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* 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>
110 lines
3.0 KiB
Python
110 lines
3.0 KiB
Python
"""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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