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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>
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title, weight
| title | weight |
|---|---|
| OpenAI | 800 |
OpenAI
Qdrant can also easily work with OpenAI embeddings. There is an official OpenAI Python package that simplifies obtaining them, and it might be installed with pip:
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:
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"]],
)
)