Azure OpenAI Prompt Chaining

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Step 3: Configure and publish the process

Configure the deployment details of the LLM model and publish the processes.

Open the

Prompt Chaining Azure OpenAI

process.

On the Temp Fields
tab of the

Start

step, enter values for the following fields:

In the api_version
field, enter the API version of the LLM model. Default is

2024-06-01

. You can optionally edit the api version.

In the deployment_id
field, enter the user-specific deployment ID.

Optionally, in the

Configure Request Parameters

step, configure the prompt instructions in the

Assignments

field by updating the

Prompt_Configuration

field using the Expression Editor, as shown in the following sample code:

 
 <GenerationConfig_AzureAI>
      <topP>1</topP>
      <max_tokens>500</max_tokens>
      <temperature>0.5</temperature>
   </GenerationConfig_AzureAI>

For the

Prompt_Configuration

field, enter values for the following properties:

Property	Description
max_tokens	Defines the maximum number of tokens that the model can generate in its response. Setting a limit ensures that the response is concise and fits within the desired length constraints.
temperature	Controls the randomness of the model's output. A lower value makes the output more deterministic, while a higher value increases randomness and creativity. For example, a temperature of 0.5 balances between deterministic and creative outputs.
topP	Determines the cumulative probability threshold for token selection. The model considers the smallest set of tokens whose cumulative probability meets or exceeds topP. For example, if topP is set to 0.1, the model considers only the top 10% most probable tokens at each step.

In the

Create Prompt 1

step, enter the prompt instructions in the

Assignments

field by updating the

Prompt_Request

field using the Expression Editor as shown in the following sample code:


<CreateChatCompletionRequest>
    <temperature>{$temp.Prompt_Configuration[1]/temperature }</temperature>
   <top_p>{$temp.Prompt_Configuration[1]/top_p }</top_p>
   <max_tokens>{$temp.Prompt_Configuration[1]/max_tokens }</max_tokens>
     <messages>
      <role>system</role>
      <content>{$input.First_System_Prompt  } </content>
    </messages>
    <messages>
      <role>user</role>
      <content>{$input.First_User_Prompt  }</content>
    </messages>
</CreateChatCompletionRequest>

After configuring the prompt instructions, the process sends the details to the LLM to fetch the required response, and then stores the first response.

In the

Create Prompt 2

step, in the

Assignments

field, update the

Prompt_Request

field using the Expression Editor as shown in the following sample code:

 
<CreateChatCompletionRequest>
    <temperature>{$temp.Prompt_Configuration[1]/temperature }</temperature>
    <top_p>{$temp.Prompt_Configuration[1]/top_p }</top_p>
    <max_tokens>{$temp.Prompt_Configuration[1]/max_tokens }</max_tokens>
       <messages>
         <role>system</role>
         <content> {$input.First_System_Prompt} </content>
    </messages>
     <messages>
      <role>user</role>
      <content>{ $input.First_User_Prompt  }</content>
    </messages>
     <messages>
      <role>assistant</role>
      <content>{ $temp.Prompt_Response[1]/choices[1]/message[1]/content    }</content>
    </messages>
    <messages>
      <role>user</role>
      <content>{$input.Second_User_Prompt  }</content>
    </messages>
</CreateChatCompletionRequest>

The LLM uses both the requests as an instruction to prepare the final response.

Save and publish the process.

Using the Azure OpenAI Prompt Chaining recipe

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