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

title, short_description, description

title	short_description	description
Prem AI	Generate embeddings on the PremAI generative AI platform and store them in Qdrant to ship semantic search backed by tunable, monitored models.	Use PremAI's generative AI platform to create embeddings and index them in Qdrant for semantic search, RAG, and monitored, fine-tunable retrieval workflows.

Prem AI

PremAI is a unified generative AI development platform for fine-tuning deploying, and monitoring AI models.

Qdrant is compatible with PremAI APIs.

Installing the SDKs

pip install premai qdrant-client

To install the npm package:

npm install @premai/prem-sdk @qdrant/js-client-rest

Import all required packages

from premai import Prem

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams

import Prem from '@premai/prem-sdk';
import { QdrantClient } from '@qdrant/js-client-rest';

Define all the constants

We need to define the project ID and the embedding model to use. You can learn more about obtaining these in the PremAI docs.

PROJECT_ID = 123
EMBEDDING_MODEL = "text-embedding-3-large"
COLLECTION_NAME = "prem-collection-py"
QDRANT_SERVER_URL = "http://localhost:6333"
DOCUMENTS = [
    "This is a sample python document",
    "We will be using qdrant and premai python sdk"
]

const PROJECT_ID = 123;
const EMBEDDING_MODEL = "text-embedding-3-large";
const COLLECTION_NAME = "prem-collection-js";
const SERVER_URL = "http://localhost:6333"
const DOCUMENTS = [
    "This is a sample javascript document",
    "We will be using qdrant and premai javascript sdk"
];

Set up PremAI and Qdrant clients

prem_client = Prem(api_key="xxxx-xxx-xxx")
qdrant_client = QdrantClient(url=QDRANT_SERVER_URL)

const premaiClient = new Prem({
    apiKey: "xxxx-xxx-xxx"
})
const qdrantClient = new QdrantClient({ url: SERVER_URL });

Generating Embeddings

from typing import Union, List

def get_embeddings(
    project_id: int, 
    embedding_model: str, 
    documents: Union[str, List[str]]
) -> List[List[float]]:
    """
    Helper function to get the embeddings from premai sdk 
    Args
        project_id (int): The project id from prem saas platform.
        embedding_model (str): The embedding model alias to choose
        documents (Union[str, List[str]]): Single texts or list of texts to embed
    Returns:
        List[List[int]]: A list of list of integers that represents different
            embeddings
    """
    embeddings = []
    documents = [documents] if isinstance(documents, str) else documents 
    for embedding in prem_client.embeddings.create(
        project_id=project_id,
        model=embedding_model, 
        input=documents
    ).data:
        embeddings.append(embedding.embedding)
    
    return embeddings

async function getEmbeddings(projectID, embeddingModel, documents) {
    const response = await premaiClient.embeddings.create({
        project_id: projectID,
        model: embeddingModel,
        input: documents
    });
    return response;
}

Converting Embeddings to Qdrant Points

from qdrant_client.models import PointStruct

embeddings = get_embeddings(
    project_id=PROJECT_ID,
    embedding_model=EMBEDDING_MODEL, 
    documents=DOCUMENTS 
)

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

function convertToQdrantPoints(embeddings, texts) {
    return embeddings.data.map((data, i) => {
        return {
            id: i,
            vector: data.embedding,
            payload: {
                text: texts[i]
            }
        };
    });
}

const embeddings = await getEmbeddings(PROJECT_ID, EMBEDDING_MODEL, DOCUMENTS);
const points = convertToQdrantPoints(embeddings, DOCUMENTS);

Set up a Qdrant Collection

qdrant_client.create_collection(
    collection_name=COLLECTION_NAME, 
    vectors_config=VectorParams(size=3072, distance=Distance.DOT)
)

await qdrantClient.createCollection(COLLECTION_NAME, {
    vectors: {
        size: 3072,
        distance: 'Cosine'
    }
})

Insert Documents into the Collection

doc_ids = list(range(len(embeddings)))

qdrant_client.upsert(
    collection_name=COLLECTION_NAME, 
    points=points
 )

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

Perform a Search

query = "what is the extension of python document"

query_embedding = get_embeddings(
    project_id=PROJECT_ID, 
    embedding_model=EMBEDDING_MODEL, 
    documents=query
)

qdrant_client.search(collection_name=COLLECTION_NAME, query_vector=query_embedding[0])

const query = "what is the extension of javascript document"
const query_embedding_response = await getEmbeddings(PROJECT_ID, EMBEDDING_MODEL, query)

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