2e8e63878e
- Trigger opening statement on new conversation in run/run_stream - Fix /opening endpoint to support workflow app type - Fix features field missing in workflow config release snapshot - Knowledge node returns citations alongside chunks - Aggregate citations from all knowledge nodes in result builder - Filter citations based on features.citation.enabled switch - Fix WorkflowConfigCreate circular import in app_schema
368 lines
13 KiB
Python
368 lines
13 KiB
Python
"""
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App Config Utilities
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Utility functions for converting between dict and model objects for different app configurations.
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"""
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import uuid
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from typing import Dict, Any, Optional, Union
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from datetime import datetime
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from app.db import get_db_read
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from app.models import AppRelease, WorkflowConfig
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from app.models.agent_app_config_model import AgentConfig
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from app.models.multi_agent_model import MultiAgentConfig
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from app.repositories.workflow_repository import WorkflowConfigRepository
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def model_parameters_to_dict(model_parameters: Any) -> Optional[Dict[str, Any]]:
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"""将 ModelParameters 对象转换为字典
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Args:
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model_parameters: ModelParameters 对象、字典或 None
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Returns:
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字典格式的模型参数，如果输入为 None 则返回 None
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"""
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if model_parameters is None:
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return None
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if isinstance(model_parameters, dict):
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return model_parameters
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# Pydantic v2
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if hasattr(model_parameters, 'model_dump'):
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return model_parameters.model_dump()
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# Pydantic v1
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if hasattr(model_parameters, 'dict'):
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return model_parameters.dict()
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# 其他情况尝试转换
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try:
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return dict(model_parameters)
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except (TypeError, ValueError):
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return None
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def dict_to_model_parameters(data: Optional[Dict[str, Any]]) -> Optional[Any]:
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"""将字典转换为 ModelParameters 对象
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Args:
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data: 字典格式的模型参数或 None
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Returns:
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ModelParameters 对象，如果输入为 None 则返回 None
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"""
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if data is None:
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return None
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from app.schemas.app_schema import ModelParameters
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if isinstance(data, ModelParameters):
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return data
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if isinstance(data, dict):
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return ModelParameters(**data)
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return None
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class AgentConfigProxy:
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"""Proxy class for AgentConfig (legacy compatibility)"""
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def __init__(self, release, app, config_data):
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self.id = release.id
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self.app_id = release.app_id
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self.app = app
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self.name = release.name
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self.description = release.description
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self.system_prompt = config_data.get("system_prompt")
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self.default_model_config_id = release.default_model_config_id
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def agent_config_4_app_release(release: AppRelease) -> AgentConfig:
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config_dict = release.config
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# 如果 config 是字符串，解析为字典
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if isinstance(config_dict, str):
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import json
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try:
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config_dict = json.loads(config_dict)
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except json.JSONDecodeError:
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config_dict = {}
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agent_config = AgentConfig(
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app_id=release.app_id,
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system_prompt=config_dict.get("system_prompt"),
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default_model_config_id=release.default_model_config_id,
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model_parameters=config_dict.get("model_parameters"),
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knowledge_retrieval=config_dict.get("knowledge_retrieval"),
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memory=config_dict.get("memory"),
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variables=config_dict.get("variables", []),
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tools=config_dict.get("tools", []),
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skills=config_dict.get("skills", {}),
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features=config_dict.get("features", {})
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)
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return agent_config
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def multi_agent_config_4_app_release(release: AppRelease) -> MultiAgentConfig:
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config_dict = release.config
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# 如果 config 是字符串，解析为字典
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if isinstance(config_dict, str):
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import json
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try:
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config_dict = json.loads(config_dict)
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except json.JSONDecodeError:
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config_dict = {}
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agent_config = MultiAgentConfig(
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app_id=release.app_id,
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default_model_config_id=release.default_model_config_id,
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model_parameters=config_dict.get("model_parameters"),
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master_agent_id=config_dict.get("master_agent_id"),
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master_agent_name=config_dict.get("master_agent_name"),
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orchestration_mode=config_dict.get("orchestration_mode", "supervisor"),
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sub_agents=config_dict.get("sub_agents", []),
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routing_rules=config_dict.get("routing_rules"),
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execution_config=config_dict.get("execution_config", {}),
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aggregation_strategy=config_dict.get("aggregation_strategy", "merge"),
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)
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return agent_config
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def workflow_config_4_app_release(release: AppRelease) -> WorkflowConfig:
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config_dict = release.config
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# 如果 config 是字符串，解析为字典
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if isinstance(config_dict, str):
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import json
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try:
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config_dict = json.loads(config_dict)
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except json.JSONDecodeError:
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config_dict = {}
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config = WorkflowConfig(
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id=config_dict.get("id"),
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app_id=release.app_id,
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nodes=config_dict.get("nodes", []),
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edges=config_dict.get("edges", []),
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variables=config_dict.get("variables", []),
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execution_config=config_dict.get("execution_config", {}),
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triggers=config_dict.get("triggers", []),
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features=config_dict.get("features", {})
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)
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return config
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def dict_to_multi_agent_config(config_dict: Dict[str, Any], app_id: Optional[uuid.UUID] = None):
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"""Convert dict to MultiAgentConfig model object
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Args:
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config_dict: Configuration dictionary
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app_id: Optional app ID (if not provided in dict)
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Returns:
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MultiAgentConfig model instance (not yet persisted to database)
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Example:
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>>> config_dict = {
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... "app_id": "uuid-here",
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... "master_agent_id": "master-uuid",
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... "master_agent_name": "Master Agent",
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... "orchestration_mode": "supervisor",
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... "sub_agents": [
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... {"agent_id": "sub1-uuid", "name": "Sub Agent 1", "role": "specialist", "priority": 1},
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... {"agent_id": "sub2-uuid", "name": "Sub Agent 2", "role": "specialist", "priority": 2}
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... ],
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... "routing_rules": [
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... {"condition": "intent == 'technical'", "target_agent_id": "sub1-uuid", "priority": 1}
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... ],
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... "execution_config": {"max_iterations": 5, "timeout": 60, "parallel_limit": 3},
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... "aggregation_strategy": "merge"
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... }
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>>> multi_agent_config = dict_to_multi_agent_config(config_dict)
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"""
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from app.models.multi_agent_model import MultiAgentConfig
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# Extract app_id
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final_app_id = config_dict.get("app_id") or app_id
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if not final_app_id:
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raise ValueError("app_id is required")
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# Convert string UUID to UUID object if needed
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if isinstance(final_app_id, str):
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final_app_id = uuid.UUID(final_app_id)
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# Convert master_agent_id
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master_agent_id = config_dict.get("master_agent_id")
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if not master_agent_id:
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raise ValueError("master_agent_id is required")
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if isinstance(master_agent_id, str):
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master_agent_id = uuid.UUID(master_agent_id)
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# Create MultiAgentConfig instance
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multi_agent_config = MultiAgentConfig(
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id=uuid.UUID(config_dict["id"]) if "id" in config_dict else uuid.uuid4(),
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app_id=final_app_id,
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master_agent_id=master_agent_id,
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master_agent_name=config_dict.get("master_agent_name"),
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orchestration_mode=config_dict.get("orchestration_mode", "supervisor"),
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sub_agents=config_dict.get("sub_agents", []),
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routing_rules=config_dict.get("routing_rules"),
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execution_config=config_dict.get("execution_config", {}),
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aggregation_strategy=config_dict.get("aggregation_strategy", "merge"),
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is_active=config_dict.get("is_active", True),
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created_at=config_dict.get("created_at", datetime.now()),
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updated_at=config_dict.get("updated_at", datetime.now())
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)
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return multi_agent_config
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def dict_to_workflow_config(config_dict: Dict[str, Any], app_id: Optional[uuid.UUID] = None):
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"""Convert dict to WorkflowConfig model object
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Args:
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config_dict: Configuration dictionary
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app_id: Optional app ID (if not provided in dict)
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Returns:
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WorkflowConfig model instance (not yet persisted to database)
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Example:
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>>> config_dict = {
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... "app_id": "uuid-here",
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... "nodes": [
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... {"id": "start", "type": "start", "config": {}},
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... {"id": "llm", "type": "llm", "config": {"model": "gpt-4"}},
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... {"id": "end", "type": "end", "config": {"output": "{{llm.output}}"}}
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... ],
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... "edges": [
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... {"source": "start", "target": "llm"},
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... {"source": "llm", "target": "end"}
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... ],
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... "variables": [
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... {"name": "user_input", "type": "string", "default": ""}
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... ],
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... "execution_config": {
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... "max_iterations": 10,
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... "timeout": 300,
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... "enable_streaming": True
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... },
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... "triggers": [
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... {"type": "manual", "enabled": True}
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... ]
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... }
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>>> workflow_config = dict_to_workflow_config(config_dict)
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"""
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from app.models.workflow_model import WorkflowConfig
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# Extract app_id
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final_app_id = config_dict.get("app_id") or app_id
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if not final_app_id:
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raise ValueError("app_id is required")
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# Convert string UUID to UUID object if needed
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if isinstance(final_app_id, str):
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final_app_id = uuid.UUID(final_app_id)
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# Create WorkflowConfig instance
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workflow_config = WorkflowConfig(
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id=uuid.UUID(config_dict["id"]) if "id" in config_dict else uuid.uuid4(),
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app_id=final_app_id,
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nodes=config_dict.get("nodes", []),
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edges=config_dict.get("edges", []),
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variables=config_dict.get("variables", []),
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execution_config=config_dict.get("execution_config", {}),
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triggers=config_dict.get("triggers", []),
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is_active=config_dict.get("is_active", True),
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created_at=config_dict.get("created_at", datetime.now()),
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updated_at=config_dict.get("updated_at", datetime.now())
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)
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return workflow_config
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def agent_config_to_dict(agent_config) -> Dict[str, Any]:
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"""Convert AgentConfig model to dict
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Args:
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agent_config: AgentConfig model instance
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Returns:
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Configuration dictionary
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"""
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return {
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"id": str(agent_config.id),
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"app_id": str(agent_config.app_id),
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"system_prompt": agent_config.system_prompt,
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"default_model_config_id": str(
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agent_config.default_model_config_id) if agent_config.default_model_config_id else None,
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"model_parameters": agent_config.model_parameters,
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"knowledge_retrieval": agent_config.knowledge_retrieval,
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"memory": agent_config.memory,
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"variables": agent_config.variables,
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"tools": agent_config.tools,
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"agent_role": agent_config.agent_role,
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"agent_domain": agent_config.agent_domain,
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"parent_agent_id": str(agent_config.parent_agent_id) if agent_config.parent_agent_id else None,
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"capabilities": agent_config.capabilities,
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"is_active": agent_config.is_active,
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"created_at": agent_config.created_at.isoformat() if agent_config.created_at else None,
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"updated_at": agent_config.updated_at.isoformat() if agent_config.updated_at else None
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}
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def multi_agent_config_to_dict(multi_agent_config) -> Dict[str, Any]:
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"""Convert MultiAgentConfig model to dict
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Args:
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multi_agent_config: MultiAgentConfig model instance
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Returns:
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Configuration dictionary
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"""
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return {
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"id": str(multi_agent_config.id),
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"app_id": str(multi_agent_config.app_id),
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"master_agent_id": str(multi_agent_config.master_agent_id),
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"master_agent_name": multi_agent_config.master_agent_name,
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"orchestration_mode": multi_agent_config.orchestration_mode,
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"sub_agents": multi_agent_config.sub_agents,
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"routing_rules": multi_agent_config.routing_rules,
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"execution_config": multi_agent_config.execution_config,
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"aggregation_strategy": multi_agent_config.aggregation_strategy,
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"is_active": multi_agent_config.is_active,
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"created_at": multi_agent_config.created_at.isoformat() if multi_agent_config.created_at else None,
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"updated_at": multi_agent_config.updated_at.isoformat() if multi_agent_config.updated_at else None
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}
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def workflow_config_to_dict(workflow_config) -> Dict[str, Any]:
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"""Convert WorkflowConfig model to dict
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Args:
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workflow_config: WorkflowConfig model instance
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Returns:
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Configuration dictionary
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"""
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return {
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"id": str(workflow_config.id),
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"app_id": str(workflow_config.app_id),
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"nodes": workflow_config.nodes,
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"edges": workflow_config.edges,
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"variables": workflow_config.variables,
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"execution_config": workflow_config.execution_config,
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"triggers": workflow_config.triggers,
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"is_active": workflow_config.is_active,
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"created_at": workflow_config.created_at.isoformat() if workflow_config.created_at else None,
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"updated_at": workflow_config.updated_at.isoformat() if workflow_config.updated_at else None
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}
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