How to Build Your Own Custom LLM Memory Layer from Scratch

Building a custom memory layer for Large Language Models (LLMs) addresses their stateless nature, enabling personalized interactions. The process is framed as a context engineering problem, focusing on extracting, embedding, retrieving, and maintaining information. It involves converting conversation transcripts into atomic factoids using a framework like DSPy. These factoids are then embedded into vectors and stored in a vector database such as QDrant for efficient retrieval, allowing an LLM to autonomously search for relevant memories to inform its responses.