DataStax

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

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

Astra DB Chat Memory

The Astra DB Chat Memory component retrieves and stores chat messages using an Astra DB database.

Chat memories are passed between memory storage components as the Memory data type. Specifically, the component creates an instance of AstraDBChatMessageHistory, which is a LangChain chat message history class that uses Astra DB for storage.

important

The Astra DB Chat Memory component isn't recommended for most memory storage because memories tend to be long JSON objects or strings, often exceeding the maximum size of a document or object supported by Astra DB.

However, Langflow's Agent and Language Model components include built-in chat memory that is enabled by default. Your flows don't need an external database to store chat memory.

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

Astra DB Chat Memory parameters

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

Name	Type	Description
collection_name	String	Input parameter. The name of the Astra DB collection for storing messages. Required.
token	SecretString	Input parameter. The authentication token for Astra DB access. Required.
api_endpoint	SecretString	Input parameter. The API endpoint URL for the Astra DB service. Required.
namespace	String	Input parameter. The optional namespace within Astra DB for the collection.
session_id	MessageText	Input parameter. The unique identifier for the chat session. Uses the current session ID if not provided.

Astra DB CQL

The Astra DB CQL component allows agents to query data from CQL tables in Astra DB.

The output is a list of Data objects containing the query results from the Astra DB CQL table. Each Data object contains the document fields specified by the projection fields. Limited by the number_of_results parameter.

Astra DB CQL parameters

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

Name	Type	Description
Tool Name	String	Input parameter. The name used to reference the tool in the agent's prompt.
Tool Description	String	Input parameter. A brief description of the tool to guide the model in using it.
Keyspace	String	Input parameter. The name of the keyspace.
Table Name	String	Input parameter. The name of the Astra DB CQL table to query.
Token	SecretString	Input parameter. The authentication token for Astra DB.
API Endpoint	String	Input parameter. The Astra DB API endpoint.
Projection Fields	String	Input parameter. The attributes to return, separated by commas. Default: "*".
Partition Keys	Dict	Input parameter. Required parameters that the model must fill to query the tool.
Clustering Keys	Dict	Input parameter. Optional parameters the model can fill to refine the query. Required parameters should be marked with an exclamation mark, for example, !customer_id.
Static Filters	Dict	Input parameter. Attribute-value pairs used to filter query results.
Limit	String	Input parameter. The number of records to return.

Astra DB Tool

The Astra DB Tool component enables searching data in Astra DB collections, including hybrid search, vector search, and regular filter-based search. Specialized searches require that the collection is pre-configured with the required parameters.

Outputs a list of Data objects containing the query results from Astra DB. Each Data object contains the document fields specified by the projection attributes. Limited by the number_of_results parameter and the upper limit of the Astra DB Data API, depending on the type of search.

You can use the component to execute queries directly as isolated steps in a flow, or you can connect it as a tool for an agent to allow the agent to query data from Astra DB collections as needed to respond to user queries. For more information, see Use Langflow agents.

Astra DB Tool parameters

The following parameters are for the Astra DB Tool component overall.

The values for Collection Name, Astra DB Application Token, and Astra DB API Endpoint are found in your Astra DB deployment. For more information, see the Astra DB Serverless documentation.

Name	Type	Description
Tool Name	String	Input parameter. The name used to reference the tool in the agent's prompt.
Tool Description	String	Input parameter. A brief description of the tool. This helps the model decide when to use it.
Keyspace Name	String	Input parameter. The name of the keyspace in Astra DB. Default: default_keyspace
Collection Name	String	Input parameter. The name of the Astra DB collection to query.
Token	SecretString	Input parameter. The authentication token for accessing Astra DB.
API Endpoint	String	Input parameter. The Astra DB API endpoint.
Projection Fields	String	Input parameter. Comma-separated list of attributes to return from matching documents. The default is the default projection, *, which returns all attributes except reserved fields like $vector.
Tool Parameters	Dict	Input parameter. Astra DB Data API find filters that become tools for an agent. These Filters may be used in a search, if the agent selects them. See Define tool-specific parameters.
Static Filters	Dict	Input parameter. Attribute-value pairs used to filter query results. Equivalent to Astra DB Data API find filters. Static Filters are included with every query. Use Static Filters without semantic search to perform a regular filter search.
Number of Results	Int	Input parameter. The maximum number of documents to return.
Semantic Search	Boolean	Input parameter. Whether to run a similarity search by generating a vector embedding from the chat input and following the Semantic Search Instruction. Default: False. If True, you must attach an Embedding Model component or have vectorize pre-enabled on your collection.
Use Astra DB Vectorize	Boolean	Input parameter. Whether to use the Astra DB vectorize feature for embedding generation when running a semantic search. Default: False. If True, you must have vectorize pre-enabled on your collection.
Embedding Model	Embedding	Input parameter. A port to attach an Embedding Model component to generate a vector from input text for semantic search. This can be used when Semantic Search is True with or without vectorize. Be sure to use a model that aligns with the dimensions of the embeddings already present in the collection.
Semantic Search Instruction	String	Input parameter. The query to use for similarity search. Default: "Find documents similar to the query.". This instruction is used to guide the model in performing semantic search.

Define tool-specific parameters

tip

Tool Parameters are small functions that you create within the Astra DB Tool component. They give the LLM pre-defined ways to interact with the data in your collection.

Without these filters, the LLM has no concept of the data in your collection or which attributes are important.

At runtime, the LLM can decide which filters are relevant to the current query.

Filters in Tool Parameters aren't always applied. If you want to enforce filters for every query, use the Static Filters parameter. You can use both Tool Parameters and Static Filters to set some required filters and some optional filters.

In the Astra DB Tool component's Tool Parameters field, you can create filters to query documents in your collection.

When used in Tool Mode with an agent, these filters tell the agent which document attributes are most important, which are required in searches, and which operators to use on certain attributes. The filters become available as parameters that the LLM can use when calling the tool, with a better understanding of each parameter provided by the Description field.

In the Tool Parameters pane, click Add a new row, and then edit each cell in the row. For example, the following filter allows an LLM to filter by unique customer_id values:

• Name: customer_id
• Attribute Name: Leave empty if the attribute matches the field name in the database.
• Description: "The unique identifier of the customer to filter by".
• Is Metadata: False unless the value stored in the metadata field.
• Is Mandatory: True to require this filter.
• Is Timestamp: For this example, select False because the value is an ID, not a timestamp.
• Operator: $eq to look for an exact match.

The following fields are available for each row in the Tool Parameters pane:

Parameter	Description
Name	The name of the parameter that is exposed to the LLM. It can be the same as the underlying field name or a more descriptive label. The LLM uses this name, along with the description, to infer what value to provide during execution.
Attribute Name	When the parameter name shown to the LLM differs from the actual field or property in the database, use this setting to map the user-facing name to the correct attribute. For example, to apply a range filter to the timestamp field, define two separate parameters, such as start_date and end_date, that both reference the same timestamp attribute.
Description	Provides instructions to the LLM on how the parameter should be used. Clear and specific guidance helps the LLM provide valid input. For example, if a field such as specialty is stored in lowercase, the description should indicate that the input must be lowercase.
Is Metadata	When loading data using LangChain or Langflow, additional attributes may be stored under a metadata object. If the target attribute is stored this way, enable this option. It adjusts the query by generating a filter in the format: {"metadata.<attribute_name>": "<value>"}
Is Timestamp	For date or time-based filters, enable this option to automatically convert values to the timestamp format that the Astrapy client expects. This ensures compatibility with the underlying API without requiring manual formatting.
Operator	Defines the filtering logic applied to the attribute. You can use any valid Data API filter operator. For example, to filter a time range on the timestamp attribute, use two parameters: one with the $gt operator for "greater than", and another with the $lt operator for "less than".

Cassandra Chat Memory

The Cassandra Chat Memory component retrieves and stores chat messages using an Apache Cassandra-based database, including Astra DB and Hyper-Converged Database (HCD).

Chat memories are passed between memory storage components as the Memory data type. Specifically, the component creates an instance of CassandraChatMessageHistory, which is a LangChain chat message history class that uses a Cassandra database for storage.

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

Cassandra Chat Memory parameters

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

Name	Type	Description
database_ref	MessageText	Input parameter. The contact points for the Cassandra database or Astra DB database ID. Required.
username	MessageText	Input parameter. The username for Cassandra. Leave empty for Astra DB.
token	SecretString	Input parameter. The password for Cassandra or the token for Astra DB. Required.
keyspace	MessageText	Input parameter. The keyspace in Cassandra or namespace in Astra DB. Required.
table_name	MessageText	Input parameter. The name of the table or collection for storing messages. Required.
session_id	MessageText	Input parameter. The unique identifier for the chat session. Optional.
cluster_kwargs	Dictionary	Input parameter. Additional keyword arguments for the Cassandra cluster configuration. Optional.

DataStax assistant components

The following DataStax components are used to create and manage Assistants API functions in a flow:

• Astra Assistant Agent
• Create Assistant
• Create Assistant Thread
• Get Assistant Name
• List Assistants
• Run Assistant

DataStax environment variable components

The following DataStax components are used to load and retrieve environment variables in a flow:

• Dotenv
• Get Environment Variable

Legacy DataStax components

The following components are considered legacy or deprecated. These components are no longer being developed and can be removed in future releases.

Replace them with the suggested alternatives as soon as possible.

Astra DB Vectorize

This component was deprecated in Langflow version 1.1.2. Replace it with the Astra DB vector store component as soon as possible.

The Astra DB Vectorize component was used to generate embeddings with Astra DB's vectorize feature in conjunction with an Astra DB vector store component.

The vectorize functionality is now built into the Astra DB vector store component. You no longer need a separate component for vectorize embedding generation.