embedbase-qdrant
Embedbase + Qdrant Advanced and high-performant vector similarity search technology in your AI applications
⚠️ Status: Alpha release ⚠️
If you have any feedback or issues, please let us know by opening an issue or contacting us on discord.
Please refer to the documentation
Getting started
To install the Embedbase Qdrant library, run the following command:
pip install embedbase-qdrant
Quick tour
Let's try Embedbase + Qdrant with an OpenAI embedder:
pip install openai uvicorn
import os
import uvicorn
from embedbase import get_app
from embedbase.embedding.openai import Openai
from embedbase_qdrant import Qdrant
# here we use openai to create embeddings and qdrant to store the data
app = get_app().use_embedder(Openai(os.environ["OPENAI_API_KEY"])).use_db(Qdrant()).run()
if __name__ == "__main__":
uvicorn.run(app)
Start a local Qdrant:
docker-compose up -d
Run Embedbase:
python3 main.py
Check out other examples and documentation for more details.
1 maintainer
Release files for embedbase-qdrant 1.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| embedbase_qdrant-1.0.1.tar.gz | 5.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| embedbase_qdrant-1.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 10.4 kB
Release files / embedbase_qdrant-1.0.1.tar.gz
| Download URL | embedbase_qdrant-1.0.1.tar.gz |
|---|---|
| Size | 5.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / embedbase_qdrant-1.0.1-py3-none-any.whl
| Download URL | embedbase_qdrant-1.0.1-py3-none-any.whl |
|---|---|
| Size | 5.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
poetry/1.4.2 CPython/3.8.16 Linux/5.15.0-1036-azure
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