133 lines
4.5 KiB
Python
133 lines
4.5 KiB
Python
"""
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Agent 配置格式转换器
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用于将 Pydantic 模型转换为数据库存储格式
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"""
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from typing import Dict, Any, Optional, Union
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from app.schemas.app_schema import (
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KnowledgeRetrievalConfig,
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MemoryConfig,
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VariableDefinition,
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ToolConfig,
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AgentConfigCreate,
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AgentConfigUpdate, ToolOldConfig, SkillConfig,
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)
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class AgentConfigConverter:
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"""Agent 配置格式转换器"""
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@staticmethod
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def to_storage_format(config: AgentConfigCreate | AgentConfigUpdate) -> Dict[str, Any]:
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"""
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将配置对象转换为数据库存储格式
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Args:
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config: AgentConfigCreate 或 AgentConfigUpdate 对象
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Returns:
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包含数据库字段的字典
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"""
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result = {}
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# 1. 模型参数配置
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if hasattr(config, 'model_parameters') and config.model_parameters:
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result["model_parameters"] = config.model_parameters.model_dump()
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# 2. 知识库检索配置
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if config.knowledge_retrieval:
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result["knowledge_retrieval"] = config.knowledge_retrieval.model_dump()
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# 3. 记忆配置
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if hasattr(config, 'memory') and config.memory:
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result["memory"] = config.memory.model_dump()
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# 4. 变量配置
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if hasattr(config, 'variables') and config.variables:
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result["variables"] = [var.model_dump() for var in config.variables]
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# 5. 工具配置
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if hasattr(config, 'tools') and config.tools:
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result["tools"] = [tool.model_dump() for tool in config.tools]
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if hasattr(config, "skills") and config.skills:
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result["skills"] = config.skills.model_dump()
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return result
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@staticmethod
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def from_storage_format(
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model_parameters: Optional[Dict[str, Any]],
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knowledge_retrieval: Optional[Dict[str, Any]],
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memory: Optional[Dict[str, Any]],
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variables: Optional[list],
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tools: Optional[Union[list, Dict[str, Any]]],
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skills: Optional[dict]
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) -> Dict[str, Any]:
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"""
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将数据库存储格式转换为 Pydantic 对象
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Args:
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model_parameters: 模型参数配置
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knowledge_retrieval: 知识库检索配置
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memory: 记忆配置
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variables: 变量配置
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tools: 工具配置
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skills: 技能列表
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Returns:
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包含 Pydantic 对象的字典
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"""
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result = {
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"model_parameters": None,
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"knowledge_retrieval": None,
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"memory": MemoryConfig(enabled=True),
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"variables": [],
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"tools": [],
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"skills": SkillConfig(enabled=False, all_skills=False, skill_ids=[])
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}
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# 1. 解析模型参数配置
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if model_parameters:
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from app.schemas.app_schema import ModelParameters
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if isinstance(model_parameters, ModelParameters):
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result["model_parameters"] = model_parameters
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elif isinstance(model_parameters, dict):
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result["model_parameters"] = ModelParameters(**model_parameters)
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else:
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result["model_parameters"] = ModelParameters()
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# 2. 解析知识库检索配置
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if knowledge_retrieval:
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result["knowledge_retrieval"] = KnowledgeRetrievalConfig(**knowledge_retrieval)
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else:
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# 提供默认的知识库配置（空列表）
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result["knowledge_retrieval"] = KnowledgeRetrievalConfig(
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knowledge_bases=[],
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merge_strategy="weighted"
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)
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# 3. 解析记忆配置
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if memory:
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result["memory"] = MemoryConfig(**memory)
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# 4. 解析变量配置
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if variables:
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result["variables"] = [VariableDefinition(**var) for var in variables]
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# 5. 解析工具配置
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if tools:
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if isinstance(tools, list):
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result["tools"] = [ToolConfig(**tool_config) for tool_config in tools]
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else:
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result["tools"] = {
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name: ToolOldConfig(**tool_data)
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for name, tool_data in tools.items()
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}
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if skills:
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result["skills"] = SkillConfig(**skills)
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else:
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result["skills"] = SkillConfig(enabled=False, all_skills=False, skill_ids=[])
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return result
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