Results for “memmap”

Adds a persistent memory layer that stores and retrieves user preferences and context across conversations using semantic search.

Machine learning on genomic interval data (BED files) with the geniml Python package — region embeddings (Region2Vec), joint region+metadata embeddings (BEDspace/StarSpace), single-cell ATAC-seq embeddings (scEmbed), consensus peak sets / universes (build-universe), tokenization, BEDshift randomization, and BBClient/BEDbase caching. Use when training or using region/cell embeddings, clustering scATAC-seq, building a tokenization universe from BED collections, or any ML/feature-learning task over genomic regions. NOT for plain interval arithmetic (overlap/intersect/merge counts) — that is gtars, not geniml. Part of the AlterLab Academic Skills suite.

Indexes document metadata in SQLite and manages ephemeral key-value state in Redis for cross-session context.

Persists context across sessions by compiling sources into a local SQLite store and markdown tree, then retrieves relevant memory via embedding search.

Searches a local-first memory index and returns the top matching entries with paths and headers.

Builds a local RAG memory system that indexes session logs and notes into ChromaDB for semantic recall across agent restarts.

Detects available memory tools and maps them to a structured memory system for cross-session recall, with file-based fallback.

Creates, reads, deletes, and lists memos on a Memos instance via its API, using a personal access token.