Background and core programs
Memory storage for AI intelligences needs to deal with three core requirements: information persistence, version evolution and fast retrieval. While traditional solutions such as vector databases have difficulty in tracking information change history, DiffMem provides an innovative solution through the combination of Git version control system + Markdown files:
- Git Commit History: Generate Git commits with session IDs for each memory update, with support for the
git diffView Changes - dual storage structure: the current state is saved as a Markdown file (for LLM processing), and historical versions are stored via the Git object repository
- BM25 Index: In-memory maintenance of a backward index of the latest documents for millisecond retrieval
Specific steps
- Initialize the memory bank:
memory = DiffMemory(repo_path="/path/to/repo", user_name="AI") - Submit a memory update:
process_and_commit_session("对话内容", session_id="unique123") - Query Evolution History: Calls via GitPython
git.log()maybegit.diff("commit1..commit2")
advanced skill
For production environments, it is recommended:
1. Setting up periodicgit gcCompression Repository
2. Remote backups via Git hooks
3. Applying a sub-warehousing strategy for large memory banks
This answer comes from the articleDiffMem: a Git-based versioned memory repository for AI intelligencesThe
May not be reproduced without permission:AI productivity tools " How to solve the challenges of long-term memory storage and version tracking for AI intelligences?
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