from datetime import datetime
from typing import Any

from pydantic import BaseModel, ConfigDict


class ProviderBase(BaseModel):
    name: str
    code: str
    provider_type: str = "openai_compatible"
    base_url: str = ""
    api_key: str = ""
    status: str = "enabled"


class ProviderOut(ProviderBase):
    id: str
    created_at: datetime
    model_config = ConfigDict(from_attributes=True)


class ModelBase(BaseModel):
    provider_id: str
    name: str
    display_name: str = ""
    context_length: int = 8192
    supports_streaming: bool = True
    supports_function_call: bool = False
    default_temperature: float = 0.7
    default_max_tokens: int = 2048
    status: str = "enabled"


class ModelOut(ModelBase):
    id: str
    created_at: datetime
    model_config = ConfigDict(from_attributes=True)


class AgentBase(BaseModel):
    name: str
    code: str
    description: str = ""
    avatar: str = ""
    status: str = "draft"
    persona: dict[str, Any] = {}
    system_prompt: str = ""
    model_id: str | None = None
    temperature: float = 0.7
    max_tokens: int = 2048
    tools: list[Any] = []
    knowledge_base_ids: list[str] = []
    enable_memory: bool = True
    memory_window: int = 10


class AgentOut(AgentBase):
    id: str
    created_at: datetime
    model_config = ConfigDict(from_attributes=True)


class ChatIn(BaseModel):
    message: str
    conversation_id: str | None = None


class ConversationOut(BaseModel):
    id: str
    agent_id: str
    title: str
    total_tokens: int = 0
    created_at: datetime
    model_config = ConfigDict(from_attributes=True)


class MessageOut(BaseModel):
    id: str
    conversation_id: str
    role: str
    content: str
    status: str
    total_tokens: int = 0
    elapsed_time: int = 0
    created_at: datetime
    model_config = ConfigDict(from_attributes=True)


class WorkflowBase(BaseModel):
    name: str
    code: str
    description: str = ""
    status: str = "draft"
    definition: dict[str, Any] = {}
    input_variables: list[Any] = []
    output_variables: list[Any] = []


class WorkflowOut(WorkflowBase):
    id: str
    version: int
    published_version: int | None = None
    published_definition: dict[str, Any] = {}
    run_count: int = 0
    created_at: datetime
    model_config = ConfigDict(from_attributes=True)


class WorkflowRunIn(BaseModel):
    inputs: dict[str, Any] = {}


class WorkflowRunOut(BaseModel):
    id: str
    workflow_id: str
    status: str
    inputs: dict[str, Any]
    outputs: dict[str, Any]
    execution_log: list[Any]
    error_message: str = ""
    total_tokens: int = 0
    elapsed_time: int = 0
    created_at: datetime
    model_config = ConfigDict(from_attributes=True)


class KnowledgeBaseIn(BaseModel):
    name: str
    code: str
    description: str = ""
    status: str = "enabled"


class KnowledgeBaseOut(KnowledgeBaseIn):
    id: str
    created_at: datetime
    model_config = ConfigDict(from_attributes=True)


class TeamBase(BaseModel):
    name: str
    code: str
    description: str = ""
    mode: str = "sequential"
    members: list[dict[str, Any]] = []
    status: str = "enabled"


class TeamOut(TeamBase):
    id: str
    created_at: datetime
    model_config = ConfigDict(from_attributes=True)


class CollaborationRunIn(BaseModel):
    task: str


class CollaborationRunOut(BaseModel):
    id: str
    team_id: str
    task: str
    status: str
    messages: list[Any]
    final_answer: str
    elapsed_time: int
    created_at: datetime
    model_config = ConfigDict(from_attributes=True)