97 lines
3.0 KiB
Python
97 lines
3.0 KiB
Python
"""
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AI Model logging configuration.
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Controls whether AI model requests/responses are logged to disk.
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Easy to extend with additional log targets, formats, or filters.
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"""
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import os
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import json
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import base64
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from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
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from cryptography.hazmat.backends import default_backend
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from cryptography.hazmat.primitives import padding
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from app.config import settings
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#16字节密钥
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ENCRYPTION_KEY = b'videogen@202605!'
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def encrypt_data(data: dict, is_encrypt: bool = False) -> str:
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data_str = json.dumps(data, ensure_ascii=False, sort_keys=True)
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if is_encrypt:
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return data_str
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data_bytes = data_str.encode("utf-8")
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padder = padding.PKCS7(128).padder()
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padded_data = padder.update(data_bytes) + padder.finalize()
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iv = os.urandom(16)
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cipher = Cipher(algorithms.AES(ENCRYPTION_KEY), modes.CBC(iv), backend=default_backend())
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encryptor = cipher.encryptor()
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encrypted_data = encryptor.update(padded_data) + encryptor.finalize()
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return base64.b64encode(iv + encrypted_data).decode("utf-8")
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def decrypt_data(encrypted_str: str) -> dict:
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try:
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encrypted_bytes = base64.b64decode(encrypted_str)
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iv = encrypted_bytes[:16]
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ciphertext = encrypted_bytes[16:]
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cipher = Cipher(algorithms.AES(ENCRYPTION_KEY), modes.CBC(iv), backend=default_backend())
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decryptor = cipher.decryptor()
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padded_data = decryptor.update(ciphertext) + decryptor.finalize()
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unpadder = padding.PKCS7(128).unpadder()
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data_bytes = unpadder.update(padded_data) + unpadder.finalize()
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return json.loads(data_bytes.decode("utf-8"))
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except Exception:
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return {}
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# ── Feature toggle ──────────────────────────────────────────
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AI_LOG_ENABLED: bool = True # Set True to enable logging, or use env var AI_LOG_ENABLED=true
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# ── Log output settings ────────────────────────────────────
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LOG_DIR = os.path.join(
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os.path.dirname(os.path.dirname(os.path.dirname(__file__))),
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"log", "AiModel",
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)
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# 请求响应日志目录
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LOG_R_Q_DIR = os.path.join(
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os.path.dirname(os.path.dirname(os.path.dirname(__file__))),
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"log", "RequestResponse",
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)
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LOG_FILENAME_FORMAT = "{date}.log" # e.g. 2026-05-12.log
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LOG_DATE_FORMAT = "%Y-%m-%d"
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# ── Fields to include in each log entry ────────────────────
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LOG_FIELDS = [
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"timestamp",
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"model_name",
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"model_id",
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"provider",
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"api_base",
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"request",
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"response",
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"error",
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]
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def is_enabled() -> bool:
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"""Check if AI model logging is enabled."""
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env_val = os.environ.get("AI_LOG_ENABLED", "").lower()
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if env_val in ("true", "1", "yes"):
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return True
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if env_val in ("false", "0", "no"):
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return False
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return AI_LOG_ENABLED
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