Language Model

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Language Model components in Robility flow generate text using a specified Large Language Model (LLM).

Robility flow includes a Language Model core component that has built-in support for many LLMs. Alternatively, you can use any additional language model in place of the core Language Model component.

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 and Output components 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 Language Model core component to create a chatbot flow like the Basic Prompting template. It also explains how you can replace the core component with another LLM.

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.

My preferred provider or model isn't listed If you want to use a provider or model that isn't built-in to the Language Model core component, you can replace this component with another compatible component, as explained in Additional language models. Then, you can continue following these steps to build your flow.

1. In the component’s header menu, click Controls, enable the System Message parameter, and then click Close.
2. Add a Prompt Template component to your flow.
3. In the Template field, enter some instructions for the LLM, such as You are an expert in geography who is tutoring high school students.
4. Connect the Prompt Template component’s output to the Language Model component’s System Message input.
5. Add Chat Input and Chat Output components to your flow.
6. 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.

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:

a. In the Language Model component, change the model provider to Anthropic.
b. Select an Anthropic model, such as Claude 3.5 Haiku.
c. 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.

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.

Sources:

Utah State Government Official Website (utah.gov)

U.S. Census Bureau

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:

a. 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.

b. 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 example, the Smart Function component uses an LLM to create a function from natural language input.

Additional language models

If your provider or model isn’t supported by the Language Model core component, additional provider-specific models are available in the Bundles section of the Components menu.

You can use these provider-specific components directly in your flows in the same place that you would use the Language Model core component. Or you can connect them to other components that accept a LanguageModel input, such as the Smart Function and Agent components.

For example, to connect a provider-specific component to the Agent component, do the following:

1. In the Components menu, search for your preferred model provider, and then add the provider’s LLM component to your flow. The component may not have model in the name. For example, Azure OpenAI LLMs are in the Azure OpenAI component.
2. Configure the LLM component as needed to connect to your preferred model.
3. Change the LLM component’s output type from Model Response to Language Model. The output port changes to a LanguageModel port. For more information, see Language Model output types.
4. Add an Agent component to the flow and then set Model Provider to Custom. The Model Provider field changes to a Language Model field with a LanguageModel port.
5. Connect the LLM component’s output to the Agent component’s Language Model input. The Agent component now inherits the LLM settings from the connected LLM component instead of using any of the built-in models.

Language Model

Use Language Model components in a flow

Language Model parameters

Language Model output types

Additional language models

Language Model

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