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Build AI Agent Memory From Scratch — Tutorial For Dummies
- This guide explains AI memory in a simple way for beginners without a tech background, focusing on PocketFlow framework.
- AI memory involves short-term memory for recent info and long-term memory for older info, using methods like embeddings and summaries.
- PocketFlow provides basic tools for learning AI memory, with clear instructions and room for expansion at your own pace.
- AI memory is compared to a note-taking system, using short-term and long-term memory with retrieval methods like embeddings and summaries.
- Frameworks like LangChain offer tools like Conversation Buffer Window and Vector Store Retriever Memory for applying memory in AI systems.
- The tutorial explains how to DIY memory from scratch using nodes like Question, Retrieve, Answer, and Embed in a self-loop flow.
- Nodes like Question receive input, Retrieve searches archives, Answer generates responses, and Embed archives older conversations.
- Connecting these nodes in a self-loop flow ensures the system continues to handle input, retrieve old info, and generate responses.
- The guide also provides steps to run and test the code for a practical example of how AI memory would work in practice.
- AI memory involves self-loop flows, shared stores, memory retrieval, and contextual responses to enable efficient recall and interaction.
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