- Embedded
- Python SDK
- JS/TS
similarity_search_by_vector(
embedding: Union[List[float], np.ndarray],
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
**kwargs
) -> List[Document]
Parameters
| Parameter | Type | Description |
|---|---|---|
embedding | Union[List[float], np.ndarray] | Embedding vector to search with |
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[Document]: List of most similar Document objectsExample Usage
# Get embedding for a query
query_embedding = store.get_embeddings("data science concepts")
# Search using the embedding
results = store.similarity_search_by_vector(query_embedding, k=5)
# Search with custom embedding
custom_embedding = np.random.rand(384) # Example 384-dim embedding
results = store.similarity_search_by_vector(custom_embedding, k=3)
similarity_search_by_vector(
embedding: Union[List[float], np.ndarray],
k: Optional[int] = None,
filter: Optional[Dict[str, Any]] = None,
**kwargs
) -> List[Document]
Parameters
| Parameter | Type | Description |
|---|---|---|
embedding | Union[List[float], np.ndarray] | Embedding vector to search with |
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[Document]: List of most similar Document objectsExample Usage
query_embedding = store.get_embeddings("data science concepts")
results = store.similarity_search_by_vector(query_embedding, k=5)
similaritySearchVectorWithScore(
query: number[],
k: number,
filter?: Record<string, any>
): Promise<[Document, number][]>
Parameters
| Parameter | Type | Description |
|---|---|---|
query | number[] | Embedding vector to search with |
k | number | Number of documents to return |
filter | Record<string, any> | (Optional) Metadata filters to apply |
Returns
Promise<[Document, number][]>: Array of [Document, score] tuplesThe JS/TS method name is
similaritySearchVectorWithScore (not similaritySearchByVector), and it returns score tuples unlike the Python SDKs which return documents only.Example Usage
const queryEmbedding = [0.1, 0.2, 0.3, /* ... */];
const results = await store.similaritySearchVectorWithScore(queryEmbedding, 5);
for (const [doc, score] of results) {
console.log(`Score: ${score.toFixed(4)} - ${doc.pageContent.slice(0, 100)}...`);
}
Async
Python async variants
Python async variants
The Embedded and Python SDK provide async versions of this method prefixed with
a:# asimilarity_search_by_vector — async variant
docs = await store.asimilarity_search_by_vector(embedding, k=5)
JS/TS methods are natively async — all signatures above already return
Promise<...>. No separate async variant is needed.