Author(s): Jageen Shukla

Originally published on Towards AI.

Learn how to build an AI agent that remembers unlimited conversation history using Redis, ChromaDB vector search, and intelligent context management. Full source code available on GitHub and you can read this blog free from here.

Imagine having a conversation with an AI assistant that truly remembers your past discussions. Not just the last few messages, but meaningful context from conversations that happened days or even weeks ago. This is the promise of a long-running agent an AI system designed to maintain context across extended interactions without hitting token limits or losing important information.

The article details the development of a long-running conversational AI agent that utilizes advanced context management techniques to enhance user interaction by preserving significant conversation details over extended periods. It discusses challenges such as managing token limits of modern language models and proposes a multi-layered context management system that leverages technologies like Redis for session management and ChromaDB for semantic search. Key insights emphasize the importance of context retention, token-aware processing, and the necessity for intelligent retrieval methods to maintain meaningful interactions, ultimately leading to a system that enhances user experience without losing relevant dialogue history.

Read the full blog for free on Medium.

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Published via Towards AI