Improved Nested Docs and Link Grouping Support (#147)

* 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>
2026-09-27 15:08:30 +02:00 · 2023-05-29 15:11:40 +02:00
parent b9874e5c56
commit 3215985d1d
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+---
+title: OpenAI
+weight: 800
+---
+
+# OpenAI
+
+Qdrant can also easily work with [OpenAI embeddings](https://beta.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
+
+# Provide OpenAI API key and choose one of the available models:
+# https://beta.openai.com/docs/models/overview
+openai.api_key = "<< your_api_key >>"
+embedding_model = "text-embedding-ada-002"
+
+response = openai.Embedding.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"]],
+    )
+)
+```
+