Inspiration
The idea stemmed from the need for a truly adaptable AI assistant—one that can take on any role, switch between different LLM models, and decide when to use web search for real-time data. Instead of rigid, predefined responses, this chatbot offers flexibility and intelligence tailored to the user's needs.
What it does
Our agentic chatbot allows users to:
- Assign it a specific role (e.g., tutor, assistant, researcher).
- Choose an LLM model best suited for the task.
- Enable or disable web search for up-to-date information.
How we built it
We leveraged:
- LLM frameworks to support multiple AI models.
- Agentic design to allow dynamic role-switching.
- Tool integrations like web search for real-time information retrieval.
- Efficient API handling to optimize response times and costs.
Challenges we ran into
- Balancing model flexibility with response accuracy.
- Implementing an efficient tool-switching mechanism.
- Ensuring seamless transitions between different roles and knowledge sources.
Accomplishments that we're proud of
- Successfully integrating role-based adaptability.
- Enabling real-time web search alongside trained AI responses.
- Creating a scalable, modular framework for future improvements.
What we learned
- The importance of dynamic tool selection in AI assistants.
- Optimizing response flow for accuracy and speed.
- Balancing pre-trained knowledge with real-time data retrieval.
What's next for Untitled
- Expanding support for more AI models.
- Adding custom user preferences for an even more personalized experience.
- Enhancing tool integrations with APIs for deeper web and data access.
