ClickHouse

Bundles contain custom components that support specific third-party integrations with Langflow.

This page describes the components that are available in the ClickHouse bundle.

ClickHouse vector store

The ClickHouse component reads and writes to a ClickHouse vector store using an instance of ClickHouse vector store.

About vector store instances

Because Langflow is based on LangChain, vector store components use an instance of LangChain vector store to drive the underlying read and write functions. These instances are provider-specific and configured according to the component's parameters, such as the connection string, index name, and schema.

In component code, this is often instantiated as vector_store, but some vector store components use a different name, such as the provider name.

Some LangChain classes don't expose all possible options as component parameters. Depending on the provider, these options might use default values or allow modification through environment variables, if they are supported in Langflow. For information about specific options, see the LangChain API reference and vector store provider's documentation.

If you use a vector store component to query your vector database, it produces search results that you can pass to downstream components in your flow as a list of Data objects or a tabular DataFrame. If both types are supported, you can set the format near the vector store component's output port in the visual editor.

tip

For a tutorial using a vector database in a flow, see Create a vector RAG chatbot.

ClickHouse parameters

You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.

Some parameters are hidden by default in the visual editor. You can modify all parameters through the Controls in the component's header menu.

Some parameters are conditional, and they are only available after you set other parameters or select specific options for other parameters. Conditional parameters may not be visible on the Controls pane until you set the required dependencies.

For information about accepted values and functionality, see the ClickHouse Documentation or inspect component code.

Name	Display Name	Info
host	hostname	Input parameter. The ClickHouse server hostname. Required. Default: localhost.
port	port	Input parameter. The ClickHouse server port. Required. Default: 8123.
database	database	Input parameter. The ClickHouse database name. Required.
table	Table name	Input parameter. The ClickHouse table name. Required.
username	Username	Input parameter. ClickHouse username for authentication. Required.
password	Password	Input parameter. ClickHouse password for authentication. Required.
index_type	index_type	Input parameter. Type of the index, either annoy (default) or vector_similarity.
metric	metric	Input parameter. Metric to compute distance for similarity search. The options are angular (default), euclidean, manhattan, hamming, dot.
secure	Use HTTPS/TLS	Input parameter. If true, enables HTTPS/TLS for the ClickHouse server and overrides inferred values for interface or port arguments. Default: false.
index_param	Param of the index	Input parameter. Index parameters. Default: 100,'L2Distance'.
index_query_params	index query params	Input parameter. Additional index query parameters.
search_query	Search Query	Input parameter. The query string for similarity search. Only relevant for reads.
ingest_data	Ingest Data	Input parameter. The records to load into the vector store.
cache_vector_store	Cache Vector Store	Input parameter. If true, the component caches the vector store in memory for faster reads. Default: Enabled (true).
embedding	Embedding	Input parameter. The embedding model to use.
number_of_results	Number of Results	Input parameter. The number of search results to return. Default: 4. Only relevant for reads.
score_threshold	Score threshold	Input parameter. The threshold for similarity score comparison. Default: Unset (no threshold). Only relevant for reads.