spfcraze
89f74d58f6
FactRetriever.search() re-encoded the query vector once per candidate, related() re-encoded both role atoms once per fact row, and probe() re-encoded the role-content atom once per row. All three encoders are deterministic (SHA-256 counter blocks), so the hoisted vectors are bit-identical to the per-iteration values they replace. Measured (300-fact store, dim=1024, median of 30 calls): search() 11.62 -> 1.46 ms/call (8.0x; encode_text 30 -> 1 per call), related() 63.08 -> 16.17 ms/call (3.9x; encode_atom 601 -> 3 per call), probe() 431.93 -> 389.36 ms/call (1.1x; dominated by per-fact content encoding, which is inherent to the algorithm and unchanged). Tests: call-count regression tests for each hoist plus a bit-exact parity test of search() against the pre-fix per-candidate loop.
Holographic Memory Provider
Local SQLite fact store with FTS5 search, trust scoring, entity resolution, and HRR-based compositional retrieval.
Requirements
None — uses SQLite (always available). NumPy optional for HRR algebra.
Setup
hermes memory setup # select "holographic"
Or manually:
hermes config set memory.provider holographic
Config
Config in config.yaml under plugins.hermes-memory-store:
| Key | Default | Description |
|---|---|---|
db_path |
$HERMES_HOME/memory_store.db |
SQLite database path |
auto_extract |
false |
Auto-extract facts at session end |
default_trust |
0.5 |
Default trust score for new facts |
hrr_dim |
1024 |
HRR vector dimensions |
Tools
| Tool | Description |
|---|---|
fact_store |
9 actions: add, search, probe, related, reason, contradict, update, remove, list |
fact_feedback |
Rate facts as helpful/unhelpful (trains trust scores) |