Configuring and publishing the processes

1To publish the Salesforce Executor process, click Actions in the row that contains the process and select Publish.
2Open the Prompt Engineering process.
3On the Temp Fields tab of the Start step, the Model_LLM field is set to gemini-1.5 pro by default. You can optionally edit the model version. For information about changing the model version, see the Gemini documentation.
4 Save and publish the process.
5Open the GeminiAI Agent for Salesforce process.
6Optionally, you can change the tracing level from Verbose to None on the Advanced tab.
7Optionally, in the Prepare First Prompt To LLM step, enter the prompt instructions in the Assignments field by updating the Prompt_Configuration and Prompt_Request fields using the Expression Editor, as shown in the following sample code:
For Prompt_Configuration:
<generationConfig>
<candidateCount>1</candidateCount>
<maxOutputTokens>5000</maxOutputTokens>
<temperature>0.5</temperature>
<topP>0.5</topP>
<topK>1</topK>
</generationConfig>
For Prompt_Request:
<Generate_Content_Request>
<contents>
<parts>
<text>{$input.System_Instruction_Planning}</text>
</parts>
<parts>
<text>input:{$input.User_Prompt}</text>
</parts>
<parts>
<text>output:</text>
</parts>
</contents>
<generationConfig>
<candidateCount>{$temp.Prompt_Configuration[1]/candidateCount }</candidateCount>
<maxOutputTokens>{$temp.Prompt_Configuration[1]/maxOutputTokens }</maxOutputTokens>
<temperature>{$temp.Prompt_Configuration[1]/temperature }</temperature>
<topP>{$temp.Prompt_Configuration[1]/topP }</topP>
<topK>{$temp.Prompt_Configuration[1]/topK }</topK>
</generationConfig>
</Generate_Content_Request>
For the Prompt_Configuration field, enter values for the following properties:

Property	Description
stopSequences	Contains sequences of characters or strings that stop the model's output. This property controls where the model must end its response.
candidateCount	Specifies the number of response candidates that the model must generate. For example, if the value is set to 1, the model generates one response. If set to a higher number, the model generates that many alternative responses for the same input.
maxOutputTokens	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 close to 0 makes the output more deterministic, while a higher value close to 1 increases randomness and creativity. For example, if temperature is set to 0.5, the model 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.
topK	Limits the number of the highest-probability tokens to consider during response generation. For example, if topK is set to 2, the model considers only the top 2 tokens at each step, controlling output diversity and quality.

8 Save and publish the process.