Returns documents most similar to the query along with relevance scores. 
similarity_search_with_score(
    query: str,
    k: int = 4,
    filter: Optional[Dict[str, Any]] = None,
    **kwargs
) -> List[Tuple[Document, float]]

​
Parameters

ParameterTypeDescription
querystrQuery text to search for
kintNumber of documents to return (default: 4)
filterOptional[Dict[str, Any]](Optional) Metadata filters to apply
**kwargsAnyAdditional keyword arguments (currently unused)

​
Returns

List[Tuple[Document, float]]: List of (Document, score) tuples where score is normalized [0, 1]

​
Example Usage

# Search with scores
results = store.similarity_search_with_score("neural networks", k=3)

for doc, score in results:
    print(f"Score: {score:.4f}")
    print(f"Content: {doc.page_content[:100]}...")
    print(f"Metadata: {doc.metadata}")
    print("---")

# Filter results by score threshold
threshold = 0.7
high_score_results = [
    (doc, score) for doc, score in results if score >= threshold
]