k (int) – Number of documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata.
Defaults to None.
Returns
List of Documents most similar to the query
text and score for each.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document][source]¶
Return meilisearch documents most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding to look up similar documents.
k (int) – Number of documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata.
Defaults to None.
Returns
List of Documents most similar to the queryvector and score for each.
Return type
List[Document]
similarity_search_by_vector_with_scores(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return meilisearch documents most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding to look up similar documents.
k (int) – Number of documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata.
Defaults to None.
Returns
List of Documents most similar to the queryvector and score for each.
Return type
List[Document]
similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1].