landing_page/qdrant-landing/content/documentation/embeddings/bedrock.md

title, short_description, description

title	short_description	description
AWS Bedrock	Generate embeddings with AWS Bedrock and store them in Qdrant to run semantic search and RAG fully within your AWS environment.	Combine AWS Bedrock embedding models with Qdrant for scalable semantic search and retrieval-augmented generation across your AWS-hosted data.

Bedrock Embeddings

You can use AWS Bedrock with Qdrant. AWS Bedrock supports multiple embedding model providers.

You'll need the following information from your AWS account:

• Region
• Access key ID
• Secret key

To configure your credentials, review the following AWS article: How do I create an AWS access key.

With the following code sample, you can generate embeddings using the Titan Embeddings G1 - Text model which produces sentence embeddings of size 1536.

# Install the required dependencies
# pip install boto3 qdrant_client

import json
import boto3

from qdrant_client import QdrantClient, models

session = boto3.Session()

bedrock_client = session.client(
    "bedrock-runtime",
    region_name="<YOUR_AWS_REGION>",
    aws_access_key_id="<YOUR_AWS_ACCESS_KEY_ID>",
    aws_secret_access_key="<YOUR_AWS_SECRET_KEY>",
)

qdrant_client = QdrantClient(url="http://localhost:6333")

qdrant_client.create_collection(
    "{collection_name}",
    vectors_config=models.VectorParams(size=1536, distance=models.Distance.COSINE),
)

body = json.dumps({"inputText": "Some text to generate embeddings for"})

response = bedrock_client.invoke_model(
    body=body,
    modelId="amazon.titan-embed-text-v1",
    accept="application/json",
    contentType="application/json",
)

response_body = json.loads(response.get("body").read())

qdrant_client.upsert(
    "{collection_name}",
    points=[models.PointStruct(id=1, vector=response_body["embedding"])],
)

// Install the required dependencies
// npm install @aws-sdk/client-bedrock-runtime @qdrant/js-client-rest

import {
    BedrockRuntimeClient,
    InvokeModelCommand,
} from "@aws-sdk/client-bedrock-runtime";
import { QdrantClient } from '@qdrant/js-client-rest';

const main = async () => {
    const bedrockClient = new BedrockRuntimeClient({
        region: "<YOUR_AWS_REGION>",
        credentials: {
            accessKeyId: "<YOUR_AWS_ACCESS_KEY_ID>",,
            secretAccessKey: "<YOUR_AWS_SECRET_KEY>",
        },
    });

    const qdrantClient = new QdrantClient({ url: 'http://localhost:6333' });

    await qdrantClient.createCollection("{collection_name}", {
        vectors: {
            size: 1536,
            distance: 'Cosine',
        }
    });

    const response = await bedrockClient.send(
        new InvokeModelCommand({
            modelId: "amazon.titan-embed-text-v1",
            body: JSON.stringify({
                inputText: "Some text to generate embeddings for",
            }),
            contentType: "application/json",
            accept: "application/json",
        })
    );

    const body = new TextDecoder().decode(response.body);

    await qdrantClient.upsert("{collection_name}", {
        points: [
            {
                id: 1,
                vector: JSON.parse(body).embedding,
            },
        ],
    });
}

main();