码农丁丁

Langchain-Chatchat-win10本地安装部署成功笔记（CPU）

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

最近因工作需要，想体验一下ChatGLM的功能，就用手头的think P70笔记本安装部署了一下，自己记录一下，因为网上大部分的作者可能是NLP大咖，所以会忽略一些基础的东西，自己花了不少时间踩坑，分享一下自己的安装日志。

一、硬件环境

笔记本：Thinkpad P70 （二手），内存：64G，硬盘：1G固态，win10, 带有8G显存的显卡NVIDIA RTX

二、安装步骤

1、安装 Anaconda软件，用于管理python虚拟环境

官网地址： Anaconda | The World’s Most Popular Data Science Platform，下载free版的anaconda，按导航默认安装即可。

参考文章：Anaconda安装和入门(超级简单详细的安装步骤)-CSDN博客

安装后，命令行运行 conda --version

D:\opt\l2>conda --version
conda 23.7.4

2、创建python运行虚拟环境

执行命令：conda create -n l2 python=3.10.12

D:\opt\l2>conda create -n l2 python=3.10.12
...

done
#
# To activate this environment, use
#
#     $ conda activate l2
#
# To deactivate an active environment, use
#
#     $ conda deactivate

激活虚拟环境: conda activate l2

D:\opt\l2>conda activate l2

(l2) D:\opt\l2>python --version
Python 3.10.12

(l2) D:\opt\l2>

3、安装pytorch

安装pytorch版本是比较麻烦的事情，有一个比较简单的方法：访问url: Start Locally | PyTorch，自动判断当前系统可安装的版本。如下图：

运行推荐的命令安装pytorch，用清华源：

(l2) D:\opt\l2>pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 -i https://pypi.tuna.tsinghua.edu.cn/simple/
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple/
Collecting torch
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/fa/47/1a7daf04f40715fc1cdc6f1cc3200228a556d06c843e6ceb58883b745e1b/torch-2.1.0-cp310-cp310-win_amd64.whl (192.3 MB)
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Collecting torchvision
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/59/af/426c2b90f5c4f8aba778746465af9e662680570e950e02379e91c6138285/torchvision-0.16.0-cp310-cp310-win_amd64.whl (1.3 MB)
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Collecting torchaudio
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6b/ba/0e26883fd90f280452bed7edc7906ef9253255f395702751f65fa02afb5c/torchaudio-2.1.0-cp310-cp310-win_amd64.whl (2.3 MB)
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Collecting filelock (from torch)
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Collecting typing-extensions (from torch)
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Collecting sympy (from torch)
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Collecting networkx (from torch)
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Collecting jinja2 (from torch)
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Collecting fsspec (from torch)
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Collecting numpy (from torchvision)
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Collecting requests (from torchvision)
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Collecting pillow!=8.3.*,>=5.3.0 (from torchvision)
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Collecting MarkupSafe>=2.0 (from jinja2->torch)
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Collecting urllib3<3,>=1.21.1 (from requests->torchvision)
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Collecting certifi>=2017.4.17 (from requests->torchvision)
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Collecting mpmath>=0.19 (from sympy->torch)
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Installing collected packages: mpmath, urllib3, typing-extensions, sympy, pillow, numpy, networkx, MarkupSafe, idna, fsspec, filelock, charset-normalizer, certifi, requests, jinja2, torch, torchvision, torchaudio
Successfully installed MarkupSafe-2.1.3 certifi-2023.7.22 charset-normalizer-3.3.2 filelock-3.13.1 fsspec-2023.10.0 idna-3.4 jinja2-3.1.2 mpmath-1.3.0 networkx-3.2.1 numpy-1.26.1 pillow-10.1.0 requests-2.31.0 sympy-1.12 torch-2.1.0 torchaudio-2.1.0 torchvision-0.16.0 typing-extensions-4.8.0 urllib3-2.0.7

验证是否安装成功：

(l2) D:\opt\l2>python
Python 3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:34:57) [MSC v.1936 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.cuda.is_available()
False
>>> x = torch.rand(5, 3)
>>> print(x)
tensor([[0.8827, 0.8297, 0.5390],
        [0.4590, 0.8473, 0.4074],
        [0.4045, 0.0082, 0.4121],
        [0.7649, 0.3901, 0.1535],
        [0.5408, 0.8168, 0.8615]])
>>>

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
git clone https://github.com/chatchat-space/Langchain-Chatchat.git
# 指定版本获取代码
git clone -b v0.2.6 https://github.com/chatchat-space/Langchain-Chatchat.git

(l2) D:\opt\l2>git clone https://github.com/chatchat-space/Langchain-Chatchat.git
Cloning into 'Langchain-Chatchat'...
remote: Enumerating objects: 8144, done.
remote: Counting objects: 100% (902/902), done.
remote: Compressing objects: 100% (450/450), done.
remote: Total 8144 (delta 594), reused 690 (delta 449), pack-reused 7242Receiving objects: 100% (8144/8144), 54.07 MiB |

Resolving deltas: 100% (4900/4900), done.

5、安装依赖包

cd Langchain-Chatchat

# 安装全部依赖 用清华源
pip3 install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple/

(l2) D:\opt\l2\Langchain-Chatchat>pip3 install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple/
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple/
Ignoring vllm: markers 'sys_platform == "linux"' don't match your environment
Collecting langchain>=0.0.319 (from -r requirements.txt (line 1))
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6a/b6/b8687bf07dfd272a62a9b655de96d9e1e5e9199de1a2dc4f6989ace2e9d5/langchain-0.0.330-py3-none-any.whl (2.0 MB)
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Requirement already satisfied: torch>=2.0.1 in c:\users\chenjd\.conda\envs\l2\lib\site-packages (from -r requirements.txt (line 8)) (2.1.0)
Requirement already satisfied: torchvision in c:\users\chenjd\.conda\envs\l2\lib\site-packages (from -r requirements.txt (line 9)) (0.16.0)
Requirement already satisfied: torchaudio in c:\users\chenjd\.conda\envs\l2\lib\site-packages (from -r requirements.txt (line 10)) (2.1.0)
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Collecting cycler>=0.10 (from matplotlib>=2.1.0->pycocotools>=2.0.2->effdet->layoutparser[layoutmodels,tesseract]->unstructured-inference==0.7.10->unstructured[all-docs]>=0.10.12->-r requirements.txt (line 16))
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Collecting kiwisolver>=1.3.1 (from matplotlib>=2.1.0->pycocotools>=2.0.2->effdet->layoutparser[layoutmodels,tesseract]->unstructured-inference==0.7.10->unstructured[all-docs]>=0.10.12->-r requirements.txt (line 16))
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Installing collected packages: wcwidth, sentencepiece, pywin32, pytz, python-magic-bin, python-decouple, pyreadline3, pyclipper, pathlib, nh3, flatbuffers, filetype, faiss-cpu, cymem, antlr4-python3-runtime, zipp, XlsxWriter, xlrd, websockets, watchdog, validators, tzdata, tornado, toolz, tomli, toml, threadpoolctl, tenacity, tabulate, svgwrite, spacy-loggers, spacy-legacy, soupsieve, sniffio, smmap, smart-open, six, simplejson, shortuuid, safetensors, rpds-py, regex, rapidfuzz, PyYAML, python-multipart, python-magic, python-iso639, pypdfium2, pyparsing, pypandoc, PyMuPDFb, pygments, pydantic, pycparser, psutil, protobuf, prompt-toolkit, portalocker, pluggy, pillow, packaging, olefile, numpy, mypy-extensions, murmurhash, multidict, mdurl, markdown2, markdown, lxml, langcodes, kiwisolver, jsonpointer, joblib, iniconfig, humanfriendly, h11, greenlet, frozenlist, fonttools, exceptiongroup, et-xmlfile, emoji, einops, cycler, colorama, cloudpathlib, chardet, catalogue, cachetools, blinker, backoff, attrs, async-timeout, yarl, wavedrom, wasabi, unstructured.pytesseract, tzlocal, typing-inspect, tqdm, tiktoken, srsly, SQLAlchemy, Shapely, scipy, referencing, python-pptx, python-docx, python-dateutil, pytest, pytesseract, PyMuPDF, pydeck, pyarrow, preshed, pdf2image, openpyxl, opencv-python, onnx, omegaconf, numexpr, msg-parser, marshmallow, markdown-it-py, langsmith, langdetect, jsonpatch, importlib-metadata, gitdb, contourpy, coloredlogs, click, cffi, blis, beautifulsoup4, anyio, aiosignal, xformers, uvicorn, typer, starlette, scikit-learn, rich, pandas, onnxruntime, nltk, matplotlib, jsonschema-specifications, iopath, huggingface-hub, httpcore, gitpython, dataclasses-json, cryptography, confection, aiohttp, weasel, unstructured, tokenizers, timm, thinc, rapidocr_onnxruntime, pycocotools, pdfminer.six, openai, langchain, jsonschema, httpx, fastapi, accelerate, transformers, spacy, pdfplumber, langchain-experimental, fschat, effdet, altair, transformers_stream_generator, streamlit, sentence_transformers, peft, layoutparser, streamlit-option-menu, streamlit-chatbox, streamlit-antd-components, streamlit-aggrid, unstructured-inference
  Attempting uninstall: pillow
    Found existing installation: Pillow 10.1.0
    Uninstalling Pillow-10.1.0:
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  Attempting uninstall: numpy
    Found existing installation: numpy 1.26.1
    Uninstalling numpy-1.26.1:
      Successfully uninstalled numpy-1.26.1
Successfully installed PyMuPDF-1.23.5 PyMuPDFb-1.23.5 PyYAML-6.0.1 SQLAlchemy-2.0.19 Shapely-2.0.2 XlsxWriter-3.1.9 accelerate-0.24.1 aiohttp-3.8.6 aiosignal-1.3.1 altair-5.1.2 antlr4-python3-runtime-4.9.3 anyio-3.7.1 async-timeout-4.0.3 attrs-23.1.0 backoff-2.2.1 beautifulsoup4-4.12.2 blinker-1.7.0 blis-0.7.11 cachetools-5.3.2 catalogue-2.0.10 cffi-1.16.0 chardet-5.2.0 click-8.1.7 cloudpathlib-0.16.0 colorama-0.4.6 coloredlogs-15.0.1 confection-0.1.3 contourpy-1.2.0 cryptography-41.0.5 cycler-0.12.1 cymem-2.0.8 dataclasses-json-0.6.1 effdet-0.4.1 einops-0.7.0 emoji-2.8.0 et-xmlfile-1.1.0 exceptiongroup-1.1.3 faiss-cpu-1.7.4 fastapi-0.104.1 filetype-1.2.0 flatbuffers-23.5.26 fonttools-4.44.0 frozenlist-1.4.0 fschat-0.2.31 gitdb-4.0.11 gitpython-3.1.40 greenlet-3.0.1 h11-0.14.0 httpcore-0.17.3 httpx-0.24.1 huggingface-hub-0.17.3 humanfriendly-10.0 importlib-metadata-6.8.0 iniconfig-2.0.0 iopath-0.1.10 joblib-1.3.2 jsonpatch-1.33 jsonpointer-2.4 jsonschema-4.19.2 jsonschema-specifications-2023.7.1 kiwisolver-1.4.5 langchain-0.0.330 langchain-experimental-0.0.37 langcodes-3.3.0 langdetect-1.0.9 langsmith-0.0.57 layoutparser-0.3.4 lxml-4.9.3 markdown-3.5.1 markdown-it-py-3.0.0 markdown2-2.4.10 marshmallow-3.20.1 matplotlib-3.8.1 mdurl-0.1.2 msg-parser-1.2.0 multidict-6.0.4 murmurhash-1.0.10 mypy-extensions-1.0.0 nh3-0.2.14 nltk-3.8.1 numexpr-2.8.7 numpy-1.24.4 olefile-0.46 omegaconf-2.3.0 onnx-1.15.0 onnxruntime-1.15.1 openai-0.28.1 opencv-python-4.8.1.78 openpyxl-3.1.2 packaging-23.2 pandas-2.0.3 pathlib-1.0.1 pdf2image-1.16.3 pdfminer.six-20221105 pdfplumber-0.10.3 peft-0.6.0 pillow-9.5.0 pluggy-1.3.0 portalocker-2.8.2 preshed-3.0.9 prompt-toolkit-3.0.39 protobuf-4.25.0 psutil-5.9.6 pyarrow-14.0.0 pyclipper-1.3.0.post5 pycocotools-2.0.7 pycparser-2.21 pydantic-1.10.13 pydeck-0.8.1b0 pygments-2.16.1 pypandoc-1.12 pyparsing-3.1.1 pypdfium2-4.23.1 pyreadline3-3.4.1 pytesseract-0.3.10 pytest-7.4.3 python-dateutil-2.8.2 python-decouple-3.8 python-docx-1.1.0 python-iso639-2023.6.15 python-magic-0.4.27 python-magic-bin-0.4.14 python-multipart-0.0.6 python-pptx-0.6.21 pytz-2023.3.post1 pywin32-306 rapidfuzz-3.5.2 rapidocr_onnxruntime-1.3.8 referencing-0.30.2 regex-2023.10.3 rich-13.6.0 rpds-py-0.12.0 safetensors-0.4.0 scikit-learn-1.3.2 scipy-1.11.3 sentence_transformers-2.2.2 sentencepiece-0.1.99 shortuuid-1.0.11 simplejson-3.19.2 six-1.16.0 smart-open-6.4.0 smmap-5.0.1 sniffio-1.3.0 soupsieve-2.5 spacy-3.7.2 spacy-legacy-3.0.12 spacy-loggers-1.0.5 srsly-2.4.8 starlette-0.27.0 streamlit-1.28.1 streamlit-aggrid-0.3.4.post3 streamlit-antd-components-0.2.3 streamlit-chatbox-1.1.10 streamlit-option-menu-0.3.6 svgwrite-1.4.3 tabulate-0.9.0 tenacity-8.2.3 thinc-8.2.1 threadpoolctl-3.2.0 tiktoken-0.5.1 timm-0.9.10 tokenizers-0.14.1 toml-0.10.2 tomli-2.0.1 toolz-0.12.0 tornado-6.3.3 tqdm-4.66.1 transformers-4.35.0 transformers_stream_generator-0.0.4 typer-0.9.0 typing-inspect-0.9.0 tzdata-2023.3 tzlocal-5.2 unstructured-0.10.28 unstructured-inference-0.7.10 unstructured.pytesseract-0.3.12 uvicorn-0.23.2 validators-0.22.0 wasabi-1.1.2 watchdog-3.0.0 wavedrom-2.0.3.post3 wcwidth-0.2.9 weasel-0.3.3 websockets-12.0 xformers-0.0.22.post7 xlrd-2.0.1 yarl-1.9.2 zipp-3.17.0


(l2) D:\opt\l2\Langchain-Chatchat>

6、下载模型

下载两个模型：M3e-base内置模型和chatglm2-6b-int4模型。

https://huggingface.co/moka-ai/m3e-base
#备选
https://aistudio.baidu.com/datasetdetail/234251/0

#配置文件
https://huggingface.co/THUDM/chatglm2-6b-int4

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：

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

(l2) D:\opt\l2\Langchain-Chatchat>python copy_config_example.py

(l2) D:\opt\l2\Langchain-Chatchat>

执行命令后：

修改model_config.py文件

修改m3e-base的模型本地路径：

MODEL_PATH = {
    "embed_model": {
        "ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
        "ernie-base": "nghuyong/ernie-3.0-base-zh",
        "text2vec-base": "shibing624/text2vec-base-chinese",
        "text2vec": "GanymedeNil/text2vec-large-chinese",
        "text2vec-paraphrase": "shibing624/text2vec-base-chinese-paraphrase",
        "text2vec-sentence": "shibing624/text2vec-base-chinese-sentence",
        "text2vec-multilingual": "shibing624/text2vec-base-multilingual",
        "text2vec-bge-large-chinese": "shibing624/text2vec-bge-large-chinese",
        "m3e-small": "moka-ai/m3e-small",
       # "m3e-base": "moka-ai/m3e-base",
       # 修改为本地的 路径
        "m3e-base": "E:\\llm_models\\m3e-base",
        "m3e-large": "moka-ai/m3e-large",
        "bge-small-zh": "BAAI/bge-small-zh",
        "bge-base-zh": "BAAI/bge-base-zh",
        "bge-large-zh": "BAAI/bge-large-zh",
        "bge-large-zh-noinstruct": "BAAI/bge-large-zh-noinstruct",
        "bge-base-zh-v1.5": "BAAI/bge-base-zh-v1.5",
        "bge-large-zh-v1.5": "BAAI/bge-large-zh-v1.5",
        "piccolo-base-zh": "sensenova/piccolo-base-zh",
        "piccolo-large-zh": "sensenova/piccolo-large-zh",
        "text-embedding-ada-002": "your OPENAI_API_KEY",
    },

修改chatglm2-6b-int4模型本地路径：

"llm_model": {
        # 以下部分模型并未完全测试，仅根据fastchat和vllm模型的模型列表推定支持
        "chatglm-6b": "THUDM/chatglm-6b",
        "chatglm2-6b": "THUDM/chatglm2-6b",
        "chatglm2-6b-int4": "E:\\llm_models\\chatglm2-6b-int4", #"THUDM/chatglm2-6b-int4",
        "chatglm2-6b-32k": "THUDM/chatglm2-6b-32k",

修改LLM模型为chatglm2-6b-int4：

# LLM 名称 ,修改为chatglm2-6b-int4
LLM_MODEL = "chatglm2-6b-int4"

修改LLM_DEVICE运行设备，看使用cpu、cuda（带GPU）或mps( mac本）

# 选用的 Embedding 名称
EMBEDDING_MODEL = "m3e-base" # 可以尝试最新的嵌入式sota模型：bge-large-zh-v1.5


# Embedding 模型运行设备。设为"auto"会自动检测，也可手动设定为"cuda","mps","cpu"其中之一。
EMBEDDING_DEVICE = "cpu"

# LLM 名称
LLM_MODEL = "chatglm2-6b-int4"

# LLM 运行设备。设为"auto"会自动检测，也可手动设定为"cuda","mps","cpu"其中之一。
LLM_DEVICE = "cpu"

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

# 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
DEFAULT_BIND_HOST = “127.0.0.1"

8、初始化数据库

(l2) D:\opt\l2\Langchain-Chatchat>python init_database.py --recreate-vs
database talbes reseted
recreating all vector stores
2023-11-06 18:21:06,059 - faiss_cache.py[line:75] - INFO: loading vector store in 'samples/vector_store' from disk.
2023-11-06 18:21:06,566 - SentenceTransformer.py[line:66] - INFO: Load pretrained SentenceTransformer: E:\llm_models\m3e-base
Batches: 100%|███████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 11.15it/s]
2023-11-06 18:21:11,388 - loader.py[line:54] - INFO: Loading faiss with AVX2 support.
2023-11-06 18:21:11,389 - loader.py[line:58] - INFO: Could not load library with AVX2 support due to:
ModuleNotFoundError("No module named 'faiss.swigfaiss_avx2'")
2023-11-06 18:21:11,397 - loader.py[line:64] - INFO: Loading faiss.
2023-11-06 18:21:11,472 - loader.py[line:66] - INFO: Successfully loaded faiss.
2023-11-06 18:21:11,561 - faiss_cache.py[line:75] - INFO: loading vector store in 'samples/vector_store' from disk.
Batches: 100%|███████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 12.99it/s]
2023-11-06 18:21:11,713 - utils.py[line:287] - INFO: UnstructuredFileLoader used for D:\opt\l2\Langchain-Chatchat\knowledge_base\samples\content\test.txt
2023-11-06 18:21:17,676 - utils.py[line:160] - INFO: NumExpr defaulting to 8 threads.
2023-11-06 18:21:21,338 - font_manager.py[line:1578] - INFO: generated new fontManager
文档切分示例：page_content='ChatGPT是OpenAI开发的一个大型语言模型，可以提供各种主题的信息，\n# 如何向 ChatGPT 提问以获得高质量答案：提示技巧工程完全指南\n## 介绍\n我很高兴欢迎您阅读我的最新书籍《The Art of Asking ChatGPT for High-Quality Answers: A complete Guide to Prompt Engineering Techniques》。本书是一本全面指南，介绍了各种提示技术，用于从ChatGPT中生成 高质量的答案。\n我们将探讨如何使用不同的提示工程技术来实现不同的目标。ChatGPT是一款最先进的语言模型，能够生成类似人类的 文本。然而，理解如何正确地向ChatGPT提问以获得我们所需的高质量输出非常重要。而这正是本书的目的。' metadata={'source': 'D:\\opt\\l2\\Langchain-Chatchat\\knowledge_base\\samples\\content\\test.txt'}
正在将 samples/test.txt 添加到向量库，共包含66条文档
Batches: 100%|███████████████████████████████████████████████████████████████████████████| 3/3 [00:48<00:00, 16.10s/it]
2023-11-06 18:22:17,663 - faiss_cache.py[line:20] - INFO: 已将向量库 ('samples', 'vector_store') 保存到磁盘
总计用时： 0:01:11.719423


(l2) D:\opt\l2\Langchain-Chatchat>

9、一键启动项目

运行：

python startup.py -a

(l2) D:\opt\l2\Langchain-Chatchat>python startup.py -a

==============================Langchain-Chatchat Configuration==============================
操作系统：Windows-10-10.0.19044-SP0.
python版本：3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:34:57) [MSC v.1936 64 bit (AMD64)]
项目版本：v0.2.6
langchain版本：0.0.330. fastchat版本：0.2.31


当前使用的分词器：ChineseRecursiveTextSplitter
当前启动的LLM模型：['chatglm2-6b-int4'] @ cpu
{'device': 'cpu',
 'host': '127.0.0.1',
 'infer_turbo': False,
 'model_path': 'E:\\llm_models\\chatglm2-6b-int4',
 'port': 20002}
当前Embbedings模型： m3e-base @ cpu
==============================Langchain-Chatchat Configuration==============================


2023-11-06 18:35:19,714 - startup.py[line:626] - INFO: 正在启动服务：
2023-11-06 18:35:19,714 - startup.py[line:627] - INFO: 如需查看 llm_api 日志，请前往 D:\opt\l2\Langchain-Chatchat\logs
2023-11-06 18:35:27 | ERROR | stderr | [32mINFO[0m:     Started server process [[36m16092[0m]
2023-11-06 18:35:27 | ERROR | stderr | [32mINFO[0m:     Waiting for application startup.
2023-11-06 18:35:27 | ERROR | stderr | [32mINFO[0m:     Application startup complete.
2023-11-06 18:35:27 | ERROR | stderr | [32mINFO[0m:     Uvicorn running on [1mhttp://127.0.0.1:20000[0m (Press CTRL+C to quit)
2023-11-06 18:35:35 | INFO | model_worker | Register to controller
2023-11-06 18:35:35 | INFO | model_worker | Loading the model ['chatglm2-6b-int4'] on worker f816f43b ...
2023-11-06 18:35:45 | INFO | model_worker | Register to controller
INFO:     Started server process [18388]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:7861 (Press CTRL+C to quit)


==============================Langchain-Chatchat Configuration==============================
操作系统：Windows-10-10.0.19044-SP0.
python版本：3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:34:57) [MSC v.1936 64 bit (AMD64)]
项目版本：v0.2.6
langchain版本：0.0.330. fastchat版本：0.2.31


当前使用的分词器：ChineseRecursiveTextSplitter
当前启动的LLM模型：['chatglm2-6b-int4'] @ cpu
{'device': 'cpu',
 'host': '127.0.0.1',
 'infer_turbo': False,
 'model_path': 'E:\\llm_models\\chatglm2-6b-int4',
 'port': 20002}
当前Embbedings模型： m3e-base @ cpu


服务端运行信息：
    OpenAI API Server: http://127.0.0.1:20000/v1
    Chatchat  API  Server: http://127.0.0.1:7861
    Chatchat WEBUI Server: http://127.0.0.1:8501
==============================Langchain-Chatchat Configuration==============================



  You can now view your Streamlit app in your browser.

  URL: http://127.0.0.1:8501

10、浏览器访问使用

http://127.0.0.1:8501

11、安装成功

继续研究。

三、FAQ

1、Q：Failed to load cpm_kernels:No module named 'cpm_kernels'

2023-11-06 18:26:44 | INFO | model_worker | Loading the model ['chatglm2-6b-int4'] on worker ac9ad6b5 ...
2023-11-06 18:26:44 | INFO | model_worker | Register to controller
2023-11-06 18:26:49 | WARNING | transformers_modules.chatglm2-6b-int4.quantization | Failed to load cpm_kernels:No module named 'cpm_kernels'

A：pip3 install cpm_kernels -i https://pypi.tuna.tsinghua.edu.cn/simple/

(l2) D:\opt\l2\Langchain-Chatchat>pip3 install cpm_kernels -i https://pypi.tuna.tsinghua.edu.cn/simple/
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple/
Collecting cpm_kernels
  Using cached https://pypi.tuna.tsinghua.edu.cn/packages/af/84/1831ce6ffa87b8fd4d9673c3595d0fc4e6631c0691eb43f406d3bf89b951/cpm_kernels-1.0.11-py3-none-any.whl (416 kB)
Installing collected packages: cpm_kernels
Successfully installed cpm_kernels-1.0.11

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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml&quo
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