Current AI chat assistants face a fundamental challenge: context management in long conversations. While current LLM apps use multiple separate conversations to bypass context limits, a truly human-like AI assistant should maintain a single, coherent conversation thread, making efficient context management critical. Although modern LLMs have longer contexts, they still suffer from the long-context problem (e.g. context rot problem) - reasoning ability decreases as context grows longer.

Memory-based systems have been invented to alleviate the context rot problem, however, memory-based representations are inherently lossy and inevitably lose information from the original conversation. In principle, no lossy representation is universally perfect for all downstream tasks. This leads to two key requirements for defining a flexible in-context management system:

1. Preserve raw data: An index system that can retrieve the original conversation when necessary.

2. Multi-resolution access: Ability to retrieve information at different levels of detail on-demand.

ChatIndex is a context management system that enables LLMs to efficiently navigate and utilize long conversation histories through hierarchical tree-based indexing and intelligent reasoning-based retrieval.

Open-sourced repo: https://github.com/VectifyAI/ChatIndex