Returns documents most similar to the query text.
Embedded
Python SDK
JS/TS
similarity_search(
query: str,
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
**kwargs
) -> List[Document]
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[Document]: List of most similar Document objectsExample Usage
# Basic similarity search
results = store.similarity_search("machine learning algorithms", k=5)
for doc in results:
print(f"Content: {doc.page_content[:100]}...")
print(f"Metadata: {doc.metadata}")
# Search with metadata filter
filter_dict = {"language": "python", "level": "beginner"}
results = store.similarity_search(
"python tutorial",
k=3,
filter=filter_dict
)
similarity_search(
query: str,
k: Optional[int] = None,
filter: Optional[Dict[str, Any]] = None,
**kwargs
) -> List[Document]
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[Document]: List of most similar Document objectsWhen k is None, the server default number of results is returned.
Example Usage
results = store.similarity_search("machine learning algorithms", k=5)
for doc in results:
print(f"Content: {doc.page_content[:100]}...")
similaritySearch(
query: string,
k?: number,
filter?: Record<string, any>
): Promise<Document[]>
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[]>: Array of most similar Document objectsExample Usage
const results = await store.similaritySearch("machine learning algorithms", 5);
for (const doc of results) {
console.log(`Content: ${doc.pageContent.slice(0, 100)}...`);
console.log(`Metadata:`, doc.metadata);
}
// Search with metadata filter
const filtered = await store.similaritySearch(
"python tutorial",
3,
{ language: "python", level: "beginner" }
);
Async
The Embedded and Python SDK provide async versions of this method prefixed with a:# asimilarity_search — async variant
docs = await store.asimilarity_search("query", k=5)
JS/TS methods are natively async — all signatures above already return Promise<...>. No separate async variant is needed.
