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Semantic Kernel: Using Memories to Create Intelligent AI Agents
- Semantic Kernel uses memories to enable intelligent AI agents to recall relevant information from past interactions and provide context-aware responses.
- Memories help agents learn from experience, personalize their behavior, and provide intelligent and consistent user experiences.
- There are two types of memory storage mechanisms in Semantic Kernel: volatile memory and non-volatile memory.
- Semantic Memory, which is a library within the Semantic Kernel SDK, and Kernel Memory, a service that can run in the background, are the two ways to store agentic memory in Semantic Kernel.
- Semantic Memory is not a deployable service, whereas Kernel Memory is a deployable service, making it a more reasonable option for building an enterprise-grade solution.
- Volatile memory holds data for the duration of an interaction, whereas non-volatile memory is stored long-term and can be accessed across different sessions.
- Kernel Memory combines multiple types of memories to manage context and state within an application, making it more a more comprehensive memory management layer than Semantic Memory.
- The fundamental difference between Semantic Memory and Kernel Memory is that the former operates as a library within Semantic Kernel and the latter as a deployable service.
- The comparison table included in the article can help you decide which memory storage mechanism to opt for based on your requirements.
- Creating a simple implementation of Semantic Memory involves creating a new memory system, choosing a text embedding model and memory storage mechanism, and adding contents to the memory store.
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