docs: make sure to use Gemini 1.5 Flash as the default

docs/dotprompt.md

@@ -15,7 +15,7 @@ might call `greeting.prompt`:
 
 ```none
 ---
-model: vertexai/gemini-1.0-pro
+model: vertexai/gemini-1.5-flash
 config:
   temperature: 0.9
 input:
@@ -193,7 +193,7 @@ the file itself, you can also override these values on a per-call basis:
 
 ```ts
 const result = await greetingPrompt.generate({
-  model: 'google-genai/gemini-pro',
+  model: 'vertexai/gemini-1.5-pro',
   config: {
     temperature: 1.0,
   },
@@ -210,7 +210,7 @@ You can set the format and output schema of a prompt to coerce into JSON:
 
 ```none
 ---
-model: vertexai/gemini-1.0-pro
+model: vertexai/gemini-1.5-flash
 input:
   schema:
     theme: string
@@ -267,7 +267,7 @@ The `{{role}}` helper provides a simple way to construct multi-message prompts:
 
 ```none
 ---
-model: vertexai/gemini-1.0-pro
+model: vertexai/gemini-1.5-flash
 input:
   schema:
     userQuestion: string
@@ -317,7 +317,7 @@ use the `{{media}}` helper:
 
 ```none
 ---
-model: vertexai/gemini-1.0-pro-vision
+model: vertexai/gemini-1.5-flash
 input:
   schema:
     photoUrl: string
@@ -416,16 +416,16 @@ production environment side-by-side with existing versions. Dotprompt supports
 this through its **variants** feature.
 
 To create a variant, create a `[name].[variant].prompt` file. For instance, if
-you were using Gemini 1.0 Pro in your prompt but wanted to see if Gemini 1.5 Pro
-would perform better, you might create two files:
+you were using Gemini 1.5 Flash in your prompt but wanted to see if Gemini 1.5
+Pro would perform better, you might create two files:
 
 - `my_prompt.prompt`: the "baseline" prompt
-- `my_prompt.gemini15.prompt`: a variant named "gemini"
+- `my_prompt.geminipro.prompt`: a variant named "geminipro"
 
 To use a prompt variant, specify the `variant` option when loading:
 
 ```ts
-const myPrompt = await prompt('my_prompt', { variant: 'gemini15' });
+const myPrompt = await prompt('my_prompt', { variant: 'geminipro' });
 ```
 
 The name of the variant is included in the metadata of generation traces, so you
@@ -447,7 +447,7 @@ Once a helper is defined you can use it in any prompt:
 
 ```none
 ---
-model: vertexai/gemini-1.5-pro
+model: vertexai/gemini-1.5-flash
 input:
   schema:
     name: string
@@ -491,7 +491,7 @@ const myPrompt = await loadPromptUrl('https://example.com/my_prompt.prompt');
 // Define a prompt in code
 const myPrompt = defineDotprompt(
   {
-    model: 'vertexai/gemini-1.0-pro',
+    model: 'vertexai/gemini-1.5-flash',
     input: {
       schema: z.object({
         name: z.string(),

docs/index.md

@@ -186,7 +186,7 @@ into a single file for easier testing and organization.
 
 ```none
 ---
-model: vertexai/gemini-1.0-pro
+model: vertexai/gemini-1.5-flash
 config:
   temperature: 0.9
 input:

docs/models.md

@@ -31,7 +31,7 @@ authentication. For example, Vertex API uses the Google Auth Library so it can
 pull required credentials using Application Default Credentials.
 
 To use models provided by the plugin, you can either refer to them by name (e.g.
-`'vertexai/gemini-1.0-pro'`) or some plugins export model ref objects which
+`'vertexai/gemini-1.5-flash'`) or some plugins export model ref objects which
 provide additional type info about the model capabilities and options.
 
 ```js

docs/prompts.md

@@ -23,7 +23,7 @@ call models this way for straight-forward use cases.
 import { generate } from '@genkit-ai/ai';
 
 generate({
-  model: 'googleai/gemini-pro',
+  model: 'googleai/gemini-1.5-flash-latest',
   prompt: 'You are a helpful AI assistant named Walt.',
 });
 ```
@@ -37,7 +37,7 @@ function helloPrompt(name: string) {
 }
 
 generate({
-  model: 'googleai/gemini-pro',
+  model: 'googleai/gemini-1.5-flash-latest',
   prompt: helloPrompt('Fred'),
 });
 ```
@@ -85,7 +85,7 @@ generate(
   renderPrompt({
     prompt: helloPrompt,
     input: { name: 'Fred' },
-    model: 'googleai/gemini-pro',
+    model: 'googleai/gemini-1.5-flash-latest',
   })
 );
 ```