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Clip As Service

Clip As Service is a Python server-and-client system for serving CLIP models that generate image and text embeddings and rank image–sentence matches. It provides a model-serving layer for cross-modal retrieval and neural-search applications.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Clip As Service — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when an application needs a separately served CLIP model for image/text embedding or image-to-text candidate ranking, and you can provide the surrounding data indexing and search workflow.

Who it is for

  • Developers building neural-search systems that need image or text embeddings.
  • Teams integrating CLIP inference as a microservice into cross-modal retrieval applications.
  • Developers comparing candidate text descriptions against images using CLIP scores.

Documented uses

  • Generate embeddings for sentences and image inputs.
  • Use image and text representations for cross-modal retrieval, such as text-to-image search.
  • Rank candidate sentences against an image with the `/rank` endpoint.
  • Serve CLIP models using PyTorch, ONNX Runtime, or TensorRT.
Before you choose

Requirements & limits

The server and client are independently installable Python packages and require Python 3.7 or later. Repository metadata identifies the public GitHub project as open source.

  • The project supplies model serving and client interfaces; the README's search example also uses DocArray to load and work with image collections.
  • Server runtime options listed in the README are PyTorch, ONNX Runtime, and TensorRT.
  • The README documents HTTP, gRPC, and WebSocket protocol options, plus asynchronous client support.
  • The hosted HTTP and gRPC examples use an inference address and access token; the basic local example connects to a server address and port.
  • The cited 800-QPS figure is tied to a single-replica PyTorch configuration without JIT on a GeForce RTX 3090, not a general performance guarantee.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up a local embedding request

Start the CLIP server and use the Python client to create sentence or image embeddings.

Before you start

  • Python 3.7 or later.
  • Install the independently distributed clip-server and clip-client packages.
  • A running CLIP server; the README's local example uses an address and port.

Steps

  1. Install the server package with the command below.

    Documented command
    pip install clip-server

  2. Install the client package with the command below.

    Documented command
    pip install clip-client

  3. Start the server with the command below and note its address and port.

    Documented command
    python -m clip_server

  4. Create a clip_client.Client for that address, then call encode with sentences or image inputs such as a local image path or remote image URL.

Expected outcome

The client returns embeddings; the README example shows three sentence embeddings with shape [3, 512].

Guide limitations

  • The guide covers a basic local setup and embedding requests, not deployment configuration or production scaling.
  • The documented example's output shape is an example, not a compatibility guarantee.
Documentation 1 ↗
Source-based practical guide

Rank text descriptions against an image

Submit an image and candidate text descriptions to the documented HTTP ranking endpoint and inspect the returned CLIP scores.

Before you start

  • A hosted inference address and access token.
  • An image URI and candidate text descriptions.
  • HTTP access; jq is suggested by the README to format the response.

Steps

  1. Send an HTTP POST request to the documented /post address with the access-token Authorization header.

  2. Provide the image URI and candidate descriptions in matches, and set execEndpoint to /rank.

  3. Inspect the returned matches and their clip_score.value scores; use jq to format the response if desired.

Expected outcome

A ranked set of candidate descriptions with CLIP scores, as illustrated by the repository examples.

Guide limitations

  • The example assumes an already available inference address and token; it does not document how to obtain them in this request recipe.
  • Scores are model outputs for candidate descriptions, not a standalone verification of the image.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Clip As ServiceGenerate embeddings for sentences and image inputs.Cloud, ServerThe server and client are independently installable Python packages and require Python 3.7 or later. Repository metadata identifies the public GitHub project as open source.
MorphingdbImport a libtorch model and create predictions from values in a PostgreSQL table.Docker, Linux, Self-hosted, ServerThe README documents a Docker image and a build-from-repository option, followed by connecting to PostgreSQL and enabling the pgdl extension.
Gateway — pyadavSend chat-completion requests to a provider through a common server endpoint.ServerThe repository is public and identifies the project as Apache-2.0 licensed. README installation instructions include binaries, platform package managers and Docker.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
jina-ai/clip-as-service · Official homepage title · retained in its source language
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