feat: 准备loop
This commit is contained in:
@@ -0,0 +1,118 @@
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"""输入解析、意图分流和需求理解流程"""
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import inspect
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from langgraph.graph import END, StateGraph
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from app.processor.nodes.clarify_node import ClarifyNode
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from app.processor.nodes.input_node import InputNode
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from app.processor.nodes.intent_node import IntentNode
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from app.processor.nodes.requirement_node import RequirementNode
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from app.processor.state import QueryState
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def route_after_intent(state: QueryState) -> str:
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return state["intent"]
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def route_after_requirement(state: QueryState) -> bool:
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return state["requirement"].needs_clarification
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def build_graph():
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# 定义工作流
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workflow = StateGraph(QueryState)
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# 实例化节点
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nodes = {
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"input_node": InputNode(),
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"intent_node": IntentNode(),
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"requirement_node": RequirementNode(),
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"clarify_node": ClarifyNode(),
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}
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# 注册节点，异步节点使用 BaseNode 的异步入口
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for node_name, node in nodes.items():
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action = node._acall if inspect.iscoroutinefunction(node.process) else node
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workflow.add_node(node_name, action)
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# 定义入口
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workflow.set_entry_point("input_node")
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# 添加业务边
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workflow.add_edge("input_node", "intent_node")
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# 新建和修改进入需求理解，意图不明进入澄清
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workflow.add_conditional_edges(
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source="intent_node",
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path=route_after_intent,
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path_map={
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"create_screen": "requirement_node",
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"modify_screen": "requirement_node",
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"qa": END,
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"unclear": "clarify_node",
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},
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)
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workflow.add_conditional_edges(
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source="requirement_node",
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path=route_after_requirement,
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path_map={True: "clarify_node", False: END},
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)
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# 本轮只整理问题，用户补充后的恢复流程后续接入
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workflow.add_edge("clarify_node", END)
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return workflow.compile()
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# 创建全局图实例
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query_app = build_graph()
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if __name__ == "__main__":
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import asyncio
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import json
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from pydantic import BaseModel
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from app.clients.decision import close_decision_engine
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async def main():
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# 1. 修改请求，测试完整流程
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state: QueryState = {
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"request": {
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"prompt": "做个大屏",
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"screen": {
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"canvas": {"width": 1920, "height": 1080},
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"nodes": [
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{
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"id": "sales-chart",
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"type": "bar-chart",
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"name": "销售额统计",
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"layout": {"x": 26, "y": 34.8, "width": 400, "height": 260},
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"props": {"option": {"title": {"text": "销售额统计"}}},
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}
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],
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"dataSource": [],
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},
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"context": {"selected_node_ids": ["sales-chart"]},
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}
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}
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# 2. 执行图，程序退出时关闭共享连接
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try:
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final_state = await query_app.ainvoke(state)
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finally:
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await close_decision_engine()
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# 3. 打印完整 state
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print(
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json.dumps(
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{
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key: value.model_dump(mode="json", exclude_unset=True)
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if isinstance(value, BaseModel)
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else value
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for key, value in final_state.items()
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},
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ensure_ascii=False,
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indent=2,
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)
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)
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asyncio.run(main())
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@@ -0,0 +1,73 @@
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"""整理澄清问题，等待用户补充"""
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from langchain_core.messages import HumanMessage, SystemMessage
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from app.clients.llm import get_llm
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from app.processor.base import BaseNode
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from app.processor.state import QueryState
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from app.prompt.clarify import CLARIFY_SYSTEM_PROMPT
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from app.schemas.clarification import Clarification
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class ClarifyNode(BaseNode):
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name = "clarify_node"
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async def process(self, state: QueryState) -> QueryState:
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intent = state["intent"]
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print(intent)
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print("=================")
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if intent == "unclear":
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llm = get_llm(Clarification)
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clarification = await llm.ainvoke(
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[
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SystemMessage(content=CLARIFY_SYSTEM_PROMPT),
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HumanMessage(
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content=state["input_context"].model_dump_json(
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exclude_unset=True
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)
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),
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]
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)
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else:
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# 需求理解已经给出问题，直接复用
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clarification = Clarification(
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question=state["requirement"].clarification_question
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)
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return {**state, "clarification": clarification}
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if __name__ == "__main__":
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import asyncio
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import json
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from pydantic import BaseModel
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from app.clients.decision import close_decision_engine
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from app.processor.graph import query_app
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async def main():
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# 修改请求，通过完整流程测试澄清分支
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state: QueryState = {
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"request": {
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"prompt": "这个图不太对",
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"screen": {"canvas": {"width": 1920, "height": 1080}, "nodes": []},
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}
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}
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try:
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final_state = await query_app.ainvoke(state)
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finally:
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await close_decision_engine()
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print(
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json.dumps(
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{
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key: value.model_dump(mode="json")
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if isinstance(value, BaseModel)
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else value
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for key, value in final_state.items()
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},
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ensure_ascii=False,
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indent=2,
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)
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)
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asyncio.run(main())
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@@ -0,0 +1,149 @@
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"""意图识别节点"""
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from typing import cast
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from app.clients.decision import close_decision_engine, get_decision_engine
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from app.processor.base import BaseNode
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from app.processor.state import QueryState
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from app.prompt.intent import build_intent_question
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from app.schemas.base import JsonObject
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from app.schemas.decision import DecisionRequest
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from app.schemas.input_context import InputContext
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from app.schemas.requirement import Intent
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class IntentNode(BaseNode):
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name = "intent_node"
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@staticmethod
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def build_decision_state(context: InputContext) -> JsonObject:
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"""只整理判断意图需要的摘要，完整 Screen 仍保留在输入上下文"""
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nodes: list[JsonObject] = []
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for node in context.screen.nodes:
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option = node.props.get("option")
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title = option.get("title") if isinstance(option, dict) else None
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titles = title if isinstance(title, list) else [title]
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nodes.append(
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{
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"id": node.id,
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"type": node.type,
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"name": node.name,
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"titles": [
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item["text"]
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for item in titles
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if isinstance(item, dict) and isinstance(item.get("text"), str)
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],
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"layout": node.layout.model_dump(mode="json", exclude_unset=True),
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}
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)
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return {
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"prompt": context.prompt,
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"context": context.context.model_dump(mode="json", exclude_unset=True),
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"screen_summary": {
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"canvas": {
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"width": context.screen.canvas.width,
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"height": context.screen.canvas.height,
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},
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"nodes": nodes,
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"data_sources": [
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{
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key: source[key]
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for key in ("id", "name", "type")
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if key in source
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}
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for source in context.screen.dataSource
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],
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},
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"attachments": [
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{
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key: attachment[key]
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for key in ("id", "kind", "name")
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if key in attachment
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}
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for attachment in context.attachments
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],
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}
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async def process(self, state: QueryState) -> QueryState:
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context = state["input_context"]
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# 构建jev请求
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request = DecisionRequest(
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# 将 InputNode 的上下文整理为意图判断摘要
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state=self.build_decision_state(context),
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decisions={"intent": build_intent_question()},
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)
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# 调用 jev
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decision_engine = get_decision_engine()
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# 获取 jev 结果
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decision_res = await decision_engine.run(request)
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# 原生 unclear 或低可信时澄清，原始结果完整保留
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decision = decision_res.results["intent"]
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if (
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decision.choice == "unclear"
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or decision.confidence < self.config.intent_min_confidence
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):
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intent: Intent = "unclear"
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else:
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intent = cast(Intent, decision.choice)
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updated_state: QueryState = {
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**state,
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"intent_decision": decision_res,
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"intent": intent,
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}
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return updated_state
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if __name__ == "__main__":
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import asyncio
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import json
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from pydantic import BaseModel
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from app.processor.nodes.input_node import InputNode
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async def main():
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# 1. 修改这里的请求，测试不同意图
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state: QueryState = {
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"request": {
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"prompt": "这个图太乱了，帮我重新排一下",
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"screen": {
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"canvas": {"width": 1920, "height": 1080},
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"nodes": [
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{
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"id": "sales-chart",
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"type": "bar-chart",
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"name": "销售额统计",
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"layout": {"x": 26, "y": 34.8, "width": 400, "height": 260},
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"props": {"option": {"title": {"text": "销售额统计"}}},
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}
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],
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"dataSource": [],
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},
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"context": {"selected_node_ids": ["sales-chart"]},
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}
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}
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# 2. 先校验输入，再真实调用 Jev，退出时关闭 Jev 连接
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state = InputNode().process(state)
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node = IntentNode()
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try:
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final_state = await node.process(state)
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finally:
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await close_decision_engine()
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# 3. 打印完整 state，包含选项、概率、置信度和调用信息
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print(
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json.dumps(
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{
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key: value.model_dump(mode="json", exclude_unset=True)
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if isinstance(value, BaseModel)
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else value
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for key, value in final_state.items()
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},
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ensure_ascii=False,
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indent=2,
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)
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)
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asyncio.run(main())
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@@ -1,100 +1,63 @@
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"""意图识别节点"""
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"""沿用意图判断，提取具体需求"""
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from app.clients.decision import close_decision_engine, get_decision_engine
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import json
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from langchain_core.messages import HumanMessage, SystemMessage
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from app.clients.llm import get_llm
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from app.processor.base import BaseNode
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from app.processor.state import QueryState
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from app.prompt.requirement import build_intent_question
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from app.schemas.base import JsonObject
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from app.schemas.decision import DecisionRequest
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from app.schemas.input_context import InputContext
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from app.prompt.requirement import REQUIREMENT_SYSTEM_PROMPT
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from app.schemas.requirement import Requirement
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class RequirementNode(BaseNode):
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name = "requirement_node"
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@staticmethod
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def build_decision_state(context: InputContext) -> JsonObject:
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"""只整理判断意图需要的摘要，完整 Screen 仍保留在输入上下文"""
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nodes: list[JsonObject] = []
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for node in context.screen.nodes:
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option = node.props.get("option")
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title = option.get("title") if isinstance(option, dict) else None
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titles = title if isinstance(title, list) else [title]
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nodes.append(
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{
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"id": node.id,
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"type": node.type,
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"name": node.name,
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"titles": [
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item["text"]
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for item in titles
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if isinstance(item, dict) and isinstance(item.get("text"), str)
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],
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"layout": node.layout.model_dump(mode="json", exclude_unset=True),
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}
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)
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return {
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"prompt": context.prompt,
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"context": context.context.model_dump(mode="json", exclude_unset=True),
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"screen_summary": {
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"canvas": {
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"width": context.screen.canvas.width,
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"height": context.screen.canvas.height,
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},
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"nodes": nodes,
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"data_sources": [
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{
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key: source[key]
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for key in ("id", "name", "type")
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if key in source
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}
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for source in context.screen.dataSource
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],
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},
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"attachments": [
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{
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key: attachment[key]
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for key in ("id", "kind", "name")
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if key in attachment
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}
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for attachment in context.attachments
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],
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}
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async def process(self, state: QueryState) -> QueryState:
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context = state["input_context"]
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# 构建jev请求
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request = DecisionRequest(
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# 将 InputNode 的上下文整理为意图判断摘要
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state=self.build_decision_state(context),
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decisions={"intent": build_intent_question()},
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intent = state["intent"]
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payload = {
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"intent": intent,
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"input_context": context.model_dump(mode="json", exclude_unset=True),
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}
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llm = get_llm(Requirement)
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result = await llm.ainvoke(
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[
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SystemMessage(content=REQUIREMENT_SYSTEM_PROMPT),
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HumanMessage(content=json.dumps(payload, ensure_ascii=False)),
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]
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)
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# 调用 jev
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decision_engine = get_decision_engine()
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# 获取 jev 结果
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decision_res = await decision_engine.run(request)
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print(f"LLM 返回的需求: {result}")
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print("=" * 66)
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updated_state: QueryState = {
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**state,
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"intent_decision": decision_res,
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}
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return updated_state
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# 沿用门控后的意图，合并后重新校验字段一致性
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requirement = Requirement.model_validate(
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{**result.model_dump(), "intent": intent}
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)
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node_ids = {node.id for node in context.screen.nodes}
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for change in requirement.changes:
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if unknown_ids := set(change.target_ids) - node_ids:
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raise ValueError(f"需求引用了不存在的节点: {sorted(unknown_ids)}")
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return {**state, "requirement": requirement}
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if __name__ == "__main__":
|
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import asyncio
|
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import json
|
||||
|
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from pydantic import BaseModel
|
||||
|
||||
from app.clients.decision import close_decision_engine
|
||||
from app.processor.nodes.input_node import InputNode
|
||||
from app.processor.nodes.intent_node import IntentNode
|
||||
|
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async def main():
|
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# 1. 修改这里的请求，测试不同意图
|
||||
# 1. 修改这里的请求，测试具体需求提取
|
||||
state: QueryState = {
|
||||
"request": {
|
||||
"prompt": "这个图太乱了，帮我重新排一下",
|
||||
"prompt": "把销售额统计向右移动 50px，其他不变",
|
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"screen": {
|
||||
"canvas": {"width": 1920, "height": 1080},
|
||||
"nodes": [
|
||||
@@ -111,14 +74,16 @@ if __name__ == "__main__":
|
||||
"context": {"selected_node_ids": ["sales-chart"]},
|
||||
}
|
||||
}
|
||||
# 2. 先校验输入，再真实调用 Jev，退出时关闭 Jev 连接
|
||||
state = InputNode().process(state)
|
||||
node = RequirementNode()
|
||||
# 2. 依次执行输入、意图判断和需求理解
|
||||
try:
|
||||
state = InputNode().process(state)
|
||||
state = await IntentNode().process(state)
|
||||
node = RequirementNode()
|
||||
final_state = await node.process(state)
|
||||
finally:
|
||||
await close_decision_engine()
|
||||
# 3. 打印完整 state，包含选项、概率、置信度和调用信息
|
||||
|
||||
# 3. 打印完整 state
|
||||
print(
|
||||
json.dumps(
|
||||
{
|
||||
|
||||
@@ -3,9 +3,14 @@ from typing import NotRequired, TypedDict
|
||||
from app.schemas.base import JsonObject
|
||||
from app.schemas.input_context import InputContext
|
||||
from app.schemas.decision import DecisionResponse
|
||||
from app.schemas.requirement import Intent, Requirement
|
||||
from app.schemas.clarification import Clarification
|
||||
|
||||
|
||||
class QueryState(TypedDict):
|
||||
request: JsonObject
|
||||
input_context: NotRequired[InputContext]
|
||||
intent_decision: NotRequired[DecisionResponse]
|
||||
intent: NotRequired[Intent]
|
||||
requirement: NotRequired[Requirement]
|
||||
clarification: NotRequired[Clarification]
|
||||
|
||||
Reference in New Issue
Block a user