Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query vector.
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].
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 4, radius: float = - 1.0, epsilon: float = 0.01, timeout: int = 3000000000, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, radius: float = - 1.0, epsilon: float = 0.01, timeout: int = 3000000000, **kwargs: Any) → List[Tuple[Document, float]][source]¶