# Multimodal Embedding Serving


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  <a class="icon_github" href="https://github.com/open-edge-platform/edge-ai-libraries/tree/release-2026.0.0/microservices/multimodal-embedding-serving">
     GitHub project
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  <a class="icon_document" href="https://github.com/open-edge-platform/edge-ai-libraries/blob/release-2026.0.0/microservices/multimodal-embedding-serving/README.md">
     Readme
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The Multimodal Embedding Serving microservice provides a scalable and efficient solution for generating multimodal embeddings from text, images, and videos. Built on state-of-the-art vision-language models, it enables applications to perform cross-modal search, retrieval, and similarity tasks through a simple, production-ready service.

## Architecture

The microservice is designed as a RESTful API service that:

- Accepts text, image, and video inputs through OpenAI-compatible endpoints
- Loads and manages multiple vision-language models dynamically
- Provides hardware-accelerated inference using OpenVINO for Intel hardware
- Returns high-dimensional embeddings in a shared semantic space
- Supports both synchronous and batch processing workflows

## Model Support

The service supports multiple model families:

- **CLIP**: General-purpose vision-language understanding
- **CN-CLIP**: Chinese-optimized models for multilingual applications
- **MobileCLIP**: Lightweight models for mobile and edge deployment
- **SigLIP**: Models with sigmoid loss function
- **BLIP-2**: Advanced multimodal models with Q-Former architecture

For complete model specifications, see [Supported Models](./supported-models.md).

## Key Capabilities

- **OpenAI-Compatible API**: Standard embeddings API format for seamless integration
- **Multi-Modal Processing**: Handle text, images (URL/base64), and videos (URL/base64/file)
- **Hardware Optimization**: CPU and GPU support with OpenVINO acceleration
- **Video Processing**: Advanced frame extraction with configurable sampling strategies
- **Production Features**: Health checks, monitoring, logging, and scalability

## Deployment Architecture

The microservice can be deployed in multiple configurations:

- **Docker Containers**: Single-node deployment using Docker Compose
- **Kubernetes**: Multi-node scalable deployment
- **Python SDK**: Direct integration into Python applications

The same container image supports both CPU and GPU deployments through runtime configuration.

## Supporting Resources

- [Get Started Guide](./get-started.md) - Step-by-step deployment instructions
- [System Requirements](./get-started/system-requirements.md) - Hardware and software prerequisites
- [SDK Usage Guide](./sdk-usage.md) - Python SDK integration examples
- [Supported Models](./supported-models.md) - Complete model list and specifications
- [API Reference](./api-reference.md) - Complete REST API documentation


:::{toctree}
:hidden:

./get-started.md
./sdk-usage.md
./wheel-installation.md
./supported-models.md
./api-reference.md
Release Notes <./release-notes.md>

:::