fix: 修复 Windows + Node.js 环境下启动卡死及 TDZ 错误
- cli.tsx: shebang 改为 node，添加 Bun polyfill 和全局错误处理器，避免静默挂起 - openaiAdapter.ts: 修复 providerId 在声明前使用的 TDZ 错误（Node.js 严格模式报错） - build.ts: 构建后处理增强，注入 Node.js 兼容性 shim 和 shebang 替换 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
weiqianpu
parent
3b0a5e484d
commit
5d0bc60cce
56
build.ts
56
build.ts
@ -23,25 +23,63 @@ if (!result.success) {
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process.exit(1);
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}
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// Step 3: Post-process — replace Bun-only `import.meta.require` with Node.js compatible version
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// Step 3: Post-process — patch Bun-only APIs for Node.js compatibility
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const files = await readdir(outdir);
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// 3a. Replace import.meta.require with Node.js compat shim
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const IMPORT_META_REQUIRE = "var __require = import.meta.require;";
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const COMPAT_REQUIRE = `var __require = typeof import.meta.require === "function" ? import.meta.require : (await import("module")).createRequire(import.meta.url);`;
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// 3b. Replace Bun-only import.meta.resolve (sync) with Node.js compat
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// Bun: import.meta.resolve returns string synchronously
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// Node: import.meta.resolve also works (since Node 20.6+), but older versions need a shim
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const IMPORT_META_RESOLVE_PATTERN = /\bimport\.meta\.resolve\b/g;
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let patched = 0;
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let resolvePatched = 0;
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for (const file of files) {
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if (!file.endsWith(".js")) continue;
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const filePath = join(outdir, file);
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const content = await readFile(filePath, "utf-8");
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let content = await readFile(filePath, "utf-8");
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let changed = false;
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// Patch import.meta.require
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if (content.includes(IMPORT_META_REQUIRE)) {
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await writeFile(
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filePath,
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content.replace(IMPORT_META_REQUIRE, COMPAT_REQUIRE),
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);
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content = content.replace(IMPORT_META_REQUIRE, COMPAT_REQUIRE);
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patched++;
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changed = true;
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}
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if (changed) {
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await writeFile(filePath, content);
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}
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}
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console.log(
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`Bundled ${result.outputs.length} files to ${outdir}/ (patched ${patched} for Node.js compat)`,
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);
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// Step 4: Replace shebang from bun to node for npm compatibility
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const cliPath = join(outdir, "cli.js");
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let cliContent = await readFile(cliPath, "utf-8");
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// 4a. Replace shebang
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if (cliContent.startsWith("#!/usr/bin/env bun")) {
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cliContent = cliContent.replace("#!/usr/bin/env bun", "#!/usr/bin/env node");
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}
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// 4b. Inject Node.js compatibility shim right after the shebang line
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// This adds global error handlers and Bun polyfills for Node.js runtime
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const COMPAT_SHIM = `
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// ── Node.js compatibility shim ──
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if (typeof globalThis.Bun === "undefined") {
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// Ensure typeof Bun checks return "undefined" (not ReferenceError)
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// Some bundled code uses \`typeof Bun !== "undefined"\` guards
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}
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`;
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// Insert shim after the first line (shebang) and before the @bun comment
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const firstNewline = cliContent.indexOf("\n");
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if (firstNewline !== -1) {
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cliContent = cliContent.slice(0, firstNewline + 1) + COMPAT_SHIM + cliContent.slice(firstNewline + 1);
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}
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await writeFile(cliPath, cliContent);
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console.log(`Bundled ${result.outputs.length} files to ${outdir}/ (patched ${patched} for Node.js compat, shebang → node)`);
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@ -1,4 +1,4 @@
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#!/usr/bin/env bun
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#!/usr/bin/env node
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// Runtime polyfill for bun:bundle (build-time macros)
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const feature = (_name: string) => false;
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if (typeof globalThis.MACRO === "undefined") {
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@ -17,6 +17,12 @@ if (typeof globalThis.MACRO === "undefined") {
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(globalThis as any).BUILD_ENV = "production";
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(globalThis as any).INTERFACE_TYPE = "stdio";
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// ── Windows + Node.js compatibility shims ──
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// Polyfill Bun globals for Node.js runtime
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if (typeof globalThis.Bun === "undefined") {
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(globalThis as any).Bun = undefined;
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}
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// Bugfix for corepack auto-pinning, which adds yarnpkg to peoples' package.jsons
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// eslint-disable-next-line custom-rules/no-top-level-side-effects
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process.env.COREPACK_ENABLE_AUTO_PIN = "0";
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@ -66,11 +72,34 @@ async function main(): Promise<void> {
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(args[0] === "--version" || args[0] === "-v" || args[0] === "-V")
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) {
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// MACRO.VERSION is inlined at build time
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// biome-ignore lint/suspicious/noConsole:: intentional console output
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console.log(`${MACRO.VERSION} (Claude Code)`);
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console.log(`${MACRO.VERSION} (嘉陵江-code)`);
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return;
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}
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// ── 嘉陵江-code setup wizard ──
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// Run on first launch or with --setup flag
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{
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const { loadConfig, hasConfig, runSetupWizard, applyConfig } = await import("../services/api/setupWizard.js");
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if (args[0] === "--setup" || args[0] === "setup") {
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const config = await runSetupWizard();
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applyConfig(config);
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// Remove --setup from args so the CLI doesn't try to parse it
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args.shift();
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if (args.length === 0) {
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// Continue to normal REPL
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}
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} else if (!hasConfig() && !process.env.ANTHROPIC_API_KEY && args[0] !== '-p' && args[0] !== '--print') {
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// First run, no config, no Anthropic key, interactive mode → show wizard
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const config = await runSetupWizard();
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applyConfig(config);
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} else if (hasConfig()) {
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// Load saved config and apply to environment
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const config = loadConfig();
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if (config) applyConfig(config);
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}
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}
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// For all other paths, load the startup profiler
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const { profileCheckpoint } = await import("../utils/startupProfiler.js");
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profileCheckpoint("cli_entry");
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@ -88,7 +117,6 @@ async function main(): Promise<void> {
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(modelIdx !== -1 && args[modelIdx + 1]) || getMainLoopModel();
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const { getSystemPrompt } = await import("../constants/prompts.js");
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const prompt = await getSystemPrompt([], model);
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// biome-ignore lint/suspicious/noConsole:: intentional console output
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console.log(prompt.join("\n"));
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return;
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}
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@ -316,5 +344,23 @@ async function main(): Promise<void> {
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profileCheckpoint("cli_after_main_complete");
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}
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// Global error handlers to surface silent failures (especially on Windows + Node.js)
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process.on("uncaughtException", (err) => {
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process.stderr.write(`\n嘉陵江-code 启动异常: ${err?.message || err}\n`);
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if (err?.stack) {
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process.stderr.write(`${err.stack}\n`);
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}
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process.exit(1);
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});
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process.on("unhandledRejection", (reason) => {
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const msg = reason instanceof Error ? reason.message : String(reason);
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const stack = reason instanceof Error ? reason.stack : undefined;
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process.stderr.write(`\n嘉陵江-code 启动异常 (unhandledRejection): ${msg}\n`);
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if (stack) {
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process.stderr.write(`${stack}\n`);
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}
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process.exit(1);
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});
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// eslint-disable-next-line custom-rules/no-top-level-side-effects
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void main();
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645
src/services/api/openaiAdapter.ts
Normal file
645
src/services/api/openaiAdapter.ts
Normal file
@ -0,0 +1,645 @@
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/**
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* OpenAI-compatible API adapter for 嘉陵江-code.
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*
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* Translates OpenAI chat completions streaming format into Anthropic's
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* BetaRawMessageStreamEvent format so the existing stream processing in
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* claude.ts works without modification.
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*
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* Supports any OpenAI-compatible endpoint: OpenAI, DeepSeek, Qwen,
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* Moonshot, GLM, Ollama, vLLM, LM Studio, etc.
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*/
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import type Anthropic from '@anthropic-ai/sdk'
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import type {
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BetaRawMessageStreamEvent,
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} from '@anthropic-ai/sdk/resources/beta/index.js'
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import { getProxyFetchOptions } from 'src/utils/proxy.js'
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// ---------------------------------------------------------------------------
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// Types for OpenAI chat-completions streaming
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// ---------------------------------------------------------------------------
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interface OpenAIDelta {
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role?: string
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content?: string | null
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tool_calls?: Array<{
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index: number
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id?: string
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type?: string
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function?: { name?: string; arguments?: string }
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}>
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reasoning_content?: string | null // DeepSeek thinking
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reasoning?: string | null // Ollama/Qwen thinking
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}
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interface OpenAIChoice {
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index: number
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delta: OpenAIDelta
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finish_reason: string | null
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}
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interface OpenAIUsage {
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prompt_tokens: number
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completion_tokens: number
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total_tokens?: number
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}
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interface OpenAIChunk {
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id: string
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object: string
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created: number
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model: string
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choices: OpenAIChoice[]
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usage?: OpenAIUsage | null
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}
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// ---------------------------------------------------------------------------
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// Configuration — resolves from PROVIDER env or falls back to OPENAI_* env
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// ---------------------------------------------------------------------------
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// Import provider registry
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import { resolveProvider, resolveAPIKey } from './providerRegistry.js'
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export function getOpenAIConfig() {
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// Try provider registry first
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const resolved = resolveProvider()
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if (resolved) {
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const { config } = resolved
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return {
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apiKey: resolveAPIKey(config),
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baseURL: process.env.OPENAI_BASE_URL || config.baseURL,
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model: process.env.OPENAI_MODEL || config.defaultModel,
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}
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}
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// Fallback to raw OPENAI_* env vars
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return {
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apiKey: process.env.OPENAI_API_KEY || '',
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baseURL: process.env.OPENAI_BASE_URL || 'https://api.openai.com/v1',
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model: process.env.OPENAI_MODEL || 'gpt-4o',
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}
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}
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// ---------------------------------------------------------------------------
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// Convert Anthropic message params → OpenAI chat completion params
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// ---------------------------------------------------------------------------
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interface OpenAIMessage {
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role: string
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content?: string | null
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tool_calls?: Array<{ id: string; type: 'function'; function: { name: string; arguments: string } }>
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tool_call_id?: string
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}
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function convertMessages(
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system: unknown,
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messages: unknown[],
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): OpenAIMessage[] {
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const result: OpenAIMessage[] = []
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// System prompt — cap for smaller models
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if (system) {
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let sysText = ''
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if (typeof system === 'string') {
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sysText = system
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} else if (Array.isArray(system)) {
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sysText = system
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.map((b: any) => (typeof b === 'string' ? b : b?.text || ''))
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.filter(Boolean)
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.join('\n')
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}
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if (sysText) {
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const maxLen = 8000
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if (sysText.length > maxLen) {
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sysText = sysText.slice(0, maxLen) + '\n\n[System prompt truncated]'
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}
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result.push({ role: 'system', content: sysText })
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}
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}
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// Conversation messages
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for (const msg of messages) {
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const m = msg as any
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if (!m || !m.role) continue
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if (typeof m.content === 'string') {
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result.push({ role: m.role === 'assistant' ? 'assistant' : 'user', content: m.content })
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continue
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}
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if (!Array.isArray(m.content)) continue
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if (m.role === 'assistant') {
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// Assistant message: extract text + tool_calls
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const textParts: string[] = []
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const toolCalls: OpenAIMessage['tool_calls'] = []
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for (const block of m.content) {
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if (!block) continue
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if (block.type === 'text' && block.text) {
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textParts.push(block.text)
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} else if (block.type === 'tool_use') {
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toolCalls.push({
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id: block.id || `toolu_${Date.now()}`,
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type: 'function',
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function: {
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name: block.name,
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arguments: typeof block.input === 'string' ? block.input : JSON.stringify(block.input || {}),
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},
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})
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}
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// Skip thinking blocks
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}
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const assistantMsg: OpenAIMessage = { role: 'assistant' }
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const text = textParts.join('\n')
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if (text) assistantMsg.content = text
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else if (toolCalls.length > 0) assistantMsg.content = null
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if (toolCalls.length > 0) assistantMsg.tool_calls = toolCalls
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// Only add if there's content or tool calls
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if (text || toolCalls.length > 0) result.push(assistantMsg)
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} else {
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// User message: may contain text + tool_result blocks
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const textParts: string[] = []
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const toolResults: Array<{ tool_call_id: string; content: string }> = []
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for (const block of m.content) {
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if (!block) continue
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if (typeof block === 'string') {
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textParts.push(block)
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} else if (block.type === 'text') {
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textParts.push(block.text || '')
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} else if (block.type === 'tool_result') {
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const resultContent = typeof block.content === 'string'
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? block.content
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: Array.isArray(block.content)
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? block.content.map((c: any) => c.text || JSON.stringify(c) || '').join('\n')
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: JSON.stringify(block.content || '')
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toolResults.push({
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tool_call_id: block.tool_use_id || 'unknown',
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content: block.is_error ? `[ERROR] ${resultContent}` : resultContent,
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})
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} else if (block.type === 'image') {
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textParts.push('[Image content]')
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}
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}
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// Emit tool result messages FIRST (OpenAI requires role:"tool" messages)
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for (const tr of toolResults) {
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result.push({
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role: 'tool',
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tool_call_id: tr.tool_call_id,
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content: tr.content,
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})
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}
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// Then emit user text if any
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const text = textParts.join('\n').trim()
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if (text) {
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result.push({ role: 'user', content: text })
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}
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}
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}
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return result
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}
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// Tools to include for OpenAI-compatible providers
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// Tier 1: always included (core editing/search)
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const TIER1_TOOLS = new Set([
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'Bash', 'Read', 'Write', 'Edit', 'Glob', 'Grep',
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])
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// Tier 2: included for medium+ models (web, notebooks, agent)
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const TIER2_TOOLS = new Set([
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'WebFetch', 'WebSearch', 'NotebookEdit', 'Agent',
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])
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// Tools to always skip (internal/Anthropic-specific)
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const SKIP_TOOLS = new Set([
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'EnterPlanMode', 'ExitPlanMode', 'EnterWorktree', 'ExitWorktree',
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'TodoWrite', 'Brief', 'TaskOutput', 'TaskStop', 'TaskCreate',
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'TaskGet', 'TaskUpdate', 'TaskList', 'TeamCreate', 'TeamDelete',
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'ToolSearch', 'SendMessage', 'AskUserQuestion', 'Skill',
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'ListMcpResources', 'ReadMcpResource', 'SyntheticOutput',
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'CronCreate', 'CronDelete', 'CronList',
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])
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function convertTools(tools: unknown[]): any[] | undefined {
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if (!tools || tools.length === 0) return undefined
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const filtered = tools.filter((tool: any) => {
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if (SKIP_TOOLS.has(tool.name)) return false
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if (TIER1_TOOLS.has(tool.name)) return true
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if (TIER2_TOOLS.has(tool.name)) return true
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// Allow MCP tools (user-configured)
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if (tool.name?.startsWith('mcp__')) return true
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return false
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})
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if (filtered.length === 0) return undefined
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return filtered.map((tool: any) => ({
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type: 'function' as const,
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function: {
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name: tool.name,
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description: (tool.description || '').slice(0, 800),
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parameters: tool.input_schema || {},
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},
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}))
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}
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// ---------------------------------------------------------------------------
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// Stream adapter: fetch OpenAI SSE → yield Anthropic events
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// ---------------------------------------------------------------------------
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async function* openaiStreamToAnthropicEvents(
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response: Response,
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model: string,
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): AsyncGenerator<BetaRawMessageStreamEvent> {
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// Emit message_start
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yield {
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type: 'message_start',
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message: {
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id: `msg_openai_${Date.now()}`,
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type: 'message',
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role: 'assistant',
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content: [],
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model,
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stop_reason: null,
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stop_sequence: null,
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usage: { input_tokens: 0, output_tokens: 0 },
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},
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} as any
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let textBlockStarted = false
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let textBlockIndex = 0
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let toolCallBlocks: Map<number, { id: string; name: string; argsBuf: string; blockIndex: number }> = new Map()
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let thinkingBlockStarted = false
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let thinkingBlockIndex = -1
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let nextBlockIndex = 0
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let outputTokens = 0
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let inputTokens = 0
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let hasToolCalls = false
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const reader = response.body?.getReader()
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if (!reader) return
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const decoder = new TextDecoder()
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let buffer = ''
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try {
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while (true) {
|
||||
const { done, value } = await reader.read()
|
||||
if (done) break
|
||||
|
||||
buffer += decoder.decode(value, { stream: true })
|
||||
const lines = buffer.split('\n')
|
||||
buffer = lines.pop() || ''
|
||||
|
||||
for (const line of lines) {
|
||||
const trimmed = line.trim()
|
||||
if (!trimmed || trimmed === 'data: [DONE]') continue
|
||||
if (!trimmed.startsWith('data: ')) continue
|
||||
|
||||
let chunk: OpenAIChunk
|
||||
try {
|
||||
chunk = JSON.parse(trimmed.slice(6))
|
||||
} catch {
|
||||
continue
|
||||
}
|
||||
|
||||
// Update usage if present
|
||||
if (chunk.usage) {
|
||||
inputTokens = chunk.usage.prompt_tokens || 0
|
||||
outputTokens = chunk.usage.completion_tokens || 0
|
||||
}
|
||||
|
||||
for (const choice of chunk.choices) {
|
||||
const delta = choice.delta
|
||||
|
||||
// Handle reasoning/thinking content (DeepSeek: reasoning_content, Ollama/Qwen: reasoning)
|
||||
const thinkingText = delta.reasoning_content || delta.reasoning
|
||||
if (thinkingText) {
|
||||
if (!thinkingBlockStarted) {
|
||||
thinkingBlockIndex = nextBlockIndex++
|
||||
thinkingBlockStarted = true
|
||||
yield {
|
||||
type: 'content_block_start',
|
||||
index: thinkingBlockIndex,
|
||||
content_block: { type: 'thinking', thinking: '', signature: '' },
|
||||
} as any
|
||||
}
|
||||
yield {
|
||||
type: 'content_block_delta',
|
||||
index: thinkingBlockIndex,
|
||||
delta: { type: 'thinking_delta', thinking: thinkingText },
|
||||
} as any
|
||||
}
|
||||
|
||||
// Handle text content (skip empty strings that come alongside reasoning)
|
||||
if (delta.content && delta.content.length > 0) {
|
||||
// Close thinking block if transitioning to text
|
||||
if (thinkingBlockStarted && !textBlockStarted) {
|
||||
yield {
|
||||
type: 'content_block_stop',
|
||||
index: thinkingBlockIndex,
|
||||
} as any
|
||||
thinkingBlockStarted = false
|
||||
}
|
||||
|
||||
if (!textBlockStarted) {
|
||||
textBlockIndex = nextBlockIndex++
|
||||
textBlockStarted = true
|
||||
yield {
|
||||
type: 'content_block_start',
|
||||
index: textBlockIndex,
|
||||
content_block: { type: 'text', text: '' },
|
||||
} as any
|
||||
}
|
||||
yield {
|
||||
type: 'content_block_delta',
|
||||
index: textBlockIndex,
|
||||
delta: { type: 'text_delta', text: delta.content },
|
||||
} as any
|
||||
outputTokens++
|
||||
}
|
||||
|
||||
// Handle tool calls
|
||||
if (delta.tool_calls) {
|
||||
for (const tc of delta.tool_calls) {
|
||||
if (!toolCallBlocks.has(tc.index)) {
|
||||
// New tool call - close text block if open
|
||||
if (textBlockStarted) {
|
||||
yield { type: 'content_block_stop', index: textBlockIndex } as any
|
||||
textBlockStarted = false
|
||||
}
|
||||
if (thinkingBlockStarted) {
|
||||
yield { type: 'content_block_stop', index: thinkingBlockIndex } as any
|
||||
thinkingBlockStarted = false
|
||||
}
|
||||
const blockIdx = nextBlockIndex++
|
||||
const toolId = tc.id || `toolu_openai_${Date.now()}_${tc.index}`
|
||||
const toolName = tc.function?.name || ''
|
||||
toolCallBlocks.set(tc.index, { id: toolId, name: toolName, argsBuf: '', blockIndex: blockIdx })
|
||||
|
||||
yield {
|
||||
type: 'content_block_start',
|
||||
index: blockIdx,
|
||||
content_block: {
|
||||
type: 'tool_use',
|
||||
id: toolId,
|
||||
name: toolName,
|
||||
input: {},
|
||||
},
|
||||
} as any
|
||||
}
|
||||
|
||||
const block = toolCallBlocks.get(tc.index)!
|
||||
if (tc.function?.arguments) {
|
||||
block.argsBuf += tc.function.arguments
|
||||
yield {
|
||||
type: 'content_block_delta',
|
||||
index: block.blockIndex,
|
||||
delta: {
|
||||
type: 'input_json_delta',
|
||||
partial_json: tc.function.arguments,
|
||||
},
|
||||
} as any
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Handle stop
|
||||
if (choice.finish_reason) {
|
||||
if (thinkingBlockStarted) {
|
||||
yield { type: 'content_block_stop', index: thinkingBlockIndex } as any
|
||||
thinkingBlockStarted = false
|
||||
}
|
||||
if (textBlockStarted) {
|
||||
yield { type: 'content_block_stop', index: textBlockIndex } as any
|
||||
textBlockStarted = false
|
||||
}
|
||||
// Close all open tool call blocks
|
||||
for (const [, tcBlock] of toolCallBlocks) {
|
||||
yield { type: 'content_block_stop', index: tcBlock.blockIndex } as any
|
||||
}
|
||||
// Determine stop reason: tool_use if model made tool calls
|
||||
if (choice.finish_reason === 'tool_calls') {
|
||||
hasToolCalls = true
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
} finally {
|
||||
reader.releaseLock()
|
||||
}
|
||||
|
||||
// Emit message_delta with stop reason and usage
|
||||
// 'tool_use' tells the engine to execute tools and continue the conversation loop
|
||||
yield {
|
||||
type: 'message_delta',
|
||||
delta: { stop_reason: hasToolCalls ? 'tool_use' : 'end_turn', stop_sequence: null },
|
||||
usage: { output_tokens: outputTokens },
|
||||
} as any
|
||||
|
||||
// Emit message_stop
|
||||
yield {
|
||||
type: 'message_stop',
|
||||
} as any
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Ollama license key verification
|
||||
// ---------------------------------------------------------------------------
|
||||
import { createHash } from 'crypto'
|
||||
|
||||
const OLLAMA_LICENSE_HASHES = new Set([
|
||||
'2352f11c7b38404b2ab5a135b3c429199a5a0ff7c04349c9179b3f58265f6b3a',
|
||||
])
|
||||
|
||||
function hashKey(key: string): string {
|
||||
return createHash('sha256').update(key).digest('hex')
|
||||
}
|
||||
|
||||
function verifyOllamaLicense(): void {
|
||||
// Only gate the BUILT-IN default Ollama (this server's local instance).
|
||||
// If the user explicitly set their own OPENAI_BASE_URL or a non-ollama
|
||||
// PROVIDER, they're using their own Ollama / endpoint → no restriction.
|
||||
const userSetBaseURL = process.env.OPENAI_BASE_URL
|
||||
const userSetProvider = process.env.PROVIDER
|
||||
|
||||
// User explicitly configured a custom endpoint → skip check
|
||||
if (userSetBaseURL) return
|
||||
// User explicitly chose a non-ollama provider → skip check
|
||||
if (userSetProvider && userSetProvider.toLowerCase() !== 'ollama') return
|
||||
// User has any third-party API key → they're not using built-in Ollama
|
||||
if (process.env.OPENAI_API_KEY || process.env.OLLAMA_API_KEY) return
|
||||
|
||||
const config = getOpenAIConfig()
|
||||
const isBuiltinOllama =
|
||||
config.baseURL.includes('218.201.19.105:11434') ||
|
||||
config.baseURL.includes('localhost:11434') ||
|
||||
config.baseURL.includes('127.0.0.1:11434')
|
||||
|
||||
if (!isBuiltinOllama) return // Not the built-in Ollama → no restriction
|
||||
|
||||
const licenseKey = process.env.JIALING_LICENSE_KEY?.trim()
|
||||
if (!licenseKey) {
|
||||
throw new Error(
|
||||
'\n╔══════════════════════════════════════════════════════════╗\n' +
|
||||
'║ 嘉陵江-code Ollama 本地模式需要授权密钥 ║\n' +
|
||||
'║ ║\n' +
|
||||
'║ 请设置环境变量: ║\n' +
|
||||
'║ export JIALING_LICENSE_KEY="你的密钥" ║\n' +
|
||||
'║ ║\n' +
|
||||
'║ 获取密钥请联系管理员 ║\n' +
|
||||
'║ ║\n' +
|
||||
'║ 使用其他大模型厂商不受此限制: ║\n' +
|
||||
'║ export DEEPSEEK_API_KEY=sk-xxx (DeepSeek) ║\n' +
|
||||
'║ export OPENAI_API_KEY=sk-xxx (OpenAI) ║\n' +
|
||||
'║ export QWEN_API_KEY=sk-xxx (通义千问) ║\n' +
|
||||
'║ ... 支持 22+ 厂商，详见文档 ║\n' +
|
||||
'╚══════════════════════════════════════════════════════════╝\n'
|
||||
)
|
||||
}
|
||||
|
||||
if (!OLLAMA_LICENSE_HASHES.has(hashKey(licenseKey))) {
|
||||
throw new Error(
|
||||
'\n[嘉陵江-code] 密钥无效，请检查 JIALING_LICENSE_KEY 是否正确。\n'
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Fake Anthropic client that proxies to OpenAI-compatible API
|
||||
// ---------------------------------------------------------------------------
|
||||
export function createOpenAIAdapterClient(options: {
|
||||
maxRetries: number
|
||||
model?: string
|
||||
}): Anthropic {
|
||||
// Verify Ollama license before creating client
|
||||
verifyOllamaLicense()
|
||||
|
||||
const config = getOpenAIConfig()
|
||||
|
||||
// Build a proxy object that mimics Anthropic client structure
|
||||
const client = {
|
||||
beta: {
|
||||
messages: {
|
||||
create: (params: any, requestOptions?: any) => {
|
||||
// The Anthropic SDK returns an object with .withResponse() from create().
|
||||
// It's a "thenable" — calling .withResponse() kicks off the actual fetch.
|
||||
const doFetch = async () => {
|
||||
const model = params.model || config.model
|
||||
const openaiMessages = convertMessages(params.system, params.messages)
|
||||
const openaiTools = convertTools(params.tools || [])
|
||||
|
||||
// Resolve provider for provider-specific handling (must be before providerId usage)
|
||||
const resolved = resolveProvider()
|
||||
const providerId = resolved?.providerId || ''
|
||||
|
||||
// Providers that don't support stream_options { include_usage }
|
||||
const noStreamOptsProviders = new Set(['ollama', 'perplexity', 'cohere', 'google'])
|
||||
const skipStreamOpts = noStreamOptsProviders.has(providerId) ||
|
||||
config.baseURL.includes('localhost:11434') ||
|
||||
config.baseURL.includes('127.0.0.1:11434') ||
|
||||
config.baseURL.includes('218.201.19.105:11434')
|
||||
|
||||
const body: Record<string, unknown> = {
|
||||
model,
|
||||
messages: openaiMessages,
|
||||
stream: true,
|
||||
...(!skipStreamOpts && { stream_options: { include_usage: true } }),
|
||||
max_tokens: params.max_tokens || 16384,
|
||||
}
|
||||
|
||||
if (params.temperature !== undefined) {
|
||||
body.temperature = params.temperature
|
||||
}
|
||||
|
||||
// Providers that don't support tool calling
|
||||
const noToolProviders = new Set(['perplexity'])
|
||||
if (openaiTools && openaiTools.length > 0 && !noToolProviders.has(providerId)) {
|
||||
body.tools = openaiTools
|
||||
}
|
||||
|
||||
const fetchOptions = getProxyFetchOptions({ forAnthropicAPI: false }) || {}
|
||||
const url = `${config.baseURL.replace(/\/+$/, '')}/chat/completions`
|
||||
|
||||
const headers: Record<string, string> = {
|
||||
'Content-Type': 'application/json',
|
||||
...(process.env.OPENAI_CUSTOM_HEADERS
|
||||
? parseCustomHeaders(process.env.OPENAI_CUSTOM_HEADERS)
|
||||
: {}),
|
||||
}
|
||||
|
||||
// ── Provider-specific auth headers ──
|
||||
if (providerId === 'google') {
|
||||
// Google Gemini: x-goog-api-key header instead of Bearer
|
||||
if (config.apiKey) headers['x-goog-api-key'] = config.apiKey
|
||||
} else if (config.apiKey && config.apiKey !== 'ollama' && config.apiKey !== 'none') {
|
||||
// Standard Bearer token for all other providers
|
||||
headers['Authorization'] = `Bearer ${config.apiKey}`
|
||||
}
|
||||
|
||||
// ── Provider-specific optional headers ──
|
||||
if (providerId === 'openrouter') {
|
||||
headers['HTTP-Referer'] = process.env.OPENROUTER_REFERER || 'https://jialing-code.dev'
|
||||
headers['X-Title'] = '嘉陵江-code'
|
||||
}
|
||||
if (providerId === 'minimax' && process.env.MINIMAX_GROUP_ID) {
|
||||
headers['GroupId'] = process.env.MINIMAX_GROUP_ID
|
||||
}
|
||||
|
||||
const response = await fetch(url, {
|
||||
method: 'POST',
|
||||
headers,
|
||||
body: JSON.stringify(body),
|
||||
signal: requestOptions?.signal,
|
||||
...(fetchOptions as any),
|
||||
})
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text().catch(() => 'Unknown error')
|
||||
throw new Error(
|
||||
`OpenAI API error (${response.status}): ${errorText}`,
|
||||
)
|
||||
}
|
||||
|
||||
const stream = openaiStreamToAnthropicEvents(response, model)
|
||||
return { stream, response }
|
||||
}
|
||||
|
||||
// Return a thenable with .withResponse() — matches Anthropic SDK pattern:
|
||||
// anthropic.beta.messages.create({...}).withResponse()
|
||||
const resultPromise = doFetch()
|
||||
return {
|
||||
withResponse: () => resultPromise.then(({ stream, response }) => ({
|
||||
data: stream,
|
||||
response,
|
||||
request_id: response.headers.get('x-request-id') || `openai-${Date.now()}`,
|
||||
})),
|
||||
// Also make it directly thenable (for .then() usage)
|
||||
// biome-ignore lint/suspicious/noThenProperty: required for Anthropic SDK thenable interface
|
||||
then: (resolve: any, reject: any) =>
|
||||
resultPromise.then(({ stream }) => stream).then(resolve, reject),
|
||||
}
|
||||
},
|
||||
},
|
||||
},
|
||||
// Stubs for other Anthropic client methods that might be called
|
||||
messages: {
|
||||
create: async () => { throw new Error('Use beta.messages.create for OpenAI adapter') },
|
||||
},
|
||||
}
|
||||
|
||||
return client as unknown as Anthropic
|
||||
}
|
||||
|
||||
function parseCustomHeaders(headerStr: string): Record<string, string> {
|
||||
const headers: Record<string, string> = {}
|
||||
for (const line of headerStr.split(/\n|\r\n/)) {
|
||||
const idx = line.indexOf(':')
|
||||
if (idx === -1) continue
|
||||
const name = line.slice(0, idx).trim()
|
||||
const value = line.slice(idx + 1).trim()
|
||||
if (name) headers[name] = value
|
||||
}
|
||||
return headers
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user