Helper components in Langflow

Helper components provide utility functions to help manage data, tasks, and other components in your flow.

Use a helper component in a flow

Chat memory in Langflow is stored either in local Langflow tables with LCBufferMemory, or connected to an external database.

The Store Message helper component stores chat memories as Data objects, and the Message History helper component retrieves chat messages as data objects or strings.

This example flow stores and retrieves chat history from an AstraDBChatMemory component with Store Message and Chat Memory components.

Batch Run

The Batch Run component runs a language model over each row of a DataFrame text column and returns a new DataFrame with the original text and an LLM response.

The response contains the following columns:

• text_input: The original text from the input DataFrame.
• model_response: The model's response for each input.
• batch_index: The processing order, with a 0-based index.
• metadata (optional): Additional information about the processing.

These columns, when connected to a Parser component, can be used as variables within curly braces.

To use the Batch Run component with a Parser component, do the following:

1. Connect a Model component to the Batch Run component's Language model port.
2. Connect a component that outputs DataFrame, like File component, to the Batch Run component's DataFrame input.
3. Connect the Batch Run component's Batch Results output to a Parser component's DataFrame input. The flow looks like this:

4. In the Column Name field of the Batch Run component, enter a column name based on the data you're loading from the File loader. For example, to process a column of name, enter name.
5. Optionally, in the System Message field of the Batch Run component, enter a System Message to instruct the connected LLM on how to process your file. For example, Create a business card for each name.
6. In the Template field of the Parser component, enter a template for using the Batch Run component's new DataFrame columns. To use all three columns from the Batch Run component, include them like this:

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record_number: {batch_index}, name: {text_input}, summary: {model_response}

7. To run the flow, in the Parser component, click .
8. To view your created DataFrame, in the Parser component, click .
9. Optionally, connect a Chat Output component, and open the Playground to see the output.

Inputs

Name	Display Name	Type	Info
model	Language Model	HandleInput	Connect the 'Language Model' output from your LLM component here. Required.
system_message	System Message	MultilineInput	Multi-line system instruction for all rows in the DataFrame.
df	DataFrame	DataFrameInput	The DataFrame whose column is treated as text messages, as specified by 'column_name'. Required.
column_name	Column Name	MessageTextInput	The name of the DataFrame column to treat as text messages. Default='text'. Required.
enable_metadata	Enable Metadata	BoolInput	If True, add metadata to the output DataFrame.

Outputs

Name	Display Name	Method	Info
batch_results	Batch Results	run_batch	A DataFrame with columns: 'text_input', 'model_response', 'batch_index', and optional 'metadata' containing processing information.

Current date

The Current Date component returns the current date and time in a selected timezone. This component provides a flexible way to obtain timezone-specific date and time information within a Langflow pipeline.

Inputs

Name	Display Name	Info
timezone	Timezone	Select the timezone for the current date and time.

Outputs

Name	Display Name	Info
current_date	Current Date	The resulting current date and time in the selected timezone.

ID Generator

This component generates a unique ID.

Inputs

Name	Display Name	Info
unique_id	Value	The generated unique ID.

Outputs

Name	Display Name	Info
id	ID	The generated unique ID.

Message history

info

Prior to Langflow 1.1, this component was known as the Chat Memory component.

This component retrieves chat messages from Langflow tables or external memory.

In this example, the Message Store component stores the complete chat history in a local Langflow table, which the Message History component retrieves as context for the LLM to answer each question.

For more information on configuring memory in Langflow, see Memory.

Inputs

Name	Display Name	Info
memory	External Memory	Retrieve messages from an external memory. If empty, it will use the Langflow tables.
sender	Sender Type	Filter by sender type.
sender_name	Sender Name	Filter by sender name.
n_messages	Number of Messages	Number of messages to retrieve.
session_id	Session ID	The session ID of the chat. If empty, the current session ID parameter will be used.
order	Order	Order of the messages.
template	Template	The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.

Outputs

Name	Display Name	Info
messages	Messages (Data)	Retrieved messages as Data objects.
messages_text	Messages (Text)	Retrieved messages formatted as text.
lc_memory	Memory	A constructed Langchain ConversationBufferMemory object

Message store

This component stores chat messages or text in Langflow tables or external memory.

In this example, the Message Store component stores the complete chat history in a local Langflow table, which the Message History component retrieves as context for the LLM to answer each question.

For more information on configuring memory in Langflow, see Memory.

Inputs

Name	Display Name	Info
message	Message	The chat message to be stored. (Required)
memory	External Memory	The external memory to store the message. If empty, it will use the Langflow tables.
sender	Sender	The sender of the message. Can be Machine or User. If empty, the current sender parameter will be used.
sender_name	Sender Name	The name of the sender. Can be AI or User. If empty, the current sender parameter will be used.
session_id	Session ID	The session ID of the chat. If empty, the current session ID parameter will be used.

Outputs

Name	Display Name	Info
stored_messages	Stored Messages	The list of stored messages after the current message has been added.

Structured output

This component transforms LLM responses into structured data formats.

In this example from the Financial Support Parser template, the Structured Output component transforms unstructured financial reports into structured data.

The connected LLM model is prompted by the Structured Output component's Format Instructions parameter to extract structured output from the unstructured text. Format Instructions is utilized as the system prompt for the Structured Output component.

In the Structured Output component, click the Open table button to view the Output Schema table. The Output Schema parameter defines the structure and data types for the model's output using a table with the following fields:

• Name: The name of the output field.
• Description: The purpose of the output field.
• Type: The data type of the output field. The available types are str, int, float, bool, list, or dict. The default is text.
• Multiple: This feature is deprecated. Currently, it is set to True by default if you expect multiple values for a single field. For example, a list of features is set to True to contain multiple values, such as ["waterproof", "durable", "lightweight"]. Default: True.

The Parse DataFrame component parses the structured output into a template for orderly presentation in chat output. The template receives the values from the output_schema table with curly braces.

For example, the template EBITDA: {EBITDA} , Net Income: {NET_INCOME} , GROSS_PROFIT: {GROSS_PROFIT} presents the extracted values in the Playground as EBITDA: 900 million , Net Income: 500 million , GROSS_PROFIT: 1.2 billion.

Inputs

Name	Display Name	Info
llm	Language Model	The language model to use to generate the structured output.
input_value	Input Message	The input message to the language model.
system_prompt	Format Instructions	Instructions to the language model for formatting the output.
schema_name	Schema Name	The name for the output data schema.
output_schema	Output Schema	Defines the structure and data types for the model's output.
multiple	Generate Multiple	[Deprecated] Always set to True.

Outputs

Name	Display Name	Info
structured_output	Structured Output	The structured output is a Data object based on the defined schema.
structured_output_dataframe	DataFrame	The structured output converted to a DataFrame format.

Legacy components

Legacy components are available for use but are no longer supported.

Create List

This component dynamically creates a record with a specified number of fields.

Inputs

Name	Display Name	Info
n_fields	Number of Fields	Number of fields to be added to the record.
text_key	Text Key	Key used as text.

Outputs

Name	Display Name	Info
list	List	The dynamically created list with the specified number of fields.

Output Parser

This component transforms the output of a language model into a specified format. It supports CSV format parsing, which converts LLM responses into comma-separated lists using Langchain's CommaSeparatedListOutputParser.

note

This component only provides formatting instructions and parsing functionality. It does not include a prompt. You'll need to connect it to a separate Prompt component to create the actual prompt template for the LLM to use.

Both the Output Parser and Structured Output components format LLM responses, but they have different use cases. The Output Parser is simpler and focused on converting responses into comma-separated lists. Use this when you just need a list of items, for example ["item1", "item2", "item3"]. The Structured Output is more complex and flexible, and allows you to define custom schemas with multiple fields of different types. Use this when you need to extract structured data with specific fields and types.

To use this component:

1. Create a Prompt component and connect the Output Parser's format_instructions output to it. This ensures the LLM knows how to format its response.
2. Write your actual prompt text in the Prompt component, including the {format_instructions} variable. For example, in your Prompt component, the template might look like:

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{format_instructions}

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Please list three fruits.

3. Connect the output_parser output to your LLM model.

4. The output parser converts this into a Python list: ["apple", "banana", "orange"].

Inputs

Name	Display Name	Info
parser_type	Parser	Select the parser type. Currently supports "CSV".

Outputs

Name	Display Name	Info
format_instructions	Format Instructions	Pass to a prompt template to include formatting instructions for LLM responses.
output_parser	Output Parser	The constructed output parser that can be used to parse LLM responses.