从“ollama安装模型失败“到“显卡驱动升级“记录

1、定义问题我在执行ollama模型安装时失败了报错如下D:\Ollma\ollmaollama run deepseek-r1:1.5b pulling manifest pulling aabd4debf0c8: 100% ▕██████████████████████████████████████████████████████████▏ 1.1 GB pulling c5ad996bda6e: 100% ▕██████████████████████████████████████████████████████████▏ 556 B pulling 6e4c38e1172f: 100% ▕██████████████████████████████████████████████████████████▏ 1.1 KB pulling f4d24e9138dd: 100% ▕██████████████████████████████████████████████████████████▏ 148 B pulling a85fe2a2e58e: 100% ▕██████████████████████████████████████████████████████████▏ 487 B verifying sha256 digest writing manifest success Error: 500 Internal Server Error: llama-server process has terminated: exit status 0xc0000409: The system detected an overrun of a stack-based buffer in this application. This overrun could potentially allow a malicious user to gain control of this application.: CUDA error: the provided PTX was compiled with an unsupported toolchain.------------------报错了请解决这个报错主要是由于你的NVIDIA 显卡驱动版本与 Ollama 运行 DeepSeek-R1通常使用 CUDA 加速所需要的CUDA 工具链版本不匹配导致的。具体原因在于这一句CUDA error: the provided PTX was compiled with an unsupported toolchainCUDA 错误提供的 PTX 文件是由不支持的工具链编译的。同时引发了栈溢出错误0xc0000409导致llama-server进程直接崩溃。我查看了我的显卡信息如下所示根据图片可知显卡型号是NVIDIA GeForce MX230专用显存为 4.0 GB当前的驱动程序版本为32.0.15.5612即英伟达官方的 556.12 版本发布日期是 2024 年 6 月。针对“是否支持升级”我们需要从硬件和驱动软件两个维度来回答1. 硬件升级不支持原因NVIDIA GeForce MX230 是一款笔记本电脑上的入门级独立显卡。这类显卡芯片是直接焊接在笔记本主板上的BGA 封装无法像台式机那样拔下来更换成更新、更强的显卡。2. 驱动升级支持但有局限性支持升级你的驱动是 2024 运作的版本英伟达官方目前依然会为 Pascal 架构包括 MX230提供安全更新驱动例如 582.xx 或更高版本。你可以前往 NVIDIA 官网下载最新的笔记本显卡驱动进行安装。局限性大模型兼容问题MX230 属于 2019 年发布的旧架构。英伟达早已停止了对它的 “Game Ready” 性能优化仅提供基础维护。你之前遇到的 Ollama 报错PTX was compiled with an unsupported toolchain正是因为 Ollama 新版本使用的 CUDA 工具链较新而老旧显卡架构在编译指令集上产生了断层。这意味着即使你把驱动升到最新新版 Ollama 也有可能因硬件架构过老而拒绝提供 GPU 加速。给我的最佳建议是不用GPU直接使用CPU运行。方法如下右键点击任务栏右下角的 Ollama 图标选择Quit Ollama彻底退出。打开 Windows系统环境变量设置在 Windows 搜索栏输入“环境变量”选择“编辑系统环境变量”。点击“环境变量”按钮。在“用户变量”或“系统变量”中点击“新建”变量名OLLAMA_NUM_PARALLEL可选设置为 1或者更直接的禁用GPU新建变量名CUDA_VISIBLE_DEVICES变量值设为-1。重新启动 Ollama 软件然后再运行ollama run deepseek-r1:1.5b。2、NVIDIA驱动更新但我想先尝试去官网碰碰运气更新一下驱动操作如下1、打开 NVIDIA 驱动下载官网在下拉菜单中精准选择产品类型 (Product Type)GeForce产品系列 (Product Series)GeForce MX200 Series (Notebooks)产品 (Product)GeForce MX230操作系统 (Operating System)根据你的电脑选择Windows 10 64-bit或Windows 11下载类型 (Download Type)选择Game Ready 驱动程序 (GRD)如果没有就选默认的所有或Studio语言 (Language)Chinese (Simplified)(简体中文)2、点击右侧的View按钮进入下载页面并把安装包下载到本地。3、运行安装程序记得在安装过程中选择“自定义高级”并勾选“执行清洁安装”。为什么这一步很重要清洁安装会彻底卸载你原有的旧驱动并清除残留的 CUDA PTX 缓存这能最大概率解决报错里提到的llama-server栈溢出和工具链断层问题。4、安装完成后重启电脑然后再次去命令行执行ollama run deepseek-r1:1.5b。安装过程中弹窗这个路径怎么选 这其实不是真正的安装路径而是安装包的临时解压路径直接点击 “OK” 即可。这个临时文件夹里的内容通常会自动被系统或安装程序清理掉不会长期占用你的 C 盘空间。这两个选项怎么选鼠标点击勾选第二个NVIDIA 图形驱动程序。点击右下角的“同意并继续(A)”按钮。为什么这么选第一个选项NVIDIA 显卡驱动程序和 NVIDIA App会额外安装一个 NVIDIA App用于游戏录屏、优化、串流等功能。这个软件常驻后台不仅会占用内存和 CPU 资源对于你目前运行大模型或进行日常科研、运维任务来说没有任何帮助反而属于“捆绑垃圾”。第二个选项NVIDIA 图形驱动程序只安装核心的纯驱动包含大模型所需的 CUDA 核心组件。这样安装最干净、最省资源。鼠标点击勾选自定义(C)高级。点击右下角的“下一步(N)”按钮。重要提示点击下一步后会弹出一个组件列表窗口。请在那个窗口的下方寻找一个名为“执行清洁安装” (Perform a clean installation)的复选框务必把它勾选上然后再点击继续安装。组件列表保持默认即可图形驱动程序是灰色强制勾选的。NVIDIA App保持不要勾选为空。PhysX 系统软件保持勾选即可。核心一步请务必用鼠标勾选下方的执行清洁安装(P)把那个空白的正方形方框点亮。勾选完“执行清洁安装”之后直接点击右下角的“下一步(N)”按钮。接下来系统就会自动开始卸载老驱动并安装全新的582.66驱动安装过程中屏幕可能会闪烁几次这属于正常现象。安装完成后建议重启一下电脑但是我没有重启电脑我想直接在命令行里输入ollama run deepseek-r1:1.5b碰碰运气最后发现直接成功了可能会出现的两种结果直接成功Windows 10/11 的新版驱动架构很多时候支持热加载如果不重启就能跑通那就最省事了。依然报错或找不到 GPU因为没有重启Ollama 后台服务可能还在沿用旧驱动的缓存或者系统还没完全识别到新驱动的 CUDA 环境。新驱动已经完美识别并生效了驱动程序版本成功更新为了32.0.15.8266即官方的 582.66。驱动程序日期也变成了2026/6/9。这意味着你的系统已经彻底加载了新驱动确实不需要重启也能看到更新结果。3、全局代理惹的祸我重启了并在cmd终端和ollama客户端都成功运行ollama本地模型deepseek-r1:1.5b但是在python代码中运行还是报错代码如下from langchain_ollama import ChatOllama model ChatOllama( modeldeepseek-r1:1.5b, base_urlhttp://localhost:11434 # 如果Ollama在本地默认端口运行则可省略或使用http://localhost:11434 ) print(model.invoke(介绍一下你自己))报错如下一切成功了接下来啊帮我分析以下报错信息{ name: ResponseError, message: (status code: 502), stack: \u001b[31m---------------------------------------------------------------------------\u001b[39m\n\u001b[31mResponseError\u001b[39m Traceback (most recent call last)\n\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m 4\u001b[39m model\u001b[33m\deepseek-r1:1.5b\\u001b[39m,\n\u001b[32m 5\u001b[39m base_url\u001b[33m\http://localhost:11434\\u001b[39m \u001b[38;5;66;03m# 如果Ollama在本地默认端口运行则可省略或使用http://localhost:11434\u001b[39;00m\n\u001b[32m 6\u001b[39m )\n\u001b[32m 7\u001b[39m \n\u001b[32m---- \u001b[39m\u001b[32m8\u001b[39m print(model.invoke(\u001b[33m\介绍一下你自己\\u001b[39m))\n\n\u001b[36mFile \u001b[39m\u001b[32md:\\python\\Anaconda\\envs\\langchain1.2\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:402\u001b[39m, in \u001b[36mBaseChatModel.invoke\u001b[39m\u001b[34m(self, input, config, stop, **kwargs)\u001b[39m\n\u001b[32m 388\u001b[39m \u001b[38;5;129moverride\u001b[39m\n\u001b[32m 389\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minvoke\u001b[39m(\n\u001b[32m 390\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 395\u001b[39m **kwargs: Any,\n\u001b[32m 396\u001b[39m ) - AIMessage:\n\u001b[32m 397\u001b[39m config ensure_config(config)\n\u001b[32m 398\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[32m 399\u001b[39m \u001b[33m\\u001b[39m\u001b[33mAIMessage\u001b[39m\u001b[33m\\u001b[39m,\n\u001b[32m 400\u001b[39m cast(\n\u001b[32m 401\u001b[39m \u001b[33m\\u001b[39m\u001b[33mChatGeneration\u001b[39m\u001b[33m\\u001b[39m,\n\u001b[32m-- \u001b[39m\u001b[32m402\u001b[39m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mgenerate_prompt\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 403\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_convert_input\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43minput\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 404\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mstop\u001b[39;49m\u001b[30;43m\u001b[39;49m\u001b[30;43mstop\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 405\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mcallbacks\u001b[39;49m\u001b[30;43m\u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43mcallbacks\u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 406\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtags\u001b[39;49m\u001b[30;43m\u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43mtags\u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 407\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mmetadata\u001b[39;49m\u001b[30;43m\u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43mmetadata\u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 408\u001b[39m \u001b[30;43m 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\u001b[39;49m\u001b[30;43m)\u001b[39;49m.generations[\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m],\n\u001b[32m 412\u001b[39m ).message,\n\u001b[32m 413\u001b[39m )\n\n\u001b[36mFile \u001b[39m\u001b[32md:\\python\\Anaconda\\envs\\langchain1.2\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:1123\u001b[39m, in \u001b[36mBaseChatModel.generate_prompt\u001b[39m\u001b[34m(self, prompts, stop, callbacks, **kwargs)\u001b[39m\n\u001b[32m 1114\u001b[39m \u001b[38;5;129moverride\u001b[39m\n\u001b[32m 1115\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mgenerate_prompt\u001b[39m(\n\u001b[32m 1116\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1120\u001b[39m **kwargs: Any,\n\u001b[32m 1121\u001b[39m ) - LLMResult:\n\u001b[32m 1122\u001b[39m prompt_messages [p.to_messages() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[32m- \u001b[39m\u001b[32m1123\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mgenerate\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mprompt_messages\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mstop\u001b[39;49m\u001b[30;43m\u001b[39;49m\u001b[30;43mstop\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mcallbacks\u001b[39;49m\u001b[30;43m\u001b[39;49m\u001b[30;43mcallbacks\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\n\u001b[36mFile \u001b[39m\u001b[32md:\\python\\Anaconda\\envs\\langchain1.2\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:933\u001b[39m, in \u001b[36mBaseChatModel.generate\u001b[39m\u001b[34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[39m\n\u001b[32m 930\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(input_messages):\n\u001b[32m 931\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 932\u001b[39m results.append(\n\u001b[32m-- \u001b[39m\u001b[32m933\u001b[39m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_generate_with_cache\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 934\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mm\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 935\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mstop\u001b[39;49m\u001b[30;43m\u001b[39;49m\u001b[30;43mstop\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 936\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mrun_manager\u001b[39;49m\u001b[30;43m\u001b[39;49m\u001b[30;43mrun_managers\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43mi\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mrun_managers\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01melse\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 937\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 938\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 939\u001b[39m )\n\u001b[32m 940\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 941\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n\n\u001b[36mFile \u001b[39m\u001b[32md:\\python\\Anaconda\\envs\\langchain1.2\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:1235\u001b[39m, in \u001b[36mBaseChatModel._generate_with_cache\u001b[39m\u001b[34m(self, messages, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m 1233\u001b[39m result generate_from_stream(\u001b[38;5;28miter\u001b[39m(chunks))\n\u001b[32m 1234\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m inspect.signature(\u001b[38;5;28mself\u001b[39m._generate).parameters.get(\u001b[33m\\u001b[39m\u001b[33mrun_manager\u001b[39m\u001b[33m\\u001b[39m):\n\u001b[32m- \u001b[39m\u001b[32m1235\u001b[39m result \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_generate\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 1236\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mmessages\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mstop\u001b[39;49m\u001b[30;43m\u001b[39;49m\u001b[30;43mstop\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mrun_manager\u001b[39;49m\u001b[30;43m\u001b[39;49m\u001b[30;43mrun_manager\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\n\u001b[32m 1237\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1238\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1239\u001b[39m result \u001b[38;5;28mself\u001b[39m._generate(messages, stopstop, **kwargs)\n\n\u001b[36mFile \u001b[39m\u001b[32md:\\python\\Anaconda\\envs\\langchain1.2\\Lib\\site-packages\\langchain_ollama\\chat_models.py:1030\u001b[39m, in \u001b[36mChatOllama._generate\u001b[39m\u001b[34m(self, messages, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m 1023\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_generate\u001b[39m(\n\u001b[32m 1024\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 1025\u001b[39m messages: \u001b[38;5;28mlist\u001b[39m[BaseMessage],\n\u001b[32m (...)\u001b[39m\u001b[32m 1028\u001b[39m **kwargs: Any,\n\u001b[32m 1029\u001b[39m ) - ChatResult:\n\u001b[32m- \u001b[39m\u001b[32m1030\u001b[39m final_chunk \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_chat_stream_with_aggregation\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 1031\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mmessages\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mstop\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mrun_manager\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mverbose\u001b[39;49m\u001b[30;43m\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mverbose\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\n\u001b[32m 1032\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1033\u001b[39m generation_info final_chunk.generation_info\n\u001b[32m 1034\u001b[39m chat_generation ChatGeneration(\n\u001b[32m 1035\u001b[39m messageAIMessage(\n\u001b[32m 1036\u001b[39m contentfinal_chunk.text,\n\u001b[32m (...)\u001b[39m\u001b[32m 1043\u001b[39m generation_infogeneration_info,\n\u001b[32m 1044\u001b[39m )\n\n\u001b[36mFile \u001b[39m\u001b[32md:\\python\\Anaconda\\envs\\langchain1.2\\Lib\\site-packages\\langchain_ollama\\chat_models.py:965\u001b[39m, in \u001b[36mChatOllama._chat_stream_with_aggregation\u001b[39m\u001b[34m(self, messages, stop, run_manager, verbose, **kwargs)\u001b[39m\n\u001b[32m 956\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_chat_stream_with_aggregation\u001b[39m(\n\u001b[32m 957\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 958\u001b[39m messages: \u001b[38;5;28mlist\u001b[39m[BaseMessage],\n\u001b[32m (...)\u001b[39m\u001b[32m 962\u001b[39m **kwargs: Any,\n\u001b[32m 963\u001b[39m ) - ChatGenerationChunk:\n\u001b[32m 964\u001b[39m final_chunk \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-- \u001b[39m\u001b[32m965\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;01mfor\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mchunk\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01min\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_iterate_over_stream\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mmessages\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mstop\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m:\u001b[39;49m\n\u001b[32m 966\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mfinal_chunk\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mis\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m:\u001b[39;49m\n\u001b[32m 967\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mfinal_chunk\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mchunk\u001b[39;49m\n\n\u001b[36mFile \u001b[39m\u001b[32md:\\python\\Anaconda\\envs\\langchain1.2\\Lib\\site-packages\\langchain_ollama\\chat_models.py:1054\u001b[39m, in \u001b[36mChatOllama._iterate_over_stream\u001b[39m\u001b[34m(self, messages, stop, **kwargs)\u001b[39m\n\u001b[32m 1047\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_iterate_over_stream\u001b[39m(\n\u001b[32m 1048\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 1049\u001b[39m messages: \u001b[38;5;28mlist\u001b[39m[BaseMessage],\n\u001b[32m 1050\u001b[39m stop: \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m] | \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 1051\u001b[39m **kwargs: Any,\n\u001b[32m 1052\u001b[39m ) - Iterator[ChatGenerationChunk]:\n\u001b[32m 1053\u001b[39m reasoning kwargs.get(\u001b[33m\\u001b[39m\u001b[33mreasoning\u001b[39m\u001b[33m\\u001b[39m, \u001b[38;5;28mself\u001b[39m.reasoning)\n\u001b[32m- \u001b[39m\u001b[32m1054\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;01mfor\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mstream_resp\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01min\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_create_chat_stream\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mmessages\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mstop\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m:\u001b[39;49m\n\u001b[32m 1055\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mnot\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43misinstance\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mstream_resp\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mstr\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m:\u001b[39;49m\n\u001b[32m 1056\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mcontent\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 1057\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mstream_resp\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43mmessage\u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43mcontent\u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43m]\u001b[39;49m\n\u001b[32m 1058\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43mmessage\u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01min\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mstream_resp\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mand\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43mcontent\u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01min\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mstream_resp\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43mmessage\u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43m]\u001b[39;49m\n\u001b[32m 1059\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43;01melse\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43m\\u001b[39;49m\u001b[30;43m\\u001b[39;49m\n\u001b[32m 1060\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\n\u001b[36mFile \u001b[39m\u001b[32md:\\python\\Anaconda\\envs\\langchain1.2\\Lib\\site-packages\\langchain_ollama\\chat_models.py:952\u001b[39m, in \u001b[36mChatOllama._create_chat_stream\u001b[39m\u001b[34m(self, messages, stop, **kwargs)\u001b[39m\n\u001b[32m 950\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chat_params[\u001b[33m\\u001b[39m\u001b[33mstream\u001b[39m\u001b[33m\\u001b[39m]:\n\u001b[32m 951\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._client:\n\u001b[32m-- \u001b[39m\u001b[32m952\u001b[39m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m._client.chat(**chat_params)\n\u001b[32m 953\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._client:\n\u001b[32m 954\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28mself\u001b[39m._client.chat(**chat_params)\n\n\u001b[36mFile \u001b[39m\u001b[32md:\\python\\Anaconda\\envs\\langchain1.2\\Lib\\site-packages\\ollama\\_client.py:189\u001b[39m, in \u001b[36mClient._request.locals.inner\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 187\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m httpx.HTTPStatusError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 188\u001b[39m e.response.read()\n\u001b[32m-- \u001b[39m\u001b[32m189\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m ResponseError(e.response.text, e.response.status_code) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 191\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m r.iter_lines():\n\u001b[32m 192\u001b[39m part json.loads(line)\n\n\u001b[31mResponseError\u001b[39m: (status code: 502) }报错原因在终端cmd可以成功运行本地模型但通过 Python 代码LangChain / Ollama SDK调用时返回502错误Bad Gateway这通常说明请求虽然发送出去了但在中途遭遇了阻断或错误的路由。最常见的原因是你的电脑上开启了全局代理软件。Python 的网络库如httpx,requests默认会读取系统的环境变量http_proxy和https_proxy。当代码尝试连接localhost或127.0.0.1时请求被错误地发送到了代理服务器而代理服务器无法处理本地网络请求从而返回了502错误。将全局模式改成规则模式后再次运行代码即可解决