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96641a93f6
Introduce a new unified Agent V2 workflow node that combines LLM capabilities with agent tool-calling loops, along with a new AppMode.AGENT for standalone agent apps backed by single-node workflows. Phase 1 — Agent Patterns: - Add core/agent/patterns/ module (AgentPattern, FunctionCallStrategy, ReActStrategy, StrategyFactory) ported from feat/support-agent-sandbox - Add ExecutionContext, AgentLog, AgentResult entities - Add Tool.to_prompt_message_tool() for LLM-consumable tool conversion Phase 2 — Agent V2 Workflow Node: - Add core/workflow/nodes/agent_v2/ (AgentV2Node, AgentV2NodeData, AgentV2ToolManager, AgentV2EventAdapter) - Register agent-v2 node type in DifyNodeFactory - No-tools path: single LLM call (LLM Node equivalent) - Tools path: FC/ReAct loop via StrategyFactory Phase 3 — Agent App Type: - Add AppMode.AGENT to model enum - Add WorkflowGraphFactory for auto-generating start->agent_v2->answer graphs - AppService.create_app() creates workflow draft for AGENT mode - AppGenerateService.generate() routes AGENT to AdvancedChatAppGenerator - Console API and DSL import/export support AGENT mode - Default app template for AGENT mode Old agent/agent-chat/LLM node paths are fully preserved. 38 unit tests all passing. Made-with: Cursor
114 lines
3.6 KiB
Python
114 lines
3.6 KiB
Python
"""Factory for programmatically building workflow graphs.
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Used by AppService to auto-generate single-node workflow graphs when
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creating a new Agent app (AppMode.AGENT).
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"""
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from typing import Any
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from core.workflow.nodes.agent_v2.entities import AGENT_V2_NODE_TYPE
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class WorkflowGraphFactory:
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"""Builds workflow graph dicts for special app creation flows."""
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@staticmethod
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def create_single_agent_graph(
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model_config: dict[str, Any],
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is_chat: bool = True,
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) -> dict[str, Any]:
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"""Create a minimal start -> agent_v2 -> answer/end graph.
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Args:
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model_config: Model configuration dict with provider, name, mode, completion_params.
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is_chat: If True, creates chatflow (with answer node); otherwise workflow (with end node).
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Returns:
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Graph dict with nodes and edges, ready for WorkflowService.sync_draft_workflow().
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"""
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agent_node_data: dict[str, Any] = {
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"type": AGENT_V2_NODE_TYPE,
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"title": "Agent",
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"model": model_config,
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"prompt_template": [
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{"role": "system", "text": "You are a helpful assistant."},
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{"role": "user", "text": "{{#sys.query#}}"},
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],
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"tools": [],
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"max_iterations": 10,
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"agent_strategy": "auto",
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"context": {"enabled": False},
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"vision": {"enabled": False},
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}
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if is_chat:
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agent_node_data["memory"] = {"window": {"enabled": True, "size": 50}}
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nodes: list[dict[str, Any]] = [
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{
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"id": "start",
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"type": "custom",
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"data": {"type": "start", "title": "Start", "variables": []},
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"position": {"x": 80, "y": 282},
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},
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{
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"id": "agent",
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"type": "custom",
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"data": agent_node_data,
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"position": {"x": 400, "y": 282},
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},
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]
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if is_chat:
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nodes.append(
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{
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"id": "answer",
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"type": "custom",
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"data": {
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"type": "answer",
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"title": "Answer",
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"answer": "{{#agent.text#}}",
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},
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"position": {"x": 720, "y": 282},
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}
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)
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end_node_id = "answer"
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else:
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nodes.append(
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{
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"id": "end",
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"type": "custom",
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"data": {
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"type": "end",
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"title": "End",
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"outputs": [
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{
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"value_selector": ["agent", "text"],
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"variable": "result",
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}
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],
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},
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"position": {"x": 720, "y": 282},
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}
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)
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end_node_id = "end"
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edges: list[dict[str, str]] = [
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{
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"id": "start-agent",
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"source": "start",
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"target": "agent",
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"sourceHandle": "source",
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"targetHandle": "target",
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},
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{
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"id": f"agent-{end_node_id}",
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"source": "agent",
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"target": end_node_id,
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"sourceHandle": "source",
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"targetHandle": "target",
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},
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]
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return {"nodes": nodes, "edges": edges}
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