feat(govdoc): 新增内部公文模块全链路（后端58+前端11文件）

fastapi_modules/fastapi_leaudit/govdoc_engine/llm/cache.py

@@ -0,0 +1,101 @@
+"""LLM 响应缓存（SQLite）。
+
+缓存键 = sha256(model + canonical_json(messages, temperature, top_p, max_tokens))。
+仅缓存成功返回的文本；JSON 解析失败、API 错误、超时一律不入库。
+"""
+
+from __future__ import annotations
+import hashlib
+import json
+import logging
+import sqlite3
+import time
+from pathlib import Path
+from threading import Lock
+from typing import Any
+
+_log = logging.getLogger(__name__)
+
+_SCHEMA = """
+CREATE TABLE IF NOT EXISTS llm_cache (
+    cache_key TEXT PRIMARY KEY,
+    model TEXT NOT NULL,
+    response_text TEXT NOT NULL,
+    created_at REAL NOT NULL,
+    hit_count INTEGER NOT NULL DEFAULT 0,
+    last_hit_at REAL
+);
+CREATE INDEX IF NOT EXISTS idx_llm_cache_created ON llm_cache(created_at);
+"""
+
+# 影响响应的关键参数。其他 kwargs 不入 hash（如 stream/timeout）。
+_KEY_PARAMS = ("temperature", "top_p", "max_tokens", "response_format")
+
+
+def _canonical(messages: list[dict[str, str]], **kwargs: Any) -> str:
+    payload = {
+        "messages": messages,
+        "params": {k: kwargs.get(k) for k in _KEY_PARAMS},
+    }
+    return json.dumps(payload, sort_keys=True, ensure_ascii=False)
+
+
+def make_key(model: str, messages: list[dict[str, str]], **kwargs: Any) -> str:
+    raw = model + "\x00" + _canonical(messages, **kwargs)
+    return hashlib.sha256(raw.encode("utf-8")).hexdigest()
+
+
+class LlmCache:
+    def __init__(self, path: str | Path):
+        self.path = Path(path)
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+        self._lock = Lock()
+        self._conn = sqlite3.connect(str(self.path), check_same_thread=False)
+        self._conn.executescript(_SCHEMA)
+        self._conn.commit()
+
+    def get(self, key: str) -> str | None:
+        with self._lock:
+            row = self._conn.execute(
+                "SELECT response_text FROM llm_cache WHERE cache_key = ?",
+                (key,),
+            ).fetchone()
+            if row is None:
+                return None
+            self._conn.execute(
+                "UPDATE llm_cache "
+                "SET hit_count = hit_count + 1, last_hit_at = ? "
+                "WHERE cache_key = ?",
+                (time.time(), key),
+            )
+            self._conn.commit()
+            return row[0]
+
+    def put(self, key: str, model: str, response_text: str) -> None:
+        if not response_text:
+            return
+        with self._lock:
+            self._conn.execute(
+                "INSERT OR IGNORE INTO llm_cache "
+                "(cache_key, model, response_text, created_at) "
+                "VALUES (?, ?, ?, ?)",
+                (key, model, response_text, time.time()),
+            )
+            self._conn.commit()
+
+    def stats(self) -> dict[str, int]:
+        with self._lock:
+            row = self._conn.execute(
+                "SELECT COUNT(*), COALESCE(SUM(hit_count), 0) FROM llm_cache"
+            ).fetchone()
+            return {"entries": int(row[0] or 0), "total_hits": int(row[1] or 0)}
+
+    def clear(self) -> int:
+        with self._lock:
+            cur = self._conn.execute("DELETE FROM llm_cache")
+            self._conn.commit()
+            return cur.rowcount
+
+    def close(self) -> None:
+        with self._lock:
+            self._conn.close()