
+                options={
+                    "jina-api-key": "<your_jinaai_api_key>",
+                    "dimensions": 512
+                }
+            )
+        )
+    ]
+)
+```

qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/rust.md

@@ -0,0 +1,25 @@
+```rust
+use qdrant_client::{
+    Payload, Qdrant, QdrantError,
+    qdrant::{Image, PointStruct, UpsertPointsBuilder},
+};
+use std::collections::HashMap;
+
+let client = Qdrant::from_url("<your-qdrant-url>").build()?;
+let mut options = HashMap::new();
+options.insert("jina-api-key".to_string(), "<YOUR_JINAAI_API_KEY>".into());
+options.insert("dimensions".to_string(), 512.into());
+
+client
+    .upsert_points(UpsertPointsBuilder::new("{collection_name}",
+        vec![
+            PointStruct::new(1,
+                Image {
+                  image: Some("https://qdrant.tech/example.png".into()),
+                  model: "jinaai/jina-clip-v2".into(),
+                  options,
+                  },
+                Payload::default())
+            ]).wait(true))
+        .await?;
+```

qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/typescript.md

@@ -0,0 +1,21 @@
+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 });
+
+client.upsert("{collection_name}", {
+    points: [
+        {
+            id: 1,
+            vector: {
+                image: 'https://qdrant.tech/example.png',
+                model: 'jinaai/jina-clip-v2',
+                options: {
+                    'jina-api-key': '<your_jinaai_api_key>',
+                    dimensions: 512,
+                },
+            },
+        },
+    ],
+});
+```

qdrant-landing/content/documentation/headless/snippets/inference/multiple/csharp.md

@@ -0,0 +1,33 @@
+```csharp
+using Qdrant.Client;
+using Qdrant.Client.Grpc;
+
+var client = new QdrantClient(
+host: "xyz-example.qdrant.io", port: 6334, https: true, apiKey: "<your-api-key>");
+
+await client.UpsertAsync(
+    collectionName: "{collection_name}",
+    points: new List<PointStruct>
+    {
+        new()
+        {
+            Id = 1,
+            Vectors = new Dictionary<string, Vector>
+            {
+                ["image"] = new Image()
+                {
+                    Model = "jinaai/jina-clip-v2",
+                    Image_ = "https://qdrant.tech/example.png",
+                    Options = { ["jina-api-key"] = "<YOUR_JINAAI_API_KEY>", ["dimensions"] = 512 },
+                },
+                ["text"] = new Document()
+                {
+                    Model = "sentence-transformers/all-minilm-l6-v2",
+                    Text = "Mars, the red planet",
+                },
+                ["bm25"] = new Document() { Model = "qdrant/bm25", Text = "Mars, the red planet" },
+            },
+        },
+    }
+);
+```

qdrant-landing/content/documentation/headless/snippets/inference/multiple/go.md

@@ -0,0 +1,42 @@
+```go
+import (
+	"context"
+	"time"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+client, err := qdrant.NewClient(&qdrant.Config{
+	Host:   "xyz-example.qdrant.io",
+	Port:   6334,
+	APIKey: "<paste-your-api-key-here>",
+	UseTLS: true,
+})
+
+client.Upsert(ctx, &qdrant.UpsertPoints{
+	CollectionName: "{collection_name}",
+	Points: []*qdrant.PointStruct{
+		{
+			Id: qdrant.NewIDNum(uint64(1)),
+			Vectors: qdrant.NewVectorsMap(map[string]*qdrant.Vector{
+				"image": qdrant.NewVectorImage(&qdrant.Image{
+					Model: "jinaai/jina-clip-v2",
+					Image: qdrant.NewValueString("https://qdrant.tech/example.png"),
+					Options: qdrant.NewValueMap(map[string]any{
+						"jina-api-key": "<YOUR_JINAAI_API_KEY>",
+						"dimensions":   512,
+					}),
+				}),
+				"text": qdrant.NewVectorDocument(&qdrant.Document{
+					Model: "sentence-transformers/all-minilm-l6-v2",
+					Text:  "Mars, the red planet",
+				}),
+				"my-bm25-vector": qdrant.NewVectorDocument(&qdrant.Document{
+					Model: "qdrant/bm25",
+					Text:  "Recipe for baking chocolate chip cookies",
+				}),
+			}),
+		},
+	},
+})
+```

qdrant-landing/content/documentation/headless/snippets/inference/multiple/http.md

@@ -1,5 +1,5 @@
 ```http
-PUT /collections/<your-collection_name>/points?wait=true
+PUT /collections/{collection_name}/points?wait=true
 {
   "points": [
     {

qdrant-landing/content/documentation/headless/snippets/inference/multiple/java.md

@@ -0,0 +1,56 @@
+```java
+import static io.qdrant.client.PointIdFactory.id;
+import static io.qdrant.client.ValueFactory.value;
+import static io.qdrant.client.VectorFactory.vector;
+import static io.qdrant.client.VectorsFactory.namedVectors;
+
+import io.qdrant.client.QdrantClient;
+import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.grpc.Points.Document;
+import io.qdrant.client.grpc.Points.Image;
+import io.qdrant.client.grpc.Points.PointStruct;
+import java.util.List;
+import java.util.Map;
+
+QdrantClient client =
+    new QdrantClient(
+        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+            .withApiKey("<your-api-key")
+            .build());
+
+client
+    .upsertAsync(
+        "{collection_name}",
+        List.of(
+            PointStruct.newBuilder()
+                .setId(id(1))
+                .setVectors(
+                    namedVectors(
+                        Map.of(
+                            "image",
+                            vector(
+                                Image.newBuilder()
+                                    .setModel("jinaai/jina-clip-v2")
+                                    .setImage(value("https://qdrant.tech/example.png"))
+                                    .putAllOptions(
+                                        Map.of(
+                                            "jina-api-key",
+                                            value("<YOUR_JINAAI_API_KEY>"),
+                                            "dimensions",
+                                            value(512)))
+                                    .build()),
+                            "text",
+                            vector(
+                                Document.newBuilder()
+                                    .setModel("sentence-transformers/all-minilm-l6-v2")
+                                    .setText("Mars, the red planet")
+                                    .build()),
+                            "bm25",
+                            vector(
+                                Document.newBuilder()
+                                    .setModel("qdrant/bm25")
+                                    .setText("Mars, the red planet")
+                                    .build()))))
+                .build()))
+    .get();
+```

qdrant-landing/content/documentation/headless/snippets/inference/multiple/python.md

@@ -0,0 +1,37 @@
+```python
+from qdrant_client import QdrantClient, models
+
+client = QdrantClient(
+    url="https://xyz-example.qdrant.io:6333",
+    api_key="<your-api-key>",
+    cloud_inference=True
+)
+
+client.upsert(
+    collection_name="{collection_name}",
+    points=[
+        models.PointStruct(
+            id=1,
+            vector={
+                "image": models.Image(
+                    image="https://qdrant.tech/example.png",
+                    model="jinaai/jina-clip-v2",
+                    options={
+                        "jina-api-key": "<your_jinaai_api_key>",
+                        "dimensions": 512
+                    },
+                ),
+                "text": models.Document(
+                    text="Mars, the red planet",
+                    model="sentence-transformers/all-minilm-l6-v2",
+                ),
+                "bm25": models.Document(
+                    text="Mars, the red planet",
+                    model="Qdrant/bm25",
+                ),
+            },
+        )
+    ],
+)
+
+```

qdrant-landing/content/documentation/headless/snippets/inference/multiple/rust.md

@@ -0,0 +1,51 @@
+```rust
+use qdrant_client::{
+    Payload, Qdrant, QdrantError,
+    qdrant::{Document, PointStruct, UpsertPointsBuilder, Vectors},
+};
+use std::collections::HashMap;
+
+let client = Qdrant::from_url("<your-qdrant-url>").build()?;
+
+let mut jina_options = HashMap::new();
+jina_options.insert("jina-api-key".to_string(), "<YOUR_JINAAI_API_KEY>".into());
+jina_options.insert("dimensions".to_string(), 512.into());
+
+client
+    .upsert_points(
+        UpsertPointsBuilder::new(
+            "{collection_name}",
+            vec![PointStruct::new(
+                1,
+                NamedVectors::default()
+                    .add_vector(
+                        "image",
+                        Image {
+                            image: Some("https://qdrant.tech/example.png".into()),
+                            model: "jinaai/jina-clip-v2".into(),
+                            options: jina_options,
+                        },
+                    )
+                    .add_vector(
+                        "text",
+                        Document {
+                            text: "Mars, the red planet".into(),
+                            model: "sentence-transformers/all-minilm-l6-v2".into(),
+                            ..Default::default()
+                        },
+                    )
+                    .add_vector(
+                        "bm25",
+                        Document {
+                            text: "How to bake cookies?".into(),
+                            model: "qdrant/bm25".into(),
+                            ..Default::default()
+                        },
+                    ),
+                Payload::default(),
+            )],
+        )
+        .wait(true),
+    )
+    .await?;
+```

qdrant-landing/content/documentation/headless/snippets/inference/multiple/typescript.md

@@ -0,0 +1,31 @@
+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 });
+
+client.upsert("{collection_name}", {
+    points: [
+        {
+            id: 1,
+            vector: {
+                image: {
+                    image: 'https://qdrant.tech/example.png',
+                    model: 'jinaai/jina-clip-v2',
+                    options: {
+                        'jina-api-key': '<your_jinaai_api_key>',
+                        dimensions: 512,
+                    },
+                },
+                text: {
+                    text: 'Mars, the red planet',
+                    model: 'sentence-transformers/all-minilm-l6-v2',
+                },
+                bm25: {
+                    text: 'Mars, the red planet',
+                    model: 'Qdrant/bm25',
+                },
+            },
+        },
+    ],
+});
+```

qdrant-landing/content/documentation/headless/snippets/inference/openai-query/csharp.md

@@ -0,0 +1,21 @@
+```csharp
+using Qdrant.Client;
+using Qdrant.Client.Grpc;
+
+var client = new QdrantClient(
+  host: "xyz-example.qdrant.io",
+  port: 6334,
+  https: true,
+  apiKey: "<your-api-key>"
+);
+
+await client.QueryAsync(
+    collectionName: "{collection_name}",
+    query: new Document()
+    {
+        Model = "openai/text-embedding-3-large",
+        Text = "How to bake cookies?",
+        Options = { ["openai-api-key"] = "<YOUR_OPENAI_API_KEY>", ["dimensions"] = 512 },
+    }
+);
+```

qdrant-landing/content/documentation/headless/snippets/inference/openai-query/go.md

@@ -0,0 +1,29 @@
+```go
+import (
+	"context"
+	"time"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+client, err := qdrant.NewClient(&qdrant.Config{
+	Host:   "xyz-example.qdrant.io",
+	Port:   6334,
+	APIKey: "<paste-your-api-key-here>",
+	UseTLS: true,
+})
+
+client.Query(ctx, &qdrant.QueryPoints{
+	CollectionName: "{collection_name}",
+	Query: qdrant.NewQueryNearest(
+		qdrant.NewVectorInputDocument(&qdrant.Document{
+			Model: "openai/text-embedding-3-large",
+			Text:  "How to bake cookies?",
+			Options: qdrant.NewValueMap(map[string]any{
+				"openai-api-key": "<YOUR_OPENAI_API_KEY>",
+				"dimensions":     512,
+			}),
+		}),
+	),
+})
+```

qdrant-landing/content/documentation/headless/snippets/inference/openai-query/http.md

@@ -1,5 +1,5 @@
 ```http
-POST /collections/<your-collection_name>/points/query
+POST /collections/{collection_name}/points/query
 {
   "query": {
     "text": "How to bake cookies?",

qdrant-landing/content/documentation/headless/snippets/inference/openai-query/java.md

@@ -0,0 +1,33 @@
+```java
+import static io.qdrant.client.QueryFactory.nearest;
+import static io.qdrant.client.ValueFactory.value;
+
+import io.qdrant.client.QdrantClient;
+import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.grpc.Points.Document;
+import java.util.Map;
+
+QdrantClient client =
+    new QdrantClient(
+        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+            .withApiKey("<your-api-key")
+            .build());
+client
+    .queryAsync(
+        Points.QueryPoints.newBuilder()
+            .setCollectionName("{collection_name}")
+            .setQuery(
+                nearest(
+                    Document.newBuilder()
+                        .setModel("openai/text-embedding-3-large")
+                        .setText("How to bake cookies?")
+                        .putAllOptions(
+                            Map.of(
+                                "openai-api-key",
+                                value("<YOUR_OPENAI_API_KEY>"),
+                                "dimensions",
+                                value(512)))
+                        .build()))
+            .build())
+    .get();
+```

qdrant-landing/content/documentation/headless/snippets/inference/openai-query/python.md

@@ -0,0 +1,21 @@
+```python
+from qdrant_client import QdrantClient, models
+
+client = QdrantClient(
+    url="https://xyz-example.qdrant.io:6333",
+    api_key="<your-api-key>",
+    cloud_inference=True
+)
+
+client.query_points(
+    collection_name="{collection_name}",
+    query=models.Document(
+        text="How to bake cookies?",
+        model="openai/text-embedding-3-large",
+        options={
+            "openai-api-key": "<your_openai_api_key>",
+            "dimensions": 512
+        }
+    )
+)
+```

qdrant-landing/content/documentation/headless/snippets/inference/openai-query/rust.md

@@ -0,0 +1,25 @@
+```rust
+use qdrant_client::{
+    Qdrant, QdrantError,
+    qdrant::{Document, Query, QueryPointsBuilder, Value},
+};
+use std::collections::HashMap;
+
+let client = Qdrant::from_url("<your-qdrant-url>").build().unwrap();
+
+let mut options = HashMap::<String, Value>::new();
+options.insert("openai-api-key".to_string(), "<YOUR_OPENAI_API_KEY>".into());
+options.insert("dimensions".to_string(), 512.into());
+
+client
+    .query(
+        QueryPointsBuilder::new("{collection_name}")
+            .query(Query::new_nearest(Document {
+                text: "How to bake cookies?".into(),
+                model: "openai/text-embedding-3-large".into(),
+                options,
+            }))
+            .build(),
+    )
+    .await?;
+```

qdrant-landing/content/documentation/headless/snippets/inference/openai-query/typescript.md

@@ -0,0 +1,16 @@
+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 });
+
+client.query("{collection_name}", {
+    query: {
+        text: 'How to bake cookies?',
+        model: 'openai/text-embedding-3-large',
+        options: {
+            'openai-api-key': '<your_openai_api_key>',
+            dimensions: 512,
+        },
+    },
+});
+```

qdrant-landing/content/documentation/headless/snippets/inference/openai-upsert/csharp.md

@@ -0,0 +1,24 @@
+```csharp
+using Qdrant.Client;
+using Qdrant.Client.Grpc;
+
+var client = new QdrantClient(
+host: "xyz-example.qdrant.io", port: 6334, https: true, apiKey: "<your-api-key>");
+
+await client.UpsertAsync(
+    collectionName: "{collection_name}",
+    points: new List<PointStruct>
+    {
+        new()
+        {
+            Id = 1,
+            Vectors = new Document()
+            {
+                Model = "openai/text-embedding-3-large",
+                Text = "Recipe for baking chocolate chip cookies",
+                Options = { ["openai-api-key"] = "<YOUR_OPENAI_API_KEY>", ["dimensions"] = 512 },
+            },
+        },
+    }
+);
+```

qdrant-landing/content/documentation/headless/snippets/inference/openai-upsert/go.md

@@ -0,0 +1,32 @@
+```go
+import (
+	"context"
+	"time"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+client, err := qdrant.NewClient(&qdrant.Config{
+	Host:   "xyz-example.qdrant.io",
+	Port:   6334,
+	APIKey: "<paste-your-api-key-here>",
+	UseTLS: true,
+})
+
+client.Upsert(ctx, &qdrant.UpsertPoints{
+	CollectionName: "{collection_name}",
+	Points: []*qdrant.PointStruct{
+		{
+			Id: qdrant.NewIDNum(uint64(1)),
+			Vectors: qdrant.NewVectorsDocument(&qdrant.Document{
+				Model: "openai/text-embedding-3-large",
+				Text:  "Recipe for baking chocolate chip cookies",
+				Options: qdrant.NewValueMap(map[string]any{
+					"openai-api-key": "<YOUR_OPENAI_API_KEY>",
+					"dimensions":     512,
+				}),
+			}),
+		},
+	},
+})
+```

qdrant-landing/content/documentation/headless/snippets/inference/openai-upsert/http.md

@@ -1,5 +1,5 @@
 ```http
-PUT /collections/<your-collection_name>/points?wait=true
+PUT /collections/{collection_name}/points?wait=true
 {
   "points": [
     {

qdrant-landing/content/documentation/headless/snippets/inference/openai-upsert/java.md

@@ -0,0 +1,39 @@
+```java
+import static io.qdrant.client.PointIdFactory.id;
+import static io.qdrant.client.ValueFactory.value;
+import static io.qdrant.client.VectorsFactory.vectors;
+
+import io.qdrant.client.QdrantClient;
+import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.grpc.Points.Document;
+import io.qdrant.client.grpc.Points.PointStruct;
+import java.util.List;
+import java.util.Map;
+
+QdrantClient client =
+    new QdrantClient(
+        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+            .withApiKey("<your-api-key")
+            .build());
+
+client
+    .upsertAsync(
+        "{collection_name}",
+        List.of(
+            PointStruct.newBuilder()
+                .setId(id(1))
+                .setVectors(
+                    vectors(
+                        Document.newBuilder()
+                            .setModel("openai/text-embedding-3-large")
+                            .setText("Recipe for baking chocolate chip cookies")
+                            .putAllOptions(
+                                Map.of(
+                                    "openai-api-key",
+                                    value("<YOUR_OPENAI_API_KEY>"),
+                                    "dimensions",
+                                    value(512)))
+                            .build()))
+                .build()))
+    .get();
+```

qdrant-landing/content/documentation/headless/snippets/inference/openai-upsert/python.md

@@ -0,0 +1,26 @@
+```python
+from qdrant_client import QdrantClient, models
+
+client = QdrantClient(
+    url="https://xyz-example.qdrant.io:6333", 
+    api_key="<your-api-key>", 
+    cloud_inference=True
+)
+
+client.upsert(
+    collection_name="{collection_name}",
+    points=[
+        models.PointStruct(
+            id=1,
+            vector=models.Document(
+                text="Recipe for baking chocolate chip cookies",
+                model="openai/text-embedding-3-large",
+                options={
+                    "openai-api-key": "<your_openai_api_key>",
+                    "dimensions": 512
+                }
+            )
+        )
+    ]
+)
+```

qdrant-landing/content/documentation/headless/snippets/inference/openai-upsert/rust.md

@@ -0,0 +1,25 @@
+```rust
+use qdrant_client::{
+    Payload, Qdrant, QdrantError,
+    qdrant::{Document, PointStruct, UpsertPointsBuilder},
+};
+use std::collections::HashMap;
+
+let client = Qdrant::from_url("<your-qdrant-url>").build()?;
+let mut options = HashMap::new();
+options.insert("openai-api-key".to_string(), "<YOUR_OPENAI_API_KEY>".into());
+options.insert("dimensions".to_string(), 512.into());
+
+client
+    .upsert_points(UpsertPointsBuilder::new("{collection_name}",
+        vec![
+            PointStruct::new(1,
+                Document {
+                  text: "Recipe for baking chocolate chip cookies".into(),
+                  model: "openai/text-embedding-3-large".into(),
+                  options,
+                  },
+                Payload::default())
+            ]).wait(true))
+        .await?;
+```

qdrant-landing/content/documentation/headless/snippets/inference/openai-upsert/typescript.md

@@ -0,0 +1,21 @@
+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 });
+
+client.upsert("{collection_name}", {
+    points: [
+        {
+            id: 1,
+            vector: {
+                text: 'Recipe for baking chocolate chip cookies',
+                model: 'openai/text-embedding-3-large',
+                options: {
+                    'openai-api-key': '<your_openai_api_key>',
+                    dimensions: 512,
+                },
+            },
+        },
+    ],
+});
+```

qdrant-landing/content/documentation/headless/snippets/inference/query/csharp.md

@@ -0,0 +1,17 @@
+```csharp
+using Qdrant.Client;
+using Qdrant.Client.Grpc;
+
+var client = new QdrantClient(
+  host: "xyz-example.qdrant.io",
+  port: 6334,
+  https: true,
+  apiKey: "<your-api-key>"
+);
+
+await client.QueryAsync(
+    collectionName: "{collection_name}",
+    query: new Document() { Model = "qdrant/bm25", Text = "How to bake cookies?" },
+    usingVector: "my-bm25-vector"
+);
+```

qdrant-landing/content/documentation/headless/snippets/inference/query/go.md

@@ -0,0 +1,26 @@
+```go
+import (
+	"context"
+	"time"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+client, err := qdrant.NewClient(&qdrant.Config{
+	Host:   "xyz-example.qdrant.io",
+	Port:   6334,
+	APIKey: "<paste-your-api-key-here>",
+	UseTLS: true,
+})
+
+client.Query(ctx, &qdrant.QueryPoints{
+	CollectionName: "{collection_name}",
+	Query: qdrant.NewQueryNearest(
+		qdrant.NewVectorInputDocument(&qdrant.Document{
+			Model: "qdrant/bm25",
+			Text:  "How to bake cookies?",
+		}),
+	),
+	Using: qdrant.PtrOf("my-bm25-vector"),
+})
+```

qdrant-landing/content/documentation/headless/snippets/inference/query/http.md

@@ -1,5 +1,5 @@
 ```http
-POST /collections/<your-collection_name>/points/query
+POST /collections/{collection_name}/points/query
 {
   "query": {
     "text": "How to bake cookies?",

qdrant-landing/content/documentation/headless/snippets/inference/query/java.md

@@ -0,0 +1,27 @@
+```java
+import static io.qdrant.client.QueryFactory.nearest;
+
+import io.qdrant.client.QdrantClient;
+import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.grpc.Points;
+import io.qdrant.client.grpc.Points.Document;
+
+QdrantClient client =
+    new QdrantClient(
+        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+            .withApiKey("<your-api-key")
+            .build());
+client
+    .queryAsync(
+        Points.QueryPoints.newBuilder()
+            .setCollectionName("{collection_name}")
+            .setQuery(
+                nearest(
+                    Document.newBuilder()
+                        .setModel("qdrant/bm25")
+                        .setText("How to bake cookies?")
+                        .build()))
+            .setUsing("my-bm25-vector")
+            .build())
+    .get();
+```

qdrant-landing/content/documentation/headless/snippets/inference/query/python.md

@@ -0,0 +1,18 @@
+```python
+from qdrant_client import QdrantClient, models
+
+client = QdrantClient(
+    url="https://xyz-example.qdrant.io:6333", 
+    api_key="<your-api-key>", 
+    cloud_inference=True
+)
+
+client.query_points(
+    collection_name="{collection_name}",
+    query=models.Document(
+        text="How to bake cookies?", 
+        model="Qdrant/bm25",
+    ),
+    using="my-bm25-vector",
+)
+```

qdrant-landing/content/documentation/headless/snippets/inference/query/rust.md

@@ -0,0 +1,21 @@
+```rust
+use qdrant_client::{
+    Qdrant, QdrantError,
+    qdrant::{Document, Query, QueryPointsBuilder},
+};
+
+let client = Qdrant::from_url("<your-qdrant-url>").build().unwrap();
+
+client
+    .query(
+        QueryPointsBuilder::new("{collection_name}")
+            .query(Query::new_nearest(Document {
+                text: "How to bake cookies?".into(),
+                model: "qdrant/bm25".into(),
+                ..Default::default()
+            }))
+            .using("my-bm25-vector")
+            .build(),
+    )
+    .await?;
+```

qdrant-landing/content/documentation/headless/snippets/inference/query/typescript.md

@@ -0,0 +1,13 @@
+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 });
+
+client.query("{collection_name}", {
+    query: {
+        text: 'How to bake cookies?',
+        model: 'qdrant/bm25',
+    },
+    using: 'my-bm25-vector',
+});
+```