https://github.com/skitsanos/streamlit-ell
A demonstration application showcasing how to integrate ELL and Streamlit
https://github.com/skitsanos/streamlit-ell
Last synced: about 1 month ago
JSON representation
A demonstration application showcasing how to integrate ELL and Streamlit
- Host: GitHub
- URL: https://github.com/skitsanos/streamlit-ell
- Owner: skitsanos
- Created: 2025-01-22T13:58:02.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-22T14:03:57.000Z (9 months ago)
- Last Synced: 2025-01-22T15:19:23.124Z (9 months ago)
- Language: Python
- Size: 4.88 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# ELL Integration with Streamlit Demo
A demonstration application showcasing how to integrate ELL with Streamlit to create an interactive chat interface. This
app demonstrates explicitly ELL's tool calling capabilities and session management in a Streamlit environment.
> `ell` is a lightweight prompt engineering library treating prompts as functions. Documentation can be
> found [here](https://docs.ell.so/).
## Key Demonstration Points
- **ELL Integration**: Shows how to initialize and use ELL within a Streamlit application
- **Tool Calling**: Demonstrates ELL's tool calling functionality using a weather service example
- **Streamlit State Management**: Examples of managing ELL chat state with Streamlit's session state
- **Complex Function Handling**: Shows how to use ELL's `@complex` decorator with Streamlit's dynamic state
## Technical Highlights
```python
@ell.complex(model=lambda: st.session_state.model or DEFAULT_MODEL,
tools=[weather.get_weather],
temperature=0.0)
def execute_with_tools(message_history: List[Message]):
return message_history
```
This example demonstrates:
- Dynamic model selection using Streamlit's session state
- Tool registration with ELL
- Message history management
## Prerequisites
- Python 3.9+
- OpenAI API key
- Required packages:
```
streamlit
openai
ell
```
## Installation
1. Clone this repository:
```bash
git clone https://github.com/skitsanos/streamlit-ell.git
cd streamlit-ell
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
3. Set up your OpenAI API key as an environment variable:
```bash
export OPENAI_API_KEY='your-api-key-here'
```
## Running the Demo
1. Start the application:
```bash
streamlit run app.py
```
2. Open your web browser and navigate to the provided URL (typically http://localhost:8501)
## Demo Features
### ELL Tool Integration
- Weather information tool demonstration
- Example of how to structure and register tools with ELL
- Tool execution flow in a Streamlit environment
### State Management
- ELL chat history preservation using Streamlit session state
- Model selection state management
- Message threading with tool calls
### UI Components
- Model selector in sidebar
- Chat interface with message history
- Tool response display
## Project Structure
```
.
├── app.py # Main demonstration file
├── tools/
│ └── weather.py # Example tool integration
├── logdir/ # ELL logging directory
└── requirements.txt # Project dependencies
```
## Learning Points
1. How to initialize ELL in a Streamlit application
2. Managing tool calls with ELL and Streamlit
3. Handling chat state and history
4. Error handling in ELL tool calls
5. Integrating external services (weather API example)
## Contributing
Feel free to submit issues and pull requests with additional examples or improvements to the demonstration.
## License
MIT
## Support
For questions about:
- ELL integration: https://docs.ell.so/
- Streamlit usage: https://streamlit.io/