[agent builder] define name/desc for structured output calls (#234914)

## Summary

Provide proper names and descriptions for the "structured output tools"
we're using to provide more guidances to the LLM

Author: pgayvallet

x-pack/platform/packages/shared/onechat/onechat-genai-utils/tools/index_explorer.ts

@@ -205,20 +205,33 @@ export const createIndexSelectorPrompt = ({
       'system',
       `You are an AI assistant for the Elasticsearch company.
 
-       Based on a natural language query provided by the user, your task is to select up to ${limit} most relevant targets
-       to perform that search, from a list of indices, aliases and datastreams.`,
+Your sole function is to identify the most relevant Elasticsearch resources (indices, aliases, data streams) based on a user's query.
+
+You MUST call the 'select_resources' tool to provide your answer. Do NOT respond with conversational text, explanations, or any data outside of the tool call.
+
+- The user's query will be provided.
+- A list of available resources will be provided in XML format.
+- You must analyze the query against the resource names, descriptions, and fields.
+- Select up to a maximum of ${limit} of the most relevant resources.
+- For each selected resource, you MUST provide its 'name', 'type', and a brief 'reason' for your choice.
+- If NO resources are relevant, you MUST call the 'select_resources' tool with an empty 'targets' array.
+
+Now, perform your function for the following query and resources.
+`,
     ],
     [
       'human',
-      `*The natural language query is:* "${nlQuery}"
+      `## Query
+
+*The natural language query is:* "${nlQuery}"
 
-       ## Available resources
-       <resources>
-        ${resources.map(formatResource).join('\n')}
-       </resources>
+## Available resources
+<resources>
+${resources.map(formatResource).join('\n')}
+</resources>
 
-       Based on the natural language query and the index descriptions, please return the most relevant indices with your reasoning.
-       Remember, you should select at maximum ${limit} targets. If none match, just return an empty list.`,
+Based on the natural language query and the index descriptions, please return the most relevant indices with your reasoning.
+Remember, you should select at maximum ${limit} targets. If none match, just return an empty list.`,
     ],
   ];
 };
@@ -236,23 +249,26 @@ const selectResources = async ({
 }): Promise<SelectedResource[]> => {
   const { chatModel } = model;
   const indexSelectorModel = chatModel.withStructuredOutput(
-    z.object({
-      reasoning: z
-        .string()
-        .optional()
-        .describe(
-          'optional brief overall reasoning. Can be used to explain why you did not return any target.'
+    z
+      .object({
+        reasoning: z
+          .string()
+          .optional()
+          .describe(
+            'optional brief overall reasoning. Can be used to explain why you did not return any target.'
+          ),
+        targets: z.array(
+          z.object({
+            reason: z.string().describe('brief explanation of why this resource could be relevant'),
+            type: z
+              .enum([EsResourceType.index, EsResourceType.alias, EsResourceType.dataStream])
+              .describe('the type of the resource'),
+            name: z.string().describe('name of the index, alias or data stream'),
+          })
         ),
-      targets: z.array(
-        z.object({
-          reason: z.string().describe('brief explanation of why this resource could be relevant'),
-          type: z
-            .enum([EsResourceType.index, EsResourceType.alias, EsResourceType.dataStream])
-            .describe('the type of the resource'),
-          name: z.string().describe('name of the index, alias or data stream'),
-        })
-      ),
-    })
+      })
+      .describe('Tool to use to select the relevant targets to search against'),
+    { name: 'select_resources' }
   );
 
   const promptContent = createIndexSelectorPrompt({

x-pack/platform/plugins/shared/onechat/server/services/chat/utils/generate_title.ts

@@ -25,13 +25,13 @@ export const generateTitle$ = ({
 }): Observable<string> => {
   return conversation$.pipe(
     switchMap((conversation) => {
-      return defer(async () =>
-        generateConversationTitle({
+      return defer(async () => {
+        return generateConversationTitle({
           previousRounds: conversation.rounds,
           nextInput,
           chatModel,
-        })
-      ).pipe(shareReplay());
+        });
+      }).pipe(shareReplay());
     })
   );
 };
@@ -50,15 +50,28 @@ export const generateConversationTitle = async ({
     { attributes: { [ElasticGenAIAttributes.InferenceSpanKind]: 'CHAIN' } },
     async (span) => {
       const structuredModel = chatModel.withStructuredOutput(
-        z.object({
-          title: z.string().describe('The title for the conversation'),
-        })
+        z
+          .object({
+            title: z.string().describe('The title for the conversation'),
+          })
+          .describe('Tool to use to provide the title for the conversation'),
+        { name: 'set_title' }
       );
 
       const prompt: BaseMessageLike[] = [
         [
           'system',
-          "'You are a helpful assistant. Assume the following messages is the start of a conversation between you and a user; give this conversation a title based on the content below",
+          `You are a title-generation utility. Your ONLY purpose is to create a short, relevant title for the provided conversation.
+
+You MUST call the 'set_title' tool to provide the title. Do NOT respond with plain text or any other conversational language.
+
+Here is an example:
+Conversation:
+- User: "Hey, can you help me find out how to configure a new role in Kibana for read-only access to dashboards?"
+- Assistant: "Of course! To create a read-only role..."
+=> Your response MUST be a call to the 'set_title' tool like this: {"title": "Kibana Read-Only Role Configuration"}
+
+Now, generate a title for the following conversation.`,
         ],
         ...conversationToLangchainMessages({ previousRounds, nextInput }),
       ];