Nvidia

Qdrant supports working with Nvidia embeddings.

You can generate an API key to authenticate the requests from the Nvidia Playground.

Setting up the Qdrant client and Nvidia session

import requests
from qdrant_client import QdrantClient

NVIDIA_BASE_URL = "https://ai.api.nvidia.com/v1/retrieval/nvidia/embeddings"

NVIDIA_API_KEY = "<YOUR_API_KEY>"

nvidia_session = requests.Session()

client = QdrantClient(":memory:")

headers = {
    "Authorization": f"Bearer {NVIDIA_API_KEY}",
    "Accept": "application/json",
}

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 NVIDIA_BASE_URL = "https://ai.api.nvidia.com/v1/retrieval/nvidia/embeddings"
const NVIDIA_API_KEY = "<YOUR_API_KEY>"

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

const headers = {
    "Authorization": "Bearer " + NVIDIA_API_KEY,
    "Accept": "application/json",
    "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 embed-qa-4 model that generates sentence embeddings of size 1024.

Embedding documents

payload = {
    "input": texts,
    "input_type": "passage",
    "model": "NV-Embed-QA",
}

response_body = nvidia_session.post(
    NVIDIA_BASE_URL, headers=headers, json=payload
).json()

let body = {
    "input": texts,
    "input_type": "passage",
    "model": "NV-Embed-QA"
}

let response = await fetch(NVIDIA_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=data["embedding"],
        payload={"text": text},
    )
    for idx, (data, text) in enumerate(zip(response_body["data"], 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"

client.create_collection(
    collection_name,
    vectors_config=VectorParams(
        size=1024,
        distance=Distance.COSINE,
    ),
)
client.upsert(collection_name, points)

const COLLECTION_NAME = "example_collection"

await client.createCollection(COLLECTION_NAME, {
    vectors: {
        size: 1024,
        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.

payload = {
    "input": "What is the best to use for vector search scaling?",
    "input_type": "query",
    "model": "NV-Embed-QA",
}

response_body = nvidia_session.post(
    NVIDIA_BASE_URL, headers=headers, json=payload
).json()

client.search(
    collection_name=collection_name,
    query_vector=response_body["data"][0]["embedding"],
)

body = {
    "input": "What is the best to use for vector search scaling?",
    "input_type": "query",
    "model": "NV-Embed-QA",
}

response = await fetch(NVIDIA_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,
});

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