Model components in LLM Controls

Model components generate text using large language models. Refer to your specific component’s documentation for more information on parameters.

Use a model component in a flow

Model components receive inputs and prompts for generating text, and the generated text is sent to an output component. The model output can also be sent to the Language Model port and on to a Parse Data component, where the output can be parsed into structured Data objects. This example has the OpenAI model in a chatbot flow.

LLMC Executor

The LLMC Executor is a user-friendly interface designed to streamline interactions with AI language models, such as those from OpenAI. It plays a central role in allowing users to experiment with different prompts and models to generate high-quality AI responses. By integrating with API keys, the executor enables seamless communication with external AI services. Users can effortlessly select prompts , either manually or through integration with a Prompt Optimizer Flow and choose from available AI models using intuitive dropdown menus. Once configured, the tool provides a clean and efficient workflow where users input text, trigger the request with a single click, and receive intelligent outputs in real-time. This makes LLMC Executor an essential component for testing, optimizing, and comparing AI-generated responses across various configurations.

Key usage highlights

Prompt & Model Selection: Choose from a set of predefined prompts and models using dropdowns. Your selections are automatically displayed for clarity.
Secure API Access: Enter your API key to enable access to the chosen model. This is required for the tool to work.

AIML

This component creates a ChatOpenAI model instance using the AIML API. For more information, see AIML documentation.

Parameters

Inputs

Name	Type	Description
max_tokens	Integer	The maximum number of tokens to generate. Set to 0 for unlimited tokens. Range: 0-128000.
model_kwargs	Dictionary	Additional keyword arguments for the model.
model_name	String	The name of the AIML model to use. Options are predefined in `AIML_CHAT_MODELS`.
aiml_api_base	String	The base URL of the AIML API. Defaults to `https://api.aimlapi.com`.
api_key	SecretString	The AIML API Key to use for the model.
temperature	Float	Controls randomness in the output. Default: `0.1`.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatOpenAI configured with the specified parameters.

Amazon Bedrock

This component generates text using Amazon Bedrock LLMs. For more information, see Amazon Bedrock documentation.

Parameters

Inputs

Name	Type	Description
model_id	String	The ID of the Amazon Bedrock model to use. Options include various models.
aws_access_key	SecretString	AWS Access Key for authentication.
aws_secret_key	SecretString	AWS Secret Key for authentication.
aws_session_token	SecretString	The session key for your AWS account.
credentials_profile_name	String	Name of the AWS credentials profile to use.
region_name	String	AWS region name. Default: `us-east-1`.
model_kwargs	Dictionary	Additional keyword arguments for the model.
endpoint_url	String	Custom endpoint URL for the Bedrock service.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatBedrock configured with the specified parameters.

Anthropic

This component allows the generation of text using Anthropic Chat and Language models. For more information, see the Anthropic documentation.

Parameters

Inputs

Name	Type	Description
max_tokens	Integer	The maximum number of tokens to generate. Set to 0 for unlimited tokens. Default: `4096`.
model	String	The name of the Anthropic model to use. Options include various Claude 3 models.
anthropic_api_key	SecretString	Your Anthropic API key for authentication.
temperature	Float	Controls randomness in the output. Default: `0.1`.
anthropic_api_url	String	Endpoint of the Anthropic API. Defaults to `https://api.anthropic.com` if not specified (advanced).
prefill	String	Prefill text to guide the model’s response (advanced).

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatAnthropic configured with the specified parameters.

Azure OpenAI

This component generates text using Azure OpenAI LLM. For more information, see the Azure OpenAI documentation.

Parameters

Inputs

Name	Type	Description
Model Name	String	Specifies the name of the Azure OpenAI model to be used for text generation.
Azure Endpoint	String	Your Azure endpoint, including the resource.
Deployment Name	String	Specifies the name of the deployment.
API Version	String	Specifies the version of the Azure OpenAI API to be used.
API Key	SecretString	Your Azure OpenAI API key.
Temperature	Float	Specifies the sampling temperature. Defaults to `0.7`.
Max Tokens	Integer	Specifies the maximum number of tokens to generate. Defaults to `1000`.
Input Value	String	Specifies the input text for text generation.
Stream	Boolean	Specifies whether to stream the response from the model. Defaults to `False`.

Outputs

Name	Type	Description
model	LanguageModel	An instance of AzureOpenAI configured with the specified parameters.

Cohere

This component generates text using Cohere’s language models. For more information, see the Cohere documentation.

Parameters

Inputs

Name	Type	Description
Cohere API Key	SecretString	Your Cohere API key.
Max Tokens	Integer	Specifies the maximum number of tokens to generate. Defaults to `256`.
Temperature	Float	Specifies the sampling temperature. Defaults to `0.75`.
Input Value	String	Specifies the input text for text generation.

Outputs

Name	Type	Description
model	LanguageModel	An instance of the Cohere model configured with the specified parameters.

DeepSeek

This component generates text using DeepSeek’s language models. For more information, see the DeepSeek documentation.

Parameters

Inputs

Name	Type	Description
max_tokens	Integer	Maximum number of tokens to generate. Set to `0` for unlimited. Range: `0-128000`.
model_kwargs	Dictionary	Additional keyword arguments for the model.
json_mode	Boolean	If `True`, outputs JSON regardless of passing a schema.
model_name	String	The DeepSeek model to use. Default: `deepseek-chat`.
api_base	String	Base URL for API requests. Default: `https://api.deepseek.com`.
api_key	SecretString	Your DeepSeek API key for authentication.
temperature	Float	Controls randomness in responses. Range: `[0.0, 2.0]`. Default: `1.0`.
seed	Integer	Number initialized for random number generation. Use the same seed integer for more reproducible results, and use a different seed number for more random results.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatOpenAI configured with the specified parameters.

Google Generative AI

This component generates text using Google’s Generative AI models. For more information, see the Google Generative AI documentation.

Parameters

Inputs

Name	Type	Description
Google API Key	SecretString	Your Google API key to use for the Google Generative AI.
Model	String	The name of the model to use, such as `"gemini-pro"`.
Max Output Tokens	Integer	The maximum number of tokens to generate.
Temperature	Float	Run inference with this temperature.
Top K	Integer	Consider the set of top K most probable tokens.
Top P	Float	The maximum cumulative probability of tokens to consider when sampling.
N	Integer	Number of chat completions to generate for each prompt.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatGoogleGenerativeAI configured with the specified parameters.

Groq

This component generates text using Groq’s language models.

To use this component in a flow, connect it as a Model in a flow like the Basic prompting flow, or select it as the Model Provider if you’re using an Agent component.
In the Groq API Key field, paste your Groq API key. The Groq model component automatically retrieves a list of the latest models. To refresh your list of models, click Refresh.
In the Model field, select the model you want to use for your LLM.
Click Playground and ask your Groq LLM a question. The responses include a list of sources.

For more information, see the Groq documentation.

Parameters

Inputs

Name	Type	Description
groq_api_key	SecretString	API key for the Groq API.
groq_api_base	String	Base URL path for API requests. Default: `https://api.groq.com`.
max_tokens	Integer	The maximum number of tokens to generate.
temperature	Float	Controls randomness in the output. Range: `[0.0, 1.0]`. Default: `0.1`.
n	Integer	Number of chat completions to generate for each prompt.
model_name	String	The name of the Groq model to use. Options are dynamically fetched from the Groq API.
tool_mode_enabled	Bool	If enabled, the component only displays models that work with tools.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatGroq configured with the specified parameters.

Hugging Face API

This component sends requests to the Hugging Face API to generate text using the model specified in the Model ID field. The Hugging Face API is a hosted inference API for models hosted on Hugging Face, and requires a Hugging Face API token to authenticate.

from llmc.components.inputs import ChatInput
from llmc.components.models import HuggingFaceEndpointsComponent
from llmc.components.outputs import ChatOutput
from llmc.components.prompts import PromptComponent
from llmc.graph import Graph

def basic_prompting_with_huggingface():
    # Create chat input
    chat_input = ChatInput()
    
    # Create prompt component
    prompt_component = PromptComponent()
    prompt_component.set(
        template="Answer the user as if you were a GenAI expert.\n\nUser: {user_input}\n\nAnswer:",
        user_input=chat_input.message_response,
    )
    
    # Replace OpenAI with Hugging Face API model
    huggingface_component = HuggingFaceEndpointsComponent()
    # Set your Hugging Face API token in the huggingfacehub_api_token field
    # Select a model from the Model ID dropdown (e.g., "meta-llama/Llama-3.3-70B-Instruct")
    huggingface_component.set(input_value=prompt_component.build_prompt)
    
    # Create chat output
    chat_output = ChatOutput()
    chat_output.set(input_value=huggingface_component.text_response)
    
    graph = Graph(start=chat_input, end=chat_output)
    return graph

In this example based on the Basic prompting flow, the Hugging Face API model component replaces the Open AI model. By selecting different hosted models, you can see how different models return different results.

Steps to use Hugging Face API in Basic prompting flow:

Create a Basic prompting flow.
Replace the OpenAI model component with a Hugging Face API model component.
In the Hugging Face API component, add your Hugging Face API token to the API Token field.
Select a model from the Model ID dropdown (e.g., meta-llama/Llama-3.3-70B-Instruct).
Open the Playground and ask a question to the model, and see how it responds.
Try different models, and see how they perform differently.

For more information, see the Hugging Face documentation.

Parameters

Inputs

Name	Type	Description
model_id	String	The model ID from Hugging Face Hub. For example, “gpt2”, “facebook/bart-large”.
huggingfacehub_api_token	SecretString	Your Hugging Face API token for authentication.
temperature	Float	Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.7.
max_new_tokens	Integer	Maximum number of tokens to generate. Default: 512.
top_p	Float	Nucleus sampling parameter. Range: [0.0, 1.0]. Default: 0.95.
top_k	Integer	Top-k sampling parameter. Default: 50.
model_kwargs	Dictionary	Additional keyword arguments to pass to the model.

Outputs

Name	Type	Description
model	LanguageModel	An instance of HuggingFaceHub configured with the specified parameters.

IBM watsonx.ai

This component generates text using IBM WatsonX.ai foundation models. To use IBM watsonx.ai model components, replace a model component with the IBM watsonx.ai component in a flow. An example flow looks like the following:

The values for API endpoint, Project ID, API key, and Model Name are found in your IBM watsonx.ai deployment. For more information, see the Langchain documentation.

Parameters

Inputs

Name	Type	Description
url	String	The base URL of the watsonx API.
project_id	String	Your watsonx Project ID.
api_key	SecretString	Your IBM watsonx API Key.
model_name	String	The name of the watsonx model to use. Options are dynamically fetched from the API.
max_tokens	Integer	The maximum number of tokens to generate. Default: `1000`.
stop_sequence	String	The sequence where generation should stop.
temperature	Float	Controls randomness in the output. Default: `0.1`.
top_p	Float	Controls nucleus sampling, which limits the model to tokens whose probability is below the `top_p` value. Range: Default: `0.9`.
frequency_penalty	Float	Controls frequency penalty. A positive value decreases the probability of repeating tokens, and a negative value increases the probability. Range: Default: `0.5`.
presence_penalty	Float	Controls presence penalty. A positive value increases the likelihood of new topics being introduced. Default: `0.3`.
seed	Integer	A random seed for the model. Default: `8`.
logprobs	Boolean	Whether to return log probabilities of output tokens or not. Default: `True`.
top_logprobs	Integer	The number of most likely tokens to return at each position. Default: `3`.
logit_bias	String	A JSON string of token IDs to bias or suppress.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatWatsonx configured with the specified parameters.

Language model

This component generates text using either OpenAI or Anthropic language models. Use this component as a drop-in replacement for LLM models to switch between different model providers and models. Instead of swapping out model components when you want to try a different provider, like switching between OpenAI and Anthropic components, change the provider dropdown in this single component. This makes it easier to experiment with and compare different models while keeping the rest of your flow intact. For more information, see the OpenAI documentation and Anthropic documentation.

Parameters

Inputs

Name	Type	Description
provider	String	The model provider to use. Options: “OpenAI”, “Anthropic”. Default: “OpenAI”.
model_name	String	The name of the model to use. Options depend on the selected provider.
api_key	SecretString	The API Key for authentication with the selected provider.
input_value	String	The input text to send to the model.
system_message	String	A system message that helps set the behavior of the assistant (advanced).
stream	Boolean	Whether to stream the response. Default: `False` (advanced).
temperature	Float	Controls randomness in responses. Range: `[0.0, 1.0]`. Default: `0.1` (advanced).

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatOpenAI or ChatAnthropic configured with the specified parameters.

LMStudio

This component generates text using LM Studio’s local language models. For more information, see LM Studio documentation.

Parameters

Inputs

Name	Type	Description
base_url	String	The URL where LM Studio is running. Default: `"http://localhost:1234"`.
max_tokens	Integer	Maximum number of tokens to generate in the response. Default: `512`.
temperature	Float	Controls randomness in the output. Range: `[0.0, 2.0]`. Default: `0.7`.
top_p	Float	Controls diversity via nucleus sampling. Range: `[0.0, 1.0]`. Default: `1.0`.
stop	List[String]	List of strings that stop generation when encountered.
stream	Boolean	Whether to stream the response. Default: `False`.
presence_penalty	Float	Penalizes repeated tokens. Range: `[-2.0, 2.0]`. Default: `0.0`.
frequency_penalty	Float	Penalizes frequent tokens. Range: `[-2.0, 2.0]`. Default: `0.0`.

Outputs

Name	Type	Description
model	LanguageModel	An instance of LMStudio configured with the specified parameters.

Maritalk

This component generates text using Maritalk LLMs. For more information, see Maritalk documentation.

Parameters

Inputs

Name	Type	Description
max_tokens	Integer	The maximum number of tokens to generate. Set to `0` for unlimited tokens. Default: `512`.
model_name	String	The name of the Maritalk model to use. Options: `sabia-2-small`, `sabia-2-medium`. Default: `sabia-2-small`.
api_key	SecretString	The Maritalk API Key to use for authentication.
temperature	Float	Controls randomness in the output. Range: `[0.0, 1.0]`. Default: `0.5`.
endpoint_url	String	The Maritalk API endpoint. Default: `https://api.maritalk.com`.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatMaritalk configured with the specified parameters.

Mistral

This component generates text using MistralAI LLMs. For more information, see Mistral AI documentation.

Parameters

Inputs

Name	Type	Description
max_tokens	Integer	The maximum number of tokens to generate. Set to 0 for unlimited tokens (advanced).
model_name	String	The name of the Mistral AI model to use. Options include `open-mixtral-8x7b`, `open-mixtral-8x22b`, `mistral-small-latest`, `mistral-medium-latest`, `mistral-large-latest`, and `codestral-latest`. Default: `codestral-latest`.
mistral_api_base	String	The base URL of the Mistral API. Defaults to `https://api.mistral.ai/v1` (advanced).
api_key	SecretString	The Mistral API Key to use for authentication.
temperature	Float	Controls randomness in the output. Default: 0.5.
max_retries	Integer	Maximum number of retries for API calls. Default: 5 (advanced).
timeout	Integer	Timeout for API calls in seconds. Default: 60 (advanced).
max_concurrent_requests	Integer	Maximum number of concurrent API requests. Default: 3 (advanced).
top_p	Float	Nucleus sampling parameter. Default: 1 (advanced).
random_seed	Integer	Seed for random number generation. Default: 1 (advanced).
safe_mode	Boolean	Enables safe mode for content generation (advanced).

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatMistralAI configured with the specified parameters.

Novita AI

This component generates text using Novita AI’s language models. For more information, see Novita AI documentation.

Parameters

Inputs

Name	Type	Description
api_key	SecretString	Your Novita AI API Key.
model	String	The id of the Novita AI model to use.
max_tokens	Integer	The maximum number of tokens to generate. Set to 0 for unlimited tokens.
temperature	Float	Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.7.
top_p	Float	Controls the nucleus sampling. Range: [0.0, 1.0]. Default: 1.0.
frequency_penalty	Float	Controls the frequency penalty. Range: [0.0, 2.0]. Default: 0.0.
presence_penalty	Float	Controls the presence penalty. Range: [0.0, 2.0]. Default: 0.0.

Outputs

Name	Type	Description
model	LanguageModel	An instance of Novita AI model configured with the specified parameters.

NVIDIA

This component generates text using NVIDIA LLMs. For more information, see NVIDIA AI documentation.

Parameters

Inputs

Name	Type	Description
max_tokens	Integer	The maximum number of tokens to generate. Set to `0` for unlimited tokens (advanced).
model_name	String	The name of the NVIDIA model to use. Default: `mistralai/mixtral-8x7b-instruct-v0.1`.
base_url	String	The base URL of the NVIDIA API. Default: `https://integrate.api.nvidia.com/v1`.
nvidia_api_key	SecretString	The NVIDIA API Key for authentication.
temperature	Float	Controls randomness in the output. Default: `0.1`.
seed	Integer	The seed controls the reproducibility of the job (advanced). Default: `1`.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatNVIDIA configured with the specified parameters.

Ollama

This component generates text using Ollama’s language models. To use this component in a flow, connect LLM Controls to your locally running Ollama server and select a model.

In the Ollama component, in the Base URL field, enter the address for your locally running Ollama server. This value is set as the OLLAMA_HOST environment variable in Ollama. The default base URL is http://localhost:11434.
To refresh the server’s list of models, click Refresh.
In the Model Name field, select a model. This example uses llama3.2:latest.
Connect the Ollama model component to a flow. For example, this flow connects a local Ollama server running a Llama 3.2 model as the custom model for an Agent component.

For more information, see the Ollama documentation.

Parameters

Inputs

Name	Type	Description
Base URL	String	Endpoint of the Ollama API.
Model Name	String	The model name to use.
Temperature	Float	Controls the creativity of model responses.

Outputs

Name	Type	Description
model	LanguageModel	An instance of an Ollama model configured with the specified parameters.

OpenAI

This component generates text using OpenAI’s language models. For more information, see OpenAI documentation.

Parameters

Inputs

Name	Type	Description
api_key	SecretString	Your OpenAI API Key.
model	String	The name of the OpenAI model to use. Options include “gpt-3.5-turbo” and “gpt-4”.
max_tokens	Integer	The maximum number of tokens to generate. Set to 0 for unlimited tokens.
temperature	Float	Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.7.
top_p	Float	Controls the nucleus sampling. Range: [0.0, 1.0]. Default: 1.0.
frequency_penalty	Float	Controls the frequency penalty. Range: [0.0, 2.0]. Default: 0.0.
presence_penalty	Float	Controls the presence penalty. Range: [0.0, 2.0]. Default: 0.0.

Outputs

Name	Type	Description
model	LanguageModel	An instance of OpenAI model configured with the specified parameters.

OpenRouter

This component generates text using OpenRouter’s unified API for multiple AI models from different providers. For more information, see OpenRouter documentation.

Parameters

Inputs

Name	Type	Description
api_key	SecretString	Your OpenRouter API key for authentication.
site_url	String	Your site URL for OpenRouter rankings (advanced).
app_name	String	Your app name for OpenRouter rankings (advanced).
provider	String	The AI model provider to use.
model_name	String	The specific model to use for chat completion.
temperature	Float	Controls randomness in the output. Range: [0.0, 2.0]. Default: 0.7.
max_tokens	Integer	The maximum number of tokens to generate (advanced).

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatOpenAI configured with the specified parameters.

Perplexity

This component generates text using Perplexity’s language models. For more information, see Perplexity documentation.

Parameters

Inputs

Name	Type	Description
model_name	String	The name of the Perplexity model to use. Options include various Llama 3.1 models.
max_output_tokens	Integer	The maximum number of tokens to generate.
api_key	SecretString	The Perplexity API Key for authentication.
temperature	Float	Controls randomness in the output. Default: 0.75.
top_p	Float	The maximum cumulative probability of tokens to consider when sampling (advanced).
n	Integer	Number of chat completions to generate for each prompt (advanced).
top_k	Integer	Number of top tokens to consider for top-k sampling. Must be positive (advanced).

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatPerplexity configured with the specified parameters.

Qianfan

This component generates text using Qianfan’s language models. For more information, see Qianfan documentation.

Parameters

Inputs

Name	Display Name	Info
input_value	Input	(inherited from base)
system_message	System Message	System message to pass to the model.
stream	Stream	(inherited from base, advanced)
model	Model Name	Options: ERNIE-4.0-8K, ERNIE-3.5-8K, ERNIE-Bot, ERNIE-Bot-4, ERNIE-Bot-turbo-AI, ERNIE-Speed, ERNIE-Speed-8K, ERNIE-Speed-128k, ERNIE-Lite-8K-0308, Llama-2-70b-chat, Llama-2-13b-chat, Llama-2-7b-chat, Mixtral-8x7B-Instruct, Yi-34B-Chat, ChatGLM2-6B-32K, BLOOMZ-7B, and more.
qianfan_ak	Qianfan Ak	Which you could get from https://cloud.baidu.com/product/wenxinworkshop
qianfan_sk	Qianfan Sk	Which you could get from https://cloud.baidu.com/product/wenxinworkshop
top_p	Top p	Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. Default: 0.8.
temperature	Temperature	Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. Default: 0.95.
penalty_score	Penalty Score	Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. Default: 1.0.
endpoint	Endpoint	Endpoint of the Qianfan LLM, required if custom model used.

Outputs

Name	Display Name	Info
text_output	Message	Model response as a Message object.
model_output	Language Model	An instance of Qianfan model configured with the specified parameters.

SambaNova

This component generates text using SambaNova LLMs. For more information, see Sambanova Cloud documentation.

Parameters

Inputs

Name	Type	Description
sambanova_url	String	Base URL path for API requests. Default: `https://api.sambanova.ai/v1/chat/completions`.
sambanova_api_key	SecretString	Your SambaNova API Key.
model_name	String	The name of the Sambanova model to use. Options include various Llama models.
max_tokens	Integer	The maximum number of tokens to generate. Set to 0 for unlimited tokens.
temperature	Float	Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.07.

Outputs

Name	Type	Description
model	LanguageModel	An instance of SambaNova model configured with the specified parameters.

VertexAI

This component generates text using Vertex AI LLMs. For more information, see Google Vertex AI documentation.

Parameters

Inputs

Name	Type	Description
credentials	File	JSON credentials file. Leave empty to fall back to environment variables. File type: JSON.
model_name	String	The name of the Vertex AI model to use. Default: “gemini-1.5-pro”.
project	String	The project ID (advanced).
location	String	The location for the Vertex AI API. Default: “us-central1” (advanced).
max_output_tokens	Integer	The maximum number of tokens to generate (advanced).
max_retries	Integer	Maximum number of retries for API calls. Default: 1 (advanced).
temperature	Float	Controls randomness in the output. Default: 0.0.
top_k	Integer	The number of highest probability vocabulary tokens to keep for top-k-filtering (advanced).
top_p	Float	The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Default: 0.95 (advanced).
verbose	Boolean	Whether to print verbose output. Default: False (advanced).

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatVertexAI configured with the specified parameters.

xAI

This component generates text using xAI models like Grok. For more information, see the xAI documentation.

Parameters

Inputs

Name	Type	Description
max_tokens	Integer	Maximum number of tokens to generate. Set to `0` for unlimited. Range: `0-128000`.
model_kwargs	Dictionary	Additional keyword arguments for the model.
json_mode	Boolean	If `True`, outputs JSON regardless of passing a schema.
model_name	String	The xAI model to use. Default: `grok-2-latest`.
base_url	String	Base URL for API requests. Default: `https://api.x.ai/v1`.
api_key	SecretString	Your xAI API key for authentication.
temperature	Float	Controls randomness in the output. Range: `[0.0, 2.0]`. Default: `0.1`.
seed	Integer	Controls reproducibility of the job.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatOpenAI configured with the specified parameters.

DALL-E 3

This component generates images using OpenAI’s DALL-E 3 model. It accepts a text prompt and returns the generated image as a URL, base64-encoded data, or a Message with generation details.

Parameters

Inputs

Name	Display Name	Info
prompt	Prompt	A text description of the desired image(s).
model	Model	The model to use for image generation.
size	Size	The size of the generated images.
quality	Quality	The quality of the generated images. ‘hd’ creates more detailed images but costs more.
style	Style	The style of the generated images. ‘vivid’ favors vibrant colors and high contrast, while ‘natural’ produces more realistic images with less stylized colors.
n	Number of Images	The number of images to generate. Maximum is 1 for DALL-E 3.
openai_api_base	OpenAI API Base	The base URL of the OpenAI API.
api_key	OpenAI API Key	The OpenAI API Key to use for the OpenAI model.

Outputs

Name	Display Name	Info
image_url	Image URL	The URL of the first generated image.
images_base64	Images as base64	The generated images as a list of base64-encoded strings.
message	Message	A Message object containing generation details and image URLs.

Welcome

Get started

Templates

Concepts

Components

Integrations

Model components in LLM Controls

Use a model component in a flow

LLMC Executor

Key usage highlights

AIML

Amazon Bedrock

Anthropic

Azure OpenAI

Cohere

DeepSeek

Google Generative AI

Groq

Hugging Face API

Steps to use Hugging Face API in Basic prompting flow:

IBM watsonx.ai

Language model

LMStudio

Maritalk

Mistral

Novita AI

NVIDIA

Ollama

OpenAI

OpenRouter

Perplexity

Qianfan

SambaNova

VertexAI

xAI

DALL-E 3

Outputs

Welcome

Get started

Templates

Concepts

Components

Integrations

Documentation Index

​Use a model component in a flow

​LLMC Executor

​Key usage highlights

​AIML

​Amazon Bedrock

​Anthropic

​Azure OpenAI

​Cohere

​DeepSeek

​Google Generative AI

​Groq

​Hugging Face API

​Steps to use Hugging Face API in Basic prompting flow:

​IBM watsonx.ai

​Language model

​LMStudio

​Maritalk

​Mistral

​Novita AI

​NVIDIA

​Ollama

​OpenAI

​OpenRouter

​Perplexity

​Qianfan

​SambaNova

​VertexAI

​xAI

​DALL-E 3

​Outputs

Use a model component in a flow

LLMC Executor

Key usage highlights

AIML

Amazon Bedrock

Anthropic

Azure OpenAI

Cohere

DeepSeek

Google Generative AI

Groq

Hugging Face API

Steps to use Hugging Face API in Basic prompting flow:

IBM watsonx.ai

Language model

LMStudio

Maritalk

Mistral

Novita AI

NVIDIA

Ollama

OpenAI

OpenRouter

Perplexity

Qianfan

SambaNova

VertexAI

xAI

DALL-E 3

Outputs