feat: add llm-client.js with prompt builder and JSON extractor

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frontend/scene-generator/llm-client.js

@@ -0,0 +1,121 @@
+const http = require("http");
+
+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。
+
+scene-id 规则：
+- 使用英文短横线连接，如 tq-lineloss-report
+- 全小写，有业务含义
+
+scene-name 规则：
+- 使用中文，简短描述性名称
+- 如 "台区线损报表"、"知乎热榜导出"
+
+请以 JSON 格式返回：{"sceneId": "...", "sceneName": "..."}`;
+
+function buildAnalyzePrompt(sourceDir, dirContents) {
+  const parts = [];
+
+  parts.push(`=== 目录结构 ===`);
+  parts.push(dirContents.tree || "(empty)");
+
+  if (dirContents["scene.toml"]) {
+    parts.push(`\n=== scene.toml ===`);
+    parts.push(dirContents["scene.toml"]);
+  }
+
+  if (dirContents["SKILL.toml"]) {
+    parts.push(`\n=== SKILL.toml ===`);
+    parts.push(dirContents["SKILL.toml"]);
+  }
+
+  if (dirContents["SKILL.md"]) {
+    parts.push(`\n=== SKILL.md ===`);
+    parts.push(dirContents["SKILL.md"]);
+  }
+
+  if (dirContents.scripts && Object.keys(dirContents.scripts).length > 0) {
+    parts.push(`\n=== 脚本文件 ===`);
+    for (const [name, content] of Object.entries(dirContents.scripts)) {
+      parts.push(`\n--- ${name} ---`);
+      parts.push(content.substring(0, 2000));
+    }
+  }
+
+  return `以下是场景目录 "${sourceDir}" 的内容：\n\n${parts.join("\n")}\n\n请以 JSON 格式返回：{"sceneId": "...", "sceneName": "..."}`;
+}
+
+function extractJsonFromResponse(text) {
+  const codeBlockMatch = text.match(/```(?:json)?\s*\n([\s\S]*?)\n```/);
+  if (codeBlockMatch) return JSON.parse(codeBlockMatch[1]);
+
+  const jsonMatch = text.match(
+    /\{[\s\S]*"sceneId"[\s\S]*"sceneName"[\s\S]*\}/
+  );
+  if (jsonMatch) return JSON.parse(jsonMatch[0]);
+
+  return JSON.parse(text);
+}
+
+function analyzeScene(sourceDir, dirContents, { apiKey, baseUrl, model }) {
+  const userPrompt = buildAnalyzePrompt(sourceDir, dirContents);
+
+  const requestBody = JSON.stringify({
+    model,
+    messages: [
+      { role: "system", content: SYSTEM_PROMPT },
+      { role: "user", content: userPrompt },
+    ],
+    temperature: 0.1,
+    max_tokens: 256,
+  });
+
+  return new Promise((resolve, reject) => {
+    const url = new URL(baseUrl.replace(/\/v1\/?$/, "") + "/v1/chat/completions");
+    const options = {
+      hostname: url.hostname,
+      port: url.port || (url.protocol === "https:" ? 443 : 80),
+      path: url.pathname,
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+        Authorization: `Bearer ${apiKey}`,
+        "Content-Length": Buffer.byteLength(requestBody),
+      },
+    };
+
+    const req = http.request(options, (res) => {
+      let data = "";
+      res.on("data", (chunk) => (data += chunk));
+      res.on("end", () => {
+        if (res.statusCode !== 200) {
+          return reject(new Error(`LLM API error ${res.statusCode}: ${data}`));
+        }
+
+        try {
+          const parsed = JSON.parse(data);
+          const content = parsed.choices?.[0]?.message?.content;
+          if (!content) return reject(new Error("LLM returned empty response"));
+          const result = extractJsonFromResponse(content);
+          if (!result.sceneId || !result.sceneName) {
+            return reject(
+              new Error(`LLM response missing sceneId/sceneName: ${content}`)
+            );
+          }
+          resolve(result);
+        } catch (err) {
+          reject(new Error(`Failed to parse LLM response: ${err.message}`));
+        }
+      });
+    });
+
+    req.on("error", reject);
+    req.setTimeout(30000, () => {
+      req.destroy(new Error("LLM API request timed out"));
+    });
+
+    req.write(requestBody);
+    req.end();
+  });
+}
+
+module.exports = { buildAnalyzePrompt, extractJsonFromResponse, analyzeScene };

tests/scene_generator_llm_test.js

@@ -0,0 +1,45 @@
+const assert = require("assert");
+const {
+  buildAnalyzePrompt,
+  extractJsonFromResponse,
+} = require("../frontend/scene-generator/llm-client");
+
+function testBuildAnalyzePromptIncludesFileContents() {
+  const dirContents = {
+    "scene.toml": '[scene]\nid = "test-scene"',
+    scripts: { "collect_test.js": "async function main() {}" },
+    tree: "├── scene.toml\n└── collect_test.js",
+  };
+
+  const prompt = buildAnalyzePrompt("D:/test/scenario", dirContents);
+
+  assert.ok(prompt.includes("scene.toml"), "should include scene.toml");
+  assert.ok(prompt.includes("collect_test.js"), "should include script name");
+  assert.ok(prompt.includes("D:/test/scenario"), "should include sourceDir");
+  console.log("PASS: testBuildAnalyzePromptIncludesFileContents");
+}
+
+function testExtractJsonFromResponse() {
+  const withMarkdown =
+    '```json\n{"sceneId": "test", "sceneName": "测试"}\n```';
+  const plain = '{"sceneId": "test", "sceneName": "测试"}';
+  const withPrefix =
+    'Here is the result:\n{"sceneId": "test", "sceneName": "测试"}';
+
+  assert.deepStrictEqual(extractJsonFromResponse(withMarkdown), {
+    sceneId: "test",
+    sceneName: "测试",
+  });
+  assert.deepStrictEqual(extractJsonFromResponse(plain), {
+    sceneId: "test",
+    sceneName: "测试",
+  });
+  assert.deepStrictEqual(extractJsonFromResponse(withPrefix), {
+    sceneId: "test",
+    sceneName: "测试",
+  });
+  console.log("PASS: testExtractJsonFromResponse");
+}
+
+testBuildAnalyzePromptIncludesFileContents();
+testExtractJsonFromResponse();