similarity_search_with_score

Returns documents most similar to the query along with relevance scores.

Embedded
Python SDK
JS/TS

similarity_search_with_score(
    query: str,
    k: int = 4,
    filter: Optional[Dict[str, Any]] = None,
    **kwargs
) -> List[Tuple[Document, float]]

Parameters

Parameter	Type	Description
`query`	`str`	Query text to search for
`k`	`int`	Number of documents to return (default: 4)
`filter`	`Optional[Dict[str, Any]]`	(Optional) Metadata filters to apply
`**kwargs`	`Any`	Additional 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
]

_similarity_search_with_relevance_scores (internal method)

The Embedded library also exposes _similarity_search_with_relevance_scores, an internal method that supports a score_threshold keyword argument for server-side score filtering:

_similarity_search_with_relevance_scores(
    query: str,
    k: int,
    **kwargs
) -> List[Tuple[Document, float]]

Keyword Arguments:

Parameter	Type	Description
`filter`	`Dict[str, Any]`	(Optional) Metadata filters to apply
`score_threshold`	`float`	(Optional) Minimum score threshold for results

Example:

# Search with relevance scores and threshold
results = store._similarity_search_with_relevance_scores(
    "machine learning",
    k=10,
    score_threshold=0.5
)

print(f"Found {len(results)} documents above threshold")
for doc, score in results:
    print(f"Relevance: {score:.2%} - {doc.page_content[:50]}...")

similarity_search_with_score(
    query: str,
    k: Optional[int] = None,
    filter: Optional[Dict[str, Any]] = None,
    **kwargs
) -> List[Tuple[Document, float]]

Parameters

Parameter	Type	Description
`query`	`str`	Query text to search for
`k`	`Optional[int]`	(Optional) Number of documents to return (default: `None`, uses server default)
`filter`	`Optional[Dict[str, Any]]`	(Optional) Metadata filters to apply
`**kwargs`	`Any`	Additional keyword arguments

Returns

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

Example Usage

results = store.similarity_search_with_score("neural networks", k=3)

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

similaritySearchWithScore(
    query: string,
    k?: number,
    filter?: Record<string, any>
): Promise<[Document, number][]>

Parameters

Parameter	Type	Description
`query`	`string`	Query text to search for
`k`	`number`	(Optional) Number of documents to return
`filter`	`Record<string, any>`	(Optional) Metadata filters to apply

Returns

Promise<[Document, number][]>: Array of [Document, score] tuples

Example Usage

const results = await store.similaritySearchWithScore("neural networks", 3);

for (const [doc, score] of results) {
    console.log(`Score: ${score.toFixed(4)}`);
    console.log(`Content: ${doc.pageContent.slice(0, 100)}...`);
}

Async

Python async variants

The Embedded and Python SDK provide async versions of this method prefixed with a:

# asimilarity_search_with_score — async variant
results = await store.asimilarity_search_with_score("query", k=3)

JS/TS methods are natively async — all signatures above already return Promise<...>. No separate async variant is needed.

Introduction

CyborgVectorStore

Types

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