embedbase-qdrant 1.0.1

Embedbase + Qdrant - Advanced and high-performant vector similarity search technology in your AI applications.

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.