In the world of AI and large language models (LLMs), understanding how to manage memory is crucial for creating applications that feel responsive and intelligent. Many developers are turning to Semantic Kernel, a lightweight and open-source development kit, to integrate these capabilities into their applications. For those already familiar with Semantic Kernel, let’s dive into how memory functions within this framework, especially when interacting with LLMs via chat completions.
Chat Completions: The Most Common Interaction with LLMs
When it comes to interacting with LLMs, one of the most intuitive and widely used methods is through chat completions. This allows developers to simulate a conversation between a user and an AI agent, facilitating various use cases like building chatbots, automating business processes, or even generating code.
In Semantic Kernel, chat completions are implemented through models from popular providers like OpenAI, Google, and others. These models enable developers to manage the flow of conversation seamlessly. While using chat completions, one key aspect to keep in mind is how the conversation history is stored and managed.
Temporary Memory: ChatHistory and Kernel String Arguments
Within the Semantic Kernel framework, the memory that a chat completion model uses is managed by the ChatHistory object. This object stores the conversation history temporarily, meaning it captures the back-and-forth between the user and the model during an active session. Alternatively, you can use a string argument passed to the kernel, which contains context information for the conversation. However, like the ChatHistory, this method is also not persistent.
Once the host class is disposed of, all stored context and memory from both the ChatHistory object and the string argument are lost. This transient nature of memory means that these methods are useful only for short-term interactions and are destroyed after the session ends.
What’s Next? Exploring Long-Term Memory Options
In this article, we’ve discussed how Semantic Kernel manages short-term memory with ChatHistory and kernel string arguments. However, for more complex applications that require retaining memory over longer periods—think customer support agents or business process automation—temporary memory might not be sufficient. In the next article, we’ll explore the options available for implementing long-term memory within Semantic Kernel, providing insights on how to make your AI applications even more powerful and context-aware.
Stay tuned for the deep dive into long-term memory solutions!