feat(langchain-chat): LangGraph 重写 agent 内核
主要变化: - 新增 agent_v2.py: 用 LangGraph create_react_agent + astream_events 替代原 agent_chat_test 的 LLM step-routing 死循环 - 新增 tools_v2.py: 闭包工厂模式，每个请求按 uuid 生成工具列表， 消除 toolinput 字符串拼 JSON 注入 uuid 的旧 hack - chat_test.py:266-346: 删 11 次 count_process 重试外层和事件 分发 spaghetti，换成 agent_run 单次调用 + 简单事件 dispatcher - policy_fun_iast.py:168-187: 修 broken <think> filter 老代码把 start_flag 设反了（看见 <think> 才开始 yield）导致 非 think 模型 yield 不出任何内容；改为正确跳过 <think>...</think> 块 模型函数调用通过 langchain_openai.ChatOpenAI（不能用旧版 langchain_community.chat_models.ChatOpenAI，没有现代 tool calling）。 依赖: langgraph==0.0.49 + langchain-core==0.1.53（已在服务器装好）。 非 stream 分支保留旧 agent_chat_test 路径（极少触发，回归风险低）。 旧版回滚: git checkout backup/pre-langgraph 实测对比: - 旧版 30-60s，答案 0 字（filter 卡死后展示 11 次重试） - 新版 25-40s，答案完整（含工具调用、参考文献、推荐问题、摘要） Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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@@ -264,63 +264,45 @@ async def chat_test(
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count_process = 0
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# await agent_chat_test(query=query, history=history, model_name=model_name,temperature=temperature,max_tokens=max_tokens,prompt_name="answer_question_history",think_content=res)
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if stream:
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while i<1:
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if count_process>10:
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# ============================================================
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# LangGraph 版 agent（v2）—— 替换原来 11 次外层重试 + LLM 路由
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# 旧代码见 git tag: checkpoint-pre-langgraph
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# ============================================================
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from server.chat.agent_v2 import agent_run
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# 初始化共享状态（工具内部仍用它写 source_docs）
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tip["END"] = ""
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tip["source_docs"] = []
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tip["num"] = 0
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tip["title"] = []
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utils.set_shared_variable(time_based_uuid, tip)
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async for response in agent_run(
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query=query,
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uuid=time_based_uuid,
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history=history,
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model_name=model_name,
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temperature=temperature,
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max_tokens=max_tokens,
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user_prompt_name=user_prompt_name,
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think_content=res["text"],
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):
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if not utils.get_shared_variable(time_based_uuid)["status"]:
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logging.info("\n==========STOPPED==========\n")
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break
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tip["END"]=""
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stop = ""
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temp = ""
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tip["source_docs"]=[]
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tip["num"]=0
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tip["title"]=[]
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# tip["status"] = True
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utils.set_shared_variable(time_based_uuid,tip)
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count = 0
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count_process += 1
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logging.info(f"\n\ncount_process:{count_process}\n\n")
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async for response in agent_chat_test(user_prompt_name = user_prompt_name,query=query,uuid=time_based_uuid, history=history, model_name=model_name,temperature=temperature,max_tokens=max_tokens,prompt_name="Think Test",think_content=res["text"]):
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# print("------------"+response)
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if not utils.get_shared_variable(time_based_uuid)["status"]:
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logging.info("\n==============================STOPPED==============================\n")
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break
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if "answer" in json.loads(response):
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# logging.info(f"answer:{json.loads(response)['answer']}")
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answer = json.loads(response)["answer"]
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history_summary+=answer
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stop = "1"
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yield json.dumps({"text": answer}, ensure_ascii=False)
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elif "tools" in json.loads(response):
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# print("tools:", json.loads(response)["tools"])
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tools.append(json.loads(response)["tools"])
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# yield json.dumps({"tools": tools}, ensure_ascii=False)
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elif "search_answer" in json.loads(response):
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search_answer = json.loads(response)["search_answer"]
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# history_summary+= search_answer
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yield json.dumps({"text": search_answer}, ensure_ascii=False)
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elif "docs" in json.loads(response):
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docs = json.loads(response)["docs"]
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elif "detail" in json.loads(response):
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docs_detail += json.loads(response)["detail"]
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elif "pic" in json.loads(response):
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# 获取图片路径
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image_name = json.loads(response)["pic"]
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image_name = f"\n\n\n\n"
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# yield json.dumps({"text": image_name}, ensure_ascii=False)
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else :
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#history_summary += json.loads(response)["final_answer"]
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yield json.dumps({"text": json.loads(response)["final_answer"]}, ensure_ascii=False)
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if stop == "":
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continue
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else:
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stop = ""
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temp1 = utils.get_shared_variable(time_based_uuid)
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temp1["END"]=""
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i+=1
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# if index3 == 0 and not "Action" in answer:
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# yield json.dumps({"text": answer}, ensure_ascii=False)
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yield json.dumps({"text":"\n"}, ensure_ascii=False)
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import importlib
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importlib.reload(prompt_config)
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msg = json.loads(response)
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if "answer" in msg:
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history_summary += msg["answer"]
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yield json.dumps({"text": msg["answer"]}, ensure_ascii=False)
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elif "think" in msg:
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yield json.dumps({"think": msg["think"]}, ensure_ascii=False)
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elif "docs" in msg:
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docs += msg["docs"]
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elif "detail" in msg:
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docs_detail += msg["detail"]
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yield json.dumps({"text": "\n"}, ensure_ascii=False)
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if not docs_detail.strip() == "" and uid and uid in prompt_config.detail_answer_uid:
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yield json.dumps({"text": f"\n\n"}, ensure_ascii=False)
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async for chunk in thinking_generator("正在进行幻觉校验，请稍等待..."):
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