Hermes Agent
169 lines
3.5 KiB
Python
169 lines
3.5 KiB
Python
from datetime import datetime
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from typing import Any
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from pydantic import BaseModel, ConfigDict
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class ProviderBase(BaseModel):
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name: str
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code: str
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provider_type: str = "openai_compatible"
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base_url: str = ""
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api_key: str = ""
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status: str = "enabled"
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class ProviderOut(ProviderBase):
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id: str
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created_at: datetime
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model_config = ConfigDict(from_attributes=True)
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class ModelBase(BaseModel):
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provider_id: str
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name: str
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display_name: str = ""
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context_length: int = 8192
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supports_streaming: bool = True
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supports_function_call: bool = False
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default_temperature: float = 0.7
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default_max_tokens: int = 2048
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status: str = "enabled"
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class ModelOut(ModelBase):
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id: str
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created_at: datetime
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model_config = ConfigDict(from_attributes=True)
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class AgentBase(BaseModel):
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name: str
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code: str
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description: str = ""
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avatar: str = ""
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status: str = "draft"
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persona: dict[str, Any] = {}
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system_prompt: str = ""
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model_id: str | None = None
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temperature: float = 0.7
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max_tokens: int = 2048
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tools: list[Any] = []
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knowledge_base_ids: list[str] = []
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enable_memory: bool = True
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memory_window: int = 10
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class AgentOut(AgentBase):
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id: str
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created_at: datetime
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model_config = ConfigDict(from_attributes=True)
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class ChatIn(BaseModel):
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message: str
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conversation_id: str | None = None
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class ConversationOut(BaseModel):
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id: str
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agent_id: str
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title: str
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total_tokens: int = 0
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created_at: datetime
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model_config = ConfigDict(from_attributes=True)
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class MessageOut(BaseModel):
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id: str
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conversation_id: str
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role: str
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content: str
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status: str
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total_tokens: int = 0
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elapsed_time: int = 0
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created_at: datetime
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model_config = ConfigDict(from_attributes=True)
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class WorkflowBase(BaseModel):
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name: str
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code: str
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description: str = ""
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status: str = "draft"
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definition: dict[str, Any] = {}
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input_variables: list[Any] = []
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output_variables: list[Any] = []
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class WorkflowOut(WorkflowBase):
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id: str
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version: int
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published_version: int | None = None
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published_definition: dict[str, Any] = {}
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run_count: int = 0
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created_at: datetime
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model_config = ConfigDict(from_attributes=True)
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class WorkflowRunIn(BaseModel):
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inputs: dict[str, Any] = {}
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class WorkflowRunOut(BaseModel):
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id: str
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workflow_id: str
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status: str
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inputs: dict[str, Any]
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outputs: dict[str, Any]
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execution_log: list[Any]
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error_message: str = ""
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total_tokens: int = 0
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elapsed_time: int = 0
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created_at: datetime
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model_config = ConfigDict(from_attributes=True)
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class KnowledgeBaseIn(BaseModel):
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name: str
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code: str
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description: str = ""
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status: str = "enabled"
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class KnowledgeBaseOut(KnowledgeBaseIn):
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id: str
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created_at: datetime
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model_config = ConfigDict(from_attributes=True)
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class TeamBase(BaseModel):
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name: str
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code: str
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description: str = ""
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mode: str = "sequential"
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members: list[dict[str, Any]] = []
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status: str = "enabled"
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class TeamOut(TeamBase):
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id: str
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created_at: datetime
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model_config = ConfigDict(from_attributes=True)
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class CollaborationRunIn(BaseModel):
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task: str
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class CollaborationRunOut(BaseModel):
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id: str
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team_id: str
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task: str
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status: str
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messages: list[Any]
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final_answer: str
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elapsed_time: int
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created_at: datetime
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model_config = ConfigDict(from_attributes=True)
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