fix：优化工具解析
This commit is contained in:
SengokuCola
@@ -70,55 +70,6 @@ def extract_tool_call_arguments(tool_call: Dict[str, Any], default_value: Dict[s
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return default_result
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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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if key.isdigit() and isinstance(current, list):
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index = int(key)
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if 0 <= index < len(current):
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current = current[index]
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else:
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return default_value
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# 处理字典键
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elif isinstance(current, dict):
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if key in current:
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current = current[key]
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else:
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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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return current
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except Exception as e:
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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, pretty: bool = False) -> str:
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"""
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@@ -144,21 +95,6 @@ def safe_json_dumps(obj: Any, default_value: str = "{}", ensure_ascii: bool = Fa
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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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result = {}
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for obj in objects:
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if obj and isinstance(obj, dict):
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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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@@ -172,6 +108,9 @@ def normalize_llm_response(response: Any, log_prefix: str = "") -> Tuple[bool, L
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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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@@ -201,114 +140,60 @@ def normalize_llm_response(response: Any, log_prefix: str = "") -> Tuple[bool, L
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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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def process_llm_tool_calls(tool_calls: List[Dict[str, 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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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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response: 标准化后的LLM响应列表
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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, 工具调用列表, 错误消息)
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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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# 确保响应格式正确
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print(response)
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print(11111111111111111)
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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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# 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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# 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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# 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__}")
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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(f"{log_prefix}工具调用[{i}]不是函数类型: {tool_call.get('type', '未知')}")
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logger.warning(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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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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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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if "arguments" not in func_details or not isinstance(func_details.get("arguments"), str): # 参数是字符串形式的JSON
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logger.warning(f"{log_prefix}工具调用[{i}]的'function'字段缺少'arguments'或类型不正确: {func_details}")
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continue
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# 可选：尝试解析参数JSON，确保其有效
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args_str = func_details["arguments"]
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try:
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json.loads(args_str) # 尝试解析，但不存储结果
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except json.JSONDecodeError as e:
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logger.warning(f"{log_prefix}工具调用[{i}]的'arguments'不是有效的JSON字符串: {e}, 内容: {args_str[:100]}...")
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continue
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except Exception as e:
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logger.warning(f"{log_prefix}解析工具调用[{i}]的'arguments'时发生意外错误: {e}, 内容: {args_str[:100]}...")
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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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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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def process_llm_tool_response(
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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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- 如果解析失败，返回(False, {}, 错误描述)
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"""
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# 使用新的标准化函数
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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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# 新增检查：确保响应包含预期的工具调用部分
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if len(normalized_response) != 3:
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# 如果长度不为3，说明LLM响应不包含工具调用部分，这在期望工具调用的上下文中是错误的
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error_msg = (
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f"LLM响应未包含预期的工具调用部分: 元素数量{len(normalized_response)}，响应内容：{normalized_response}"
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)
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logger.warning(f"{log_prefix}{error_msg}")
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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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# 此时已知 normalized_response 长度必定为 3
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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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