Version: latest
agrag.evidence.entities.find_entities
find_entities(store:GraphStore, embedder:Embedder, query:str, *, scope:frozenset[str], labels:Sequence[str] | None = None, limit:int, mode:DiscoveryMode = DiscoveryMode.HYBRID, support_limit:int = 3) -> EntityResult
Find up to limit entities that match a query by name or by meaning.
The lexical leg matches the words of the query against the name of each
entity. The semantic leg embeds the query and searches the embedding of
each entity. The hybrid mode runs both and fuses the rankings with reciprocal
rank fusion. Each label needs a full-text index on name and a vector index
on embedding. A missing or offline index gives index_not_ready.
An entity is returned only when a passage in scope mentions it. Its
support lists up to support_limit visible passages, as evidence ids.
An entity with passages in scope and no visible passage is returned with kind
unsupported and an empty support list.
Parameters:
- store (
GraphStore) – The graph store to search. - embedder (
Embedder) – Embeds the query for the semantic leg. The lexical leg does not use it. - query (
str) – The query text. Lucene syntax is not applied: each word is searched as a plain term. - scope (
frozenset[str]) – Keys of the documents whose passages may be used. - labels (
Sequence[str] | None) – The entity labels to search. None searches every entity label that has a vector index, which is every label the graph schema declares. - limit (
int) – The most entities to return. Must be positive. - mode (
DiscoveryMode) – Which legs to run. - support_limit (
int) – The most supporting passages listed for each entity. Must be positive.
Returns:
EntityResult– The entities, best first, with the status and the index state.
Raises:
ValueError–limitorsupport_limitis not positive.