mirror of
https://github.com/qdrant/landing_page.git
synced 2026-09-29 07:58:31 +02:00
103 lines
3.0 KiB
Markdown
103 lines
3.0 KiB
Markdown
---
|
|
title: OpenAI
|
|
aliases: [ ../integrations/openai/ ]
|
|
---
|
|
|
|
# OpenAI
|
|
|
|
Qdrant supports working with [OpenAI embeddings](https://platform.openai.com/docs/guides/embeddings/embeddings).
|
|
|
|
There is an official OpenAI Python package that simplifies obtaining them, and it can be installed with pip:
|
|
|
|
```bash
|
|
pip install openai
|
|
```
|
|
|
|
### Setting up the OpenAI and Qdrant clients
|
|
|
|
```python
|
|
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](https://platform.openai.com/docs/models/embeddings).
|
|
|
|
### Embedding a document
|
|
|
|
```python
|
|
embedding_model = "text-embedding-3-small"
|
|
|
|
result = openai_client.embeddings.create(input=texts, model=embedding_model)
|
|
```
|
|
|
|
### Converting the model outputs to Qdrant points
|
|
|
|
```python
|
|
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
|
|
|
|
```python
|
|
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.
|
|
|
|
```python
|
|
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](/articles/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](https://huggingface.co/datasets/Qdrant/dbpedia-entities-openai3-text-embedding-3-large-3072-1M) | 0.9966|3x|
|
|
|OpenAI text-embedding-3-small|1536|[DBpedia 100K](https://huggingface.co/datasets/Qdrant/dbpedia-entities-openai3-text-embedding-3-small-1536-100K)| 0.9847|3x|
|
|
|OpenAI text-embedding-3-large|1536|[DBpedia 1M](https://huggingface.co/datasets/Qdrant/dbpedia-entities-openai3-text-embedding-3-large-1536-1M)| 0.9826|3x|
|
|
|OpenAI text-embedding-ada-002|1536|[DbPedia 1M](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M) |0.98|4x|
|