docs: C# snippets

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

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

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

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

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

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

@@ -14,7 +14,7 @@ client, err := qdrant.NewClient(&qdrant.Config{
 })
 
 client.Query(ctx, &qdrant.QueryPoints{
-	CollectionName: "<your-collection>",
+	CollectionName: "{collection_name}",
 	Query: qdrant.NewQueryNearest(
 		qdrant.NewVectorInputDocument(&qdrant.Document{
 			Text:  "a green square",

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

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

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

@@ -8,7 +8,7 @@ client = QdrantClient(
 )
 
 client.query_points(
-    collection_name="<your_collection_name>",
+    collection_name="{collection_name}",
     query=models.Document(
         text="a green square", 
         model="cohere/embed-v4.0", 

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

@@ -13,7 +13,7 @@ options.insert("output_dimension".to_string(), 512.into());
 
 client
     .query(
-        QueryPointsBuilder::new("<your-collection_name>")
+        QueryPointsBuilder::new("{collection_name}")
             .query(Query::new_nearest(Document {
                 text: "a green square".into(),
                 model: "cohere/embed-v4.0".into(),

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

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

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

@@ -14,7 +14,7 @@ client, err := qdrant.NewClient(&qdrant.Config{
 })
 
 client.Upsert(ctx, &qdrant.UpsertPoints{
-	CollectionName: "<your-collection>",
+	CollectionName: "{collection_name}",
 	Points: []*qdrant.PointStruct{
 		{
 			Id: qdrant.NewIDNum(uint64(1)),

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

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

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

@@ -8,7 +8,7 @@ client = QdrantClient(
 )
 
 client.upsert(
-    collection_name="<your_collection_name>",
+    collection_name="{collection_name}",
     points=[
         models.PointStruct(
             id=1,

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

@@ -11,7 +11,7 @@ options.insert("cohere-api-key".to_string(), "<YOUR_COHERE_API_KEY>".into());
 options.insert("output_dimension".to_string(), 512.into());
 
 client
-    .upsert_points(UpsertPointsBuilder::new("<your-collection>",
+    .upsert_points(UpsertPointsBuilder::new("{collection_name}",
         vec![
             PointStruct::new(1,
                 Document {

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

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

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

@@ -14,7 +14,7 @@ client, err := qdrant.NewClient(&qdrant.Config{
 })
 
 client.Upsert(ctx, &qdrant.UpsertPoints{
-	CollectionName: "<your-collection>",
+	CollectionName: "{collection_name}",
 	Points: []*qdrant.PointStruct{
 		{
 			Id: qdrant.NewIDNum(uint64(1)),

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

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

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

@@ -8,7 +8,7 @@ client = QdrantClient(
 )
 
 client.upsert(
-    collection_name="<your-collection_name",
+    collection_name="{collection_name}",
     points=[
         models.PointStruct(
             id=1,

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

@@ -7,7 +7,7 @@ use qdrant_client::{
 let client = Qdrant::from_url("<your-qdrant-url>").build()?;
 
 client
-    .upsert_points(UpsertPointsBuilder::new("<your-collection>",
+    .upsert_points(UpsertPointsBuilder::new("{collection_name}",
         vec![
             PointStruct::new(1,
                 HashMap::from([("my-bm25-vector".to_string(),

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

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

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

@@ -14,7 +14,7 @@ client, err := qdrant.NewClient(&qdrant.Config{
 })
 
 client.Query(ctx, &qdrant.QueryPoints{
-	CollectionName: "<your-collection>",
+	CollectionName: "{collection_name}",
 	Query: qdrant.NewQueryNearest(
 		qdrant.NewVectorInputDocument(&qdrant.Document{
 			Text:  "Mission to Mars",

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

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

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

@@ -8,7 +8,7 @@ client = QdrantClient(
 )
 
 client.query_points(
-    collection_name="<your_collection_name>",
+    collection_name="{collection_name}",
     query=models.Document(
         text="Mission to Mars", 
         model="jinaai/jina-clip-v2", 

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

@@ -13,7 +13,7 @@ options.insert("dimensions".to_string(), 512.into());
 
 client
     .query(
-        QueryPointsBuilder::new("<your-collection_name>")
+        QueryPointsBuilder::new("{collection_name}")
             .query(Query::new_nearest(Document {
                 text: "Mission to Mars".into(),
                 model: "jinaai/jina-clip-v2".into(),

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

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

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

@@ -14,7 +14,7 @@ client, err := qdrant.NewClient(&qdrant.Config{
 })
 
 client.Upsert(ctx, &qdrant.UpsertPoints{
-	CollectionName: "<your-collection>",
+	CollectionName: "{collection_name}",
 	Points: []*qdrant.PointStruct{
 		{
 			Id: qdrant.NewIDNum(uint64(1)),

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

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

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

@@ -8,7 +8,7 @@ client = QdrantClient(
 )
 
 client.upsert(
-    collection_name="<your_collection_name>",
+    collection_name="{collection_name}",
     points=[
         models.PointStruct(
             id=1,

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

@@ -11,7 +11,7 @@ options.insert("jina-api-key".to_string(), "<YOUR_JINAAI_API_KEY>".into());
 options.insert("dimensions".to_string(), 512.into());
 
 client
-    .upsert_points(UpsertPointsBuilder::new("<your-collection>",
+    .upsert_points(UpsertPointsBuilder::new("{collection_name}",
         vec![
             PointStruct::new(1,
                 Image {

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

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

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

@@ -14,7 +14,7 @@ client, err := qdrant.NewClient(&qdrant.Config{
 })
 
 client.Upsert(ctx, &qdrant.UpsertPoints{
-	CollectionName: "<your-collection>",
+	CollectionName: "{collection_name}",
 	Points: []*qdrant.PointStruct{
 		{
 			Id: qdrant.NewIDNum(uint64(1)),

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

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

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

@@ -8,7 +8,7 @@ client = QdrantClient(
 )
 
 client.upsert(
-    collection_name="<your_collection_name>",
+    collection_name="{collection_name}",
     points=[
         models.PointStruct(
             id=1,

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

@@ -14,7 +14,7 @@ jina_options.insert("dimensions".to_string(), 512.into());
 client
     .upsert_points(
         UpsertPointsBuilder::new(
-            "<your-collection>",
+            "{collection_name}",
             vec![PointStruct::new(
                 1,
                 NamedVectors::default()

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

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

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

@@ -14,7 +14,7 @@ client, err := qdrant.NewClient(&qdrant.Config{
 })
 
 client.Query(ctx, &qdrant.QueryPoints{
-	CollectionName: "<your-collection>",
+	CollectionName: "{collection_name}",
 	Query: qdrant.NewQueryNearest(
 		qdrant.NewVectorInputDocument(&qdrant.Document{
 			Model: "openai/text-embedding-3-large",

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

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

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

@@ -8,7 +8,7 @@ client = QdrantClient(
 )
 
 client.query_points(
-    collection_name="<your_collection_name>",
+    collection_name="{collection_name}",
     query=models.Document(
         text="How to bake cookies?",
         model="openai/text-embedding-3-large",

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

@@ -13,7 +13,7 @@ options.insert("dimensions".to_string(), 512.into());
 
 client
     .query(
-        QueryPointsBuilder::new("<your-collection_name>")
+        QueryPointsBuilder::new("{collection_name}")
             .query(Query::new_nearest(Document {
                 text: "How to bake cookies?".into(),
                 model: "openai/text-embedding-3-large".into(),

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

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

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

@@ -14,7 +14,7 @@ client, err := qdrant.NewClient(&qdrant.Config{
 })
 
 client.Upsert(ctx, &qdrant.UpsertPoints{
-	CollectionName: "<your-collection>",
+	CollectionName: "{collection_name}",
 	Points: []*qdrant.PointStruct{
 		{
 			Id: qdrant.NewIDNum(uint64(1)),

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

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

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

@@ -8,7 +8,7 @@ client = QdrantClient(
 )
 
 client.upsert(
-    collection_name="<your_collection_name>",
+    collection_name="{collection_name}",
     points=[
         models.PointStruct(
             id=1,

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

@@ -11,7 +11,7 @@ options.insert("openai-api-key".to_string(), "<YOUR_OPENAI_API_KEY>".into());
 options.insert("dimensions".to_string(), 512.into());
 
 client
-    .upsert_points(UpsertPointsBuilder::new("<your-collection>",
+    .upsert_points(UpsertPointsBuilder::new("{collection_name}",
         vec![
             PointStruct::new(1,
                 Document {

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

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

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

@@ -14,7 +14,7 @@ client, err := qdrant.NewClient(&qdrant.Config{
 })
 
 client.Query(ctx, &qdrant.QueryPoints{
-	CollectionName: "<your-collection>",
+	CollectionName: "{collection_name}",
 	Query: qdrant.NewQueryNearest(
 		qdrant.NewVectorInputDocument(&qdrant.Document{
 			Model: "qdrant/bm25",

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

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

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

@@ -8,7 +8,7 @@ client = QdrantClient(
 )
 
 client.query_points(
-    collection_name="<your_collection_name>",
+    collection_name="{collection_name}",
     query=models.Document(
         text="How to bake cookies?", 
         model="Qdrant/bm25",

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

@@ -8,7 +8,7 @@ let client = Qdrant::from_url("<your-qdrant-url>").build().unwrap();
 
 client
     .query(
-        QueryPointsBuilder::new("<your-collection_name>")
+        QueryPointsBuilder::new("{collection_name}")
             .query(Query::new_nearest(Document {
                 text: "How to bake cookies?".into(),
                 model: "qdrant/bm25".into(),