Chroma

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

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

Chroma DB

You can use the Chroma DB component to read and write to a Chroma database using an instance of Chroma vector store. Includes support for remote or in-memory instances with or without persistence.

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.

When writing, the component can create a new database or collection at the specified location.

tip

An ephemeral (non-persistent) local Chroma vector store is helpful for testing vector search flows where you don't need to retain the database.

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.

Use the Chroma DB component in a flow

The following example flow uses one Chroma DB component for both reads and writes:

• When writing, it splits Data from a URL component into chunks, computes embeddings with attached Embedding Model component, and then loads the chunks and embeddings into the Chroma vector store. To trigger writes, click Run component on the Chroma DB component.

• When reading, it uses chat input to perform a similarity search on the vector store, and then print the search results to the chat. To trigger reads, open the Playground and enter a chat message.

After running the flow once, you can click Inspect Output on each component to understand how the data transformed as it passed from component to component.

Chroma DB 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 provider's documentation or inspect component code.

Name	Type	Description
Collection Name (collection_name)	String	Input parameter. The name of your Chroma vector store collection. Default: langflow.
Persist Directory (persist_directory)	String	Input parameter. To persist the Chroma database, enter a relative or absolute path to a directory to store the chroma.sqlite3 file. Leave empty for an ephemeral database. When reading or writing to an existing persistent database, specify the path to the persistent directory.
Ingest Data (ingest_data)	Data or DataFrame	Input parameter. Data or DataFrame input containing the records to write to the vector store. Only relevant for writes.
Search Query (search_query)	String	Input parameter. The query to use for vector search. Only relevant for reads.
Cache Vector Store (cache_vector_store)	Boolean	Input parameter. If true, the component caches the vector store in memory for faster reads. Default: Enabled (true).
Embedding (embedding)	Embeddings	Input parameter. The embedding function to use for the vector store. By default, Chroma DB uses its built-in embeddings model, or you can attach an Embedding Model component to use a different provider or model.
CORS Allow Origins (chroma_server_cors_allow_origins)	String	Input parameter. The allowed CORS origins for the Chroma server.
Chroma Server Host (chroma_server_host)	String	Input parameter. The host for the Chroma server.
Chroma Server HTTP Port (chroma_server_http_port)	Integer	Input parameter. The HTTP port for the Chroma server.
Chroma Server gRPC Port (chroma_server_grpc_port)	Integer	Input parameter. The gRPC port for the Chroma server.
Chroma Server SSL Enabled (chroma_server_ssl_enabled)	Boolean	Input parameter. Enable SSL for the Chroma server.
Allow Duplicates (allow_duplicates)	Boolean	Input parameter. If true (default), writes don't check for existing duplicates in the collection, allowing you to store multiple copies of the same content. If false, writes won't add documents that match existing documents already present in the collection. If false, it can strictly enforce deduplication by searching the entire collection or only search the number of records, specified in limit. Only relevant for writes.
Search Type (search_type)	String	Input parameter. The type of search to perform, either Similarity or MMR. Only relevant for reads.
Number of Results (number_of_results)	Integer	Input parameter. The number of search results to return. Default: 10. Only relevant for reads.
Limit (limit)	Integer	Input parameter. Limit the number of records to compare when Allow Duplicates is false. This can help improve performance when writing to large collections, but it can result in some duplicate records. Only relevant for writes.

See also

• Local DB component