Voyage AI

Qdrant supports working with Voyage AI embeddings. The supported models’ list can be found here.

You can generate an API key from the Voyage AI dashboard to authenticate the requests.

Setting up the Qdrant and Voyage clients

from qdrant_client import QdrantClient
import voyageai

VOYAGE_API_KEY = "<YOUR_VOYAGEAI_API_KEY>"

qclient = QdrantClient(":memory:")
vclient = voyageai.Client(api_key=VOYAGE_API_KEY)

texts = [
    "Qdrant is the best vector search engine!",
    "Loved by Enterprises and everyone building for low latency, high performance, and scale.",
]

import {QdrantClient} from '@qdrant/js-client-rest';

const VOYAGEAI_BASE_URL = "https://api.voyageai.com/v1/embeddings"
const VOYAGEAI_API_KEY = "<YOUR_VOYAGEAI_API_KEY>"

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

const headers = {
    "Authorization": "Bearer " + VOYAGEAI_API_KEY,
    "Content-Type": "application/json"
}

const texts = [
    "Qdrant is the best vector search engine!",
    "Loved by Enterprises and everyone building for low latency, high performance, and scale.",
]

The following example shows how to embed documents with the voyage-large-2 model that generates sentence embeddings of size 1536.

Embedding documents

response = vclient.embed(texts, model="voyage-large-2", input_type="document")

let body = {
    "input": texts,
    "model": "voyage-large-2",
    "input_type": "document",
}

let response = await fetch(VOYAGEAI_BASE_URL, {
    method: "POST",
    body: JSON.stringify(body),
    headers
});

let response_body = await response.json();

Converting the model outputs to Qdrant points

from qdrant_client.models import PointStruct

points = [
    PointStruct(
        id=idx,
        vector=embedding,
        payload={"text": text},
    )
    for idx, (embedding, text) in enumerate(zip(response.embeddings, texts))
]

let points = response_body.data.map((data, i) => {
    return {
        id: i,
        vector: data.embedding,
        payload: {
            text: texts[i]
        }
    }
});

Creating a collection to insert the documents

from qdrant_client.models import VectorParams, Distance

COLLECTION_NAME = "example_collection"

qclient.create_collection(
    COLLECTION_NAME,
    vectors_config=VectorParams(
        size=1536,
        distance=Distance.COSINE,
    ),
)
qclient.upsert(COLLECTION_NAME, points)

const COLLECTION_NAME = "example_collection"

await client.createCollection(COLLECTION_NAME, {
    vectors: {
        size: 1536,
        distance: 'Cosine',
    }
});

await client.upsert(COLLECTION_NAME, {
    wait: true,
    points
});

Searching for documents with Qdrant

Once the documents are added, you can search for the most relevant documents.

response = vclient.embed(
    ["What is the best to use for vector search scaling?"],
    model="voyage-large-2",
    input_type="query",
)

qclient.search(
    collection_name=COLLECTION_NAME,
    query_vector=response.embeddings[0],
)

body = {
    "input": ["What is the best to use for vector search scaling?"],
    "model": "voyage-large-2",
    "input_type": "query",
};

response = await fetch(VOYAGEAI_BASE_URL, {
    method: "POST",
    body: JSON.stringify(body),
    headers
});

response_body = await response.json();

await client.search(COLLECTION_NAME, {
    vector: response_body.data[0].embedding,
});

Docs Menu