mempalace.backends.embedding_wrapper
Source: mempalace/backends/embedding_wrapper.py
Core-side embedding adapter for explicit-vector backends.
Classes
class EmbeddingCollection(BaseCollection)
Wrap a collection that requires explicit vectors.
Backends opt in with the requires_explicit_embeddings capability. Core callers can keep using documents= and query_texts=; this wrapper computes vectors locally before delegating to the backend.
__init__
python
def __init__(self, inner: BaseCollection)distance_metric
python
def distance_metric(self) -> strget_stored_embedder_identity
python
def get_stored_embedder_identity(self)set_embedder_identity
python
def set_embedder_identity(self, identity) -> Noneeffective_embedder_identity
python
def effective_embedder_identity(self)maintenance_state
python
def maintenance_state(self) -> dictrun_maintenance
python
def run_maintenance(self, kind: str)add
python
def add(self, *, documents, ids, metadatas = None, embeddings = None)upsert
python
def upsert(self, *, documents, ids, metadatas = None, embeddings = None)query
python
def query(self, *, query_texts: Optional[list[str] | str] = None, query_embeddings: Optional[list[list[float]]] = None, n_results: int = 10, where: Optional[dict] = None, where_document: Optional[dict] = None, include: Optional[list[str]] = None)get
python
def get(self, *, ids = None, where = None, where_document = None, limit = None, offset = None, include = None)delete
python
def delete(self, *, ids = None, where = None)count
python
def count(self) -> intestimated_count
python
def estimated_count(self) -> intclose
python
def close(self) -> Nonehealth
python
def health(self)lexical_search
python
def lexical_search(self, *, query: str, n_results: int = 10, where: Optional[dict] = None)facet_counts
python
def facet_counts(self, field: str, where: Optional[dict] = None, limit: int = 1000) -> dict[str, int]get_all_metadata
python
def get_all_metadata(self, where: Optional[dict] = None) -> list[dict]update
python
def update(self, *, ids, documents = None, metadatas = None, embeddings = None)rename_wing
python
def rename_wing(self, *, from_wing: str, to_wing: str, batch_size: int = 500) -> dict