fix(llm): reject anthropic empty text responses

Author: MouseWW

RELEASE_NOTES.md

@@ -1,16 +1,16 @@
-# Anything Analyzer v3.6.29
+# Anything Analyzer v3.6.30
 
 ## 修复
 
-- **LLM 非对象 JSON 响应诊断** — 避免兼容服务返回 `null` 等合法但非对象 JSON 时抛出内部属性访问错误
-  - `safeParseJson` 现在会将非对象 JSON 归一为空对象，由各调用路径继续给出明确格式错误
-  - OpenAI Chat Completions 回归测试覆盖 `null` 响应，确保诊断保持为缺少 `choices` 字段
+- **Anthropic/MiniMax 非流式空文本诊断** — 避免兼容服务只返回工具块或非文本块时被误判为空成功结果
+  - 非流式 Anthropic 兼容响应现在要求至少包含一个文本内容块
+  - MiniMax 回归测试覆盖无 `text` 内容的响应，确保给出明确格式错误
 
 ## 下载
 
 | 平台 | 文件 |
 |------|------|
-| Windows | Anything-Analyzer-Setup-3.6.29.exe |
-| macOS (Apple Silicon) | Anything-Analyzer-3.6.29-arm64.dmg |
-| macOS (Intel) | Anything-Analyzer-3.6.29-x64.dmg |
-| Linux | Anything-Analyzer-3.6.29.AppImage |
+| Windows | Anything-Analyzer-Setup-3.6.30.exe |
+| macOS (Apple Silicon) | Anything-Analyzer-3.6.30-arm64.dmg |
+| macOS (Intel) | Anything-Analyzer-3.6.30-x64.dmg |
+| Linux | Anything-Analyzer-3.6.30.AppImage |

package.json

@@ -1,6 +1,6 @@
 {
   "name": "anything-analyzer",
-  "version": "3.6.29",
+  "version": "3.6.30",
   "description": "Universal web protocol analyzer with embedded browser and AI-powered analysis",
   "packageManager": "pnpm@10.24.0",
   "main": "./out/main/index.js",

src/main/ai/llm-router.ts

@@ -688,14 +688,17 @@ export class LLMRouter {
     if (!Array.isArray(data.content)) {
       throw new Error(`LLM 响应格式异常: 缺少 content 字段 — ${JSON.stringify(data).slice(0, 200)}`);
     }
-    const content = data.content
-      .filter((c) => c.type === "text")
-      .map((c) => c.text)
-      .join("");
-    return {
-      content,
-      promptTokens: data.usage?.input_tokens || 0,
-      completionTokens: data.usage?.output_tokens || 0,
+    const content = data.content
+      .filter((c) => c.type === "text")
+      .map((c) => c.text)
+      .join("");
+    if (content.length === 0) {
+      throw new Error(`LLM 响应格式异常: 缺少 text content 字段 — ${JSON.stringify(data).slice(0, 200)}`);
+    }
+    return {
+      content,
+      promptTokens: data.usage?.input_tokens || 0,
+      completionTokens: data.usage?.output_tokens || 0,
     };
   }
 

tests/main/ai/llm-router.test.ts

@@ -187,11 +187,11 @@ describe("LLMRouter", () => {
       expect(options.headers).not.toHaveProperty("Authorization");
     });
 
-    it("should parse MiniMax response content and usage correctly", async () => {
-      const config: LLMProviderConfig = {
-        name: "minimax",
-        baseUrl: "https://api.minimax.io/anthropic/v1",
-        apiKey: "test-minimax-key",
+    it("should parse MiniMax response content and usage correctly", async () => {
+      const config: LLMProviderConfig = {
+        name: "minimax",
+        baseUrl: "https://api.minimax.io/anthropic/v1",
+        apiKey: "test-minimax-key",
         model: "MiniMax-M2.7-highspeed",
         maxTokens: 4096,
       };
@@ -206,15 +206,36 @@ describe("LLMRouter", () => {
       const result = await router.complete([{ role: "user", content: "hello" }]);
 
       expect(result.content).toBe("MiniMax response");
-      expect(result.promptTokens).toBe(20);
-      expect(result.completionTokens).toBe(10);
-    });
-
-    it("should route to completions endpoint when apiType is undefined", async () => {
-      const config: LLMProviderConfig = { ...baseConfig };
-      fetchSpy.mockResolvedValueOnce(
-        createJSONResponse({
-          choices: [{ message: { content: "hello" } }],
+      expect(result.promptTokens).toBe(20);
+      expect(result.completionTokens).toBe(10);
+    });
+
+    it("should reject Anthropic-compatible responses without text content", async () => {
+      const config: LLMProviderConfig = {
+        name: "minimax",
+        baseUrl: "https://api.minimax.io/anthropic/v1",
+        apiKey: "test-minimax-key",
+        model: "MiniMax-M2.7-highspeed",
+        maxTokens: 4096,
+      };
+      fetchSpy.mockResolvedValueOnce(
+        createJSONResponse({
+          content: [{ type: "tool_use", id: "call-1", name: "lookup", input: {} }],
+        }),
+      );
+
+      const router = new LLMRouter(config);
+
+      await expect(
+        router.complete([{ role: "user", content: "hello" }]),
+      ).rejects.toThrow("LLM 响应格式异常: 缺少 text content 字段");
+    });
+
+    it("should route to completions endpoint when apiType is undefined", async () => {
+      const config: LLMProviderConfig = { ...baseConfig };
+      fetchSpy.mockResolvedValueOnce(
+        createJSONResponse({
+          choices: [{ message: { content: "hello" } }],
           usage: { prompt_tokens: 10, completion_tokens: 5 },
         }),
       );