This service allows to search relevant images with a text prompt. To this end, it uses a pretrained image-and-text encoder model, such as CLIP to encode images and text queries into an n-dimensional vector space. The service then returns the images whose embeddings are closest to the text query's embedding, as measured with the cosine similarity.
- Build the image:
docker compose build
- Start the service(s):
docker compose up
- Install project requirements using
pip install -r requirements.txt
- Prepare image data
- Start vector database, e.g., with Docker:
docker run -p 6333:6333 --mount type=bind,source=$HOME/.qdrant_data,target=/qdrant_data --name vector-db qdrant/qdrant:v1.8.4 - Start encoding task queue
bash deploy/entrypoint.sh task-queue
- Start HTTP server
bash deploy/entrypoint.sh server