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https://github.com/qdrant/landing_page.git
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Extract code examples into snippets
This commit is contained in:
Bastian Hofmann
+1
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This code snippet demonstrates how to use cloud inference in Qdrant Cloud to automatically create vector embeddings from text or image documents during upseart and query operations.
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```bash
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# Create a new vector
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curl -X PUT "https://xyz-example.cloud-region.cloud-provider.cloud.qdrant.io:6333/collections/<your-collection>/points?wait=true" \\
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-H "Content-Type: application/json" \\
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-H "api-key: <paste-your-api-key-here>" \\
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-d '{
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"points": [
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{
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"id": 1,
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"payload": { "topic": "cooking", "type": "dessert" },
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"vector": {
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"text": "Recipe for baking chocolate chip cookies requires flour, sugar, eggs, and chocolate chips.",
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"model": "<the-model-to-use>"
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}
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}
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]
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}'
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# Perform a search query
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curl -X POST "https://xyz-example.cloud-region.cloud-provider.cloud.qdrant.io:6333/collections/<your-collection>/points/query" \\
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-H "Content-Type: application/json" \\
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-H "api-key: <paste-your-api-key-here>" \\
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-d '{
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"query": {
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"text": "Recipe for baking chocolate chip cookies",
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"model": "<the-model-to-use>"
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}
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}'
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```
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+41
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```csharp
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using Qdrant.Client;
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using Qdrant.Client.Grpc;
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using Value = Qdrant.Client.Grpc.Value;
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var client = new QdrantClient(
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host: "xyz-example.cloud-region.cloud-provider.cloud.qdrant.io",
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port: 6334,
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https: true,
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apiKey: "<paste-your-api-key-here>"
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);
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await client.UpsertAsync(
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collectionName: "<your-collection>",
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points: new List <PointStruct> {
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new() {
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Id = 1,
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Vectors = new Document() {
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Text =
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"Recipe for baking chocolate chip cookies requires flour, sugar, eggs, and chocolate chips.",
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Model = "<the-model-to-use>",
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},
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Payload = {
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["topic"] = "cooking",
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["type"] = "dessert"
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},
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},
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}
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);
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var points = await client.QueryAsync(
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collectionName: "<your-collection>",
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query: new Document() {
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Text = "Recipe for baking chocolate chip cookies", Model = "<the-model-to-use>"
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}
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);
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foreach(var point in points) {
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Console.WriteLine(point);
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}
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```
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```go
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package main
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import (
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"context"
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"log"
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"time"
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"github.com/qdrant/go-client/qdrant"
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)
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func main() {
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ctx, cancel := context.WithTimeout(context.Background(), time.Second)
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defer cancel()
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client, err := qdrant.NewClient(&qdrant.Config{
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Host: "xyz-example.cloud-region.cloud-provider.cloud.qdrant.io",
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Port: 6334,
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APIKey: "<paste-your-api-key-here>",
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UseTLS: true,
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})
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if err != nil {
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log.Fatalf("did not connect: %v", err)
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}
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defer client.Close()
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_, err = client.GetPointsClient().Upsert(ctx, &qdrant.UpsertPoints{
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CollectionName: "<your-collection>",
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Points: []*qdrant.PointStruct{
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{
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Id: qdrant.NewIDNum(uint64(1)),
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Vectors: qdrant.NewVectorsDocument(&qdrant.Document{
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Text: "Recipe for baking chocolate chip cookies requires flour, sugar, eggs, and chocolate chips.",
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Model: "<the-model-to-use>",
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}),
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Payload: qdrant.NewValueMap(map[string]any{
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"topic": "cooking",
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"type": "dessert",
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}),
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},
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},
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})
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if err != nil {
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log.Fatalf("error creating point: %v", err)
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}
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points, err := client.Query(ctx, &qdrant.QueryPoints{
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CollectionName: "<your-collection>",
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Query: qdrant.NewQueryNearest(
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qdrant.NewVectorInputDocument(&qdrant.Document{
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Text: "Recipe for baking chocolate chip cookies",
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Model: "<the-model-to-use>",
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}),
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),
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})
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log.Printf("List of points: %s", points)
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}
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})
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```
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```java
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package org.example;
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import static io.qdrant.client.PointIdFactory.id;
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import static io.qdrant.client.QueryFactory.nearest;
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import static io.qdrant.client.ValueFactory.value;
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import static io.qdrant.client.VectorsFactory.vectors;
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import io.qdrant.client.grpc.Points;
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import io.qdrant.client.grpc.Points.Document;
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import io.qdrant.client.grpc.Points.PointStruct;
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import java.util.List;
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import java.util.Map;
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import java.util.concurrent.ExecutionException;
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public class Main {
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public static void main(String[] args) throws ExecutionException, InterruptedException {
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QdrantClient client =
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new QdrantClient(
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QdrantGrpcClient.newBuilder("xyz-example.cloud-region.cloud-provider.cloud.qdrant.io", 6334, true)
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.withApiKey("<paste-your-api-key-here>")
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.build());
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client
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.upsertAsync(
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"<your-collection>",
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List.of(
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PointStruct.newBuilder()
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.setId(id(1))
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.setVectors(
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vectors(
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Document.newBuilder()
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.setText(
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"Recipe for baking chocolate chip cookies requires flour, sugar, eggs, and chocolate chips.")
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.setModel("<the-model-to-use>")
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.build()))
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.putAllPayload(Map.of("topic", value("cooking"), "type", value("dessert")))
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.build()))
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.get();
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List <Points.ScoredPoint> points =
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client
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.queryAsync(
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Points.QueryPoints.newBuilder()
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.setCollectionName("<your-collection>")
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.setQuery(
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nearest(
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Document.newBuilder()
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.setText("Recipe for baking chocolate chip cookies")
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.setModel("<the-model-to-use>")
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.build()))
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.build())
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.get();
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System.out.printf(points.toString());
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}
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}
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```
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+30
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```python
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from qdrant_client import QdrantClient
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from qdrant_client.http.models import PointStruct, Document
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client = QdrantClient(
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url="https://xyz-example.cloud-region.cloud-provider.cloud.qdrant.io:6333",
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api_key="<paste-your-api-key-here>",
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cloud_inference=True,
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)
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points = [
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PointStruct(
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id=1,
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payload={"topic": "cooking", "type": "dessert"},
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vector=Document(
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text="Recipe for baking chocolate chip cookies requires flour, sugar, eggs, and chocolate chips.",
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model="<the-model-to-use>"
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)
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)
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]
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client.upsert(collection_name="<your-collection>", points=points)
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points = client.query_points(collection_name="<your-collection>", query=Document(
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text="Recipe for baking chocolate chip cookies requires flour",
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model="<the-model-to-use>"
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))
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print(points)
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```
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```rust
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use qdrant_client::qdrant::vector;
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use qdrant_client::qdrant::vector_input;
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use qdrant_client::qdrant::QueryPointsBuilder;
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use qdrant_client::qdrant::Vector;
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use qdrant_client::qdrant::VectorInput;
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use qdrant_client::Payload;
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use qdrant_client::Qdrant;
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use qdrant_client::qdrant::{Document};
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use qdrant_client::qdrant::{PointStruct, UpsertPointsBuilder};
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#[tokio::main]
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async fn main() {
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let client = Qdrant::from_url("https://xyz-example.cloud-region.cloud-provider.cloud.qdrant.io:6334")
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.api_key("<paste-your-api-key-here>")
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.build()
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.unwrap();
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let mut points = Vec::new();
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let vector = Vector {
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vector: Some(vector::Vector::Document(Document {
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text: "Recipe for baking chocolate chip cookies requires flour, sugar, eggs, and chocolate chips.".to_string(),
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model: "<the-model-to-use>".to_string(),
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options: Default::default(),
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})),
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..Default::default()
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};
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points.push(PointStruct::new(1, vector, Payload::default()));
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let _ = client
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.upsert_points(UpsertPointsBuilder::new("<your-collection>", points).wait(true))
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.await;
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let document = Document {
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text: "Recipe for baking chocolate chip cookies".to_string(),
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model: "<the-model-to-use>".to_string(),
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options: Default::default(),
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};
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let query = VectorInput {
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variant: Some(vector_input::Variant::Document(document)),
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};
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let query_request = QueryPointsBuilder::new("<your-collection>").query(query);
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let result = client.query(query_request).await.unwrap();
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println!("Result: {:?}", result);
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```
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+33
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```typescript
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import {QdrantClient} from "@qdrant/js-client-rest";
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const client = new QdrantClient({
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url: 'https://xyz-example.cloud-region.cloud-provider.cloud.qdrant.io:6333',
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apiKey: '<paste-your-api-key-here>',
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});
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const points = [
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{
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id: 1,
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payload: { topic: "cooking", type: "dessert" },
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vector: {
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text: "Recipe for baking chocolate chip cookies requires flour, sugar, eggs, and chocolate chips.",
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model: "<the-model-to-use>"
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}
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}
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];
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await client.upsert("<your-collection>", { wait: true, points });
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const result = await client.query(
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"<your-collection>",
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{
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query: {
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text: "What ingredients are needed for baking chocolate chip cookies?",
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model: "<the-model-to-use>"
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},
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}
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)
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console.log(result);
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```
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