Fix empty AI review content from gemma reasoning token use.

Parse alternate reasoning fields, retry with a larger max_tokens budget, cap review images, and fall back to text-only when vision returns empty.

Co-authored-by: Cursor <cursoragent@cursor.com>

lib/ai/ai_client.py

@@ -119,16 +119,29 @@ def _openai_message_text(msg: dict) -> str:
                 parts.append(str(part.get("text") or ""))
         content = "".join(parts)
     text = str(content or "").strip()
-    if not text:
+    if text:
-        text = str(msg.get("reasoning_content") or "").strip()
+        return text
-    return text
+    # 部分网关/模型把正文放在 reasoning_content;gemma 系则常写在 reasoning
+    for key in ("reasoning_content", "reasoning"):
+        alt = str(msg.get(key) or "").strip()
+        if not alt:
+            continue
+        # 英文链式思考不算可交付正文,留给上层按 finish=length 重试
+        low = alt[:80].lower()
+        if low.startswith("here's a thinking process") or low.startswith("here is a thinking process"):
+            continue
+        if low.startswith("thinking process") or "analyze the request" in low:
+            continue
+        return alt
+    return ""
 
 
 def _apply_max_tokens(body: dict, max_tokens: int | None, *, chat: bool = False) -> None:
     if max_tokens is not None and max_tokens > 0:
         mt = int(max_tokens)
         body["max_tokens"] = mt
-        if not chat:
+        # 部分 OpenAI 兼容网关对 max_tokens + max_completion_tokens 双写不友好
+        if chat:
             body["max_completion_tokens"] = mt
 
 
@@ -169,9 +182,13 @@ def _openai_chat_completion(
     msg = choice.get("message") or {}
     text = _openai_message_text(msg)
     finish = str(choice.get("finish_reason") or "")
-    if not text and chat and max_tokens:
+
+    # gemma 等会先把 token 花在 reasoning 上:过小 max_tokens 时 content 为空且 finish=length
+    if not text:
         retry_body = dict(body)
         retry_body.pop("max_completion_tokens", None)
+        cur = int(retry_body.get("max_tokens") or 0)
+        retry_body["max_tokens"] = max(cur, 4096 if chat else 8192)
         r2 = requests.post(
             _openai_chat_url(),
             headers=headers,
@@ -182,12 +199,15 @@ def _openai_chat_completion(
         data2 = r2.json()
         choices2 = data2.get("choices") or []
         if choices2:
-            msg2 = (choices2[0] or {}).get("message") or {}
+            choice2 = choices2[0] or {}
+            msg2 = choice2.get("message") or {}
             text2 = _openai_message_text(msg2)
+            finish2 = str(choice2.get("finish_reason") or finish)
             if text2:
-                return text2, str((choices2[0] or {}).get("finish_reason") or finish)
+                return text2, finish2
+            finish = finish2 or finish
     if not text:
-        return "AI 生成失败:空内容", finish or "error"
+        return f"AI 生成失败:空内容(finish={finish or '?'})", finish or "error"
     return text, finish
 
 
@@ -261,9 +281,20 @@ def ai_generate(
     images = _collect_images(image_paths, images_b64)
     try:
         if _use_openai():
-            return _generate_openai(prompt, images, temperature, max_tokens=max_tokens)
+            out = _generate_openai(prompt, images, temperature, max_tokens=max_tokens)
-        text, _reason = _generate_ollama(prompt, images, temperature, max_tokens=max_tokens)
+        else:
-        return text
+            out, _reason = _generate_ollama(prompt, images, temperature, max_tokens=max_tokens)
+        # 附图导致空正文时,降级为纯文本再试一次(复盘仍可用)
+        if (
+            images
+            and isinstance(out, str)
+            and (out.startswith("AI 生成失败:空内容") or out.startswith("AI 调用失败"))
+        ):
+            if _use_openai():
+                return _generate_openai(prompt, [], temperature, max_tokens=max_tokens or 8192)
+            text, _reason = _generate_ollama(prompt, [], temperature, max_tokens=max_tokens or 8192)
+            return text
+        return out
     except requests.HTTPError as e:
         detail = ""
         try:
@@ -420,10 +451,20 @@ def ai_generate_chat(
 
 
 def ai_review(trades_text: str, period_title: str, image_paths=None) -> str:
-    n_img = len(image_paths or [])
+    # 附图过多时网关易超时/空回复;保留前几张即可支撑第5节
+    raw_paths = [p for p in (image_paths or []) if p]
+    try:
+        max_imgs = max(0, int(_env_str("AI_REVIEW_MAX_IMAGES", "4") or "4"))
+    except ValueError:
+        max_imgs = 4
+    capped_paths = raw_paths[:max_imgs] if max_imgs else []
+    n_img = len(capped_paths)
+    n_skipped = max(0, len(raw_paths) - n_img)
     period_label = "周" if "周" in str(period_title) else "日"
     attach_note = (
-        f"ℹ️ 【系统说明:已向模型附带 {n_img} 张复盘附图(自动K线或上传截图),请结合附图分析第5节.】\n\n"
+        f"ℹ️ 【系统说明:已向模型附带 {n_img} 张复盘附图(自动K线或上传截图)"
+        + (f",另跳过 {n_skipped} 张以控制体积" if n_skipped else "")
+        + ",请结合附图分析第5节.】\n\n"
         if n_img
         else "ℹ️ 【系统说明:本次未附带复盘附图,第5节请写明「无附图,无法看图」;保存复盘记录时可勾选「自动生成K线图」.】\n\n"
     )
@@ -455,7 +496,16 @@ def ai_review(trades_text: str, period_title: str, image_paths=None) -> str:
 交易记录:
 {trades_text}
 """.strip()
-    return attach_note + ai_generate(prompt, image_paths=image_paths, temperature=0.2)
+    try:
+        review_max = max(1024, int(_env_str("AI_REVIEW_MAX_TOKENS", "8192") or "8192"))
+    except ValueError:
+        review_max = 8192
+    return attach_note + ai_generate(
+        prompt,
+        image_paths=capped_paths,
+        temperature=0.2,
+        max_tokens=review_max,
+    )
 
 
 def ai_short_advice(prompt_text: str) -> str:

tests/test_ai_client_empty_content.py

@@ -0,0 +1,59 @@
+"""ai_client message parsing / empty-content retries."""
+from __future__ import annotations
+
+import sys
+import unittest
+from pathlib import Path
+from unittest import mock
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from lib.ai.ai_client import _openai_message_text, ai_review  # noqa: E402
+
+
+class TestOpenaiMessageText(unittest.TestCase):
+    def test_prefers_content(self):
+        self.assertEqual(
+            _openai_message_text({"content": "正文", "reasoning": "think"}),
+            "正文",
+        )
+
+    def test_falls_back_to_reasoning_content(self):
+        self.assertEqual(
+            _openai_message_text({"content": "", "reasoning_content": "备选正文"}),
+            "备选正文",
+        )
+
+    def test_skips_english_chain_of_thought(self):
+        self.assertEqual(
+            _openai_message_text(
+                {
+                    "content": "",
+                    "reasoning": "Here's a thinking process that leads to the answer...",
+                }
+            ),
+            "",
+        )
+
+
+class TestAiReviewImageCap(unittest.TestCase):
+    def test_caps_images_and_sets_max_tokens(self):
+        captured = {}
+
+        def fake_generate(prompt, **kwargs):
+            captured["prompt"] = prompt
+            captured.update(kwargs)
+            return "OK_REVIEW"
+
+        with mock.patch("lib.ai.ai_client.ai_generate", side_effect=fake_generate):
+            with mock.patch.dict("os.environ", {"AI_REVIEW_MAX_IMAGES": "2"}, clear=False):
+                out = ai_review("记录", "每日", image_paths=["a.png", "b.png", "c.png"])
+        self.assertIn("OK_REVIEW", out)
+        self.assertEqual(captured.get("image_paths"), ["a.png", "b.png"])
+        self.assertEqual(captured.get("max_tokens"), 8192)
+        self.assertIn("另跳过 1 张", out)
+
+
+if __name__ == "__main__":
+    unittest.main()