Open WebUI is one of those incredibly powerful tools emerging in the AI open-source space. While it might initially seem like just a chat interface --- a convenient drop-in replacement for ChatGPT --- it's actually a full-fledged platform. You can tweak and customize almost every single piece of functionality: the models, API usage, user interface, prompts, and much, much more.
In this comprehensive guide I will walk you through the various ways you can extend Open WebUI's functionality, from integrating new models to building complex, automated workflows.
But as with many powerful tools, having features is one thing; understanding and effectively using them is another. The Open WebUI website highlights many of these customization options, but some can be easily overlooked or not emphasized enough.
This blog post aims to provide a comprehensive overview of Open WebUI's extensibility, from simple UI tweaks with Canvas to powerful integrations like Ollama or adding your own fine-tuned models.
Many of the extensions mentioned here, such as Tools, Functions, Prompt, are also available for free from the OpenWebUI tools community.
We'll explore everything from basic function extensions to pipeline architectures, empowering you to unlock the full potential of this versatile platform.
Now, let's dive into how you can extend Open WebUI's functionality...
1. Core App Extensions: Expanding the Foundation
Open WebUI's modular backend is built around specialized apps, each handling a specific aspect of the system. These apps provide a solid foundation for extension:
1.1 Model Integration Apps
- Ollama Integration (
apps/ollama/): Connect custom model runners, fine-tune parameter handling, and even process unique model file formats. - OpenAI Integration (
apps/openai/): Integrate with OpenAI-compatible APIs, define custom model configurations, and manage API routing and parameter mapping.
1.2 Media Processing Apps
- Audio Processing (
apps/audio/): Build custom audio handlers, speech-to-text integrations, and even implement your own audio models. - Image Generation (
apps/images/): Integrate custom image generators and models, and create custom image processing pipelines.
1.3 Knowledge & Retrieval
- Retrieval System (
apps/retrieval/): Extend knowledge capabilities by integrating custom vector databases, document loaders, web search APIs, and Retrieval-Augmented Generation (RAG) implementations.
1.4 Real-Time Communication
- Socket Handling (
apps/socket/): Enable real-time features with custom event handlers and streaming implementations.
2. Function System: Lightweight Logic Enhancements
The built-in function system (webui/models/functions.py) offers a database-driven approach to adding custom logic. Functions are defined with the following structure:
class Function:
id: str
type: str # 'filter', 'action', or 'global'
content: str # Python code implementing the function
meta: dict # Metadata (e.g., description)
valves: dict # Configuration parameters
is_active: bool # Enable/disable the function
is_global: bool # Make the function available to all users
2.1 Function Types
- Filter Functions: Modify or filter messages on the fly.
def my_filter(message: dict) -> dict:
# ... modify message ...
return modified_message
- Action Functions: Execute custom actions triggered by user input.
def my_action(input: str) -> str:
# ... perform action ...
return result
- Global Functions: Provide system-wide functionality available to all users.
def global_handler(context: dict) -> dict:
# ... process context ...
return processed_context
3. Model Extensions: Tailoring AI Behavior
Customize how models behave within Open WebUI by modifying their configurations (webui/models/models.py):
3.1 Extension Points
- Parameter Customization: Fine-tune model behavior by adjusting parameters like temperature, top_p, and frequency_penalty.
{
"temperature": 0.7,
"top_p": 0.9,
"frequency_penalty": 0.0
}
- System Prompts: Define custom system prompts to shape the model's personality and instructions, even using variables for dynamic context.
{
"system_prompt": "You are a specialized assistant for {domain}",
"variables": {
"domain": "technical support"
}
}
- Knowledge Integration: Equip models with specific knowledge by attaching documents or functions.
4. Vector Database Extensions: Powering Knowledge Retrieval
Open WebUI's retrieval system supports a variety of vector databases for efficient knowledge storage and retrieval (apps/retrieval/vector/dbs/):
- ChromaDB
- Milvus
- OpenSearch
- pgvector
- Qdrant
5. Web Search Extensions: Bringing the Web into Your UI
Extend Open WebUI's reach to the internet by implementing custom search providers (apps/retrieval/web/):
- Google PSE
- Brave Search
- DuckDuckGo
- Bing
- Custom providers
5.1 Custom Search Provider
Create a function to interact with your desired search API:
def custom_search(query: str, api_key: str) -> list[SearchResult]:
# ... call search API and format results ...
return results
6. Document Processing Extensions: Handling Diverse Content
Customize how Open WebUI handles various document formats:
6.1 Custom Loaders (apps/retrieval/loaders/)
Load data from different file types into a standardized document format.
class CustomLoader:
def load(self, file_path: str) -> list[Document]:
# ... load and parse document content ...
return documents
6.2 Text Splitters
Control how text is divided into chunks for processing.
class CustomSplitter:
def split_text(self, text: str) -> list[str]:
# ... split text into chunks based on custom logic ...
return chunks
7. Pipeline Extensions: Orchestrating Complex Workflows
Pipelines provide the most powerful and flexible way to create complex features by chaining together multiple processing steps:
7.1 Pipeline Types
- Filter Pipelines: Transform or filter messages as they flow through the system.
- Action Pipelines: Add new capabilities by executing actions based on messages.
- Provider Pipelines: Integrate external models and services.
- RAG Pipelines: Implement custom Retrieval-Augmented Generation logic.
Wrap-Up
In summary, Open WebUI offers a wide range of extension mechanisms to customize its functionality. From basic function extensions to complex pipelines, you can tailor Open WebUI to your specific needs. This guide has covered the key methods for extending Open WebUI, including core apps, functions, models, vector databases, search providers, document processors, and pipelines.
By understanding these options and following best practices for security and performance, you can effectively leverage Open WebUI's flexibility to build powerful and customized AI solutions.
For more information, see the official documentation:
Top comments (1)
This is so cool. Thank you for writing. Does Open WebUI have support for Astra DB as a vector database yet?