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Langflow objects

In Langflow, the Data and Message objects are Pydantic models that serve as structured, functional representations of data.

Data object​

The Data object is a Pydantic model that serves as a container for storing and manipulating data. It carries data—a dictionary that can be accessed as attributes—and uses text_key to specify which key in the dictionary should be considered the primary text content.

  • Main Attributes:
    • text_key: Specifies the key to retrieve the primary text data.
    • data: A dictionary to store additional data.
    • default_value: default value when the text_key is not present in the data dictionary.

Create a Data Object​

Create a Data object by directly assigning key-value pairs to it. For example:


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from langflow.schema import Data
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# Creating a Data object with specified key-value pairs
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data = Data(text="my_string", bar=3, foo="another_string")
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# Outputs:
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print(data.text) # Outputs: "my_string"
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print(data.bar) # Outputs: 3
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print(data.foo) # Outputs: "another_string"

The text_key specifies which key in the data dictionary should be considered the primary text content. The default_value provides a fallback if the text_key is not present.


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# Creating a Data object with a specific text_key and default_value
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data = Data(data={"title": "Hello, World!"}, text_key="content", default_value="No content available")
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# Accessing the primary text using text_key and default_value
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print(data.get_text()) # Outputs: "No content available" because "content" key is not in the data dictionary
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# Accessing data keys by calling the attribute directly
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print(data.title) # Outputs: "Hello, World!" because "title" key is in the data dictionary

The Data object is also convenient for visualization of outputs, since the output preview has visual elements to inspect data as a table and its cells as pop ups for basic types. The idea is to create a unified way to work and visualize complex information in Langflow.

To receive Data objects in a component input, use the DataInput input type.


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inputs = [
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DataInput(name="data", display_name="Data", info="Helpful info about the incoming data object.", is_list=True),
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]

Message object​

The Message object extends the functionality of Data and includes additional attributes and methods for chat interactions.

  • Core message data:

    • text: The main text content of the message
    • sender: Identifier for the sender ("User" or "AI")
    • sender_name: Name of the sender
    • session_id: Identifier for the chat session (string or UUID)
    • timestamp: Timestamp when the message was created (UTC)
    • flow_id: Identifier for the flow (string or UUID)
    • id: Unique identifier for the message

  • Content and files:

    • files: List of files or images associated with the message
    • content_blocks: List of structured content block objects
    • properties: Additional properties including visual styling and source information

  • Message state:

    • error: Boolean indicating if there was an error
    • edit: Boolean indicating if the message was edited
    • category: Message category ("message", "error", "warning", "info")

The Message object can be used to send, store, and manipulate chat messages within Langflow.

Create a Message object​

You can create a Message object by directly assigning key-value pairs to it. For example:


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from langflow.schema.message import Message
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message = Message(text="Hello, AI!", sender="User", sender_name="John Doe")

To receive Message objects in a component input, you can use the MessageInput input type or MessageTextInput when the goal is to extract just the text field of the Message object.

ContentBlock object​

The ContentBlock object is a list of multiple ContentTypes. It allows you to include multiple types of content within a single Message, including images, videos, and text.

Content types are Pydantic base classes constructed from the types in content_types.py.

Each content type has specific fields related to its data type. For example:

  • TextContent has a text field for storing strings of text
  • MediaContent has a urls field for storing media file URLs
  • CodeContent has code and language fields for code snippets
  • JSONContent has a data field for storing arbitrary JSON data
  • ToolContent has a tool_input field for storing input parameters for the tool

Create a ContentBlock object​

Create a ContentBlock object with a list of different content types.


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content_block = ContentBlock(
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title="Mixed Content Example",
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contents=[
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TextContent(text="This is a text content"),
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MediaContent(urls=["http://example.com/image.jpg"]),
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JSONContent(data={"key": "value"}),
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CodeContent(code="print('Hello')", language="python")
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],
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media_url=["http://example.com/additional_image.jpg"]
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)

Add ContentBlocks objects to a message​

In this example, a text and a media ContentBlock are added to a message.


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from langflow.schema.message import Message
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from langflow.schema.content_block import ContentBlock
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from langflow.schema.content_types import TextContent, MediaContent
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message = Message(
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text="Main message text",
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sender="User",
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sender_name="John Doe",
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content_blocks=[
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ContentBlock(
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title="Text Block",
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contents=[
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TextContent(type="text", text="This is some text content")
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]
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),
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ContentBlock(
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title="Media Block",
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contents=[
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MediaContent(type="media", urls=["http://example.com/image.jpg"])
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]
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)
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]
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)

DataFrame object​

The DataFrame class is a custom extension of the Pandas DataFrame class, specifically designed to work seamlessly with Langflow's Data objects. The class includes methods for converting between DataFrame and lists of Data objects.

A DataFrame object accepts various input formats, including lists of Data objects, dictionaries, and existing DataFrames.

Create a DataFrame object​

You can create a DataFrame object using different data formats:


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from langflow.schema import Data
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from langflow.schema.data import DataFrame
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# From a list of Data objects
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data_list = [Data(data={"name": "John"}), Data(data={"name": "Jane"})]
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df = DataFrame(data_list)
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# From a list of dictionaries
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dict_list = [{"name": "John"}, {"name": "Jane"}]
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df = DataFrame(dict_list)
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# From a dictionary of lists
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data_dict = {"name": ["John", "Jane"], "age": [30, 25]}
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df = DataFrame(data_dict)
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Key Methods
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to_data_list(): Converts the DataFrame back to a list of Data objects.
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add_row(data): Adds a single row (either a Data object or a dictionary) to the DataFrame.
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add_rows(data): Adds multiple rows (list of Data objects or dictionaries) to the DataFrame.
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Usage Example
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python
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# Create a DataFrame
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df = DataFrame([Data(data={"name": "John"}), Data(data={"name": "Jane"})])
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# Add a new row
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df = df.add_row({"name": "Alice"})
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# Convert back to a list of Data objects
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data_list = df.to_data_list()
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# Use pandas functionality
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filtered_df = df[df["name"].str.startswith("J")]

To use DataFrame objects in a component input,use the DataFrameInput input type.


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DataFrameInput(
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name="dataframe_input", display_name="DataFrame Input", info="Input for DataFrame objects.", tool_mode=True
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),

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