fix(llm): reject invalid anthropic text content

Author: Mouseww

RELEASE_NOTES.md

@@ -1,16 +1,16 @@
-# Anything Analyzer v3.6.35
+# Anything Analyzer v3.6.36
 
 ## 修复
 
-- **流式文本增量格式校验** — 避免 SSE 文本增量不是字符串时被拼成 `[object Object]`
-  - OpenAI Chat、Responses API、Anthropic/MiniMax 流式解析现在都会拒绝非字符串文本 delta
-  - 新增回归测试覆盖三个流式接口的非字符串增量异常路径
+- **Anthropic/MiniMax 非流式文本格式校验** — 避免 text content 不是字符串时被拼成 `[object Object]`
+  - 普通请求和工具调用最终轮次复用同一段 Anthropic 文本块校验逻辑
+  - 新增回归测试覆盖非字符串 text content 的异常路径
 
 ## 下载
 
 | 平台 | 文件 |
 |------|------|
-| Windows | Anything-Analyzer-Setup-3.6.35.exe |
-| macOS (Apple Silicon) | Anything-Analyzer-3.6.35-arm64.dmg |
-| macOS (Intel) | Anything-Analyzer-3.6.35-x64.dmg |
-| Linux | Anything-Analyzer-3.6.35.AppImage |
+| Windows | Anything-Analyzer-Setup-3.6.36.exe |
+| macOS (Apple Silicon) | Anything-Analyzer-3.6.36-arm64.dmg |
+| macOS (Intel) | Anything-Analyzer-3.6.36-x64.dmg |
+| Linux | Anything-Analyzer-3.6.36.AppImage |

package.json

@@ -1,6 +1,6 @@
 {
   "name": "anything-analyzer",
-  "version": "3.6.35",
+  "version": "3.6.36",
   "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

@@ -105,10 +105,26 @@ function readStreamTextDelta(
   throw new Error(`${providerName} stream error: ${fieldName} must be a string`);
 }
 
+function readAnthropicTextContent(data: {
+  content: Array<{ type: string; text?: unknown }>;
+}): string {
+  const textBlocks = data.content.filter((block) => block.type === "text");
+  if (textBlocks.some((block) => typeof block.text !== "string")) {
+    throw new Error(`LLM 响应格式异常: text content 必须是字符串 — ${JSON.stringify(data).slice(0, 200)}`);
+  }
+
+  const content = textBlocks.map((block) => block.text as string).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;
+}
+
 /**
  * Sanitize string content in LLM request body to remove control characters
  * that may break JSON parsing in intermediate proxies.
- */
+ */
 function sanitizeForJson(obj: unknown): unknown {
   if (typeof obj === 'string') {
     // Remove ASCII control chars (except \n \r \t) and Unicode replacement char
@@ -457,14 +473,7 @@ export class LLMRouter {
       }
 
       // No tool use → extract text content as final answer
-      const textContent = data.content
-        .filter((b): b is AnthropicTextBlock => b.type === "text")
-        .map((b) => b.text)
-        .join("");
-
-      if (textContent.length === 0) {
-        throw new Error(`LLM 响应格式异常: 缺少 text content 字段 — ${JSON.stringify(data).slice(0, 200)}`);
-      }
+      const textContent = readAnthropicTextContent(data);
       if (onChunk && textContent) onChunk(textContent);
       return {
         content: textContent,
@@ -730,13 +739,7 @@ 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("");
-    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)}`);
-    }
+    const content = readAnthropicTextContent(data);
     return {
       content,
       promptTokens: data.usage?.input_tokens || 0,

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

@@ -231,6 +231,27 @@ describe("LLMRouter", () => {
       ).rejects.toThrow("LLM 响应格式异常: 缺少 text content 字段");
     });
 
+    it("should reject Anthropic-compatible responses with non-string 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: "text", text: { value: "not a string" } }],
+        }),
+      );
+
+      const router = new LLMRouter(config);
+
+      await expect(
+        router.complete([{ role: "user", content: "hello" }]),
+      ).rejects.toThrow("LLM 响应格式异常: text content 必须是字符串");
+    });
+
     it("should route to completions endpoint when apiType is undefined", async () => {
       const config: LLMProviderConfig = { ...baseConfig };
       fetchSpy.mockResolvedValueOnce(