Building a Database That Works Like Human Memory
The article describes the development of MNHEME, an append-only binary database engine written from scratch in Python, which enforces true immutability and models human memory.
Why it matters
MNHEME represents a novel approach to database design, inspired by the immutable nature of human memory, with potential applications in personal information management, knowledge representation, and AI-powered cognitive systems.
Key Points
- 1Append-only database with no update or delete operations
- 2Inspired by the immutable nature of human memory
- 3Stores memories with concepts, emotions, and optional media
- 4Includes an LLM-powered cognitive layer for perception, retrieval, reflection, and introspection
- 5Filesystem-aware media storage with hard links, reflinks, and atomic copies
Details
The author built MNHEME, a database engine that enforces true immutability, unlike traditional databases that only provide an audit log. The core data structure is the 'Memory' dataclass, which is frozen and cannot be modified after creation. The database is implemented as a binary log, with each record containing a header and the serialized memory data. This ensures crash-safety, as any incomplete writes are detected and skipped on startup. MNHEME also includes in-memory indexes to enable efficient querying. On top of the storage engine, the author has built an LLM-powered cognitive layer that can perceive, ask, reflect, dream, and introspect on the stored memories, mimicking human memory processes. The database is provider-agnostic, allowing the use of any LLM via a .env file, including local models like LM Studio/Ollama.
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