[DRAFT] Update AI Toolkit docs

site/lib/src/style_hash.dart

@@ -2,4 +2,4 @@
 // dart format off
 
 /// The generated hash of the `main.css` file.
-const generatedStylesHash = '0lnBUTa5o0lF';
+const generatedStylesHash = 'lWAnsjm6RjR2';

src/content/ai-toolkit/chat-client-sample.md

@@ -7,13 +7,11 @@ prev:
   path: /ai-toolkit/custom-llm-providers
 ---
 
-The AI Chat sample is meant to be a full-fledged chat app
-built using the Flutter AI Toolkit and Vertex AI for Firebase.
-In addition to all of the multi-shot, multi-media,
-streaming features that it gets from the AI Toolkit,
-the AI Chat sample shows how to store and manage
-multiple chats at once in your own apps.
-On desktop form-factors, the AI Chat sample looks like the following:
+The AI Chat sample is meant to be a full-fledged chat app built using the
+Flutter AI Toolkit and Vertex AI for Firebase. In addition to all of the
+multi-shot, multi-media, streaming features that it gets from the AI Toolkit,
+the AI Chat sample shows how to store and manage multiple chats at once in your
+own apps. On desktop form-factors, the AI Chat sample looks like the following:
 
 ![Desktop app UI](/assets/images/docs/ai-toolkit/desktop-pluto-convo.png)
 

src/content/ai-toolkit/custom-llm-providers.md

@@ -10,8 +10,8 @@ next:
   path: /ai-toolkit/chat-client-sample
 ---
 
-The protocol connecting an LLM and the `LlmChatView`
-is expressed in the [`LlmProvider` interface][]:
+The protocol connecting an LLM and the `LlmChatView` is expressed in the
+[`LlmProvider` interface][]:
 
 ```dart
 abstract class LlmProvider implements Listenable {
@@ -22,48 +22,42 @@ abstract class LlmProvider implements Listenable {
 }
 ```
 
-The LLM could be in the cloud or local,
-it could be hosted in the Google Cloud Platform
-or on some other cloud provider,
-it could be a proprietary LLM or open source.
-Any LLM or LLM-like endpoint that can be used
-to implement this interface can be plugged into
-the chat view as an LLM provider. The AI Toolkit
-comes with three providers out of the box,
-all of which implement the `LlmProvider` interface
-that is required to plug the provider into the following:
-
-* The [Gemini provider][],
-  which wraps the `google_generative_ai` package
-* The [Vertex provider][],
-  which wraps the `firebase_vertexai` package
-* The [Echo provider][],
-  which is useful as a minimal provider example
-
-[Echo provider]: {{site.pub-api}}/flutter_ai_toolkit/latest/flutter_ai_toolkit/EchoProvider-class.html
-[Gemini provider]: {{site.pub-api}}/flutter_ai_toolkit/latest/flutter_ai_toolkit/GeminiProvider-class.html
-[`LlmProvider` interface]: {{site.pub-api}}/flutter_ai_toolkit/latest/flutter_ai_toolkit/LlmProvider-class.html
-[Vertex provider]: {{site.pub-api}}/flutter_ai_toolkit/latest/flutter_ai_toolkit/VertexProvider-class.html
+The LLM could be in the cloud or local, it could be hosted in the Google Cloud
+Platform or on some other cloud provider, it could be a proprietary LLM or open
+source. Any LLM or LLM-like endpoint that can be used to implement this
+interface can be plugged into the chat view as an LLM provider. The AI Toolkit
+comes with two providers out of the box, both of which implement the
+`LlmProvider` interface that is required to plug the provider into the
+following:
+
+* The [Firebase provider][], which wraps the `firebase_ai` package
+* The [Echo provider][], which is useful as a minimal provider example
+
+[Echo provider]:
+    {{site.pub-api}}/flutter_ai_toolkit/latest/flutter_ai_toolkit/EchoProvider-class.html
+[Firebase provider]:
+    {{site.pub-api}}/flutter_ai_toolkit/latest/flutter_ai_toolkit/FirebaseProvider-class.html
+[`LlmProvider` interface]:
+    {{site.pub-api}}/flutter_ai_toolkit/latest/flutter_ai_toolkit/LlmProvider-class.html
 
 ## Implementation
 
-To build your own provider, you need to implement
-the `LlmProvider` interface with these things in mind:
+To build your own provider, you need to implement the `LlmProvider` interface
+with these things in mind:
 
 1. Providing for full configuration support
 1. Handling history
 1. Translating messages and attachments to the underlying LLM
 1. Calling the underlying LLM
 
-1. Configuration
-   To support full configurability in your custom provider,
-   you should allow the user to create the underlying model
-   and pass that in as a parameter, as the Gemini provider does:
+1. Configuration To support full configurability in your custom provider, you
+   should allow the user to create the underlying model and pass that in as a
+   parameter, as the Firebase provider does:
 
 ```dart
-class GeminiProvider extends LlmProvider ... {
+class FirebaseProvider extends LlmProvider ... {
   @immutable
-  GeminiProvider({
+  FirebaseProvider({
     required GenerativeModel model,
     ...
   })  : _model = model,
@@ -74,25 +68,22 @@ class GeminiProvider extends LlmProvider ... {
 }
 ```
 
-In this way, no matter what changes come
-to the underlying model in the future,
-the configuration knobs will all be available
-to the user of your custom provider.
+In this way, no matter what changes come to the underlying model in the future,
+the configuration knobs will all be available to the user of your custom
+provider.
 
-2. History
-  History is a big part of any provider—not only
-  does the provider need to allow history to be
-  manipulated directly, but it has to notify listeners
-  as it changes. In addition, to support serialization
-  and changing provider parameters, it must also support
-  saving history as part of the construction process.
+2. History History is a big part of any provider—not only does the provider need
+  to allow history to be manipulated directly, but it has to notify listeners as
+  it changes. In addition, to support serialization and changing provider
+  parameters, it must also support saving history as part of the construction
+  process.
 
-  The Gemini provider handles this as shown:
+  The Firebase provider handles this as shown:
 
 ```dart
-class GeminiProvider extends LlmProvider with ChangeNotifier {
+class FirebaseProvider extends LlmProvider with ChangeNotifier {
   @immutable
-  GeminiProvider({
+  FirebaseProvider({
     required GenerativeModel model,
     Iterable<ChatMessage>? history,
     ...
@@ -143,37 +134,32 @@ class GeminiProvider extends LlmProvider with ChangeNotifier {
 ```
 
 You'll notice several things in this code:
-* The use of `ChangeNotifier` to implement the `Listenable`
-  method requirements from the `LlmProvider` interface
+* The use of `ChangeNotifier` to implement the `Listenable` method requirements
+  from the `LlmProvider` interface
 * The ability to pass initial history in as a constructor parameter
-* Notifying listeners when there's a new user
-  prompt/LLM response pair
+* Notifying listeners when there's a new user prompt/LLM response pair
 * Notifying listeners when the history is changed manually
 * Creating a new chat when the history changes, using the new history
 
-Essentially, a custom provider manages the history
-for a single chat session with the underlying LLM.
-As the history changes, the underlying chat either
-needs to be kept up to date automatically
-(as the Gemini AI SDK for Dart does when you call
-the underlying chat-specific methods) or manually recreated
-(as the Gemini provider does whenever the history is set manually).
+Essentially, a custom provider manages the history for a single chat session
+with the underlying LLM. As the history changes, the underlying chat either
+needs to be kept up to date automatically (as the Gemini AI SDK for Dart does
+when you call the underlying chat-specific methods) or manually recreated (as
+the Firebase provider does whenever the history is set manually).
 
 3. Messages and attachments
 
-Attachments must be mapped from the standard
-`ChatMessage` class exposed by the `LlmProvider`
-type to whatever is handled by the underlying LLM.
-For example, the Gemini provider maps from the
-`ChatMessage` class from the AI Toolkit to the
-`Content` type provided by the Gemini AI SDK for Dart,
-as shown in the following example:
+Attachments must be mapped from the standard `ChatMessage` class exposed by the
+`LlmProvider` type to whatever is handled by the underlying LLM. For example,
+the Firebase provider maps from the `ChatMessage` class from the AI Toolkit to the
+`Content` type provided by the Gemini AI SDK for Dart, as shown in the following
+example:
 
 ```dart
 import 'package:google_generative_ai/google_generative_ai.dart';
 ...
 
-class GeminiProvider extends LlmProvider with ChangeNotifier {
+class FirebaseProvider extends LlmProvider with ChangeNotifier {
   ...
   static Part _partFrom(Attachment attachment) => switch (attachment) {
         (final FileAttachment a) => DataPart(a.mimeType, a.bytes),
@@ -190,22 +176,19 @@ class GeminiProvider extends LlmProvider with ChangeNotifier {
 }
 ```
 
-The `_contentFrom` method is called whenever a user prompt
-needs to be sent to the underlying LLM.
-Every provider needs to provide for its own mapping.
+The `_contentFrom` method is called whenever a user prompt needs to be sent to
+the underlying LLM. Every provider needs to provide for its own mapping.
 
 4. Calling the LLM
 
-How you call the underlying LLM to implement
-`generateStream` and `sendMessageStream` methods
-depends on the protocol it exposes.
-The Gemini provider in the AI Toolkit
-handles configuration and history but calls to
-`generateStream` and `sendMessageStream` each
-end up in a call to an API from the Gemini AI SDK for Dart:
+How you call the underlying LLM to implement `generateStream` and
+`sendMessageStream` methods depends on the protocol it exposes. The Firebase
+provider in the AI Toolkit handles configuration and history but calls to
+`generateStream` and `sendMessageStream` each end up in a call to an API from
+the Firebase AI Logic SDK:
 
 ```dart
-class GeminiProvider extends LlmProvider with ChangeNotifier {
+class FirebaseProvider extends LlmProvider with ChangeNotifier {
   ...
 
   @override
@@ -275,13 +258,11 @@ class GeminiProvider extends LlmProvider with ChangeNotifier {
 
 ## Examples
 
-The [Gemini provider][] and [Vertex provider][]
-implementations are nearly identical and provide
-a good starting point for your own custom provider.
-If you'd like to see an example provider implementation with
-all of the calls to the underlying LLM stripped away,
-check out the [Echo example app][], which simply formats
-the user's prompt and attachments as Markdown
-to send back to the user as its response.
+The [Firebase provider][] implementation provides a good starting point for your
+own custom provider. If you'd like to see an example provider implementation
+with all of the calls to the underlying LLM stripped away, check out the [Echo
+example app][], which simply formats the user's prompt and attachments as
+Markdown to send back to the user as its response.
 
-[Echo example app]: {{site.github}}/flutter/ai/blob/main/lib/src/providers/implementations/echo_provider.dart
+[Echo example app]:
+    {{site.github}}/flutter/ai/blob/main/lib/src/providers/implementations/echo_provider.dart