fix:FFUF
This commit is contained in:
SengokuCola
@@ -1,27 +1,28 @@
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import json
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import logging
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from typing import Any, Dict, Optional, TypeVar, Generic, List, Union, Callable, Tuple
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from typing import Any, Dict, TypeVar, List, Union, Callable, Tuple
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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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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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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:
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return default_value
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try:
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return json.loads(json_str)
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except json.JSONDecodeError as e:
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@@ -31,66 +32,67 @@ def safe_json_loads(json_str: str, default_value: T = None) -> Union[Any, T]:
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logger.error(f"JSON解析过程中发生意外错误: {e}")
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return default_value
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def extract_tool_call_arguments(tool_call: Dict[str, Any],
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default_value: Dict[str, Any] = None) -> Dict[str, Any]:
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def extract_tool_call_arguments(tool_call: Dict[str, Any], default_value: Dict[str, Any] = None) -> 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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# 提取arguments
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arguments_str = function_data.get("arguments", "{}")
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if not arguments_str:
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return default_result
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# 解析JSON
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return safe_json_loads(arguments_str, 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 get_json_value(json_obj: Dict[str, Any], key_path: str,
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default_value: T = None,
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transform_func: Callable[[Any], T] = None) -> Union[Any, T]:
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def get_json_value(
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json_obj: Dict[str, Any], key_path: str, default_value: T = None, transform_func: Callable[[Any], T] = None
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) -> Union[Any, T]:
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"""
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从JSON对象中按照路径提取值，支持点表示法路径，如"data.items.0.name"
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参数:
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json_obj: JSON对象(已解析的字典)
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key_path: 键路径，使用点表示法，如"data.items.0.name"
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default_value: 获取失败时返回的默认值
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transform_func: 可选的转换函数，用于对获取的值进行转换
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返回:
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路径指向的值，或在获取失败时返回default_value
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"""
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if not json_obj or not key_path:
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return default_value
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try:
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# 分割路径
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keys = key_path.split(".")
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current = json_obj
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# 遍历路径
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for key in keys:
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# 处理数组索引
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@@ -108,7 +110,7 @@ def get_json_value(json_obj: Dict[str, Any], key_path: str,
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return default_value
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else:
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return default_value
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# 应用转换函数(如果提供)
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if transform_func and current is not None:
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return transform_func(current)
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@@ -117,17 +119,17 @@ def get_json_value(json_obj: Dict[str, Any], key_path: str,
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logger.error(f"从JSON获取值时出错: {e}, 路径: {key_path}")
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return default_value
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def safe_json_dumps(obj: Any, default_value: str = "{}", ensure_ascii: bool = False,
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pretty: bool = False) -> str:
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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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@@ -141,13 +143,14 @@ def safe_json_dumps(obj: Any, default_value: str = "{}", ensure_ascii: bool = Fa
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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 merge_json_objects(*objects: Dict[str, Any]) -> Dict[str, Any]:
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"""
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合并多个JSON对象(字典)
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参数:
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*objects: 要合并的JSON对象(字典)
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返回:
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合并后的字典，后面的对象会覆盖前面对象的相同键
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"""
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@@ -157,109 +160,110 @@ def merge_json_objects(*objects: Dict[str, Any]) -> Dict[str, Any]:
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result.update(obj)
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return result
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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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# 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(response: List[Any], log_prefix: str = "") -> Tuple[bool, List[Dict[str, Any]], str]:
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"""
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处理并提取LLM响应中的工具调用列表
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参数:
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response: 标准化后的LLM响应列表
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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, 工具调用列表, 错误消息)
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"""
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# 确保响应格式正确
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if len(response) != 3:
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return False, [], f"LLM响应元素数量不正确: 预期3个元素，实际{len(response)}个"
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# 提取工具调用部分
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tool_calls = response[2]
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# 检查工具调用是否有效
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if tool_calls is None:
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return False, [], "工具调用部分为None"
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if not isinstance(tool_calls, list):
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return False, [], f"工具调用部分不是列表: {type(tool_calls).__name__}"
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if len(tool_calls) == 0:
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return False, [], "工具调用列表为空"
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# 检查工具调用是否格式正确
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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__}")
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continue
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if tool_call.get("type") != "function":
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logger.warning(f"{log_prefix}工具调用[{i}]不是函数类型: {tool_call.get('type', '未知')}")
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continue
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if "function" not in tool_call or not isinstance(tool_call["function"], dict):
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logger.warning(f"{log_prefix}工具调用[{i}]缺少function字段或格式不正确")
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continue
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valid_tool_calls.append(tool_call)
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# 检查是否有有效的工具调用
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if not valid_tool_calls:
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return False, [], "没有找到有效的工具调用"
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return True, valid_tool_calls, ""
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def process_llm_tool_response(
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response: Any,
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expected_tool_name: str = None,
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log_prefix: str = ""
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response: Any, expected_tool_name: str = None, log_prefix: str = ""
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) -> Tuple[bool, Dict[str, Any], str]:
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"""
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处理LLM返回的工具调用响应，进行常见错误检查并提取参数
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参数:
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response: LLM的响应，预期是[content, reasoning, tool_calls]格式的列表或元组
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expected_tool_name: 预期的工具名称，如不指定则不检查
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log_prefix: 日志前缀，用于标识日志来源
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返回:
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三元组(成功标志, 参数字典, 错误描述)
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- 如果成功解析，返回(True, 参数字典, "")
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@@ -269,29 +273,29 @@ def process_llm_tool_response(
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success, normalized_response, error_msg = normalize_llm_response(response, log_prefix)
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if not success:
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return False, {}, error_msg
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# 使用新的工具调用处理函数
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success, valid_tool_calls, error_msg = process_llm_tool_calls(normalized_response, log_prefix)
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if not success:
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return False, {}, error_msg
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# 检查是否有工具调用
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if not valid_tool_calls:
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return False, {}, "没有有效的工具调用"
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# 获取第一个工具调用
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tool_call = valid_tool_calls[0]
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# 检查工具名称(如果提供了预期名称)
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if expected_tool_name:
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actual_name = tool_call.get("function", {}).get("name")
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if actual_name != expected_tool_name:
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return False, {}, f"工具名称不匹配: 预期'{expected_tool_name}'，实际'{actual_name}'"
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# 提取并解析参数
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try:
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arguments = extract_tool_call_arguments(tool_call, {})
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return True, arguments, ""
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except Exception as e:
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logger.error(f"{log_prefix}解析工具参数时出错: {e}")
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return False, {}, f"解析参数失败: {str(e)}"
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return False, {}, f"解析参数失败: {str(e)}"
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