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Weaviate Podcast
Agentic RAG with Erika Cardenas - Weaviate Podcast #109!
Nov 13, 2024
In this engaging discussion, Erika Cardenas, Technology Partner Manager at Weaviate, dives deep into the innovative world of Agentic RAG systems. She explains how Agentic RAG outperforms traditional approaches by enhancing complex querying and reasoning. The conversation explores the importance of memory in AI, the evolution of multi-agent systems, and the role of generative feedback loops in advancing AI capabilities. Erika also emphasizes the necessity of human oversight in AI, underscoring collaborative approaches between machines and human input.
34:08
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Quick takeaways
- Agentic RAG enhances decision-making through strategic planning, enabling agents to break down complex queries and utilize chain-of-thought reasoning.
- Multi-agent systems allow specialized agents to collaborate effectively, improving processing efficiency and adapting to evolving tasks through flexible orchestration.
Deep dives
Understanding Agentic RAG Components
An agent consists of key components, including a language model, memory, and tools that enable it to process information. The memory can be classified as short-term or long-term and is crucial for keeping track of conversation history, while frameworks like Leta allow updates based on previous interactions. Different planning strategies, such as chain of thought queries or sub-questions, can enhance the decision-making process when an agent is responding to a user query. The capacity for function calling further differentiates agents, allowing them to interact with external databases and APIs to obtain or manipulate information dynamically.
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