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* added a table of content * wide layout for docs, styles for the table of content * fixes for docs layout * wide footer at the docs section * added support for nested docs, added toggling groups of links, delimiters, external links * external link icon * added active state for nested links, styles for the external link icon * styles fix * remove doc sync * update directory structure and doc titles * fix outstanding links * fix more links for merge * Revert "fix more links for merge" This reverts commit 46c9ccaf1b7765f2cda8dc85d625fa6b4e3f5436. * Revert "fix outstanding links" This reverts commit 28e6380b74f1ab74690c8184551f186656d6d4e9. * fix remaining broken links * move how-to tutorials in the different page * split tutorials * fix link * upd github edit link * skip empty index pages --------- Co-authored-by: Andrey Vasnetsov <andrey@vasnetsov.com> Co-authored-by: David Sertic <62056091+davidmyriel@users.noreply.github.com>
45 lines
1.1 KiB
Markdown
45 lines
1.1 KiB
Markdown
---
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title: OpenAI
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weight: 800
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---
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# OpenAI
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Qdrant can also easily work with [OpenAI embeddings](https://beta.openai.com/docs/guides/embeddings/embeddings). There is an
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official OpenAI Python package that simplifies obtaining them, and it might be installed with pip:
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```bash
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pip install openai
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```
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Once installed, the package exposes the method allowing to retrieve the embedding for given text. OpenAI requires an API key
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that has to be provided either as an environmental variable `OPENAI_API_KEY` or set in the source code directly, as
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presented below:
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```python
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import openai
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import qdrant_client
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from qdrant_client.http.models import Batch
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# Provide OpenAI API key and choose one of the available models:
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# https://beta.openai.com/docs/models/overview
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openai.api_key = "<< your_api_key >>"
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embedding_model = "text-embedding-ada-002"
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response = openai.Embedding.create(
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input="The best vector database",
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model=embedding_model,
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)
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qdrant_client = qdrant_client.QdrantClient()
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qdrant_client.upsert(
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collection_name="MyCollection",
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points=Batch(
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ids=[1],
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vectors=[response["data"][0]["embedding"]],
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)
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)
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```
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