Setup:

Install langchain_community and clickhouse-connect:

.. code-block:: bash

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:

.. code-block:: python

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:

.. code-block:: python

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:

.. code-block:: python

vector_store.delete(ids=["3"])

TODO: Fill out example output.

Search: .. code-block:: python

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

.. code-block:: python

    # TODO: Example output

TODO: Fill out with relevant variables and example output.

Search with filter: .. code-block:: python

    # 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}]")

.. code-block:: python

    # TODO: Example output

TODO: Fill out with example output.

Search with score: .. code-block:: python

    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}]")

.. code-block:: python

    # TODO: Example output

TODO: Fill out with example output.

Async: .. code-block:: python

    # 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}]")

.. code-block:: python

    # TODO: Example output

TODO: Fill out with example output.

Use as Retriever: .. code-block:: python

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

.. code-block:: python

    # TODO: Example output