Merge pull request #7709 from microsoft/isidorn/xenogeneic-aphid

update docs to specify request.model usage, and not selectChatModel

api/extension-guides/chat.md

@@ -140,7 +140,7 @@ const handler: vscode.ChatRequestHandler = async (request: vscode.ChatRequest, c
 
 #### Determine the request intent
 
-To determine the intent of the user's request, you can reference the `vscode.ChatRequest` parameter to access the prompt, [commands](#register-commands), chat location, and [chat variables](#variables) that the user entered in the Chat view. Optionally, you can take advantage of the language model to determine the user's intent, rather than using traditional logic. Learn how you can use the [Language Model API](/api/extension-guides/language-model) in your extension.
+To determine the intent of the user's request, you can reference the `vscode.ChatRequest` parameter to access the prompt, [commands](#register-commands), chat location, and [chat variables](#variables) that the user entered in the Chat view. Optionally, you can take advantage of the language model to determine the user's intent, rather than using traditional logic. As part of the `request` object you get a language model instance that the user picked in the chat model dropdown. Learn how you can use the [Language Model API](/api/extension-guides/language-model) in your extension.
 
 The following code snippet shows the basic structure of first using the command, and then the user prompt to determine the user intent:
 
@@ -156,7 +156,7 @@ const handler: vscode.ChatRequestHandler = async (request: vscode.ChatRequest, c
     } else {
 
         // Determine the user's intent
-        const intent = determineUserIntent(request.prompt, request.variables);
+        const intent = determineUserIntent(request.prompt, request.variables, request.model);
 
         // Add logic here to handle other scenarios
     }
@@ -165,7 +165,7 @@ const handler: vscode.ChatRequestHandler = async (request: vscode.ChatRequest, c
 
 #### Process the request
 
-Next, you need to implement the actual logic for processing the user request. Often, chat extensions use the [Language Model API](/api/extension-guides/language-model) to process the request. In this case, you might adjust the language model prompt to match the user's intent. Alternately, you can implement the extension logic by invoking a backend service, by using traditional programming logic, or by using a combination of all these options. For example, you could invoke a web search to gather additional information, which you then provide as context to the language model.
+Next, you need to implement the actual logic for processing the user request. Often, chat extensions use the `request.model` language model instance to process the request. In this case, you might adjust the language model prompt to match the user's intent. Alternately, you can implement the extension logic by invoking a backend service, by using traditional programming logic, or by using a combination of all these options. For example, you could invoke a web search to gather additional information, which you then provide as context to the language model.
 
 While processing the current request, you might want to refer to previous chat messages. For example, if a previous response returned a C# code snippet, the user's current request might be "give the code in Python". Learn how you can [use the chat message history](#use-the-chat-message-history).
 
@@ -468,7 +468,6 @@ Once you have created your AI extension, you can publish your extension to the V
 
 - Before publishing to the VS Marketplace we recommend that you read the [Microsoft AI tools and practices guidelines](https://www.microsoft.com/en-us/ai/tools-practices). These guidelines provide best practices for the responsible development and use of AI technologies.
 - By publishing to the VS Marketplace, your extension is adhering to the [GitHub Copilot extensibility acceptable development and use policy](https://docs.github.com/en/early-access/copilot/github-copilot-extensibility-platform-partnership-plugin-acceptable-development-and-use-policy).
-- Update the attributes in the `package.json` to make it easy for users to find your extension. Add "AI" and "Chat" to the `categories` field in your `package.json`.
 - Upload to the Marketplace as described in [Publishing Extension](https://code.visualstudio.com/api/working-with-extensions/publishing-extension).
 - If your extension already contributes functionality other than chat, we recommend that you do not introduce an extension dependency on GitHub Copilot in the [extension manifest](/api/references/extension-manifest). This ensures that extension users that do not use GitHub Copilot can use the non-chat functionality without having to install GitHub Copilot.
 

api/extension-guides/language-model.md

@@ -69,7 +69,7 @@ const craftedPrompt = [
 
 ## Send the language model request
 
-Once you've built the prompt for the language model, you first select the language model you want to use with the [`selectChatModels`](/api/references/vscode-api#lm.selectChatModels) method. This method returns an array of language models that match the specified criteria. Then, you send the request to the language model by using the [`sendRequest`](/api/references/vscode-api#LanguageModelChat) method.
+Once you've built the prompt for the language model, you first select the language model you want to use with the [`selectChatModels`](/api/references/vscode-api#lm.selectChatModels) method. This method returns an array of language models that match the specified criteria. If you are implementing a chat participant, we recommend that you instead use the model that is passed as part of the `request` object in your chat request handler. This ensures that your extension respects the model that the user chose in the chat model dropdown. Then, you send the request to the language model by using the [`sendRequest`](/api/references/vscode-api#LanguageModelChat) method.
 
 To select the language model, you can specify the following properties: `vendor`, `id`, `family`, or `version`. Use these properties to either broadly match all models of a given vendor or family, or select one specific model by its ID. Learn more about these properties in the [API reference](/api/references/vscode-api#LanguageModelChat).
 
@@ -214,7 +214,6 @@ Once you have created your AI extension, you can publish your extension to the V
 
 - Before publishing to the VS Marketplace we recommend that you read the [Microsoft AI tools and practices guidelines](https://www.microsoft.com/en-us/ai/tools-practices). These guidelines provide best practices for the responsible development and use of AI technologies.
 - By publishing to the VS Marketplace, your extension is adhering to the [GitHub Copilot extensibility acceptable development and use policy](https://docs.github.com/en/early-access/copilot/github-copilot-extensibility-platform-partnership-plugin-acceptable-development-and-use-policy).
-- Update the attributes in the `package.json` to make it easy for users to find your extension. Add "AI" to the `categories` field in your `package.json`. If your extension contributes a Chat Participant, add "Chat" as well.
 - If your extension already contributes functionality other than using the Language Model API, we recommend that you do not introduce an extension dependency on GitHub Copilot in the [extension manifest](/api/references/extension-manifest). This ensures that extension users that do not use GitHub Copilot can use the non language model functionality without having to install GitHub Copilot. Make sure to have appropriate error handling when accessing language models for this case.
 - Upload to the Marketplace as described in [Publishing Extension](https://code.visualstudio.com/api/working-with-extensions/publishing-extension).