script.js

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.

Author: trimbakeshmadhan-109

Server-Side Components/Script Includes/GetServiceDeskAgentHelpAIUtil/script.js

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+var GetServiceDeskAgentHelpAIUtil = Class.create();
+GetServiceDeskAgentHelpAIUtil.prototype = Object.extendsObject(global.AbstractAjaxProcessor, {
+
+  /**
+     * Main function that processes a search term from a client-side call,
+     * sends it to a Databricks-powered AI, and returns the response.
+     * This function is intended to assist Service Desk agents.
+     *
+     * @returns {string} A JSON string containing the AI's response, model details, and metrics.
+     */
+    getSearchResults: function() {
+        // Defines the use case for logging and metric purposes.
+        var usecase = "ServiceDesk Helper";
+        // Gets the current user's Sys ID, though it is not used in the current implementation.
+        var user = gs.getUserID();
+        // Retrieves the search term passed from the client-side script.
+        var searchText = this.getParameter("sysparm_search_key");
+        // Replaces double quotes with single quotes in the search text to prevent JSON parsing issues.
+        searchText = searchText.replaceAll('"', "'");
+        
+        var searchObj = {
+            "searchValue": searchText.toString()
+        };
+
+        // Extracts the raw search value from the object.
+        var search = searchObj["searchValue"];
+
+        // This object is structured to create a prompt for another potential AI endpoint (possibly for a brief statement),
+        // but it is currently not used.
+        var brief_statement_payload = {
+            "messages": [{
+                    "role": "system",
+                    "content": "You are an Expert ServiceNow bot that helps the users to create an incident"
+                },
+                {
+                    "role": "user",
+                    "content": gs.getProperty('user.prompt') + search
+                }
+            ]
+        };
+
+        var databricks_model_response = {};
+        // Calls the internal method to get the response from the Databricks model.
+        var response = this.getDataBricksModelResponse(search);
+        // UNCOMMENT THIS WHEN WE HAVE A PROPER SOLUTION FOR BRIEF RESPONSE GENERATION
+        // var brief_response = this.getBriefResponse(brief_statement_payload);
+        // The brief response is hardcoded to an empty JSON object
+        // Assigns the model response to the output object.
+        databricks_model_response.modelResponse = response;
+        // Assigns a hardcoded model ID.
+        databricks_model_response.model_id = "Databricks Runbook";
+
+        // Converts the final response object to a JSON string for client-side processing.
+        databricks_model_response = JSON.stringify(databricks_model_response);
+        // Logs the final JSON string for debugging purposes.
+        gs.info("Service Desk Helper Results: Testing value of the final databricks response being sent: " + databricks_model_response);
+
+        // Returns the JSON string to the calling client script.
+        return databricks_model_response;
+    },
+
+    /**
+     * This function calls the Databricks endpoint via a Flow Designer action
+     * to generate an answer for the user's query.
+     *
+     * @param {string} search - The user's search query.
+     * @returns {string} A JSON string containing the AI's response, the current date, and a trace ID.
+     */
+    getDataBricksModelResponse: function(search) {
+        try {
+            var inputs = {};
+            // Maps the search query to the input expected by the flow action.
+            inputs['search_query'] = search;
+
+            // Executes the specified Flow Designer action with the provided inputs.
+            // The action is run in the foreground, meaning the script will wait for a response.
+            var result = sn_fd.FlowAPI.getRunner().action('global.genai_action').inForeground().withInputs(inputs).run();
+            // Retrieves the outputs from the completed flow action.
+            var outputs = result.getOutputs();
+
+            // Extracts the model output from the flow action outputs.
+            var model_output = outputs['model_output'];
+            // Attempts to parse and extract vector response data, though the variable is not used after this line.
+            var databricks_vector_response = JSON.parse(model_output).databricks_output.trace.data.spans;
+
+            // Logs the raw response from the Databricks model for debugging.
+            gs.info("Helper Results: Databricks flow action response: " + JSON.stringify(model_output));
+
+            var current_date = new GlideDateTime();
+            var output = {};
+            // Parses the model output to extract the AI's content.
+            output.response = JSON.parse(model_output).choices[0].message.content;
+            // Adds the current date to the output object.
+            output.date = current_date.getDisplayValue();
+            // Logs the trace ID for tracking purposes.
+            gs.info("Helper Results: GEN AI flow action TraceID value: " + JSON.parse(model_output).id);
+            // Adds the trace ID to the output object.
+            output.traceID = JSON.parse(model_output).id;
+            // Returns the constructed output object as a JSON string.
+            return JSON.stringify(output);
+
+        } catch (ex) {
+            // Catches any exceptions during the flow execution and logs an error.
+            var message = ex.getMessage();
+            gs.error(message);
+        }
+    },
+
+  type: 'GetServiceDeskAgentHelpAIUtil'
+});