---
title: OpenAI
weight: 800
aliases: [ ../integrations/openai/ ]
---

# OpenAI

Qdrant can also easily work with [OpenAI embeddings](https://platform.openai.com/docs/guides/embeddings/embeddings). 

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

```bash
pip install openai
```

Once installed, the package exposes the method allowing to retrieve the embedding for given text. OpenAI requires an API key that has to be provided either as an environmental variable `OPENAI_API_KEY` or set in the source code directly, as presented below:

```python
import openai
import qdrant_client

from qdrant_client.http.models import Batch

# Choose one of the available models:
# https://platform.openai.com/docs/models/embeddings
embedding_model = "text-embedding-ada-002"

openai_client = openai.Client(
    api_key="<< your_api_key >>"
)
response = openai_client.embeddings.create(
    input="The best vector database",
    model=embedding_model,
)

qdrant_client = qdrant_client.QdrantClient()
qdrant_client.upsert(
    collection_name="MyCollection",
    points=Batch(
        ids=[1],
        vectors=[response.data[0].embedding],
    ),
)
```