Use cases
An agent with no server and no embedder
connect_embedded opens a directory and runs the engine in the process. Nothing is listening on a port. With no vector passed in, a cue has to share tokens with what was stored. That is the whole offline behaviour, and it was printed.
What ran
The first cue shares "dark" and "mode" with the stored sentence and came back. The second shares none and came back empty. Same brain, one call apart. Paraphrase recall needs a semantic_vector you computed, on the write and on the cue.
Run.
from fluctlightdb import connect_embedded
brain = connect_embedded("./edge-brain")
brain.experience("User prefers dark mode", context="settings", salience=0.7)
print("shares", bool(brain.activate("dark mode")["recalls"]))
print("paraphrase", bool(brain.activate("theme preference")["recalls"]))Printed
shares True
paraphrase FalseWhat this is for
A process on a laptop, a CI job, or a small machine that should not depend on a hosted memory service. The limit, in the project’s words, is the one below. Bringing an embedder is how you leave it. The engine will not choose one for you.
Stated limit
Offline, recall is lexical: a cue has to share tokens with what was stored. Paraphrase recall needs a semantic vector from an embedder you bring. Source: docs/EMBEDDINGS.md:9,24. Opens the repository in a new tab.