tools系统
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UnCLAS-Prommer
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import ast
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import json
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import logging
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from typing import Any, Dict, TypeVar, List, Union, Tuple, Optional
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# 定义类型变量用于泛型类型提示
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T = TypeVar("T")
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# 获取logger
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logger = logging.getLogger("json_utils")
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def safe_json_loads(json_str: str, default_value: T = None) -> Union[Any, T]:
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"""
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安全地解析JSON字符串，出错时返回默认值
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现在尝试处理单引号和标准JSON
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参数:
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json_str: 要解析的JSON字符串
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default_value: 解析失败时返回的默认值
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返回:
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解析后的Python对象，或在解析失败时返回default_value
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"""
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if not json_str or not isinstance(json_str, str):
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logger.warning(f"safe_json_loads 接收到非字符串输入: {type(json_str)}, 值: {json_str}")
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return default_value
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try:
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# 尝试标准的 JSON 解析
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return json.loads(json_str)
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except json.JSONDecodeError:
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# 如果标准解析失败，尝试用 ast.literal_eval 解析
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try:
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# logger.debug(f"标准JSON解析失败，尝试用 ast.literal_eval 解析: {json_str[:100]}...")
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result = ast.literal_eval(json_str)
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if isinstance(result, dict):
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return result
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logger.warning(f"ast.literal_eval 解析成功但结果不是字典: {type(result)}, 内容: {result}")
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return default_value
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except (ValueError, SyntaxError, MemoryError, RecursionError) as ast_e:
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logger.error(f"使用 ast.literal_eval 解析失败: {ast_e}, 字符串: {json_str[:100]}...")
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return default_value
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except Exception as e:
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logger.error(f"使用 ast.literal_eval 解析时发生意外错误: {e}, 字符串: {json_str[:100]}...")
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return default_value
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except Exception as e:
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logger.error(f"JSON解析过程中发生意外错误: {e}, 字符串: {json_str[:100]}...")
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return default_value
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def extract_tool_call_arguments(
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tool_call: Dict[str, Any], default_value: Optional[Dict[str, Any]] = None
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) -> Dict[str, Any]:
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"""
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从LLM工具调用对象中提取参数
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参数:
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tool_call: 工具调用对象字典
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default_value: 解析失败时返回的默认值
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返回:
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解析后的参数字典，或在解析失败时返回default_value
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"""
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default_result = default_value or {}
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if not tool_call or not isinstance(tool_call, dict):
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logger.error(f"无效的工具调用对象: {tool_call}")
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return default_result
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try:
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# 提取function参数
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function_data = tool_call.get("function", {})
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if not function_data or not isinstance(function_data, dict):
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logger.error(f"工具调用缺少function字段或格式不正确: {tool_call}")
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return default_result
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if arguments_str := function_data.get("arguments", "{}"):
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# 解析JSON
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return safe_json_loads(arguments_str, default_result)
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else:
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return default_result
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except Exception as e:
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logger.error(f"提取工具调用参数时出错: {e}")
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return default_result
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def safe_json_dumps(obj: Any, default_value: str = "{}", ensure_ascii: bool = False, pretty: bool = False) -> str:
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"""
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安全地将Python对象序列化为JSON字符串
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参数:
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obj: 要序列化的Python对象
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default_value: 序列化失败时返回的默认值
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ensure_ascii: 是否确保ASCII编码(默认False，允许中文等非ASCII字符)
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pretty: 是否美化输出JSON
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返回:
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序列化后的JSON字符串，或在序列化失败时返回default_value
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"""
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try:
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indent = 2 if pretty else None
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return json.dumps(obj, ensure_ascii=ensure_ascii, indent=indent)
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except TypeError as e:
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logger.error(f"JSON序列化失败(类型错误): {e}")
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return default_value
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except Exception as e:
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logger.error(f"JSON序列化过程中发生意外错误: {e}")
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return default_value
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def normalize_llm_response(response: Any, log_prefix: str = "") -> Tuple[bool, List[Any], str]:
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"""
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标准化LLM响应格式，将各种格式（如元组）转换为统一的列表格式
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参数:
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response: 原始LLM响应
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log_prefix: 日志前缀
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返回:
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元组 (成功标志, 标准化后的响应列表, 错误消息)
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"""
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logger.debug(f"{log_prefix}原始人 LLM响应: {response}")
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# 检查是否为None
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if response is None:
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return False, [], "LLM响应为None"
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# 记录原始类型
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logger.debug(f"{log_prefix}LLM响应原始类型: {type(response).__name__}")
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# 将元组转换为列表
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if isinstance(response, tuple):
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logger.debug(f"{log_prefix}将元组响应转换为列表")
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response = list(response)
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# 确保是列表类型
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if not isinstance(response, list):
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return False, [], f"无法处理的LLM响应类型: {type(response).__name__}"
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# 处理工具调用部分（如果存在）
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if len(response) == 3:
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content, reasoning, tool_calls = response
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# 将工具调用部分转换为列表（如果是元组）
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if isinstance(tool_calls, tuple):
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logger.debug(f"{log_prefix}将工具调用元组转换为列表")
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tool_calls = list(tool_calls)
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response[2] = tool_calls
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return True, response, ""
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def process_llm_tool_calls(
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tool_calls: List[Dict[str, Any]], log_prefix: str = ""
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) -> Tuple[bool, List[Dict[str, Any]], str]:
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"""
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处理并验证LLM响应中的工具调用列表
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参数:
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tool_calls: 从LLM响应中直接获取的工具调用列表
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log_prefix: 日志前缀
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返回:
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元组 (成功标志, 验证后的工具调用列表, 错误消息)
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"""
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# 如果列表为空，表示没有工具调用，这不是错误
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if not tool_calls:
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return True, [], "工具调用列表为空"
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# 验证每个工具调用的格式
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valid_tool_calls = []
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for i, tool_call in enumerate(tool_calls):
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if not isinstance(tool_call, dict):
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logger.warning(f"{log_prefix}工具调用[{i}]不是字典: {type(tool_call).__name__}, 内容: {tool_call}")
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continue
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# 检查基本结构
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if tool_call.get("type") != "function":
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logger.warning(
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f"{log_prefix}工具调用[{i}]不是function类型: type={tool_call.get('type', '未定义')}, 内容: {tool_call}"
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)
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continue
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if "function" not in tool_call or not isinstance(tool_call.get("function"), dict):
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logger.warning(f"{log_prefix}工具调用[{i}]缺少'function'字段或其类型不正确: {tool_call}")
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continue
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func_details = tool_call["function"]
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if "name" not in func_details or not isinstance(func_details.get("name"), str):
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logger.warning(f"{log_prefix}工具调用[{i}]的'function'字段缺少'name'或类型不正确: {func_details}")
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continue
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# 验证参数 'arguments'
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args_value = func_details.get("arguments")
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# 1. 检查 arguments 是否存在且是字符串
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if args_value is None or not isinstance(args_value, str):
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logger.warning(f"{log_prefix}工具调用[{i}]的'function'字段缺少'arguments'字符串: {func_details}")
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continue
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# 2. 尝试安全地解析 arguments 字符串
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parsed_args = safe_json_loads(args_value, None)
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# 3. 检查解析结果是否为字典
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if parsed_args is None or not isinstance(parsed_args, dict):
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logger.warning(
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f"{log_prefix}工具调用[{i}]的'arguments'无法解析为有效的JSON字典, "
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f"原始字符串: {args_value[:100]}..., 解析结果类型: {type(parsed_args).__name__}"
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)
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continue
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# 如果检查通过，将原始的 tool_call 加入有效列表
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valid_tool_calls.append(tool_call)
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if not valid_tool_calls and tool_calls: # 如果原始列表不为空，但验证后为空
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return False, [], "所有工具调用格式均无效"
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return True, valid_tool_calls, ""
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