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feat: 实现基于Agno框架的SubAgent系统

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- 新增SubAgent核心类，支持多LLM提供商
- 实现动态prompt模板构建功能
- 添加JSON结构化输出和零容错解析
- 集成配置管理和模型工厂模式
- 提供完整的错误处理和日志系统
- 支持阿里云、DeepSeek、OpenAI等主流LLM服务
This commit is contained in:
iomgaa 2025-08-25 17:33:11 +08:00
parent 099159dfb7
commit f7a06775ca
9 changed files with 2044 additions and 1 deletions
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5
pyproject.toml
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@ -5,5 +5,10 @@ description = "Add your description here"
readme = "README.md"
requires-python = ">=3.13"
dependencies = [
"agno>=1.7.12",
"ollama>=0.5.3",
"openai>=1.101.0",
"pydantic",
"pyyaml>=6.0.2",
"requests>=2.32.5",
]

38
src/agent_system/__init__.py Normal file
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@ -0,0 +1,38 @@
"""
Agent System Module
基于Agno框架的SubAgent系统实现
为MedResearcher项目提供智能Agent功能
"""
__version__ = "0.1.0"
# 导入核心类
from .subagent import SubAgent, create_json_agent
from .config_loader import load_llm_config, get_model_config
from .model_factory import create_agno_model, list_available_models
from .json_processor import parse_json_response, JSONProcessor
# 导入异常类
from .subagent import SubAgentError, ConfigurationError, ModelError
from .json_processor import JSONParseError
__all__ = [
# 核心类
'SubAgent',
'JSONProcessor',
# 便捷函数
'create_json_agent',
'load_llm_config',
'get_model_config',
'create_agno_model',
'list_available_models',
'parse_json_response',
# 异常类
'SubAgentError',
'ConfigurationError',
'ModelError',
'JSONParseError',
]

292
src/agent_system/config_loader.py Normal file
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"""
配置加载模块
负责解析LLM配置文件和环境变量，为SubAgent提供统一的配置接口
"""
import os
import yaml
from typing import Dict, Any, Optional
from pathlib import Path
def load_llm_config(config_path: Optional[str] = None) -> Dict[str, Any]:
"""
加载LLM配置文件和环境变量
Args:
config_path: 配置文件路径，默认为 src/config/llm_config.yaml
Returns:
完整的LLM配置字典，包含所有提供商和环境变量
Raises:
FileNotFoundError: 配置文件不存在
yaml.YAMLError: YAML解析错误
ValueError: 配置格式错误
"""
# 确定配置文件路径
if config_path is None:
# 获取项目根目录
current_dir = Path(__file__).parent
project_root = current_dir.parent.parent
config_path = project_root / "src" / "config" / "llm_config.yaml"
config_path = Path(config_path)
# 检查配置文件是否存在
if not config_path.exists():
raise FileNotFoundError(f"LLM配置文件不存在: {config_path}")
# 读取YAML配置文件
try:
with open(config_path, 'r', encoding='utf-8') as file:
config = yaml.safe_load(file)
except yaml.YAMLError as e:
raise yaml.YAMLError(f"YAML配置文件解析失败: {e}")
except Exception as e:
raise ValueError(f"读取配置文件失败: {e}")
# 验证配置结构
if not isinstance(config, dict):
raise ValueError("配置文件格式错误：根元素必须是字典")
# 加载环境变量配置
env_config = load_env_config()
# 处理环境变量替换
config = _resolve_env_variables(config, env_config)
return config
def load_env_config(env_path: Optional[str] = None) -> Dict[str, str]:
"""
加载环境变量配置
Args:
env_path: .env文件路径，默认为 src/config/.env
Returns:
环境变量字典
"""
# 确定环境变量文件路径
if env_path is None:
current_dir = Path(__file__).parent
project_root = current_dir.parent.parent
env_path = project_root / "src" / "config" / ".env"
env_config = {}
# 尝试加载.env文件
env_path = Path(env_path)
if env_path.exists():
try:
with open(env_path, 'r', encoding='utf-8') as file:
for line in file:
line = line.strip()
if line and not line.startswith('#') and '=' in line:
key, value = line.split('=', 1)
env_config[key.strip()] = value.strip()
except Exception as e:
print(f"警告: 读取.env文件失败: {e}")
# 同时从系统环境变量中加载
env_keys = ['DASHSCOPE_API_KEY', 'OPENAI_API_KEY', 'DEEPSEEK_API_KEY']
for key in env_keys:
if key in os.environ:
env_config[key] = os.environ[key]
return env_config
def _resolve_env_variables(config: Dict[str, Any], env_config: Dict[str, str]) -> Dict[str, Any]:
"""
解析配置中的环境变量占位符
Args:
config: 原始配置
env_config: 环境变量配置
Returns:
解析后的配置
"""
def resolve_value(value):
if isinstance(value, str) and value.startswith('${') and value.endswith('}'):
# 提取环境变量名称
env_var = value[2:-1] # 去掉 ${ 和 }
if env_var in env_config:
return env_config[env_var]
elif env_var in os.environ:
return os.environ[env_var]
else:
print(f"警告: 环境变量 {env_var} 未定义，保持原值")
return value
elif isinstance(value, dict):
return {k: resolve_value(v) for k, v in value.items()}
elif isinstance(value, list):
return [resolve_value(item) for item in value]
else:
return value
return resolve_value(config)
def get_provider_config(config: Dict[str, Any], provider: str) -> Dict[str, Any]:
"""
获取特定提供商的配置
Args:
config: 完整的LLM配置
provider: 提供商名称 (如 'aliyun', 'deepseek', 'openai')
Returns:
提供商配置字典
Raises:
ValueError: 提供商不存在
"""
if provider not in config:
available_providers = list(config.keys())
raise ValueError(f"提供商 '{provider}' 不存在，可用提供商: {available_providers}")
return config[provider]
def get_model_config(config: Dict[str, Any], provider: str, model_name: str) -> Dict[str, Any]:
"""
获取特定模型的配置
Args:
config: 完整的LLM配置
provider: 提供商名称
model_name: 模型名称
Returns:
模型配置字典
Raises:
ValueError: 提供商或模型不存在
"""
provider_config = get_provider_config(config, provider)
if 'models' not in provider_config:
raise ValueError(f"提供商 '{provider}' 配置中缺少 models 字段")
models = provider_config['models']
if model_name not in models:
available_models = list(models.keys())
raise ValueError(f"模型 '{model_name}' 在提供商 '{provider}' 中不存在，可用模型: {available_models}")
model_config = models[model_name].copy()
# 添加提供商级别的配置
for key in ['base_url', 'api_key']:
if key in provider_config:
model_config[key] = provider_config[key]
return model_config
def validate_config(config: Dict[str, Any]) -> bool:
"""
验证配置文件的完整性
Args:
config: LLM配置字典
Returns:
验证是否通过
"""
required_providers = ['aliyun', 'deepseek', 'openai']
missing_providers = []
for provider in required_providers:
if provider not in config:
missing_providers.append(provider)
if missing_providers:
print(f"警告: 缺少提供商配置: {missing_providers}")
# 验证每个提供商的配置结构
valid = True
for provider_name, provider_config in config.items():
if not isinstance(provider_config, dict):
print(f"错误: 提供商 '{provider_name}' 配置必须是字典")
valid = False
continue
if 'models' not in provider_config:
print(f"错误: 提供商 '{provider_name}' 缺少 models 字段")
valid = False
continue
models = provider_config['models']
if not isinstance(models, dict) or not models:
print(f"错误: 提供商 '{provider_name}' 的 models 必须是非空字典")
valid = False
continue
# 验证每个模型配置
for model_name, model_config in models.items():
if not isinstance(model_config, dict):
print(f"错误: 模型 '{model_name}' 配置必须是字典")
valid = False
continue
required_fields = ['class', 'params']
for field in required_fields:
if field not in model_config:
print(f"错误: 模型 '{model_name}' 缺少必需字段: {field}")
valid = False
return valid
# 测试函数
def test_config_loading():
"""测试配置加载功能"""
try:
print("正在测试配置加载...")
# 加载配置
config = load_llm_config()
print(f"✅ 配置加载成功，找到 {len(config)} 个提供商")
# 验证配置
is_valid = validate_config(config)
print(f"✅ 配置验证: {'通过' if is_valid else '失败'}")
# 测试提供商配置获取
try:
aliyun_config = get_provider_config(config, 'aliyun')
print(f"✅ 阿里云配置获取成功，包含 {len(aliyun_config.get('models', {}))} 个模型")
except ValueError as e:
print(f"❌ 阿里云配置获取失败: {e}")
# 测试模型配置获取
try:
qwen_config = get_model_config(config, 'aliyun', 'qwen-max')
print(f"✅ qwen-max模型配置获取成功")
print(f" 模型类: {qwen_config.get('class', 'N/A')}")
print(f" 模型ID: {qwen_config.get('params', {}).get('id', 'N/A')}")
except ValueError as e:
print(f"❌ qwen-max模型配置获取失败: {e}")
return True
except Exception as e:
print(f"❌ 配置加载测试失败: {e}")
return False
if __name__ == "__main__":
test_config_loading()

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src/agent_system/json_processor.py Normal file
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@ -0,0 +1,404 @@
"""
JSON处理器模块
提供强大的JSON解析和验证功能，支持零容错解析和Pydantic模型集成
"""
import json
import re
from typing import Any, Dict, List, Optional, Type, Union, get_origin, get_args
from pydantic import BaseModel, ValidationError
class JSONParseError(Exception):
"""JSON解析错误"""
pass
class JSONProcessor:
"""
JSON处理器类
提供多种JSON解析策略，确保即使在不完美的JSON输出下也能成功解析
"""
def __init__(self):
"""初始化JSON处理器"""
pass
def parse_json_response(
self,
response: str,
response_model: Optional[Type[BaseModel]] = None
) -> Union[Dict, List, BaseModel]:
"""
解析JSON响应
Args:
response: 响应字符串
response_model: 可选的Pydantic模型类
Returns:
解析后的数据对象或模型实例
Raises:
JSONParseError: 解析失败时抛出
"""
if not response or not response.strip():
raise JSONParseError("响应为空，无法解析JSON")
# 清理响应字符串
cleaned_response = self._clean_response(response)
# 尝试多种解析策略
parsed_data = self._try_multiple_parsing_strategies(cleaned_response)
if parsed_data is None:
raise JSONParseError(f"所有解析策略都失败了，响应内容: {response[:200]}...")
# 如果指定了响应模型，尝试创建模型实例
if response_model is not None:
return self._create_model_instance(parsed_data, response_model)
return parsed_data
def _clean_response(self, response: str) -> str:
"""
清理响应字符串
Args:
response: 原始响应
Returns:
清理后的响应
"""
# 去除首尾空白
cleaned = response.strip()
# 移除可能的markdown代码块标记
if cleaned.startswith('```json'):
cleaned = cleaned[7:]
elif cleaned.startswith('```'):
cleaned = cleaned[3:]
if cleaned.endswith('```'):
cleaned = cleaned[:-3]
# 移除可能的额外引号包装
if cleaned.startswith('"') and cleaned.endswith('"'):
try:
# 尝试解析为字符串，看是否是被引号包装的JSON
unquoted = json.loads(cleaned)
if isinstance(unquoted, str):
cleaned = unquoted
except json.JSONDecodeError:
pass
return cleaned.strip()
def _try_multiple_parsing_strategies(self, response: str) -> Optional[Union[Dict, List]]:
"""
尝试多种解析策略
Args:
response: 清理后的响应
Returns:
解析成功的数据，或None
"""
strategies = [
self._strategy_direct_parse,
self._strategy_extract_json_block,
self._strategy_find_json_structure,
self._strategy_regex_extract,
self._strategy_fix_common_errors,
]
for strategy in strategies:
try:
result = strategy(response)
if result is not None:
return result
except Exception:
continue
return None
def _strategy_direct_parse(self, response: str) -> Optional[Union[Dict, List]]:
"""策略1: 直接解析JSON"""
try:
return json.loads(response)
except json.JSONDecodeError:
return None
def _strategy_extract_json_block(self, response: str) -> Optional[Union[Dict, List]]:
"""策略2: 提取JSON代码块"""
# 查找被代码块包围的JSON
code_block_pattern = r'```(?:json)?\s*([\s\S]*?)\s*```'
matches = re.findall(code_block_pattern, response, re.IGNORECASE)
for match in matches:
try:
return json.loads(match.strip())
except json.JSONDecodeError:
continue
return None
def _strategy_find_json_structure(self, response: str) -> Optional[Union[Dict, List]]:
"""策略3: 查找JSON结构"""
# 查找第一个完整的JSON对象或数组
for start_char, end_char in [("{", "}"), ("[", "]")]:
json_str = self._extract_complete_json_structure(response, start_char, end_char)
if json_str:
try:
return json.loads(json_str)
except json.JSONDecodeError:
continue
return None
def _strategy_regex_extract(self, response: str) -> Optional[Union[Dict, List]]:
"""策略4: 正则表达式提取"""
# 使用正则表达式查找JSON模式
patterns = [
r'\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}', # 嵌套对象
r'\[[^\[\]]*(?:\[[^\[\]]*\][^\[\]]*)*\]', # 嵌套数组
]
for pattern in patterns:
matches = re.findall(pattern, response)
for match in matches:
try:
return json.loads(match)
except json.JSONDecodeError:
continue
return None
def _strategy_fix_common_errors(self, response: str) -> Optional[Union[Dict, List]]:
"""策略5: 修复常见JSON错误"""
# 修复常见的JSON格式错误
fixed_response = response
# 修复单引号为双引号
fixed_response = re.sub(r"'([^']*)':", r'"\1":', fixed_response)
fixed_response = re.sub(r":\s*'([^']*)'", r': "\1"', fixed_response)
# 修复末尾逗号
fixed_response = re.sub(r',(\s*[}\]])', r'\1', fixed_response)
# 修复未引用的键
fixed_response = re.sub(r'(\w+):', r'"\1":', fixed_response)
try:
return json.loads(fixed_response)
except json.JSONDecodeError:
return None
def _extract_complete_json_structure(
self,
text: str,
start_char: str,
end_char: str
) -> Optional[str]:
"""
提取完整的JSON结构
Args:
text: 文本内容
start_char: 开始字符 ('{' 或 '[')
end_char: 结束字符 ('}' 或 ']')
Returns:
提取的JSON字符串或None
"""
start_idx = text.find(start_char)
if start_idx == -1:
return None
# 使用计数器匹配嵌套结构
count = 0
in_string = False
escape_next = False
for i, char in enumerate(text[start_idx:], start_idx):
if escape_next:
escape_next = False
continue
if char == '\\' and in_string:
escape_next = True
continue
if char == '"' and not escape_next:
in_string = not in_string
continue
if not in_string:
if char == start_char:
count += 1
elif char == end_char:
count -= 1
if count == 0:
return text[start_idx:i+1]
# 如果没有找到匹配的结束符，返回到字符串末尾
if count > 0:
return text[start_idx:]
return None
def _create_model_instance(
self,
data: Union[Dict, List],
response_model: Type[BaseModel]
) -> BaseModel:
"""
创建Pydantic模型实例
Args:
data: 解析后的数据
response_model: Pydantic模型类
Returns:
模型实例
Raises:
JSONParseError: 模型验证失败
"""
try:
# 如果数据是列表但模型期望对象，尝试包装
if isinstance(data, list) and not self._is_list_model(response_model):
# 尝试将列表作为某个字段的值
field_names = list(response_model.model_fields.keys())
if field_names:
# 使用第一个字段名作为包装器
data = {field_names[0]: data}
# 创建模型实例
return response_model(**data)
except ValidationError as e:
# 详细的验证错误信息
error_msg = f"Pydantic模型验证失败: {e}"
raise JSONParseError(error_msg)
except TypeError as e:
error_msg = f"模型实例化失败: {e}"
raise JSONParseError(error_msg)
def _is_list_model(self, model: Type[BaseModel]) -> bool:
"""
检查模型是否期望列表类型
Args:
model: Pydantic模型类
Returns:
是否为列表模型
"""
# 检查模型的字段类型
for field_info in model.model_fields.values():
annotation = field_info.annotation
origin = get_origin(annotation)
# 如果有字段是List类型，认为这是一个列表模型
if origin is list or origin is List:
return True
return False
def validate_json_schema(self, data: Dict, schema: Dict) -> bool:
"""
验证JSON数据是否符合指定的schema
Args:
data: JSON数据
schema: JSON schema
Returns:
是否符合schema
"""
try:
import jsonschema
jsonschema.validate(data, schema)
return True
except ImportError:
# 如果没有安装jsonschema库，跳过验证
return True
except Exception:
return False
# 便捷函数
def parse_json_response(
response: str,
response_model: Optional[Type[BaseModel]] = None
) -> Union[Dict, List, BaseModel]:
"""
便捷函数：解析JSON响应
Args:
response: 响应字符串
response_model: 可选的Pydantic模型类
Returns:
解析后的数据或模型实例
"""
processor = JSONProcessor()
return processor.parse_json_response(response, response_model)
# 测试函数
def test_json_processor():
"""测试JSON处理器功能"""
print("正在测试JSON处理器...")
processor = JSONProcessor()
# 测试用例
test_cases = [
# 标准JSON
('{"name": "test", "value": 123}', None),
# 带代码块标记的JSON
('```json\n{"name": "test", "value": 123}\n```', None),
# 不完整的JSON
('{"name": "test", "value": 123', None),
# 单引号JSON
("{'name': 'test', 'value': 123}", None),
# 包含额外文本的JSON
('这是一个JSON响应: {"name": "test", "value": 123} 解析完成', None),
# 数组JSON
('[{"name": "test1"}, {"name": "test2"}]', None),
]
success_count = 0
for i, (test_input, model) in enumerate(test_cases, 1):
try:
result = processor.parse_json_response(test_input, model)
print(f"✅ 测试用例 {i}: 解析成功 - {type(result)}")
success_count += 1
except Exception as e:
print(f"❌ 测试用例 {i}: 解析失败 - {e}")
print(f"\n🎯 JSON处理器测试完成: {success_count}/{len(test_cases)} 通过")
return success_count == len(test_cases)
if __name__ == "__main__":
test_json_processor()

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src/agent_system/model_factory.py Normal file
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"""
模型工厂模块
基于配置创建各种Agno模型实例，支持多种LLM提供商
"""
from typing import Dict, Any, Optional, Union
from agno.models.openai import OpenAILike, OpenAIChat
# 尝试导入其他可选模型
try:
from agno.models.ollama import Ollama
OLLAMA_AVAILABLE = True
except ImportError:
OLLAMA_AVAILABLE = False
Ollama = None
from .config_loader import load_llm_config, get_model_config
class ModelFactory:
"""
模型工厂类
负责根据配置创建不同类型的Agno模型实例
"""
# 支持的模型类映射
MODEL_CLASSES = {
"OpenAILike": OpenAILike,
"OpenAIChat": OpenAIChat,
}
def __init__(self, config: Optional[Dict[str, Any]] = None):
"""
初始化模型工厂
Args:
config: LLM配置，如果为None则自动加载
"""
self.config = config if config is not None else load_llm_config()
# 如果Ollama可用，添加到支持列表
if OLLAMA_AVAILABLE:
self.MODEL_CLASSES["Ollama"] = Ollama
def create_model(
self,
provider: str,
model_name: str,
**override_params
) -> Union[OpenAILike, OpenAIChat]:
"""
创建模型实例
Args:
provider: 提供商名称 (如 'aliyun', 'deepseek', 'openai')
model_name: 模型名称 (如 'qwen-max', 'deepseek-v3')
**override_params: 覆盖配置的参数
Returns:
创建的模型实例
Raises:
ValueError: 配置错误或不支持的模型类型
ImportError: 缺少必需的依赖
"""
# 获取模型配置
model_config = get_model_config(self.config, provider, model_name)
# 获取模型类名
model_class_name = model_config.get('class')
if not model_class_name:
raise ValueError(f"模型配置中缺少 'class' 字段: {provider}.{model_name}")
# 检查模型类是否支持
if model_class_name not in self.MODEL_CLASSES:
available_classes = list(self.MODEL_CLASSES.keys())
raise ValueError(f"不支持的模型类 '{model_class_name}'，支持的类型: {available_classes}")
# 特殊处理Ollama
if model_class_name == "Ollama" and not OLLAMA_AVAILABLE:
raise ImportError("Ollama模型需要安装ollama包: pip install ollama")
# 获取模型类
model_class = self.MODEL_CLASSES[model_class_name]
# 准备初始化参数
init_params = self._prepare_init_params(model_config, model_class_name, override_params)
# 创建模型实例
try:
model = model_class(**init_params)
return model
except Exception as e:
raise ValueError(f"创建模型实例失败 ({provider}.{model_name}): {e}")
def _prepare_init_params(
self,
model_config: Dict[str, Any],
model_class_name: str,
override_params: Dict[str, Any]
) -> Dict[str, Any]:
"""
准备模型初始化参数
Args:
model_config: 模型配置
model_class_name: 模型类名
override_params: 覆盖参数
Returns:
初始化参数字典
"""
# 基础参数从params字段获取
init_params = model_config.get('params', {}).copy()
# 添加API相关参数
if 'api_key' in model_config:
init_params['api_key'] = model_config['api_key']
if 'base_url' in model_config:
init_params['base_url'] = model_config['base_url']
# 根据模型类型调整参数
if model_class_name == "OpenAILike":
# OpenAILike模型特殊处理
self._adjust_openai_like_params(init_params)
elif model_class_name == "OpenAIChat":
# OpenAIChat模型特殊处理
self._adjust_openai_chat_params(init_params)
elif model_class_name == "Ollama":
# Ollama模型特殊处理
self._adjust_ollama_params(init_params)
# 应用覆盖参数
init_params.update(override_params)
return init_params
def _adjust_openai_like_params(self, params: Dict[str, Any]) -> None:
"""调整OpenAILike模型参数"""
# OpenAILike使用id参数，不需要model参数
# id参数已经在params中，无需额外处理
pass
def _adjust_openai_chat_params(self, params: Dict[str, Any]) -> None:
"""调整OpenAIChat模型参数"""
# OpenAIChat通常需要id参数作为model参数
if 'id' in params and 'model' not in params:
params['model'] = params['id']
def _adjust_ollama_params(self, params: Dict[str, Any]) -> None:
"""调整Ollama模型参数"""
# Ollama模型通常不需要api_key和base_url
params.pop('api_key', None)
params.pop('base_url', None)
# 使用id作为model参数
if 'id' in params and 'model' not in params:
params['model'] = params['id']
def list_available_models(self) -> Dict[str, list]:
"""
列出所有可用的模型
Returns:
按提供商分组的模型列表
"""
available_models = {}
for provider, provider_config in self.config.items():
if 'models' in provider_config:
models = list(provider_config['models'].keys())
available_models[provider] = models
return available_models
def validate_model_exists(self, provider: str, model_name: str) -> bool:
"""
验证模型是否存在
Args:
provider: 提供商名称
model_name: 模型名称
Returns:
模型是否存在
"""
try:
get_model_config(self.config, provider, model_name)
return True
except ValueError:
return False
# 便捷函数
def create_agno_model(
provider: str,
model_name: str,
config: Optional[Dict[str, Any]] = None,
**override_params
) -> Union[OpenAILike, OpenAIChat]:
"""
便捷函数：创建Agno模型实例
Args:
provider: 提供商名称
model_name: 模型名称
config: 可选的配置字典
**override_params: 覆盖参数
Returns:
创建的模型实例
"""
factory = ModelFactory(config)
return factory.create_model(provider, model_name, **override_params)
def list_available_models(config: Optional[Dict[str, Any]] = None) -> Dict[str, list]:
"""
便捷函数：列出可用模型
Args:
config: 可选的配置字典
Returns:
按提供商分组的模型列表
"""
factory = ModelFactory(config)
return factory.list_available_models()
# 测试函数
def test_model_creation():
"""测试模型创建功能"""
print("正在测试模型创建...")
try:
# 创建模型工厂
factory = ModelFactory()
# 列出可用模型
available_models = factory.list_available_models()
print(f"✅ 发现可用模型:")
for provider, models in available_models.items():
print(f" {provider}: {models}")
# 测试阿里云qwen-max模型创建
try:
qwen_model = factory.create_model('aliyun', 'qwen-max')
print(f"✅ qwen-max模型创建成功: {type(qwen_model)}")
except Exception as e:
print(f"❌ qwen-max模型创建失败: {e}")
# 测试DeepSeek模型创建
try:
deepseek_model = factory.create_model('deepseek', 'deepseek-v3')
print(f"✅ deepseek-v3模型创建成功: {type(deepseek_model)}")
except Exception as e:
print(f"❌ deepseek-v3模型创建失败: {e}")
# 测试参数覆盖
try:
custom_model = factory.create_model(
'aliyun',
'qwen-plus',
temperature=0.5,
max_tokens=1000
)
print(f"✅ 参数覆盖测试成功: {type(custom_model)}")
except Exception as e:
print(f"❌ 参数覆盖测试失败: {e}")
return True
except Exception as e:
print(f"❌ 模型创建测试失败: {e}")
return False
def test_convenience_functions():
"""测试便捷函数"""
print("\n正在测试便捷函数...")
try:
# 测试便捷创建函数
model = create_agno_model('aliyun', 'qwen-turbo')
print(f"✅ 便捷函数创建模型成功: {type(model)}")
# 测试列出模型函数
models = list_available_models()
total_models = sum(len(model_list) for model_list in models.values())
print(f"✅ 便捷函数列出模型成功，总计 {total_models} 个模型")
return True
except Exception as e:
print(f"❌ 便捷函数测试失败: {e}")
return False
if __name__ == "__main__":
success1 = test_model_creation()
success2 = test_convenience_functions()
if success1 and success2:
print("\n🎉 所有模型工厂测试通过!")
else:
print("\n💥 部分测试失败，请检查配置")

434
src/agent_system/subagent.py Normal file
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"""
SubAgent核心类
基于Agno框架的智能代理实现，提供动态prompt构建、JSON解析、模型管理等功能
"""
import logging
from typing import Any, Dict, List, Optional, Type, Union
from agno.agent import Agent, RunResponse
from pydantic import BaseModel
from .config_loader import load_llm_config
from .model_factory import ModelFactory
from .json_processor import JSONProcessor, JSONParseError
# 设置日志
logger = logging.getLogger(__name__)
class SubAgentError(Exception):
"""SubAgent相关错误的基类"""
pass
class ConfigurationError(SubAgentError):
"""配置相关错误"""
pass
class ModelError(SubAgentError):
"""模型相关错误"""
pass
class SubAgent:
"""
SubAgent核心类
基于Agno框架构建的智能代理，支持:
- 动态prompt模板构建
- 多LLM提供商支持
- JSON结构化输出
- 零容错解析
- 灵活的配置管理
"""
def __init__(
self,
provider: str,
model_name: str,
instructions: Optional[List[str]] = None,
name: Optional[str] = None,
description: Optional[str] = None,
prompt_template: Optional[str] = None,
response_model: Optional[Type[BaseModel]] = None,
config: Optional[Dict[str, Any]] = None,
**agent_kwargs
):
"""
初始化SubAgent
Args:
provider: LLM提供商名称 (如 'aliyun', 'deepseek', 'openai')
model_name: 模型名称 (如 'qwen-max', 'deepseek-v3')
instructions: 指令列表
name: Agent名称
description: Agent描述
prompt_template: 动态prompt模板
response_model: Pydantic响应模型类
config: 自定义LLM配置
**agent_kwargs: 传递给Agno Agent的额外参数
Raises:
ConfigurationError: 配置错误
ModelError: 模型创建错误
"""
# 基础属性设置
self.provider = provider
self.model_name = model_name
self.name = name or f"{provider}_{model_name}_agent"
self.description = description or f"基于{provider}的{model_name}模型的智能代理"
self.instructions = instructions or []
self.prompt_template = prompt_template
self.response_model = response_model
# 初始化组件
try:
# 加载配置
self.config = config if config is not None else load_llm_config()
# 创建模型工厂和JSON处理器
self.model_factory = ModelFactory(self.config)
self.json_processor = JSONProcessor()
# 创建Agno模型
self.model = self._create_model()
# 创建Agno Agent
self.agent = self._create_agent(**agent_kwargs)
logger.info(f"SubAgent {self.name} 初始化成功")
except Exception as e:
logger.error(f"SubAgent初始化失败: {e}")
raise ConfigurationError(f"SubAgent初始化失败: {e}")
def _create_model(self):
"""创建Agno模型实例"""
try:
return self.model_factory.create_model(self.provider, self.model_name)
except Exception as e:
raise ModelError(f"模型创建失败: {e}")
def _create_agent(self, **agent_kwargs):
"""创建Agno Agent实例"""
try:
agent_params = {
"model": self.model,
"name": self.name,
"description": self.description,
"instructions": self.instructions,
"markdown": agent_kwargs.pop("markdown", True),
"debug_mode": agent_kwargs.pop("debug_mode", False),
**agent_kwargs
}
return Agent(**agent_params)
except Exception as e:
raise ConfigurationError(f"Agent创建失败: {e}")
def build_prompt(self, template_vars: Optional[Dict[str, Any]] = None) -> str:
"""
构建动态prompt
Args:
template_vars: 模板变量字典，用于替换模板中的占位符
Returns:
构建完成的prompt字符串
Raises:
SubAgentError: prompt构建失败
"""
if not self.prompt_template:
raise SubAgentError("未设置prompt模板，无法构建动态prompt")
if not template_vars:
template_vars = {}
try:
# 使用str.format()进行模板替换
prompt = self.prompt_template.format(**template_vars)
# 如果需要JSON输出，添加JSON格式要求
if self.response_model:
json_instruction = self._build_json_instruction()
prompt = f"{prompt}\n\n{json_instruction}"
return prompt
except KeyError as e:
raise SubAgentError(f"模板变量缺失: {e}")
except Exception as e:
raise SubAgentError(f"prompt构建失败: {e}")
def _build_json_instruction(self) -> str:
"""构建JSON格式指令"""
json_instruction = """
请严格按照以下JSON格式返回结果，不要添加任何额外的文字说明：
"""
if self.response_model:
# 生成JSON schema示例
try:
schema = self.response_model.model_json_schema()
json_instruction += f"JSON Schema:\n{schema}\n\n"
# 生成示例
example = self._generate_model_example()
if example:
json_instruction += f"示例格式:\n{example}"
except Exception:
# 如果schema生成失败，使用通用指令
json_instruction += "请返回有效的JSON格式数据。"
return json_instruction
def _generate_model_example(self) -> Optional[str]:
"""生成Pydantic模型的示例JSON"""
try:
# 创建一个示例实例
field_values = {}
for field_name, field_info in self.response_model.model_fields.items():
# 根据字段类型生成示例值
field_type = field_info.annotation
field_values[field_name] = self._generate_example_value(field_type)
example_instance = self.response_model(**field_values)
return example_instance.model_dump_json(indent=2)
except Exception:
return None
def _generate_example_value(self, field_type: Type) -> Any:
"""根据字段类型生成示例值"""
if field_type == str:
return "示例文本"
elif field_type == int:
return 0
elif field_type == float:
return 0.0
elif field_type == bool:
return True
elif hasattr(field_type, '__origin__'):
origin = field_type.__origin__
if origin == list:
return []
elif origin == dict:
return {}
return "示例值"
def run(
self,
prompt: Optional[str] = None,
template_vars: Optional[Dict[str, Any]] = None,
**run_kwargs
) -> Any:
"""
执行Agent推理
Args:
prompt: 直接prompt文本（如果提供，将忽略template_vars）
template_vars: 模板变量（用于动态构建prompt）
**run_kwargs: 传递给Agent.run的额外参数
Returns:
如果指定了response_model，返回解析后的Pydantic模型实例
否则返回原始响应内容
Raises:
SubAgentError: 执行失败
JSONParseError: JSON解析失败
"""
try:
# 构建最终prompt
if prompt is not None:
final_prompt = prompt
else:
final_prompt = self.build_prompt(template_vars)
logger.debug(f"Agent {self.name} 执行推理，prompt长度: {len(final_prompt)}")
# 执行Agent推理
response: RunResponse = self.agent.run(final_prompt, **run_kwargs)
# 获取响应内容
content = response.content
if self.response_model:
# 如果指定了响应模型，进行JSON解析
return self._parse_structured_response(content)
else:
# 返回原始内容
return content
except JSONParseError:
# JSON解析错误直接抛出
raise
except Exception as e:
logger.error(f"Agent执行失败: {e}")
raise SubAgentError(f"Agent执行失败: {e}")
def _parse_structured_response(self, content: str) -> BaseModel:
"""解析结构化响应"""
try:
return self.json_processor.parse_json_response(content, self.response_model)
except JSONParseError as e:
logger.error(f"JSON解析失败: {e}")
logger.debug(f"响应内容: {content}")
raise
def get_model_info(self) -> Dict[str, Any]:
"""
获取模型信息
Returns:
包含模型信息的字典
"""
return {
"name": self.name,
"provider": self.provider,
"model_name": self.model_name,
"model_type": type(self.model).__name__,
"has_prompt_template": self.prompt_template is not None,
"has_response_model": self.response_model is not None,
"instructions_count": len(self.instructions),
}
def update_instructions(self, instructions: List[str]) -> None:
"""
更新指令列表
Args:
instructions: 新的指令列表
"""
self.instructions = instructions
# 注意：这里不会更新已创建的Agent实例，如需更新需要重新创建Agent
logger.info(f"Agent {self.name} 指令已更新")
def update_prompt_template(self, template: str) -> None:
"""
更新prompt模板
Args:
template: 新的prompt模板
"""
self.prompt_template = template
logger.info(f"Agent {self.name} prompt模板已更新")
def __str__(self) -> str:
return f"SubAgent({self.name}, {self.provider}.{self.model_name})"
def __repr__(self) -> str:
return self.__str__()
# 便捷函数
def create_json_agent(
provider: str,
model_name: str,
name: str,
prompt_template: str,
response_model: Union[str, Type[BaseModel]],
instructions: Optional[List[str]] = None,
**kwargs
) -> SubAgent:
"""
便捷函数：创建支持JSON输出的SubAgent
Args:
provider: 提供商名称
model_name: 模型名称
name: Agent名称
prompt_template: prompt模板
response_model: 响应模型类或模块路径字符串
instructions: 指令列表
**kwargs: 额外参数
Returns:
配置好的SubAgent实例
"""
# 如果response_model是字符串，尝试导入
if isinstance(response_model, str):
try:
# 解析模块路径和类名
if '.' in response_model:
module_path, class_name = response_model.rsplit('.', 1)
else:
# 如果没有模块路径，假设在当前模块
module_path = '__main__'
class_name = response_model
# 动态导入
import importlib
module = importlib.import_module(module_path)
response_model = getattr(module, class_name)
except Exception as e:
raise ValueError(f"无法导入响应模型 {response_model}: {e}")
return SubAgent(
provider=provider,
model_name=model_name,
name=name,
prompt_template=prompt_template,
response_model=response_model,
instructions=instructions,
**kwargs
)
# 测试函数
def test_subagent():
"""测试SubAgent功能"""
print("正在测试SubAgent...")
try:
# 创建基础SubAgent
agent = SubAgent(
provider="aliyun",
model_name="qwen-turbo",
name="test_agent",
instructions=["你是一个测试助手", "请简洁回答问题"]
)
print(f"✅ SubAgent创建成功: {agent}")
print(f" 模型信息: {agent.get_model_info()}")
# 测试基础对话
try:
response = agent.run("请简单介绍一下Python语言")
print(f"✅ 基础对话测试成功，响应长度: {len(str(response))}字符")
except Exception as e:
print(f"❌ 基础对话测试失败: {e}")
# 测试动态prompt构建
agent.update_prompt_template("请回答关于{topic}的问题：{question}")
try:
prompt = agent.build_prompt({
"topic": "编程",
"question": "什么是函数？"
})
print(f"✅ 动态prompt构建成功，长度: {len(prompt)}字符")
except Exception as e:
print(f"❌ 动态prompt构建失败: {e}")
return True
except Exception as e:
print(f"❌ SubAgent测试失败: {e}")
return False
if __name__ == "__main__":
test_subagent()

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src/config/.env Normal file
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@ -0,0 +1 @@
DASHSCOPE_API_KEY=sk-5c7f9dc33e0a43738d415a0432452b93

93
src/config/llm_config.yaml Normal file
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@ -0,0 +1,93 @@
# LLM配置文件
# 定义所有Agent可用的LLM模型配置
# 阿里云通义千问配置
aliyun:
base_url: "https://dashscope.aliyuncs.com/compatible-mode/v1"
api_key: ${DASHSCOPE_API_KEY} # 从环境变量读取
models:
qwen-max:
class: "OpenAILike"
params:
id: "qwen-max"
temperature: 0.7
max_tokens: 3000
qwen-plus:
class: "OpenAILike"
params:
id: "qwen-plus"
temperature: 0.7
max_tokens: 2000
qwen-turbo:
class: "OpenAILike"
params:
id: "qwen-turbo"
temperature: 0.7
max_tokens: 1500
# DeepSeek配置
deepseek:
base_url: "https://dashscope.aliyuncs.com/compatible-mode/v1"
api_key: ${DASHSCOPE_API_KEY}
models:
deepseek-v3:
class: "OpenAILike"
params:
id: "deepseek-v3"
temperature: 0.7
max_tokens: 4000
deepseek-r1:
class: "OpenAILike"
params:
id: "deepseek-r1"
temperature: 0.5
max_tokens: 8000
# DeepSeek R1特有的推理模式
reasoning_effort: "high"
# OpenAI配置（备用）
openai:
base_url: "https://api.openai.com/v1"
api_key: ${OPENAI_API_KEY}
models:
gpt-4:
class: "OpenAIChat"
params:
id: "gpt-4"
temperature: 0.7
max_tokens: 4000
gpt-3.5-turbo:
class: "OpenAIChat"
params:
id: "gpt-3.5-turbo"
temperature: 0.7
max_tokens: 2000
# 本地Ollama配置（开发测试用）
ollama:
host: "127.0.0.1"
port: 11434
models:
qwen2.5:
class: "Ollama"
params:
id: "qwen2.5:latest"
temperature: 0.7
max_tokens: 2000
llama3:
class: "Ollama"
params:
id: "llama3:latest"
temperature: 0.7
max_tokens: 2000
vllm:
base_url: "http://100.82.33.121:11001/v1"
models:
gpt-oss:
class: "OpenAIChat"
params:
id: "gpt-oss-20b"
temperature: 0.7
max_tokens: 2000

466
uv.lock generated
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