mirror of
https://github.com/qdrant/landing_page.git
synced 2026-10-06 11:28:31 +02:00
Anush008
+1
-1
@@ -16,7 +16,7 @@ await client.UpsertAsync(
|
||||
new() {
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||||
Id = 1,
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||||
Vectors = new Image() {
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||||
Image = "https://qdrant.tech/example.png",
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Image_ = "https://qdrant.tech/example.png",
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Model = "qdrant/clip-vit-b-32-vision",
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||||
},
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Payload = {
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+21
@@ -0,0 +1,21 @@
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```csharp
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using Qdrant.Client;
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using Qdrant.Client.Grpc;
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var client = new QdrantClient(
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host: "xyz-example.qdrant.io",
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port: 6334,
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https: true,
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apiKey: "<your-api-key>"
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||||
);
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await client.QueryAsync(
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collectionName: "{collection_name}",
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query: new Document()
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{
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Model = "cohere/embed-v4.0",
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Text = "a green square",
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Options = { ["cohere-api-key"] = "<YOUR_COHERE_API_KEY>", ["output_dimension"] = 512 },
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}
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);
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```
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@@ -14,7 +14,7 @@ client, err := qdrant.NewClient(&qdrant.Config{
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})
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client.Query(ctx, &qdrant.QueryPoints{
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CollectionName: "<your-collection>",
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CollectionName: "{collection_name}",
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Query: qdrant.NewQueryNearest(
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qdrant.NewVectorInputDocument(&qdrant.Document{
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Text: "a green square",
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+1
-1
@@ -1,5 +1,5 @@
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```http
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POST /collections/<your-collection_name>/points/query
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POST /collections/{collection_name}/points/query
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{
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"query": {
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"text": "a green square",
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+1
-1
@@ -8,7 +8,7 @@ client = QdrantClient(
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)
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client.query_points(
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collection_name="<your_collection_name>",
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collection_name="{collection_name}",
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query=models.Document(
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text="a green square",
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model="cohere/embed-v4.0",
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+1
-1
@@ -13,7 +13,7 @@ options.insert("output_dimension".to_string(), 512.into());
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client
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.query(
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QueryPointsBuilder::new("<your-collection_name>")
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QueryPointsBuilder::new("{collection_name}")
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.query(Query::new_nearest(Document {
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text: "a green square".into(),
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model: "cohere/embed-v4.0".into(),
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+29
@@ -0,0 +1,29 @@
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```csharp
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using Qdrant.Client;
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using Qdrant.Client.Grpc;
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var client = new QdrantClient(
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host: "xyz-example.qdrant.io", port: 6334, https: true, apiKey: "<your-api-key>");
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await client.UpsertAsync(
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collectionName: "{collection_name}",
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points: new List<PointStruct>
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{
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new()
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{
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Id = 1,
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Vectors = new Image()
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{
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Model = "cohere/embed-v4.0",
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Image_ =
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"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC",
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Options =
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{
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["cohere-api-key"] = "<YOUR_COHERE_API_KEY>",
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["output_dimension"] = 512,
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},
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},
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},
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}
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);
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```
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+1
-1
@@ -14,7 +14,7 @@ client, err := qdrant.NewClient(&qdrant.Config{
|
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})
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client.Upsert(ctx, &qdrant.UpsertPoints{
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CollectionName: "<your-collection>",
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CollectionName: "{collection_name}",
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Points: []*qdrant.PointStruct{
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{
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Id: qdrant.NewIDNum(uint64(1)),
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+1
-1
@@ -1,5 +1,5 @@
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```http
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PUT /collections/<your-collection_name>/points?wait=true
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PUT /collections/{collection_name}/points?wait=true
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{
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"points": [
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{
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+1
-1
@@ -8,7 +8,7 @@ client = QdrantClient(
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)
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client.upsert(
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collection_name="<your_collection_name>",
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collection_name="{collection_name}",
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points=[
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models.PointStruct(
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id=1,
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+1
-1
@@ -11,7 +11,7 @@ options.insert("cohere-api-key".to_string(), "<YOUR_COHERE_API_KEY>".into());
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options.insert("output_dimension".to_string(), 512.into());
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client
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.upsert_points(UpsertPointsBuilder::new("<your-collection>",
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.upsert_points(UpsertPointsBuilder::new("{collection_name}",
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vec![
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PointStruct::new(1,
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Document {
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@@ -0,0 +1,26 @@
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```csharp
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using Qdrant.Client;
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using Qdrant.Client.Grpc;
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var client = new QdrantClient(
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host: "xyz-example.qdrant.io", port: 6334, https: true, apiKey: "<your-api-key>");
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|
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await client.UpsertAsync(
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collectionName: "{collection_name}",
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points: new List<PointStruct>
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{
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new()
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{
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Id = 1,
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Vectors = new Dictionary<string, Vector>
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{
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["my-bm25-vector"] = new Document()
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{
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Model = "qdrant/bm25",
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Text = "Recipe for baking chocolate chip cookies",
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},
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},
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},
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}
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);
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```
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@@ -14,7 +14,7 @@ client, err := qdrant.NewClient(&qdrant.Config{
|
||||
})
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|
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client.Upsert(ctx, &qdrant.UpsertPoints{
|
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CollectionName: "<your-collection>",
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CollectionName: "{collection_name}",
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Points: []*qdrant.PointStruct{
|
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{
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Id: qdrant.NewIDNum(uint64(1)),
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@@ -1,5 +1,5 @@
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```http
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PUT /collections/<your-collection_name>/points
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PUT /collections/{collection_name}/points
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{
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"points": [
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{
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|
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@@ -8,7 +8,7 @@ client = QdrantClient(
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)
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client.upsert(
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collection_name="<your-collection_name",
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collection_name="{collection_name}",
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points=[
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models.PointStruct(
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id=1,
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@@ -7,7 +7,7 @@ use qdrant_client::{
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let client = Qdrant::from_url("<your-qdrant-url>").build()?;
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|
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client
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.upsert_points(UpsertPointsBuilder::new("<your-collection>",
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.upsert_points(UpsertPointsBuilder::new("{collection_name}",
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vec![
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PointStruct::new(1,
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HashMap::from([("my-bm25-vector".to_string(),
|
||||
|
||||
+21
@@ -0,0 +1,21 @@
|
||||
```csharp
|
||||
using Qdrant.Client;
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||||
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 },
|
||||
}
|
||||
);
|
||||
```
|
||||
@@ -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",
|
||||
|
||||
+1
-1
@@ -1,5 +1,5 @@
|
||||
```http
|
||||
POST /collections/<your-collection_name>/points/query
|
||||
POST /collections/{collection_name}/points/query
|
||||
{
|
||||
"query": {
|
||||
"text": "Mission to Mars",
|
||||
|
||||
+1
-1
@@ -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",
|
||||
|
||||
+1
-1
@@ -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(),
|
||||
|
||||
+28
@@ -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 },
|
||||
},
|
||||
},
|
||||
}
|
||||
);
|
||||
```
|
||||
+1
-1
@@ -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)),
|
||||
|
||||
+1
-1
@@ -1,5 +1,5 @@
|
||||
```http
|
||||
PUT /collections/<your-collection_name>/points?wait=true
|
||||
PUT /collections/{collection_name}/points?wait=true
|
||||
{
|
||||
"points": [
|
||||
{
|
||||
|
||||
+1
-1
@@ -8,7 +8,7 @@ client = QdrantClient(
|
||||
)
|
||||
|
||||
client.upsert(
|
||||
collection_name="<your_collection_name>",
|
||||
collection_name="{collection_name}",
|
||||
points=[
|
||||
models.PointStruct(
|
||||
id=1,
|
||||
|
||||
+1
-1
@@ -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 {
|
||||
|
||||
@@ -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" },
|
||||
},
|
||||
},
|
||||
}
|
||||
);
|
||||
```
|
||||
@@ -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)),
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
```http
|
||||
PUT /collections/<your-collection_name>/points?wait=true
|
||||
PUT /collections/{collection_name}/points?wait=true
|
||||
{
|
||||
"points": [
|
||||
{
|
||||
|
||||
@@ -8,7 +8,7 @@ client = QdrantClient(
|
||||
)
|
||||
|
||||
client.upsert(
|
||||
collection_name="<your_collection_name>",
|
||||
collection_name="{collection_name}",
|
||||
points=[
|
||||
models.PointStruct(
|
||||
id=1,
|
||||
|
||||
@@ -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()
|
||||
|
||||
+21
@@ -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 },
|
||||
}
|
||||
);
|
||||
```
|
||||
@@ -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",
|
||||
|
||||
+1
-1
@@ -1,5 +1,5 @@
|
||||
```http
|
||||
POST /collections/<your-collection_name>/points/query
|
||||
POST /collections/{collection_name}/points/query
|
||||
{
|
||||
"query": {
|
||||
"text": "How to bake cookies?",
|
||||
|
||||
+1
-1
@@ -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",
|
||||
|
||||
+1
-1
@@ -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(),
|
||||
|
||||
+24
@@ -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 },
|
||||
},
|
||||
},
|
||||
}
|
||||
);
|
||||
```
|
||||
+1
-1
@@ -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)),
|
||||
|
||||
+1
-1
@@ -1,5 +1,5 @@
|
||||
```http
|
||||
PUT /collections/<your-collection_name>/points?wait=true
|
||||
PUT /collections/{collection_name}/points?wait=true
|
||||
{
|
||||
"points": [
|
||||
{
|
||||
|
||||
+1
-1
@@ -8,7 +8,7 @@ client = QdrantClient(
|
||||
)
|
||||
|
||||
client.upsert(
|
||||
collection_name="<your_collection_name>",
|
||||
collection_name="{collection_name}",
|
||||
points=[
|
||||
models.PointStruct(
|
||||
id=1,
|
||||
|
||||
+1
-1
@@ -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 {
|
||||
|
||||
@@ -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"
|
||||
);
|
||||
```
|
||||
@@ -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",
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
```http
|
||||
POST /collections/<your-collection_name>/points/query
|
||||
POST /collections/{collection_name}/points/query
|
||||
{
|
||||
"query": {
|
||||
"text": "How to bake cookies?",
|
||||
|
||||
@@ -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",
|
||||
|
||||
@@ -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(),
|
||||
|
||||
Reference in New Issue
Block a user