269 lines
7.8 KiB
TypeScript
269 lines
7.8 KiB
TypeScript
import type { RuneConfig } from "../../src/types.ts";
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import type { AgentRunner, AgentResult } from "./runner.ts";
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import {
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assemblePlanPrompt,
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assembleBuildPrompt,
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assembleArchivePrompt,
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} from "../../src/core/assembler.ts";
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export interface LLMAction {
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type: "write_file" | "check_task" | "done";
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path?: string;
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content?: string;
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taskIndex?: number;
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}
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export interface LLMPlan {
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actions: LLMAction[];
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}
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export function isLLMAvailable(): boolean {
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return !!process.env.RUNE_E2E_LLM_API_KEY;
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}
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export function getLLMEnv() {
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return {
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provider: process.env.RUNE_E2E_LLM_PROVIDER || "openai",
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model: process.env.RUNE_E2E_LLM_MODEL || "gpt-4o-mini",
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apiKey: process.env.RUNE_E2E_LLM_API_KEY || "",
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baseUrl: process.env.RUNE_E2E_LLM_BASE_URL || "https://api.openai.com/v1",
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};
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}
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async function callLLM(prompt: string): Promise<LLMPlan> {
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const { provider, model, apiKey, baseUrl } = getLLMEnv();
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if (provider === "openai" || provider === "openrouter") {
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const response = await fetch(`${baseUrl}/chat/completions`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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Authorization: `Bearer ${apiKey}`,
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},
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body: JSON.stringify({
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model,
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messages: [
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{
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role: "system",
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content: `你是一个自动化构建工具，负责根据提示词生成精确的文件操作计划。
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请严格按以下 JSON 格式输出行动计划（不要包含其他内容）：
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{
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"actions": [
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{ "type": "write_file", "path": "相对路径", "content": "文件内容" },
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{ "type": "check_task", "taskIndex": 0 }
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]
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}
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可用的 action 类型：
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- write_file: 写入文件，path 和 content 必填
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- check_task: 标记任务为已完成，taskIndex 是任务列表中从 0 开始的索引
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- done: 表示所有操作已完成
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根据提示词要求，生成完整的操作计划。不要跳过任何步骤。`,
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},
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{
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role: "user",
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content: prompt,
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},
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],
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temperature: undefined,
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}),
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});
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if (!response.ok) {
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const body = await response.text();
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throw new Error(`LLM API error ${response.status}: ${body}`);
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}
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const data = await response.json();
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const text = data.choices?.[0]?.message?.content || "";
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return parseLLMResponse(text);
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}
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if (provider === "anthropic") {
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const response = await fetch(`https://api.anthropic.com/v1/messages`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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"x-api-key": apiKey,
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"anthropic-version": "2023-06-01",
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},
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body: JSON.stringify({
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model,
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max_tokens: 4096,
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system: `你是一个自动化构建工具，负责根据提示词生成精确的文件操作计划。
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请严格按以下 JSON 格式输出行动计划（不要包含其他内容）：
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{
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"actions": [
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{ "type": "write_file", "path": "相对路径", "content": "文件内容" },
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{ "type": "check_task", "taskIndex": 0 }
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]
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}
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可用的 action 类型：
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- write_file: 写入文件，path 和 content 必填
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- check_task: 标记任务为已完成，taskIndex 是任务列表中从 0 开始的索引
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- done: 表示所有操作已完成
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根据提示词要求，生成完整的操作计划。不要跳过任何步骤。`,
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messages: [{ role: "user", content: prompt }],
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temperature: undefined,
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}),
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});
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if (!response.ok) {
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const body = await response.text();
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throw new Error(`LLM API error ${response.status}: ${body}`);
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}
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const data = await response.json();
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const text = data.content?.[0]?.text || "";
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return parseLLMResponse(text);
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}
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throw new Error(`不支持的 LLM provider: ${provider}`);
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}
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function parseLLMResponse(text: string): LLMPlan {
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const jsonMatch = text.match(/\{[\s\S]*\}/);
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if (!jsonMatch) {
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throw new Error(`LLM 输出中未找到 JSON: ${text.slice(0, 200)}`);
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}
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try {
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const plan: LLMPlan = JSON.parse(jsonMatch[0]);
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if (!plan.actions || !Array.isArray(plan.actions)) {
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throw new Error("LLM 输出缺少 actions 数组");
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}
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return plan;
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} catch (e) {
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throw new Error(`LLM 输出 JSON 解析失败: ${text.slice(0, 200)}`, { cause: e });
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}
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}
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import { mkdir, writeFile, readFile, rename } from "node:fs/promises";
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import { join } from "node:path";
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import { getChangeDir, getArchiveDir } from "../../src/core/config.ts";
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import { parseTasks } from "../../src/core/task-parser.ts";
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async function executeActions(projectDir: string, plan: LLMPlan): Promise<string[]> {
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const files: string[] = [];
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for (const action of plan.actions) {
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switch (action.type) {
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case "write_file": {
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if (!action.path || action.content === undefined) {
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throw new Error("write_file action 缺少 path 或 content");
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}
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const fullPath = join(projectDir, action.path);
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await mkdir(join(fullPath, ".."), { recursive: true });
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await writeFile(fullPath, action.content);
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files.push(action.path);
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break;
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}
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case "check_task": {
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if (action.taskIndex === undefined) {
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throw new Error("check_task action 缺少 taskIndex");
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}
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break;
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}
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case "done":
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break;
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}
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}
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return files;
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}
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function extractTaskCheckActions(plan: LLMPlan): number[] {
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return plan.actions
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.filter((a) => a.type === "check_task" && a.taskIndex !== undefined)
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.map((a) => a.taskIndex!);
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}
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export class LLMJudgeRunner implements AgentRunner {
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readonly tier = 3;
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async runPlan(
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projectDir: string,
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changeName: string,
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docName: string,
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config: RuneConfig,
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): Promise<AgentResult> {
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if (!isLLMAvailable()) {
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throw new Error("RUNE_E2E_LLM_PROVIDER 和 RUNE_E2E_LLM_API_KEY 未设置");
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}
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const prompt = await assemblePlanPrompt(config, projectDir, changeName, docName);
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const plan = await callLLM(prompt);
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return {
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files: [],
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rawPlan: plan,
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};
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}
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async runBuild(projectDir: string, changeName: string, config: RuneConfig): Promise<AgentResult> {
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if (!isLLMAvailable()) {
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throw new Error("RUNE_E2E_LLM_PROVIDER 和 RUNE_E2E_LLM_API_KEY 未设置");
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}
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const changeDir = getChangeDir(projectDir, changeName);
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const prompt = await assembleBuildPrompt(config, projectDir, changeName);
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const plan = await callLLM(prompt);
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const files = await executeActions(projectDir, plan);
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const taskIndices = extractTaskCheckActions(plan);
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if (taskIndices.length > 0) {
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const taskPath = join(changeDir, "task.md");
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let taskContent = await readFile(taskPath, "utf-8");
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const tasks = parseTasks(taskContent);
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for (const index of taskIndices) {
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if (index < tasks.length) {
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const task = tasks[index];
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const oldLine = `- [ ] ${task.text}`;
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const newLine = `- [x] ${task.text}`;
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taskContent = taskContent.replace(oldLine, newLine);
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}
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}
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await writeFile(taskPath, taskContent);
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files.push("task.md");
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}
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return {
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files: [...new Set(files)],
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rawPlan: plan,
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};
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}
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async runArchive(
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projectDir: string,
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changeName: string,
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config: RuneConfig,
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): Promise<AgentResult> {
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if (!isLLMAvailable()) {
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throw new Error("RUNE_E2E_LLM_PROVIDER 和 RUNE_E2E_LLM_API_KEY 未设置");
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}
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const prompt = await assembleArchivePrompt(config, projectDir, changeName);
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const changeDir = getChangeDir(projectDir, changeName);
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const plan = await callLLM(prompt);
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const today = new Date().toISOString().slice(0, 10);
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const archiveDir = getArchiveDir(projectDir);
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await mkdir(archiveDir, { recursive: true });
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const dest = join(archiveDir, `${today}-${changeName}`);
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await rename(changeDir, dest);
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return {
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files: [],
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rawPlan: plan,
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};
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}
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}
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