mempalace.entities

Source: mempalace/entities.py

No-LLM structural entity extraction for the associative graph.

Pulls deterministic, structural tokens from text — author-quoted code spans, URLs, file paths, qualified identifiers, and CamelCase symbols — to populate the entities drawer-metadata field that hallways/tunnels consume. Structural-only by design: no wordlists, no NLP models, no domain vocabulary, so it stays language-neutral and predictable, and biases to precision (only tokens that are unambiguously "a thing being referred to") over recall.

The output format matches what hallways._parse_entities expects: a ;-joined string.

Functions

extract_structural_entities

def extract_structural_entities(text, max_entities = _MAX_ENTITIES)

Return up to max_entities structural entities from text.

Deterministic and order-stable: entities are ranked by occurrence count (ties broken by first appearance), deduplicated case-insensitively, preserving the first-seen surface form.

entities_metadata

def entities_metadata(text, max_entities = _MAX_ENTITIES)

;-joined entity string for drawer metadata, or "" when none are found.