Language Model

Language model components in Langflow generate text using a specified Large Language Model (LLM).

Langflow includes a core Language Model component that has built-in support for many LLMs, as well as an interface to connect any additional language model component. The built-in LLMs are appropriate for most text-based language model use cases in Langflow.

Use language model components in a flow

Use language model components anywhere you would use an LLM in a flow.

These components accept inputs like chat messages, files, and instructions in order to generate a text response. The flow must include Chat Input/Output component to allow chat-based interactions with the LLM. However, you can also use the Language Model component for actions that don't emit chat output directly, such as the Smart Function component.

The following example uses the core Language Model component and a built-in LLM to create a chatbot flow similar to the Basic Prompting template. The example focuses on using the built-in models, but it also indicates where you can integrate another model.

1. Add the Language Model component to your flow.

2. In the OpenAI API Key field, enter your OpenAI API key.

   This example uses the default OpenAI model and a built-in Anthropic model to compare responses from different providers.

   If you want to use a different provider, edit the Model Provider, Model Name, and API Key fields accordingly.

   If you want to use provider or model that isn't built-in to the Language Model component, see Additional language model components to learn how to connect a Custom model provider to the Language Model component. Then, you can continue following these steps to build your flow.

3. In the component's header menu, click Controls, enable the System Message parameter, and then click Close.

4. Add a Prompt Template component to your flow.

5. In the Template field, enter some instructions for the LLM, such as You are an expert in geography who is tutoring high school students.

6. Connect the Prompt Template component's output to the Language Model component's System Message input.

7. Add Chat Input and Chat Output components to your flow.

8. Connect the Chat Input component to the Language Model component's Input, and then connect the Language Model component's Message output to the Chat Output component.

   

9. Open the Playground, and ask a question to chat with the LLM and test the flow, such as What is the capital of Utah?.

   Result

   The following response is an example of an OpenAI model's response. Your actual response may vary based on the model version at the time of your request, your template, and input.

   
   

   _10

   The capital of Utah is Salt Lake City. It is not only the largest city in the state but also serves as the cultural and economic center of Utah. Salt Lake City was founded in 1847 by Mormon pioneers and is known for its proximity to the Great Salt Lake and its role in the history of the Church of Jesus Christ of Latter-day Saints. For more information, you can refer to sources such as the U.S. Geological Survey or the official state website of Utah.

   
   

10. Try a different model or provider to see how the response changes. For example:

    1. In the Language Model component, change the model provider to Anthropic.
    2. Select an Anthropic model, such as Claude 3.5 Haiku.
    3. Enter an Anthropic API key.

11. Open the Playground, ask the same question as you did before, and then compare the content and format of the responses.

    This helps you understand how different models handle the same request so you can choose the best model for your use case. You can also learn more about different models in each model provider's documentation.

    Result

    The following response is an example of an Anthropic model's response. Your actual response may vary based on the model version at the time of your request, your template, and input.

    Note that this response is shorter and includes sources, whereas the OpenAI response was more encyclopedic and didn't cite sources.

    
    

    _10

    The capital of Utah is Salt Lake City. It is also the most populous city in the state. Salt Lake City has been the capital of Utah since 1896, when Utah became a state.

    _10

    Sources:

    _10

    Utah State Government Official Website (utah.gov)

    _10

    U.S. Census Bureau

    _10

    Encyclopedia Britannica

    
    

Language Model parameters

Some Language Model 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
provider	String	Input parameter. The model provider to use.
model_name	String	Input parameter. The name of the model to use. Options depend on the selected provider.
api_key	SecretString	Input parameter. The API Key for authentication with the selected provider.
input_value	String	Input parameter. The input text to send to the model.
system_message	String	Input parameter. A system message that helps set the behavior of the assistant.
stream	Boolean	Input parameter. Whether to stream the response. Default: False.
temperature	Float	Input parameter. Controls randomness in responses. Range: [0.0, 1.0]. Default: 0.1.
model	LanguageModel	Output parameter. Alternative output type to the default Message output. Produces an instance of Chat configured with the specified parameters. See Language Model output types.

Language Model output types

Language Model components, including the core component and bundled components, can produce two types of output:

• Model Response: The default output type emits the model's generated response as Message data. Use this output type when you want the typical LLM interaction where the LLM produces a text response based on given input.

• Language Model: Change the Language Model component's output type to LanguageModel when you need to attach an LLM to another component in your flow. This is a specific data type that is only required by certain components, such as the Smart Function component.

  With this configuration, the Language Model component is meant to support an action completed by another component, rather than producing a text response for a standard chat-based interaction. For an example, the Smart Function component uses an LLM to create a function from natural language input.

Additional language model components

If your provider or model isn't supported by the core Language Model component, additional single-provider language model components are available in the Bundles section of the Components menu.

You can use bundled components directly in your flows or you can connect them to other components that accept a LanguageModel input, such as the Language Model and Agent components.

For example, to connect bundled components to the core Language Model component, do the following:

1. In the Language Model component, set Model Provider to Custom.

   The field name changes to Language Model and the input port changes to a LanguageModel port.

2. Add a compatible bundled component to your flow, such as the Vertex AI component for text generation.

3. Change the bundled component's output type to LanguageModel. To do this, click Model Response near the component's output port, and then select Language Model. For more information, see Language Model output types.

4. Connect the bundled component's output to the Language Model component's LanguageModel input port.

   The bundled component now provides the LLM configuration for the component that it is connected to, and you can continue building your flow as needed.