arcee-ai/pipelines

arcee-ai/pipelines

Description: Pipelines: Versatile, UI-Agnostic OpenAI-Compatible Plugin Framework

License: MIT

Stars: 1

Forks: 0

Open issues: 0

Created: 2025-02-17T16:02:04Z

Pushed: 2025-02-14T21:45:25Z

Default branch: main

Fork: yes

Parent repository: open-webui/pipelines

Archived: no

README:

Pipelines: UI-Agnostic OpenAI API Plugin Framework

> [!TIP] > If your goal is simply to add support for additional providers like Anthropic or basic filters, you likely don't need Pipelines . For those cases, Open WebUI Functions are a better fit—it's built-in, much more convenient, and easier to configure. Pipelines, however, comes into play when you're dealing with computationally heavy tasks (e.g., running large models or complex logic) that you want to offload from your main Open WebUI instance for better performance and scalability.

Welcome to Pipelines, an Open WebUI initiative. Pipelines bring modular, customizable workflows to any UI client supporting OpenAI API specs – and much more! Easily extend functionalities, integrate unique logic, and create dynamic workflows with just a few lines of code.

🚀 Why Choose Pipelines?

• Limitless Possibilities: Easily add custom logic and integrate Python libraries, from AI agents to home automation APIs.
• Seamless Integration: Compatible with any UI/client supporting OpenAI API specs. (Only pipe-type pipelines are supported; filter types require clients with Pipelines support.)
• Custom Hooks: Build and integrate custom pipelines.

Examples of What You Can Achieve:

• [Function Calling Pipeline](/examples/filters/function_calling_filter_pipeline.py): Easily handle function calls and enhance your applications with custom logic.
• [Custom RAG Pipeline](/examples/pipelines/rag/llamaindex_pipeline.py): Implement sophisticated Retrieval-Augmented Generation pipelines tailored to your needs.
• [Message Monitoring Using Langfuse](/examples/filters/langfuse_filter_pipeline.py): Monitor and analyze message interactions in real-time using Langfuse.
• [Rate Limit Filter](/examples/filters/rate_limit_filter_pipeline.py): Control the flow of requests to prevent exceeding rate limits.
• [Real-Time Translation Filter with LibreTranslate](/examples/filters/libretranslate_filter_pipeline.py): Seamlessly integrate real-time translations into your LLM interactions.
• [Toxic Message Filter](/examples/filters/detoxify_filter_pipeline.py): Implement filters to detect and handle toxic messages effectively.
• And Much More!: The sky is the limit for what you can accomplish with Pipelines and Python. [Check out our scaffolds](/examples/scaffolds) to get a head start on your projects and see how you can streamline your development process!

🔧 How It Works

Integrating Pipelines with any OpenAI API-compatible UI client is simple. Launch your Pipelines instance and set the OpenAI URL on your client to the Pipelines URL. That's it! You're ready to leverage any Python library for your needs.

⚡ Quick Start with Docker

> [!WARNING] > Pipelines are a plugin system with arbitrary code execution — don't fetch random pipelines from sources you don't trust.

For a streamlined setup using Docker:

1. Run the Pipelines container:

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

2. Connect to Open WebUI:

• Navigate to the Settings > Connections > OpenAI API section in Open WebUI.
• Set the API URL to http://localhost:9099 and the API key to 0p3n-w3bu!. Your pipelines should now be active.

> [!NOTE] > If your Open WebUI is running in a Docker container, replace localhost with host.docker.internal in the API URL.

3. Manage Configurations:

• In the admin panel, go to Admin Settings > Pipelines tab.
• Select your desired pipeline and modify the valve values directly from the WebUI.

> [!TIP] > If you are unable to connect, it is most likely a Docker networking issue. We encourage you to troubleshoot on your own and share your methods and solutions in the discussions forum.

If you need to install a custom pipeline with additional dependencies:

• Run the following command:

docker run -d -p 9099:9099 --add-host=host.docker.internal:host-gateway -e PIPELINES_URLS="https://github.com/open-webui/pipelines/blob/main/examples/filters/detoxify_filter_pipeline.py" -v pipelines:/app/pipelines --name pipelines --restart always ghcr.io/open-webui/pipelines:main

Alternatively, you can directly install pipelines from the admin settings by copying and pasting the pipeline URL, provided it doesn't have additional dependencies.

That's it! You're now ready to build customizable AI integrations effortlessly with Pipelines. Enjoy!

📦 Installation and Setup

Get started with Pipelines in a few easy steps:

1. Ensure Python 3.11 is installed. 2. Clone the Pipelines repository:

git clone https://github.com/open-webui/pipelines.git
cd pipelines

3. Install the required dependencies:

pip install -r requirements.txt

4. Start the Pipelines server:

sh ./start.sh

Once the server is running, set the OpenAI URL on your client to the Pipelines URL. This unlocks the full capabilities of Pipelines, integrating any Python library and creating custom workflows tailored to your needs.

Advanced Docker Builds

If you create your own pipelines, you can install them when the Docker image is built. For example, create a bash script with the snippet below to collect files from a path, add them as install URLs, and build the Docker image with the new pipelines automatically installed.

NOTE: The pipelines module will still attempt to install any package dependencies found at in your file headers at start time, but they will not be downloaded again.

# build in the specific pipelines
PIPELINE_DIR="pipelines-custom"
# assuming the above directory is in your source repo and not skipped by `.dockerignore`, it will get copied to the image
PIPELINE_PREFIX="file:///app"

# retrieve all the sub files
export PIPELINES_URLS=
for file in "$PIPELINE_DIR"/*; do
if [[ -f "$file" ]]; then
if [[ "$file" == *.py ]]; then
if [ -z "$PIPELINES_URLS" ]; then
PIPELINES_URLS="$PIPELINE_PREFIX/$file"
else
PIPELINES_URLS="$PIPELINES_URLS;$PIPELINE_PREFIX/$file"
fi
fi
fi
done
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