Upstage

Qdrant supports working with the Solar Embeddings API from Upstage.

Solar Embeddings API features dual models for user queries and document embedding, within a unified vector space, designed for performant text processing.

You can generate an API key to authenticate the requests from the Upstage Console.

Setting up the Qdrant client and Upstage session

import requests
from qdrant_client import QdrantClient

UPSTAGE_BASE_URL = "https://api.upstage.ai/v1/solar/embeddings"

UPSTAGE_API_KEY = "<YOUR_API_KEY>"

upstage_session = requests.Session()

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

headers = {
    "Authorization": f"Bearer {UPSTAGE_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 UPSTAGE_BASE_URL = "https://api.upstage.ai/v1/solar/embeddings"
const UPSTAGE_API_KEY = "<YOUR_API_KEY>"

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

const headers = {
    "Authorization": "Bearer " + UPSTAGE_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 recommended solar-embedding-1-large-passage and solar-embedding-1-large-query models that generates sentence embeddings of size 4096.

Embedding documents

body = {
    "input": texts,
    "model": "solar-embedding-1-large-passage",
}

response_body = upstage_session.post(
    UPSTAGE_BASE_URL, headers=headers, json=body
).json()

let body = {
    "input": texts,
    "model": "solar-embedding-1-large-passage",
}

let response = await fetch(UPSTAGE_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=4096,
        distance=Distance.COSINE,
    ),
)
client.upsert(collection_name, points)

const COLLECTION_NAME = "example_collection"

await client.createCollection(COLLECTION_NAME, {
    vectors: {
        size: 4096,
        distance: 'Cosine',
    }
});

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

Searching for documents with Qdrant

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

body = {
    "input": "What is the best to use for vector search scaling?",
    "model": "solar-embedding-1-large-query",
}

response_body = upstage_session.post(
    UPSTAGE_BASE_URL, headers=headers, json=body
).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?",
    "model": "solar-embedding-1-large-query",
}

response = await fetch(UPSTAGE_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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