langchain.vectorstores.clickhouse.Clickhouse

class langchain.vectorstores.clickhouse.Clickhouse(embedding: Embeddings, config: Optional[ClickhouseSettings] = None, **kwargs: Any)

Bases: VectorStore

Wrapper around ClickHouse vector database

You need a clickhouse-connect python package, and a valid account to connect to ClickHouse.

ClickHouse can not only search with simple vector indexes, it also supports complex query with multiple conditions, constraints and even sub-queries.

For more information, please visit

[ClickHouse official site](https://clickhouse.com/clickhouse)

ClickHouse Wrapper to LangChain

embedding_function (Embeddings): config (ClickHouseSettings): Configuration to ClickHouse Client Other keyword arguments will pass into

[clickhouse-connect](https://docs.clickhouse.com/)

Methods

__init__(embedding[, config])	ClickHouse Wrapper to LangChain
aadd_documents(documents, **kwargs)	Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])	Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)	Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, batch_size, ids])	Insert more texts through the embeddings and add to the VectorStore.
afrom_documents(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])	Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])	Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)	Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
asearch(query, search_type, **kwargs)	Return docs most similar to query using specified search type.
asimilarity_search(query[, k])	Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])	Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)	Return docs most similar to query.
delete([ids])	Delete by vector ID or other criteria.
drop()	Helper function: Drop data
escape_str(value)
from_documents(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])	Create ClickHouse wrapper with existing texts
max_marginal_relevance_search(query[, k, ...])	Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)	Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)	Return docs most similar to query using specified search type.
similarity_search(query[, k, where_str])	Perform a similarity search with ClickHouse
similarity_search_by_vector(embedding[, k, ...])	Perform a similarity search with ClickHouse by vectors
similarity_search_with_relevance_scores(query)	Perform a similarity search with ClickHouse
similarity_search_with_score(*args, **kwargs)	Run similarity search with distance.

Attributes

embeddings	Access the query embedding object if available.
metadata_column

async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]

Run more documents through the embeddings and add to the vectorstore.

Parameters

(List[Document] (documents) – Documents to add to the vectorstore.

Returns

List of IDs of the added texts.

Return type

List[str]

async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]

Run more texts through the embeddings and add to the vectorstore.

add_documents(documents: List[Document], **kwargs: Any) → List[str]

Run more documents through the embeddings and add to the vectorstore.

Parameters

(List[Document] (documents) – Documents to add to the vectorstore.

Returns

List of IDs of the added texts.

Return type

List[str]

add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, batch_size: int = 32, ids: Optional[Iterable[str]] = None, **kwargs: Any) → List[str]

Insert more texts through the embeddings and add to the VectorStore.

Parameters

• texts – Iterable of strings to add to the VectorStore.

• ids – Optional list of ids to associate with the texts.

• batch_size – Batch size of insertion

• metadata – Optional column data to be inserted

Returns

List of ids from adding the texts into the VectorStore.

async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

Return VectorStore initialized from documents and embeddings.

async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST

Return VectorStore initialized from texts and embeddings.

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

as_retriever(**kwargs: Any) → VectorStoreRetriever

async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using specified search type.

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to query.

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to embedding vector.

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to query.

delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]

Delete by vector ID or other criteria.

Parameters

• ids – List of ids to delete.

• **kwargs – Other keyword arguments that subclasses might use.

Returns

True if deletion is successful, False otherwise, None if not implemented.

Return type

Optional[bool]

drop() → None

Helper function: Drop data

escape_str(value: str) → str

classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

Return VectorStore initialized from documents and embeddings.

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[Dict[Any, Any]]] = None, config: Optional[ClickhouseSettings] = None, text_ids: Optional[Iterable[str]] = None, batch_size: int = 32, **kwargs: Any) → Clickhouse

Create ClickHouse wrapper with existing texts

Parameters

• embedding_function (Embeddings) – Function to extract text embedding

• texts (Iterable[str]) – List or tuple of strings to be added

• config (ClickHouseSettings, Optional) – ClickHouse configuration

• text_ids (Optional[Iterable], optional) – IDs for the texts. Defaults to None.

• batch_size (int, optional) – Batchsize when transmitting data to ClickHouse. Defaults to 32.

• metadata (List[dict], optional) – metadata to texts. Defaults to None.

• into (Other keyword arguments will pass) – [clickhouse-connect](https://clickhouse.com/docs/en/integrations/python#clickhouse-connect-driver-api)

Returns

ClickHouse Index

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters

• query – Text to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• fetch_k – Number of Documents to fetch to pass to MMR algorithm.

• lambda_mult – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.

Returns

List of Documents selected by maximal marginal relevance.

max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters

• embedding – Embedding to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• fetch_k – Number of Documents to fetch to pass to MMR algorithm.

• lambda_mult – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.

Returns

List of Documents selected by maximal marginal relevance.

search(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using specified search type.

similarity_search(query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any) → List[Document]

Perform a similarity search with ClickHouse

Parameters

• query (str) – query string

• k (int, optional) – Top K neighbors to retrieve. Defaults to 4.

• where_str (Optional[str], optional) – where condition string. Defaults to None.

• NOTE – Please do not let end-user to fill this and always be aware of SQL injection. When dealing with metadatas, remember to use {self.metadata_column}.attribute instead of attribute alone. The default name for it is metadata.

Returns

List of Documents

Return type

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, where_str: Optional[str] = None, **kwargs: Any) → List[Document]

Perform a similarity search with ClickHouse by vectors

Parameters

• query (str) – query string

• k (int, optional) – Top K neighbors to retrieve. Defaults to 4.

• where_str (Optional[str], optional) – where condition string. Defaults to None.

• NOTE – Please do not let end-user to fill this and always be aware of SQL injection. When dealing with metadatas, remember to use {self.metadata_column}.attribute instead of attribute alone. The default name for it is metadata.

Returns

List of (Document, similarity)

Return type

List[Document]

similarity_search_with_relevance_scores(query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]]

Perform a similarity search with ClickHouse

Parameters

• query (str) – query string

• k (int, optional) – Top K neighbors to retrieve. Defaults to 4.

• where_str (Optional[str], optional) – where condition string. Defaults to None.

• NOTE – Please do not let end-user to fill this and always be aware of SQL injection. When dealing with metadatas, remember to use {self.metadata_column}.attribute instead of attribute alone. The default name for it is metadata.

Returns

List of documents

Return type

List[Document]

similarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]

Run similarity search with distance.

property embeddings: langchain.embeddings.base.Embeddings

Access the query embedding object if available.

property metadata_column: str

Examples using Clickhouse

• ClickHouse Vector Search