Milvus

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

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

Milvus vector store

The Milvus component reads and writes to Milvus vector stores using an instance of Milvus 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.

Milvus 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 Milvus documentation or inspect component code.

Name	Type	Description
collection_name	String	Input parameter. Name of the Milvus collection.
collection_description	String	Input parameter. Description of the Milvus collection.
uri	String	Input parameter. Connection URI for Milvus.
password	SecretString	Input parameter. Password for Milvus.
username	SecretString	Input parameter. Username for Milvus.
batch_size	Integer	Input parameter. Number of data to process in a single batch.
search_query	String	Input parameter. Query for similarity search.
ingest_data	Data	Input parameter. Data to be ingested into the vector store.
embedding	Embeddings	Input parameter. Embedding function to use.
number_of_results	Integer	Input parameter. Number of results to return in search.
search_type	String	Input parameter. Type of search to perform.
search_score_threshold	Float	Input parameter. Minimum similarity score for search results.
search_filter	Dict	Input parameter. Metadata filters for search query.
setup_mode	String	Input parameter. Configuration mode for setting up the vector store.
vector_dimensions	Integer	Input parameter. Number of dimensions of the vectors.
pre_delete_collection	Boolean	Input parameter. Whether to delete the collection before creating a new one.