1ba96c6662
- support multi-line SKILL.md frontmatter parsing in manage_skills - write multi-line skill descriptions as YAML block scalars - sync README, docs, and release notes for v0.12.2
124 lines
3.6 KiB
Markdown
124 lines
3.6 KiB
Markdown
# Pipe Plugins
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Pipe plugins create custom model integrations or transform LLM responses. They appear as selectable models in the OpenWebUI interface.
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## What are Pipes?
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Pipes allow you to:
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- :material-api: Connect to external AI APIs (Gemini, Claude, etc.)
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- :material-robot: Create custom model wrappers
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- :material-cog-transfer: Transform requests and responses
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- :material-middleware: Implement middleware logic
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---
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## Available Pipe Plugins
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- [GitHub Copilot SDK](github-copilot-sdk.md) (v0.12.2) - Official GitHub Copilot SDK integration. Features **Workspace Isolation**, **Zero-config OpenWebUI Tool Bridge**, **BYOK** support, and **dynamic MCP discovery**. **NEW in v0.12.2: `manage_skills` multi-line `SKILL.md` frontmatter support and safer YAML writes**. [View Deep Dive](github-copilot-sdk-deep-dive.md) | [**View Advanced Tutorial**](github-copilot-sdk-tutorial.md) | [**View Detailed Usage Guide**](github-copilot-sdk-usage-guide.md).
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- **[Case Study: GitHub 100 Star Growth Analysis](star-prediction-example.md)** - Learn how to use the GitHub Copilot SDK Pipe with Minimax 2.1 to automatically analyze CSV data and generate project growth reports.
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- **[Case Study: High-Quality Video to GIF Conversion](video-processing-example.md)** - See how the model uses system-level FFmpeg to accelerate, scale, and optimize colors for screen recordings.
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---
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## How Pipes Work
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```mermaid
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graph LR
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A[User selects Pipe as Model] --> B[Pipe receives request]
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B --> C[Transform/Route request]
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C --> D[External API / Custom Logic]
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D --> E[Return response]
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E --> F[Display to User]
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```
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### The `pipes` Method
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Defines what models this pipe provides:
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```python
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def pipes(self):
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return [
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{"id": "my-model", "name": "My Custom Model"},
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{"id": "my-model-fast", "name": "My Custom Model (Fast)"}
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]
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```
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### The `pipe` Method
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Handles the actual request processing:
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```python
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def pipe(self, body: dict) -> Generator:
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# Process the request
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messages = body.get("messages", [])
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# Call external API or custom logic
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response = call_external_api(messages)
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# Return response (can be streaming)
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return response
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```
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---
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## Quick Installation
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1. Download the desired pipe `.py` file
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2. Navigate to **Admin Panel** → **Settings** → **Functions**
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3. Upload the file and configure API keys
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4. The pipe will appear as a selectable model
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---
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## Development Template
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```python
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"""
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title: My Custom Pipe
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author: Your Name
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version: 1.0.0
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description: Description of your pipe plugin
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"""
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from pydantic import BaseModel, Field
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from typing import Generator, Iterator, Union
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class Pipe:
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class Valves(BaseModel):
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API_KEY: str = Field(
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default="",
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description="API key for the external service"
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)
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API_URL: str = Field(
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default="https://api.example.com",
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description="API endpoint URL"
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)
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def __init__(self):
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self.valves = self.Valves()
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def pipes(self) -> list[dict]:
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"""Define available models."""
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return [
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{"id": "my-model", "name": "My Custom Model"},
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]
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def pipe(
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self,
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body: dict
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) -> Union[str, Generator, Iterator]:
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"""Process the request and return response."""
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messages = body.get("messages", [])
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model = body.get("model", "")
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# Your logic here
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# Can return:
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# - str: Single response
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# - Generator/Iterator: Streaming response
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return "Response from custom pipe"
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```
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For more details, check our [Plugin Development Guide](../../development/plugin-guide.md).
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