Restructure inference docs (#2225)

* Break Inference page into several pages

* Make all inference code snippets testable and clean up

* Make more snippets testable

* Edits

* Document automatic query and passage prefix injection in Cloud Inference

Qdrant Cloud Inference silently applies model-specific prefixes (e.g.
"query: "/"passage: " for E5, BGE-style instruction prefix for BGE/mxbai/
Snowflake arctic-embed) so users don't need to manage them manually.
Add a section explaining this behavior, the idempotency guarantee, and
the scope (Qdrant-hosted models only; external providers handle their own).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* Document short query optimization in Cloud Inference

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* Update links

* Expand on external provider API key usage

* Add section about external provider API keys

* Default to header for external API keys

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>

qdrant-landing/content/blog/qdrant-1.17.x.md

@@ -124,7 +124,7 @@ Additionally, this release adds the following features:
 - Reciprocal Rank Fusion (RRF) combines multiple query results into one list, but its default equal weighting can let weaker rankers dilute stronger ones. [Weighted RRF](/documentation/search/hybrid-queries/#reciprocal-rank-fusion-rrf) in Qdrant 1.17 addresses this by letting you assign weights to individual queries.
 - A new [user interface in the Web UI enables resharding collections](https://github.com/qdrant/qdrant-web-ui/pull/341) on Qdrant Cloud.
 - Qdrant now supports [audit logging](/documentation/security/#audit-logging) to track all API operations that require authentication or authorization.
-- [External provider API keys for inference requests](/documentation/inference/#external-embedding-model-providers) can now be provided in the request header.
+- [External provider API keys for inference requests](/documentation/inference/external-inference-providers/) can now be provided in the request header.
 
 For a full list of all changes in version 1.17, please refer to the [change log](https://github.com/qdrant/qdrant/releases/tag/v1.17.0).
 

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

@@ -6,12 +6,14 @@ public class Snippet
 {
 	public static async Task Run()
 	{
+		// @hide-start
 		var client = new QdrantClient(
 		  host: "xyz-example.qdrant.io",
 		  port: 6334,
 		  https: true,
 		  apiKey: "<paste-your-api-key-here>"
 		);
+		// @hide-end
 
 		await client.UpsertAsync(
 		  collectionName: "<your-collection>",

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

@@ -3,13 +3,6 @@ using Qdrant.Client;
 using Qdrant.Client.Grpc;
 using Value = Qdrant.Client.Grpc.Value;
 
-var client = new QdrantClient(
-  host: "xyz-example.qdrant.io",
-  port: 6334,
-  https: true,
-  apiKey: "<paste-your-api-key-here>"
-);
-
 await client.UpsertAsync(
   collectionName: "<your-collection>",
   points: new List <PointStruct> {

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

@@ -0,0 +1,33 @@
+```go
+import (
+	"context"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+client.Upsert(context.Background(), &qdrant.UpsertPoints{
+	CollectionName: "<your-collection>",
+	Points: []*qdrant.PointStruct{
+		{
+			Id: qdrant.NewIDNum(1),
+			Vectors: qdrant.NewVectorsImage(&qdrant.Image{
+				Model: "qdrant/clip-vit-b-32-vision",
+				Image: qdrant.NewValueString("https://qdrant.tech/example.png"),
+			}),
+			Payload: qdrant.NewValueMap(map[string]any{
+				"title": "Example image",
+			}),
+		},
+	},
+})
+
+client.Query(context.Background(), &qdrant.QueryPoints{
+	CollectionName: "<your-collection>",
+	Query: qdrant.NewQueryNearest(
+		qdrant.NewVectorInputDocument(&qdrant.Document{
+			Text:  "Mission to Mars",
+			Model: "qdrant/clip-vit-b-32-text",
+		}),
+	),
+})
+```

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

@@ -0,0 +1,47 @@
+```java
+import static io.qdrant.client.PointIdFactory.id;
+import static io.qdrant.client.QueryFactory.nearest;
+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;
+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;
+
+client
+    .upsertAsync(
+        "<your-collection>",
+        List.of(
+            PointStruct.newBuilder()
+                .setId(id(1))
+                .setVectors(
+                    vectors(
+                        Image.newBuilder()
+                            .setImage(value("https://qdrant.tech/example.png"))
+                            .setModel("qdrant/clip-vit-b-32-vision")
+                            .build()))
+                .putAllPayload(Map.of("title", value("Example Image")))
+                .build()))
+    .get();
+
+List<Points.ScoredPoint> points =
+    client
+        .queryAsync(
+            Points.QueryPoints.newBuilder()
+                .setCollectionName("<your-collection>")
+                .setQuery(
+                    nearest(
+                        Document.newBuilder()
+                            .setText("Mission to Mars")
+                            .setModel("qdrant/clip-vit-b-32-text")
+                            .build()))
+                .build())
+        .get();
+
+System.out.printf(points.toString());
+```

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

@@ -0,0 +1,38 @@
+```rust
+use qdrant_client::{
+    Payload, Qdrant,
+    qdrant::{Document, Image, PointStruct, Query, QueryPointsBuilder, UpsertPointsBuilder},
+};
+
+let points = vec![PointStruct::new(
+    1,
+    Image {
+        image: Some("https://qdrant.tech/example.png".into()),
+        model: "qdrant/clip-vit-b-32-vision".into(),
+        ..Default::default()
+    },
+    Payload::try_from(serde_json::json!({
+        "title": "Example Image"
+    }))?,
+)];
+
+client
+    .upsert_points(UpsertPointsBuilder::new("<your-collection>", points).wait(true))
+    .await?;
+
+let query_document = Document {
+    text: "Mission to Mars".into(),
+    model: "qdrant/clip-vit-b-32-text".into(),
+    ..Default::default()
+};
+
+let result = client
+    .query(
+        QueryPointsBuilder::new("<your-collection>")
+            .query(Query::new_nearest(query_document))
+            .build(),
+    )
+    .await?;
+
+println!("Result: {:?}", result);
+```

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

@@ -1,11 +1,6 @@
 ```typescript
 import {QdrantClient} from "@qdrant/js-client-rest";
 
-const client = new QdrantClient({
-    url: 'https://xyz-example.qdrant.io:6333',
-    apiKey: '<paste-your-api-key-here>',
-});
-
 const points = [
   {
     id: 1,

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

@@ -0,0 +1,47 @@
+package snippet
+
+import (
+	"context"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+func Main() {
+	// @hide-start
+	client, err := qdrant.NewClient(&qdrant.Config{
+		Host:   "xyz-example.qdrant.io",
+		Port:   6334,
+		APIKey: "<paste-your-api-key-here>",
+		UseTLS: true,
+	})
+	// @hide-end
+
+	if err != nil { panic(err) } // @hide
+	defer client.Close()         // @hide
+
+	client.Upsert(context.Background(), &qdrant.UpsertPoints{
+		CollectionName: "<your-collection>",
+		Points: []*qdrant.PointStruct{
+			{
+				Id: qdrant.NewIDNum(1),
+				Vectors: qdrant.NewVectorsImage(&qdrant.Image{
+					Model: "qdrant/clip-vit-b-32-vision",
+					Image: qdrant.NewValueString("https://qdrant.tech/example.png"),
+				}),
+				Payload: qdrant.NewValueMap(map[string]any{
+					"title": "Example image",
+				}),
+			},
+		},
+	})
+
+	client.Query(context.Background(), &qdrant.QueryPoints{
+		CollectionName: "<your-collection>",
+		Query: qdrant.NewQueryNearest(
+			qdrant.NewVectorInputDocument(&qdrant.Document{
+				Text:  "Mission to Mars",
+				Model: "qdrant/clip-vit-b-32-text",
+			}),
+		),
+	})
+}

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

@@ -1,57 +0,0 @@
-```go
-package main
-
-import (
-    "context"
-    "log"
-    "time"
-
-    "github.com/qdrant/go-client/qdrant"
-)
-
-func main() {
-    ctx, cancel := context.WithTimeout(context.Background(), time.Second)
-    defer cancel()
-
-    client, err := qdrant.NewClient(&qdrant.Config{
-        Host:   "xyz-example.qdrant.io",
-        Port:   6334,
-        APIKey: "<paste-your-api-key-here>",
-        UseTLS: true,
-    })
-    if err != nil {
-        log.Fatalf("did not connect: %v", err)
-    }
-    defer client.Close()
-
-    _, err = client.Upsert(ctx, &qdrant.UpsertPoints{
-        CollectionName: "<your-collection>",
-        Points: []*qdrant.PointStruct{
-            {
-                Id: qdrant.NewIDNum(1),
-                Vectors: qdrant.NewVectorsImage(&qdrant.Image{
-                    Model: "qdrant/clip-vit-b-32-vision",
-                    Image: qdrant.NewValueString("https://qdrant.tech/example.png"),
-                }),
-                Payload: qdrant.NewValueMap(map[string]any{
-                    "title": "Example image",
-                }),
-            },
-        },
-    })
-    if err != nil {
-        log.Fatalf("error creating point: %v", err)
-    }
-
-    points, err := client.Query(ctx, &qdrant.QueryPoints{
-        CollectionName: "<your-collection>",
-        Query: qdrant.NewQueryNearest(
-            qdrant.NewVectorInputDocument(&qdrant.Document{
-                Text:  "Mission to Mars",
-                Model: "qdrant/clip-vit-b-32-text",
-            }),
-        ),
-    })
-    log.Printf("List of points: %s", points)
-}
-```

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

@@ -0,0 +1,59 @@
+package com.example.snippets_amalgamation;
+
+import static io.qdrant.client.PointIdFactory.id;
+import static io.qdrant.client.QueryFactory.nearest;
+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;
+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;
+
+public class Snippet {
+    public static void run() throws Exception {
+        // @hide-start
+        QdrantClient client =
+            new QdrantClient(
+                QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+                    .withApiKey("<paste-your-api-key-here>")
+                    .build());
+        // @hide-end
+
+        client
+            .upsertAsync(
+                "<your-collection>",
+                List.of(
+                    PointStruct.newBuilder()
+                        .setId(id(1))
+                        .setVectors(
+                            vectors(
+                                Image.newBuilder()
+                                    .setImage(value("https://qdrant.tech/example.png"))
+                                    .setModel("qdrant/clip-vit-b-32-vision")
+                                    .build()))
+                        .putAllPayload(Map.of("title", value("Example Image")))
+                        .build()))
+            .get();
+
+        List<Points.ScoredPoint> points =
+            client
+                .queryAsync(
+                    Points.QueryPoints.newBuilder()
+                        .setCollectionName("<your-collection>")
+                        .setQuery(
+                            nearest(
+                                Document.newBuilder()
+                                    .setText("Mission to Mars")
+                                    .setModel("qdrant/clip-vit-b-32-text")
+                                    .build()))
+                        .build())
+                .get();
+
+        System.out.printf(points.toString());
+    }
+}

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

@@ -1,59 +0,0 @@
-```java
-package org.example;
-
-import static io.qdrant.client.PointIdFactory.id;
-import static io.qdrant.client.QueryFactory.nearest;
-import static io.qdrant.client.ValueFactory.value;
-import static io.qdrant.client.VectorsFactory.vectors;
-
-import io.qdrant.client.grpc.Points;
-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;
-import java.util.concurrent.ExecutionException;
-
-public class Main {
-  public static void main(String[] args)
-      throws ExecutionException, InterruptedException {
-    QdrantClient client =
-      new QdrantClient(
-        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
-        .withApiKey("<paste-your-api-key-here>")
-        .build());
-
-    client
-      .upsertAsync(
-        "<your-collection>",
-        List.of(
-          PointStruct.newBuilder()
-          .setId(id(1))
-          .setVectors(
-            vectors(
-              Image.newBuilder()
-              .setImage(value("https://qdrant.tech/example.png"))
-              .setModel("qdrant/clip-vit-b-32-vision")
-              .build()))
-          .putAllPayload(Map.of("title", value("Example Image")))
-          .build()))
-      .get();
-
-    List <Points.ScoredPoint> points =
-      client
-      .queryAsync(
-        Points.QueryPoints.newBuilder()
-        .setCollectionName("<your-collection>")
-        .setQuery(
-          nearest(
-            Document.newBuilder()
-            .setText("Mission to Mars")
-            .setModel("qdrant/clip-vit-b-32-text")
-            .build()))
-        .build())
-      .get();
-
-    System.out.printf(points.toString());
-  }
-}
-```

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

@@ -1,47 +0,0 @@
-```rust
-use qdrant_client::qdrant::Query;
-use qdrant_client::qdrant::QueryPointsBuilder;
-use qdrant_client::Payload;
-use qdrant_client::Qdrant;
-use qdrant_client::qdrant::{Document, Image};
-use qdrant_client::qdrant::{PointStruct, UpsertPointsBuilder};
-
-#[tokio::main]
-async fn main() {
-    let client = Qdrant::from_url("https://xyz-example.qdrant.io:6334")
-        .api_key("<paste-your-api-key-here>")
-        .build()
-        .unwrap();
-
-    let points = vec![
-        PointStruct::new(
-            1,
-            Image::new_from_url(
-                "https://qdrant.tech/example.png",
-                "qdrant/clip-vit-b-32-vision"
-            ),
-            Payload::try_from(serde_json::json!({
-                "title": "Example Image"
-            })).unwrap(),
-        )
-    ];
-
-    let upsert_request = UpsertPointsBuilder::new(
-        "<your-collection>",
-        points
-    ).wait(true);
-
-    let _ = client.upsert_points(upsert_request).await;
-
-    let query_document = Document::new(
-        "Mission to Mars",
-        "qdrant/clip-vit-b-32-text"
-    );
-
-    let query_request = QueryPointsBuilder::new("<your-collection>")
-        .query(Query::new_nearest(query_document));
-
-    let result = client.query(query_request).await.unwrap();
-    println!("Result: {:?}", result);
-}
-```

qdrant-landing/content/documentation/headless/snippets/cloud-inference/image/rust.rs

@@ -0,0 +1,46 @@
+use qdrant_client::{
+    Payload, Qdrant,
+    qdrant::{Document, Image, PointStruct, Query, QueryPointsBuilder, UpsertPointsBuilder},
+};
+
+pub async fn main() -> anyhow::Result<()> {
+    // @hide-start
+    let client = Qdrant::from_url("https://xyz-example.qdrant.io:6334")
+        .api_key("<paste-your-api-key-here>")
+        .build()?;
+    // @hide-end
+
+    let points = vec![PointStruct::new(
+        1,
+        Image {
+            image: Some("https://qdrant.tech/example.png".into()),
+            model: "qdrant/clip-vit-b-32-vision".into(),
+            ..Default::default()
+        },
+        Payload::try_from(serde_json::json!({
+            "title": "Example Image"
+        }))?,
+    )];
+
+    client
+        .upsert_points(UpsertPointsBuilder::new("<your-collection>", points).wait(true))
+        .await?;
+
+    let query_document = Document {
+        text: "Mission to Mars".into(),
+        model: "qdrant/clip-vit-b-32-text".into(),
+        ..Default::default()
+    };
+
+    let result = client
+        .query(
+            QueryPointsBuilder::new("<your-collection>")
+                .query(Query::new_nearest(query_document))
+                .build(),
+        )
+        .await?;
+
+    println!("Result: {:?}", result);
+
+    Ok(())
+}

qdrant-landing/content/documentation/headless/snippets/cloud-inference/image/typescript.ts

@@ -1,9 +1,11 @@
 import {QdrantClient} from "@qdrant/js-client-rest";
 
+// @hide-start
 const client = new QdrantClient({
     url: 'https://xyz-example.qdrant.io:6333',
     apiKey: '<paste-your-api-key-here>',
 });
+// @hide-end
 
 const points = [
   {

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

@@ -0,0 +1,34 @@
+```go
+import (
+	"context"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+client.Upsert(context.Background(), &qdrant.UpsertPoints{
+	CollectionName: "<your-collection>",
+	Points: []*qdrant.PointStruct{
+		{
+			Id: qdrant.NewIDNum(1),
+			Vectors: qdrant.NewVectorsDocument(&qdrant.Document{
+				Text:  "Recipe for baking chocolate chip cookies",
+				Model: "<the-model-to-use>",
+			}),
+			Payload: qdrant.NewValueMap(map[string]any{
+				"topic": "cooking",
+				"type":  "dessert",
+			}),
+		},
+	},
+})
+
+client.Query(context.Background(), &qdrant.QueryPoints{
+	CollectionName: "<your-collection>",
+	Query: qdrant.NewQueryNearest(
+		qdrant.NewVectorInputDocument(&qdrant.Document{
+			Text:  "How to bake cookies?",
+			Model: "<the-model-to-use>",
+		}),
+	),
+})
+```

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

@@ -0,0 +1,46 @@
+```java
+import static io.qdrant.client.PointIdFactory.id;
+import static io.qdrant.client.QueryFactory.nearest;
+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;
+import io.qdrant.client.grpc.Points.Document;
+import io.qdrant.client.grpc.Points.PointStruct;
+import java.util.List;
+import java.util.Map;
+
+client
+    .upsertAsync(
+        "<your-collection>",
+        List.of(
+            PointStruct.newBuilder()
+                .setId(id(1))
+                .setVectors(
+                    vectors(
+                        Document.newBuilder()
+                            .setText("Recipe for baking chocolate chip cookies")
+                            .setModel("<the-model-to-use>")
+                            .build()))
+                .putAllPayload(Map.of("topic", value("cooking"), "type", value("dessert")))
+                .build()))
+    .get();
+
+List<Points.ScoredPoint> points =
+    client
+        .queryAsync(
+            Points.QueryPoints.newBuilder()
+                .setCollectionName("<your-collection>")
+                .setQuery(
+                    nearest(
+                        Document.newBuilder()
+                            .setText("How to bake cookies?")
+                            .setModel("<the-model-to-use>")
+                            .build()))
+                .build())
+        .get();
+
+System.out.printf(points.toString());
+```

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

@@ -0,0 +1,38 @@
+```rust
+use qdrant_client::{
+    Payload, Qdrant,
+    qdrant::{Document, PointStruct, Query, QueryPointsBuilder, UpsertPointsBuilder},
+};
+
+let points = vec![PointStruct::new(
+    1,
+    Document {
+        text: "Recipe for baking chocolate chip cookies".into(),
+        model: "<the-model-to-use>".into(),
+        ..Default::default()
+    },
+    Payload::try_from(serde_json::json!(
+        {"topic": "cooking", "type": "dessert"}
+    ))?,
+)];
+
+client
+    .upsert_points(UpsertPointsBuilder::new("<your-collection>", points).wait(true))
+    .await?;
+
+let query_document = Document {
+    text: "How to bake cookies?".into(),
+    model: "<the-model-to-use>".into(),
+    ..Default::default()
+};
+
+let result = client
+    .query(
+        QueryPointsBuilder::new("<your-collection>")
+            .query(Query::new_nearest(query_document))
+            .build(),
+    )
+    .await?;
+
+println!("Result: {:?}", result);
+```

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

@@ -1,11 +1,6 @@
 ```typescript
 import {QdrantClient} from "@qdrant/js-client-rest";
 
-const client = new QdrantClient({
-    url: 'https://xyz-example.qdrant.io:6333',
-    apiKey: '<paste-your-api-key-here>',
-});
-
 const points = [
   {
     id: 1,

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

@@ -0,0 +1,48 @@
+package snippet
+
+import (
+	"context"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+func Main() {
+	// @hide-start
+	client, err := qdrant.NewClient(&qdrant.Config{
+		Host:   "xyz-example.qdrant.io",
+		Port:   6334,
+		APIKey: "<paste-your-api-key-here>",
+		UseTLS: true,
+	})
+	// @hide-end
+
+	if err != nil { panic(err) } // @hide
+	defer client.Close()         // @hide
+
+	client.Upsert(context.Background(), &qdrant.UpsertPoints{
+		CollectionName: "<your-collection>",
+		Points: []*qdrant.PointStruct{
+			{
+				Id: qdrant.NewIDNum(1),
+				Vectors: qdrant.NewVectorsDocument(&qdrant.Document{
+					Text:  "Recipe for baking chocolate chip cookies",
+					Model: "<the-model-to-use>",
+				}),
+				Payload: qdrant.NewValueMap(map[string]any{
+					"topic": "cooking",
+					"type":  "dessert",
+				}),
+			},
+		},
+	})
+
+	client.Query(context.Background(), &qdrant.QueryPoints{
+		CollectionName: "<your-collection>",
+		Query: qdrant.NewQueryNearest(
+			qdrant.NewVectorInputDocument(&qdrant.Document{
+				Text:  "How to bake cookies?",
+				Model: "<the-model-to-use>",
+			}),
+		),
+	})
+}

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

@@ -1,58 +0,0 @@
-```go
-package main
-
-import (
-    "context"
-    "log"
-    "time"
-
-    "github.com/qdrant/go-client/qdrant"
-)
-
-func main() {
-    ctx, cancel := context.WithTimeout(context.Background(), time.Second)
-    defer cancel()
-
-    client, err := qdrant.NewClient(&qdrant.Config{
-        Host:   "xyz-example.qdrant.io",
-        Port:   6334,
-        APIKey: "<paste-your-api-key-here>",
-        UseTLS: true,
-    })
-    if err != nil {
-        log.Fatalf("did not connect: %v", err)
-    }
-    defer client.Close()
-
-    _, err = client.Upsert(ctx, &qdrant.UpsertPoints{
-        CollectionName: "<your-collection>",
-        Points: []*qdrant.PointStruct{
-            {
-                Id: qdrant.NewIDNum(1),
-                Vectors: qdrant.NewVectorsDocument(&qdrant.Document{
-                    Text:  "Recipe for baking chocolate chip cookies",
-                    Model: "<the-model-to-use>",
-                }),
-                Payload: qdrant.NewValueMap(map[string]any{
-                    "topic": "cooking",
-                    "type":  "dessert",
-                }),
-            },
-        },
-    })
-    if err != nil {
-        log.Fatalf("error creating point: %v", err)
-    }
-
-    points, err := client.Query(ctx, &qdrant.QueryPoints{
-        CollectionName: "<your-collection>",
-        Query: qdrant.NewQueryNearest(
-            qdrant.NewVectorInputDocument(&qdrant.Document{
-                Text:  "How to bake cookies?",
-                Model: "<the-model-to-use>",
-            }),
-        ),
-    })
-    log.Printf("List of points: %s", points)
-}
-```

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

@@ -0,0 +1,58 @@
+package com.example.snippets_amalgamation;
+
+import static io.qdrant.client.PointIdFactory.id;
+import static io.qdrant.client.QueryFactory.nearest;
+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;
+import io.qdrant.client.grpc.Points.Document;
+import io.qdrant.client.grpc.Points.PointStruct;
+import java.util.List;
+import java.util.Map;
+
+public class Snippet {
+    public static void run() throws Exception {
+        // @hide-start
+        QdrantClient client =
+            new QdrantClient(
+                QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+                    .withApiKey("<paste-your-api-key-here>")
+                    .build());
+        // @hide-end
+
+        client
+            .upsertAsync(
+                "<your-collection>",
+                List.of(
+                    PointStruct.newBuilder()
+                        .setId(id(1))
+                        .setVectors(
+                            vectors(
+                                Document.newBuilder()
+                                    .setText("Recipe for baking chocolate chip cookies")
+                                    .setModel("<the-model-to-use>")
+                                    .build()))
+                        .putAllPayload(Map.of("topic", value("cooking"), "type", value("dessert")))
+                        .build()))
+            .get();
+
+        List<Points.ScoredPoint> points =
+            client
+                .queryAsync(
+                    Points.QueryPoints.newBuilder()
+                        .setCollectionName("<your-collection>")
+                        .setQuery(
+                            nearest(
+                                Document.newBuilder()
+                                    .setText("How to bake cookies?")
+                                    .setModel("<the-model-to-use>")
+                                    .build()))
+                        .build())
+                .get();
+
+        System.out.printf(points.toString());
+    }
+}

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

@@ -1,58 +0,0 @@
-```java
-package org.example;
-
-import static io.qdrant.client.PointIdFactory.id;
-import static io.qdrant.client.QueryFactory.nearest;
-import static io.qdrant.client.ValueFactory.value;
-import static io.qdrant.client.VectorsFactory.vectors;
-
-import io.qdrant.client.grpc.Points;
-import io.qdrant.client.grpc.Points.Document;
-import io.qdrant.client.grpc.Points.PointStruct;
-import java.util.List;
-import java.util.Map;
-import java.util.concurrent.ExecutionException;
-
-public class Main {
-  public static void main(String[] args)
-      throws ExecutionException, InterruptedException {
-    QdrantClient client =
-      new QdrantClient(
-        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
-        .withApiKey("<paste-your-api-key-here>")
-        .build());
-
-    client
-      .upsertAsync(
-        "<your-collection>",
-        List.of(
-          PointStruct.newBuilder()
-          .setId(id(1))
-          .setVectors(
-            vectors(
-              Document.newBuilder()
-              .setText("Recipe for baking chocolate chip cookies")
-              .setModel("<the-model-to-use>")
-              .build()))
-          .putAllPayload(Map.of("topic", value("cooking"), "type", value("dessert")))
-          .build()))
-      .get();
-
-    List <Points.ScoredPoint> points =
-      client
-      .queryAsync(
-        Points.QueryPoints.newBuilder()
-        .setCollectionName("<your-collection>")
-        .setQuery(
-          nearest(
-            Document.newBuilder()
-            .setText("How to bake cookies?")
-            .setModel("<the-model-to-use>")
-            .build()))
-        .build())
-      .get();
-
-    System.out.printf(points.toString());
-  }
-}
-```

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

@@ -1,47 +0,0 @@
-```rust
-use qdrant_client::qdrant::Query;
-use qdrant_client::qdrant::QueryPointsBuilder;
-use qdrant_client::Payload;
-use qdrant_client::Qdrant;
-use qdrant_client::qdrant::{Document};
-use qdrant_client::qdrant::{PointStruct, UpsertPointsBuilder};
-
-#[tokio::main]
-async fn main() {
-    let client = Qdrant::from_url("https://xyz-example.qdrant.io:6334")
-        .api_key("<paste-your-api-key-here>")
-        .build()
-        .unwrap();
-
-    let points = vec![
-        PointStruct::new(
-            1,
-            Document::new(
-                "Recipe for baking chocolate chip cookies",
-                "<the-model-to-use>"
-            ),
-            Payload::try_from(serde_json::json!(
-                {"topic": "cooking", "type": "dessert"}
-            )).unwrap(),
-        )
-    ];
-
-    let upsert_request = UpsertPointsBuilder::new(
-        "<your-collection>",
-        points
-    ).wait(true);
-
-    let _ = client.upsert_points(upsert_request).await;
-
-    let query_document = Document::new(
-        "How to bake cookies?",
-        "<the-model-to-use>"
-    );
-
-    let query_request = QueryPointsBuilder::new("<your-collection>")
-        .query(Query::new_nearest(query_document));
-
-    let result = client.query(query_request).await.unwrap();
-    println!("Result: {:?}", result);
-}
-```

qdrant-landing/content/documentation/headless/snippets/cloud-inference/simple/rust.rs

@@ -0,0 +1,46 @@
+use qdrant_client::{
+    Payload, Qdrant,
+    qdrant::{Document, PointStruct, Query, QueryPointsBuilder, UpsertPointsBuilder},
+};
+
+pub async fn main() -> anyhow::Result<()> {
+    // @hide-start
+    let client = Qdrant::from_url("https://xyz-example.qdrant.io:6334")
+        .api_key("<paste-your-api-key-here>")
+        .build()?;
+    // @hide-end
+
+    let points = vec![PointStruct::new(
+        1,
+        Document {
+            text: "Recipe for baking chocolate chip cookies".into(),
+            model: "<the-model-to-use>".into(),
+            ..Default::default()
+        },
+        Payload::try_from(serde_json::json!(
+            {"topic": "cooking", "type": "dessert"}
+        ))?,
+    )];
+
+    client
+        .upsert_points(UpsertPointsBuilder::new("<your-collection>", points).wait(true))
+        .await?;
+
+    let query_document = Document {
+        text: "How to bake cookies?".into(),
+        model: "<the-model-to-use>".into(),
+        ..Default::default()
+    };
+
+    let result = client
+        .query(
+            QueryPointsBuilder::new("<your-collection>")
+                .query(Query::new_nearest(query_document))
+                .build(),
+        )
+        .await?;
+
+    println!("Result: {:?}", result);
+
+    Ok(())
+}

qdrant-landing/content/documentation/headless/snippets/cloud-inference/simple/typescript.ts

@@ -1,10 +1,11 @@
 import {QdrantClient} from "@qdrant/js-client-rest";
 
+// @hide-start
 const client = new QdrantClient({
     url: 'https://xyz-example.qdrant.io:6333',
     apiKey: '<paste-your-api-key-here>',
 });
-
+// @hide-end
 const points = [
   {
     id: 1,

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

@@ -5,21 +5,24 @@ public class Snippet
 {
 	public static async Task Run()
 	{
+		// @hide-start
 		var client = new QdrantClient(
 		  host: "xyz-example.qdrant.io",
 		  port: 6334,
 		  https: true,
 		  apiKey: "<your-api-key>"
 		);
+		// @hide-end
 
-		await client.QueryAsync(
+		using (RequestHeaders.Use("cohere-api-key", "<YOUR_COHERE_API_KEY>"))
-		    collectionName: "{collection_name}",
+		    await client.QueryAsync(
-		    query: new Document()
+		        collectionName: "{collection_name}",
-		    {
+		        query: new Document()
-		        Model = "cohere/embed-v4.0",
+		        {
-		        Text = "a green square",
+		            Model = "cohere/embed-v4.0",
-		        Options = { ["cohere-api-key"] = "<YOUR_COHERE_API_KEY>", ["output_dimension"] = 512 },
+		            Text = "a green square",
-		    }
+		            Options = { ["output_dimension"] = 512 },
-		);
+		        }
+		    );
 	}
 }

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

@@ -2,20 +2,14 @@
 using Qdrant.Client;
 using Qdrant.Client.Grpc;
 
-var client = new QdrantClient(
+using (RequestHeaders.Use("cohere-api-key", "<YOUR_COHERE_API_KEY>"))
-  host: "xyz-example.qdrant.io",
+    await client.QueryAsync(
-  port: 6334,
+        collectionName: "{collection_name}",
-  https: true,
+        query: new Document()
-  apiKey: "<your-api-key>"
+        {
-);
+            Model = "cohere/embed-v4.0",
-
+            Text = "a green square",
-await client.QueryAsync(
+            Options = { ["output_dimension"] = 512 },
-    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/generated/go.md

@@ -5,21 +5,15 @@ import (
 	"github.com/qdrant/go-client/qdrant"
 )
 
-client, err := qdrant.NewClient(&qdrant.Config{
+ctx := qdrant.WithHeader(context.Background(), "cohere-api-key", "<YOUR_COHERE_API_KEY>")
-    Host:   "xyz-example.qdrant.io",
-    Port:   6334,
-    APIKey: "<paste-your-api-key-here>",
-    UseTLS: true,
-})
 
-client.Query(context.Background(), &qdrant.QueryPoints{
+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/generated/java.md

@@ -2,34 +2,31 @@
 import static io.qdrant.client.QueryFactory.nearest;
 import static io.qdrant.client.ValueFactory.value;
 
+import io.grpc.Context;
 import io.qdrant.client.QdrantClient;
 import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.RequestHeaders;
 import io.qdrant.client.grpc.Points.Document;
 import io.qdrant.client.grpc.Points.QueryPoints;
 import java.util.Map;
 
-QdrantClient client =
+    Context ctx = RequestHeaders.withHeader(
-    new QdrantClient(
+        Context.current(), "cohere-api-key", "<YOUR_COHERE_API_KEY>");
-        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
-            .withApiKey("<your-api-key")
-            .build());
 
-client
+    ctx.call(() -> client
-    .queryAsync(
+        .queryAsync(
-        QueryPoints.newBuilder()
+            QueryPoints.newBuilder()
-            .setCollectionName("{collection_name}")
+                .setCollectionName("{collection_name}")
-            .setQuery(
+                .setQuery(
-                nearest(
+                    nearest(
-                    Document.newBuilder()
+                        Document.newBuilder()
-                        .setModel("cohere/embed-v4.0")
+                            .setModel("cohere/embed-v4.0")
-                        .setText("a green square")
+                            .setText("a green square")
-                        .putAllOptions(
+                            .putAllOptions(
-                            Map.of(
+                                Map.of(
-                                "cohere-api-key",
+                                    "output_dimension",
-                                value("<YOUR_COHERE_API_KEY>"),
+                                    value(512)))
-                                "output_dimension",
+                            .build()))
-                                value(512)))
+                .build())
-                        .build()))
+        .get());
-            .build())
-    .get();
 ```

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

@@ -1,21 +1,22 @@
 ```python
 from qdrant_client import QdrantClient, models
+from qdrant_client.context_headers import headers
 
 client = QdrantClient(
     url="https://xyz-example.qdrant.io:6333", 
-    api_key="<your-api-key>", 
+    api_key="<your-qdrant-api-key>",
     cloud_inference=True
 )
 
-client.query_points(
+with headers({"cohere-api-key": "<YOUR_COHERE_API_KEY>"}):
-    collection_name="{collection_name}",
+    client.query_points(
-    query=models.Document(
+        collection_name="{collection_name}",
-        text="a green square", 
+        query=models.Document(
-        model="cohere/embed-v4.0", 
+            text="a green square", 
-        options={
+            model="cohere/embed-v4.0", 
-            "cohere-api-key": "<your_cohere_api_key>", 
+            options={
-            "output_dimension": 512
+                "output_dimension": 512
-        }
+            }
+        )
     )
-)
 ```

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

@@ -5,13 +5,11 @@ use qdrant_client::{
 };
 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
+    .with_header("cohere-api-key", "<YOUR_COHERE_API_KEY>")
     .query(
         QueryPointsBuilder::new("{collection_name}")
             .query(Query::new_nearest(Document {

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

@@ -1,16 +1,15 @@
 ```typescript
-import { QdrantClient } from "@qdrant/js-client-rest";
+import { QdrantClient, withHeaders } from "@qdrant/js-client-rest";
 
-const client = new QdrantClient({ host: "localhost", port: 6333 });
+await withHeaders({ 'cohere-api-key': '<YOUR_COHERE_API_KEY>' }, () =>
-
+    client.query("{collection_name}", {
-client.query("{collection_name}", {
+        query: {
-    query: {
+            text: 'a green square',
-        text: 'a green square',
+            model: 'cohere/embed-v4.0',
-        model: 'cohere/embed-v4.0',
+            options: {
-        options: {
+                output_dimension: 512,
-            'cohere-api-key': '<your_cohere_api_key>',
+            },
-            output_dimension: 512,
         },
-    },
+    })
-});
+);
 ```

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

@@ -7,23 +7,26 @@ import (
 )
 
 func Main() {
+	// @hide-start
 	client, err := qdrant.NewClient(&qdrant.Config{
 	    Host:   "xyz-example.qdrant.io",
 	    Port:   6334,
 	    APIKey: "<paste-your-api-key-here>",
 	    UseTLS: true,
 	})
+	// @hide-end
 
 	if err != nil { panic(err) } // @hide
 
-	client.Query(context.Background(), &qdrant.QueryPoints{
+	ctx := qdrant.WithHeader(context.Background(), "cohere-api-key", "<YOUR_COHERE_API_KEY>")
+
+	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/java.java

@@ -3,21 +3,28 @@ package com.example.snippets_amalgamation;
 import static io.qdrant.client.QueryFactory.nearest;
 import static io.qdrant.client.ValueFactory.value;
 
+import io.grpc.Context;
 import io.qdrant.client.QdrantClient;
 import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.RequestHeaders;
 import io.qdrant.client.grpc.Points.Document;
 import io.qdrant.client.grpc.Points.QueryPoints;
 import java.util.Map;
 
 public class Snippet {
         public static void run() throws Exception {
+            // @hide-start
                 QdrantClient client =
                     new QdrantClient(
                         QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
                             .withApiKey("<your-api-key")
                             .build());
+            // @hide-end
 
-                client
+                Context ctx = RequestHeaders.withHeader(
+                    Context.current(), "cohere-api-key", "<YOUR_COHERE_API_KEY>");
+
+                ctx.call(() -> client
                     .queryAsync(
                         QueryPoints.newBuilder()
                             .setCollectionName("{collection_name}")
@@ -28,12 +35,10 @@ public class Snippet {
                                         .setText("a green square")
                                         .putAllOptions(
                                             Map.of(
-                                                "cohere-api-key",
-                                                value("<YOUR_COHERE_API_KEY>"),
                                                 "output_dimension",
                                                 value(512)))
                                         .build()))
                             .build())
-                    .get();
+                    .get());
         }
 }

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

@@ -1,19 +1,20 @@
 from qdrant_client import QdrantClient, models
+from qdrant_client.context_headers import headers
 
 client = QdrantClient(
     url="https://xyz-example.qdrant.io:6333", 
-    api_key="<your-api-key>", 
+    api_key="<your-qdrant-api-key>",
     cloud_inference=True
 )
 
-client.query_points(
+with headers({"cohere-api-key": "<YOUR_COHERE_API_KEY>"}):
-    collection_name="{collection_name}",
+    client.query_points(
-    query=models.Document(
+        collection_name="{collection_name}",
-        text="a green square", 
+        query=models.Document(
-        model="cohere/embed-v4.0", 
+            text="a green square", 
-        options={
+            model="cohere/embed-v4.0", 
-            "cohere-api-key": "<your_cohere_api_key>", 
+            options={
-            "output_dimension": 512
+                "output_dimension": 512
-        }
+            }
+        )
     )
-)

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

@@ -5,13 +5,13 @@ use qdrant_client::{
 use std::collections::HashMap;
 
 pub async fn main() -> anyhow::Result<()> {
-    let client = Qdrant::from_url("http://localhost:6333").build().unwrap();
+    let client = Qdrant::from_url("http://localhost:6333").build().unwrap(); // @hide
 
     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
+        .with_header("cohere-api-key", "<YOUR_COHERE_API_KEY>")
         .query(
             QueryPointsBuilder::new("{collection_name}")
                 .query(Query::new_nearest(Document {

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

@@ -1,14 +1,15 @@
-import { QdrantClient } from "@qdrant/js-client-rest";
+import { QdrantClient, withHeaders } from "@qdrant/js-client-rest";
 
-const client = new QdrantClient({ host: "localhost", port: 6333 });
+const client = new QdrantClient({ host: "localhost", port: 6333 }); // @hide
 
-client.query("{collection_name}", {
+await withHeaders({ 'cohere-api-key': '<YOUR_COHERE_API_KEY>' }, () =>
-    query: {
+    client.query("{collection_name}", {
-        text: 'a green square',
+        query: {
-        model: 'cohere/embed-v4.0',
+            text: 'a green square',
-        options: {
+            model: 'cohere/embed-v4.0',
-            'cohere-api-key': '<your_cohere_api_key>',
+            options: {
-            output_dimension: 512,
+                output_dimension: 512,
+            },
         },
-    },
+    })
-});
+);

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

@@ -5,29 +5,31 @@ public class Snippet
 {
 	public static async Task Run()
 	{
+		// @hide-start
 		var client = new QdrantClient(
 		host: "xyz-example.qdrant.io", port: 6334, https: true, apiKey: "<your-api-key>");
+		// @hide-end
 
-		await client.UpsertAsync(
+		using (RequestHeaders.Use("cohere-api-key", "<YOUR_COHERE_API_KEY>"))
-		    collectionName: "{collection_name}",
+		    await client.UpsertAsync(
-		    points: new List<PointStruct>
+		        collectionName: "{collection_name}",
-		    {
+		        points: new List<PointStruct>
-		        new()
 		        {
-		            Id = 1,
+		            new()
-		            Vectors = new Image()
 		            {
-		                Model = "cohere/embed-v4.0",
+		                Id = 1,
-		                Image_ =
+		                Vectors = new Image()
-		                    "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC",
-		                Options =
 		                {
-		                    ["cohere-api-key"] = "<YOUR_COHERE_API_KEY>",
+		                    Model = "cohere/embed-v4.0",
-		                    ["output_dimension"] = 512,
+		                    Image_ =
+		                        "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC",
+		                    Options =
+		                    {
+		                        ["output_dimension"] = 512,
+		                    },
 		                },
 		            },
-		        },
+		        }
-		    }
+		    );
-		);
 	}
 }

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

@@ -2,28 +2,25 @@
 using Qdrant.Client;
 using Qdrant.Client.Grpc;
 
-var client = new QdrantClient(
+using (RequestHeaders.Use("cohere-api-key", "<YOUR_COHERE_API_KEY>"))
-host: "xyz-example.qdrant.io", port: 6334, https: true, apiKey: "<your-api-key>");
+    await client.UpsertAsync(
-
+        collectionName: "{collection_name}",
-await client.UpsertAsync(
+        points: new List<PointStruct>
-    collectionName: "{collection_name}",
-    points: new List<PointStruct>
-    {
-        new()
         {
-            Id = 1,
+            new()
-            Vectors = new Image()
             {
-                Model = "cohere/embed-v4.0",
+                Id = 1,
-                Image_ =
+                Vectors = new Image()
-                    "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC",
-                Options =
                 {
-                    ["cohere-api-key"] = "<YOUR_COHERE_API_KEY>",
+                    Model = "cohere/embed-v4.0",
-                    ["output_dimension"] = 512,
+                    Image_ =
+                        "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC",
+                    Options =
+                    {
+                        ["output_dimension"] = 512,
+                    },
                 },
             },
-        },
+        }
-    }
+    );
-);
 ```

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

@@ -5,14 +5,9 @@ import (
 	"github.com/qdrant/go-client/qdrant"
 )
 
-client, err := qdrant.NewClient(&qdrant.Config{
+ctx := qdrant.WithHeader(context.Background(), "cohere-api-key", "<YOUR_COHERE_API_KEY>")
-    Host:   "xyz-example.qdrant.io",
-    Port:   6334,
-    APIKey: "<paste-your-api-key-here>",
-    UseTLS: true,
-})
 
-client.Upsert(context.Background(), &qdrant.UpsertPoints{
+client.Upsert(ctx, &qdrant.UpsertPoints{
 	CollectionName: "{collection_name}",
 	Points: []*qdrant.PointStruct{
 		{
@@ -21,7 +16,6 @@ client.Upsert(context.Background(), &qdrant.UpsertPoints{
 				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/generated/java.md

@@ -3,39 +3,36 @@ import static io.qdrant.client.PointIdFactory.id;
 import static io.qdrant.client.ValueFactory.value;
 import static io.qdrant.client.VectorsFactory.vectors;
 
+import io.grpc.Context;
 import io.qdrant.client.QdrantClient;
 import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.RequestHeaders;
 import io.qdrant.client.grpc.Points.Image;
 import io.qdrant.client.grpc.Points.PointStruct;
 import java.util.List;
 import java.util.Map;
 
-QdrantClient client =
+    Context ctx = RequestHeaders.withHeader(
-    new QdrantClient(
+        Context.current(), "cohere-api-key", "<YOUR_COHERE_API_KEY>");
-        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
-            .withApiKey("<your-api-key")
-            .build());
 
-client
+    ctx.call(() -> client
-    .upsertAsync(
+        .upsertAsync(
-        "{collection_name}",
+            "{collection_name}",
-        List.of(
+            List.of(
-            PointStruct.newBuilder()
+                PointStruct.newBuilder()
-                .setId(id(1))
+                    .setId(id(1))
-                .setVectors(
+                    .setVectors(
-                    vectors(
+                        vectors(
-                        Image.newBuilder()
+                            Image.newBuilder()
-                            .setModel("cohere/embed-v4.0")
+                                .setModel("cohere/embed-v4.0")
-                            .setImage(
+                                .setImage(
-                                value(
+                                    value(
-                                    "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC"))
+                                        "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC"))
-                            .putAllOptions(
+                                .putAllOptions(
-                                Map.of(
+                                    Map.of(
-                                    "cohere-api-key",
+                                        "output_dimension",
-                                    value("<YOUR_COHERE_API_KEY>"),
+                                        value(512)))
-                                    "output_dimension",
+                                .build()))
-                                    value(512)))
+                    .build()))
-                            .build()))
+        .get());
-                .build()))
-    .get();
 ```

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

@@ -1,26 +1,27 @@
 ```python
 from qdrant_client import QdrantClient, models
+from qdrant_client.context_headers import headers
 
 client = QdrantClient(
     url="https://xyz-example.qdrant.io:6333", 
-    api_key="<your-api-key>", 
+    api_key="<your-qdrant-api-key>",
     cloud_inference=True
 )
 
-client.upsert(
+with headers({"cohere-api-key": "<YOUR_COHERE_API_KEY>"}):
-    collection_name="{collection_name}",
+    client.upsert(
-    points=[
+        collection_name="{collection_name}",
-        models.PointStruct(
+        points=[
-            id=1,
+            models.PointStruct(
-            vector=models.Document(
+                id=1,
-                text="a green square",
+                vector=models.Image(
-                model="cohere/embed-v4.0",
+                    image="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC",
-                options={
+                    model="cohere/embed-v4.0",
-                    "cohere-api-key": "<your_cohere_api_key>",
+                    options={
-                    "output_dimension": 512
+                        "output_dimension": 512
-                }
+                    }
+                )
             )
-        )
+        ]
-    ]
+    )
-)
 ```

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

@@ -1,22 +1,21 @@
 ```rust
 use qdrant_client::{
     Payload, Qdrant,
-    qdrant::{Document, PointStruct, UpsertPointsBuilder},
+    qdrant::{Image, 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
+    .with_header("cohere-api-key", "<YOUR_COHERE_API_KEY>")
     .upsert_points(UpsertPointsBuilder::new("{collection_name}",
         vec![
             PointStruct::new(1,
-                Document {
+                Image {
-                  text: "Recipe for baking chocolate chip cookies requires flour, sugar, eggs, and chocolate chips.".into(),
+                  image: Some("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC".into()),
-                  model: "openai/text-embedding-3-small".into(),
+                  model: "cohere/embed-v4.0".into(),
                   options,
                   },
                 Payload::default())

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

@@ -1,21 +1,20 @@
 ```typescript
-import { QdrantClient } from "@qdrant/js-client-rest";
+import { QdrantClient, withHeaders } from "@qdrant/js-client-rest";
 
-const client = new QdrantClient({ host: "localhost", port: 6333 });
+await withHeaders({ 'cohere-api-key': '<YOUR_COHERE_API_KEY>' }, () =>
-
+    client.upsert("{collection_name}", {
-client.upsert("{collection_name}", {
+        points: [
-    points: [
+            {
-        {
+                id: 1,
-            id: 1,
+                vector: {
-            vector: {
+                    image: 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC',
-                text: 'a green square',
+                    model: 'cohere/embed-v4.0',
-                model: 'cohere/embed-v4.0',
+                    options: {
-                options: {
+                        output_dimension: 512,
-                    'cohere-api-key': '<your_cohere_api_key>',
+                    },
-                    output_dimension: 512,
                 },
             },
-        },
+        ],
-    ],
+    })
-});
+);
 ```

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

@@ -7,16 +7,20 @@ import (
 )
 
 func Main() {
+	// @hide-start
 	client, err := qdrant.NewClient(&qdrant.Config{
 	    Host:   "xyz-example.qdrant.io",
 	    Port:   6334,
 	    APIKey: "<paste-your-api-key-here>",
 	    UseTLS: true,
 	})
+	// @hide-end
 
 	if err != nil { panic(err) } // @hide
 
-	client.Upsert(context.Background(), &qdrant.UpsertPoints{
+	ctx := qdrant.WithHeader(context.Background(), "cohere-api-key", "<YOUR_COHERE_API_KEY>")
+
+	client.Upsert(ctx, &qdrant.UpsertPoints{
 		CollectionName: "{collection_name}",
 		Points: []*qdrant.PointStruct{
 			{
@@ -25,7 +29,6 @@ func Main() {
 					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/java.java

@@ -4,8 +4,10 @@ import static io.qdrant.client.PointIdFactory.id;
 import static io.qdrant.client.ValueFactory.value;
 import static io.qdrant.client.VectorsFactory.vectors;
 
+import io.grpc.Context;
 import io.qdrant.client.QdrantClient;
 import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.RequestHeaders;
 import io.qdrant.client.grpc.Points.Image;
 import io.qdrant.client.grpc.Points.PointStruct;
 import java.util.List;
@@ -13,13 +15,18 @@ import java.util.Map;
 
 public class Snippet {
         public static void run() throws Exception {
+            // @hide-start
                 QdrantClient client =
                     new QdrantClient(
                         QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
                             .withApiKey("<your-api-key")
                             .build());
+            // @hide-end
 
-                client
+                Context ctx = RequestHeaders.withHeader(
+                    Context.current(), "cohere-api-key", "<YOUR_COHERE_API_KEY>");
+
+                ctx.call(() -> client
                     .upsertAsync(
                         "{collection_name}",
                         List.of(
@@ -34,12 +41,10 @@ public class Snippet {
                                                     "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();
+                    .get());
         }
 }

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

@@ -1,24 +1,25 @@
 from qdrant_client import QdrantClient, models
+from qdrant_client.context_headers import headers
 
 client = QdrantClient(
     url="https://xyz-example.qdrant.io:6333", 
-    api_key="<your-api-key>", 
+    api_key="<your-qdrant-api-key>",
     cloud_inference=True
 )
 
-client.upsert(
+with headers({"cohere-api-key": "<YOUR_COHERE_API_KEY>"}):
-    collection_name="{collection_name}",
+    client.upsert(
-    points=[
+        collection_name="{collection_name}",
-        models.PointStruct(
+        points=[
-            id=1,
+            models.PointStruct(
-            vector=models.Document(
+                id=1,
-                text="a green square",
+                vector=models.Image(
-                model="cohere/embed-v4.0",
+                    image="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC",
-                options={
+                    model="cohere/embed-v4.0",
-                    "cohere-api-key": "<your_cohere_api_key>",
+                    options={
-                    "output_dimension": 512
+                        "output_dimension": 512
-                }
+                    }
+                )
             )
-        )
+        ]
-    ]
+    )
-)

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

@@ -1,22 +1,22 @@
 use qdrant_client::{
     Payload, Qdrant,
-    qdrant::{Document, PointStruct, UpsertPointsBuilder},
+    qdrant::{Image, PointStruct, UpsertPointsBuilder},
 };
 use std::collections::HashMap;
 
 pub async fn main() -> anyhow::Result<()> {
-    let client = Qdrant::from_url("<your-qdrant-url>").build()?;
+    let client = Qdrant::from_url("<your-qdrant-url>").build()?; // @hide
     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
+        .with_header("cohere-api-key", "<YOUR_COHERE_API_KEY>")
         .upsert_points(UpsertPointsBuilder::new("{collection_name}",
             vec![
                 PointStruct::new(1,
-                    Document {
+                    Image {
-                      text: "Recipe for baking chocolate chip cookies requires flour, sugar, eggs, and chocolate chips.".into(),
+                      image: Some("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC".into()),
-                      model: "openai/text-embedding-3-small".into(),
+                      model: "cohere/embed-v4.0".into(),
                       options,
                       },
                     Payload::default())

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

@@ -1,19 +1,20 @@
-import { QdrantClient } from "@qdrant/js-client-rest";
+import { QdrantClient, withHeaders } from "@qdrant/js-client-rest";
 
-const client = new QdrantClient({ host: "localhost", port: 6333 });
+const client = new QdrantClient({ host: "localhost", port: 6333 }); // @hide
 
-client.upsert("{collection_name}", {
+await withHeaders({ 'cohere-api-key': '<YOUR_COHERE_API_KEY>' }, () =>
-    points: [
+    client.upsert("{collection_name}", {
-        {
+        points: [
-            id: 1,
+            {
-            vector: {
+                id: 1,
-                text: 'a green square',
+                vector: {
-                model: 'cohere/embed-v4.0',
+                    image: 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC',
-                options: {
+                    model: 'cohere/embed-v4.0',
-                    'cohere-api-key': '<your_cohere_api_key>',
+                    options: {
-                    output_dimension: 512,
+                        output_dimension: 512,
+                    },
                 },
             },
-        },
+        ],
-    ],
+    })
-});
+);

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-body/_description.md

@@ -0,0 +1 @@
+This example upserts a point using OpenAI's `text-embedding-3-large` model. The OpenAI API key is passed in the `options` object in the request body.

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-body/csharp.cs

@@ -0,0 +1,30 @@
+using Qdrant.Client;
+using Qdrant.Client.Grpc;
+
+public class Snippet
+{
+	public static async Task Run()
+	{
+		// @hide-start
+		var client = new QdrantClient(
+		host: "xyz-example.qdrant.io", port: 6334, https: true, apiKey: "<your-api-key>");
+		// @hide-end
+
+		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>"},
+		            },
+		        },
+		    }
+		);
+	}
+}

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-body/generated/csharp.md

@@ -0,0 +1,21 @@
+```csharp
+using Qdrant.Client;
+using Qdrant.Client.Grpc;
+
+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>"},
+            },
+        },
+    }
+);
+```

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-body/generated/go.md

@@ -0,0 +1,23 @@
+```go
+import (
+	"context"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+client.Upsert(context.Background(), &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>",
+				}),
+			}),
+		},
+	},
+})
+```

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-body/generated/java.md

@@ -0,0 +1,31 @@
+```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;
+
+    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>")))
+                                .build()))
+                    .build()))
+        .get();
+```

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-body/generated/python.md

@@ -0,0 +1,25 @@
+```python
+from qdrant_client import QdrantClient, models
+
+client = QdrantClient(
+    url="https://xyz-example.qdrant.io:6333",
+    api_key="<your-qdrant-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>"
+                }
+            )
+        )
+    ]
+)
+```

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-body/generated/rust.md

@@ -0,0 +1,23 @@
+```rust
+use qdrant_client::{
+    Payload, Qdrant,
+    qdrant::{Document, PointStruct, UpsertPointsBuilder},
+};
+use std::collections::HashMap;
+
+let mut options = HashMap::new();
+options.insert("openai-api-key".to_string(), "<YOUR_OPENAI_API_KEY>".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/external-api-key-body/generated/typescript.md

@@ -0,0 +1,18 @@
+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+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>',
+                },
+            },
+        },
+    ],
+});
+```

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-body/go.go

@@ -0,0 +1,36 @@
+package snippet
+
+import (
+	"context"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+func Main() {
+	// @hide-start
+	client, err := qdrant.NewClient(&qdrant.Config{
+		Host:   "xyz-example.qdrant.io",
+		Port:   6334,
+		APIKey: "<paste-your-api-key-here>",
+		UseTLS: true,
+	})
+	// @hide-end
+
+	if err != nil { panic(err) } // @hide
+
+	client.Upsert(context.Background(), &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>",
+					}),
+				}),
+			},
+		},
+	})
+}

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-body/http.md

@@ -0,0 +1,17 @@
+```http
+PUT /collections/{collection_name}/points?wait=true
+{
+  "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>"
+        }
+      }
+    }
+  ]
+}
+```

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-body/java.java

@@ -0,0 +1,43 @@
+package com.example.snippets_amalgamation;
+
+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;
+
+public class Snippet {
+        public static void run() throws Exception {
+            // @hide-start
+                QdrantClient client =
+                    new QdrantClient(
+                        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+                            .withApiKey("<your-api-key")
+                            .build());
+            // @hide-end
+
+                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>")))
+                                            .build()))
+                                .build()))
+                    .get();
+        }
+}

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-body/python.py

@@ -0,0 +1,23 @@
+from qdrant_client import QdrantClient, models
+
+client = QdrantClient(
+    url="https://xyz-example.qdrant.io:6333",
+    api_key="<your-qdrant-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>"
+                }
+            )
+        )
+    ]
+)

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-body/rust.rs

@@ -0,0 +1,26 @@
+use qdrant_client::{
+    Payload, Qdrant,
+    qdrant::{Document, PointStruct, UpsertPointsBuilder},
+};
+use std::collections::HashMap;
+
+pub async fn main() -> anyhow::Result<()> {
+    let client = Qdrant::from_url("<your-qdrant-url>").build()?; // @hide
+    let mut options = HashMap::new();
+    options.insert("openai-api-key".to_string(), "<YOUR_OPENAI_API_KEY>".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?;
+
+    Ok(())
+}

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-body/typescript.ts

@@ -0,0 +1,18 @@
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 }); // @hide
+
+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>',
+                },
+            },
+        },
+    ],
+});

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-header/_description.md

@@ -0,0 +1 @@
+This example upserts a point using OpenAI's `text-embedding-3-large` model. The OpenAI API key is passed in the `openai-api-key` request header.

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-header/csharp.cs

@@ -0,0 +1,34 @@
+using Qdrant.Client;
+using Qdrant.Client.Grpc;
+
+public class Snippet
+{
+	public static async Task Run()
+	{
+		// @hide-start
+		var client = new QdrantClient(
+		    host: "xyz-example.qdrant.io",
+		    port: 6334,
+		    https: true,
+		    apiKey: "<your-api-key>"
+		);
+		// @hide-end
+
+		using (RequestHeaders.Use("openai-api-key", "<YOUR_OPENAI_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",
+		                },
+		            },
+		        }
+		    );
+	}
+}

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-header/generated/csharp.md

@@ -0,0 +1,21 @@
+```csharp
+using Qdrant.Client;
+using Qdrant.Client.Grpc;
+
+using (RequestHeaders.Use("openai-api-key", "<YOUR_OPENAI_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",
+                },
+            },
+        }
+    );
+```

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-header/generated/go.md

@@ -0,0 +1,22 @@
+```go
+import (
+	"context"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+ctx := qdrant.WithHeader(context.Background(), "openai-api-key", "<YOUR_OPENAI_API_KEY>")
+
+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",
+			}),
+		},
+	},
+})
+```

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-header/generated/java.md

@@ -0,0 +1,30 @@
+```java
+import static io.qdrant.client.PointIdFactory.id;
+import static io.qdrant.client.VectorsFactory.vectors;
+
+import io.grpc.Context;
+import io.qdrant.client.QdrantClient;
+import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.RequestHeaders;
+import io.qdrant.client.grpc.Points.Document;
+import io.qdrant.client.grpc.Points.PointStruct;
+import java.util.List;
+
+    Context ctx = RequestHeaders.withHeader(
+        Context.current(), "openai-api-key", "<YOUR_OPENAI_API_KEY>");
+
+    ctx.call(() -> 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")
+                                .build()))
+                    .build()))
+        .get());
+```

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-header/generated/python.md

@@ -0,0 +1,24 @@
+```python
+from qdrant_client import QdrantClient, models
+from qdrant_client.context_headers import headers
+
+client = QdrantClient(
+    url="https://xyz-example.qdrant.io:6333",
+    api_key="<your-qdrant-api-key>",
+    cloud_inference=True
+)
+
+with headers({"openai-api-key": "<YOUR_OPENAI_API_KEY>"}):
+    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",
+                )
+            )
+        ]
+    )
+```

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-header/generated/rust.md

@@ -0,0 +1,26 @@
+```rust
+use qdrant_client::{
+    Payload, Qdrant,
+    qdrant::{Document, PointStruct, UpsertPointsBuilder},
+};
+use std::collections::HashMap;
+
+client
+    .with_header("openai-api-key", "<YOUR_OPENAI_API_KEY>")
+    .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: HashMap::new(),
+                },
+                Payload::default(),
+            )],
+        )
+        .wait(true),
+    )
+    .await?;
+```

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-header/generated/typescript.md

@@ -0,0 +1,17 @@
+```typescript
+import { QdrantClient, withHeaders } from "@qdrant/js-client-rest";
+
+await withHeaders({ 'openai-api-key': '<YOUR_OPENAI_API_KEY>' }, () =>
+    client.upsert("{collection_name}", {
+        points: [
+            {
+                id: 1,
+                vector: {
+                    text: 'Recipe for baking chocolate chip cookies',
+                    model: 'openai/text-embedding-3-large',
+                },
+            },
+        ],
+    })
+);
+```

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-header/go.go

@@ -0,0 +1,35 @@
+package snippet
+
+import (
+	"context"
+
+	"github.com/qdrant/go-client/qdrant"
+)
+
+func Main() {
+	// @hide-start
+	client, err := qdrant.NewClient(&qdrant.Config{
+		Host:   "xyz-example.qdrant.io",
+		Port:   6334,
+		APIKey: "<paste-your-api-key-here>",
+		UseTLS: true,
+	})
+	// @hide-end
+
+	if err != nil { panic(err) } // @hide
+
+	ctx := qdrant.WithHeader(context.Background(), "openai-api-key", "<YOUR_OPENAI_API_KEY>")
+
+	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",
+				}),
+			},
+		},
+	})
+}

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-header/java.java

@@ -0,0 +1,42 @@
+package com.example.snippets_amalgamation;
+
+import static io.qdrant.client.PointIdFactory.id;
+import static io.qdrant.client.VectorsFactory.vectors;
+
+import io.grpc.Context;
+import io.qdrant.client.QdrantClient;
+import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.RequestHeaders;
+import io.qdrant.client.grpc.Points.Document;
+import io.qdrant.client.grpc.Points.PointStruct;
+import java.util.List;
+
+public class Snippet {
+        public static void run() throws Exception {
+            // @hide-start
+                QdrantClient client =
+                    new QdrantClient(
+                        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+                            .withApiKey("<your-api-key")
+                            .build());
+            // @hide-end
+
+                Context ctx = RequestHeaders.withHeader(
+                    Context.current(), "openai-api-key", "<YOUR_OPENAI_API_KEY>");
+
+                ctx.call(() -> 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")
+                                            .build()))
+                                .build()))
+                    .get());
+        }
+}

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-header/python.py

@@ -0,0 +1,22 @@
+from qdrant_client import QdrantClient, models
+from qdrant_client.context_headers import headers
+
+client = QdrantClient(
+    url="https://xyz-example.qdrant.io:6333",
+    api_key="<your-qdrant-api-key>",
+    cloud_inference=True
+)
+
+with headers({"openai-api-key": "<YOUR_OPENAI_API_KEY>"}):
+    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",
+                )
+            )
+        ]
+    )

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-header/rust.rs

@@ -0,0 +1,30 @@
+use qdrant_client::{
+    Payload, Qdrant,
+    qdrant::{Document, PointStruct, UpsertPointsBuilder},
+};
+use std::collections::HashMap;
+
+pub async fn main() -> anyhow::Result<()> {
+    let client = Qdrant::from_url("<your-qdrant-url>").build()?; // @hide
+
+    client
+        .with_header("openai-api-key", "<YOUR_OPENAI_API_KEY>")
+        .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: HashMap::new(),
+                    },
+                    Payload::default(),
+                )],
+            )
+            .wait(true),
+        )
+        .await?;
+
+    Ok(())
+}

qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-header/typescript.ts

@@ -0,0 +1,17 @@
+import { QdrantClient, withHeaders } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 }); // @hide
+
+await withHeaders({ 'openai-api-key': '<YOUR_OPENAI_API_KEY>' }, () =>
+    client.upsert("{collection_name}", {
+        points: [
+            {
+                id: 1,
+                vector: {
+                    text: 'Recipe for baking chocolate chip cookies',
+                    model: 'openai/text-embedding-3-large',
+                },
+            },
+        ],
+    })
+);

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

@@ -5,8 +5,10 @@ public class Snippet
 {
 	public static async Task Run()
 	{
+		// @hide-start
 		var client = new QdrantClient(
 		host: "xyz-example.qdrant.io", port: 6334, https: true, apiKey: "<your-api-key>");
+		// @hide-end
 
 		await client.UpsertAsync(
 		    collectionName: "{collection_name}",

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

@@ -2,9 +2,6 @@
 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>

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

@@ -5,13 +5,6 @@ import (
 	"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(context.Background(), &qdrant.UpsertPoints{
 	CollectionName: "{collection_name}",
 	Points: []*qdrant.PointStruct{

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

@@ -12,27 +12,21 @@ import io.qdrant.client.grpc.Points.PointStruct;
 import java.util.List;
 import java.util.Map;
 
-QdrantClient client =
+    client
-    new QdrantClient(
+        .upsertAsync(
-        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+            "{collection_name}",
-            .withApiKey("<your-api-key")
+            List.of(
-            .build());
+                PointStruct.newBuilder()
-
+                    .setId(id(1))
-client
+                    .setVectors(
-    .upsertAsync(
+                        namedVectors(
-        "{collection_name}",
+                            Map.of(
-        List.of(
+                                "my-bm25-vector",
-            PointStruct.newBuilder()
+                                vector(
-                .setId(id(1))
+                                    Document.newBuilder()
-                .setVectors(
+                                        .setModel("qdrant/bm25")
-                    namedVectors(
+                                        .setText("Recipe for baking chocolate chip cookies")
-                        Map.of(
+                                        .build()))))
-                            "my-bm25-vector",
+                    .build()))
-                            vector(
+        .get();
-                                Document.newBuilder()
-                                    .setModel("qdrant/bm25")
-                                    .setText("Recipe for baking chocolate chip cookies")
-                                    .build()))))
-                .build()))
-    .get();
 ```

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

@@ -1,12 +1,6 @@
 ```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=[

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

@@ -5,8 +5,6 @@ use qdrant_client::{
 };
 use std::collections::HashMap;
 
-let client = Qdrant::from_url("<your-qdrant-url>").build()?;
-
 client
     .upsert_points(UpsertPointsBuilder::new(
         "{collection_name}",

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

@@ -1,8 +1,6 @@
 ```typescript
 import { QdrantClient } from "@qdrant/js-client-rest";
 
-const client = new QdrantClient({ host: "localhost", port: 6333 });
-
 client.upsert("{collection_name}", {
     points: [
         {

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

@@ -7,12 +7,14 @@ import (
 )
 
 func Main() {
+	// @hide-start
 	client, err := qdrant.NewClient(&qdrant.Config{
 	    Host:   "xyz-example.qdrant.io",
 	    Port:   6334,
 	    APIKey: "<paste-your-api-key-here>",
 	    UseTLS: true,
 	})
+	// @hide-end
 
 	if err != nil { panic(err) } // @hide
 

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

@@ -15,11 +15,13 @@ import java.util.Map;
 
 public class Snippet {
         public static void run() throws Exception {
+            // @hide-start
                 QdrantClient client =
                     new QdrantClient(
                         QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
                             .withApiKey("<your-api-key")
                             .build());
+            // @hide-end
 
                 client
                     .upsertAsync(

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

@@ -1,10 +1,12 @@
 from qdrant_client import QdrantClient, models
 
+# @hide-start
 client = QdrantClient(
     url="https://xyz-example.qdrant.io:6333", 
-    api_key="<your-api-key>", 
+    api_key="<your-qdrant-api-key>",
     cloud_inference=True
 )
+# @hide-end
 
 client.upsert(
     collection_name="{collection_name}",

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

@@ -5,7 +5,7 @@ use qdrant_client::{
 use std::collections::HashMap;
 
 pub async fn main() -> anyhow::Result<()> {
-    let client = Qdrant::from_url("<your-qdrant-url>").build()?;
+    let client = Qdrant::from_url("<your-qdrant-url>").build()?; // @hide
 
     client
         .upsert_points(UpsertPointsBuilder::new(

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

@@ -1,6 +1,6 @@
 import { QdrantClient } from "@qdrant/js-client-rest";
 
-const client = new QdrantClient({ host: "localhost", port: 6333 });
+const client = new QdrantClient({ host: "localhost", port: 6333 }); // @hide
 
 client.upsert("{collection_name}", {
     points: [

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

@@ -5,21 +5,24 @@ public class Snippet
 {
 	public static async Task Run()
 	{
+		// @hide-start
 		var client = new QdrantClient(
 		  host: "xyz-example.qdrant.io",
 		  port: 6334,
 		  https: true,
 		  apiKey: "<your-api-key>"
 		);
+		// @hide-end
 
-		await client.QueryAsync(
+		using (RequestHeaders.Use("jina-api-key", "<YOUR_JINAAI_API_KEY>"))
-		    collectionName: "{collection_name}",
+		    await client.QueryAsync(
-		    query: new Document()
+		        collectionName: "{collection_name}",
-		    {
+		        query: new Document()
-		        Model = "jinaai/jina-clip-v2",
+		        {
-		        Text = "Mission to Mars",
+		            Model = "jinaai/jina-clip-v2",
-		        Options = { ["jina-api-key"] = "<YOUR_JINAAI_API_KEY>", ["dimensions"] = 512 },
+		            Text = "Mission to Mars",
-		    }
+		            Options = { ["dimensions"] = 512 },
-		);
+		        }
+		    );
 	}
 }

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

@@ -2,20 +2,14 @@
 using Qdrant.Client;
 using Qdrant.Client.Grpc;
 
-var client = new QdrantClient(
+using (RequestHeaders.Use("jina-api-key", "<YOUR_JINAAI_API_KEY>"))
-  host: "xyz-example.qdrant.io",
+    await client.QueryAsync(
-  port: 6334,
+        collectionName: "{collection_name}",
-  https: true,
+        query: new Document()
-  apiKey: "<your-api-key>"
+        {
-);
+            Model = "jinaai/jina-clip-v2",
-
+            Text = "Mission to Mars",
-await client.QueryAsync(
+            Options = { ["dimensions"] = 512 },
-    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/generated/go.md

@@ -5,22 +5,16 @@ import (
 	"github.com/qdrant/go-client/qdrant"
 )
 
-client, err := qdrant.NewClient(&qdrant.Config{
+ctx := qdrant.WithHeader(context.Background(), "jina-api-key", "<YOUR_JINAAI_API_KEY>")
-    Host:   "xyz-example.qdrant.io",
-    Port:   6334,
-    APIKey: "<paste-your-api-key-here>",
-    UseTLS: true,
-})
 
-client.Query(context.Background(), &qdrant.QueryPoints{
+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,
-				"dimensions":   512,
 			}),
 		}),
 	),

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

@@ -2,18 +2,18 @@
 import static io.qdrant.client.QueryFactory.nearest;
 import static io.qdrant.client.ValueFactory.value;
 
+import io.grpc.Context;
 import io.qdrant.client.QdrantClient;
 import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.RequestHeaders;
 import io.qdrant.client.grpc.Points.Document;
 import io.qdrant.client.grpc.Points.QueryPoints;
 import java.util.Map;
 
-QdrantClient client =
+Context ctx = RequestHeaders.withHeader(
-    new QdrantClient(
+    Context.current(), "jina-api-key", "<YOUR_JINAAI_API_KEY>");
-        QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
+
-            .withApiKey("<your-api-key")
+ctx.call(() -> client
-            .build());
-client
     .queryAsync(
         QueryPoints.newBuilder()
             .setCollectionName("{collection_name}")
@@ -24,11 +24,9 @@ client
                         .setText("Mission to Mars")
                         .putAllOptions(
                             Map.of(
-                                "jina-api-key",
-                                value("<YOUR_JINAAI_API_KEY>"),
                                 "dimensions",
                                 value(512)))
                         .build()))
             .build())
-    .get();
+    .get());
 ```

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

@@ -1,21 +1,22 @@
 ```python
 from qdrant_client import QdrantClient, models
+from qdrant_client.context_headers import headers
 
 client = QdrantClient(
     url="https://xyz-example.qdrant.io:6333", 
-    api_key="<your-api-key>", 
+    api_key="<your-qdrant-api-key>",
     cloud_inference=True
 )
 
-client.query_points(
+with headers({"jina-api-key": "<YOUR_JINAAI_API_KEY>"}):
-    collection_name="{collection_name}",
+    client.query_points(
-    query=models.Document(
+        collection_name="{collection_name}",
-        text="Mission to Mars", 
+        query=models.Document(
-        model="jinaai/jina-clip-v2", 
+            text="Mission to Mars", 
-        options={
+            model="jinaai/jina-clip-v2", 
-            "jina-api-key": "<your_jinaai_api_key>", 
+            options={
-            "dimensions": 512
+                "dimensions": 512
-        }
+            }
+        )
     )
-)
 ```

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

@@ -5,13 +5,11 @@ use qdrant_client::{
 };
 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
+    .with_header("jina-api-key", "<YOUR_JINAAI_API_KEY>")
     .query(
         QueryPointsBuilder::new("{collection_name}")
             .query(Query::new_nearest(Document {

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

@@ -1,16 +1,15 @@
 ```typescript
-import { QdrantClient } from "@qdrant/js-client-rest";
+import { QdrantClient, withHeaders } from "@qdrant/js-client-rest";
 
-const client = new QdrantClient({ host: "localhost", port: 6333 });
+await withHeaders({ 'jina-api-key': '<YOUR_JINAAI_API_KEY>' }, () =>
-
+    client.query("{collection_name}", {
-client.query("{collection_name}", {
+        query: {
-    query: {
+            text: 'Mission to Mars',
-        text: 'Mission to Mars',
+            model: 'jinaai/jina-clip-v2',
-        model: 'jinaai/jina-clip-v2',
+            options: {
-        options: {
+                dimensions: 512,
-            'jina-api-key': '<your_jinaai_api_key>',
+            },
-            dimensions: 512,
         },
-    },
+    })
-});
+);
 ```

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

@@ -7,24 +7,27 @@ import (
 )
 
 func Main() {
+	// @hide-start
 	client, err := qdrant.NewClient(&qdrant.Config{
 	    Host:   "xyz-example.qdrant.io",
 	    Port:   6334,
 	    APIKey: "<paste-your-api-key-here>",
 	    UseTLS: true,
 	})
+	// @hide-end
 
 	if err != nil { panic(err) } // @hide
 
-	client.Query(context.Background(), &qdrant.QueryPoints{
+	ctx := qdrant.WithHeader(context.Background(), "jina-api-key", "<YOUR_JINAAI_API_KEY>")
+
+	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,
-					"dimensions":   512,
 				}),
 			}),
 		),

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

@@ -3,20 +3,28 @@ package com.example.snippets_amalgamation;
 import static io.qdrant.client.QueryFactory.nearest;
 import static io.qdrant.client.ValueFactory.value;
 
+import io.grpc.Context;
 import io.qdrant.client.QdrantClient;
 import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.RequestHeaders;
 import io.qdrant.client.grpc.Points.Document;
 import io.qdrant.client.grpc.Points.QueryPoints;
 import java.util.Map;
 
 public class Snippet {
         public static void run() throws Exception {
+                // @hide-start
                 QdrantClient client =
                     new QdrantClient(
                         QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
                             .withApiKey("<your-api-key")
                             .build());
-                client
+                // @hide-end
+
+                Context ctx = RequestHeaders.withHeader(
+                    Context.current(), "jina-api-key", "<YOUR_JINAAI_API_KEY>");
+
+                ctx.call(() -> client
                     .queryAsync(
                         QueryPoints.newBuilder()
                             .setCollectionName("{collection_name}")
@@ -27,12 +35,10 @@ public class Snippet {
                                         .setText("Mission to Mars")
                                         .putAllOptions(
                                             Map.of(
-                                                "jina-api-key",
-                                                value("<YOUR_JINAAI_API_KEY>"),
                                                 "dimensions",
                                                 value(512)))
                                         .build()))
                             .build())
-                    .get();
+                    .get());
         }
 }

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

@@ -1,19 +1,20 @@
 from qdrant_client import QdrantClient, models
+from qdrant_client.context_headers import headers
 
 client = QdrantClient(
     url="https://xyz-example.qdrant.io:6333", 
-    api_key="<your-api-key>", 
+    api_key="<your-qdrant-api-key>",
     cloud_inference=True
 )
 
-client.query_points(
+with headers({"jina-api-key": "<YOUR_JINAAI_API_KEY>"}):
-    collection_name="{collection_name}",
+    client.query_points(
-    query=models.Document(
+        collection_name="{collection_name}",
-        text="Mission to Mars", 
+        query=models.Document(
-        model="jinaai/jina-clip-v2", 
+            text="Mission to Mars", 
-        options={
+            model="jinaai/jina-clip-v2", 
-            "jina-api-key": "<your_jinaai_api_key>", 
+            options={
-            "dimensions": 512
+                "dimensions": 512
-        }
+            }
+        )
     )
-)

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

@@ -5,13 +5,13 @@ use qdrant_client::{
 use std::collections::HashMap;
 
 pub async fn main() -> anyhow::Result<()> {
-    let client = Qdrant::from_url("<your-qdrant-url>").build().unwrap();
+    let client = Qdrant::from_url("<your-qdrant-url>").build().unwrap(); // @hide
 
     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
+        .with_header("jina-api-key", "<YOUR_JINAAI_API_KEY>")
         .query(
             QueryPointsBuilder::new("{collection_name}")
                 .query(Query::new_nearest(Document {

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

@@ -1,14 +1,15 @@
-import { QdrantClient } from "@qdrant/js-client-rest";
+import { QdrantClient, withHeaders } from "@qdrant/js-client-rest";
 
-const client = new QdrantClient({ host: "localhost", port: 6333 });
+const client = new QdrantClient({ host: "localhost", port: 6333 }); // @hide
 
-client.query("{collection_name}", {
+await withHeaders({ 'jina-api-key': '<YOUR_JINAAI_API_KEY>' }, () =>
-    query: {
+    client.query("{collection_name}", {
-        text: 'Mission to Mars',
+        query: {
-        model: 'jinaai/jina-clip-v2',
+            text: 'Mission to Mars',
-        options: {
+            model: 'jinaai/jina-clip-v2',
-            'jina-api-key': '<your_jinaai_api_key>',
+            options: {
-            dimensions: 512,
+                dimensions: 512,
+            },
         },
-    },
+    })
-});
+);