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feat: agent workflow builder (#2264)
* feat: implement WorkflowAgent and GraphExecutor for workflow management and execution * refactor: workflow schemas and introduce WorkflowEngine - Updated schemas in `schemas.py` to include new agent types and configurations. - Created `WorkflowEngine` class in `workflow_engine.py` to manage workflow execution. - Enhanced `StreamProcessor` to handle workflow-related data. - Added new routes and utilities for managing workflows in the user API. - Implemented validation and serialization functions for workflows. - Established MongoDB collections and indexes for workflows and related entities. * refactor: improve WorkflowAgent documentation and update type hints in WorkflowEngine * feat: workflow builder and managing in frontend - Added new endpoints for workflows in `endpoints.ts`. - Implemented `getWorkflow`, `createWorkflow`, and `updateWorkflow` methods in `userService.ts`. - Introduced new UI components for alerts, buttons, commands, dialogs, multi-select, popovers, and selects. - Enhanced styling in `index.css` with new theme variables and animations. - Refactored modal components for better layout and styling. - Configured TypeScript paths and Vite aliases for cleaner imports. * feat: add workflow preview component and related state management - Implemented WorkflowPreview component for displaying workflow execution. - Created WorkflowPreviewSlice for managing workflow preview state, including queries and execution steps. - Added WorkflowMiniMap for visual representation of workflow nodes and their statuses. - Integrated conversation handling with the ability to fetch answers and manage query states. - Introduced reusable Sheet component for UI overlays. - Updated Redux store to include workflowPreview reducer. * feat: enhance workflow execution details and state management in WorkflowEngine and WorkflowPreview * feat: enhance workflow components with improved UI and functionality - Updated WorkflowPreview to allow text truncation for better display of long names. - Enhanced BaseNode with connectable handles and improved styling for better visibility. - Added MobileBlocker component to inform users about desktop requirements for the Workflow Builder. - Introduced PromptTextArea component for improved variable insertion and search functionality, including upstream variable extraction and context addition. * feat(workflow): add owner validation and graph version support * fix: ruff lint --------- Co-authored-by: Alex <a@tushynski.me>
This commit is contained in:
Siddhant Rai
109
application/agents/workflows/node_agent.py
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109
application/agents/workflows/node_agent.py
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@@ -0,0 +1,109 @@
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"""Workflow Node Agents - defines specialized agents for workflow nodes."""
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from typing import Any, Dict, List, Optional, Type
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from application.agents.base import BaseAgent
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from application.agents.classic_agent import ClassicAgent
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from application.agents.react_agent import ReActAgent
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from application.agents.workflows.schemas import AgentType
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class ToolFilterMixin:
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"""Mixin that filters fetched tools to only those specified in tool_ids."""
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_allowed_tool_ids: List[str]
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def _get_user_tools(self, user: str = "local") -> Dict[str, Dict[str, Any]]:
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all_tools = super()._get_user_tools(user)
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if not self._allowed_tool_ids:
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return {}
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filtered_tools = {
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tool_id: tool
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for tool_id, tool in all_tools.items()
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if str(tool.get("_id", "")) in self._allowed_tool_ids
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}
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return filtered_tools
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def _get_tools(self, api_key: str = None) -> Dict[str, Dict[str, Any]]:
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all_tools = super()._get_tools(api_key)
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if not self._allowed_tool_ids:
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return {}
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filtered_tools = {
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tool_id: tool
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for tool_id, tool in all_tools.items()
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if str(tool.get("_id", "")) in self._allowed_tool_ids
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}
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return filtered_tools
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class WorkflowNodeClassicAgent(ToolFilterMixin, ClassicAgent):
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def __init__(
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self,
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endpoint: str,
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llm_name: str,
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model_id: str,
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api_key: str,
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tool_ids: Optional[List[str]] = None,
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**kwargs,
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):
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super().__init__(
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endpoint=endpoint,
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llm_name=llm_name,
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model_id=model_id,
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api_key=api_key,
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**kwargs,
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)
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self._allowed_tool_ids = tool_ids or []
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class WorkflowNodeReActAgent(ToolFilterMixin, ReActAgent):
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def __init__(
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self,
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endpoint: str,
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llm_name: str,
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model_id: str,
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api_key: str,
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tool_ids: Optional[List[str]] = None,
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**kwargs,
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):
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super().__init__(
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endpoint=endpoint,
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llm_name=llm_name,
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model_id=model_id,
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api_key=api_key,
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**kwargs,
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)
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self._allowed_tool_ids = tool_ids or []
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class WorkflowNodeAgentFactory:
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_agents: Dict[AgentType, Type[BaseAgent]] = {
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AgentType.CLASSIC: WorkflowNodeClassicAgent,
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AgentType.REACT: WorkflowNodeReActAgent,
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}
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@classmethod
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def create(
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cls,
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agent_type: AgentType,
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endpoint: str,
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llm_name: str,
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model_id: str,
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api_key: str,
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tool_ids: Optional[List[str]] = None,
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**kwargs,
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) -> BaseAgent:
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agent_class = cls._agents.get(agent_type)
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if not agent_class:
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raise ValueError(f"Unsupported agent type: {agent_type}")
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return agent_class(
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endpoint=endpoint,
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llm_name=llm_name,
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model_id=model_id,
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api_key=api_key,
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tool_ids=tool_ids,
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**kwargs,
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)
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215
application/agents/workflows/schemas.py
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215
application/agents/workflows/schemas.py
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@@ -0,0 +1,215 @@
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from datetime import datetime, timezone
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from enum import Enum
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from typing import Any, Dict, List, Optional, Union
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from bson import ObjectId
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from pydantic import BaseModel, ConfigDict, Field, field_validator
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class NodeType(str, Enum):
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START = "start"
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END = "end"
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AGENT = "agent"
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NOTE = "note"
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STATE = "state"
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class AgentType(str, Enum):
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CLASSIC = "classic"
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REACT = "react"
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class ExecutionStatus(str, Enum):
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PENDING = "pending"
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RUNNING = "running"
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COMPLETED = "completed"
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FAILED = "failed"
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class Position(BaseModel):
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model_config = ConfigDict(extra="forbid")
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x: float = 0.0
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y: float = 0.0
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class AgentNodeConfig(BaseModel):
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model_config = ConfigDict(extra="allow")
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agent_type: AgentType = AgentType.CLASSIC
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llm_name: Optional[str] = None
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system_prompt: str = "You are a helpful assistant."
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prompt_template: str = ""
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output_variable: Optional[str] = None
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stream_to_user: bool = True
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tools: List[str] = Field(default_factory=list)
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sources: List[str] = Field(default_factory=list)
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chunks: str = "2"
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retriever: str = ""
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model_id: Optional[str] = None
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json_schema: Optional[Dict[str, Any]] = None
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class WorkflowEdgeCreate(BaseModel):
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model_config = ConfigDict(populate_by_name=True)
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id: str
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workflow_id: str
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source_id: str = Field(..., alias="source")
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target_id: str = Field(..., alias="target")
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source_handle: Optional[str] = Field(None, alias="sourceHandle")
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target_handle: Optional[str] = Field(None, alias="targetHandle")
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class WorkflowEdge(WorkflowEdgeCreate):
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mongo_id: Optional[str] = Field(None, alias="_id")
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@field_validator("mongo_id", mode="before")
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@classmethod
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def convert_objectid(cls, v: Any) -> Optional[str]:
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if isinstance(v, ObjectId):
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return str(v)
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return v
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def to_mongo_doc(self) -> Dict[str, Any]:
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return {
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"id": self.id,
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"workflow_id": self.workflow_id,
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"source_id": self.source_id,
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"target_id": self.target_id,
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"source_handle": self.source_handle,
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"target_handle": self.target_handle,
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}
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class WorkflowNodeCreate(BaseModel):
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model_config = ConfigDict(extra="allow")
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id: str
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workflow_id: str
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type: NodeType
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title: str = "Node"
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description: Optional[str] = None
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position: Position = Field(default_factory=Position)
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config: Dict[str, Any] = Field(default_factory=dict)
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@field_validator("position", mode="before")
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@classmethod
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def parse_position(cls, v: Union[Dict[str, float], Position]) -> Position:
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if isinstance(v, dict):
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return Position(**v)
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return v
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class WorkflowNode(WorkflowNodeCreate):
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mongo_id: Optional[str] = Field(None, alias="_id")
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@field_validator("mongo_id", mode="before")
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@classmethod
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def convert_objectid(cls, v: Any) -> Optional[str]:
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if isinstance(v, ObjectId):
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return str(v)
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return v
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def to_mongo_doc(self) -> Dict[str, Any]:
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return {
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"id": self.id,
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"workflow_id": self.workflow_id,
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"type": self.type.value,
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"title": self.title,
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"description": self.description,
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"position": self.position.model_dump(),
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"config": self.config,
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}
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class WorkflowCreate(BaseModel):
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model_config = ConfigDict(extra="allow")
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name: str = "New Workflow"
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description: Optional[str] = None
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user: Optional[str] = None
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class Workflow(WorkflowCreate):
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id: Optional[str] = Field(None, alias="_id")
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created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
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updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
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@field_validator("id", mode="before")
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@classmethod
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def convert_objectid(cls, v: Any) -> Optional[str]:
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if isinstance(v, ObjectId):
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return str(v)
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return v
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def to_mongo_doc(self) -> Dict[str, Any]:
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return {
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"name": self.name,
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"description": self.description,
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"user": self.user,
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"created_at": self.created_at,
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"updated_at": self.updated_at,
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}
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class WorkflowGraph(BaseModel):
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workflow: Workflow
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nodes: List[WorkflowNode] = Field(default_factory=list)
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edges: List[WorkflowEdge] = Field(default_factory=list)
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def get_node_by_id(self, node_id: str) -> Optional[WorkflowNode]:
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for node in self.nodes:
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if node.id == node_id:
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return node
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return None
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def get_start_node(self) -> Optional[WorkflowNode]:
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for node in self.nodes:
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if node.type == NodeType.START:
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return node
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return None
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def get_outgoing_edges(self, node_id: str) -> List[WorkflowEdge]:
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return [edge for edge in self.edges if edge.source_id == node_id]
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class NodeExecutionLog(BaseModel):
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model_config = ConfigDict(extra="forbid")
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node_id: str
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node_type: str
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status: ExecutionStatus
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started_at: datetime
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completed_at: Optional[datetime] = None
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error: Optional[str] = None
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state_snapshot: Dict[str, Any] = Field(default_factory=dict)
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class WorkflowRunCreate(BaseModel):
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workflow_id: str
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inputs: Dict[str, str] = Field(default_factory=dict)
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class WorkflowRun(BaseModel):
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model_config = ConfigDict(extra="allow")
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id: Optional[str] = Field(None, alias="_id")
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workflow_id: str
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status: ExecutionStatus = ExecutionStatus.PENDING
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inputs: Dict[str, str] = Field(default_factory=dict)
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outputs: Dict[str, Any] = Field(default_factory=dict)
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steps: List[NodeExecutionLog] = Field(default_factory=list)
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created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
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completed_at: Optional[datetime] = None
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@field_validator("id", mode="before")
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@classmethod
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def convert_objectid(cls, v: Any) -> Optional[str]:
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if isinstance(v, ObjectId):
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return str(v)
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return v
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def to_mongo_doc(self) -> Dict[str, Any]:
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return {
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"workflow_id": self.workflow_id,
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"status": self.status.value,
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"inputs": self.inputs,
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"outputs": self.outputs,
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"steps": [step.model_dump() for step in self.steps],
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"created_at": self.created_at,
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"completed_at": self.completed_at,
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}
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276
application/agents/workflows/workflow_engine.py
Normal file
276
application/agents/workflows/workflow_engine.py
Normal file
@@ -0,0 +1,276 @@
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import logging
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from datetime import datetime, timezone
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from typing import Any, Dict, Generator, List, Optional, TYPE_CHECKING
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from application.agents.workflows.node_agent import WorkflowNodeAgentFactory
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from application.agents.workflows.schemas import (
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AgentNodeConfig,
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ExecutionStatus,
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NodeExecutionLog,
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NodeType,
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WorkflowGraph,
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WorkflowNode,
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)
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if TYPE_CHECKING:
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from application.agents.base import BaseAgent
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logger = logging.getLogger(__name__)
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StateValue = Any
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WorkflowState = Dict[str, StateValue]
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class WorkflowEngine:
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MAX_EXECUTION_STEPS = 50
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def __init__(self, graph: WorkflowGraph, agent: "BaseAgent"):
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self.graph = graph
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self.agent = agent
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self.state: WorkflowState = {}
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self.execution_log: List[Dict[str, Any]] = []
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def execute(
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self, initial_inputs: WorkflowState, query: str
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) -> Generator[Dict[str, str], None, None]:
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self._initialize_state(initial_inputs, query)
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start_node = self.graph.get_start_node()
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if not start_node:
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yield {"type": "error", "error": "No start node found in workflow."}
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return
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current_node_id: Optional[str] = start_node.id
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steps = 0
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while current_node_id and steps < self.MAX_EXECUTION_STEPS:
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node = self.graph.get_node_by_id(current_node_id)
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if not node:
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yield {"type": "error", "error": f"Node {current_node_id} not found."}
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break
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log_entry = self._create_log_entry(node)
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yield {
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"type": "workflow_step",
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"node_id": node.id,
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"node_type": node.type.value,
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"node_title": node.title,
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"status": "running",
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}
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try:
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yield from self._execute_node(node)
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log_entry["status"] = ExecutionStatus.COMPLETED.value
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log_entry["completed_at"] = datetime.now(timezone.utc)
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output_key = f"node_{node.id}_output"
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node_output = self.state.get(output_key)
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yield {
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"type": "workflow_step",
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"node_id": node.id,
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"node_type": node.type.value,
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"node_title": node.title,
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"status": "completed",
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"state_snapshot": dict(self.state),
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"output": node_output,
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}
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except Exception as e:
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logger.error(f"Error executing node {node.id}: {e}", exc_info=True)
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log_entry["status"] = ExecutionStatus.FAILED.value
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log_entry["error"] = str(e)
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log_entry["completed_at"] = datetime.now(timezone.utc)
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log_entry["state_snapshot"] = dict(self.state)
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self.execution_log.append(log_entry)
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yield {
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"type": "workflow_step",
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"node_id": node.id,
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"node_type": node.type.value,
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"node_title": node.title,
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"status": "failed",
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"state_snapshot": dict(self.state),
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"error": str(e),
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||||
}
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yield {"type": "error", "error": str(e)}
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break
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log_entry["state_snapshot"] = dict(self.state)
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self.execution_log.append(log_entry)
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||||
|
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if node.type == NodeType.END:
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break
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||||
current_node_id = self._get_next_node_id(current_node_id)
|
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steps += 1
|
||||
if steps >= self.MAX_EXECUTION_STEPS:
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||||
logger.warning(
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f"Workflow reached max steps limit ({self.MAX_EXECUTION_STEPS})"
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||||
)
|
||||
|
||||
def _initialize_state(self, initial_inputs: WorkflowState, query: str) -> None:
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self.state.update(initial_inputs)
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self.state["query"] = query
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self.state["chat_history"] = str(self.agent.chat_history)
|
||||
|
||||
def _create_log_entry(self, node: WorkflowNode) -> Dict[str, Any]:
|
||||
return {
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||||
"node_id": node.id,
|
||||
"node_type": node.type.value,
|
||||
"started_at": datetime.now(timezone.utc),
|
||||
"completed_at": None,
|
||||
"status": ExecutionStatus.RUNNING.value,
|
||||
"error": None,
|
||||
"state_snapshot": {},
|
||||
}
|
||||
|
||||
def _get_next_node_id(self, current_node_id: str) -> Optional[str]:
|
||||
edges = self.graph.get_outgoing_edges(current_node_id)
|
||||
if edges:
|
||||
return edges[0].target_id
|
||||
return None
|
||||
|
||||
def _execute_node(
|
||||
self, node: WorkflowNode
|
||||
) -> Generator[Dict[str, str], None, None]:
|
||||
logger.info(f"Executing node {node.id} ({node.type.value})")
|
||||
|
||||
node_handlers = {
|
||||
NodeType.START: self._execute_start_node,
|
||||
NodeType.NOTE: self._execute_note_node,
|
||||
NodeType.AGENT: self._execute_agent_node,
|
||||
NodeType.STATE: self._execute_state_node,
|
||||
NodeType.END: self._execute_end_node,
|
||||
}
|
||||
|
||||
handler = node_handlers.get(node.type)
|
||||
if handler:
|
||||
yield from handler(node)
|
||||
|
||||
def _execute_start_node(
|
||||
self, node: WorkflowNode
|
||||
) -> Generator[Dict[str, str], None, None]:
|
||||
yield from ()
|
||||
|
||||
def _execute_note_node(
|
||||
self, node: WorkflowNode
|
||||
) -> Generator[Dict[str, str], None, None]:
|
||||
yield from ()
|
||||
|
||||
def _execute_agent_node(
|
||||
self, node: WorkflowNode
|
||||
) -> Generator[Dict[str, str], None, None]:
|
||||
from application.core.model_utils import get_api_key_for_provider
|
||||
|
||||
node_config = AgentNodeConfig(**node.config)
|
||||
|
||||
if node_config.prompt_template:
|
||||
formatted_prompt = self._format_template(node_config.prompt_template)
|
||||
else:
|
||||
formatted_prompt = self.state.get("query", "")
|
||||
node_llm_name = node_config.llm_name or self.agent.llm_name
|
||||
node_api_key = get_api_key_for_provider(node_llm_name) or self.agent.api_key
|
||||
|
||||
node_agent = WorkflowNodeAgentFactory.create(
|
||||
agent_type=node_config.agent_type,
|
||||
endpoint=self.agent.endpoint,
|
||||
llm_name=node_llm_name,
|
||||
model_id=node_config.model_id or self.agent.model_id,
|
||||
api_key=node_api_key,
|
||||
tool_ids=node_config.tools,
|
||||
prompt=node_config.system_prompt,
|
||||
chat_history=self.agent.chat_history,
|
||||
decoded_token=self.agent.decoded_token,
|
||||
json_schema=node_config.json_schema,
|
||||
)
|
||||
|
||||
full_response = ""
|
||||
first_chunk = True
|
||||
for event in node_agent.gen(formatted_prompt):
|
||||
if "answer" in event:
|
||||
full_response += event["answer"]
|
||||
if node_config.stream_to_user:
|
||||
if first_chunk and hasattr(self, "_has_streamed"):
|
||||
yield {"answer": "\n\n"}
|
||||
first_chunk = False
|
||||
yield event
|
||||
|
||||
if node_config.stream_to_user:
|
||||
self._has_streamed = True
|
||||
|
||||
output_key = node_config.output_variable or f"node_{node.id}_output"
|
||||
self.state[output_key] = full_response
|
||||
|
||||
def _execute_state_node(
|
||||
self, node: WorkflowNode
|
||||
) -> Generator[Dict[str, str], None, None]:
|
||||
config = node.config
|
||||
operations = config.get("operations", [])
|
||||
|
||||
if operations:
|
||||
for op in operations:
|
||||
key = op.get("key")
|
||||
operation = op.get("operation", "set")
|
||||
value = op.get("value")
|
||||
|
||||
if not key:
|
||||
continue
|
||||
if operation == "set":
|
||||
formatted_value = (
|
||||
self._format_template(str(value))
|
||||
if isinstance(value, str)
|
||||
else value
|
||||
)
|
||||
self.state[key] = formatted_value
|
||||
elif operation == "increment":
|
||||
current = self.state.get(key, 0)
|
||||
try:
|
||||
self.state[key] = int(current) + int(value or 1)
|
||||
except (ValueError, TypeError):
|
||||
self.state[key] = 1
|
||||
elif operation == "append":
|
||||
if key not in self.state:
|
||||
self.state[key] = []
|
||||
if isinstance(self.state[key], list):
|
||||
self.state[key].append(value)
|
||||
else:
|
||||
updates = config.get("updates", {})
|
||||
if not updates:
|
||||
var_name = config.get("variable")
|
||||
var_value = config.get("value")
|
||||
if var_name and isinstance(var_name, str):
|
||||
updates = {var_name: var_value or ""}
|
||||
if isinstance(updates, dict):
|
||||
for key, value in updates.items():
|
||||
if isinstance(value, str):
|
||||
self.state[key] = self._format_template(value)
|
||||
else:
|
||||
self.state[key] = value
|
||||
yield from ()
|
||||
|
||||
def _execute_end_node(
|
||||
self, node: WorkflowNode
|
||||
) -> Generator[Dict[str, str], None, None]:
|
||||
config = node.config
|
||||
output_template = str(config.get("output_template", ""))
|
||||
if output_template:
|
||||
formatted_output = self._format_template(output_template)
|
||||
yield {"answer": formatted_output}
|
||||
|
||||
def _format_template(self, template: str) -> str:
|
||||
formatted = template
|
||||
for key, value in self.state.items():
|
||||
placeholder = f"{{{{{key}}}}}"
|
||||
if placeholder in formatted and value is not None:
|
||||
formatted = formatted.replace(placeholder, str(value))
|
||||
return formatted
|
||||
|
||||
def get_execution_summary(self) -> List[NodeExecutionLog]:
|
||||
return [
|
||||
NodeExecutionLog(
|
||||
node_id=log["node_id"],
|
||||
node_type=log["node_type"],
|
||||
status=ExecutionStatus(log["status"]),
|
||||
started_at=log["started_at"],
|
||||
completed_at=log.get("completed_at"),
|
||||
error=log.get("error"),
|
||||
state_snapshot=log.get("state_snapshot", {}),
|
||||
)
|
||||
for log in self.execution_log
|
||||
]
|
||||
Reference in New Issue
Block a user