网关首选的推理模型一次生成约 160 秒,每次打开建议页都重跑不可用。 结果落库缓存,页面读缓存,用户想要新的再手动触发。 db.py: - 新增 ai_recommendations 表,每用户一行(重新生成是替换不是累积) - fingerprint 列记录这条建议是基于哪份数据算出来的 services/analysis.py: - _fingerprint() 对全部每日指标 + 运动条数取 sha256,任何一次同步 新增或修正了数值都会让摘要变化,从而使缓存失效 - TTL 默认 24 小时(AI_CACHE_TTL_HOURS 可调) - 指定 model 参数时绕过缓存:点名某个模型意味着想要那个模型的答案 - 降级到规则引擎的结果不写缓存,避免把兜底答案当成 AI 结果存下来 - 缓存写入失败只打日志,不影响本次请求返回 routes: ?refresh=1 强制重新生成 前端: - "重新生成" 按钮走 refresh,并提示需要 1-3 分钟、可以离开本页 - meta 栏显示是否为缓存结果及生成时间,以及网关的上游厂商 - axios 该请求超时放宽到 240s(冷生成远超默认超时) tests/test_ai_cache.py (20 通过): - 第二次调用不再打模型 - 新增一天数据 / 修正某天数值 / 新增一条运动记录,三种情况都失效 - TTL 边界两侧各一条(刚过期重算、未过期沿用) - 缓存按用户隔离,A 的结果不会答给 B - payload 损坏时重新生成而不是抛异常 - 规则兜底结果和无数据用户都不落缓存 Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
244 lines
6.3 KiB
Python
244 lines
6.3 KiB
Python
"""
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Pluggable data layer for Garmin Health Lab.
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Supports both SQLite (stdlib, local dev) and MariaDB (PyMySQL, NAS production)
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through a single unified API:
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init_db() -> create tables if missing
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execute(sql, params) -> INSERT/UPDATE/DELETE, returns {id, changes}
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query_one(sql, params) -> one row as dict or None
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query_all(sql, params) -> list of row dicts
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Both backends accept `?` placeholders; the SQL is translated to `%s` for
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MariaDB automatically. Upserts must use backend-specific SQL (see services).
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"""
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import os
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import sqlite3
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import threading
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import queue
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import datetime
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from config import (
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DB_TYPE,
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SQLITE_PATH,
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MARIADB_SOCKET,
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MARIADB_HOST,
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MARIADB_PORT,
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MARIADB_USER,
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MARIADB_PASSWORD,
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MARIADB_DATABASE,
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)
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SCHEMA = """
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CREATE TABLE IF NOT EXISTS users (
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id VARCHAR(64) PRIMARY KEY,
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email VARCHAR(255) NOT NULL UNIQUE,
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garmin_email VARCHAR(255) NOT NULL,
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garmin_password_hash TEXT NOT NULL,
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jwt_token TEXT,
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created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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updated_at DATETIME DEFAULT CURRENT_TIMESTAMP
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);
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CREATE TABLE IF NOT EXISTS health_data (
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id VARCHAR(64) PRIMARY KEY,
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user_id VARCHAR(64) NOT NULL,
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date DATE NOT NULL,
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steps INT,
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heart_rate INT,
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heart_rate_variability DOUBLE,
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blood_pressure_systolic INT,
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blood_pressure_diastolic INT,
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sleep_duration INT,
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sleep_quality DOUBLE,
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stress INT,
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calories_burned DOUBLE,
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created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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updated_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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UNIQUE(user_id, date),
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FOREIGN KEY (user_id) REFERENCES users(id)
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);
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CREATE TABLE IF NOT EXISTS activities (
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id VARCHAR(64) PRIMARY KEY,
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user_id VARCHAR(64) NOT NULL,
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activity_type VARCHAR(255) NOT NULL,
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start_time DATETIME NOT NULL,
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end_time DATETIME NOT NULL,
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duration INT,
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distance DOUBLE,
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calories DOUBLE,
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heart_rate_average INT,
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heart_rate_max INT,
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created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (user_id) REFERENCES users(id)
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);
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CREATE TABLE IF NOT EXISTS sync_status (
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user_id VARCHAR(64) PRIMARY KEY,
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last_sync_time DATETIME,
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status VARCHAR(32) DEFAULT 'idle',
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last_error TEXT,
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records_synced INT DEFAULT 0,
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updated_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (user_id) REFERENCES users(id)
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);
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-- One cached LLM answer per user. Generating one takes minutes against a
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-- large reasoning model, which is far too slow to sit in a page load, so the
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-- result is stored and reused until the underlying data changes.
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-- `fingerprint` identifies the health data the advice was derived from.
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CREATE TABLE IF NOT EXISTS ai_recommendations (
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user_id VARCHAR(64) PRIMARY KEY,
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fingerprint VARCHAR(64) NOT NULL,
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model VARCHAR(64),
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upstream VARCHAR(64),
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days INT,
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payload TEXT NOT NULL,
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created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (user_id) REFERENCES users(id)
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);
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"""
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# --- MariaDB pool (lazy) ----------------------------------------------------
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_mariadb_pool = None
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_pool_lock = threading.Lock()
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def _new_mariadb_conn():
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import pymysql
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from pymysql.cursors import DictCursor
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kwargs = dict(
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user=MARIADB_USER,
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password=MARIADB_PASSWORD,
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database=MARIADB_DATABASE,
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charset="utf8mb4",
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autocommit=True,
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cursorclass=DictCursor,
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connect_timeout=10,
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)
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if MARIADB_SOCKET:
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kwargs["unix_socket"] = MARIADB_SOCKET
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else:
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kwargs["host"] = MARIADB_HOST
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kwargs["port"] = MARIADB_PORT
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return pymysql.connect(**kwargs)
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def _mariadb_acquire():
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global _mariadb_pool
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if _mariadb_pool is None:
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with _pool_lock:
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if _mariadb_pool is None:
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_mariadb_pool = queue.Queue(maxsize=10)
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for _ in range(10):
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_mariadb_pool.put(_new_mariadb_conn())
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try:
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return _mariadb_pool.get(block=False)
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except queue.Empty:
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return _new_mariadb_conn()
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def _mariadb_release(conn):
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try:
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conn.ping(reconnect=False)
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_mariadb_pool.put(conn)
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except Exception:
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try:
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conn.close()
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except Exception:
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pass
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# --- SQLite connection ------------------------------------------------------
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def _sqlite_connect():
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data_dir = os.path.dirname(SQLITE_PATH)
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if data_dir and not os.path.exists(data_dir):
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os.makedirs(data_dir, exist_ok=True)
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conn = sqlite3.connect(SQLITE_PATH, isolation_level=None)
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conn.row_factory = sqlite3.Row
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conn.execute("PRAGMA foreign_keys = ON")
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return conn
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def _connect():
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if DB_TYPE == "mariadb":
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return _mariadb_acquire()
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return _sqlite_connect()
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def _disconnect(conn):
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if DB_TYPE == "mariadb":
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_mariadb_release(conn)
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else:
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conn.close()
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def _adapt_sql(sql):
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# pymysql uses %s placeholders; sqlite3 uses ?. Business code writes ?.
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return sql.replace("?", "%s") if DB_TYPE == "mariadb" else sql
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def _serialize(value):
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if value is None:
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return None
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if isinstance(value, (datetime.datetime, datetime.date)):
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return value.isoformat()
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return value
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def _row_to_dict(row):
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if row is None:
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return None
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if isinstance(row, dict):
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return {k: _serialize(v) for k, v in row.items()}
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return {k: _serialize(row[k]) for k in row.keys()}
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# --- Public API -------------------------------------------------------------
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def init_db():
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conn = _connect()
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try:
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cur = conn.cursor()
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for stmt in SCHEMA.split(";"):
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stmt = stmt.strip()
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if not stmt:
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continue
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cur.execute(_adapt_sql(stmt))
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finally:
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_disconnect(conn)
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def execute(sql, params=None):
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params = params or []
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conn = _connect()
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try:
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cur = conn.cursor()
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cur.execute(_adapt_sql(sql), params)
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return {"id": cur.lastrowid, "changes": cur.rowcount}
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finally:
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_disconnect(conn)
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def query_one(sql, params=None):
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params = params or []
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conn = _connect()
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try:
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cur = conn.cursor()
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cur.execute(_adapt_sql(sql), params)
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return _row_to_dict(cur.fetchone())
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finally:
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_disconnect(conn)
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def query_all(sql, params=None):
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params = params or []
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conn = _connect()
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try:
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cur = conn.cursor()
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cur.execute(_adapt_sql(sql), params)
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return [_row_to_dict(r) for r in cur.fetchall()]
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finally:
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_disconnect(conn)
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