feat 接入report

src/server/agent/agents.py

@@ -2,7 +2,7 @@ from pathlib import Path
 from typing import AsyncGenerator, Dict, Any
 from deepagents import create_deep_agent
 from deepagents.backends import FilesystemBackend
-from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage, AIMessage
+from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage, AIMessage, AIMessageChunk
 from langchain_core.runnables import RunnableConfig
 from langchain_qwq import ChatQwen
 
@@ -26,6 +26,7 @@ MAIN_DIR = Path(__file__).resolve().parent
 PROJECT_ROOT = MAIN_DIR
 
 model = ChatQwen(
+    enable_thinking=False,
     model="qwen3.5-flash",
     max_tokens=3_000,
     timeout=None,
@@ -48,6 +49,7 @@ research_agent = create_deep_agent(
 def get_model(config: RunnableConfig):
     temp = config["configurable"].get("llm_temperature", 0.5)
     return ChatQwen(
+        enable_thinking=False,
         model="qwen3.5-flash",
         max_tokens=3_000,
         timeout=None,
@@ -114,110 +116,69 @@ async def researcher_node(
         )]
     }
 
-    async for event in research_agent.astream_events(
+    async for chunk in research_agent.astream(
             {"messages": messages[-12:]},
-            version="v2",
-            config=config,
-            stream_subgraphs=True
+            config=config
     ):
-        event_type = event["event"]
-        name = event.get("name", "未知")
-
-        if event["event"] == "on_custom_event":
-            custom_data = event["data"]
-            # 你的 writer 发的是 dict，所以这里 custom_data 就是你写的 {"type": "report_delta", "delta": "..."}
-            if isinstance(custom_data, dict) and custom_data.get("type") == "report_delta":
-                delta = custom_data.get("delta", "")
-                print(delta, end="", flush=True)  # 实时打印，不换行
-
-        # ────────────── 工具结束事件：重点处理并 yield 输出 ──────────────
-        if event["event"] in {"on_tool_start", "on_tool_end"}:
-            tool_name = event.get("name", "未知")
-            is_start = event["event"] == "on_tool_start"
-
-            if is_start:
-                tool_input = event["data"].get("input", {})
-                current_step = f"正在執行工具：{tool_name}"
-                print(f"| {current_step} | {tool_input}")
-                yield {
-                    "messages": [AIMessage(
-                        content=full_content,
-                        name="Researcher",
-                        additional_kwargs={
-                            "current_step": current_step,
-                            "tool_name": tool_name,
-                            "tool_input": tool_input,
-                            "tool_status": "start",
-                            "streaming": True
-                        }
-                    )]
-                }
-            else:  # on_tool_end
-                tool_output = event["data"].get("output", "")
-                current_step = f"工具 {tool_name} 已完成"
-                print(f"| {current_step} | {tool_output}")
-                yield {
-                    "messages": [AIMessage(
-                        content=full_content,
-                        name="Researcher",
-                        additional_kwargs={
-                            "current_step": current_step,
-                            "tool_name": tool_name,
-                            "tool_output": tool_output,
-                            "tool_status": "end",
-                            "streaming": True
-                        }
-                    )]
-                }
-
-
-        # ────────────── LLM 内容生成（保持原有逻辑） ──────────────
-        elif event_type == "on_chat_model_stream":
-            chunk = event["data"]["chunk"].content or ""
-            if chunk:
-                print(chunk, end="", flush=True)
-                full_content += chunk
-                if "\n" in chunk or len(full_content) % 4 == 0:
-                    yield {
-                        "messages": [AIMessage(
-                            content=full_content,
-                            name="Researcher",
-                            additional_kwargs={
-                                "current_step": current_step,
-                                "streaming": True
-                            }
-                        )]
-                    }
-
-        # ────────────── 其他链路事件（可选补充） ──────────────
-        elif event_type in ("on_chain_start", "on_chain_end"):
-            status = "开始" if event_type == "on_chain_start" else "完成"
-            current_step = f"[{status}] {name.upper()}"
+        if "messages" in chunk and isinstance(chunk["messages"], AIMessageChunk):
             yield {
-                "messages": [AIMessage(
-                    content=full_content,
-                    name="Researcher",
-                    additional_kwargs={
-                        "current_step": current_step,
-                        "streaming": True
-                    }
-                )]
+                "messages": chunk["messages"],  # 逐 token 追加
+                # 可以額外 yield 一些 metadata，例如
+                # "node": "Researcher",
+                # "status": "thinking"
             }
-
-    # 最终输出
-    yield {
-        "messages": [AIMessage(
-            content=full_content.strip() or "报告生成完成",
-            name="Researcher",
-            additional_kwargs={
-                "current_step": "报告已完成",
-                "streaming": False
-            }
-        )],
-        "next": "Suggester"
-    }
+        else:
+            # 其他類型的 chunk
+            yield chunk
 
 
+#
+# async def researcher_node(
+#         state: AgentState,
+#         config: RunnableConfig
+# ) -> Dict[str, Any]:
+#     """
+#     薄節點：只判斷是否要跑深度報告，並準備初始訊息
+#     真正的 report 生成與 streaming 交給外層或子圖處理
+#     """
+#     use_report = config["configurable"].get("use_report", False)
+#
+#     if not use_report:
+#         return {
+#             "messages": [AIMessage(
+#                 content="深度報告功能未啟用，請通過前端按鈕觸發。",
+#                 name="Researcher"
+#             )],
+#             "next": "Supervisor"
+#         }
+#
+#     # 發送初始訊息，讓前端馬上看到「正在啟動」
+#     # initial_msg = AIMessage(
+#     #     content="正在啟動深度報告生成...",
+#     #     name="Researcher",
+#     #     additional_kwargs={
+#     #         "current_step": "正在啟動深度報告生成...",
+#     #         "streaming": True
+#     #     }
+#     # )
+#
+#     # 方式一：最簡單，直接把 research_agent 當作下一個要執行的東西
+#     # （假設 research_agent 已 compile 好，且支援 astream）
+#     # return {
+#     #     "messages": state["messages"] + [initial_msg],
+#     #     # 可以選擇加一個自訂 key 標記
+#     #     "report_in_progress": True,
+#     #     # next 留空或回 Supervisor，由 conditional edges 決定
+#     # }
+#
+#     # 方式二：如果你想更明確（推薦用 Send，未來好擴充）
+#     return Send(
+#         "research_sub_agent",  # 你要在 graph.add_node("research_sub_agent", research_agent)
+#         {
+#             "messages": state["messages"][-12:],
+#             "configurable": config["configurable"]
+#         }
+#     )
 # --- 3. Visualizer Agent (视觉专家) ---
 async def visualizer_node(state: AgentState, config: RunnableConfig):
     """负责将自然语言转化为绘图 Prompt 并调用绘图工具"""

src/server/agent/graph.py

@@ -2,6 +2,7 @@ from typing import Literal
 from langchain_core.messages import AIMessage
 from langchain_core.runnables import RunnableConfig
 from langchain_qwq import ChatQwen
+from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
 from langgraph.graph import StateGraph, END, START
 from pydantic import BaseModel
 from pymongo import MongoClient
@@ -91,6 +92,7 @@ client = MongoClient(MONGO_URI)
 checkpointer = MongoDBSaver(
     client=client["furniture_agent_db"],
     db_name="langgraph",
-    collection_name="checkpoints"
+    collection_name="checkpoints",
+    serde=JsonPlusSerializer(pickle_fallback=True),  # ← 關鍵這一行
 )
 app = workflow.compile(checkpointer=checkpointer)

src/server/agent/tools/report_generator_tool.py

@@ -3,6 +3,7 @@ import json
 import re
 from typing import Optional, List, Dict
 from langchain_qwq import ChatQwen
+from langgraph.config import get_stream_writer
 from pydantic import BaseModel, Field
 from langchain_core.tools import tool
 from langchain_core.messages import SystemMessage, HumanMessage
@@ -15,6 +16,7 @@ from src.core.config import settings
 
 
 llm = ChatQwen(
+    enable_thinking=False,
     model="qwen3.5-flash",
     temperature=0.2,
     max_tokens=3_000,
@@ -47,7 +49,7 @@ class ReportInput(BaseModel):
 # =========================
 
 @tool("report_generator", args_schema=ReportInput)
-async def report_generator(
+def report_generator(
         report_topic: str,
         structured_data: List[Dict],
         language: str = "English"
@@ -57,11 +59,11 @@ async def report_generator(
     directly from structured retrieval results.
     """
 
+    writer = get_stream_writer()
     if not structured_data:
-        return {
-            "status": "error",
-            "message": "No structured data provided."
-        }
+        error_msg = "Error: No structured data provided."
+        writer({"type": "report_error", "message": error_msg})
+        return error_msg
 
     collected_data_str = json.dumps(
         structured_data,
@@ -103,55 +105,40 @@ Input Data:
     # =========================
     # 调用 LLM
     # =========================
+    writer({"type": "report_start", "topic": report_topic, "language": language})
 
+    full_report = ""
     try:
-        response = await llm.ainvoke([
+        for chunk in llm.stream([
             SystemMessage(content=system_prompt),
             HumanMessage(content=user_prompt)
-        ])
-
-        report_content = response.content.strip()
-
-        # 清理 markdown block 包裹
-        report_content = (
-            report_content
-            .replace("```markdown", "")
-            .replace("```", "")
-            .strip()
-        )
-
+        ]):
+            if chunk.content:  # Gemini 返回的 chunk.content
+                delta = chunk.content
+                full_report += delta
+                writer({"type": "report_delta", "delta": delta})  # ← 实时推送给前端
     except Exception as e:
-        return {
-            "status": "error",
-            "message": f"LLM generation failed: {str(e)}"
-        }
+        error_msg = f"LLM generation failed: {str(e)}"
+        writer({"type": "report_error", "message": error_msg})
+        return error_msg
+
+    report_content = full_report.strip()
 
     # =========================
     # 保存报告
     # =========================
-
     output_dir = "workspace/reports"
     os.makedirs(output_dir, exist_ok=True)
 
-    safe_topic = re.sub(
-        r'[\\/*?:"<>|]',
-        "",
-        report_topic.replace(" ", "_")
-    )
-
+    safe_topic = re.sub(r'[\\/*?:"<>|]', "", report_topic.replace(" ", "_"))
     filename = f"{output_dir}/{safe_topic}.md"
 
     try:
         with open(filename, "w", encoding="utf-8") as f:
             f.write(report_content)
+        writer({"type": "report_complete", "file_path": filename})
     except Exception as e:
-        return {
-            "status": "error",
-            "message": f"Failed to save report: {str(e)}"
-        }
+        writer({"type": "report_save_warning", "message": str(e)})
 
-    return {
-        "status": "success",
-        "file_path": filename,
-        "message": "Report generated successfully."
-    }
+    # 返回完整内容（作为 tool result），同时正文已通过 delta 流式输出
+    return report_content + f"\n\n✅ Report saved to: {filename}"