agentguard-llm

Create a small but powerful chatbot application using Python that leverages the 'agentguard-llm' package to ensure robustness and reliability. This chatbot will serve as a customer service representative for a fictional tech company, handling common queries about products and services. It will integrate seamlessly with various backend systems to provide accurate and up-to-date information to users.

The application should include the following key features:
1. **User Interaction**: Allow users to input questions via a simple command-line interface or a web-based frontend.
2. **LLM Integration**: Utilize 'agentguard-llm' to manage interactions with one or more Large Language Models (LLMs), such as those from OpenAI, Anthropic, or other providers.
3. **Fault Tolerance**: Implement 'agentguard-llm' to handle potential failures gracefully. For example, if the primary LLM fails, the system should automatically switch to a fallback model without interrupting the user interaction.
4. **Loop Detection**: Ensure that the chatbot does not get stuck in repetitive loops by leveraging 'agentguard-llm's loop detection capabilities.
5. **Async Support**: Use asynchronous programming techniques provided by 'agentguard-llm' to improve the responsiveness of the chatbot.
6. **Health Monitoring**: Continuously monitor the health of the chatbot and its integration with external services. If any issues arise, notify the administrator through email or SMS.
7. **Budget Enforcement**: Set a daily budget for API calls to the LLMs and enforce it using 'agentguard-llm'. When the budget limit is reached, switch to a less expensive model or stop further interactions until the next day.
8. **Logging and Analytics**: Maintain logs of all interactions for auditing purposes and use analytics to understand user behavior and improve the chatbot over time.

Steps to Build the Application:
1. Install necessary packages including 'agentguard-llm', FastAPI for the web server, and any required LLM clients.
2. Configure 'agentguard-llm' to work with your chosen LLM(s), setting up fallback chains, async support, and health checks.
3. Develop the user interface (command-line or web-based).
4. Implement the logic for handling user inputs, integrating with the LLM(s) through 'agentguard-llm', and providing responses back to the user.
5. Add functionality for fault tolerance, loop detection, and asynchronous operations.
6. Set up budget enforcement and health monitoring.
7. Integrate logging and analytics to track usage and performance.
8. Test the application thoroughly under different conditions to ensure reliability and efficiency.
9. Deploy the application on a cloud platform like AWS or Google Cloud for easy access and scalability.