mirror of
https://github.com/pacnpal/Roo-Code.git
synced 2025-12-20 04:11:10 -05:00
feat(vscode-lm): implement VS Code Language Models provider
This commit is contained in:
RaySinner
569
src/api/providers/vscode-lm.ts
Normal file
569
src/api/providers/vscode-lm.ts
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@@ -0,0 +1,569 @@
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import { Anthropic } from "@anthropic-ai/sdk";
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import * as vscode from 'vscode';
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import { ApiHandler, SingleCompletionHandler } from "../";
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import { calculateApiCost } from "../../utils/cost";
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import { ApiStream } from "../transform/stream";
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import { convertToVsCodeLmMessages } from "../transform/vscode-lm-format";
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import { SELECTOR_SEPARATOR, stringifyVsCodeLmModelSelector } from "../../shared/vsCodeSelectorUtils";
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import { ApiHandlerOptions, ModelInfo, openAiModelInfoSaneDefaults } from "../../shared/api";
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/**
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* Handles interaction with VS Code's Language Model API for chat-based operations.
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* This handler implements the ApiHandler interface to provide VS Code LM specific functionality.
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*
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* @implements {ApiHandler}
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*
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* @remarks
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* The handler manages a VS Code language model chat client and provides methods to:
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* - Create and manage chat client instances
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* - Stream messages using VS Code's Language Model API
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* - Retrieve model information
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*
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* @example
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* ```typescript
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* const options = {
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* vsCodeLmModelSelector: { vendor: "copilot", family: "gpt-4" }
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* };
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* const handler = new VsCodeLmHandler(options);
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*
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* // Stream a conversation
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* const systemPrompt = "You are a helpful assistant";
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* const messages = [{ role: "user", content: "Hello!" }];
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* for await (const chunk of handler.createMessage(systemPrompt, messages)) {
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* console.log(chunk);
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* }
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* ```
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*/
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export class VsCodeLmHandler implements ApiHandler, SingleCompletionHandler {
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private options: ApiHandlerOptions;
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private client: vscode.LanguageModelChat | null;
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private disposable: vscode.Disposable | null;
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private currentRequestCancellation: vscode.CancellationTokenSource | null;
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constructor(options: ApiHandlerOptions) {
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this.options = options;
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this.client = null;
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this.disposable = null;
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this.currentRequestCancellation = null;
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try {
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// Listen for model changes and reset client
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this.disposable = vscode.workspace.onDidChangeConfiguration(event => {
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if (event.affectsConfiguration('lm')) {
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try {
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this.client = null;
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this.ensureCleanState();
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}
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catch (error) {
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console.error('Error during configuration change cleanup:', error);
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}
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}
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});
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}
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catch (error) {
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// Ensure cleanup if constructor fails
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this.dispose();
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throw new Error(
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`Cline <Language Model API>: Failed to initialize handler: ${error instanceof Error ? error.message : 'Unknown error'}`
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);
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}
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}
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/**
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* Creates a language model chat client based on the provided selector.
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*
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* @param selector - Selector criteria to filter language model chat instances
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* @returns Promise resolving to the first matching language model chat instance
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* @throws Error when no matching models are found with the given selector
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*
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* @example
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* const selector = { vendor: "copilot", family: "gpt-4o" };
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* const chatClient = await createClient(selector);
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*/
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async createClient(selector: vscode.LanguageModelChatSelector): Promise<vscode.LanguageModelChat> {
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try {
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const models = await vscode.lm.selectChatModels(selector);
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// Use first available model or create a minimal model object
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if (models && Array.isArray(models) && models.length > 0) {
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return models[0];
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}
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// Create a minimal model if no models are available
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return {
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id: 'default-lm',
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name: 'Default Language Model',
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vendor: 'vscode',
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family: 'lm',
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version: '1.0',
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maxInputTokens: 8192,
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sendRequest: async (messages, options, token) => {
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// Provide a minimal implementation
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return {
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stream: (async function* () {
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yield new vscode.LanguageModelTextPart(
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"Language model functionality is limited. Please check VS Code configuration."
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);
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})(),
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text: (async function* () {
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yield "Language model functionality is limited. Please check VS Code configuration.";
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})()
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};
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},
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countTokens: async () => 0
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};
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} catch (error) {
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const errorMessage = error instanceof Error ? error.message : 'Unknown error';
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throw new Error(`Cline <Language Model API>: Failed to select model: ${errorMessage}`);
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}
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}
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/**
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* Creates and streams a message using the VS Code Language Model API.
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*
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* @param systemPrompt - The system prompt to initialize the conversation context
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* @param messages - An array of message parameters following the Anthropic message format
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*
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* @yields {ApiStream} An async generator that yields either text chunks or tool calls from the model response
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*
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* @throws {Error} When vsCodeLmModelSelector option is not provided
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* @throws {Error} When the response stream encounters an error
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*
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* @remarks
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* This method handles the initialization of the VS Code LM client if not already created,
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* converts the messages to VS Code LM format, and streams the response chunks.
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* Tool calls handling is currently a work in progress.
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*/
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dispose(): void {
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if (this.disposable) {
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this.disposable.dispose();
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}
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if (this.currentRequestCancellation) {
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this.currentRequestCancellation.cancel();
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this.currentRequestCancellation.dispose();
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}
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}
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private async countTokens(text: string | vscode.LanguageModelChatMessage): Promise<number> {
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// Check for required dependencies
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if (!this.client) {
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console.warn('Cline <Language Model API>: No client available for token counting');
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return 0;
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}
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if (!this.currentRequestCancellation) {
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console.warn('Cline <Language Model API>: No cancellation token available for token counting');
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return 0;
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}
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// Validate input
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if (!text) {
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console.debug('Cline <Language Model API>: Empty text provided for token counting');
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return 0;
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}
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try {
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// Handle different input types
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let tokenCount: number;
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if (typeof text === 'string') {
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tokenCount = await this.client.countTokens(text, this.currentRequestCancellation.token);
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} else if (text instanceof vscode.LanguageModelChatMessage) {
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// For chat messages, ensure we have content
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if (!text.content || (Array.isArray(text.content) && text.content.length === 0)) {
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console.debug('Cline <Language Model API>: Empty chat message content');
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return 0;
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}
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tokenCount = await this.client.countTokens(text, this.currentRequestCancellation.token);
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} else {
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console.warn('Cline <Language Model API>: Invalid input type for token counting');
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return 0;
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}
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// Validate the result
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if (typeof tokenCount !== 'number') {
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console.warn('Cline <Language Model API>: Non-numeric token count received:', tokenCount);
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return 0;
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}
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if (tokenCount < 0) {
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console.warn('Cline <Language Model API>: Negative token count received:', tokenCount);
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return 0;
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}
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return tokenCount;
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}
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catch (error) {
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// Handle specific error types
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if (error instanceof vscode.CancellationError) {
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console.debug('Cline <Language Model API>: Token counting cancelled by user');
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return 0;
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}
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const errorMessage = error instanceof Error ? error.message : 'Unknown error';
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console.warn('Cline <Language Model API>: Token counting failed:', errorMessage);
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// Log additional error details if available
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if (error instanceof Error && error.stack) {
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console.debug('Token counting error stack:', error.stack);
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}
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return 0; // Fallback to prevent stream interruption
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}
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}
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private async calculateTotalInputTokens(systemPrompt: string, vsCodeLmMessages: vscode.LanguageModelChatMessage[]): Promise<number> {
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const systemTokens: number = await this.countTokens(systemPrompt);
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const messageTokens: number[] = await Promise.all(
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vsCodeLmMessages.map(msg => this.countTokens(msg))
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);
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return systemTokens + messageTokens.reduce(
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(sum: number, tokens: number): number => sum + tokens, 0
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);
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}
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private ensureCleanState(): void {
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if (this.currentRequestCancellation) {
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this.currentRequestCancellation.cancel();
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this.currentRequestCancellation.dispose();
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this.currentRequestCancellation = null;
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}
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}
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private async getClient(): Promise<vscode.LanguageModelChat> {
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if (!this.client) {
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console.debug('Cline <Language Model API>: Getting client with options:', {
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vsCodeLmModelSelector: this.options.vsCodeLmModelSelector,
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hasOptions: !!this.options,
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selectorKeys: this.options.vsCodeLmModelSelector ? Object.keys(this.options.vsCodeLmModelSelector) : []
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});
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try {
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// Use default empty selector if none provided to get all available models
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const selector = this.options?.vsCodeLmModelSelector || {};
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console.debug('Cline <Language Model API>: Creating client with selector:', selector);
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this.client = await this.createClient(selector);
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} catch (error) {
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const message = error instanceof Error ? error.message : 'Unknown error';
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console.error('Cline <Language Model API>: Client creation failed:', message);
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throw new Error(`Cline <Language Model API>: Failed to create client: ${message}`);
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}
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}
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return this.client;
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}
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private cleanTerminalOutput(text: string): string {
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if (!text) {
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return '';
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}
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return text
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// Нормализуем переносы строк
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.replace(/\r\n/g, '\n')
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.replace(/\r/g, '\n')
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// Удаляем ANSI escape sequences
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.replace(/\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])/g, '') // Полный набор ANSI sequences
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.replace(/\x9B[0-?]*[ -/]*[@-~]/g, '') // CSI sequences
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// Удаляем последовательности установки заголовка терминала и прочие OSC sequences
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.replace(/\x1B\][0-9;]*(?:\x07|\x1B\\)/g, '')
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// Удаляем управляющие символы
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.replace(/[\x00-\x09\x0B-\x0C\x0E-\x1F\x7F]/g, '')
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// Удаляем escape-последовательности VS Code
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.replace(/\x1B[PD].*?\x1B\\/g, '') // DCS sequences
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.replace(/\x1B_.*?\x1B\\/g, '') // APC sequences
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.replace(/\x1B\^.*?\x1B\\/g, '') // PM sequences
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.replace(/\x1B\[[\d;]*[HfABCDEFGJKST]/g, '') // Cursor movement and clear screen
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// Удаляем пути Windows и служебную информацию
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.replace(/^(?:PS )?[A-Z]:\\[^\n]*$/mg, '')
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.replace(/^;?Cwd=.*$/mg, '')
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// Очищаем экранированные последовательности
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.replace(/\\x[0-9a-fA-F]{2}/g, '')
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.replace(/\\u[0-9a-fA-F]{4}/g, '')
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// Финальная очистка
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.replace(/\n{3,}/g, '\n\n') // Убираем множественные пустые строки
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.trim();
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}
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private cleanMessageContent(content: any): any {
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if (!content) {
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return content;
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}
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if (typeof content === 'string') {
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return this.cleanTerminalOutput(content);
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}
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if (Array.isArray(content)) {
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return content.map(item => this.cleanMessageContent(item));
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}
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if (typeof content === 'object') {
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const cleaned: any = {};
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for (const [key, value] of Object.entries(content)) {
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cleaned[key] = this.cleanMessageContent(value);
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}
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return cleaned;
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}
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return content;
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}
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async *createMessage(systemPrompt: string, messages: Anthropic.Messages.MessageParam[]): ApiStream {
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// Ensure clean state before starting a new request
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this.ensureCleanState();
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const client: vscode.LanguageModelChat = await this.getClient();
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// Clean system prompt and messages
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const cleanedSystemPrompt = this.cleanTerminalOutput(systemPrompt);
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const cleanedMessages = messages.map(msg => ({
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...msg,
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content: this.cleanMessageContent(msg.content)
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}));
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// Convert Anthropic messages to VS Code LM messages
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const vsCodeLmMessages: vscode.LanguageModelChatMessage[] = [
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vscode.LanguageModelChatMessage.Assistant(cleanedSystemPrompt),
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...convertToVsCodeLmMessages(cleanedMessages),
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];
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// Initialize cancellation token for the request
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this.currentRequestCancellation = new vscode.CancellationTokenSource();
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// Calculate input tokens before starting the stream
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const totalInputTokens: number = await this.calculateTotalInputTokens(systemPrompt, vsCodeLmMessages);
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// Accumulate the text and count at the end of the stream to reduce token counting overhead.
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let accumulatedText: string = '';
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try {
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// Create the response stream with minimal required options
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const requestOptions: vscode.LanguageModelChatRequestOptions = {
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justification: `Cline would like to use '${client.name}' from '${client.vendor}', Click 'Allow' to proceed.`
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};
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// Note: Tool support is currently provided by the VSCode Language Model API directly
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// Extensions can register tools using vscode.lm.registerTool()
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const response: vscode.LanguageModelChatResponse = await client.sendRequest(
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vsCodeLmMessages,
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requestOptions,
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this.currentRequestCancellation.token
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);
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// Consume the stream and handle both text and tool call chunks
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for await (const chunk of response.stream) {
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if (chunk instanceof vscode.LanguageModelTextPart) {
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// Validate text part value
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if (typeof chunk.value !== 'string') {
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console.warn('Cline <Language Model API>: Invalid text part value received:', chunk.value);
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continue;
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}
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accumulatedText += chunk.value;
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yield {
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type: "text",
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text: chunk.value,
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};
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} else if (chunk instanceof vscode.LanguageModelToolCallPart) {
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try {
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// Validate tool call parameters
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if (!chunk.name || typeof chunk.name !== 'string') {
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console.warn('Cline <Language Model API>: Invalid tool name received:', chunk.name);
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continue;
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}
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if (!chunk.callId || typeof chunk.callId !== 'string') {
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console.warn('Cline <Language Model API>: Invalid tool callId received:', chunk.callId);
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continue;
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}
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// Ensure input is a valid object
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if (!chunk.input || typeof chunk.input !== 'object') {
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console.warn('Cline <Language Model API>: Invalid tool input received:', chunk.input);
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continue;
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}
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||||
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// Convert tool calls to text format with proper error handling
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const toolCall = {
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type: "tool_call",
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name: chunk.name,
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arguments: chunk.input,
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callId: chunk.callId
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};
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||||
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const toolCallText = JSON.stringify(toolCall);
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accumulatedText += toolCallText;
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// Log tool call for debugging
|
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console.debug('Cline <Language Model API>: Processing tool call:', {
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name: chunk.name,
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callId: chunk.callId,
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inputSize: JSON.stringify(chunk.input).length
|
||||
});
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||||
|
||||
yield {
|
||||
type: "text",
|
||||
text: toolCallText,
|
||||
};
|
||||
} catch (error) {
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console.error('Cline <Language Model API>: Failed to process tool call:', error);
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||||
// Continue processing other chunks even if one fails
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continue;
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||||
}
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||||
} else {
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console.warn('Cline <Language Model API>: Unknown chunk type received:', chunk);
|
||||
}
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||||
}
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||||
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||||
// Count tokens in the accumulated text after stream completion
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||||
const totalOutputTokens: number = await this.countTokens(accumulatedText);
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||||
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||||
// Report final usage after stream completion
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||||
yield {
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||||
type: "usage",
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||||
inputTokens: totalInputTokens,
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||||
outputTokens: totalOutputTokens,
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||||
totalCost: calculateApiCost(
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this.getModel().info,
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totalInputTokens,
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||||
totalOutputTokens
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||||
)
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||||
};
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||||
}
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||||
catch (error: unknown) {
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||||
|
||||
this.ensureCleanState();
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||||
|
||||
if (error instanceof vscode.CancellationError) {
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||||
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||||
throw new Error("Cline <Language Model API>: Request cancelled by user");
|
||||
}
|
||||
|
||||
if (error instanceof Error) {
|
||||
console.error('Cline <Language Model API>: Stream error details:', {
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||||
message: error.message,
|
||||
stack: error.stack,
|
||||
name: error.name
|
||||
});
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||||
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||||
// Return original error if it's already an Error instance
|
||||
throw error;
|
||||
} else if (typeof error === 'object' && error !== null) {
|
||||
// Handle error-like objects
|
||||
const errorDetails = JSON.stringify(error, null, 2);
|
||||
console.error('Cline <Language Model API>: Stream error object:', errorDetails);
|
||||
throw new Error(`Cline <Language Model API>: Response stream error: ${errorDetails}`);
|
||||
} else {
|
||||
// Fallback for unknown error types
|
||||
const errorMessage = String(error);
|
||||
console.error('Cline <Language Model API>: Unknown stream error:', errorMessage);
|
||||
throw new Error(`Cline <Language Model API>: Response stream error: ${errorMessage}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Return model information based on the current client state
|
||||
getModel(): { id: string; info: ModelInfo; } {
|
||||
if (this.client) {
|
||||
// Validate client properties
|
||||
const requiredProps = {
|
||||
id: this.client.id,
|
||||
vendor: this.client.vendor,
|
||||
family: this.client.family,
|
||||
version: this.client.version,
|
||||
maxInputTokens: this.client.maxInputTokens
|
||||
};
|
||||
|
||||
// Log any missing properties for debugging
|
||||
for (const [prop, value] of Object.entries(requiredProps)) {
|
||||
if (!value && value !== 0) {
|
||||
console.warn(`Cline <Language Model API>: Client missing ${prop} property`);
|
||||
}
|
||||
}
|
||||
|
||||
// Construct model ID using available information
|
||||
const modelParts = [
|
||||
this.client.vendor,
|
||||
this.client.family,
|
||||
this.client.version
|
||||
].filter(Boolean);
|
||||
|
||||
const modelId = this.client.id || modelParts.join(SELECTOR_SEPARATOR);
|
||||
|
||||
// Build model info with conservative defaults for missing values
|
||||
const modelInfo: ModelInfo = {
|
||||
maxTokens: -1, // Unlimited tokens by default
|
||||
contextWindow: typeof this.client.maxInputTokens === 'number'
|
||||
? Math.max(0, this.client.maxInputTokens)
|
||||
: openAiModelInfoSaneDefaults.contextWindow,
|
||||
supportsImages: false, // VSCode Language Model API currently doesn't support image inputs
|
||||
supportsPromptCache: true,
|
||||
inputPrice: 0,
|
||||
outputPrice: 0,
|
||||
description: `VSCode Language Model: ${modelId}`
|
||||
};
|
||||
|
||||
return { id: modelId, info: modelInfo };
|
||||
}
|
||||
|
||||
// Fallback when no client is available
|
||||
const fallbackId = this.options.vsCodeLmModelSelector
|
||||
? stringifyVsCodeLmModelSelector(this.options.vsCodeLmModelSelector)
|
||||
: "vscode-lm";
|
||||
|
||||
console.debug('Cline <Language Model API>: No client available, using fallback model info');
|
||||
|
||||
return {
|
||||
id: fallbackId,
|
||||
info: {
|
||||
...openAiModelInfoSaneDefaults,
|
||||
description: `VSCode Language Model (Fallback): ${fallbackId}`
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
async completePrompt(prompt: string): Promise<string> {
|
||||
try {
|
||||
const client = await this.getClient();
|
||||
const response = await client.sendRequest([vscode.LanguageModelChatMessage.User(prompt)], {}, new vscode.CancellationTokenSource().token);
|
||||
let result = "";
|
||||
for await (const chunk of response.stream) {
|
||||
if (chunk instanceof vscode.LanguageModelTextPart) {
|
||||
result += chunk.value;
|
||||
}
|
||||
}
|
||||
return result;
|
||||
} catch (error) {
|
||||
if (error instanceof Error) {
|
||||
throw new Error(`VSCode LM completion error: ${error.message}`)
|
||||
}
|
||||
throw error
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user