agentforge-litellm

Create a mini-application named 'MultiModelQA' that serves as a question-answering system capable of handling queries from various users via a simple web interface. This application will leverage the 'agentforge-litellm' package to route questions to different large language models (LLMs) based on predefined criteria such as model availability, query type, or user preference. Here’s how you will develop it:

1. **Setup Project**: Initialize a new Python project using Flask as the web framework. Ensure you have Flask installed and set up a basic web server.
2. **Integrate agentforge-litellm**: Install the 'agentforge-litellm' package and configure it to connect to multiple LLM providers. Define functions to route incoming queries to the appropriate LLM based on certain conditions.
3. **Design User Interface**: Create a simple HTML form where users can input their questions. The form should also allow users to select a preferred LLM if they wish.
4. **Implement Query Handling**: Write backend logic to process user inputs. Use 'agentforge-litellm' to send these queries to the selected or recommended LLM. Handle responses appropriately and return them to the user.
5. **Add Error Handling**: Implement error handling to manage scenarios where a selected LLM might not be available or returns an unexpected response.
6. **Enhance Functionality**: Consider adding features like session management to track user interactions over time, sentiment analysis on user queries to better understand user satisfaction, or even integrating a feedback loop where user feedback improves future responses.
7. **Testing and Deployment**: Thoroughly test your application locally before deploying it to a cloud service like Heroku or AWS.

By following these steps, you will create a versatile mini-application that demonstrates the flexibility and power of 'agentforge-litellm' in routing complex tasks to multiple AI models.