clip-as-service is a microservice for serving CLIP models to generate vector embeddings for images and text. It is designed for low latency and high scalability, integrating smoothly into neural search ecosystems.
Project overview
Building a low-latency, high-scalability serving layer for CLIP image and text embeddings from scratch is complex; clip-as-service provides an easy-to-use microservice that addresses this directly and fits into broader neural search stacks.
Project type
RAG · Model Runtime
Use cases
Search & Research
Deployment
Refer to project documentation
License
License pending
Best for
Developers and AI engineers looking to integrate a low-latency, highly scalable text and image embedding microservice into their neural search applications.
Key capabilities
Serves CLIP models to generate embeddings for images and sentences.
Provides a `/rank` endpoint that re-ranks cross-modal matches according to their joint likelihood in the CLIP model.
Async client support with the ability to switch between gRPC, HTTP, and WebSocket protocols.
Limitations and risks
Embedding large datasets without a GPU can be very slow. While a GPU is beneficial for performance, it is not strictly required to run the service.
Getting started
Setup is considered easy because the client and server can be installed independently via pip. You can start quickly by running `pip install clip-server` followed by `python -m clip_server`.
Evidence and sources
README: CLIP-as-service is a low-latency high-scalability service for embedding images and text. It can be easily integrated as a microservice into neural search solutions.
README: 🍱 **Integration**: Smooth integration with neural search ecosystem including [Jina](https://github.com/jina-ai/jina) and [DocArray](https://github.com/jina-ai/docarray). Build cro…
README: CLIP-as-service is backed by [Jina AI](https://jina.ai) and licensed under [Apache-2.0](./LICENSE).
README: You can also [host the server on Google Colab](https://clip-as-service.jina.ai/hosting/colab/), leveraging its free GPU/TPU.
README: From `0.3.0` CLIP-as-service adds a new `/rank` endpoint that re-ranks cross-modal matches according to their joint likelihood in CLIP model.