Vector Store API

Complete API reference for AsyncCockroachDBVectorStore and CockroachDBVectorStore.

langchain_cockroachdb.async_vectorstore.AsyncCockroachDBVectorStore

Bases: VectorStore

Async vector store using CockroachDB native VECTOR type and C-SPANN indexes.

embeddings property

Get embeddings model.

__init__(engine, embeddings, collection_name, *, schema='public', distance_strategy=DistanceStrategy.COSINE, content_column='content', embedding_column='embedding', metadata_column='metadata', id_column='id', namespace_column='namespace', namespace=None, hybrid_search_config=None, batch_size=100, retry_max_attempts=3, retry_initial_backoff=0.1, retry_max_backoff=5.0, retry_backoff_multiplier=2.0, retry_jitter=True)

Initialize async vector store.

Parameters:

Name	Type	Description	Default
engine	CockroachDBEngine	CockroachDBEngine instance	required
embeddings	Embeddings	Embeddings model	required
collection_name	str	Table name for this collection	required
schema	str	Database schema (default: public)	'public'
distance_strategy	DistanceStrategy	Distance metric for similarity (default: COSINE)	COSINE
content_column	str	Name of content column (default: content)	'content'
embedding_column	str	Name of embedding column (default: embedding)	'embedding'
metadata_column	str	Name of metadata column (default: metadata)	'metadata'
id_column	str	Name of ID column (default: id)	'id'
namespace_column	str	Name of namespace column (default: namespace)	'namespace'
namespace	str | None	Namespace for multi-tenancy isolation. When set, all operations are scoped to this namespace. Requires the table to have a namespace column (created with namespace_column param in ainit_vectorstore_table). Default: None (no namespace filtering, backward compatible).	None
hybrid_search_config	HybridSearchConfig | None	Optional hybrid search configuration	None
batch_size	int	Batch size for inserts - CockroachDB works best with smaller batches (default: 100)	100
retry_max_attempts	int	Maximum retry attempts for operations (default: 3)	3
retry_initial_backoff	float	Initial backoff delay in seconds (default: 0.1)	0.1
retry_max_backoff	float	Maximum backoff delay in seconds (default: 5.0)	5.0
retry_backoff_multiplier	float	Backoff multiplier (default: 2.0)	2.0
retry_jitter	bool	Add randomization to backoff (default: True)	True

aadd_texts(texts, metadatas=None, ids=None, **kwargs) async

Add texts to vector store with automatic retry on failures.

Parameters:

Name	Type	Description	Default
texts	Iterable[str]	Texts to add	required
metadatas	list[dict] | None	Optional metadata for each text	None
ids	list[str] | None	Optional IDs for texts	None
**kwargs	Any	Additional arguments (batch_size override supported)	{}

Returns:

Type	Description
list[str]	List of IDs for added texts

aapply_vector_index(index, prefix_columns=None) async

Apply C-SPANN vector index.

Parameters:

Name	Type	Description	Default
index	CSPANNIndex	Index configuration	required
prefix_columns	list[str] | None	Optional prefix columns for multi-tenant indexes	None

add_texts(texts, metadatas=None, **kwargs)

Sync wrapper - not implemented for async-only store.

adelete(ids=None, **kwargs) async

Delete documents by IDs.

Parameters:

Name	Type	Description	Default
ids	list[str] | None	Document IDs to delete	None
**kwargs	Any	Additional arguments	{}

Returns:

Type	Description
bool | None	True if successful

adrop_vector_index(index) async

Drop vector index.

Parameters:

Name	Type	Description	Default
index	CSPANNIndex	Index configuration	required

afrom_texts(texts, embedding, metadatas=None, engine=None, connection_string=None, collection_name='langchain_vectors', **kwargs) async classmethod

Create vector store from texts.

Parameters:

Name	Type	Description	Default
texts	list[str]	Texts to add	required
embedding	Embeddings	Embeddings model	required
metadatas	list[dict] | None	Optional metadata	None
engine	CockroachDBEngine | None	CockroachDBEngine instance	None
connection_string	str | None	Connection string (if engine not provided)	None
collection_name	str	Table name	'langchain_vectors'
**kwargs	Any	Additional arguments	{}

Returns:

Type	Description
AsyncCockroachDBVectorStore	AsyncCockroachDBVectorStore instance

aget_by_ids(ids) async

Get documents by their IDs.

Parameters:

Name	Type	Description	Default
ids	Sequence[str]	List of IDs to retrieve.	required

Returns:

Type	Description
list[Document]	List of Document objects found.

amax_marginal_relevance_search(query, k=4, fetch_k=20, lambda_mult=0.5, filter=None, **kwargs) async

Max marginal relevance search.

Parameters:

Name	Type	Description	Default
query	str	Query text	required
k	int	Number of results	4
fetch_k	int	Number of candidates to fetch	20
lambda_mult	float	Diversity parameter (0=max diversity, 1=max relevance)	0.5
filter	dict | None	Metadata filter	None
**kwargs	Any	Additional arguments	{}

Returns:

Type	Description
list[Document]	List of documents

asimilarity_search(query, k=4, filter=None, **kwargs) async

Search for similar documents.

Parameters:

Name	Type	Description	Default
query	str	Query text	required
k	int	Number of results	4
filter	dict | None	Metadata filter	None
**kwargs	Any	Additional arguments	{}

Returns:

Type	Description
list[Document]	List of documents

asimilarity_search_by_vector(embedding, k=4, filter=None, **kwargs) async

Return docs most similar to embedding vector.

Parameters:

Name	Type	Description	Default
embedding	list[float]	Embedding to look up documents similar to.	required
k	int	Number of results to return.	4
filter	dict | None	Metadata filter.	None
**kwargs	Any	Additional arguments.	{}

Returns:

Type	Description
list[Document]	List of documents most similar to the query vector.

asimilarity_search_with_score(query, k=4, filter=None, query_options=None, **kwargs) async

Search for similar documents with scores.

Parameters:

Name	Type	Description	Default
query	str	Query text	required
k	int	Number of results	4
filter	dict | None	Metadata filter	None
query_options	CSPANNQueryOptions | None	C-SPANN query options	None
**kwargs	Any	Additional arguments	{}

Returns:

Type	Description
list[tuple[Document, float]]	List of (document, score) tuples

asimilarity_search_with_score_by_vector(embedding, k=4, filter=None, query_options=None, **kwargs) async

Search for similar documents by embedding vector.

Parameters:

Name	Type	Description	Default
embedding	list[float]	Query embedding vector	required
k	int	Number of results	4
filter	dict | None	Metadata filter	None
query_options	CSPANNQueryOptions | None	C-SPANN query options	None
**kwargs	Any	Additional arguments	{}

Returns:

Type	Description
list[tuple[Document, float]]	List of (document, score) tuples

from_texts(texts, embedding, metadatas=None, **kwargs) classmethod

Sync wrapper - not implemented for async-only store.

get_by_ids(ids)

Get documents by their IDs (sync).

Parameters:

Name	Type	Description	Default
ids	Sequence[str]	List of IDs to retrieve.	required

Returns:

Type	Description
list[Document]	List of Document objects found.

similarity_search(query, k=4, **kwargs)

Sync wrapper - not implemented for async-only store.

langchain_cockroachdb.vectorstores.CockroachDBVectorStore

Bases: AsyncCockroachDBVectorStore

Sync wrapper for AsyncCockroachDBVectorStore using background event loop.

add_texts(texts, metadatas=None, ids=None, **kwargs)

Add texts to vector store (sync).

Parameters:

Name	Type	Description	Default
texts	Iterable[str]	Texts to add	required
metadatas	list[dict] | None	Optional metadata	None
ids	list[str] | None	Optional IDs	None
**kwargs	Any	Additional arguments	{}

Returns:

Type	Description
list[str]	List of IDs

apply_vector_index(index, **kwargs)

Apply vector index to the table (sync).

Parameters:

Name	Type	Description	Default
index	Any	Index configuration	required
**kwargs	Any	Additional arguments	{}

delete(ids=None, **kwargs)

Delete documents (sync).

Parameters:

Name	Type	Description	Default
ids	list[str] | None	Document IDs	None
**kwargs	Any	Additional arguments	{}

Returns:

Type	Description
bool | None	True if successful

from_texts(texts, embedding, metadatas=None, engine=None, connection_string=None, collection_name='langchain_vectors', **kwargs) classmethod

Create from texts (sync).

Parameters:

Name	Type	Description	Default
texts	list[str]	Texts to add	required
embedding	Embeddings	Embeddings model	required
metadatas	list[dict] | None	Optional metadata	None
engine	CockroachDBEngine | None	CockroachDBEngine instance	None
connection_string	str | None	Connection string	None
collection_name	str	Table name	'langchain_vectors'
**kwargs	Any	Additional arguments	{}

Returns:

Type	Description
CockroachDBVectorStore	CockroachDBVectorStore instance

max_marginal_relevance_search(query, k=4, fetch_k=20, lambda_mult=0.5, filter=None, **kwargs)

MMR search (sync).

Parameters:

Name	Type	Description	Default
query	str	Query text	required
k	int	Number of results	4
fetch_k	int	Candidate pool size	20
lambda_mult	float	Diversity parameter	0.5
filter	dict | None	Metadata filter	None
**kwargs	Any	Additional arguments	{}

Returns:

Type	Description
list[Document]	List of documents

similarity_search(query, k=4, filter=None, **kwargs)

Search for similar documents (sync).

Parameters:

Name	Type	Description	Default
query	str	Query text	required
k	int	Number of results	4
filter	dict | None	Metadata filter	None
**kwargs	Any	Additional arguments	{}

Returns:

Type	Description
list[Document]	List of documents

similarity_search_by_vector(embedding, k=4, filter=None, **kwargs)

Return docs most similar to embedding vector (sync).

Parameters:

Name	Type	Description	Default
embedding	list[float]	Embedding to look up documents similar to.	required
k	int	Number of results to return.	4
filter	dict | None	Metadata filter.	None
**kwargs	Any	Additional arguments.	{}

Returns:

Type	Description
list[Document]	List of documents most similar to the query vector.

similarity_search_with_score(query, k=4, filter=None, query_options=None, **kwargs)

Search with scores (sync).

Parameters:

Name	Type	Description	Default
query	str	Query text	required
k	int	Number of results	4
filter	dict | None	Metadata filter	None
query_options	CSPANNQueryOptions | None	Query options	None
**kwargs	Any	Additional arguments	{}

Returns:

Type	Description
list[tuple[Document, float]]	List of (document, score) tuples

Key Methods

Adding Documents

Method	Async	Description
aadd_texts()	✓	Add text documents
add_texts()	-	Sync wrapper
aadd_documents()	✓	Add Document objects
add_documents()	-	Sync wrapper

Searching

Method	Async	Description
asimilarity_search()	✓	Search by text
similarity_search()	-	Sync wrapper
asimilarity_search_with_score()	✓	Search with scores
asimilarity_search_by_vector()	✓	Search by vector
amax_marginal_relevance_search()	✓	MMR search

Index Management

Method	Async	Description
aapply_vector_index()	✓	Create C-SPANN index
apply_vector_index()	-	Sync wrapper
adrop_vector_index()	✓	Drop index
drop_vector_index()	-	Sync wrapper

Deleting

Method	Async	Description
adelete()	✓	Delete documents
delete()	-	Sync wrapper

Examples

See Vector Store Guide for comprehensive examples.