Motivation
No method exists to fetch N concept embeddings and return their weighted mean as a query vector.
Pitch
Use case: "find concepts similar to the combination of GLP-1 receptor agonist + type 2 diabetes" --> compute centroid of the two stored embeddings, then pass to get_nearest_concepts
Alternatives
No response
Additional context
Proposed signature on EmbeddingReaderInterface:
def get_joint_embedding(
self,
concept_ids: tuple[int, ...],
weights: tuple[float, ...] | None = None,
) -> np.ndarray
Implementation should use get_embeddings_by_concept_ids and normalise the weighted sum for cosine space
Motivation
No method exists to fetch N concept embeddings and return their weighted mean as a query vector.
Pitch
Use case: "find concepts similar to the combination of GLP-1 receptor agonist + type 2 diabetes" --> compute centroid of the two stored embeddings, then pass to
get_nearest_conceptsAlternatives
No response
Additional context
Proposed signature on
EmbeddingReaderInterface:Implementation should use
get_embeddings_by_concept_idsand normalise the weighted sum for cosine space