refactor: 使用 openai SDK 替换 fetch 调用 LLM

- 安装 openai 包
- 重命名 zhipu.ts -> llm.ts
- 使用 OpenAI SDK 替代原生 fetch 实现
- 更新所有导入路径

src/lib/bigmodel/dictionary/stage1-preprocess.ts

@@ -1,4 +1,4 @@
-import { getAnswer } from "../zhipu";
+import { getAnswer } from "../llm";
 import { parseAIGeneratedJSON } from "@/utils/json";
 import { PreprocessResult } from "./types";
 import { createLogger } from "@/lib/logger";

src/lib/bigmodel/dictionary/stage4-entriesGeneration.ts

@@ -1,4 +1,4 @@
-import { getAnswer } from "../zhipu";
+import { getAnswer } from "../llm";
 import { parseAIGeneratedJSON } from "@/utils/json";
 import { EntriesGenerationResult } from "./types";
 import { createLogger } from "@/lib/logger";

src/lib/bigmodel/llm.ts

@@ -0,0 +1,37 @@
+"use server";
+
+import OpenAI from "openai";
+
+const openai = new OpenAI({
+  apiKey: process.env.ZHIPU_API_KEY,
+  baseURL: "https://open.bigmodel.cn/api/paas/v4",
+});
+
+type Messages = Array<
+  | { role: "system"; content: string }
+  | { role: "user"; content: string }
+  | { role: "assistant"; content: string }
+>;
+
+async function getAnswer(prompt: string): Promise<string>;
+async function getAnswer(prompt: Messages): Promise<string>;
+async function getAnswer(prompt: string | Messages): Promise<string> {
+  const messages: Messages = typeof prompt === "string"
+    ? [{ role: "user", content: prompt }]
+    : prompt;
+
+  const response = await openai.chat.completions.create({
+    model: process.env.ZHIPU_MODEL_NAME || "glm-4",
+    messages: messages as OpenAI.Chat.Completions.ChatCompletionMessageParam[],
+    temperature: 0.2,
+  });
+
+  const content = response.choices[0]?.message?.content;
+  if (!content) {
+    throw new Error("AI API 返回空响应");
+  }
+
+  return content.trim();
+}
+
+export { getAnswer };

src/lib/bigmodel/translator/orchestrator.ts

@@ -1,4 +1,4 @@
-import { getAnswer } from "../zhipu";
+import { getAnswer } from "../llm";
 import { parseAIGeneratedJSON } from "@/utils/json";
 import { LanguageDetectionResult, TranslationLLMResponse } from "./types";
 import { createLogger } from "@/lib/logger";

src/lib/bigmodel/zhipu.ts

@@ -1,58 +0,0 @@
-"use server";
-
-type Messages = { role: string; content: string; }[];
-
-const LLM_TIMEOUT_MS = 30000;
-
-async function callZhipuAPI(
-  messages: Messages,
-  model = process.env.ZHIPU_MODEL_NAME,
-) {
-  const controller = new AbortController();
-  const timeoutId = setTimeout(() => controller.abort(), LLM_TIMEOUT_MS);
-
-  const url = "https://open.bigmodel.cn/api/paas/v4/chat/completions";
-
-  const response = await fetch(url, {
-    method: "POST",
-    headers: {
-      Authorization: "Bearer " + process.env.ZHIPU_API_KEY,
-      "Content-Type": "application/json",
-    },
-    body: JSON.stringify({
-      model: model,
-      messages: messages,
-      temperature: 0.2,
-      thinking: {
-        type: "disabled",
-      },
-    }),
-    signal: controller.signal,
-  });
-
-  clearTimeout(timeoutId);
-
-  if (!response.ok) {
-    throw new Error(`API 调用失败: ${response.status} ${response.statusText}`);
-  }
-
-  return await response.json();
-}
-
-async function getAnswer(prompt: string): Promise<string>;
-async function getAnswer(prompt: Messages): Promise<string>;
-async function getAnswer(prompt: string | Messages): Promise<string> {
-  const messages = typeof prompt === "string"
-    ? [{ role: "user", content: prompt }]
-    : prompt;
-
-  const response = await callZhipuAPI(messages);
-  
-  if (!response.choices?.[0]?.message?.content) {
-    throw new Error("AI API 返回空响应");
-  }
-  
-  return response.choices[0].message.content.trim();
-}
-
-export { getAnswer };

src/modules/translator/translator-action.ts

@@ -8,7 +8,7 @@ import {
 import { ValidateError } from "@/lib/errors";
 import { createLogger } from "@/lib/logger";
 import { serviceTranslateText } from "./translator-service";
-import { getAnswer } from "@/lib/bigmodel/zhipu";
+import { getAnswer } from "@/lib/bigmodel/llm";
 
 const log = createLogger("translator-action");