From f336ee0271041bd27b66e25c57d731a68901e67b Mon Sep 17 00:00:00 2001
From: Giorgio Garasto <giorgio@garasto.me>
Date: Tue, 14 May 2024 09:31:38 +0200
Subject: [PATCH] docs: update descriptions & examples to latest genkit version

---
 plugins/anthropic/README.md | 35 ++++++++++++++++++++++++++---------
 plugins/openai/README.md    | 32 +++++++++++++++++++++++---------
 2 files changed, 49 insertions(+), 18 deletions(-)

diff --git a/plugins/anthropic/README.md b/plugins/anthropic/README.md
index 698fb98..63f817d 100644
--- a/plugins/anthropic/README.md
+++ b/plugins/anthropic/README.md
@@ -1,5 +1,10 @@
 # Anthropic AI Plugin for Firebase Genkit
 
+This Genkit plugin allows to use Anthropic AI models through their official APIs.
+
+If you want to use Anthropic AI models through Google Vertex AI, please refer
+to the [official Vertex AI plugin](https://www.npmjs.com/package/@genkit-ai/vertexai).
+
 ## Installation
 
 ```bash
@@ -23,27 +28,39 @@ yarn add @genkit-ai/core @genkit-ai/ai
 ## Example Claude 3 Haiku flow
 
 ```ts
-import { generate } from '@genkit-ai/ai/generate';
-import { flow } from '@genkit-ai/flow';
-import { claude3Haiku } from './plugins/anthropic';
+import { generate } from '@genkit-ai/ai';
+import { configureGenkit } from '@genkit-ai/core';
+import { defineFlow, startFlowsServer } from '@genkit-ai/flow';
+import { anthropic, claude3Haiku } from 'genkitx-anthropic';
 import * as z from 'zod';
 
-export const anthropicStoryFlow = flow(
+configureGenkit({
+  plugins: [
+    // Anthropic API key is required and defaults to the ANTHROPIC_API_KEY environment variable
+    anthropic({ apiKey: process.env.ANTHROPIC_API_KEY }),
+  ],
+  logLevel: 'debug',
+  enableTracingAndMetrics: true,
+});
+
+export const menuSuggestionFlow = defineFlow(
   {
-    name: 'anthropicStoryFlow',
-    input: z.string(),
-    output: z.string(),
-    streamType: z.string(),
+    name: 'menuSuggestionFlow',
+    inputSchema: z.string(),
+    outputSchema: z.string(),
   },
   async (subject, streamingCallback) => {
     const llmResponse = await generate({
+      prompt: `Suggest an item for the menu of a ${subject} themed restaurant`,
       model: claude3Haiku,
-      prompt: `Tell me a story about a ${subject}`,
       streamingCallback: ({ content }) => {
         streamingCallback?.(content[0]?.text ?? '');
       },
     });
+
     return llmResponse.text();
   },
 );
+
+startFlowsServer();
 ```
diff --git a/plugins/openai/README.md b/plugins/openai/README.md
index 9136767..6186f55 100644
--- a/plugins/openai/README.md
+++ b/plugins/openai/README.md
@@ -1,5 +1,7 @@
 # OpenAI Plugin for Firebase Genkit
 
+This Genkit plugin allows to use OpenAI models through their official APIs.
+
 ## Installation
 
 ```bash
@@ -23,27 +25,39 @@ yarn add @genkit-ai/core @genkit-ai/ai
 ## Example GPT-4 Turbo flow
 
 ```ts
-import { generate } from '@genkit-ai/ai/generate';
-import { flow } from '@genkit-ai/flow';
-import { gpt4Turbo } from '@genkit-ai/plugin-openai';
+import { generate } from '@genkit-ai/ai';
+import { configureGenkit } from '@genkit-ai/core';
+import { defineFlow, startFlowsServer } from '@genkit-ai/flow';
+import { openAI, gpt4Turbo } from 'genkitx-openai';
 import * as z from 'zod';
 
-export const openaiStoryFlow = flow(
+configureGenkit({
+  plugins: [
+    // OpenAI API key is required and defaults to the OPENAI_API_KEY environment variable
+    openAI({ apiKey: process.env.OPENAI_API_KEY }),
+  ],
+  logLevel: 'debug',
+  enableTracingAndMetrics: true,
+});
+
+export const menuSuggestionFlow = defineFlow(
   {
-    name: 'openaiStoryFlow',
-    input: z.string(),
-    output: z.string(),
-    streamType: z.string(),
+    name: 'menuSuggestionFlow',
+    inputSchema: z.string(),
+    outputSchema: z.string(),
   },
   async (subject, streamingCallback) => {
     const llmResponse = await generate({
+      prompt: `Suggest an item for the menu of a ${subject} themed restaurant`,
       model: gpt4Turbo,
-      prompt: `Tell me a story about a ${subject}`,
       streamingCallback: ({ content }) => {
         streamingCallback?.(content[0]?.text ?? '');
       },
     });
+
     return llmResponse.text();
   },
 );
+
+startFlowsServer();
 ```