Episode
Building Semantic Memory for AI With Cognee
- Podcast
- AI Engineering Podcast
- Published
- Nov 25, 2024
- Duration seconds
- 3301
- Processing state
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Summary
Summary In this episode of the AI Engineering Podcast, Vasilije Markovich talks about enhancing Large Language Models (LLMs) with memory to improve their accuracy. He discusses the concept of memory in LLMs, which involves managing context windows to enhance reasoning without the high costs of traditional training methods. He explains the challenges of forgetting in LLMs due to context window limitations and introduces the idea of hierarchical memory, where immediate retrieval and long-term information storage are balanced to improve application performance. Vasilije also shares his work on Cognee, a tool he's developing to manage semantic memory in AI systems, and discusses its potential applications beyond its core use case. He emphasizes the importance of combining cognitive science principles with data engineering to push the boundaries of AI capabilities and shares his vision for the future of AI systems, highlighting the role of personalization and the ongoing development of Cognee to support evolving AI architectures. Announcements Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems Your host is Tobias Macey and today I'm interviewing Vasilije Markovic about adding memory to LLMs to improve their accuracy Interview Introduction How did you get involved in machine learning? Can you describe what "memory" is in the context of LLM systems? What are the symptoms of "forgetting" that manifest when interacting with LLMs? How do these issues manifest between single-turn vs. multi-turn interactions? How does the lack of hierarchical and evolving memory limit the capabilities of LLM systems? What are the technical/architectural requirements to add memory to an LLM system/application? How do…