支持gimini格式，优化debug日志

utils/thinking_cache.py

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+"""轻量 Thinking 缓存
+
+纯内存缓存，在多轮对话中保存和恢复 thinking/reasoning 内容。
+解决 Cursor 不会把 thinking 内容回传给 API 的问题，
+某些模型（如推理模型）在缺少历史 thinking 时表现会下降。
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import logging
+import re
+import time
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+_THINK_RE = re.compile(r'<think>.*?</think>', re.DOTALL)
+_UNCLOSED_THINK_RE = re.compile(r'<think>.*$', re.DOTALL)
+_TOOL_ID_RE = re.compile(r'[^a-zA-Z0-9_-]')
+_TTL = 86400  # 24 hours
+
+
+class ThinkingCache:
+    """纯内存 thinking 缓存，TTL 2 小时。"""
+
+    def __init__(self):
+        self._store: dict[str, tuple[str, float]] = {}
+
+    def inject(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        """遍历 assistant 消息，缺少 reasoning_content 时从缓存注入。"""
+        sid = self._session_id(messages)
+        if not sid:
+            return messages
+
+        now = time.time()
+        for msg in messages:
+            if msg.get('role') != 'assistant':
+                continue
+            if msg.get('reasoning_content'):
+                continue
+            key = sid + ':' + self._message_hash(msg)
+            entry = self._store.get(key)
+            if entry and (now - entry[1]) < _TTL:
+                msg['reasoning_content'] = entry[0]
+                logger.debug('已从缓存注入 thinking (%d 字符)', len(entry[0]))
+
+        return messages
+
+    def store_from_response(
+        self,
+        messages: list[dict[str, Any]],
+        reasoning_content: str,
+    ) -> None:
+        """将响应中的 thinking 内容存入缓存。"""
+        if not reasoning_content:
+            return
+        sid = self._session_id(messages)
+        if not sid:
+            return
+
+        fake_msg: dict[str, Any] = {'role': 'assistant', 'content': '', 'tool_calls': []}
+        key = sid + ':' + self._message_hash(fake_msg)
+        self._store[key] = (reasoning_content, time.time())
+        self._cleanup()
+
+    def store_assistant_thinking(
+        self,
+        messages: list[dict[str, Any]],
+        assistant_msg: dict[str, Any],
+    ) -> None:
+        """从完整的 assistant 消息中提取并缓存 thinking。"""
+        rc = assistant_msg.get('reasoning_content', '')
+        if not rc:
+            return
+        sid = self._session_id(messages)
+        if not sid:
+            return
+        key = sid + ':' + self._message_hash(assistant_msg)
+        self._store[key] = (rc, time.time())
+        self._cleanup()
+
+    def _session_id(self, messages: list[dict[str, Any]]) -> str:
+        first_user = ''
+        first_assistant = ''
+        for msg in messages:
+            role = msg.get('role', '')
+            if role in ('system', 'developer'):
+                continue
+            if role == 'user' and not first_user:
+                first_user = self._normalize_content(
+                    msg.get('content', '')
+                )
+            elif role == 'assistant' and not first_assistant:
+                first_assistant = self._normalize_content(
+                    msg.get('content', '')
+                )
+            if first_user and first_assistant:
+                break
+
+        if not first_user or not first_assistant:
+            return ''
+
+        raw = first_user + '|' + first_assistant
+        return hashlib.sha256(raw.encode()).hexdigest()[:16]
+
+    def _message_hash(self, msg: dict[str, Any]) -> str:
+        content = self._normalize_content(msg.get('content', ''))
+        tool_ids = sorted(
+            self._normalize_tool_id(tc.get('id', ''))
+            for tc in msg.get('tool_calls', [])
+            if isinstance(tc, dict)
+        )
+        raw = json.dumps({'c': content, 't': tool_ids}, ensure_ascii=False)
+        return hashlib.sha256(raw.encode()).hexdigest()[:16]
+
+    @staticmethod
+    def _normalize_content(content: Any) -> str:
+        if isinstance(content, list):
+            parts = []
+            for p in content:
+                if isinstance(p, dict) and p.get('type') == 'text':
+                    parts.append(p.get('text', ''))
+                elif isinstance(p, str):
+                    parts.append(p)
+            text = '\n'.join(parts)
+        elif isinstance(content, str):
+            text = content
+        else:
+            text = str(content) if content else ''
+        text = _THINK_RE.sub('', text)
+        text = _UNCLOSED_THINK_RE.sub('', text)
+        return text.strip()
+
+    @staticmethod
+    def _normalize_tool_id(tid: str) -> str:
+        return _TOOL_ID_RE.sub('', tid)
+
+    def _cleanup(self) -> None:
+        """惰性清理过期条目（每 100 次写入触发一次全量扫描）。"""
+        if len(self._store) < 100:
+            return
+        now = time.time()
+        expired = [k for k, (_, ts) in self._store.items() if (now - ts) >= _TTL]
+        for k in expired:
+            del self._store[k]
+
+
+thinking_cache = ThinkingCache()