bugfix：修复部分提示词导致的格式错误问题及langfuse客户端复用bug

app/observability/tracing.py

@@ -77,7 +77,8 @@ async def ainvoke_screen(
     from langfuse.langchain import CallbackHandler
     from langgraph.types import Command
 
-    handler = CallbackHandler()
+    # 明确回调所属项目，避免进程中存在多个客户端时跳过追踪
+    handler = CallbackHandler(public_key=os.getenv("LANGFUSE_PUBLIC_KEY"))
     # 在当前异步上下文执行回调，手动记录的 Jev generation 才会挂到所属节点。
     handler.run_inline = True
     resume = isinstance(payload, Command) and payload.resume is not None
@@ -156,16 +157,16 @@ def observe_decision(request: Any) -> Iterator[Any]:
     只有图回调已经挂上当前 span 时才记。没有父 span 就跳过，避免 Jev 单独变成一条 trace
     这里不吞异常：失败时标 ERROR 后原样抛出，调用方的 DecisionError 类型不变
     """
-    if not tracing_enabled() or _ensure_client() is None or not _has_active_span():
+    client = _ensure_client() if tracing_enabled() else None
+    if client is None or not _has_active_span():
         yield None
         return
 
-    from langfuse import get_client
-
     model = request.model or settings.typesafe_model
     # 意图判断用固定名字，方便以后按名字做评测。别的决策题不要都叫 classify-intent
     name = "classify-intent" if set(request.decisions) == {"intent"} else "run-decision"
-    with get_client().start_as_current_observation(
+    # 复用本应用已经选定的客户端
+    with client.start_as_current_observation(
         as_type="generation",
         name=name,
         model=model,

app/processor/graph.py

@@ -6,19 +6,19 @@ from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
 from langgraph.graph import END, StateGraph
 
+from app.processor.nodes.action_node import ActionNode, can_generate_actions
 from app.processor.nodes.clarify_node import ClarifyNode
+from app.processor.nodes.component_resource_node import ComponentResourceNode
+from app.processor.nodes.fallback_node import FallbackNode
 from app.processor.nodes.input_node import InputNode
 from app.processor.nodes.intent_node import IntentNode
-from app.processor.nodes.requirement_node import RequirementNode
-from app.processor.nodes.wait_for_user_node import WaitForUserNode
-from app.processor.nodes.qa_node import QANode
-from app.processor.nodes.scope_node import ScopeNode
-from app.processor.nodes.action_node import ActionNode, can_generate_actions
-from app.processor.nodes.validator_node import ValidatorNode
-from app.processor.nodes.reflection_node import ReflectionNode
-from app.processor.nodes.fallback_node import FallbackNode
 from app.processor.nodes.planner_node import PlannerNode
-from app.processor.nodes.component_resource_node import ComponentResourceNode
+from app.processor.nodes.qa_node import QANode
+from app.processor.nodes.reflection_node import ReflectionNode
+from app.processor.nodes.requirement_node import RequirementNode
+from app.processor.nodes.scope_node import ScopeNode
+from app.processor.nodes.validator_node import ValidatorNode
+from app.processor.nodes.wait_for_user_node import WaitForUserNode
 from app.processor.state import QueryState
 
 
@@ -207,6 +207,7 @@ query_app = build_graph()
 
 
 if __name__ == "__main__":
+    import argparse
     import asyncio
     import json
     from uuid import uuid4
@@ -217,11 +218,25 @@ if __name__ == "__main__":
     from app.clients.decision import close_decision_engine
     from app.observability.tracing import ainvoke_screen, flush_tracing
 
+    # 选择场景时只切换用户需求，共用同一份画布，方便比较执行结果
+    examples = {
+        "move": "把销售额统计向右移动 50px，其他内容保持不变",
+        "layout": (
+            "重新排列现有两个图表，销售额统计放在左侧，销售占比放在右侧，"
+            "统一尺寸为 600×400，画布改为 1600×900，不新增或删除组件"
+        ),
+        "add": "保留现有组件，在下方新增一个柱状图，使用物料示例数据，不需要真实数据绑定",
+        "clarify": "这个图不太对",
+    }
+    parser = argparse.ArgumentParser(description="运行大屏 Agent 完整流程示例")
+    parser.add_argument("--case", choices=examples, default="move", help="选择联调场景")
+    args = parser.parse_args()
+
     async def main():
-        # 1. 修改请求，测试完整流程
+        # 1. 创建请求，每次运行使用独立会话，避免混入之前的澄清历史
         state: QueryState = {
             "request": {
-                "prompt": "帮我修改销售额统计这个图，但具体改哪里我还没说，请先问我",
+                "prompt": examples[args.case],
                 "screen": {
                     "canvas": {"width": 1920, "height": 1080},
                     "nodes": [
@@ -231,7 +246,19 @@ if __name__ == "__main__":
                             "name": "销售额统计",
                             "layout": {"x": 26, "y": 34.8, "width": 400, "height": 260},
                             "props": {"option": {"title": {"text": "销售额统计"}}},
-                        }
+                        },
+                        {
+                            "id": "sales-share",
+                            "type": "pie-chart",
+                            "name": "销售占比",
+                            "layout": {
+                                "x": 500,
+                                "y": 34.8,
+                                "width": 400,
+                                "height": 260,
+                            },
+                            "props": {"option": {"title": {"text": "销售占比"}}},
+                        },
                     ],
                     "dataSource": [],
                 },
@@ -245,10 +272,15 @@ if __name__ == "__main__":
             state["request"].get("context", {}).get("conversation_id") or thread_id
         )
 
+        print(f"场景：{args.case}\n需求：{examples[args.case]}")
+        if args.case == "clarify":
+            print("澄清回答示例：把销售额统计向右移动 50px，其他内容保持不变")
+
         try:
             final_state = await ainvoke_screen(
                 query_app, state, thread_id=thread_id, session_id=session_id
             )
+            # 3. 展示真实澄清问题，用同一个 thread_id 恢复流程
             while final_state.get("__interrupt__"):
                 pending = final_state["__interrupt__"][0].value
                 print(pending["question"])
@@ -277,15 +309,24 @@ if __name__ == "__main__":
             await close_decision_engine()
             flush_tracing()
 
-        # 3. 打印完整 state
+        # 4. 聚焦最终结果，校验失败时 simulated_screen 应为 None
+        result = {
+            "status": final_state.get("status"),
+            "intent": final_state.get("intent"),
+            # 保留需求与规划，方便定位模型输出和执行能力之间的不一致
+            "requirement": final_state.get("requirement"),
+            "screen_plan": final_state.get("screen_plan"),
+            "validation_passed": final_state.get("validation_passed"),
+            "retry_count": final_state.get("retry_count", 0),
+            "failure_reason": final_state.get("failure_reason"),
+            "validation_errors": final_state.get("validation_errors", []),
+            "actions": final_state.get("actions", []),
+            "simulated_screen": final_state.get("simulated_screen"),
+            "answer": final_state.get("answer"),
+        }
         print(
             json.dumps(
-                {
+                result,
-                    key: value.model_dump(mode="json", exclude_unset=True)
-                    if isinstance(value, BaseModel)
-                    else value
-                    for key, value in final_state.items()
-                },
                 ensure_ascii=False,
                 indent=2,
                 default=lambda value: (
@@ -295,5 +336,8 @@ if __name__ == "__main__":
                 ),
             )
         )
+        # 原始坐标单独展示，确认后端试执行没有修改请求中的画布
+        original = state["request"]["screen"]["nodes"][0]["layout"]
+        print(f"原始销售额坐标：{original['x']}，{original['y']}")
 
     asyncio.run(main())

app/processor/nodes/clarify_node.py

@@ -2,7 +2,9 @@
 
 import json
 
+from langchain_core.exceptions import OutputParserException
 from langchain_core.messages import HumanMessage, SystemMessage
+from pydantic import ValidationError
 
 from app.clients.llm import get_llm
 from app.processor.base import BaseNode
@@ -28,14 +30,39 @@ class ClarifyNode(BaseNode):
             payload["requirement"] = state["requirement"].model_dump(mode="json")
 
         llm_client = get_llm(Clarification)
-        clarification = await llm_client.ainvoke(
+        messages = [
-            [
             SystemMessage(content=CLARIFY_SYSTEM_PROMPT),
             HumanMessage(content=json.dumps(payload, ensure_ascii=False)),
         ]
+
+        # 格式错误重试一次
+        for attempt in range(2):
+            try:
+                clarification = await llm_client.ainvoke(messages)
+                break
+            except (ValidationError, OutputParserException):
+                if attempt == 0:
+                    # 要求重新生成完整问题，避免截断选项改变原意
+                    messages.append(
+                        HumanMessage(
+                            content=(
+                                "上次输出未通过 Clarification 格式校验，请重新生成完整结果："
+                                "question 为非空问题，options 为空或含 2–3 项，"
+                                "每项包含唯一 id、非空 label 和 description，最多一个 recommended=true"
                             )
+                        )
+                    )
+                    continue
+                # 连续格式失败时退回自由回答，需求阶段沿用已确定的问题
+                requirement = state.get("requirement") if intent != "unclear" else None
+                question = (
+                    requirement.clarification_question if requirement else None
+                ) or "你希望我对当前大屏做什么？可以描述想修改的内容，或想了解的问题"
+                clarification = Clarification(question=question)
 
         updated_state["clarification"] = clarification
+        # 在进入 interrupt 前保存等待状态，暂停中的节点尚不会返回更新
+        updated_state["status"] = "waiting_for_input"
         return updated_state
 
 

app/processor/nodes/input_node.py

@@ -68,6 +68,8 @@ class InputNode(BaseNode):
         updated_state["retry_count"] = 0
         updated_state["repair_plan"] = None
         updated_state["failure_reason"] = None
+        # 首次请求进入处理状态，恢复澄清时也使用同一状态
+        updated_state["status"] = "running"
         return updated_state
 
 

app/processor/nodes/qa_node.py

@@ -28,6 +28,8 @@ class QANode(BaseNode):
         if not answer:
             raise ValueError("问答模型未返回文本回答")
         updated_state["answer"] = answer
+        # 回答生成完成，问答分支在此结束
+        updated_state["status"] = "completed"
         return updated_state
 
 

app/processor/nodes/validator_node.py

@@ -15,6 +15,8 @@ class ValidatorNode(BaseNode):
 
     def process(self, state: QueryState) -> QueryState:
         updated_state: QueryState = state.copy()
+        # 校验失败仍可能进入修正循环，最终失败由 Fallback 标记
+        updated_state["status"] = "running"
         actions = state["actions"]
         try:
             # 重新校验模型内容，避免赋值后绕过字段校验
@@ -130,6 +132,8 @@ class ValidatorNode(BaseNode):
         updated_state["validation_errors"] = []
         # 保存整批动作执行后的 JSON，供后续检查，不代表前端已经完成更新
         updated_state["simulated_screen"] = screen
+        # 仅表示后端动作已通过校验，前端执行结果需另行反馈
+        updated_state["status"] = "completed"
         return updated_state
 
 

app/processor/state.py

@@ -49,5 +49,5 @@ class QueryState(TypedDict):
     ]  # 待合并的用户回答或取消指令
     answer: NotRequired[str | None]  # 问答节点生成的回答或建议
     status: NotRequired[
-        Literal["running", "cancelled", "failed"]
+        Literal["running", "waiting_for_input", "completed", "cancelled", "failed"]
-    ]  # 流程继续、用户取消或动作修正失败
+    ]  # 后端处理、等待补充、处理完成、用户取消或失败，完成不代表前端已执行

app/prompt/clarify.py

@@ -9,7 +9,7 @@ CLARIFY_SYSTEM_PROMPT = """## 任务
 结合 clarification_history，不重复询问已回答的信息
 
 ## 回答方式
-适合选择时提供互斥选项，简短说明各选项的影响，有依据才推荐
+适合选择时提供 2–3 个互斥选项，简短说明各选项的影响，有依据才推荐
 需要具体数值或自由描述时，options 留空；自由输入由客户端提供，不生成“其他”选项
 用户没有思路时，可提供先分析并给建议的选项，不将其视为授权修改
 

app/prompt/intent.py

@@ -8,7 +8,9 @@ def build_intent_question() -> ChoiceDecision:
         # 区分意图模糊与修改细节缺失，后者交给需求理解
         instructions=(
             "Classify the user's primary intent. "
-            "Read `prompt` together with `clarification_history`; later answers clarify or correct earlier requests. "
+            "Classify the latest user answer in `clarification_history`, or `prompt` when there is no history. "
+            "Use earlier turns and the selected option to resolve references; the latest answer overrides earlier conflicting intent. "
+            "Once the requested action or response is clear, earlier ambiguity does not require another confirmation. "
             "Choose modify_screen only when the user explicitly requests an actual edit. "
             "Choose qa for clear requests for a textual answer or general conversation, including information, explanation, diagnosis, advice, and assistant identity or capabilities. "
             "Requests need not be phrased as questions; being unrelated to screens does not make an intent unclear. "
@@ -24,6 +26,6 @@ def build_intent_question() -> ChoiceDecision:
             # 明确的文字回答或交流请求，包括助手身份和能力，但没有要求实际操作大屏
             "qa": "Clearly request a textual answer or general conversation without requesting screen creation or an actual edit; includes assistant identity and capabilities, information, reasons, diagnosis, and suggestions, e.g. 这个图为什么没有数据 or 这个图怎么优化",
             # 语义上没有明确表达要问什么或做什么，不等同于模型分数低
-            "unclear": "The user's desired response or action cannot be determined from the request and clarification history, so clarification is needed to decide what to do; includes vague feedback, references, or an unspecified request to look, e.g. 这个图不太对, 这个有点问题, 帮我看看这个",
+            "unclear": "The user's desired response or action is still undetermined after incorporating the latest answer and selected option; vague feedback such as 这个图不太对 is unclear only while subsequent answers have not resolved what to do",
         },
     )