Persistent HDF5-backed memory with vector search, BM25 hybrid retrieval, Hebbian learning, and temporal decay.
# Install the CLI (one-time)
cargo install edgehdf5-cli
# Or from source:
cargo install --path crates/edgehdf5-cli
Set EDGEHDF5_PATH env var or pass --path to every command.
All output is JSON for easy parsing.
edgehdf5 --path agent.h5 create --agent-id myagent --dim 384 --wal
Pass JSON via --json or stdin:
edgehdf5 --path agent.h5 save --json '{"chunk":"User asked about weather","embedding":[0.1,0.2,...],"source_channel":"discord","timestamp":1700000000.0,"session_id":"s1","tags":"weather"}'
Embedding must match the dimension specified at creation.
edgehdf5 --path agent.h5 search --embedding '[0.1,0.2,...]' --query 'weather forecast' -k 5
Optional: --vector-weight 0.7 --keyword-weight 0.3 (defaults).
edgehdf5 --path agent.h5 recall 42
edgehdf5 --path agent.h5 stats
Returns: count, active entries, WAL pending, config details.
edgehdf5 --path agent.h5 flush-wal
edgehdf5 --path agent.h5 agents-md
# Or write to file:
edgehdf5 --path agent.h5 agents-md --output AGENTS.md
edgehdf5 --path agent.h5 export
Outputs one JSON object per line (JSONL).
edgehdf5 --path agent.h5 snapshot backup.h5
MemoryEntry JSON with the conversation chunk and its embedding vectoredgehdf5 saveflush-waledgehdf5 search --embedding '[...]' --query 'user text' -k 10.h5 file is a standard HDF5 file readable by any HDF5 library.h5 file as .h5.wal 共 1 个版本