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Requesty provider fixes (#3193)
Co-authored-by: Chris Estreich <cestreich@gmail.com>
This commit is contained in:
Daniel Trugman
GitHub
parent
8ab0de3d02
commit
ce8fbbdafa
24 changed files with 365 additions and 324 deletions
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@ -2,338 +2,227 @@
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import { Anthropic } from "@anthropic-ai/sdk"
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import OpenAI from "openai"
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import { ApiHandlerOptions, ModelInfo } from "../../../shared/api"
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import { RequestyHandler } from "../requesty"
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import { convertToOpenAiMessages } from "../../transform/openai-format"
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import { convertToR1Format } from "../../transform/r1-format"
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// Mock OpenAI and transform functions
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import { RequestyHandler } from "../requesty"
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import { ApiHandlerOptions } from "../../../shared/api"
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jest.mock("openai")
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jest.mock("../../transform/openai-format")
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jest.mock("../../transform/r1-format")
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jest.mock("delay", () => jest.fn(() => Promise.resolve()))
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jest.mock("../fetchers/cache", () => ({
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getModels: jest.fn().mockResolvedValue({
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"test-model": {
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maxTokens: 8192,
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contextWindow: 200_000,
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supportsImages: true,
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supportsComputerUse: true,
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supportsPromptCache: true,
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inputPrice: 3.0,
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outputPrice: 15.0,
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cacheWritesPrice: 3.75,
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cacheReadsPrice: 0.3,
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description: "Test model description",
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},
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getModels: jest.fn().mockImplementation(() => {
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return Promise.resolve({
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"coding/claude-3-7-sonnet": {
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maxTokens: 8192,
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contextWindow: 200000,
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supportsImages: true,
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supportsPromptCache: true,
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supportsComputerUse: true,
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inputPrice: 3,
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outputPrice: 15,
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cacheWritesPrice: 3.75,
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cacheReadsPrice: 0.3,
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description: "Claude 3.7 Sonnet",
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},
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})
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}),
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}))
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describe("RequestyHandler", () => {
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let handler: RequestyHandler
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let mockCreate: jest.Mock
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const modelInfo: ModelInfo = {
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maxTokens: 8192,
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contextWindow: 200_000,
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supportsImages: true,
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supportsComputerUse: true,
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supportsPromptCache: true,
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inputPrice: 3.0,
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outputPrice: 15.0,
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cacheWritesPrice: 3.75,
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cacheReadsPrice: 0.3,
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description:
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"Claude 3.7 Sonnet is an advanced large language model with improved reasoning, coding, and problem-solving capabilities. It introduces a hybrid reasoning approach, allowing users to choose between rapid responses and extended, step-by-step processing for complex tasks. The model demonstrates notable improvements in coding, particularly in front-end development and full-stack updates, and excels in agentic workflows, where it can autonomously navigate multi-step processes. Claude 3.7 Sonnet maintains performance parity with its predecessor in standard mode while offering an extended reasoning mode for enhanced accuracy in math, coding, and instruction-following tasks. Read more at the [blog post here](https://www.anthropic.com/news/claude-3-7-sonnet)",
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}
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const defaultOptions: ApiHandlerOptions = {
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const mockOptions: ApiHandlerOptions = {
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requestyApiKey: "test-key",
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requestyModelId: "test-model",
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openAiStreamingEnabled: true,
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includeMaxTokens: true, // Add this to match the implementation
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requestyModelId: "coding/claude-3-7-sonnet",
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}
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beforeEach(() => {
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// Clear mocks
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jest.clearAllMocks()
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beforeEach(() => jest.clearAllMocks())
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// Setup mock create function that preserves params
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mockCreate = jest.fn().mockImplementation((_params) => {
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return {
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[Symbol.asyncIterator]: async function* () {
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yield {
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choices: [{ delta: { content: "Hello" } }],
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}
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yield {
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choices: [{ delta: { content: " world" } }],
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usage: {
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prompt_tokens: 30,
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completion_tokens: 10,
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prompt_tokens_details: {
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cached_tokens: 15,
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caching_tokens: 5,
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},
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},
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}
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},
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}
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it("initializes with correct options", () => {
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const handler = new RequestyHandler(mockOptions)
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expect(handler).toBeInstanceOf(RequestyHandler)
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expect(OpenAI).toHaveBeenCalledWith({
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baseURL: "https://router.requesty.ai/v1",
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apiKey: mockOptions.requestyApiKey,
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defaultHeaders: {
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"HTTP-Referer": "https://github.com/RooVetGit/Roo-Cline",
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"X-Title": "Roo Code",
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},
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})
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// Mock OpenAI constructor
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;(OpenAI as jest.MockedClass<typeof OpenAI>).mockImplementation(
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() =>
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({
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chat: {
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completions: {
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create: (params: any) => {
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// Store params for verification
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const result = mockCreate(params)
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// Make params available for test assertions
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;(result as any).params = params
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return result
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},
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},
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},
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}) as unknown as OpenAI,
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)
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// Mock transform functions
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;(convertToOpenAiMessages as jest.Mock).mockImplementation((messages) => messages)
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;(convertToR1Format as jest.Mock).mockImplementation((messages) => messages)
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// Create handler instance
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handler = new RequestyHandler(defaultOptions)
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})
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describe("constructor", () => {
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it("should initialize with correct options", () => {
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expect(OpenAI).toHaveBeenCalledWith({
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baseURL: "https://router.requesty.ai/v1",
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apiKey: defaultOptions.requestyApiKey,
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defaultHeaders: {
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"HTTP-Referer": "https://github.com/RooVetGit/Roo-Cline",
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"X-Title": "Roo Code",
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describe("fetchModel", () => {
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it("returns correct model info when options are provided", async () => {
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const handler = new RequestyHandler(mockOptions)
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const result = await handler.fetchModel()
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expect(result).toMatchObject({
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id: mockOptions.requestyModelId,
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info: {
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maxTokens: 8192,
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contextWindow: 200000,
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supportsImages: true,
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supportsPromptCache: true,
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supportsComputerUse: true,
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inputPrice: 3,
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outputPrice: 15,
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cacheWritesPrice: 3.75,
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cacheReadsPrice: 0.3,
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description: "Claude 3.7 Sonnet",
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},
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})
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})
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it("returns default model info when options are not provided", async () => {
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const handler = new RequestyHandler({})
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const result = await handler.fetchModel()
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expect(result).toMatchObject({
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id: mockOptions.requestyModelId,
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info: {
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maxTokens: 8192,
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contextWindow: 200000,
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supportsImages: true,
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supportsPromptCache: true,
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supportsComputerUse: true,
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inputPrice: 3,
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outputPrice: 15,
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cacheWritesPrice: 3.75,
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cacheReadsPrice: 0.3,
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description: "Claude 3.7 Sonnet",
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},
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})
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})
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})
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describe("createMessage", () => {
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const systemPrompt = "You are a helpful assistant"
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const messages: Anthropic.Messages.MessageParam[] = [{ role: "user", content: "Hello" }]
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it("generates correct stream chunks", async () => {
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const handler = new RequestyHandler(mockOptions)
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describe("with streaming enabled", () => {
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beforeEach(() => {
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const stream = {
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[Symbol.asyncIterator]: async function* () {
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yield {
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choices: [{ delta: { content: "Hello" } }],
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}
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yield {
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choices: [{ delta: { content: " world" } }],
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usage: {
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prompt_tokens: 30,
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completion_tokens: 10,
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prompt_tokens_details: {
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cached_tokens: 15,
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caching_tokens: 5,
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},
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const mockStream = {
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async *[Symbol.asyncIterator]() {
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yield {
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id: mockOptions.requestyModelId,
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choices: [{ delta: { content: "test response" } }],
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}
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yield {
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id: "test-id",
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choices: [{ delta: {} }],
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usage: {
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prompt_tokens: 10,
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completion_tokens: 20,
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prompt_tokens_details: {
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caching_tokens: 5,
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cached_tokens: 2,
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},
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}
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},
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}
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mockCreate.mockResolvedValue(stream)
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},
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}
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},
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}
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// Mock OpenAI chat.completions.create
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const mockCreate = jest.fn().mockResolvedValue(mockStream)
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;(OpenAI as jest.MockedClass<typeof OpenAI>).prototype.chat = {
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completions: { create: mockCreate },
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} as any
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const systemPrompt = "test system prompt"
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const messages: Anthropic.Messages.MessageParam[] = [{ role: "user" as const, content: "test message" }]
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const generator = handler.createMessage(systemPrompt, messages)
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const chunks = []
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for await (const chunk of generator) {
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chunks.push(chunk)
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}
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// Verify stream chunks
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expect(chunks).toHaveLength(2) // One text chunk and one usage chunk
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expect(chunks[0]).toEqual({ type: "text", text: "test response" })
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expect(chunks[1]).toEqual({
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type: "usage",
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inputTokens: 10,
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outputTokens: 20,
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cacheWriteTokens: 5,
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cacheReadTokens: 2,
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totalCost: expect.any(Number),
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})
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it("should handle streaming response correctly", async () => {
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const stream = handler.createMessage(systemPrompt, messages)
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const results = []
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for await (const chunk of stream) {
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results.push(chunk)
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}
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expect(results).toEqual([
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{ type: "text", text: "Hello" },
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{ type: "text", text: " world" },
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{
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type: "usage",
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inputTokens: 30,
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outputTokens: 10,
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cacheWriteTokens: 5,
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cacheReadTokens: 15,
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totalCost: 0.00020325000000000003, // (10 * 3 / 1,000,000) + (5 * 3.75 / 1,000,000) + (15 * 0.3 / 1,000,000) + (10 * 15 / 1,000,000) (the ...0 is a fp skew)
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},
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])
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// Get the actual params that were passed
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const calls = mockCreate.mock.calls
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expect(calls.length).toBe(1)
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const actualParams = calls[0][0]
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expect(actualParams).toEqual({
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model: defaultOptions.requestyModelId,
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temperature: 0,
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// Verify OpenAI client was called with correct parameters
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expect(mockCreate).toHaveBeenCalledWith(
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expect.objectContaining({
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max_tokens: undefined,
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messages: [
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{
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role: "system",
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content: [
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{
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cache_control: {
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type: "ephemeral",
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},
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text: systemPrompt,
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type: "text",
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},
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],
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content: "test system prompt",
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},
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{
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role: "user",
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content: [
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{
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cache_control: {
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type: "ephemeral",
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},
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text: "Hello",
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type: "text",
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},
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],
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content: "test message",
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},
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],
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model: "coding/claude-3-7-sonnet",
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stream: true,
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stream_options: { include_usage: true },
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max_tokens: modelInfo.maxTokens,
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})
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})
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it("should not include max_tokens when includeMaxTokens is false", async () => {
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handler = new RequestyHandler({
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...defaultOptions,
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includeMaxTokens: false,
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})
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await handler.createMessage(systemPrompt, messages).next()
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expect(mockCreate).toHaveBeenCalledWith(
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expect.not.objectContaining({
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max_tokens: expect.any(Number),
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}),
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)
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})
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it("should handle deepseek-reasoner model format", async () => {
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handler = new RequestyHandler({
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...defaultOptions,
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requestyModelId: "deepseek-reasoner",
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})
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await handler.createMessage(systemPrompt, messages).next()
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expect(convertToR1Format).toHaveBeenCalledWith([{ role: "user", content: systemPrompt }, ...messages])
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})
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temperature: undefined,
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}),
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)
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})
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describe("with streaming disabled", () => {
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beforeEach(() => {
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handler = new RequestyHandler({
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...defaultOptions,
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openAiStreamingEnabled: false,
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})
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it("handles API errors", async () => {
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const handler = new RequestyHandler(mockOptions)
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const mockError = new Error("API Error")
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const mockCreate = jest.fn().mockRejectedValue(mockError)
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;(OpenAI as jest.MockedClass<typeof OpenAI>).prototype.chat = {
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completions: { create: mockCreate },
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} as any
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mockCreate.mockResolvedValue({
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choices: [{ message: { content: "Hello world" } }],
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usage: {
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prompt_tokens: 10,
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completion_tokens: 5,
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},
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})
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})
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it("should handle non-streaming response correctly", async () => {
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const stream = handler.createMessage(systemPrompt, messages)
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const results = []
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for await (const chunk of stream) {
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results.push(chunk)
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}
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expect(results).toEqual([
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{ type: "text", text: "Hello world" },
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{
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type: "usage",
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inputTokens: 10,
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outputTokens: 5,
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cacheWriteTokens: 0,
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cacheReadTokens: 0,
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totalCost: 0.000105, // (10 * 3 / 1,000,000) + (5 * 15 / 1,000,000)
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},
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])
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expect(mockCreate).toHaveBeenCalledWith({
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model: defaultOptions.requestyModelId,
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messages: [
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{ role: "user", content: systemPrompt },
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{
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role: "user",
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content: [
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{
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cache_control: {
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type: "ephemeral",
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},
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text: "Hello",
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type: "text",
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},
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],
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},
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],
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})
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})
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})
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})
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describe("getModel", () => {
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it("should return correct model information", () => {
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const result = handler.getModel()
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expect(result).toEqual({
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id: defaultOptions.requestyModelId,
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info: modelInfo,
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})
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})
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it("should use sane defaults when no model info provided", () => {
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handler = new RequestyHandler(defaultOptions)
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const result = handler.getModel()
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expect(result).toEqual({
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id: defaultOptions.requestyModelId,
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info: modelInfo,
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})
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const generator = handler.createMessage("test", [])
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await expect(generator.next()).rejects.toThrow("API Error")
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})
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})
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describe("completePrompt", () => {
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beforeEach(() => {
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mockCreate.mockResolvedValue({
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choices: [{ message: { content: "Completed response" } }],
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})
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})
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it("returns correct response", async () => {
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const handler = new RequestyHandler(mockOptions)
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const mockResponse = { choices: [{ message: { content: "test completion" } }] }
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const mockCreate = jest.fn().mockResolvedValue(mockResponse)
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;(OpenAI as jest.MockedClass<typeof OpenAI>).prototype.chat = {
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completions: { create: mockCreate },
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} as any
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const result = await handler.completePrompt("test prompt")
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expect(result).toBe("test completion")
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it("should complete prompt successfully", async () => {
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const result = await handler.completePrompt("Test prompt")
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expect(result).toBe("Completed response")
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expect(mockCreate).toHaveBeenCalledWith({
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model: defaultOptions.requestyModelId,
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messages: [{ role: "user", content: "Test prompt" }],
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model: mockOptions.requestyModelId,
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max_tokens: undefined,
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messages: [{ role: "system", content: "test prompt" }],
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temperature: undefined,
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})
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})
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it("should handle errors correctly", async () => {
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const errorMessage = "API error"
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mockCreate.mockRejectedValue(new Error(errorMessage))
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it("handles API errors", async () => {
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const handler = new RequestyHandler(mockOptions)
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const mockError = new Error("API Error")
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const mockCreate = jest.fn().mockRejectedValue(mockError)
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;(OpenAI as jest.MockedClass<typeof OpenAI>).prototype.chat = {
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completions: { create: mockCreate },
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} as any
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await expect(handler.completePrompt("Test prompt")).rejects.toThrow(
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`OpenAI completion error: ${errorMessage}`,
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)
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await expect(handler.completePrompt("test prompt")).rejects.toThrow("API Error")
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})
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it("handles unexpected errors", async () => {
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const handler = new RequestyHandler(mockOptions)
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const mockCreate = jest.fn().mockRejectedValue(new Error("Unexpected error"))
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||||
;(OpenAI as jest.MockedClass<typeof OpenAI>).prototype.chat = {
|
||||
completions: { create: mockCreate },
|
||||
} as any
|
||||
|
||||
await expect(handler.completePrompt("test prompt")).rejects.toThrow("Unexpected error")
|
||||
})
|
||||
})
|
||||
})
|
||||
|
|
|
|||
|
|
@ -38,9 +38,8 @@ async function readModels(router: RouterName): Promise<ModelRecord | undefined>
|
|||
* @param router - The router to fetch models from.
|
||||
* @returns The models from the cache or the fetched models.
|
||||
*/
|
||||
export const getModels = async (router: RouterName): Promise<ModelRecord> => {
|
||||
export const getModels = async (router: RouterName, apiKey: string | undefined = undefined): Promise<ModelRecord> => {
|
||||
let models = memoryCache.get<ModelRecord>(router)
|
||||
|
||||
if (models) {
|
||||
// console.log(`[getModels] NodeCache hit for ${router} -> ${Object.keys(models).length}`)
|
||||
return models
|
||||
|
|
@ -51,7 +50,8 @@ export const getModels = async (router: RouterName): Promise<ModelRecord> => {
|
|||
models = await getOpenRouterModels()
|
||||
break
|
||||
case "requesty":
|
||||
models = await getRequestyModels()
|
||||
// Requesty models endpoint requires an API key for per-user custom policies
|
||||
models = await getRequestyModels(apiKey)
|
||||
break
|
||||
case "glama":
|
||||
models = await getGlamaModels()
|
||||
|
|
@ -80,3 +80,11 @@ export const getModels = async (router: RouterName): Promise<ModelRecord> => {
|
|||
|
||||
return models ?? {}
|
||||
}
|
||||
|
||||
/**
|
||||
* Flush models memory cache for a specific router
|
||||
* @param router - The router to flush models for.
|
||||
*/
|
||||
export const flushModels = async (router: RouterName) => {
|
||||
memoryCache.del(router)
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,11 +1,19 @@
|
|||
import { Anthropic } from "@anthropic-ai/sdk"
|
||||
import OpenAI from "openai"
|
||||
|
||||
import { ModelInfo, ModelRecord, requestyDefaultModelId, requestyDefaultModelInfo } from "../../shared/api"
|
||||
import {
|
||||
ApiHandlerOptions,
|
||||
ModelInfo,
|
||||
ModelRecord,
|
||||
requestyDefaultModelId,
|
||||
requestyDefaultModelInfo,
|
||||
} from "../../shared/api"
|
||||
import { convertToOpenAiMessages } from "../transform/openai-format"
|
||||
import { calculateApiCostOpenAI } from "../../utils/cost"
|
||||
import { ApiStream, ApiStreamUsageChunk } from "../transform/stream"
|
||||
import { OpenAiHandler, OpenAiHandlerOptions } from "./openai"
|
||||
import { SingleCompletionHandler } from "../"
|
||||
import { BaseProvider } from "./base-provider"
|
||||
import { DEFAULT_HEADERS } from "./constants"
|
||||
import { getModels } from "./fetchers/cache"
|
||||
import OpenAI from "openai"
|
||||
|
||||
// Requesty usage includes an extra field for Anthropic use cases.
|
||||
// Safely cast the prompt token details section to the appropriate structure.
|
||||
|
|
@ -17,25 +25,28 @@ interface RequestyUsage extends OpenAI.CompletionUsage {
|
|||
total_cost?: number
|
||||
}
|
||||
|
||||
export class RequestyHandler extends OpenAiHandler {
|
||||
type RequestyChatCompletionParams = OpenAI.Chat.ChatCompletionCreateParams & {}
|
||||
|
||||
export class RequestyHandler extends BaseProvider implements SingleCompletionHandler {
|
||||
protected options: ApiHandlerOptions
|
||||
protected models: ModelRecord = {}
|
||||
private client: OpenAI
|
||||
|
||||
constructor(options: OpenAiHandlerOptions) {
|
||||
if (!options.requestyApiKey) {
|
||||
throw new Error("Requesty API key is required. Please provide it in the settings.")
|
||||
}
|
||||
constructor(options: ApiHandlerOptions) {
|
||||
super()
|
||||
this.options = options
|
||||
|
||||
super({
|
||||
...options,
|
||||
openAiApiKey: options.requestyApiKey,
|
||||
openAiModelId: options.requestyModelId ?? requestyDefaultModelId,
|
||||
openAiBaseUrl: "https://router.requesty.ai/v1",
|
||||
})
|
||||
const apiKey = this.options.requestyApiKey ?? "not-provided"
|
||||
const baseURL = "https://router.requesty.ai/v1"
|
||||
|
||||
const defaultHeaders = DEFAULT_HEADERS
|
||||
|
||||
this.client = new OpenAI({ baseURL, apiKey, defaultHeaders })
|
||||
}
|
||||
|
||||
override async *createMessage(systemPrompt: string, messages: Anthropic.Messages.MessageParam[]): ApiStream {
|
||||
public async fetchModel() {
|
||||
this.models = await getModels("requesty")
|
||||
yield* super.createMessage(systemPrompt, messages)
|
||||
return this.getModel()
|
||||
}
|
||||
|
||||
override getModel(): { id: string; info: ModelInfo } {
|
||||
|
|
@ -44,7 +55,7 @@ export class RequestyHandler extends OpenAiHandler {
|
|||
return { id, info }
|
||||
}
|
||||
|
||||
protected override processUsageMetrics(usage: any, modelInfo?: ModelInfo): ApiStreamUsageChunk {
|
||||
protected processUsageMetrics(usage: any, modelInfo?: ModelInfo): ApiStreamUsageChunk {
|
||||
const requestyUsage = usage as RequestyUsage
|
||||
const inputTokens = requestyUsage?.prompt_tokens || 0
|
||||
const outputTokens = requestyUsage?.completion_tokens || 0
|
||||
|
|
@ -64,8 +75,74 @@ export class RequestyHandler extends OpenAiHandler {
|
|||
}
|
||||
}
|
||||
|
||||
override async completePrompt(prompt: string): Promise<string> {
|
||||
this.models = await getModels("requesty")
|
||||
return super.completePrompt(prompt)
|
||||
override async *createMessage(systemPrompt: string, messages: Anthropic.Messages.MessageParam[]): ApiStream {
|
||||
const model = await this.fetchModel()
|
||||
|
||||
let openAiMessages: OpenAI.Chat.ChatCompletionMessageParam[] = [
|
||||
{ role: "system", content: systemPrompt },
|
||||
...convertToOpenAiMessages(messages),
|
||||
]
|
||||
|
||||
let maxTokens = undefined
|
||||
if (this.options.includeMaxTokens) {
|
||||
maxTokens = model.info.maxTokens
|
||||
}
|
||||
|
||||
const temperature = this.options.modelTemperature
|
||||
|
||||
const completionParams: RequestyChatCompletionParams = {
|
||||
model: model.id,
|
||||
max_tokens: maxTokens,
|
||||
messages: openAiMessages,
|
||||
temperature: temperature,
|
||||
stream: true,
|
||||
stream_options: { include_usage: true },
|
||||
}
|
||||
|
||||
const stream = await this.client.chat.completions.create(completionParams)
|
||||
|
||||
for await (const chunk of stream) {
|
||||
const delta = chunk.choices[0]?.delta
|
||||
if (delta?.content) {
|
||||
yield {
|
||||
type: "text",
|
||||
text: delta.content,
|
||||
}
|
||||
}
|
||||
|
||||
if (delta && "reasoning_content" in delta && delta.reasoning_content) {
|
||||
yield {
|
||||
type: "reasoning",
|
||||
text: (delta.reasoning_content as string | undefined) || "",
|
||||
}
|
||||
}
|
||||
|
||||
if (chunk.usage) {
|
||||
yield this.processUsageMetrics(chunk.usage, model.info)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async completePrompt(prompt: string): Promise<string> {
|
||||
const model = await this.fetchModel()
|
||||
|
||||
let openAiMessages: OpenAI.Chat.ChatCompletionMessageParam[] = [{ role: "system", content: prompt }]
|
||||
|
||||
let maxTokens = undefined
|
||||
if (this.options.includeMaxTokens) {
|
||||
maxTokens = model.info.maxTokens
|
||||
}
|
||||
|
||||
const temperature = this.options.modelTemperature
|
||||
|
||||
const completionParams: RequestyChatCompletionParams = {
|
||||
model: model.id,
|
||||
max_tokens: maxTokens,
|
||||
messages: openAiMessages,
|
||||
temperature: temperature,
|
||||
}
|
||||
|
||||
const response: OpenAI.Chat.ChatCompletion = await this.client.chat.completions.create(completionParams)
|
||||
return response.choices[0]?.message.content || ""
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ import * as vscode from "vscode"
|
|||
import { ClineProvider } from "./ClineProvider"
|
||||
import { Language, ApiConfigMeta } from "../../schemas"
|
||||
import { changeLanguage, t } from "../../i18n"
|
||||
import { ApiConfiguration } from "../../shared/api"
|
||||
import { ApiConfiguration, RouterName, toRouterName } from "../../shared/api"
|
||||
import { supportPrompt } from "../../shared/support-prompt"
|
||||
|
||||
import { checkoutDiffPayloadSchema, checkoutRestorePayloadSchema, WebviewMessage } from "../../shared/WebviewMessage"
|
||||
|
|
@ -34,7 +34,7 @@ import { TelemetrySetting } from "../../shared/TelemetrySetting"
|
|||
import { getWorkspacePath } from "../../utils/path"
|
||||
import { Mode, defaultModeSlug } from "../../shared/modes"
|
||||
import { GlobalState } from "../../schemas"
|
||||
import { getModels } from "../../api/providers/fetchers/cache"
|
||||
import { getModels, flushModels } from "../../api/providers/fetchers/cache"
|
||||
import { generateSystemPrompt } from "./generateSystemPrompt"
|
||||
|
||||
const ALLOWED_VSCODE_SETTINGS = new Set(["terminal.integrated.inheritEnv"])
|
||||
|
|
@ -282,12 +282,18 @@ export const webviewMessageHandler = async (provider: ClineProvider, message: We
|
|||
case "resetState":
|
||||
await provider.resetState()
|
||||
break
|
||||
case "flushRouterModels":
|
||||
const routerName: RouterName = toRouterName(message.text)
|
||||
await flushModels(routerName)
|
||||
break
|
||||
case "requestRouterModels":
|
||||
const { apiConfiguration } = await provider.getState()
|
||||
|
||||
const [openRouterModels, requestyModels, glamaModels, unboundModels] = await Promise.all([
|
||||
getModels("openrouter"),
|
||||
getModels("requesty"),
|
||||
getModels("glama"),
|
||||
getModels("unbound"),
|
||||
getModels("openrouter", apiConfiguration.openRouterApiKey),
|
||||
getModels("requesty", apiConfiguration.requestyApiKey),
|
||||
getModels("glama", apiConfiguration.glamaApiKey),
|
||||
getModels("unbound", apiConfiguration.unboundApiKey),
|
||||
])
|
||||
|
||||
provider.postMessageToWebview({
|
||||
|
|
|
|||
|
|
@ -42,6 +42,7 @@ export interface WebviewMessage {
|
|||
| "importSettings"
|
||||
| "exportSettings"
|
||||
| "resetState"
|
||||
| "flushRouterModels"
|
||||
| "requestRouterModels"
|
||||
| "requestOpenAiModels"
|
||||
| "requestOllamaModels"
|
||||
|
|
|
|||
|
|
@ -437,7 +437,7 @@ export const glamaDefaultModelInfo: ModelInfo = {
|
|||
|
||||
// Requesty
|
||||
// https://requesty.ai/router-2
|
||||
export const requestyDefaultModelId = "anthropic/claude-3-7-sonnet-latest"
|
||||
export const requestyDefaultModelId = "coding/claude-3-7-sonnet"
|
||||
export const requestyDefaultModelInfo: ModelInfo = {
|
||||
maxTokens: 8192,
|
||||
contextWindow: 200_000,
|
||||
|
|
@ -449,7 +449,7 @@ export const requestyDefaultModelInfo: ModelInfo = {
|
|||
cacheWritesPrice: 3.75,
|
||||
cacheReadsPrice: 0.3,
|
||||
description:
|
||||
"Claude 3.7 Sonnet is an advanced large language model with improved reasoning, coding, and problem-solving capabilities. It introduces a hybrid reasoning approach, allowing users to choose between rapid responses and extended, step-by-step processing for complex tasks. The model demonstrates notable improvements in coding, particularly in front-end development and full-stack updates, and excels in agentic workflows, where it can autonomously navigate multi-step processes. Claude 3.7 Sonnet maintains performance parity with its predecessor in standard mode while offering an extended reasoning mode for enhanced accuracy in math, coding, and instruction-following tasks. Read more at the [blog post here](https://www.anthropic.com/news/claude-3-7-sonnet)",
|
||||
"The best coding model, optimized by Requesty, and automatically routed to the fastest provider. Claude 3.7 Sonnet is an advanced large language model with improved reasoning, coding, and problem-solving capabilities. It introduces a hybrid reasoning approach, allowing users to choose between rapid responses and extended, step-by-step processing for complex tasks. The model demonstrates notable improvements in coding, particularly in front-end development and full-stack updates, and excels in agentic workflows, where it can autonomously navigate multi-step processes. Claude 3.7 Sonnet maintains performance parity with its predecessor in standard mode while offering an extended reasoning mode for enhanced accuracy in math, coding, and instruction-following tasks. Read more at the [blog post here](https://www.anthropic.com/news/claude-3-7-sonnet)",
|
||||
}
|
||||
|
||||
// OpenRouter
|
||||
|
|
@ -1701,6 +1701,13 @@ export type RouterName = (typeof routerNames)[number]
|
|||
|
||||
export const isRouterName = (value: string): value is RouterName => routerNames.includes(value as RouterName)
|
||||
|
||||
export function toRouterName(value?: string): RouterName {
|
||||
if (value && isRouterName(value)) {
|
||||
return value
|
||||
}
|
||||
throw new Error(`Invalid router name: ${value}`)
|
||||
}
|
||||
|
||||
export type ModelRecord = Record<string, ModelInfo>
|
||||
|
||||
export type RouterModels = Record<RouterName, ModelRecord>
|
||||
|
|
|
|||
|
|
@ -22,9 +22,9 @@ import {
|
|||
useOpenRouterModelProviders,
|
||||
OPENROUTER_DEFAULT_PROVIDER_NAME,
|
||||
} from "@src/components/ui/hooks/useOpenRouterModelProviders"
|
||||
import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@src/components/ui"
|
||||
import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue, Button } from "@src/components/ui"
|
||||
import { VSCodeButtonLink } from "@src/components/common/VSCodeButtonLink"
|
||||
import { getRequestyAuthUrl, getGlamaAuthUrl } from "@src/oauth/urls"
|
||||
import { getRequestyApiKeyUrl, getGlamaAuthUrl } from "@src/oauth/urls"
|
||||
|
||||
// Providers
|
||||
import { Anthropic } from "./providers/Anthropic"
|
||||
|
|
@ -75,6 +75,8 @@ const ApiOptions = ({
|
|||
return Object.entries(headers)
|
||||
})
|
||||
|
||||
const [requestyShowRefreshHint, setRequestyShowRefreshHint] = useState<boolean>()
|
||||
|
||||
useEffect(() => {
|
||||
const propHeaders = apiConfiguration?.openAiHeaders || {}
|
||||
|
||||
|
|
@ -138,7 +140,7 @@ const ApiOptions = ({
|
|||
info: selectedModelInfo,
|
||||
} = useSelectedModel(apiConfiguration)
|
||||
|
||||
const { data: routerModels } = useRouterModels()
|
||||
const { data: routerModels, refetch: refetchRouterModels } = useRouterModels()
|
||||
|
||||
// Update apiConfiguration.aiModelId whenever selectedModelId changes.
|
||||
useEffect(() => {
|
||||
|
|
@ -373,13 +375,28 @@ const ApiOptions = ({
|
|||
{t("settings:providers.apiKeyStorageNotice")}
|
||||
</div>
|
||||
{!apiConfiguration?.requestyApiKey && (
|
||||
<VSCodeButtonLink
|
||||
href={getRequestyAuthUrl(uriScheme)}
|
||||
style={{ width: "100%" }}
|
||||
appearance="primary">
|
||||
<VSCodeButtonLink href={getRequestyApiKeyUrl()} style={{ width: "100%" }} appearance="primary">
|
||||
{t("settings:providers.getRequestyApiKey")}
|
||||
</VSCodeButtonLink>
|
||||
)}
|
||||
<Button
|
||||
variant="outline"
|
||||
title={t("settings:providers.refetchModels")}
|
||||
onClick={() => {
|
||||
vscode.postMessage({ type: "flushRouterModels", text: "requesty" })
|
||||
refetchRouterModels()
|
||||
setRequestyShowRefreshHint(true)
|
||||
}}>
|
||||
<div className="flex items-center gap-2">
|
||||
<span className="codicon codicon-refresh" />
|
||||
{t("settings:providers.flushModelsCache")}
|
||||
</div>
|
||||
</Button>
|
||||
{requestyShowRefreshHint && (
|
||||
<div className="flex items-center text-vscode-errorForeground">
|
||||
{t("settings:providers.flushedModelsCache")}
|
||||
</div>
|
||||
)}
|
||||
</>
|
||||
)}
|
||||
|
||||
|
|
|
|||
|
|
@ -119,6 +119,8 @@
|
|||
"glamaApiKey": "Clau API de Glama",
|
||||
"getGlamaApiKey": "Obtenir clau API de Glama",
|
||||
"requestyApiKey": "Clau API de Requesty",
|
||||
"flushModelsCache": "Netejar memòria cau de models",
|
||||
"flushedModelsCache": "Memòria cau netejada, si us plau torna a obrir la vista de configuració",
|
||||
"getRequestyApiKey": "Obtenir clau API de Requesty",
|
||||
"anthropicApiKey": "Clau API d'Anthropic",
|
||||
"getAnthropicApiKey": "Obtenir clau API d'Anthropic",
|
||||
|
|
|
|||
|
|
@ -106,6 +106,8 @@
|
|||
"awsCustomArnUse": "Geben Sie eine gültige Amazon Bedrock ARN für das Modell ein, das Sie verwenden möchten. Formatbeispiele:",
|
||||
"awsCustomArnDesc": "Stellen Sie sicher, dass die Region in der ARN mit Ihrer oben ausgewählten AWS-Region übereinstimmt.",
|
||||
"openRouterApiKey": "OpenRouter API-Schlüssel",
|
||||
"flushModelsCache": "Modell-Cache leeren",
|
||||
"flushedModelsCache": "Cache geleert, bitte öffnen Sie die Einstellungsansicht erneut",
|
||||
"getOpenRouterApiKey": "OpenRouter API-Schlüssel erhalten",
|
||||
"apiKeyStorageNotice": "API-Schlüssel werden sicher im VSCode Secret Storage gespeichert",
|
||||
"glamaApiKey": "Glama API-Schlüssel",
|
||||
|
|
|
|||
|
|
@ -118,6 +118,8 @@
|
|||
"headerValue": "Header value",
|
||||
"noCustomHeaders": "No custom headers defined. Click the + button to add one.",
|
||||
"requestyApiKey": "Requesty API Key",
|
||||
"flushModelsCache": "Flush cached models",
|
||||
"flushedModelsCache": "Flushed cache, please reopen the settings view",
|
||||
"getRequestyApiKey": "Get Requesty API Key",
|
||||
"openRouterTransformsText": "Compress prompts and message chains to the context size (<a>OpenRouter Transforms</a>)",
|
||||
"anthropicApiKey": "Anthropic API Key",
|
||||
|
|
|
|||
|
|
@ -106,6 +106,8 @@
|
|||
"awsCustomArnUse": "Ingrese un ARN de Amazon Bedrock válido para el modelo que desea utilizar. Ejemplos de formato:",
|
||||
"awsCustomArnDesc": "Asegúrese de que la región en el ARN coincida con la región de AWS seleccionada anteriormente.",
|
||||
"openRouterApiKey": "Clave API de OpenRouter",
|
||||
"flushModelsCache": "Limpiar modelos en caché",
|
||||
"flushedModelsCache": "Caché limpiada, por favor vuelva a abrir la vista de configuración",
|
||||
"getOpenRouterApiKey": "Obtener clave API de OpenRouter",
|
||||
"apiKeyStorageNotice": "Las claves API se almacenan de forma segura en el Almacenamiento Secreto de VSCode",
|
||||
"glamaApiKey": "Clave API de Glama",
|
||||
|
|
|
|||
|
|
@ -106,6 +106,8 @@
|
|||
"awsCustomArnUse": "Entrez un ARN Amazon Bedrock valide pour le modèle que vous souhaitez utiliser. Exemples de format :",
|
||||
"awsCustomArnDesc": "Assurez-vous que la région dans l'ARN correspond à la région AWS sélectionnée ci-dessus.",
|
||||
"openRouterApiKey": "Clé API OpenRouter",
|
||||
"flushModelsCache": "Vider le cache des modèles",
|
||||
"flushedModelsCache": "Cache vidé, veuillez rouvrir la vue des paramètres",
|
||||
"getOpenRouterApiKey": "Obtenir la clé API OpenRouter",
|
||||
"apiKeyStorageNotice": "Les clés API sont stockées en toute sécurité dans le stockage sécurisé de VSCode",
|
||||
"glamaApiKey": "Clé API Glama",
|
||||
|
|
|
|||
|
|
@ -118,6 +118,8 @@
|
|||
"headerValue": "हेडर मूल्य",
|
||||
"noCustomHeaders": "कोई कस्टम हेडर परिभाषित नहीं है। एक जोड़ने के लिए + बटन पर क्लिक करें।",
|
||||
"requestyApiKey": "Requesty API कुंजी",
|
||||
"flushModelsCache": "मॉडल कैश साफ़ करें",
|
||||
"flushedModelsCache": "कैश साफ़ किया गया, कृपया सेटिंग्स व्यू को फिर से खोलें",
|
||||
"getRequestyApiKey": "Requesty API कुंजी प्राप्त करें",
|
||||
"openRouterTransformsText": "संदर्भ आकार के लिए प्रॉम्प्ट और संदेश श्रृंखलाओं को संपीड़ित करें (<a>OpenRouter ट्रांसफॉर्म</a>)",
|
||||
"anthropicApiKey": "Anthropic API कुंजी",
|
||||
|
|
|
|||
|
|
@ -118,6 +118,8 @@
|
|||
"headerValue": "Valore intestazione",
|
||||
"noCustomHeaders": "Nessuna intestazione personalizzata definita. Fai clic sul pulsante + per aggiungerne una.",
|
||||
"requestyApiKey": "Chiave API Requesty",
|
||||
"flushModelsCache": "Svuota cache dei modelli",
|
||||
"flushedModelsCache": "Cache svuotata, riapri la vista delle impostazioni",
|
||||
"getRequestyApiKey": "Ottieni chiave API Requesty",
|
||||
"openRouterTransformsText": "Comprimi prompt e catene di messaggi alla dimensione del contesto (<a>Trasformazioni OpenRouter</a>)",
|
||||
"anthropicApiKey": "Chiave API Anthropic",
|
||||
|
|
|
|||
|
|
@ -118,6 +118,8 @@
|
|||
"headerValue": "ヘッダー値",
|
||||
"noCustomHeaders": "カスタムヘッダーが定義されていません。+ ボタンをクリックして追加してください。",
|
||||
"requestyApiKey": "Requesty APIキー",
|
||||
"flushModelsCache": "モデルキャッシュをクリア",
|
||||
"flushedModelsCache": "キャッシュをクリアしました。設定ビューを再開してください",
|
||||
"getRequestyApiKey": "Requesty APIキーを取得",
|
||||
"openRouterTransformsText": "プロンプトとメッセージチェーンをコンテキストサイズに圧縮 (<a>OpenRouter Transforms</a>)",
|
||||
"anthropicApiKey": "Anthropic APIキー",
|
||||
|
|
|
|||
|
|
@ -118,6 +118,8 @@
|
|||
"headerValue": "헤더 값",
|
||||
"noCustomHeaders": "정의된 사용자 정의 헤더가 없습니다. + 버튼을 클릭하여 추가하세요.",
|
||||
"requestyApiKey": "Requesty API 키",
|
||||
"flushModelsCache": "모델 캐시 지우기",
|
||||
"flushedModelsCache": "캐시가 지워졌습니다. 설정 보기를 다시 열어주세요",
|
||||
"getRequestyApiKey": "Requesty API 키 받기",
|
||||
"openRouterTransformsText": "프롬프트와 메시지 체인을 컨텍스트 크기로 압축 (<a>OpenRouter Transforms</a>)",
|
||||
"anthropicApiKey": "Anthropic API 키",
|
||||
|
|
|
|||
|
|
@ -118,6 +118,8 @@
|
|||
"headerValue": "Wartość nagłówka",
|
||||
"noCustomHeaders": "Brak zdefiniowanych niestandardowych nagłówków. Kliknij przycisk +, aby dodać.",
|
||||
"requestyApiKey": "Klucz API Requesty",
|
||||
"flushModelsCache": "Wyczyść pamięć podręczną modeli",
|
||||
"flushedModelsCache": "Pamięć podręczna wyczyszczona, proszę ponownie otworzyć widok ustawień",
|
||||
"getRequestyApiKey": "Uzyskaj klucz API Requesty",
|
||||
"openRouterTransformsText": "Kompresuj podpowiedzi i łańcuchy wiadomości do rozmiaru kontekstu (<a>Transformacje OpenRouter</a>)",
|
||||
"anthropicApiKey": "Klucz API Anthropic",
|
||||
|
|
|
|||
|
|
@ -106,6 +106,8 @@
|
|||
"awsCustomArnUse": "Insira um ARN Amazon Bedrock válido para o modelo que deseja usar. Exemplos de formato:",
|
||||
"awsCustomArnDesc": "Certifique-se de que a região no ARN corresponde à região AWS selecionada acima.",
|
||||
"openRouterApiKey": "Chave de API OpenRouter",
|
||||
"flushModelsCache": "Limpar cache de modelos",
|
||||
"flushedModelsCache": "Cache limpo, por favor reabra a visualização de configurações",
|
||||
"getOpenRouterApiKey": "Obter chave de API OpenRouter",
|
||||
"apiKeyStorageNotice": "As chaves de API são armazenadas com segurança no Armazenamento Secreto do VSCode",
|
||||
"glamaApiKey": "Chave de API Glama",
|
||||
|
|
|
|||
|
|
@ -118,6 +118,8 @@
|
|||
"headerValue": "Значение заголовка",
|
||||
"noCustomHeaders": "Пользовательские заголовки не определены. Нажмите кнопку +, чтобы добавить.",
|
||||
"requestyApiKey": "Requesty API-ключ",
|
||||
"flushModelsCache": "Очистить кэш моделей",
|
||||
"flushedModelsCache": "Кэш очищен, пожалуйста, переоткройте представление настроек",
|
||||
"getRequestyApiKey": "Получить Requesty API-ключ",
|
||||
"openRouterTransformsText": "Сжимать подсказки и цепочки сообщений до размера контекста (<a>OpenRouter Transforms</a>)",
|
||||
"anthropicApiKey": "Anthropic API-ключ",
|
||||
|
|
|
|||
|
|
@ -106,6 +106,8 @@
|
|||
"awsCustomArnUse": "Kullanmak istediğiniz model için geçerli bir Amazon Bedrock ARN'si girin. Format örnekleri:",
|
||||
"awsCustomArnDesc": "ARN içindeki bölgenin yukarıda seçilen AWS Bölgesiyle eşleştiğinden emin olun.",
|
||||
"openRouterApiKey": "OpenRouter API Anahtarı",
|
||||
"flushModelsCache": "Model önbelleğini temizle",
|
||||
"flushedModelsCache": "Önbellek temizlendi, lütfen ayarlar görünümünü yeniden açın",
|
||||
"getOpenRouterApiKey": "OpenRouter API Anahtarı Al",
|
||||
"apiKeyStorageNotice": "API anahtarları VSCode'un Gizli Depolamasında güvenli bir şekilde saklanır",
|
||||
"glamaApiKey": "Glama API Anahtarı",
|
||||
|
|
|
|||
|
|
@ -118,6 +118,8 @@
|
|||
"headerValue": "Giá trị tiêu đề",
|
||||
"noCustomHeaders": "Chưa có tiêu đề tùy chỉnh nào được định nghĩa. Nhấp vào nút + để thêm.",
|
||||
"requestyApiKey": "Khóa API Requesty",
|
||||
"flushModelsCache": "Xóa bộ nhớ đệm mô hình",
|
||||
"flushedModelsCache": "Đã xóa bộ nhớ đệm, vui lòng mở lại chế độ xem cài đặt",
|
||||
"getRequestyApiKey": "Lấy khóa API Requesty",
|
||||
"anthropicApiKey": "Khóa API Anthropic",
|
||||
"getAnthropicApiKey": "Lấy khóa API Anthropic",
|
||||
|
|
|
|||
|
|
@ -118,6 +118,8 @@
|
|||
"glamaApiKey": "Glama API 密钥",
|
||||
"getGlamaApiKey": "获取 Glama API 密钥",
|
||||
"requestyApiKey": "Requesty API 密钥",
|
||||
"flushModelsCache": "清除模型缓存",
|
||||
"flushedModelsCache": "缓存已清除，请重新打开设置视图",
|
||||
"getRequestyApiKey": "获取 Requesty API 密钥",
|
||||
"openRouterTransformsText": "自动压缩提示词和消息链到上下文长度限制内 (<a>OpenRouter转换</a>)",
|
||||
"anthropicApiKey": "Anthropic API 密钥",
|
||||
|
|
|
|||
|
|
@ -118,6 +118,8 @@
|
|||
"headerValue": "標頭值",
|
||||
"noCustomHeaders": "尚未定義自訂標頭。點擊 + 按鈕以新增。",
|
||||
"requestyApiKey": "Requesty API 金鑰",
|
||||
"flushModelsCache": "清除模型快取",
|
||||
"flushedModelsCache": "快取已清除，請重新開啟設定視圖",
|
||||
"getRequestyApiKey": "取得 Requesty API 金鑰",
|
||||
"openRouterTransformsText": "將提示和訊息鏈壓縮到上下文大小 (<a>OpenRouter 轉換</a>)",
|
||||
"anthropicApiKey": "Anthropic API 金鑰",
|
||||
|
|
|
|||
|
|
@ -14,3 +14,7 @@ export function getOpenRouterAuthUrl(uriScheme?: string) {
|
|||
export function getRequestyAuthUrl(uriScheme?: string) {
|
||||
return `https://app.requesty.ai/oauth/authorize?callback_url=${getCallbackUrl("requesty", uriScheme)}`
|
||||
}
|
||||
|
||||
export function getRequestyApiKeyUrl() {
|
||||
return "https://app.requesty.ai/api-keys"
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue