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Code Issues Packages Projects Releases Wiki Activity

Restructure inference docs (#2225)

Browse Source 
* 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>
This commit is contained in:
Abdon Pijpelink
2026-06-24 08:11:03 +02:00
committed by GitHub
co-authored by Claude Sonnet 4.6
parent 90d072eb03
commit 478b96554f
250 changed files with 3259 additions and 2187 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/csharp.cs
+12 -9
View File
@@ -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(
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 },
}
);
using (RequestHeaders.Use("cohere-api-key", "<YOUR_COHERE_API_KEY>"))
await client.QueryAsync(
collectionName: "{collection_name}",
query: new Document()
{
Model = "cohere/embed-v4.0",
Text = "a green square",
Options = { ["output_dimension"] = 512 },
}
);
}
}
qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/generated/csharp.md
+10 -16
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@@ -2,20 +2,14 @@
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 },
}
);
using (RequestHeaders.Use("cohere-api-key", "<YOUR_COHERE_API_KEY>"))
await client.QueryAsync(
collectionName: "{collection_name}",
query: new Document()
{
Model = "cohere/embed-v4.0",
Text = "a green square",
Options = { ["output_dimension"] = 512 },
}
);
```
qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/generated/go.md
+2 -8
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@@ -5,21 +5,15 @@ 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,
})
ctx := qdrant.WithHeader(context.Background(), "cohere-api-key", "<YOUR_COHERE_API_KEY>")
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
+20 -23
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@@ -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 =
new QdrantClient(
QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
.withApiKey("<your-api-key")
.build());
Context ctx = RequestHeaders.withHeader(
Context.current(), "cohere-api-key", "<YOUR_COHERE_API_KEY>");
client
.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.setQuery(
nearest(
Document.newBuilder()
.setModel("cohere/embed-v4.0")
.setText("a green square")
.putAllOptions(
Map.of(
"cohere-api-key",
value("<YOUR_COHERE_API_KEY>"),
"output_dimension",
value(512)))
.build()))
.build())
.get();
ctx.call(() -> client
.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.setQuery(
nearest(
Document.newBuilder()
.setModel("cohere/embed-v4.0")
.setText("a green square")
.putAllOptions(
Map.of(
"output_dimension",
value(512)))
.build()))
.build())
.get());
```
qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/generated/python.md
+12 -11
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@@ -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(
collection_name="{collection_name}",
query=models.Document(
text="a green square",
model="cohere/embed-v4.0",
options={
"cohere-api-key": "<your_cohere_api_key>",
"output_dimension": 512
}
with headers({"cohere-api-key": "<YOUR_COHERE_API_KEY>"}):
client.query_points(
collection_name="{collection_name}",
query=models.Document(
text="a green square",
model="cohere/embed-v4.0",
options={
"output_dimension": 512
}
)
)
)
```
qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/generated/rust.md
+1 -3
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@@ -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
+11 -12
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@@ -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 });
client.query("{collection_name}", {
query: {
text: 'a green square',
model: 'cohere/embed-v4.0',
options: {
'cohere-api-key': '<your_cohere_api_key>',
output_dimension: 512,
await withHeaders({ 'cohere-api-key': '<YOUR_COHERE_API_KEY>' }, () =>
client.query("{collection_name}", {
query: {
text: 'a green square',
model: 'cohere/embed-v4.0',
options: {
output_dimension: 512,
},
},
},
});
})
);
```
qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/go.go
+5 -2
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@@ -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
+9 -4
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@@ -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
+12 -11
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@@ -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(
collection_name="{collection_name}",
query=models.Document(
text="a green square",
model="cohere/embed-v4.0",
options={
"cohere-api-key": "<your_cohere_api_key>",
"output_dimension": 512
}
with headers({"cohere-api-key": "<YOUR_COHERE_API_KEY>"}):
client.query_points(
collection_name="{collection_name}",
query=models.Document(
text="a green square",
model="cohere/embed-v4.0",
options={
"output_dimension": 512
}
)
)
)
qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/rust.rs
+2 -2
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@@ -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
+12 -11
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@@ -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}", {
query: {
text: 'a green square',
model: 'cohere/embed-v4.0',
options: {
'cohere-api-key': '<your_cohere_api_key>',
output_dimension: 512,
await withHeaders({ 'cohere-api-key': '<YOUR_COHERE_API_KEY>' }, () =>
client.query("{collection_name}", {
query: {
text: 'a green square',
model: 'cohere/embed-v4.0',
options: {
output_dimension: 512,
},
},
},
});
})
);
qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/csharp.cs
+18 -16
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@@ -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(
collectionName: "{collection_name}",
points: new List<PointStruct>
{
new()
using (RequestHeaders.Use("cohere-api-key", "<YOUR_COHERE_API_KEY>"))
await client.UpsertAsync(
collectionName: "{collection_name}",
points: new List<PointStruct>
{
Id = 1,
Vectors = new Image()
new()
{
Model = "cohere/embed-v4.0",
Image_ =
"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC",
Options =
Id = 1,
Vectors = new Image()
{
["cohere-api-key"] = "<YOUR_COHERE_API_KEY>",
["output_dimension"] = 512,
Model = "cohere/embed-v4.0",
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
+16 -19
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@@ -2,28 +2,25 @@
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()
using (RequestHeaders.Use("cohere-api-key", "<YOUR_COHERE_API_KEY>"))
await client.UpsertAsync(
collectionName: "{collection_name}",
points: new List<PointStruct>
{
Id = 1,
Vectors = new Image()
new()
{
Model = "cohere/embed-v4.0",
Image_ =
"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC",
Options =
Id = 1,
Vectors = new Image()
{
["cohere-api-key"] = "<YOUR_COHERE_API_KEY>",
["output_dimension"] = 512,
Model = "cohere/embed-v4.0",
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
+2 -8
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@@ -5,14 +5,9 @@ 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,
})
ctx := qdrant.WithHeader(context.Background(), "cohere-api-key", "<YOUR_COHERE_API_KEY>")
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
+24 -27
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@@ -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 =
new QdrantClient(
QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
.withApiKey("<your-api-key")
.build());
Context ctx = RequestHeaders.withHeader(
Context.current(), "cohere-api-key", "<YOUR_COHERE_API_KEY>");
client
.upsertAsync(
"{collection_name}",
List.of(
PointStruct.newBuilder()
.setId(id(1))
.setVectors(
vectors(
Image.newBuilder()
.setModel("cohere/embed-v4.0")
.setImage(
value(
"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC"))
.putAllOptions(
Map.of(
"cohere-api-key",
value("<YOUR_COHERE_API_KEY>"),
"output_dimension",
value(512)))
.build()))
.build()))
.get();
ctx.call(() -> client
.upsertAsync(
"{collection_name}",
List.of(
PointStruct.newBuilder()
.setId(id(1))
.setVectors(
vectors(
Image.newBuilder()
.setModel("cohere/embed-v4.0")
.setImage(
value(
"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC"))
.putAllOptions(
Map.of(
"output_dimension",
value(512)))
.build()))
.build()))
.get());
```
qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/generated/python.md
+17 -16
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@@ -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(
collection_name="{collection_name}",
points=[
models.PointStruct(
id=1,
vector=models.Document(
text="a green square",
model="cohere/embed-v4.0",
options={
"cohere-api-key": "<your_cohere_api_key>",
"output_dimension": 512
}
with headers({"cohere-api-key": "<YOUR_COHERE_API_KEY>"}):
client.upsert(
collection_name="{collection_name}",
points=[
models.PointStruct(
id=1,
vector=models.Image(
image="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC",
model="cohere/embed-v4.0",
options={
"output_dimension": 512
}
)
)
)
]
)
]
)
```
qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/generated/rust.md
+5 -6
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@@ -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 {
text: "Recipe for baking chocolate chip cookies requires flour, sugar, eggs, and chocolate chips.".into(),
model: "openai/text-embedding-3-small".into(),
Image {
image: Some("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC".into()),
model: "cohere/embed-v4.0".into(),
options,
},
Payload::default())
qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/generated/typescript.md
+15 -16
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@@ -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 });
client.upsert("{collection_name}", {
points: [
{
id: 1,
vector: {
text: 'a green square',
model: 'cohere/embed-v4.0',
options: {
'cohere-api-key': '<your_cohere_api_key>',
output_dimension: 512,
await withHeaders({ 'cohere-api-key': '<YOUR_COHERE_API_KEY>' }, () =>
client.upsert("{collection_name}", {
points: [
{
id: 1,
vector: {
image: 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC',
model: 'cohere/embed-v4.0',
options: {
output_dimension: 512,
},
},
},
},
],
});
],
})
);
```
qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/go.go
+5 -2
View File
@@ -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
+9 -4
View File
@@ -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
+17 -16
View File
@@ -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(
collection_name="{collection_name}",
points=[
models.PointStruct(
id=1,
vector=models.Document(
text="a green square",
model="cohere/embed-v4.0",
options={
"cohere-api-key": "<your_cohere_api_key>",
"output_dimension": 512
}
with headers({"cohere-api-key": "<YOUR_COHERE_API_KEY>"}):
client.upsert(
collection_name="{collection_name}",
points=[
models.PointStruct(
id=1,
vector=models.Image(
image="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC",
model="cohere/embed-v4.0",
options={
"output_dimension": 512
}
)
)
)
]
)
]
)
qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/rust.rs
+6 -6
View File
@@ -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 {
text: "Recipe for baking chocolate chip cookies requires flour, sugar, eggs, and chocolate chips.".into(),
model: "openai/text-embedding-3-small".into(),
Image {
image: Some("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC".into()),
model: "cohere/embed-v4.0".into(),
options,
},
Payload::default())
qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/typescript.ts
+16 -15
View File
@@ -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}", {
points: [
{
id: 1,
vector: {
text: 'a green square',
model: 'cohere/embed-v4.0',
options: {
'cohere-api-key': '<your_cohere_api_key>',
output_dimension: 512,
await withHeaders({ 'cohere-api-key': '<YOUR_COHERE_API_KEY>' }, () =>
client.upsert("{collection_name}", {
points: [
{
id: 1,
vector: {
image: 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC',
model: 'cohere/embed-v4.0',
options: {
output_dimension: 512,
},
},
},
},
],
});
],
})
);
qdrant-landing/content/documentation/headless/snippets/inference/external-api-key-body/_description.md
+1
View File
@@ -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
+30
View File
@@ -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
+21
View File
@@ -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
+23
View File
@@ -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
+31
View File
@@ -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
+25
View File
@@ -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
+23
View File
@@ -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
+18
View File
@@ -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
+36
View File
@@ -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
+17
View File
@@ -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
+43
View File
@@ -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
+23
View File
@@ -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
+26
View File
@@ -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
+18
View File
@@ -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
+1
View File
@@ -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
+34
View File
@@ -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
+21
View File
@@ -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
+22
View File
@@ -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
+30
View File
@@ -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
+24
View File
@@ -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
+26
View File
@@ -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
+17
View File
@@ -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
+35
View File
@@ -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
+42
View File
@@ -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
+22
View File
@@ -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
+30
View File
@@ -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
+17
View File
@@ -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
+2
View File
@@ -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
-3
View File
@@ -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
-7
View File
@@ -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
+17 -23
View File
@@ -12,27 +12,21 @@ import io.qdrant.client.grpc.Points.PointStruct;
import java.util.List;
import java.util.Map;
QdrantClient client =
new QdrantClient(
QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
.withApiKey("<your-api-key")
.build());
client
.upsertAsync(
"{collection_name}",
List.of(
PointStruct.newBuilder()
.setId(id(1))
.setVectors(
namedVectors(
Map.of(
"my-bm25-vector",
vector(
Document.newBuilder()
.setModel("qdrant/bm25")
.setText("Recipe for baking chocolate chip cookies")
.build()))))
.build()))
.get();
client
.upsertAsync(
"{collection_name}",
List.of(
PointStruct.newBuilder()
.setId(id(1))
.setVectors(
namedVectors(
Map.of(
"my-bm25-vector",
vector(
Document.newBuilder()
.setModel("qdrant/bm25")
.setText("Recipe for baking chocolate chip cookies")
.build()))))
.build()))
.get();
```
qdrant-landing/content/documentation/headless/snippets/inference/ingest/generated/python.md
-6
View File
@@ -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
-2
View File
@@ -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
-2
View File
@@ -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
+2
View File
@@ -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
+2
View File
@@ -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
+3 -1
View File
@@ -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
+1 -1
View File
@@ -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 -1
View File
@@ -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
+12 -9
View File
@@ -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(
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 },
}
);
using (RequestHeaders.Use("jina-api-key", "<YOUR_JINAAI_API_KEY>"))
await client.QueryAsync(
collectionName: "{collection_name}",
query: new Document()
{
Model = "jinaai/jina-clip-v2",
Text = "Mission to Mars",
Options = { ["dimensions"] = 512 },
}
);
}
}
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-query/generated/csharp.md
+10 -16
View File
@@ -2,20 +2,14 @@
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 },
}
);
using (RequestHeaders.Use("jina-api-key", "<YOUR_JINAAI_API_KEY>"))
await client.QueryAsync(
collectionName: "{collection_name}",
query: new Document()
{
Model = "jinaai/jina-clip-v2",
Text = "Mission to Mars",
Options = { ["dimensions"] = 512 },
}
);
```
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-query/generated/go.md
+3 -9
View File
@@ -5,22 +5,16 @@ 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,
})
ctx := qdrant.WithHeader(context.Background(), "jina-api-key", "<YOUR_JINAAI_API_KEY>")
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
+7 -9
View File
@@ -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 =
new QdrantClient(
QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
.withApiKey("<your-api-key")
.build());
client
Context ctx = RequestHeaders.withHeader(
Context.current(), "jina-api-key", "<YOUR_JINAAI_API_KEY>");
ctx.call(() -> 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
+12 -11
View File
@@ -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(
collection_name="{collection_name}",
query=models.Document(
text="Mission to Mars",
model="jinaai/jina-clip-v2",
options={
"jina-api-key": "<your_jinaai_api_key>",
"dimensions": 512
}
with headers({"jina-api-key": "<YOUR_JINAAI_API_KEY>"}):
client.query_points(
collection_name="{collection_name}",
query=models.Document(
text="Mission to Mars",
model="jinaai/jina-clip-v2",
options={
"dimensions": 512
}
)
)
)
```
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-query/generated/rust.md
+1 -3
View File
@@ -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
+11 -12
View File
@@ -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 });
client.query("{collection_name}", {
query: {
text: 'Mission to Mars',
model: 'jinaai/jina-clip-v2',
options: {
'jina-api-key': '<your_jinaai_api_key>',
dimensions: 512,
await withHeaders({ 'jina-api-key': '<YOUR_JINAAI_API_KEY>' }, () =>
client.query("{collection_name}", {
query: {
text: 'Mission to Mars',
model: 'jinaai/jina-clip-v2',
options: {
dimensions: 512,
},
},
},
});
})
);
```
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-query/go.go
+6 -3
View File
@@ -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
+10 -4
View File
@@ -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
+12 -11
View File
@@ -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(
collection_name="{collection_name}",
query=models.Document(
text="Mission to Mars",
model="jinaai/jina-clip-v2",
options={
"jina-api-key": "<your_jinaai_api_key>",
"dimensions": 512
}
with headers({"jina-api-key": "<YOUR_JINAAI_API_KEY>"}):
client.query_points(
collection_name="{collection_name}",
query=models.Document(
text="Mission to Mars",
model="jinaai/jina-clip-v2",
options={
"dimensions": 512
}
)
)
)
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-query/rust.rs
+2 -2
View File
@@ -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
+12 -11
View File
@@ -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}", {
query: {
text: 'Mission to Mars',
model: 'jinaai/jina-clip-v2',
options: {
'jina-api-key': '<your_jinaai_api_key>',
dimensions: 512,
await withHeaders({ 'jina-api-key': '<YOUR_JINAAI_API_KEY>' }, () =>
client.query("{collection_name}", {
query: {
text: 'Mission to Mars',
model: 'jinaai/jina-clip-v2',
options: {
dimensions: 512,
},
},
},
});
})
);
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/csharp.cs
+16 -13
View File
@@ -5,28 +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(
collectionName: "{collection_name}",
points: new List<PointStruct>
{
new()
using (RequestHeaders.Use("jina-api-key", "<YOUR_JINAAI_API_KEY>"))
await client.UpsertAsync(
collectionName: "{collection_name}",
points: new List<PointStruct>
{
Id = 1,
Vectors = new Document()
new()
{
Model = "jinaai/jina-clip-v2",
Text = "Mission to Mars",
Options = { ["jina-api-key"] = "<YOUR_JINAAI_API_KEY>", ["dimensions"] = 512 },
Id = 1,
Vectors = new Image()
{
Model = "jinaai/jina-clip-v2",
Image_ = "https://qdrant.tech/example.png",
Options = { ["dimensions"] = 512 },
},
},
},
}
);
}
);
}
}
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/generated/csharp.md
+14 -20
View File
@@ -2,27 +2,21 @@
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()
using (RequestHeaders.Use("jina-api-key", "<YOUR_JINAAI_API_KEY>"))
await client.UpsertAsync(
collectionName: "{collection_name}",
points: new List<PointStruct>
{
Id = 1,
Vectors = new Document()
new()
{
Model = "jinaai/jina-clip-v2",
Text = "Mission to Mars",
Options = { ["jina-api-key"] = "<YOUR_JINAAI_API_KEY>", ["dimensions"] = 512 },
Id = 1,
Vectors = new Image()
{
Model = "jinaai/jina-clip-v2",
Image_ = "https://qdrant.tech/example.png",
Options = { ["dimensions"] = 512 },
},
},
},
}
);
}
);
```
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/generated/go.md
+3 -9
View File
@@ -5,14 +5,9 @@ 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,
})
ctx := qdrant.WithHeader(context.Background(), "jina-api-key", "<YOUR_JINAAI_API_KEY>")
client.Upsert(context.Background(), &qdrant.UpsertPoints{
client.Upsert(ctx, &qdrant.UpsertPoints{
CollectionName: "{collection_name}",
Points: []*qdrant.PointStruct{
{
@@ -21,8 +16,7 @@ client.Upsert(context.Background(), &qdrant.UpsertPoints{
Model: "jinaai/jina-clip-v2",
Image: qdrant.NewValueString("https://qdrant.tech/example.png"),
Options: qdrant.NewValueMap(map[string]any{
"jina-api-key": "<YOUR_JINAAI_API_KEY>",
"dimensions": 512,
"dimensions": 512,
}),
}),
},
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/generated/java.md
+6 -9
View File
@@ -3,20 +3,19 @@ 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 =
new QdrantClient(
QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
.withApiKey("<your-api-key")
.build());
Context ctx = RequestHeaders.withHeader(
Context.current(), "jina-api-key", "<YOUR_JINAAI_API_KEY>");
client
ctx.call(() -> client
.upsertAsync(
"{collection_name}",
List.of(
@@ -29,11 +28,9 @@ client
.setImage(value("https://qdrant.tech/example.png"))
.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-upsert/generated/python.md
+17 -16
View File
@@ -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(
collection_name="{collection_name}",
points=[
models.PointStruct(
id=1,
vector=models.Image(
image="https://qdrant.tech/example.png",
model="jinaai/jina-clip-v2",
options={
"jina-api-key": "<your_jinaai_api_key>",
"dimensions": 512
}
with headers({"jina-api-key": "<YOUR_JINAAI_API_KEY>"}):
client.upsert(
collection_name="{collection_name}",
points=[
models.PointStruct(
id=1,
vector=models.Image(
image="https://qdrant.tech/example.png",
model="jinaai/jina-clip-v2",
options={
"dimensions": 512
}
)
)
)
]
)
]
)
```
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/generated/rust.md
+1 -2
View File
@@ -5,12 +5,11 @@ use qdrant_client::{
};
use std::collections::HashMap;
let client = Qdrant::from_url("<your-qdrant-url>").build()?;
let mut options = HashMap::new();
options.insert("jina-api-key".to_string(), "<YOUR_JINAAI_API_KEY>".into());
options.insert("dimensions".to_string(), 512.into());
client
.with_header("jina-api-key", "<YOUR_JINAAI_API_KEY>")
.upsert_points(UpsertPointsBuilder::new("{collection_name}",
vec![
PointStruct::new(1,
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/generated/typescript.md
+15 -16
View File
@@ -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 });
client.upsert("{collection_name}", {
points: [
{
id: 1,
vector: {
image: 'https://qdrant.tech/example.png',
model: 'jinaai/jina-clip-v2',
options: {
'jina-api-key': '<your_jinaai_api_key>',
dimensions: 512,
await withHeaders({ 'jina-api-key': '<YOUR_JINAAI_API_KEY>' }, () =>
client.upsert("{collection_name}", {
points: [
{
id: 1,
vector: {
image: 'https://qdrant.tech/example.png',
model: 'jinaai/jina-clip-v2',
options: {
dimensions: 512,
},
},
},
},
],
});
],
})
);
```
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/go.go
+6 -3
View File
@@ -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(), "jina-api-key", "<YOUR_JINAAI_API_KEY>")
client.Upsert(ctx, &qdrant.UpsertPoints{
CollectionName: "{collection_name}",
Points: []*qdrant.PointStruct{
{
@@ -25,8 +29,7 @@ func Main() {
Model: "jinaai/jina-clip-v2",
Image: qdrant.NewValueString("https://qdrant.tech/example.png"),
Options: qdrant.NewValueMap(map[string]any{
"jina-api-key": "<YOUR_JINAAI_API_KEY>",
"dimensions": 512,
"dimensions": 512,
}),
}),
},
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/java.java
+9 -4
View File
@@ -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(), "jina-api-key", "<YOUR_JINAAI_API_KEY>");
ctx.call(() -> client
.upsertAsync(
"{collection_name}",
List.of(
@@ -32,12 +39,10 @@ public class Snippet {
.setImage(value("https://qdrant.tech/example.png"))
.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-upsert/python.py
+17 -16
View File
@@ -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(
collection_name="{collection_name}",
points=[
models.PointStruct(
id=1,
vector=models.Image(
image="https://qdrant.tech/example.png",
model="jinaai/jina-clip-v2",
options={
"jina-api-key": "<your_jinaai_api_key>",
"dimensions": 512
}
with headers({"jina-api-key": "<YOUR_JINAAI_API_KEY>"}):
client.upsert(
collection_name="{collection_name}",
points=[
models.PointStruct(
id=1,
vector=models.Image(
image="https://qdrant.tech/example.png",
model="jinaai/jina-clip-v2",
options={
"dimensions": 512
}
)
)
)
]
)
]
)
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/rust.rs
+2 -2
View File
@@ -5,12 +5,12 @@ 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
let mut options = HashMap::new();
options.insert("jina-api-key".to_string(), "<YOUR_JINAAI_API_KEY>".into());
options.insert("dimensions".to_string(), 512.into());
client
.with_header("jina-api-key", "<YOUR_JINAAI_API_KEY>")
.upsert_points(UpsertPointsBuilder::new("{collection_name}",
vec![
PointStruct::new(1,
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/typescript.ts
+16 -15
View File
@@ -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}", {
points: [
{
id: 1,
vector: {
image: 'https://qdrant.tech/example.png',
model: 'jinaai/jina-clip-v2',
options: {
'jina-api-key': '<your_jinaai_api_key>',
dimensions: 512,
await withHeaders({ 'jina-api-key': '<YOUR_JINAAI_API_KEY>' }, () =>
client.upsert("{collection_name}", {
points: [
{
id: 1,
vector: {
image: 'https://qdrant.tech/example.png',
model: 'jinaai/jina-clip-v2',
options: {
dimensions: 512,
},
},
},
},
],
});
],
})
);
qdrant-landing/content/documentation/headless/snippets/inference/mrl-multi-stage/csharp.cs
+24 -22
View File
@@ -5,37 +5,39 @@ 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(
collectionName: "{collection_name}",
prefetch:
[
new()
{
Query = new Document()
using (RequestHeaders.Use("openai-api-key", "<YOUR_OPENAI_API_KEY>"))
await client.QueryAsync(
collectionName: "{collection_name}",
prefetch:
[
new()
{
Model = "openai/text-embedding-3-small",
Text = "How to bake cookies?",
Options = { ["openai-api-key"] = "<YOUR_OPENAI_API_KEY>", ["mrl"] = 64 },
Query = new Document()
{
Model = "openai/text-embedding-3-small",
Text = "How to bake cookies?",
Options = { ["mrl"] = 64 },
},
Using = "small",
Limit = 1000,
},
Using = "small",
Limit = 1000,
],
query: new Document()
{
Model = "openai/text-embedding-3-small",
Text = "How to bake cookies?",
},
],
query: new Document()
{
Model = "openai/text-embedding-3-small",
Text = "How to bake cookies?",
Options = { ["openai-api-key"] = "<YOUR_OPENAI_API_KEY>" },
},
usingVector: "large",
limit: 10
);
usingVector: "large",
limit: 10
);
}
}
qdrant-landing/content/documentation/headless/snippets/inference/mrl-multi-stage/generated/csharp.md
+22 -29
View File
@@ -2,36 +2,29 @@
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}",
prefetch:
[
new()
{
Query = new Document()
using (RequestHeaders.Use("openai-api-key", "<YOUR_OPENAI_API_KEY>"))
await client.QueryAsync(
collectionName: "{collection_name}",
prefetch:
[
new()
{
Model = "openai/text-embedding-3-small",
Text = "How to bake cookies?",
Options = { ["openai-api-key"] = "<YOUR_OPENAI_API_KEY>", ["mrl"] = 64 },
Query = new Document()
{
Model = "openai/text-embedding-3-small",
Text = "How to bake cookies?",
Options = { ["mrl"] = 64 },
},
Using = "small",
Limit = 1000,
},
Using = "small",
Limit = 1000,
],
query: new Document()
{
Model = "openai/text-embedding-3-small",
Text = "How to bake cookies?",
},
],
query: new Document()
{
Model = "openai/text-embedding-3-small",
Text = "How to bake cookies?",
Options = { ["openai-api-key"] = "<YOUR_OPENAI_API_KEY>" },
},
usingVector: "large",
limit: 10
);
usingVector: "large",
limit: 10
);
```
qdrant-landing/content/documentation/headless/snippets/inference/mrl-multi-stage/generated/go.md
+3 -12
View File
@@ -5,14 +5,9 @@ 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,
})
ctx := qdrant.WithHeader(context.Background(), "openai-api-key", "<YOUR_OPENAI_API_KEY>")
client.Query(context.Background(), &qdrant.QueryPoints{
client.Query(ctx, &qdrant.QueryPoints{
CollectionName: "{collection_name}",
Prefetch: []*qdrant.PrefetchQuery{
{
@@ -21,8 +16,7 @@ client.Query(context.Background(), &qdrant.QueryPoints{
Model: "openai/text-embedding-3-small",
Text: "How to bake cookies?",
Options: qdrant.NewValueMap(map[string]any{
"mrl": 64,
"openai-api-key": "<YOUR_OPENAI_API_KEY>",
"mrl": 64,
}),
}),
),
@@ -34,9 +28,6 @@ client.Query(context.Background(), &qdrant.QueryPoints{
qdrant.NewVectorInputDocument(&qdrant.Document{
Model: "openai/text-embedding-3-small",
Text: "How to bake cookies?",
Options: qdrant.NewValueMap(map[string]any{
"openai-api-key": "<YOUR_OPENAI_API_KEY>",
}),
}),
),
Using: qdrant.PtrOf("large"),
qdrant-landing/content/documentation/headless/snippets/inference/mrl-multi-stage/generated/java.md
+7 -14
View File
@@ -2,20 +2,19 @@
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;
import io.qdrant.client.grpc.Points.Document;
import io.qdrant.client.grpc.Points.PrefetchQuery;
import java.util.Map;
QdrantClient client =
new QdrantClient(
QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
.withApiKey("<your-api-key")
.build());
Context ctx = RequestHeaders.withHeader(
Context.current(), "openai-api-key", "<YOUR_OPENAI_API_KEY>");
client
ctx.call(() -> client
.queryAsync(
Points.QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
@@ -26,12 +25,7 @@ client
Document.newBuilder()
.setModel("openai/text-embedding-3-small")
.setText("How to bake cookies?")
.putAllOptions(
Map.of(
"openai-api-key",
value("<YOUR_OPENAI_API_KEY>"),
"mrl",
value(64)))
.putAllOptions(Map.of("mrl", value(64)))
.build()))
.setUsing("small")
.setLimit(1000)
@@ -41,9 +35,8 @@ client
Document.newBuilder()
.setModel("openai/text-embedding-3-small")
.setText("How to bake cookies?")
.putAllOptions(Map.of("openai-api-key", value("<YOUR_OPENAI_API_KEY>")))
.build()))
.setUsing("large")
.build())
.get();
.get());
```
qdrant-landing/content/documentation/headless/snippets/inference/mrl-multi-stage/generated/python.md
+17 -19
View File
@@ -1,32 +1,30 @@
```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>",
url="https://xyz-example.qdrant.io:6333",
api_key="<your-qdrant-api-key>",
cloud_inference=True
)
client.query_points(
collection_name="{collection_name}",
query=models.Document(
text="How to bake cookies?",
model="openai/text-embedding-3-small",
options={"openai-api-key": "<YOUR_OPENAI_API_KEY>"}
),
using="large",
limit=10,
prefetch=models.Prefetch(
with headers({"openai-api-key": "<YOUR_OPENAI_API_KEY>"}):
client.query_points(
collection_name="{collection_name}",
query=models.Document(
text="How to bake cookies?",
model="openai/text-embedding-3-small",
options={
"openai-api-key": "<YOUR_OPENAI_API_KEY>",
"mrl": 64
}
),
using="small",
limit=1000,
using="large",
limit=10,
prefetch=models.Prefetch(
query=models.Document(
text="How to bake cookies?",
model="openai/text-embedding-3-small",
options={"mrl": 64},
),
using="small",
limit=1000,
)
)
)
```
qdrant-landing/content/documentation/headless/snippets/inference/mrl-multi-stage/generated/rust.md
+6 -13
View File
@@ -6,9 +6,8 @@ use qdrant_client::{
qdrant::{Document, PrefetchQueryBuilder, Query, QueryPointsBuilder, Value},
};
let client = Qdrant::from_url("http://localhost:6334").build()?;
client
.with_header("openai-api-key", "<YOUR_OPENAI_API_KEY>")
.query(
QueryPointsBuilder::new("{collection_name}")
.add_prefetch(
@@ -16,13 +15,10 @@ client
.query(Query::new_nearest(Document {
text: "How to bake cookies?".into(),
model: "openai/text-embedding-3-small".into(),
options: HashMap::<String, Value>::from_iter(vec![
(
"openai-api-key".to_string(),
Value::from("<YOUR_OPENAI_API_KEY>"),
),
("mrl".into(), Value::from(64)),
]),
options: HashMap::<String, Value>::from_iter(vec![(
"mrl".into(),
Value::from(64),
)]),
}))
.using("small")
.limit(1000_u64),
@@ -30,10 +26,7 @@ client
.query(Query::new_nearest(Document {
text: "How to bake cookies?".into(),
model: "openai/text-embedding-3-small".into(),
options: HashMap::from_iter(vec![(
"openai-api-key".into(),
"<YOUR_OPENAI_API_KEY>".into(),
)]),
options: HashMap::new(),
}))
.using("large")
.limit(10_u64)
qdrant-landing/content/documentation/headless/snippets/inference/mrl-multi-stage/generated/typescript.md
+18 -22
View File
@@ -1,29 +1,25 @@
```typescript
import { QdrantClient } from "@qdrant/js-client-rest";
import { QdrantClient, withHeaders } from "@qdrant/js-client-rest";
const client = new QdrantClient({ host: "localhost", port: 6333 });
client.query("{collection_name}", {
prefetch: {
await withHeaders({ 'openai-api-key': '<YOUR_OPENAI_API_KEY>' }, () =>
client.query("{collection_name}", {
prefetch: {
query: {
text: "How to bake cookies?",
model: "openai/text-embedding-3-small",
options: {
mrl: 64,
}
},
using: 'small',
limit: 1000,
},
query: {
text: "How to bake cookies?",
model: "openai/text-embedding-3-small",
options: {
"openai-api-key": "<YOUR_OPENAI_API_KEY>",
mrl: 64,
}
},
using: 'small',
limit: 1000,
},
query: {
text: "How to bake cookies?",
model: "openai/text-embedding-3-small",
options: {
"openai-api-key": "<YOUR_OPENAI_API_KEY>"
}
},
using: 'large',
limit: 10,
});
using: 'large',
limit: 10,
})
);
```
qdrant-landing/content/documentation/headless/snippets/inference/mrl-multi-stage/go.go
+6 -6
View File
@@ -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.Query(context.Background(), &qdrant.QueryPoints{
ctx := qdrant.WithHeader(context.Background(), "openai-api-key", "<YOUR_OPENAI_API_KEY>")
client.Query(ctx, &qdrant.QueryPoints{
CollectionName: "{collection_name}",
Prefetch: []*qdrant.PrefetchQuery{
{
@@ -25,8 +29,7 @@ func Main() {
Model: "openai/text-embedding-3-small",
Text: "How to bake cookies?",
Options: qdrant.NewValueMap(map[string]any{
"mrl": 64,
"openai-api-key": "<YOUR_OPENAI_API_KEY>",
"mrl": 64,
}),
}),
),
@@ -38,9 +41,6 @@ func Main() {
qdrant.NewVectorInputDocument(&qdrant.Document{
Model: "openai/text-embedding-3-small",
Text: "How to bake cookies?",
Options: qdrant.NewValueMap(map[string]any{
"openai-api-key": "<YOUR_OPENAI_API_KEY>",
}),
}),
),
Using: qdrant.PtrOf("large"),
qdrant-landing/content/documentation/headless/snippets/inference/mrl-multi-stage/java.java
+10 -9
View File
@@ -3,8 +3,10 @@ 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;
import io.qdrant.client.grpc.Points.Document;
import io.qdrant.client.grpc.Points.PrefetchQuery;
@@ -12,13 +14,18 @@ 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(), "openai-api-key", "<YOUR_OPENAI_API_KEY>");
ctx.call(() -> client
.queryAsync(
Points.QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
@@ -29,12 +36,7 @@ public class Snippet {
Document.newBuilder()
.setModel("openai/text-embedding-3-small")
.setText("How to bake cookies?")
.putAllOptions(
Map.of(
"openai-api-key",
value("<YOUR_OPENAI_API_KEY>"),
"mrl",
value(64)))
.putAllOptions(Map.of("mrl", value(64)))
.build()))
.setUsing("small")
.setLimit(1000)
@@ -44,10 +46,9 @@ public class Snippet {
Document.newBuilder()
.setModel("openai/text-embedding-3-small")
.setText("How to bake cookies?")
.putAllOptions(Map.of("openai-api-key", value("<YOUR_OPENAI_API_KEY>")))
.build()))
.setUsing("large")
.build())
.get();
.get());
}
}
qdrant-landing/content/documentation/headless/snippets/inference/mrl-multi-stage/python.py
+17 -19
View File
@@ -1,30 +1,28 @@
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>",
url="https://xyz-example.qdrant.io:6333",
api_key="<your-qdrant-api-key>",
cloud_inference=True
)
client.query_points(
collection_name="{collection_name}",
query=models.Document(
text="How to bake cookies?",
model="openai/text-embedding-3-small",
options={"openai-api-key": "<YOUR_OPENAI_API_KEY>"}
),
using="large",
limit=10,
prefetch=models.Prefetch(
with headers({"openai-api-key": "<YOUR_OPENAI_API_KEY>"}):
client.query_points(
collection_name="{collection_name}",
query=models.Document(
text="How to bake cookies?",
model="openai/text-embedding-3-small",
options={
"openai-api-key": "<YOUR_OPENAI_API_KEY>",
"mrl": 64
}
),
using="small",
limit=1000,
using="large",
limit=10,
prefetch=models.Prefetch(
query=models.Document(
text="How to bake cookies?",
model="openai/text-embedding-3-small",
options={"mrl": 64},
),
using="small",
limit=1000,
)
)
)
qdrant-landing/content/documentation/headless/snippets/inference/mrl-multi-stage/rust.rs
+7 -12
View File
@@ -6,9 +6,10 @@ use qdrant_client::{
};
pub async fn main() -> anyhow::Result<()> {
let client = Qdrant::from_url("http://localhost:6334").build()?;
let client = Qdrant::from_url("http://localhost:6334").build()?; // @hide
client
.with_header("openai-api-key", "<YOUR_OPENAI_API_KEY>")
.query(
QueryPointsBuilder::new("{collection_name}")
.add_prefetch(
@@ -16,13 +17,10 @@ pub async fn main() -> anyhow::Result<()> {
.query(Query::new_nearest(Document {
text: "How to bake cookies?".into(),
model: "openai/text-embedding-3-small".into(),
options: HashMap::<String, Value>::from_iter(vec![
(
"openai-api-key".to_string(),
Value::from("<YOUR_OPENAI_API_KEY>"),
),
("mrl".into(), Value::from(64)),
]),
options: HashMap::<String, Value>::from_iter(vec![(
"mrl".into(),
Value::from(64),
)]),
}))
.using("small")
.limit(1000_u64),
@@ -30,10 +28,7 @@ pub async fn main() -> anyhow::Result<()> {
.query(Query::new_nearest(Document {
text: "How to bake cookies?".into(),
model: "openai/text-embedding-3-small".into(),
options: HashMap::from_iter(vec![(
"openai-api-key".into(),
"<YOUR_OPENAI_API_KEY>".into(),
)]),
options: HashMap::new(),
}))
.using("large")
.limit(10_u64)
qdrant-landing/content/documentation/headless/snippets/inference/mrl-multi-stage/typescript.ts
+19 -21
View File
@@ -1,27 +1,25 @@
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}", {
prefetch: {
await withHeaders({ 'openai-api-key': '<YOUR_OPENAI_API_KEY>' }, () =>
client.query("{collection_name}", {
prefetch: {
query: {
text: "How to bake cookies?",
model: "openai/text-embedding-3-small",
options: {
mrl: 64,
}
},
using: 'small',
limit: 1000,
},
query: {
text: "How to bake cookies?",
model: "openai/text-embedding-3-small",
options: {
"openai-api-key": "<YOUR_OPENAI_API_KEY>",
mrl: 64,
}
},
using: 'small',
limit: 1000,
},
query: {
text: "How to bake cookies?",
model: "openai/text-embedding-3-small",
options: {
"openai-api-key": "<YOUR_OPENAI_API_KEY>"
}
},
using: 'large',
limit: 10,
});
using: 'large',
limit: 10,
})
);
qdrant-landing/content/documentation/headless/snippets/inference/mrl/csharp.cs
+22 -20
View File
@@ -5,37 +5,39 @@ 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()
using (RequestHeaders.Use("openai-api-key", "<YOUR_OPENAI_API_KEY>"))
await client.UpsertAsync(
collectionName: "{collection_name}",
points: new List<PointStruct>
{
Id = 1,
Vectors = new Dictionary<string, Vector>
new()
{
["large"] = new Document()
Id = 1,
Vectors = new Dictionary<string, Vector>
{
Model = "openai/text-embedding-3-small",
Text = "Recipe for baking chocolate chip cookies",
Options = { ["openai-api-key"] = "<YOUR_OPENAI_API_KEY>" },
},
["small"] = new Document()
{
Model = "openai/text-embedding-3-small",
Text = "Recipe for baking chocolate chip cookies",
Options = { ["openai-api-key"] = "<YOUR_OPENAI_API_KEY>", ["mrl"] = 64 },
["large"] = new Document()
{
Model = "openai/text-embedding-3-small",
Text = "Recipe for baking chocolate chip cookies",
},
["small"] = new Document()
{
Model = "openai/text-embedding-3-small",
Text = "Recipe for baking chocolate chip cookies",
Options = { ["mrl"] = 64 },
},
},
},
},
}
);
}
);
}
}

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