Introduction to RAG Systems
Retrieval-Augmented Generation (RAG) is becoming popular in leveraging large language models.
This video discusses various use cases for RAG and the role of generative feedback loops.
Challenges with Current Chatbots
A personal experience illustrates how traditional chatbots struggle with vague or complex queries.
Automated responses often rely on keyword matching, leading to misunderstandings and user frustration.
The Role of RAG in Semantic Search
RAG can improve interactions by utilizing vector databases for semantic search rather than keyword matching.
This technology helps understand the intent and context of user queries, providing better responses.
Verba: An Open-Source RAG Application
Verba offers an open-source RAG application, demonstrated as an Airbnb chatbot.
Users can inquire about bookings, receive relevant information, and access original sources interactively.
Enhancing Performance with AI Agents
Generative feedback loops can optimize application functionalities and improve response times.
AI agents autonomously analyze queries and adjust responses based on previous interactions.
Implementing RAG and Feedback Loops
RAG can be feasibly implemented using WE8's features, which support various model integrations.
The generative module allows applications to combine searches and generate contextual responses.
Use Cases for Self-Healing Databases
Generative feedback loops can create self-healing databases that autonomously correct data errors.
In healthcare, this ensures accurate patient records and dynamic treatment plans based on real-time data.
Conclusion and Resources
The video concludes by offering resources for getting started with WE8, vector search, and implementing RAG.
Links are provided for further learning about these advanced technologies.
Advanced AI Agents with RAG
Advanced AI Agents with RAG