OpenAI

Qdrant supports working with OpenAI embeddings.

There is an official OpenAI Python package that simplifies obtaining them, and it can be installed with pip:

pip install openai

Setting up the OpenAI and Qdrant clients

import openai
import qdrant_client

openai_client = openai.Client(
    api_key="<YOUR_API_KEY>"
)

client = qdrant_client.QdrantClient(":memory:")

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 a document with the text-embedding-3-small model that generates sentence embeddings of size 1536. You can find the list of all supported models here.

Embedding a document

embedding_model = "text-embedding-3-small"

result = openai_client.embeddings.create(input=texts, model=embedding_model)

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(result.data, texts))
]

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=1536,
        distance=Distance.COSINE,
    ),
)
client.upsert(collection_name, points)

Searching for documents with Qdrant

Once the documents are indexed, you can search for the most relevant documents using the same model.

client.search(
    collection_name=collection_name,
    query_vector=openai_client.embeddings.create(
        input=["What is the best to use for vector search scaling?"],
        model=embedding_model,
    )
    .data[0]
    .embedding,
)

Using OpenAI Embedding Models with Qdrant’s Binary Quantization

You can use OpenAI embedding Models with Binary Quantization - a technique that allows you to reduce the size of the embeddings by 32 times without losing the quality of the search results too much.

Method	Dimensionality	Test Dataset	Recall	Oversampling
OpenAI text-embedding-3-large	3072	DBpedia 1M	0.9966	3x
OpenAI text-embedding-3-small	1536	DBpedia 100K	0.9847	3x
OpenAI text-embedding-3-large	1536	DBpedia 1M	0.9826	3x
OpenAI text-embedding-ada-002	1536	DbPedia 1M	0.98	4x

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