Configuring and publishing the processes

1To publish the Search Tool process, click Actions in the row that contains the process and select Publish.
2To publish the Action Detail Extractor process, click Actions in the row that contains the process and select Publish.
3Open the Prompt Gemini process.
4On 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.
5 Save and publish the process.
6Open the Search Tool Workflow process.
7On the Temp Fields tab of the Start step, the Loop_Limit field is set to 5 by default. You can optionally edit the limit. This field defines the number of times the Search Tool subprocess can be called.
Note: You can adjust the number of repeat queries in the Search Tool to get new information and better results. However, this will increase the number of tokens used.
8On the Assignment tab of the Create Prompt to LLM step, the Prompt field is configured to contain the request template for the prompt to LLM. Optionally, you can configure the LLM behavior in the Prompt_Request field using the Expression Editor, as shown in the following sample code:
For Prompt_Request:
<GenerateContentRequestBody>
<contents>
<parts>
<text>{$temp.Prompt}</text>
</parts>
<role>user</role>
</contents>
<generationConfig>
<maxOutputTokens>2048</maxOutputTokens>
<temperature>0</temperature>
<topP>1</topP>
<topK>250</topK>
</generationConfig>
</GenerateContentRequestBody>

For the Prompt_Request field, enter values for the following properties:

Property	Description
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.

9 Save and publish the process.