DocsGPT/application/agents/workflows/schemas.py

from datetime import datetime, timezone
from enum import Enum
from typing import Any, Dict, List, Literal, Optional, Union

from pydantic import BaseModel, ConfigDict, Field, field_validator


class NodeType(str, Enum):
    START = "start"
    END = "end"
    AGENT = "agent"
    NOTE = "note"
    STATE = "state"
    CONDITION = "condition"
    CODE = "code"


class AgentType(str, Enum):
    CLASSIC = "classic"
    REACT = "react"
    AGENTIC = "agentic"
    RESEARCH = "research"


class ExecutionStatus(str, Enum):
    PENDING = "pending"
    RUNNING = "running"
    COMPLETED = "completed"
    FAILED = "failed"


class Position(BaseModel):
    model_config = ConfigDict(extra="forbid")
    x: float = 0.0
    y: float = 0.0


class AgentNodeConfig(BaseModel):
    model_config = ConfigDict(extra="allow")
    agent_type: AgentType = AgentType.CLASSIC
    llm_name: Optional[str] = None
    system_prompt: str = "You are a helpful assistant."
    prompt_template: str = ""
    output_variable: Optional[str] = None
    stream_to_user: bool = True
    tools: List[str] = Field(default_factory=list)
    sources: List[str] = Field(default_factory=list)
    chunks: str = "2"
    retriever: str = ""
    model_id: Optional[str] = None
    json_schema: Optional[Dict[str, Any]] = None
    # Run-scoped documents fed to this node's LLM. Entries are state-var names
    # holding artifact refs (single dict or a list of dicts), raw artifact ids,
    # short refs (``A1``), or the ``"*"``/``"input_documents"`` token meaning
    # "every ref in ``state['input_documents']``".
    input_documents: List[str] = Field(default_factory=list)
    # How selected documents reach the model: ``auto`` (native when the model
    # accepts the mime, else extract to text), ``native`` (force native; raise
    # on an unsupported mime), or ``extract`` (always inline extracted text).
    file_passing: Literal["auto", "native", "extract"] = "auto"


class CodeNodeConfig(BaseModel):
    model_config = ConfigDict(extra="allow")
    code: str = ""
    language: str = "python"
    inputs: List[str] = Field(default_factory=list)
    libraries: List[str] = Field(default_factory=list)
    output_variable: Optional[str] = None
    timeout: Optional[int] = None
    json_schema: Optional[Dict[str, Any]] = None


class ConditionCase(BaseModel):
    model_config = ConfigDict(extra="forbid", populate_by_name=True)
    name: Optional[str] = None
    expression: str = ""
    source_handle: str = Field(..., alias="sourceHandle")


class ConditionNodeConfig(BaseModel):
    model_config = ConfigDict(extra="allow")
    mode: Literal["simple", "advanced"] = "simple"
    cases: List[ConditionCase] = Field(default_factory=list)


class StateOperation(BaseModel):
    model_config = ConfigDict(extra="forbid")
    expression: str = ""
    target_variable: str = ""


class WorkflowEdgeCreate(BaseModel):
    model_config = ConfigDict(populate_by_name=True)
    id: str
    workflow_id: str
    source_id: str = Field(..., alias="source")
    target_id: str = Field(..., alias="target")
    source_handle: Optional[str] = Field(None, alias="sourceHandle")
    target_handle: Optional[str] = Field(None, alias="targetHandle")


class WorkflowEdge(WorkflowEdgeCreate):
    pass


class WorkflowNodeCreate(BaseModel):
    model_config = ConfigDict(extra="allow")
    id: str
    workflow_id: str
    type: NodeType
    title: str = "Node"
    description: Optional[str] = None
    position: Position = Field(default_factory=Position)
    config: Dict[str, Any] = Field(default_factory=dict)

    @field_validator("position", mode="before")
    @classmethod
    def parse_position(cls, v: Union[Dict[str, float], Position]) -> Position:
        if isinstance(v, dict):
            return Position(**v)
        return v


class WorkflowNode(WorkflowNodeCreate):
    pass


class WorkflowCreate(BaseModel):
    model_config = ConfigDict(extra="allow")
    name: str = "New Workflow"
    description: Optional[str] = None
    user: Optional[str] = None


class Workflow(WorkflowCreate):
    id: Optional[str] = None
    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))


class WorkflowGraph(BaseModel):
    workflow: Workflow
    nodes: List[WorkflowNode] = Field(default_factory=list)
    edges: List[WorkflowEdge] = Field(default_factory=list)

    def get_node_by_id(self, node_id: str) -> Optional[WorkflowNode]:
        for node in self.nodes:
            if node.id == node_id:
                return node
        return None

    def get_start_node(self) -> Optional[WorkflowNode]:
        for node in self.nodes:
            if node.type == NodeType.START:
                return node
        return None

    def get_outgoing_edges(self, node_id: str) -> List[WorkflowEdge]:
        return [edge for edge in self.edges if edge.source_id == node_id]


class NodeExecutionLog(BaseModel):
    model_config = ConfigDict(extra="forbid")
    node_id: str
    node_type: str
    status: ExecutionStatus
    started_at: datetime
    completed_at: Optional[datetime] = None
    error: Optional[str] = None
    state_snapshot: Dict[str, Any] = Field(default_factory=dict)


class WorkflowRunCreate(BaseModel):
    workflow_id: str
    inputs: Dict[str, str] = Field(default_factory=dict)


class WorkflowRun(BaseModel):
    model_config = ConfigDict(extra="allow")
    id: Optional[str] = None
    workflow_id: str
    user: Optional[str] = None
    status: ExecutionStatus = ExecutionStatus.PENDING
    inputs: Dict[str, str] = Field(default_factory=dict)
    outputs: Dict[str, Any] = Field(default_factory=dict)
    steps: List[NodeExecutionLog] = Field(default_factory=list)
    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    completed_at: Optional[datetime] = None