Jerry Liu: Agentic RAG
The speaker discusses a multi-agent framework called Llama Agents, which aims to build a production-grade knowledge assistant. They highlight the key features of this approach:
- Service-oriented architecture: Each agent is treated as a separate service that can communicate with others through a central API.
- Scalability and ease of deployment: The system can handle multiple requests at once and is easy to deploy on different types of services.
- Reusability: Agents can encapsulate logic but still communicate with each other, making them reusable across different tasks.
The speaker also talks about the architecture of Llama Agents:
- Agent representation as a service: Each agent is represented as a separate service that can be written using various frameworks (e.g., Llama Index) and interfaces.
- Message passing and orchestration: Agents interact with each other via message passing, and an orchestrator can control the flow between services.
They demonstrate how Llama Agents can be used for knowledge assistance by running a demo on a basic text pipeline. The demo shows how multiple agents communicate through APIs to process queries and provide responses.
Key points from the speaker:
- Production-grade knowledge assistant: The goal of Llama Agents is to build a system that can handle real-world tasks and scale with production requirements.
- Multi-agent approach: The system uses multiple agents working together to achieve a given task, making it more robust and scalable.
- Customizability and reusability: Agents can be customized and reused across different tasks, reducing development time and increasing efficiency.
- Community engagement: The speaker invites the audience to provide feedback on the project’s roadmap, communication protocol, and integration with other community-driven projects.
Overall, the presentation highlights the benefits of a multi-agent framework for building production-grade knowledge assistants and encourages collaboration from the community.