304 lines
9.3 KiB
JavaScript
304 lines
9.3 KiB
JavaScript
const http = require("http");
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const https = require("https");
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const SYSTEM_PROMPT = `你是一个场景信息提取助手。根据场景目录的内容，提取 scene-id 和 scene-name。
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scene-id 规则：
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- 使用英文短横线连接，如 tq-lineloss-report
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- 全小写，有业务含义
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scene-name 规则：
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- 使用中文，简短描述性名称
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- 如 "台区线损报表"、"知乎热榜导出"
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请以 JSON 格式返回：{"sceneId": "...", "sceneName": "..."}`;
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const DEEP_SYSTEM_PROMPT = `你是一个场景代码分析专家。分析场景源码，提取关键业务信息。
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## 分析目标
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1. **API 端点**: 识别所有 HTTP 请求地址 (URL, method, 用途)
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2. **静态参数**: 识别硬编码的业务参数 (key-value pairs)
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3. **列定义**: 识别数据表格/导出的列配置 ([field, label] pairs)
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4. **业务逻辑**: 理解数据获取和转换流程
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5. **场景类型**: 判断是 report_collection 还是 monitoring
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## 输出格式
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请以 JSON 格式返回：
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{
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"sceneId": "string - 场景标识 (英文短横线)",
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"sceneName": "string - 场景中文名",
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"sceneKind": "report_collection | monitoring",
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"sourceSystem": "string - 来源系统名 (可选)",
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"expectedDomain": "string - 目标域名 (可选)",
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"targetUrl": "string | null - 目标页面URL",
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"apiEndpoints": [
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{"name": "string", "url": "string", "method": "GET|POST", "description": "string"}
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],
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"staticParams": {"key": "value"},
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"columnDefs": [["fieldName", "中文列名"]],
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"entryMethod": "string - 入口方法名",
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"businessLogic": {
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"dataFetch": "string - 数据获取逻辑描述",
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"dataTransform": "string - 数据转换逻辑描述"
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}
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}`;
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function buildAnalyzePrompt(sourceDir, dirContents) {
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const parts = [];
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parts.push(`=== 目录结构 ===`);
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parts.push(dirContents.tree || "(empty)");
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if (dirContents["scene.toml"]) {
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parts.push(`\n=== scene.toml ===`);
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parts.push(dirContents["scene.toml"]);
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}
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if (dirContents["SKILL.toml"]) {
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parts.push(`\n=== SKILL.toml ===`);
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parts.push(dirContents["SKILL.toml"]);
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}
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if (dirContents["SKILL.md"]) {
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parts.push(`\n=== SKILL.md ===`);
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parts.push(dirContents["SKILL.md"]);
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}
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if (dirContents.scripts && Object.keys(dirContents.scripts).length > 0) {
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parts.push(`\n=== 脚本文件 ===`);
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for (const [name, content] of Object.entries(dirContents.scripts)) {
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parts.push(`\n--- ${name} ---`);
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const contentStr = typeof content === 'string' ? content : String(content || '');
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parts.push(contentStr.substring(0, 2000));
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}
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}
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return `以下是场景目录 "${sourceDir}" 的内容：\n\n${parts.join("\n")}\n\n请以 JSON 格式返回：{"sceneId": "...", "sceneName": "..."}`;
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}
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function buildDeepAnalyzePrompt(sourceDir, dirContents, indexHtmlContent) {
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const parts = [];
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parts.push(`=== 目录结构 ===`);
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parts.push(dirContents.tree || "(empty)");
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if (dirContents["scene.toml"]) {
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parts.push(`\n=== scene.toml ===`);
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parts.push(dirContents["scene.toml"]);
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}
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if (dirContents["SKILL.toml"]) {
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parts.push(`\n=== SKILL.toml ===`);
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parts.push(dirContents["SKILL.toml"]);
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}
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if (dirContents["SKILL.md"]) {
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parts.push(`\n=== SKILL.md ===`);
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parts.push(dirContents["SKILL.md"]);
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}
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// Include index.html content (key addition)
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if (indexHtmlContent && typeof indexHtmlContent === 'string') {
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parts.push(`\n=== index.html ===`);
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// Limit to first 15000 chars to avoid token limits
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parts.push(indexHtmlContent.substring(0, 15000));
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}
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if (dirContents.scripts && Object.keys(dirContents.scripts).length > 0) {
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parts.push(`\n=== 脚本文件 ===`);
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for (const [name, content] of Object.entries(dirContents.scripts)) {
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parts.push(`\n--- ${name} ---`);
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const contentStr = typeof content === 'string' ? content : String(content || '');
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parts.push(contentStr.substring(0, 3000));
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}
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}
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return `以下是场景目录 "${sourceDir}" 的内容：\n\n${parts.join("\n")}\n\n请分析以上代码，提取完整的场景信息。`;
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}
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function extractJsonFromResponse(text) {
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const codeBlockMatch = text.match(/```(?:json)?\s*\n([\s\S]*?)\n```/);
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if (codeBlockMatch) return JSON.parse(codeBlockMatch[1]);
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const jsonMatch = text.match(
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/\{[\s\S]*"sceneId"[\s\S]*"sceneName"[\s\S]*\}/
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);
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if (jsonMatch) return JSON.parse(jsonMatch[0]);
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return JSON.parse(text);
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}
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function extractSceneInfo(text) {
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// Try code block first
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const codeBlockMatch = text.match(/```(?:json)?\s*\n([\s\S]*?)\n```/);
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if (codeBlockMatch) {
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try {
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return JSON.parse(codeBlockMatch[1]);
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} catch (e) {
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// fall through
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}
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}
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// Try to find JSON object with sceneId
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const jsonMatch = text.match(/\{[\s\S]*"sceneId"[\s\S]*\}/);
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if (jsonMatch) {
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try {
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return JSON.parse(jsonMatch[0]);
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} catch (e) {
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// fall through
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}
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}
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// Last resort: parse entire text
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try {
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return JSON.parse(text);
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} catch (e) {
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throw new Error("Failed to extract valid SceneInfo JSON from LLM response");
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}
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}
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function analyzeScene(sourceDir, dirContents, { apiKey, baseUrl, model }) {
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const userPrompt = buildAnalyzePrompt(sourceDir, dirContents);
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const requestBody = JSON.stringify({
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model,
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messages: [
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{ role: "system", content: SYSTEM_PROMPT },
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{ role: "user", content: userPrompt },
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],
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temperature: 0.1,
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max_tokens: 256,
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});
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return new Promise((resolve, reject) => {
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const url = new URL(baseUrl.replace(/\/v1\/?$/, "") + "/v1/chat/completions");
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const options = {
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hostname: url.hostname,
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port: url.port || (url.protocol === "https:" ? 443 : 80),
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path: url.pathname,
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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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"Content-Length": Buffer.byteLength(requestBody),
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},
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};
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const req = https.request(options, (res) => {
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let data = "";
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res.on("data", (chunk) => (data += chunk));
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res.on("end", () => {
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if (res.statusCode !== 200) {
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return reject(new Error(`LLM API error ${res.statusCode}: ${data}`));
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}
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try {
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const parsed = JSON.parse(data);
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const content = parsed.choices?.[0]?.message?.content;
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if (!content) return reject(new Error("LLM returned empty response"));
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const result = extractJsonFromResponse(content);
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if (!result.sceneId || !result.sceneName) {
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return reject(
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new Error(`LLM response missing sceneId/sceneName: ${content}`)
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);
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}
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resolve(result);
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} catch (err) {
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reject(new Error(`Failed to parse LLM response: ${err.message}`));
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}
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});
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});
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req.on("error", reject);
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req.setTimeout(30000, () => {
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req.destroy(new Error("LLM API request timed out"));
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});
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req.write(requestBody);
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req.end();
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});
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}
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function analyzeSceneDeep(sourceDir, dirContents, indexHtmlContent, { apiKey, baseUrl, model }) {
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const userPrompt = buildDeepAnalyzePrompt(sourceDir, dirContents, indexHtmlContent);
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const requestBody = JSON.stringify({
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model,
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messages: [
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{ role: "system", content: DEEP_SYSTEM_PROMPT },
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{ role: "user", content: userPrompt },
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],
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temperature: 0.1,
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max_tokens: 2048, // Increased for detailed response
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});
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return new Promise((resolve, reject) => {
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const url = new URL(baseUrl.replace(/\/v1\/?$/, "") + "/v1/chat/completions");
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const options = {
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hostname: url.hostname,
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port: url.port || (url.protocol === "https:" ? 443 : 80),
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path: url.pathname,
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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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"Content-Length": Buffer.byteLength(requestBody),
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},
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};
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const httpModule = url.protocol === "https:" ? https : http;
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const req = httpModule.request(options, (res) => {
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let data = "";
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res.on("data", (chunk) => (data += chunk));
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res.on("end", () => {
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if (res.statusCode !== 200) {
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return reject(new Error(`LLM API error ${res.statusCode}: ${data}`));
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}
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try {
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const parsed = JSON.parse(data);
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const content = parsed.choices?.[0]?.message?.content;
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if (!content) return reject(new Error("LLM returned empty response"));
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const result = extractSceneInfo(content);
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// Validate required fields
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if (!result.sceneId || !result.sceneName) {
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return reject(new Error(`LLM response missing sceneId/sceneName: ${content}`));
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}
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// Set defaults for optional fields
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result.sceneKind = result.sceneKind || "report_collection";
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result.apiEndpoints = result.apiEndpoints || [];
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result.staticParams = result.staticParams || {};
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result.columnDefs = result.columnDefs || [];
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result.businessLogic = result.businessLogic || {};
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resolve(result);
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} catch (err) {
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reject(new Error(`Failed to parse LLM response: ${err.message}`));
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}
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});
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});
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req.on("error", reject);
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req.setTimeout(60000, () => {
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req.destroy(new Error("LLM API request timed out"));
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});
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req.write(requestBody);
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req.end();
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});
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}
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module.exports = {
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buildAnalyzePrompt,
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extractJsonFromResponse,
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analyzeScene,
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// New exports
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buildDeepAnalyzePrompt,
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extractSceneInfo,
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analyzeSceneDeep,
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};
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