langflow/docs/docs/Components/components-helpers.md
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Co-authored-by: Edwin Jose <edwin.jose@datastax.com>

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---------

Co-authored-by: Edwin Jose <edwin.jose@datastax.com>
Co-authored-by: KimberlyFields <46325568+KimberlyFields@users.noreply.github.com>
2025-02-25 23:08:18 +00:00

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Helpers /components-helpers

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.

Sample Flow storing Chat Memory in AstraDB

Batch Run Component

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 the model's response.

Inputs

Name Display Name Type Info Required
model Language Model HandleInput Connect the 'Language Model' output from your LLM component here. Yes
system_message System Message MultilineInput Multi-line system instruction for all rows in the DataFrame. No
df DataFrame DataFrameInput The DataFrame whose column (specified by 'column_name') will be treated as text messages. Yes
column_name Column Name StrInput The name of the DataFrame column to treat as text messages. Default='text'. Yes

Outputs

Name Display Name Method Info
batch_results Batch Results run_batch A DataFrame with two columns: 'text_input' and 'model_response'.

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.

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 and manages chat messages from Langflow tables or an external 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 into Langflow tables or an external memory.

It provides flexibility in managing message storage and retrieval within a chat system.

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.

Structured output example

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.