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

title, weight

title	weight
Snowflake Models	2900

Snowflake

Qdrant supports working with Snowflake text embedding models. You can find all the available models on HuggingFace.

Setting up the Qdrant and Snowflake models

from qdrant_client import QdrantClient
from fastembed import TextEmbedding

qclient = QdrantClient(":memory:")
embedding_model = TextEmbedding("snowflake/snowflake-arctic-embed-s")

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';
import { pipeline } from '@xenova/transformers';

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

const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-s');

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 snowflake-arctic-embed-s model that generates sentence embeddings of size 384.

Embedding documents

embeddings = embedding_model.embed(texts)

const embeddings = await extractor(texts, { normalize: true, pooling: 'cls' });

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(embeddings, texts))
]

let points = embeddings.tolist().map((embedding, i) => {
    return {
        id: i,
        vector: 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=384,
        distance=Distance.COSINE,
    ),
)
qclient.upsert(COLLECTION_NAME, points)

const COLLECTION_NAME = "example_collection"

await client.createCollection(COLLECTION_NAME, {
    vectors: {
        size: 384,
        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.

query_embedding = next(embedding_model.query_embed("What is the best to use for vector search scaling?"))

qclient.search(
    collection_name=COLLECTION_NAME,
    query_vector=query_embedding,
)

const query_embedding = await extractor("What is the best to use for vector search scaling?", {
    normalize: true,
    pooling: 'cls'
});

await client.search(COLLECTION_NAME, {
    vector: query_embedding.tolist()[0],
});