FAISS

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

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

FAISS vector store

The FAISS component provides access to the Facebook AI Similarity Search (FAISS) library through an instance of FAISS 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.

FAISS 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 FAISS documentation or inspect component code.

Name	Type	Description
index_name	String	Input parameter. The name of the FAISS index. Default: "langflow_index".
persist_directory	String	Input parameter. Path to save the FAISS index. It is relative to where Langflow is running.
search_query	String	Input parameter. The query to search for in the vector store.
ingest_data	Data	Input parameter. The list of data to ingest into the vector store.
allow_dangerous_deserialization	Boolean	Input parameter. Set to True to allow loading pickle files from untrusted sources. Default: True.
embedding	Embeddings	Input parameter. The embedding function to use for the vector store.
number_of_results	Integer	Input parameter. Number of results to return from the search. Default: 4.