embedding-proxy: A Bridge for Cherry Studio to Seamlessly Access Doubao Multimodal Embedding

embedding-proxy is a lightweight proxy service designed to resolve format compatibility issues between Cherry Studio and Volcano Engine Doubao Multimodal Embedding API. It supports disk caching and Ollama interface mimicry, allowing developers to seamlessly access Doubao's multimodal Embedding capabilities without modifying client configurations.

In large model application deployment, format barriers in toolchains often plague developers. Cherry Studio natively supports multiple Embedding services, but Doubao Multimodal Embedding API's request format (requiring text wrapped in {'type':'text','text':'content'}) conflicts with Cherry Studio's expected input (direct string arrays), blocking developers from using Doubao's capabilities. This temporary friction in ecosystem evolution is enough to disrupt workflows.

As an intermediate proxy layer between Cherry Studio and Doubao API, embedding-proxy automatically handles format conversion for seamless integration—users don’t need to understand underlying logic or adjust Cherry Studio configurations. Its core design philosophy is that technology serves people, not the other way around.

embedding-proxy’s core capabilities include four layers:

1. Intelligent Format Conversion: Bidirectionally converts request/response formats—turning Cherry’s string arrays into Doubao’s required text object structure and vice versa.
2. Disk Hash Caching: Hashes input text, saves vectors as .npy files, and uses cached data for repeated requests to reduce API costs.
3. Ollama Interface Mimicry: Simulates Ollama API response formats so Cherry Studio thinks it’s connected to a local Ollama service, no client adaptation needed.
4. Flexible Configuration: Supports local running, one-click deployment (deploy.sh), and containerization; API endpoints, model IDs, and API Keys can be set via environment variables or config files.

Built with Python + FastAPI, the project has a clear structure: main.py as entry point, app directory split into routing, services, models, and config subdirectories. Cache files are stored in vector_cache (only vector data retained for privacy). Docker containers run as non-root users, and sensitive info (like API Keys) is excluded from version control for security.

embedding-proxy’s value lies in addressing real pain points: AI tools often fail to collaborate due to format/protocol differences. It shows a pragmatic approach—building a lightweight adaptation layer is more efficient than waiting for official support or rearchitecting systems. For developers integrating Doubao into Cherry Studio, it’s an out-of-the-box solution.

embedding-proxy is a micro-case of ecosystem interconnection, reminding us AI infrastructure maturity depends on small but critical connection components. Every compatibility-solving project strengthens ecosystem robustness. Insight for developers: When facing format/protocol mismatches, a lightweight proxy layer is often a cost-effective solution.