Langchain-Chatchat-Ubuntu服务器本地安装摆设条记

 Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM 等语言模型的本地知识库问答 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM) QA app with langchain。
        开源网址：https://github.com/chatchat-space/Langchain-Chatchat
​        因为这是自己毕设项目所需，使用假造机实验一下是否能成功摆设。项目参考：Langchain-Chatchat-win10本地安装摆设成功条记（CPU）_file "d:\ai\virtual-digital-human\langchain-chatch-CSDN博客
其中有些是自己遇到的坑也会在这里说一下。

一、实验环境

可以查看目前使用的系统版本信息。
  

1. cat /proc/version

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1. Linux version 5.15.133.1-microsoft-standard-WSL2 (root@1c602f52c2e4) (gcc (GCC) 11.2.0, GNU ld (GNU Binutils) 2.37) #1 SMP Thu Oct 5 21:02:42 UTC 2023

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如果安装有显卡驱动，可以使用下面的代码来查看显卡信息。
  

1. nvidia-smi  #查看显卡信息
3. Sat Mar  9 19:31:33 2024
4. +-----------------------------------------------------------------------------------------+
5. | NVIDIA-SMI 550.40.06              Driver Version: 551.23         CUDA Version: 12.4     |
6. |-----------------------------------------+------------------------+----------------------+
7. | GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
8. | Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
9. |                                         |                        |               MIG M. |
10. |=========================================+========================+======================|
11. |   0  NVIDIA GeForce RTX 3090        On  |   00000000:AF:00.0 Off |                  N/A |
12. | 32%   25C    P8              6W /  350W |     134MiB /  24576MiB |      0%      Default |
13. |                                         |                        |                  N/A |
14. +-----------------------------------------+------------------------+----------------------+
15. |   1  NVIDIA GeForce RTX 3090        On  |   00000000:D8:00.0 Off |                  N/A |
16. | 32%   24C    P8             11W /  350W |     144MiB /  24576MiB |      0%      Default |
17. |                                         |                        |                  N/A |
18. +-----------------------------------------+------------------------+----------------------+
19.                                                                                    
20. +-----------------------------------------------------------------------------------------+
21. | Processes:                                                                              |
22. |  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
23. |        ID   ID                                                               Usage      |
24. |=========================================================================================|
25. |    0   N/A  N/A       227      G   /Xwayland                                   N/A      |
26. |    1   N/A  N/A       227      G   /Xwayland                                   N/A      |
27. +-----------------------------------------------------------------------------------------+

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二、安装步骤

1、安装 Anaconda软件，用于管理python假造环境

自己使用的是清华镜像：Index of /anaconda/archive/ | 清华大学开源软件镜像站 | Tsinghua Open Source Mirror
wget下载下令如下：
  

1.  wget -c 'https://repo.anaconda.com/archive/Anaconda3-2023.09-0-Linux-x86_64.sh' -P <下载到的文件的位置>

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 2、创建python运行假造环境

​
创建 conda 环境

1. conda create -n chatchat python=3.11.7

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可以通过 conda info --envs 查抄环境是否创建完成。
  

1. # conda environments:
2. #
3. base                     /home/david/anaconda3
4. NAD                      /home/david/anaconda3/envs/NAD
5. chat                     /home/david/anaconda3/envs/chat
6. chat_demo                /home/david/anaconda3/envs/chat_demo

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进入已经创建好的假造环境：conda activate 环境名称 或 source activate 环境名称
  

1. $ conda activate chat_demo
2. (chat_demo) $ python --version
3. Python 3.11.7

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3、安装pytorch

  

1. ~$ pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 -i https://pypi.tuna.tsinghua.edu.cn/simple

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1. Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
2. Collecting torch
3.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/2c/df/5810707da6f2fd4be57f0cc417987c0fa16a2eecf0b1b71f82ea555dc619/torch-2.2.1-cp311-cp311-manylinux1_x86_64.whl (755.6 MB)
4.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 755.6/755.6 MB 2.4 MB/s eta 0:00:00
5. Collecting torchvision
6.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/3a/49/12fc5188602c68a789a0fdaee63d176a71ad5c1e34d25aeb8554abe46089/torchvision-0.17.1-cp311-cp311-manylinux1_x86_64.whl (6.9 MB)
7.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.9/6.9 MB 6.6 MB/s eta 0:00:00
8. Collecting torchaudio
9.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a6/57/ccebdda4db80e384166c70d8645fa998637051b3b19aca1fd8de80602afb/torchaudio-2.2.1-cp311-cp311-manylinux1_x86_64.whl (3.3 MB)
10.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.3/3.3 MB 6.7 MB/s eta 0:00:00
11. Collecting filelock (from torch)
12.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/81/54/84d42a0bee35edba99dee7b59a8d4970eccdd44b99fe728ed912106fc781/filelock-3.13.1-py3-none-any.whl (11 kB)
13. Collecting typing-extensions>=4.8.0 (from torch)
14.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/f9/de/dc04a3ea60b22624b51c703a84bbe0184abcd1d0b9bc8074b5d6b7ab90bb/typing_extensions-4.10.0-py3-none-any.whl (33 kB)
15. Collecting sympy (from torch)
16.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/d2/05/e6600db80270777c4a64238a98d442f0fd07cc8915be2a1c16da7f2b9e74/sympy-1.12-py3-none-any.whl (5.7 MB)
17. Collecting networkx (from torch)
18.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/d5/f0/8fbc882ca80cf077f1b246c0e3c3465f7f415439bdea6b899f6b19f61f70/networkx-3.2.1-py3-none-any.whl (1.6 MB)
19. Collecting jinja2 (from torch)
20.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/30/6d/6de6be2d02603ab56e72997708809e8a5b0fbfee080735109b40a3564843/Jinja2-3.1.3-py3-none-any.whl (133 kB)
21. Collecting fsspec (from torch)
22.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/ad/30/2281c062222dc39328843bd1ddd30ff3005ef8e30b2fd09c4d2792766061/fsspec-2024.2.0-py3-none-any.whl (170 kB)
23. Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch)
24.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b6/9f/c64c03f49d6fbc56196664d05dba14e3a561038a81a638eeb47f4d4cfd48/nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)
25. Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch)
26.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/eb/d5/c68b1d2cdfcc59e72e8a5949a37ddb22ae6cade80cd4a57a84d4c8b55472/nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)
27. Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch)
28.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/7e/00/6b218edd739ecfc60524e585ba8e6b00554dd908de2c9c66c1af3e44e18d/nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)
29. Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch)
30.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/ff/74/a2e2be7fb83aaedec84f391f082cf765dfb635e7caa9b49065f73e4835d8/nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)
31. Collecting nvidia-cublas-cu12==12.1.3.1 (from torch)
32.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/37/6d/121efd7382d5b0284239f4ab1fc1590d86d34ed4a4a2fdb13b30ca8e5740/nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)
33. Collecting nvidia-cufft-cu12==11.0.2.54 (from torch)
34.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/86/94/eb540db023ce1d162e7bea9f8f5aa781d57c65aed513c33ee9a5123ead4d/nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)
35. Collecting nvidia-curand-cu12==10.3.2.106 (from torch)
36.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/44/31/4890b1c9abc496303412947fc7dcea3d14861720642b49e8ceed89636705/nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)
37. Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch)
38.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/bc/1d/8de1e5c67099015c834315e333911273a8c6aaba78923dd1d1e25fc5f217/nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)
39. Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch)
40.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/65/5b/cfaeebf25cd9fdec14338ccb16f6b2c4c7fa9163aefcf057d86b9cc248bb/nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)
41. Collecting nvidia-nccl-cu12==2.19.3 (from torch)
42.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/38/00/d0d4e48aef772ad5aebcf70b73028f88db6e5640b36c38e90445b7a57c45/nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)
43. Collecting nvidia-nvtx-cu12==12.1.105 (from torch)
44.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/da/d3/8057f0587683ed2fcd4dbfbdfdfa807b9160b809976099d36b8f60d08f03/nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)
45. Collecting triton==2.2.0 (from torch)
46.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/bd/ac/3974caaa459bf2c3a244a84be8d17561f631f7d42af370fc311defeca2fb/triton-2.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (167.9 MB)
47. Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch)
48.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/58/d1/d1c80553f9d5d07b6072bc132607d75a0ef3600e28e1890e11c0f55d7346/nvidia_nvjitlink_cu12-12.4.99-py3-none-manylinux2014_x86_64.whl (21.1 MB)
49.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 21.1/21.1 MB 6.6 MB/s eta 0:00:00
50. Collecting numpy (from torchvision)
51.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/3a/d0/edc009c27b406c4f9cbc79274d6e46d634d139075492ad055e3d68445925/numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.3 MB)
52. Collecting pillow!=8.3.*,>=5.3.0 (from torchvision)
53.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/66/9c/2e1877630eb298bbfd23f90deeec0a3f682a4163d5ca9f178937de57346c/pillow-10.2.0-cp311-cp311-manylinux_2_28_x86_64.whl (4.5 MB)
54. Collecting MarkupSafe>=2.0 (from jinja2->torch)
55.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/97/18/c30da5e7a0e7f4603abfc6780574131221d9148f323752c2755d48abad30/MarkupSafe-2.1.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (28 kB)
56. Collecting mpmath>=0.19 (from sympy->torch)
57.   Using cached https://pypi.tuna.tsinghua.edu.cn/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl (536 kB)
58. Installing collected packages: mpmath, typing-extensions, sympy, pillow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, MarkupSafe, fsspec, filelock, triton, nvidia-cusparse-cu12, nvidia-cudnn-cu12, jinja2, nvidia-cusolver-cu12, torch, torchvision, torchaudio
59. Successfully installed MarkupSafe-2.1.5 filelock-3.13.1 fsspec-2024.2.0 jinja2-3.1.3 mpmath-1.3.0 networkx-3.2.1 numpy-1.26.4 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 pillow-10.2.0 sympy-1.12 torch-2.2.1 torchaudio-2.2.1 torchvision-0.17.1 triton-2.2.0 typing-extensions-4.10.0

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验证是否安装成功：
  

1. ~$ python
2. Python 3.11.7 (main, Dec 15 2023, 18:12:31) [GCC 11.2.0] on linux
3. Type "help", "copyright", "credits" or "license" for more information.
4. >>> import torch
5. >>> x = torch.rand(5,3)
6. >>> print(x)
7. tensor([[0.8278, 0.8746, 0.1025],
8.         [0.7528, 0.6855, 0.7386],
9.         [0.6271, 0.1371, 0.1849],
10.         [0.4098, 0.3203, 0.7615],
11.         [0.5088, 0.7645, 0.8044]])

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4、拉取Langchain-Chatchat源代码

 有两种方式获取源代码，一种是获取最新代码，一种是获取指定版本的源代码。
# 拉取仓库

1. git clone https://github.com/chatchat-space/Langchain-Chatchat.git

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# 指定版本获取代码

1. git clone -b v0.2.9 https://github.com/chatchat-space/Langchain-Chatchat.git

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在拉取源代码之前先安装 git

5、安装依靠包

1. cd Langchain-Chatchat
3. pip3 install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple/

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1. Found existing installation: triton 2.2.0
2.     Uninstalling triton-2.2.0:
3.       Successfully uninstalled triton-2.2.0
4.   Attempting uninstall: pillow
5.     Found existing installation: pillow 10.2.0
6.     Uninstalling pillow-10.2.0:
7.       Successfully uninstalled pillow-10.2.0
8.   Attempting uninstall: nvidia-nccl-cu12
9.     Found existing installation: nvidia-nccl-cu12 2.19.3
10.     Uninstalling nvidia-nccl-cu12-2.19.3:
11.       Successfully uninstalled nvidia-nccl-cu12-2.19.3
12.   Attempting uninstall: numpy
13.     Found existing installation: numpy 1.26.4
14.     Uninstalling numpy-1.26.4:
15.       Successfully uninstalled numpy-1.26.4
16.   Attempting uninstall: torch
17.     Found existing installation: torch 2.2.1
18.     Uninstalling torch-2.2.1:
19.       Successfully uninstalled torch-2.2.1
20.   Attempting uninstall: torchvision
21.     Found existing installation: torchvision 0.17.1
22.     Uninstalling torchvision-0.17.1:
23.       Successfully uninstalled torchvision-0.17.1
24.   Attempting uninstall: torchaudio
25.     Found existing installation: torchaudio 2.2.1
26.     Uninstalling torchaudio-2.2.1:
27.       Successfully uninstalled torchaudio-2.2.1
28. Successfully installed PyMuPDF-1.23.16 PyMuPDFb-1.23.9 SQLAlchemy-2.0.25 Shapely-2.0.3 XlsxWriter-3.2.0 accelerate-0.24.1 aiofiles-23.2.1 aiohttp-3.9.3 aioprometheus-23.12.0 aiosignal-1.3.1 altair-5.2.0 antlr4-python3-runtime-4.9.3 anyio-4.3.0 arxiv-2.1.0 attrs-23.2.0 backoff-2.2.1 beautifulsoup4-4.12.3 blinker-1.7.0 blis-0.7.11 brotli-1.1.0 cachetools-5.3.3 catalogue-2.0.10 certifi-2024.2.2 cffi-1.16.0 chardet-5.2.0 charset-normalizer-3.3.2 click-8.1.7 cloudpathlib-0.16.0 coloredlogs-15.0.1 confection-0.1.4 contourpy-1.2.0 cryptography-42.0.5 cycler-0.12.1 cymem-2.0.8 dataclasses-json-0.6.4 deepdiff-6.7.1 deprecated-1.2.14 deprecation-2.1.0 distro-1.9.0 duckduckgo-search-3.9.9 effdet-0.4.1 einops-0.7.0 emoji-2.10.1 et-xmlfile-1.1.0 faiss-cpu-1.7.4 fastapi-0.109.0 feedparser-6.0.10 filetype-1.2.0 flatbuffers-24.3.7 fonttools-4.49.0 frozenlist-1.4.1 fschat-0.2.35 gitdb-4.0.11 gitpython-3.1.42 greenlet-3.0.3 h11-0.14.0 h2-4.1.0 hpack-4.0.0 httpcore-1.0.4 httptools-0.6.1 httpx-0.26.0 httpx_sse-0.4.0 huggingface-hub-0.21.4 humanfriendly-10.0 hyperframe-6.0.1 idna-3.6 importlib-metadata-7.0.2 iniconfig-2.0.0 iopath-0.1.10 joblib-1.3.2 jsonpatch-1.33 jsonpath-python-1.0.6 jsonpointer-2.4 jsonschema-4.21.1 jsonschema-specifications-2023.12.1 kiwisolver-1.4.5 langchain-0.0.354 langchain-community-0.0.20 langchain-core-0.1.23 langchain-experimental-0.0.47 langcodes-3.3.0 langdetect-1.0.9 langsmith-0.0.87 layoutparser-0.3.4 llama-index-0.9.35 lxml-5.1.0 markdown-3.5.2 markdown-it-py-3.0.0 markdown2-2.4.13 markdownify-0.11.6 marshmallow-3.21.1 matplotlib-3.8.3 mdurl-0.1.2 metaphor-python-0.1.23 msg-parser-1.2.0 msgpack-1.0.8 multidict-6.0.5 murmurhash-1.0.10 mypy-extensions-1.0.0 nest-asyncio-1.6.0 nh3-0.2.15 ninja-1.11.1.1 nltk-3.8.1 numexpr-2.8.6 numpy-1.24.4 nvidia-nccl-cu12-2.18.1 olefile-0.47 omegaconf-2.3.0 onnx-1.15.0 onnxruntime-1.15.1 openai-1.9.0 opencv-python-4.9.0.80 openpyxl-3.1.2 ordered-set-4.1.0 orjson-3.9.15 packaging-23.2 pandas-2.0.3 pathlib-1.0.1 pdf2image-1.17.0 pdfminer.six-20231228 pdfplumber-0.11.0 pikepdf-8.4.1 pillow-9.5.0 pillow-heif-0.15.0 pluggy-1.4.0 portalocker-2.8.2 preshed-3.0.9 prompt-toolkit-3.0.43 protobuf-4.25.3 psutil-5.9.8 pyarrow-15.0.1 pyclipper-1.3.0.post5 pycocotools-2.0.7 pycparser-2.21 pydantic-1.10.13 pydeck-0.8.1b0 pygments-2.17.2 pyjwt-2.8.0 pypandoc-1.13 pyparsing-3.1.2 pypdf-4.1.0 pypdfium2-4.27.0 pytesseract-0.3.10 pytest-7.4.3 python-dateutil-2.9.0.post0 python-decouple-3.8 python-docx-1.1.0 python-dotenv-1.0.1 python-iso639-2024.2.7 python-magic-0.4.27 python-multipart-0.0.9 python-pptx-0.6.23 pytz-2024.1 pyyaml-6.0.1 quantile-python-1.1 rapidfuzz-3.6.2 rapidocr_onnxruntime-1.3.8 ray-2.9.3 referencing-0.33.0 regex-2023.12.25 requests-2.31.0 rich-13.7.1 rpds-py-0.18.0 safetensors-0.4.2 scikit-learn-1.4.1.post1 scipy-1.12.0 sentence_transformers-2.2.2 sentencepiece-0.2.0 sgmllib3k-1.0.0 shortuuid-1.0.12 simplejson-3.19.2 six-1.16.0 smart-open-6.4.0 smmap-5.0.1 sniffio-1.3.1 socksio-1.0.0 soupsieve-2.5 spacy-3.7.2 spacy-legacy-3.0.12 spacy-loggers-1.0.5 srsly-2.4.8 sse_starlette-1.8.2 starlette-0.35.0 streamlit-1.30.0 streamlit-aggrid-0.3.4.post3 streamlit-antd-components-0.3.1 streamlit-chatbox-1.1.11 streamlit-feedback-0.1.3 streamlit-modal-0.1.0 streamlit-option-menu-0.3.12 strsimpy-0.2.1 svgwrite-1.4.3 tabulate-0.9.0 tenacity-8.2.3 thinc-8.2.3 threadpoolctl-3.3.0 tiktoken-0.5.2 timm-0.9.16 tokenizers-0.15.2 toml-0.10.2 toolz-0.12.1 torch-2.1.2 torchaudio-2.1.2 torchvision-0.16.2 tornado-6.4 tqdm-4.66.1 transformers-4.37.2 transformers_stream_generator-0.0.4 triton-2.1.0 typer-0.9.0 typing-inspect-0.9.0 tzdata-2024.1 tzlocal-5.2 unstructured-0.12.5 unstructured-client-0.21.1 unstructured-inference-0.7.23 unstructured.pytesseract-0.3.12 urllib3-2.2.1 uvicorn-0.28.0 uvloop-0.19.0 validators-0.22.0 vllm-0.2.7 wasabi-1.1.2 watchdog-3.0.0 watchfiles-0.21.0 wavedrom-2.0.3.post3 wcwidth-0.2.13 weasel-0.3.4 websockets-12.0 wrapt-1.16.0 xformers-0.0.23.post1 xlrd-2.0.1 yarl-1.9.4 youtube-search-2.1.2 zipp-3.17.0

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6、下载模型

下载两个模型：M3e-base 内置模型和 chatglm3-6b 模型。

1. sudo apt-get install git-lfs
3. git clone https://gitee.com/hf-models/m3e-base.git
5. git clone https://gitee.com/hf-models/chatglm-6b.git

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如果需要上 huggingface.co 获取模型可能需要科学上网工具。

7、修改配置文件

批量修改配置文件名

批量复制configs目录下所有的配置文件，去掉example后缀：

1. # cd Langchain-Chatchat
2. # 批量复制configs目录下所有配置文件，去掉example
3. python copy_config_example.py

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修改model_config.py文件

修改m3e-base的模型本地路径(注意是双反斜杠"\\")：

修改server_config.py

0.2.6之前版本，需要修改0.0.0.0为127.0.0.1不然会报错。

1. # 各服务器默认绑定host。如改为"0.0.0.0"需要修改下方所有XX_SERVER的host
2. DEFAULT_BIND_HOST = "127.0.0.1" if sys.platform != "win32" else "127.0.0.1"

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8、初始化数据库

1. python init_database.py --recreate-vs

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配置里面模型的运行设置设置为 auto （还是建议用显卡跑）

9、一键启动项目

运行：

1. python startup.py -a

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1. ==============================Langchain-Chatchat Configuration==============================
2. 操作系统：Linux-5.15.133.1-microsoft-standard-WSL2-x86_64-with-glibc2.35.
3. python版本：3.11.7 (main, Dec 15 2023, 18:12:31) [GCC 11.2.0]
4. 项目版本：v0.2.10
5. langchain版本：0.0.354. fastchat版本：0.2.35
8. 当前使用的分词器：ChineseRecursiveTextSplitter
9. 当前启动的LLM模型：['chatglm3-6b'] @ cuda
10. {'device': 'cuda',
11. 'host': '127.0.0.1',
12. 'infer_turbo': False,
13. 'model_path': 'model/chatglm3-6b',
14. 'model_path_exists': True,
15. 'port': 20002}
16. 当前Embbedings模型： m3e-base @ cuda
17. ==============================Langchain-Chatchat Configuration==============================
20. 2024-03-09 20:18:03,837 - startup.py[line:655] - INFO: 正在启动服务：
21. 2024-03-09 20:18:03,838 - startup.py[line:656] - INFO: 如需查看 llm_api 日志，请前往 /home/david/20240207/Langchain-Chatchat/logs
22. /home/david/anaconda3/envs/chat_demo/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: 模型启动功能将于 Langchain-Chatchat 0.3.x重写,支持更多模式和加速启动，0.2.x中相关功能将废弃
23.   warn_deprecated(
24. 2024-03-09 20:18:10 | ERROR | stderr | INFO:     Started server process [329625]
25. 2024-03-09 20:18:10 | ERROR | stderr | INFO:     Waiting for application startup.
26. 2024-03-09 20:18:10 | ERROR | stderr | INFO:     Application startup complete.
27. 2024-03-09 20:18:10 | ERROR | stderr | INFO:     Uvicorn running on http://127.0.0.1:20000 (Press CTRL+C to quit)
28. 2024-03-09 20:18:10 | INFO | model_worker | Loading the model ['chatglm3-6b'] on worker 7b784767 ...
29. Loading checkpoint shards:   0%|                                                                                               | 0/7 [00:00<?, ?it/s]
30. 2024-03-09 20:18:11 | ERROR | stderr | /home/david/anaconda3/envs/chat_demo/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
31. 2024-03-09 20:18:11 | ERROR | stderr |   return self.fget.__get__(instance, owner)()
32. Loading checkpoint shards:  14%|████████████▍                                                                          | 1/7 [00:03<00:21,  3.62s/it]
33. Loading checkpoint shards:  29%|████████████████████████▊                                                              | 2/7 [00:07<00:18,  3.74s/it]
34. Loading checkpoint shards:  43%|█████████████████████████████████████▎                                                 | 3/7 [00:08<00:10,  2.74s/it]
35. Loading checkpoint shards:  57%|█████████████████████████████████████████████████▋                                     | 4/7 [00:10<00:06,  2.09s/it]
36. Loading checkpoint shards:  71%|██████████████████████████████████████████████████████████████▏                        | 5/7 [00:11<00:03,  1.76s/it]
37. Loading checkpoint shards:  86%|██████████████████████████████████████████████████████████████████████████▌            | 6/7 [00:12<00:01,  1.63s/it]
38. Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:13<00:00,  1.31s/it]
39. Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:13<00:00,  1.90s/it]
40. 2024-03-09 20:18:24 | ERROR | stderr |
41. 2024-03-09 20:18:30 | INFO | model_worker | Register to controller
42. INFO:     Started server process [329963]
43. INFO:     Waiting for application startup.
44. INFO:     Application startup complete.
45. INFO:     Uvicorn running on http://127.0.0.1:7861 (Press CTRL+C to quit)
48. ==============================Langchain-Chatchat Configuration==============================
49. 操作系统：Linux-5.15.133.1-microsoft-standard-WSL2-x86_64-with-glibc2.35.
50. python版本：3.11.7 (main, Dec 15 2023, 18:12:31) [GCC 11.2.0]
51. 项目版本：v0.2.10
52. langchain版本：0.0.354. fastchat版本：0.2.35
55. 当前使用的分词器：ChineseRecursiveTextSplitter
56. 当前启动的LLM模型：['chatglm3-6b'] @ cuda
57. {'device': 'cuda',
58. 'host': '127.0.0.1',
59. 'infer_turbo': False,
60. 'model_path': 'model/chatglm3-6b',
61. 'model_path_exists': True,
62. 'port': 20002}
63. 当前Embbedings模型： m3e-base @ cuda
66. 服务端运行信息：
67.     OpenAI API Server: http://127.0.0.1:20000/v1
68.     Chatchat  API  Server: http://127.0.0.1:7861
69.     Chatchat WEBUI Server: http://127.0.0.1:8501
70. ==============================Langchain-Chatchat Configuration==============================
74.   You can now view your Streamlit app in your browser.
76.   URL: http://127.0.0.1:8501

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配置成功！
   常用下令：
  sudo usermod -a -G sudo username        为用户添加sudo权限
  取消代理
  git config --global --unset http.proxy
git config --global --unset https.proxy
   10、本地欣赏器打开Linux远程服务器网页

在需要进行端口转发：

1. ssh -L 8501:127.0.0.1:8501 llama@172.17.135.6 -N

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在欣赏器（推荐使用Firefox）中打开 http://localhost:8501/即可进入webUI。

踩坑

1、使用 chatglm2-6b-int4 量化模型 ，启动项目报错：

1. AttributeError: 'NoneType' object has no attribute 'int4WeightExtractionHalf'

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解决办法：

1. pip install cpm_kernels

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