Pipelines is a framework for extending OpenAI-compatible UI clients with custom Python logic, targeting developers and power users who need to integrate complex AI workflows or external APIs. It allows for modular, UI-agnostic plugin development, enabling advanced functionalities like custom RAG, function calling, and real-time data processing.

How It Works

Pipelines operates by exposing an OpenAI-compatible API endpoint. When a UI client sends a request to this endpoint, Pipelines intercepts it, processes it through user-defined Python scripts (pipelines), and returns the result. This architecture allows for offloading computationally intensive tasks from the main UI, enhancing scalability and performance, and integrating arbitrary Python libraries for diverse functionalities.

Quick Start & Requirements

• Install/Run: docker run -d -p 9099:9099 --add-host=host.docker.internal:host-gateway -v pipelines:/app/pipelines --name pipelines --restart always ghcr.io/open-webui/pipelines:main
• Prerequisites: Docker, Python 3.11 (for local build).
• Setup: Docker setup is quick, typically under 5 minutes. Local setup requires cloning the repo, installing dependencies (pip install -r requirements.txt), and running ./start.sh.
• Docs: https://github.com/open-webui/pipelines

Highlighted Details

• UI-agnostic compatibility with any client supporting OpenAI API specs.
• Supports custom Python logic, including integration with external libraries and APIs.
• Examples include function calling, RAG, message monitoring (Langfuse, Opik), rate limiting, and translation filters.
• Can be integrated with Open WebUI via Docker Compose.

Maintenance & Community

• Part of the Open WebUI initiative.
• Community engagement encouraged for feedback and feature requests.

Licensing & Compatibility

• License: Not explicitly stated in the README.
• Compatibility: Designed for OpenAI API-compatible clients. Custom pipelines with additional dependencies require custom Docker builds.

Limitations & Caveats

Pipelines involves arbitrary code execution, necessitating caution when loading pipelines from untrusted sources. Only "pipe-type" pipelines are supported by default; "filter-type" pipelines require specific client support. The project is described as "Work in Progress," with ongoing evolution and feature development.