Update genkit init templates and docs

docs/get-started.md

@@ -71,50 +71,41 @@ Node.js 18 or later.
     1. Choose default answers to the rest of the questions, which will
        initialize your project folder with some sample code.
 
-    The `genkit init` command created two sample source files:
-
-    - `genkit.config.ts`: this is where you configure Genkit for your project,
-      and select and configure the plugins you want to load. The sample config
-      loads a plugin to support the model provider you chose earlier.
-
-      ```js
-      export default configureGenkit({
-        plugins: [googleAI()],
-        logLevel: 'debug',
-        enableTracingAndMetrics: true,
-      });
-      ```
-
-    - `index.ts`: this is your project's entry point, where you export your AI
-      flows and other resources you've defined. The sample file contains a
-      single flow, `jokeFlow`, that simply calls the model provider's API with a
-      simple prompt and returns the result.
-
-      ```js
-      export const jokeFlow = defineFlow(
-        {
-          name: 'jokeFlow',
-          inputSchema: z.string(),
-          outputSchema: z.string(),
-        },
-        async (subject) => {
-          const llmResponse = await generate({
-            prompt: `Tell me a long joke about ${subject}`,
-            model: geminiPro,
-            config: {
-              temperature: 1,
-            },
-          });
-
-          return llmResponse.text();
-        }
-      );
-      ```
-
-      As you build out your app's AI features with Genkit, you will likely
-      create flows with multiple steps such as input preprocessing, more
-      sophisticated prompt construction, integrating external information
-      sources for retrieval-augmented generation (RAG), and more.
+    The `genkit init` command creates a sample source file, `index.ts`. This is your project's entry point, where you configure Genkit for your project, configure the plugins you want to load and export your AI flows and other resources you've defined. The sample file contains a config that loads a plugin to support the model provider you chose earlier. It also contains a single flow, `menuSuggestionFlow`, that prompts an LLM to suggest an item for a restaurant with a given theme.
+
+    ```js
+    configureGenkit({
+      plugins: [googleAI()],
+      logLevel: 'debug',
+      enableTracingAndMetrics: true,
+    });
+
+    export const menuSuggestionFlow = defineFlow(
+      {
+        name: 'menuSuggestionFlow',
+        inputSchema: z.string(),
+        outputSchema: z.string(),
+      },
+      async (subject) => {
+        const llmResponse = await generate({
+          prompt: `Suggest an item for the menu of a {subject} themed restaurant`,
+          model: $GENKIT_MODEL,
+          config: {
+            temperature: 1,
+          },
+        });
+
+        return llmResponse.text();
+      }
+    );
+
+    startFlowsServer();
+    ```
+
+    As you build out your app's AI features with Genkit, you will likely
+    create flows with multiple steps such as input preprocessing, more
+    sophisticated prompt construction, integrating external information
+    sources for retrieval-augmented generation (RAG), and more.
 
 1.  Now you can run and explore Genkit features and the sample project locally
     on your machine. Download and start the Genkit Developer UI:
@@ -144,9 +135,9 @@ Node.js 18 or later.
     - On the **Run** tab, you will see a list of all of the flows that you have
       defined and any models that have been configured by plugins.
 
-      Click **jokeFlow** and try running it with some input text (for example,
-      `"manatees"`). If all goes well, you'll be rewarded with a joke about
-      manatees. Run it a few more times and you might get one that's funny.
+      Click **menuSuggestionFlow** and try running it with some input text (for example,
+      `"cat"`). If all goes well, you'll be rewarded with a menu suggestion for a cat
+      themed restaurant.
 
     - On the **Inspect** tab, you'll see a history of flow executions. For each
       flow, you can see the parameters that were passed to the flow and a

genkit-tools/config/firebase.index.ts.template

@@ -14,9 +14,9 @@ $GENKIT_CONFIG_PLUGINS
   enableTracingAndMetrics: true,
 });
 
-export const jokeFlow = onFlow(
+export const menuSuggestionFlow = onFlow(
   {
-    name: "jokeFlow",
+    name: 'menuSuggestionFlow',
     inputSchema: z.string(),
     outputSchema: z.string(),
     authPolicy: firebaseAuth((user) => {
@@ -26,11 +26,14 @@ export const jokeFlow = onFlow(
     }),
   },
   async (subject) => {
-    const prompt = `Tell me a joke about ${subject}`;
+    const prompt = `Suggest an item for the menu of a ${subject} themed restaurant`;
 
     const llmResponse = await generate({
       model: $GENKIT_MODEL,
       prompt: prompt,
+      config: {
+        temperature: 1,
+      },
     });
 
     return llmResponse.text();

genkit-tools/config/googleCloud.index.ts.template

@@ -13,15 +13,15 @@ $GENKIT_CONFIG_PLUGINS
   enableTracingAndMetrics: true,
 });
 
-export const jokeFlow = defineFlow(
+export const menuSuggestionFlow = defineFlow(
   {
-    name: 'jokeFlow',
+    name: 'menuSuggestionFlow',
     inputSchema: z.string(),
     outputSchema: z.string(),
   },
   async (subject) => {
     const llmResponse = await generate({
-      prompt: `Tell me a long joke about ${subject}`,
+      prompt: `Suggest an item for the menu of a ${subject} themed restaurant`,
       model: $GENKIT_MODEL,
       config: {
         temperature: 1,

genkit-tools/config/nextjs.genkit.ts.template

@@ -15,20 +15,15 @@ $GENKIT_CONFIG_PLUGINS
   enableTracingAndMetrics: true,
 });
 
-export async function callJokeFlow() {
-  const joke = await runFlow(jokeFlow, 'banana');
-  console.log(joke)
-}
-
-const jokeFlow = defineFlow(
+const menuSuggestionFlow = defineFlow(
   {
-    name: 'jokeFlow',
+    name: 'menuSuggestionFlow',
     inputSchema: z.string(),
     outputSchema: z.string(),
   },
   async (subject) => {
     const llmResponse = await generate({
-      prompt: `Tell me a long joke about ${subject}`,
+      prompt: `Suggest an item for the menu of a ${subject} themed restaurant`,
       model: $GENKIT_MODEL,
       config: {
         temperature: 1,
@@ -38,3 +33,8 @@ const jokeFlow = defineFlow(
     return llmResponse.text();
   }
 );
+
+export async function callMenuSuggestionFlow() {
+  const flowResponse = await runFlow(menuSuggestionFlow, 'banana');
+  console.log(flowResponse);
+}