Redis

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

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

Redis Chat Memory

The Redis Chat Memory component retrieves and stores chat messages using Redis memory storage.

Chat memories are passed between memory storage components as the Memory data type.

For more information about using external chat memory in flows, see the Message History component.

Redis Chat Memory parameters

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

Name	Display Name	Info
host	hostname	Input parameter. The IP address or hostname.
port	port	Input parameter. The Redis Port Number.
database	database	Input parameter. The Redis database.
username	Username	Input parameter. The Redis username.
password	Password	Input parameter. The password for the username.
key_prefix	Key prefix	Input parameter. The key prefix.
session_id	Session ID	Input parameter. The unique session identifier for the message.

Redis vector store

The Redis vector store component reads and writes to Redis vector stores using an instance of RedisVectorStore.

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.

Redis vector store 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 Redis documentation or inspect component code.

Name	Type	Description
redis_server_url	SecretString	Input parameter. The Redis server connection string.
redis_index_name	String	Input parameter. The name of the Redis index.
code	String	Input parameter. Additional custom code for Redis, if supported.
schema	String	Input parameter. The schema for Redis index.
ingest_data	Data	Input parameter. The data to be ingested into the vector store.
search_query	String	Input parameter. The query for similarity search.
embedding	Embeddings	Input parameter. The embedding function to use.
number_of_results	Integer	Input parameter. The number of results to return in search.