Memo
Enable intelligent journaling by leveraging large language models, RAG for querying, and robust data indexing for personal entries.
Acerca de
Memo provides a local Model Context Protocol (MCP) server for intelligent personal journaling, allowing users to interact with their journal entries using natural language and large language models (LLMs). It employs Retrieval-Augmented Generation (RAG) with efficient indexing and optional GPU acceleration to enable flexible querying about past events, mood changes, and other personal reflections. Designed with privacy in mind for local use, it seamlessly integrates with various MCP-compatible LLM clients, offering a powerful way to explore and understand personal data without manual scanning.
Características Principales
- GPU and Apple MPS acceleration for faster embedding generation
- Automatic indexing and date filtering for search results
- 0 GitHub stars
- Natural language querying of journal entries
- Support for multiple local vector stores (ChromaDB, FAISS, in-memory)
- Seamless integration with MCP-compatible LLM clients
Casos de Uso
- Querying personal journal entries for insights on past events, mood, or work performance
- Automatically adding new daily entries to a personal journal
- Analyzing personal trends and patterns over time through natural language questions
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