Clickhouse#

class langchain_community.vectorstores.clickhouse.Clickhouse(embedding: Embeddings, config: ClickhouseSettings | None = None, **kwargs: Any)[source]#

ClickHouse vector store integration.

Setup:

Install langchain_community and clickhouse-connect:

pip install -qU langchain_community clickhouse-connect
Key init args — indexing params:
embedding: Embeddings

Embedding function to use.

Key init args — client params:
config: Optional[ClickhouseSettings]

ClickHouse client configuration.

Instantiate:
from langchain_community.vectorstores import Clickhouse, ClickhouseSettings
from langchain_openai import OpenAIEmbeddings

settings = ClickhouseSettings(table="clickhouse_example")
vector_store = Clickhouse(embedding=OpenAIEmbeddings(), config=settings)
Add Documents:
from langchain_core.documents import Document

document_1 = Document(page_content="foo", metadata={"baz": "bar"})
document_2 = Document(page_content="thud", metadata={"bar": "baz"})
document_3 = Document(page_content="i will be deleted :(")

documents = [document_1, document_2, document_3]
ids = ["1", "2", "3"]
vector_store.add_documents(documents=documents, ids=ids)
Delete Documents:
vector_store.delete(ids=["3"])

# TODO: Fill out example output. Search:

results = vector_store.similarity_search(query="thud",k=1)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

# TODO: Example output

# TODO: Fill out with relevant variables and example output. Search with filter:

# TODO: Edit filter if needed
results = vector_store.similarity_search(query="thud",k=1,filter="metadata.baz='bar'")
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

# TODO: Example output

# TODO: Fill out with example output. Search with score:

results = vector_store.similarity_search_with_score(query="qux",k=1)
for doc, score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

# TODO: Example output

# TODO: Fill out with example output. Async:

# add documents
# await vector_store.aadd_documents(documents=documents, ids=ids)

# delete documents
# await vector_store.adelete(ids=["3"])

# search
# results = vector_store.asimilarity_search(query="thud",k=1)

# search with score
results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
for doc,score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

# TODO: Example output

# TODO: Fill out with example output. Use as Retriever:

retriever = vector_store.as_retriever(
    search_type="mmr",
    search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
)
retriever.invoke("thud")

# TODO: Example output

ClickHouse Wrapper to LangChain

Parameters:

  • embedding_function (Embeddings) – embedding function to use

  • config (ClickHouseSettings) – Configuration to ClickHouse Client

  • kwargs (any) – Other keyword arguments will pass into [clickhouse-connect](https://docs.clickhouse.com/)

  • embedding (Embeddings) –

Attributes

embeddings

Provides access to the embedding mechanism used by the Clickhouse instance.

metadata_column

Methods

__init__(embedding[, config])

ClickHouse Wrapper to LangChain

aadd_documents(documents, **kwargs)

Async run more documents through the embeddings and add to the vectorstore.

aadd_texts(texts[, metadatas])

Async run more texts through the embeddings and add to the vectorstore.

add_documents(documents, **kwargs)

Add or update documents in the vectorstore.

add_texts(texts[, metadatas, batch_size, ids])

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

adelete([ids])

Async delete by vector ID or other criteria.

afrom_documents(documents, embedding, **kwargs)

Async return VectorStore initialized from documents and embeddings.

afrom_texts(texts, embedding[, metadatas])

Async return VectorStore initialized from texts and embeddings.

aget_by_ids(ids, /)

Async get documents by their IDs.

amax_marginal_relevance_search(query[, k, ...])

Async return docs selected using the maximal marginal relevance.

amax_marginal_relevance_search_by_vector(...)

Async return docs selected using the maximal marginal relevance.

as_retriever(**kwargs)

Return VectorStoreRetriever initialized from this VectorStore.

asearch(query, search_type, **kwargs)

Async return docs most similar to query using a specified search type.

asimilarity_search(query[, k])

Async return docs most similar to query.

asimilarity_search_by_vector(embedding[, k])

Async return docs most similar to embedding vector.

asimilarity_search_with_relevance_scores(query)

Async return docs and relevance scores in the range [0, 1].

asimilarity_search_with_score(*args, **kwargs)

Async run similarity search with distance.

delete([ids])

Delete by vector ID or other criteria.

drop()

Helper function: Drop data

escape_str(value)

Escape special characters in a string for Clickhouse SQL queries.

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

from_texts(texts, embedding[, metadatas, ...])

Create ClickHouse wrapper with existing texts

get_by_ids(ids, /)

Get documents by their IDs.

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 a 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.
__init__(embedding: Embeddings, config: ClickhouseSettings | None = None, **kwargs: Any) → None[source]#

ClickHouse Wrapper to LangChain

Parameters:

  • embedding_function (Embeddings) – embedding function to use

  • config (ClickHouseSettings) – Configuration to ClickHouse Client

  • kwargs (any) – Other keyword arguments will pass into [clickhouse-connect](https://docs.clickhouse.com/)

  • embedding (Embeddings) –

Return type:

None

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

Async run more documents through the embeddings and add to the vectorstore.

Parameters:

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

  • kwargs (Any) – Additional keyword arguments.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of IDs does not match the number of documents.

Return type:

List[str]

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

Async run more texts through the embeddings and add to the vectorstore.

Parameters:

  • texts (Iterable[str]) – Iterable of strings to add to the vectorstore.

  • metadatas (List[dict] | None) – Optional list of metadatas associated with the texts. Default is None.

  • **kwargs (Any) – vectorstore specific parameters.

Returns:

List of ids from adding the texts into the vectorstore.

Raises:

  • ValueError – If the number of metadatas does not match the number of texts.

  • ValueError – If the number of ids does not match the number of texts.

Return type:

List[str]

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

Add or update documents in the vectorstore.

Parameters:

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

  • kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of ids does not match the number of documents.

Return type:

List[str]

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

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

Parameters:

  • texts (Iterable[str]) – Iterable of strings to add to the VectorStore.

  • ids (Iterable[str] | None) – Optional list of ids to associate with the texts.

  • batch_size (int) – Batch size of insertion

  • metadata – Optional column data to be inserted

  • metadatas (List[dict] | None) –

  • kwargs (Any) –

Returns:

List of ids from adding the texts into the VectorStore.

Return type:

List[str]

async adelete(ids: List[str] | None = None, **kwargs: Any) → bool | None#

Async delete by vector ID or other criteria.

Parameters:

  • ids (List[str] | None) – List of ids to delete. If None, delete all. Default is None.

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

Returns:

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

Return type:

Optional[bool]

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

Async return VectorStore initialized from documents and embeddings.

Parameters:

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

  • embedding (Embeddings) – Embedding function to use.

  • kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

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

Async return VectorStore initialized from texts and embeddings.

Parameters:

  • texts (List[str]) – Texts to add to the vectorstore.

  • embedding (Embeddings) – Embedding function to use.

  • metadatas (List[dict] | None) – Optional list of metadatas associated with the texts. Default is None.

  • kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from texts and embeddings.

Return type:

VectorStore

async aget_by_ids(ids: Sequence[str], /) → List[Document]#

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:

ids (Sequence[str]) – List of ids to retrieve.

Returns:

List of Documents.

Return type:

List[Document]

New in version 0.2.11.

Async return docs selected using the maximal marginal relevance.

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

Parameters:

  • query (str) – Text to look up documents similar to.

  • k (int) – Number of Documents to return. Defaults to 4.

  • fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Default is 20.

  • lambda_mult (float) – 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.

  • kwargs (Any) –

Returns:

List of Documents selected by maximal marginal relevance.

Return type:

List[Document]

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]#

Async return docs selected using the maximal marginal relevance.

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

Parameters:

  • embedding (List[float]) – Embedding to look up documents similar to.

  • k (int) – Number of Documents to return. Defaults to 4.

  • fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Default is 20.

  • lambda_mult (float) – 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.

  • **kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents selected by maximal marginal relevance.

Return type:

List[Document]

as_retriever(**kwargs: Any) → VectorStoreRetriever#

Return VectorStoreRetriever initialized from this VectorStore.

Parameters:

**kwargs (Any) –

Keyword arguments to pass to the search function. Can include: search_type (Optional[str]): Defines the type of search that

the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.

search_kwargs (Optional[Dict]): Keyword arguments to pass to the
search function. Can include things like:

k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold

for similarity_score_threshold

fetch_k: Amount of documents to pass to MMR algorithm

(Default: 20)

lambda_mult: Diversity of results returned by MMR;

1 for minimum diversity and 0 for maximum. (Default: 0.5)

filter: Filter by document metadata

Returns:

Retriever class for VectorStore.

Return type:

VectorStoreRetriever

Examples:

# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 6, 'lambda_mult': 0.25}
)

# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 5, 'fetch_k': 50}
)

# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={'score_threshold': 0.8}
)

# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})

# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]#

Async return docs most similar to query using a specified search type.

Parameters:

  • query (str) – Input text.

  • search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.

  • **kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

List[Document]

Async return docs most similar to query.

Parameters:

  • query (str) – Input text.

  • k (int) – Number of Documents to return. Defaults to 4.

  • **kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query.

Return type:

List[Document]

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

Async return docs most similar to embedding vector.

Parameters:

  • embedding (List[float]) – Embedding to look up documents similar to.

  • k (int) – Number of Documents to return. Defaults to 4.

  • **kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query vector.

Return type:

List[Document]

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

Async return docs and relevance scores in the range [0, 1].

0 is dissimilar, 1 is most similar.

Parameters:

  • query (str) – Input text.

  • k (int) – Number of Documents to return. Defaults to 4.

  • **kwargs (Any) –

    kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

    filter the resulting set of retrieved docs

Returns:

List of Tuples of (doc, similarity_score)

Return type:

List[Tuple[Document, float]]

async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]#

Async run similarity search with distance.

Parameters:

  • *args (Any) – Arguments to pass to the search method.

  • **kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Tuples of (doc, similarity_score).

Return type:

List[Tuple[Document, float]]

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

Delete by vector ID or other criteria.

Parameters:

  • ids (List[str] | None) – List of ids to delete. If None, delete all. Default is None.

  • **kwargs (Any) – 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[source]#

Helper function: Drop data

Return type:

None

escape_str(value: str) → str[source]#

Escape special characters in a string for Clickhouse SQL queries.

This method is used internally to prepare strings for safe insertion into SQL queries by escaping special characters that might otherwise interfere with the query syntax.

Parameters:

value (str) – The string to be escaped.

Returns:

The escaped string, safe for insertion into SQL queries.

Return type:

str

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

Return VectorStore initialized from documents and embeddings.

Parameters:

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

  • embedding (Embeddings) – Embedding function to use.

  • kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

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

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)

  • embedding (Embeddings) –

  • metadatas (List[Dict[Any, Any]] | None) –

  • kwargs (Any) –

Returns:

ClickHouse Index

Return type:

Clickhouse

get_by_ids(ids: Sequence[str], /) → List[Document]#

Get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:

ids (Sequence[str]) – List of ids to retrieve.

Returns:

List of Documents.

Return type:

List[Document]

New in version 0.2.11.

Return docs selected using the maximal marginal relevance.

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

Parameters:

  • query (str) – Text to look up documents similar to.

  • k (int) – Number of Documents to return. Defaults to 4.

  • fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Default is 20.

  • lambda_mult (float) – 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.

  • **kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents selected by maximal marginal relevance.

Return type:

List[Document]

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 (List[float]) – Embedding to look up documents similar to.

  • k (int) – Number of Documents to return. Defaults to 4.

  • fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Default is 20.

  • lambda_mult (float) – 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.

  • **kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents selected by maximal marginal relevance.

Return type:

List[Document]

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

Return docs most similar to query using a specified search type.

Parameters:

  • query (str) – Input text

  • search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.

  • **kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

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.

  • kwargs (Any) –

Returns:

List of Documents

Return type:

List[Document]

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

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.

  • embedding (List[float]) –

  • kwargs (Any) –

Returns:

List of documents

Return type:

List[Document]

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

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.

  • kwargs (Any) –

Returns:

List of (Document, similarity)

Return type:

List[Document]

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

Run similarity search with distance.

Parameters:

  • *args (Any) – Arguments to pass to the search method.

  • **kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Tuples of (doc, similarity_score).

Return type:

List[Tuple[Document, float]]

Examples using Clickhouse

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