Fix type-checking errors in snippets (code only, no .md)

This commit both:
- Fixes pre-existing mistakes in snippets, aka wrong code samples.
- Adds hidden (`@hide`) statements that do not appear in the docs,
  but satisfy typecheckers.

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/create-collection/csharp.cs

@@ -1,9 +1,12 @@
-
+using Qdrant.Client; // @hide
+using Qdrant.Client.Grpc; // @hide
 
 public class Snippet
 {
 	public static async Task Run()
 	{
+		var client = new QdrantClient("localhost", 6334); // @hide
+
 		await client.CreateCollectionAsync(
 		  collectionName: "{collection_name}",
 		  vectorsConfig: new VectorParamsMap

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/create-collection/go.go

@@ -1,8 +1,17 @@
 package snippet
 
-
+import "context" // @hide
+import "github.com/qdrant/go-client/qdrant" // @hide
 
 func Main() {
+	// @hide-start
+	client, err := qdrant.NewClient(&qdrant.Config{
+	    Host: "localhost",
+	    Port: 6334,
+	})
+	if err != nil { panic(err) }
+	// @hide-end
+
 	client.CreateCollection(context.Background(), &qdrant.CreateCollection{
 		CollectionName: "{collection_name}",
 		VectorsConfig: qdrant.NewVectorsConfigMap(

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/create-collection/java.java

@@ -8,9 +8,15 @@ import io.qdrant.client.grpc.Collections.SparseVectorParams;
 import io.qdrant.client.grpc.Collections.VectorParams;
 import io.qdrant.client.grpc.Collections.VectorParamsMap;
 import io.qdrant.client.grpc.Collections.VectorsConfig;
+import java.util.Map;
 
 public class Snippet {
         public static void run() throws Exception {
+                // @hide-start
+                io.qdrant.client.QdrantClient client =
+                    new io.qdrant.client.QdrantClient(io.qdrant.client.QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
+                // @hide-end
+
                 client
                     .createCollectionAsync(
                         CreateCollection.newBuilder()
@@ -26,13 +32,14 @@ public class Snippet {
                                                         .setSize(384)
                                                         .setDistance(Distance.Cosine)
                                                         .build())))
-                                    .setSparseVectorsConfig(
-                                        SparseVectorConfig.newBuilder()
-                                            .putMap(
-                                                "bm25_sparse_vector",
-                                                SparseVectorParams.newBuilder()
-                                                    .setModifier(Modifier.Idf)
-                                                    .build())))
+                                    .build())
+                            .setSparseVectorsConfig(
+                                SparseVectorConfig.newBuilder()
+                                    .putMap(
+                                        "bm25_sparse_vector",
+                                        SparseVectorParams.newBuilder()
+                                            .setModifier(Modifier.Idf)
+                                            .build()))
                             .build())
                     .get();
         }

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/create-collection/rust.rs

@@ -4,6 +4,8 @@ use qdrant_client::qdrant::{
 };
 
 pub async fn main() -> anyhow::Result<()> {
+    let client = qdrant_client::Qdrant::from_url("http://localhost:6334").build()?; // @hide
+
     let mut vector_config = VectorsConfigBuilder::default();
     vector_config.add_named_vector_params(
         "dense_vector",

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/create-sample-query/csharp.cs

@@ -1,9 +1,8 @@
-
-
 public class Snippet
 {
 	public static async Task Run()
 	{
-		var queryText = "What is relapsing polychondritis?"
+		var queryText = "What is relapsing polychondritis?";
+		_ = queryText; // @hide
 	}
 }

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/create-sample-query/go.go

@@ -1,7 +1,6 @@
 package snippet
 
-
-
 func Main() {
 	queryText := "What is relapsing polychondritis?"
+	_ = queryText // @hide
 }

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/create-sample-query/rust.rs

@@ -1,7 +1,5 @@
-
-
 pub async fn main() -> anyhow::Result<()> {
     let query_text = "What is relapsing polychondritis?";
-
+    _ = query_text; // @hide
     Ok(())
 }

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/initialize-client/go.go

@@ -1,10 +1,6 @@
 package snippet
 
-import (
-    "context"
-
-    "github.com/qdrant/go-client/qdrant"
-)
+import "github.com/qdrant/go-client/qdrant"
 
 func Main() {
 	client, err := qdrant.NewClient(&qdrant.Config{
@@ -13,4 +9,7 @@ func Main() {
 		APIKey: "<paste-your-api-key-here>",
 		UseTLS: true,
 	})
+
+	if err != nil { panic(err) } // @hide
+	_ = client // @hide
 }

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/initialize-client/python.py

@@ -1,8 +1,8 @@
 from qdrant_client import QdrantClient
 
-qdrant_client = QdrantClient(
+client = QdrantClient(
     "xyz-example.cloud-region.cloud-provider.cloud.qdrant.io",
     api_key="<paste-your-api-key-here>",
     cloud_inference=True,
-    timeout=30.0
+    timeout=30,
 )

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/initialize-client/rust.rs

@@ -1,6 +1,4 @@
 use qdrant_client::Qdrant;
-use qdrant_client::qdrant::{Document};
-use qdrant_client::qdrant::{PointStruct, UpsertPointsBuilder};
 
 pub async fn main() -> anyhow::Result<()> {
     let client = Qdrant::from_url("https://xyz-example.qdrant.io:6334")
@@ -8,5 +6,7 @@ pub async fn main() -> anyhow::Result<()> {
         .build()
         .unwrap();
 
+    _ = client; // @hide
+
     Ok(())
 }

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/run-vector-search/csharp.cs

@@ -1,9 +1,17 @@
-
+using Qdrant.Client; // @hide
+using Qdrant.Client.Grpc; // @hide
 
 public class Snippet
 {
 	public static async Task Run()
 	{
+		// @hide-start
+		var client = new QdrantClient("localhost", 6334);
+		var queryText = "{query_text}";
+		var denseModel = "{dense_model_name}";
+		var bm25Model = "{bm25_model_name}";
+		// @hide-end
+
 		await client.QueryAsync(
 		collectionName: "{collection_name}", prefetch: new List <PrefetchQuery> {
 		  new() {

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/run-vector-search/go.go

@@ -1,8 +1,20 @@
 package snippet
 
-
+import "context" // @hide
+import "github.com/qdrant/go-client/qdrant" // @hide
 
 func Main() {
+	// @hide-start
+	client, err := qdrant.NewClient(&qdrant.Config{
+		Host: "localhost",
+		Port: 6334,
+	})
+	if err != nil { panic(err) }
+	queryText := "{query_text}"
+	denseModel := "{dense_model_name}"
+	bm25Model := "{bm25_model_name}"
+	// @hide-end
+
 	prefetch := []*qdrant.PrefetchQuery{
 		{
 			Query: qdrant.NewQueryDocument(&qdrant.Document{
@@ -20,7 +32,7 @@ func Main() {
 		},
 	}
 
-	client.Query(ctx, &qdrant.QueryPoints{
+	client.Query(context.Background(), &qdrant.QueryPoints{
 		CollectionName: "{collection_name}",
 		Prefetch:       prefetch,
 		Query:          qdrant.NewQueryFusion(qdrant.Fusion_RRF),

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/run-vector-search/java.java

@@ -10,6 +10,14 @@ import io.qdrant.client.grpc.Points.QueryPoints;
 
 public class Snippet {
         public static void run() throws Exception {
+                // @hide-start
+                io.qdrant.client.QdrantClient client =
+                    new io.qdrant.client.QdrantClient(io.qdrant.client.QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
+                String queryText = "{query_text}";
+                String denseModel = "{dense_model_name}";
+                String bm25Model = "{bm25_model_name}";
+                // @hide-end
+
                 PrefetchQuery densePrefetch =
                     PrefetchQuery.newBuilder()
                         .setQuery(

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/run-vector-search/python.py

@@ -1,12 +1,17 @@
-from qdrant_client import QdrantClient, models  # @hide
+# @hide-start
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient(url="http://localhost:6333")  # @hide
+client = QdrantClient(url="http://localhost:6333")
+query_text = "{query_text}"
+dense_model = "{dense_model_name}"
+bm25_model = "{bm25_model_name}"
+# @hide-end
 
 results = client.query_points(
     collection_name="{collection_name}",
     prefetch=[
         models.Prefetch(
-            query=Document(
+            query=models.Document(
                 text=query_text,
                 model=dense_model
             ),
@@ -14,7 +19,7 @@ results = client.query_points(
             limit=5
         ),
         models.Prefetch(
-            query=Document(
+            query=models.Document(
                 text=query_text,
                 model=bm25_model
             ),

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/run-vector-search/rust.rs

@@ -1,6 +1,13 @@
 use qdrant_client::qdrant::{Document, Fusion, PrefetchQueryBuilder, Query, QueryPointsBuilder};
 
 pub async fn main() -> anyhow::Result<()> {
+    // @hide-start
+    let client = qdrant_client::Qdrant::from_url("http://localhost:6334").build()?;
+    let query_text = "{query_text}";
+    let dense_model = "{dense_model_name}";
+    let bm25_model = "{bm25_model_name}";
+    // @hide-end
+
     let dense_prefetch = PrefetchQueryBuilder::default()
         .query(Query::new_nearest(Document::new(query_text, dense_model)))
         .using("dense_vector")
@@ -11,7 +18,7 @@ pub async fn main() -> anyhow::Result<()> {
         .using("bm25_sparse_vector")
         .build();
 
-    let query_request = QueryPointsBuilder::new(collection_name)
+    let query_request = QueryPointsBuilder::new("{collection_name}")
         .add_prefetch(dense_prefetch)
         .add_prefetch(bm25_prefetch)
         .query(Query::new_fusion(Fusion::Rrf))
@@ -20,5 +27,7 @@ pub async fn main() -> anyhow::Result<()> {
 
     let results = client.query(query_request).await?;
 
+    _ = results; // @hide
+
     Ok(())
 }

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/run-vector-search/typescript.ts

@@ -1,8 +1,14 @@
-import { QdrantClient } from "@qdrant/js-client-rest"; // @hide
+// @hide-start
+import { QdrantClient } from "@qdrant/js-client-rest";
 
-const client = new QdrantClient({ host: "localhost", port: 6333 }); // @hide
+const client = new QdrantClient({ host: "localhost", port: 6333 });
 
-const results = await client.query(collectionName, {
+const queryText = "{query_text}";
+const denseModel = "{dense_model_name}";
+const bm25Model = "{bm25_model_name}";
+// @hide-end
+
+const results = await client.query("{collection_name}", {
     prefetch: [
         {
             query: {

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/upload-data/csharp.cs

@@ -1,13 +1,22 @@
-
+using Qdrant.Client; // @hide
+using Qdrant.Client.Grpc; // @hide
 
 public class Snippet
 {
 	public static async Task Run()
 	{
+		var client = new QdrantClient("localhost", 6334); // @hide
+
 		var denseModel = "sentence-transformers/all-minilm-l6-v2";
 		var bm25Model = "qdrant/bm25";
 		// NOTE: LoadDataset is a user-defined function.
 		// Implement it to handle dataset loading as needed.
+		// @hide-start
+		List<Dictionary<string, object>> LoadDataset(string path, string slice)
+		{
+		    return new List<Dictionary<string, object>> {};
+		}
+		// @hide-end
 		var dataset = LoadDataset("miriad/miriad-4.4M", "train[0:100]");
 		var points = new List<PointStruct>();
 

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/upload-data/go.go

@@ -1,8 +1,27 @@
 package snippet
 
+// @hide-start
+import (
+	"context"
 
+	"github.com/google/uuid"
+	"github.com/qdrant/go-client/qdrant"
+)
+
+func loadDataset(name string, split string) []map[string]string {
+	return []map[string]string{}
+}
+// @hide-end
 
 func Main() {
+	// @hide-start
+	client, err := qdrant.NewClient(&qdrant.Config{
+		Host: "localhost",
+		Port: 6334,
+	})
+	if err != nil { panic(err) }
+	// @hide-end
+
 	denseModel := "sentence-transformers/all-minilm-l6-v2"
 	bm25Model := "qdrant/bm25"
 	// NOTE: loadDataset is a user-defined function.
@@ -27,8 +46,8 @@ func Main() {
 		}
 		points = append(points, point)
 	}
-	_, err = client.Upsert(ctx, &qdrant.UpsertPoints{
-		CollectionName: "{collection_name},
+	_, err = client.Upsert(context.Background(), &qdrant.UpsertPoints{
+		CollectionName: "{collection_name}",
 		Points:         points,
 	})
 }

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/upload-data/java.java

@@ -1,8 +1,8 @@
 package com.example.snippets_amalgamation;
 
 import static io.qdrant.client.PointIdFactory.id;
+import static io.qdrant.client.VectorFactory.vector;
 import static io.qdrant.client.VectorsFactory.namedVectors;
-import static io.qdrant.client.VectorsFactory.vectors;
 
 import io.qdrant.client.grpc.Points.Document;
 import io.qdrant.client.grpc.Points.PointStruct;
@@ -12,7 +12,18 @@ import java.util.Map;
 import java.util.UUID;
 
 public class Snippet {
+        // @hide-start
+        private static List<Map<String, String>> loadDataset(String name, String split) {
+                return new ArrayList<>();
+        }
+        // @hide-end
+
         public static void run() throws Exception {
+                // @hide-start
+                io.qdrant.client.QdrantClient client =
+                    new io.qdrant.client.QdrantClient(io.qdrant.client.QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
+                // @hide-end
+
                 String denseModel = "sentence-transformers/all-minilm-l6-v2";
                 String bm25Model = "qdrant/bm25";
                 // NOTE: loadDataset is a user-defined function.
@@ -29,10 +40,10 @@ public class Snippet {
                               namedVectors(
                                   Map.of(
                                       "dense_vector",
-                                      vectors(
+                                      vector(
                                           Document.newBuilder().setText(passage).setModel(denseModel).build()),
                                       "bm25_sparse_vector",
-                                      vectors(
+                                      vector(
                                           Document.newBuilder().setText(passage).setModel(bm25Model).build()))))
                           .build();
                   points.add(point);

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/upload-data/rust.rs

@@ -2,12 +2,20 @@ use qdrant_client::qdrant::{
     Document, NamedVectors, PointStruct, UpsertPointsBuilder,
 };
 use qdrant_client::Payload;
+use uuid::Uuid;
 
 pub async fn main() -> anyhow::Result<()> {
+    let client = qdrant_client::Qdrant::from_url("http://localhost:6334").build()?; // @hide
+
     let dense_model = "sentence-transformers/all-minilm-l6-v2";
     let bm25_model = "qdrant/bm25";
     // NOTE: load_dataset is a user-defined function.
     // Implement it to handle dataset loading as needed.
+    // @hide-start
+    fn load_dataset(_path: &str, _slice: &str) -> Vec<serde_json::Value> {
+        vec![]
+    }
+    // @hide-end
     let dataset: Vec<_> = load_dataset("miriad/miriad-4.4M", "train[0:100]");
 
     let points: Vec<PointStruct> = dataset
@@ -29,7 +37,7 @@ pub async fn main() -> anyhow::Result<()> {
         .collect();
 
     client
-        .upsert_points(UpsertPointsBuilder::new(collection_name, points))
+        .upsert_points(UpsertPointsBuilder::new("{collection_name}", points))
         .await?;
 
     Ok(())

qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/upload-data/typescript.ts

@@ -8,6 +8,11 @@ const denseModel = "sentence-transformers/all-minilm-l6-v2";
 const bm25Model = "qdrant/bm25";
 // NOTE: loadDataset is a user-defined function.
 // Implement it to handle dataset loading as needed.
+// @hide-start
+function loadDataset(name: string, slice: string): Array<{ passage_text: string }> {
+    return [];
+}
+// @hide-end
 const dataset = loadDataset("miriad/miriad-4.4M", "train[0:100]");
 
 const points = dataset.map((item) => {