Edit

Share via

LLM tool for flows in Azure AI Studio

  • Article

Important

Items marked (preview) in this article are currently in public preview. This preview is provided without a service-level agreement, and we don't recommend it for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

To use large language models (LLMs) for natural language processing, you use the prompt flow LLM tool.

Note

For embeddings to convert text into dense vector representations for various natural language processing tasks, see Embedding tool.

Prerequisites

Prepare a prompt as described in the Prompt tool documentation. The LLM tool and Prompt tool both support Jinja templates. For more information and best practices, see Prompt engineering techniques.

Build with the LLM tool

  1. Create or open a flow in Azure AI Studio. For more information, see Create a flow.

  2. Select + LLM to add the LLM tool to your flow.

    Screenshot that shows the LLM tool added to a flow in Azure AI Studio.

  3. Select the connection to one of your provisioned resources. For example, select Default_AzureOpenAI.

  4. From the Api dropdown list, select chat or completion.

  5. Enter values for the LLM tool input parameters described in the Text completion inputs table. If you selected the chat API, see the Chat inputs table. If you selected the completion API, see the Text completion inputs table. For information about how to prepare the prompt input, see Prerequisites.

  6. Add more tools to your flow, as needed. Or select Run to run the flow.

  7. The outputs are described in the Outputs table.

Inputs

The following input parameters are available.

Text completion inputs

Name Type Description Required
prompt string Text prompt for the language model. Yes
model, deployment_name string The language model to use. Yes
max_tokens integer The maximum number of tokens to generate in the completion. Default is 16. No
temperature float The randomness of the generated text. Default is 1. No
stop list The stopping sequence for the generated text. Default is null. No
suffix string The text appended to the end of the completion. No
top_p float The probability of using the top choice from the generated tokens. Default is 1. No
logprobs integer The number of log probabilities to generate. Default is null. No
echo boolean The value that indicates whether to echo back the prompt in the response. Default is false. No
presence_penalty float The value that controls the model's behavior regarding repeating phrases. Default is 0. No
frequency_penalty float The value that controls the model's behavior regarding generating rare phrases. Default is 0. No
best_of integer The number of best completions to generate. Default is 1. No
logit_bias dictionary The logit bias for the language model. Default is empty dictionary. No

Chat inputs

Name Type Description Required
prompt string The text prompt that the language model should reply to. Yes
model, deployment_name string The language model to use. Yes
max_tokens integer The maximum number of tokens to generate in the response. Default is inf. No
temperature float The randomness of the generated text. Default is 1. No
stop list The stopping sequence for the generated text. Default is null. No
top_p float The probability of using the top choice from the generated tokens. Default is 1. No
presence_penalty float The value that controls the model's behavior regarding repeating phrases. Default is 0. No
frequency_penalty float The value that controls the model's behavior regarding generating rare phrases. Default is 0. No
logit_bias dictionary The logit bias for the language model. Default is empty dictionary. No

Outputs

The output varies depending on the API you selected for inputs.

API Return type Description
Completion string The text of one predicted completion.
Chat string The text of one response of conversation.

Next steps