Create README.md

Author: trimbakeshmadhan-109

Server-Side Components/Script Includes/GetServiceDeskAgentHelpAIUtil/README.md

@@ -0,0 +1,68 @@
+GetServiceDeskAgentHelpAIUtil
+Overview
+This is a client-callable Script Include designed for the ServiceNow platform that integrates with an external Databricks AI endpoint. Its purpose is to assist Service Desk agents by providing AI-generated responses to user queries, which can be used to populate incident information or assist in troubleshooting.
+The script acts as a server-side proxy, handling the client-side request, invoking a Flow Designer Action to communicate with the Databricks service, and returning the AI's response to the client.
+Features
+AI-Powered Responses: Sends user search queries to a Databricks-powered Generative AI model to get intelligent answers.
+Client-Callable: Can be invoked from client-side scripts (e.g., a UI Action or Client Script) using GlideAjax.
+Flow Designer Integration: Uses a Flow Designer Action to execute the REST call to the Databricks endpoint, centralizing the external API logic.
+Structured Output: Returns a JSON object containing the AI's response, model metadata, and a trace ID for debugging.
+Prerequisites
+ServiceNow Configuration
+Flow Designer Action: A Flow Designer Action named global.genai_action must be created and configured to handle the REST call to the Databricks AI endpoint. This action must have:
+An input named search_query (String).
+An output named model_output (String).
+Databricks Connection: The Flow Designer Action must be correctly configured with the necessary credentials to connect to the external Databricks API.
+System Property: A system property named user.prompt is referenced in the script. It should be created and configured with the required prompt text for the AI.
+How to use
+1. Calling from a Client Script
+You can use GlideAjax to call the getSearchResults function from a client-side script, such as a UI Action or a Catalog Client Script.
+javascript
+// Example client-side script using GlideAjax
+var ga = new GlideAjax('GetServiceDeskAgentHelpAIUtil');
+ga.addParam('sysparm_name', 'getSearchResults');
+ga.addParam('sysparm_search_key', g_form.getValue('short_description')); // Pass the user's input
+ga.getXML(getResponse);
+
+function getResponse(response) {
+    var answer = response.responseXML.documentElement.getAttribute("answer");
+    if (answer) {
+        var result = JSON.parse(answer);
+        g_form.setValue('comments', result.modelResponse); // Set the AI response in a field
+    }
+}
+Use code with caution.
+
+2. Using from a Server Script
+The functions can also be called directly from other server-side scripts (e.g., Business Rules).
+javascript
+// Example server-side script
+var searchKey = 'What are the steps to reset my password?';
+var aiUtil = new GetServiceDeskAgentHelpAIUtil();
+var response = aiUtil.getSearchResults(searchKey);
+
+gs.info(response);
+Use code with caution.
+
+Script details
+getSearchResults()
+This is the main function that coordinates the process.
+Retrieves the search term from the client parameters.
+Calls the getDataBricksModelResponse() function to get the AI-generated answer.
+Constructs a JSON object with the AI's response and model information.
+Returns the JSON object as a string.
+getDataBricksModelResponse(search)
+This function handles the integration with the Databricks AI.
+Takes the search query as a parameter.
+Executes the global.genai_action Flow Designer Action.
+Parses the model_output from the Flow Action's outputs.
+Extracts the AI's message content and a trace ID for debugging.
+Returns a stringified JSON object containing the AI's response, date, and trace ID.
+Includes a try/catch block to handle and log potential errors during the integration process.
+Dependencies
+Flow Designer Action: global.genai_action
+System Property: user.prompt
+Troubleshooting
+Check the Flow Execution: If the AI response is not received, check the Flow Designer execution logs to ensure global.genai_action is running successfully and the REST call to Databricks is returning a valid response.
+Review System Logs: Examine the System Logs (gs.info and gs.error messages) for debugging information related to the script's execution or potential errors from the Databricks API.
+Verify Databricks Credentials: Ensure that the credentials and configuration within the Flow Designer action for connecting to Databricks are correct.