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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
218
application/agents/workflow_agent.py
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218
application/agents/workflow_agent.py
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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, Optional
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from application.agents.base import BaseAgent
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from application.agents.workflows.schemas import (
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ExecutionStatus,
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Workflow,
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WorkflowEdge,
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WorkflowGraph,
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WorkflowNode,
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WorkflowRun,
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)
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from application.agents.workflows.workflow_engine import WorkflowEngine
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from application.core.mongo_db import MongoDB
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from application.core.settings import settings
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from application.logging import log_activity, LogContext
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logger = logging.getLogger(__name__)
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class WorkflowAgent(BaseAgent):
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"""A specialized agent that executes predefined workflows."""
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def __init__(
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self,
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*args,
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workflow_id: Optional[str] = None,
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workflow: Optional[Dict[str, Any]] = None,
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workflow_owner: Optional[str] = None,
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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self.workflow_id = workflow_id
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self.workflow_owner = workflow_owner
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self._workflow_data = workflow
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self._engine: Optional[WorkflowEngine] = None
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@log_activity()
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def gen(
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self, query: str, log_context: LogContext = None
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) -> Generator[Dict[str, str], None, None]:
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yield from self._gen_inner(query, log_context)
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def _gen_inner(
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self, query: str, log_context: LogContext
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) -> Generator[Dict[str, str], None, None]:
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graph = self._load_workflow_graph()
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if not graph:
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yield {"type": "error", "error": "Failed to load workflow configuration."}
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return
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self._engine = WorkflowEngine(graph, self)
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yield from self._engine.execute({}, query)
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self._save_workflow_run(query)
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def _load_workflow_graph(self) -> Optional[WorkflowGraph]:
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if self._workflow_data:
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return self._parse_embedded_workflow()
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if self.workflow_id:
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return self._load_from_database()
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return None
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def _parse_embedded_workflow(self) -> Optional[WorkflowGraph]:
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try:
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nodes_data = self._workflow_data.get("nodes", [])
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edges_data = self._workflow_data.get("edges", [])
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workflow = Workflow(
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name=self._workflow_data.get("name", "Embedded Workflow"),
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description=self._workflow_data.get("description"),
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)
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nodes = []
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for n in nodes_data:
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node_config = n.get("data", {})
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nodes.append(
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WorkflowNode(
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id=n["id"],
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workflow_id=self.workflow_id or "embedded",
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type=n["type"],
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title=n.get("title", "Node"),
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description=n.get("description"),
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position=n.get("position", {"x": 0, "y": 0}),
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config=node_config,
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)
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)
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edges = []
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for e in edges_data:
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edges.append(
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WorkflowEdge(
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id=e["id"],
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workflow_id=self.workflow_id or "embedded",
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source=e.get("source") or e.get("source_id"),
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target=e.get("target") or e.get("target_id"),
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sourceHandle=e.get("sourceHandle") or e.get("source_handle"),
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targetHandle=e.get("targetHandle") or e.get("target_handle"),
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)
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)
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return WorkflowGraph(workflow=workflow, nodes=nodes, edges=edges)
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except Exception as e:
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logger.error(f"Invalid embedded workflow: {e}")
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return None
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def _load_from_database(self) -> Optional[WorkflowGraph]:
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try:
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from bson.objectid import ObjectId
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if not self.workflow_id or not ObjectId.is_valid(self.workflow_id):
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logger.error(f"Invalid workflow ID: {self.workflow_id}")
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return None
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owner_id = self.workflow_owner
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if not owner_id and isinstance(self.decoded_token, dict):
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owner_id = self.decoded_token.get("sub")
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if not owner_id:
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logger.error(
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f"Workflow owner not available for workflow load: {self.workflow_id}"
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)
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return None
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mongo = MongoDB.get_client()
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db = mongo[settings.MONGO_DB_NAME]
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workflows_coll = db["workflows"]
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workflow_nodes_coll = db["workflow_nodes"]
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workflow_edges_coll = db["workflow_edges"]
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workflow_doc = workflows_coll.find_one(
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{"_id": ObjectId(self.workflow_id), "user": owner_id}
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)
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if not workflow_doc:
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logger.error(
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f"Workflow {self.workflow_id} not found or inaccessible for user {owner_id}"
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)
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return None
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workflow = Workflow(**workflow_doc)
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graph_version = workflow_doc.get("current_graph_version", 1)
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try:
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graph_version = int(graph_version)
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if graph_version <= 0:
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graph_version = 1
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except (ValueError, TypeError):
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graph_version = 1
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nodes_docs = list(
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workflow_nodes_coll.find(
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{"workflow_id": self.workflow_id, "graph_version": graph_version}
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)
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)
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if not nodes_docs and graph_version == 1:
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nodes_docs = list(
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workflow_nodes_coll.find(
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{
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"workflow_id": self.workflow_id,
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"graph_version": {"$exists": False},
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}
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)
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)
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nodes = [WorkflowNode(**doc) for doc in nodes_docs]
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edges_docs = list(
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workflow_edges_coll.find(
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{"workflow_id": self.workflow_id, "graph_version": graph_version}
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)
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)
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if not edges_docs and graph_version == 1:
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edges_docs = list(
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workflow_edges_coll.find(
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{
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"workflow_id": self.workflow_id,
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"graph_version": {"$exists": False},
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}
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)
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)
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edges = [WorkflowEdge(**doc) for doc in edges_docs]
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return WorkflowGraph(workflow=workflow, nodes=nodes, edges=edges)
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except Exception as e:
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logger.error(f"Failed to load workflow from database: {e}")
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return None
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def _save_workflow_run(self, query: str) -> None:
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if not self._engine:
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return
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try:
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mongo = MongoDB.get_client()
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db = mongo[settings.MONGO_DB_NAME]
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workflow_runs_coll = db["workflow_runs"]
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run = WorkflowRun(
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workflow_id=self.workflow_id or "unknown",
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status=self._determine_run_status(),
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inputs={"query": query},
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outputs=self._serialize_state(self._engine.state),
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steps=self._engine.get_execution_summary(),
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created_at=datetime.now(timezone.utc),
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completed_at=datetime.now(timezone.utc),
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)
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workflow_runs_coll.insert_one(run.to_mongo_doc())
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except Exception as e:
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logger.error(f"Failed to save workflow run: {e}")
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def _determine_run_status(self) -> ExecutionStatus:
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if not self._engine or not self._engine.execution_log:
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return ExecutionStatus.COMPLETED
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for log in self._engine.execution_log:
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if log.get("status") == ExecutionStatus.FAILED.value:
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return ExecutionStatus.FAILED
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return ExecutionStatus.COMPLETED
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def _serialize_state(self, state: Dict[str, Any]) -> Dict[str, Any]:
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serialized: Dict[str, Any] = {}
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for key, value in state.items():
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if isinstance(value, (str, int, float, bool, type(None))):
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serialized[key] = value
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else:
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serialized[key] = str(value)
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return serialized
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