5. 进阶关卡-茴香豆：企业级知识库问答工具

进阶关卡-茴香豆：企业级知识库问答工具

茴香豆 是由书生·浦语团队开发的一款开源、专门针对国内企业级使用场景设计并优化的知识问答工具。

课程文档：Tutorial/docs/L2/huixiangdou/readme.md at camp4 · InternLM/Tutorial · GitHub

1. Web 版茴香豆

1.1 创建 Web 版茴香豆账户和密码

登录 https://openxlab.org.cn/apps/detail/tpoisonooo/huixiangdou-web，可以看到 Web 版茴香豆的知识库注册页面，在对应处输入想要创建的知识库名称和密码，该名称就是 Web 版茴香豆的账户和密码。

1.2 创建 Web 版茴香豆知识库

完成账户创建或者输入已有账户密码后会进入相应知识库的开发页面，当前 Web 版茴香豆功能包括：

• 添加/删除文档
• 编辑正反例
• 打通微信和飞书群
• 开启网络搜索功能（需要填入自己的 Serper token，token 获取参考 3.1 开启网络搜索）
• 聊天测试

1. 添加文档并进行问答检索

点击添加文档的 查看或上传 按钮，对知识库文档进行修改，目前支持 pdf、word、markdown、excel、ppt、html 和 txt 格式文件的上传和删除。上传或删除文件后将自动进行特征提取，生成的向量知识库被用于后续 RAG 检索和相似性比对。

茴香豆根据知识库进行了检索回答。

1.3 通过配置正反例调优知识助手效果

手动添加正例（希望模型回答的问题）和反例（希望模型拒答的问题）来调优知识助手的应答效果。

设置正例：

设置反例：

测试效果：

对于反例问题，茴香豆拒绝作答，这保证了在对话，尤其是企业级群聊中的闲聊、非问题和无关问题触发回答带来的回答混乱和资源浪费。

2. 茴香豆本地标准版搭建

将茴香豆从源码部署到本地服务器（以 InternlmStudio 为例），并开发一款简单的知识助手 Demo。

2.1 环境搭建

2.1.1 配置服务器

登录 InternStudio ，选择创建开发机：镜像选择 Cuda11.7-conda ，资源类型选择 30% A\*100。输入开发机名称 huixiangdou, 点击立即创建。

2.1.2 搭建茴香豆虚拟环境

命令行中输入一下命令，创建茴香豆专用 conda 环境：

studio-conda -o internlm-base -t huixiangdou

创建成功，用下面的命令激活环境：

conda activate huixiangdou

2.2 安装茴香豆

下面开始茴香豆本地标准版的安装。

2.2.1 下载茴香豆

先从茴香豆仓库拉取代码到服务器：

cd /root
# 克隆代码仓库
git clone https://github.com/internlm/huixiangdou && cd huixiangdou
git checkout 79fa810

拉取完成后进入茴香豆文件夹，开始安装。

2.2.2 安装茴香豆所需依赖

conda activate huixiangdou
# parsing `word` format requirements
apt update
apt install python-dev libxml2-dev libxslt1-dev antiword unrtf poppler-utils pstotext tesseract-ocr flac ffmpeg lame libmad0 libsox-fmt-mp3 sox libjpeg-dev swig libpulse-dev
# python requirements
pip install BCEmbedding==0.1.5 cmake==3.30.2 lit==18.1.8 sentencepiece==0.2.0 protobuf==5.27.3 accelerate==0.33.0
pip install -r requirements.txt
# python3.8 安装 faiss-gpu 而不是 faiss

2.2.3 下载模型文件

# 创建模型文件夹
cd /root && mkdir models

# 复制BCE模型
ln -s /root/share/new_models/maidalun1020/bce-embedding-base_v1 /root/models/bce-embedding-base_v1
ln -s /root/share/new_models/maidalun1020/bce-reranker-base_v1 /root/models/bce-reranker-base_v1

# 复制大模型参数（下面的模型，根据作业进度和任务进行**选择一个**就行）
ln -s /root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-7b /root/models/internlm2-chat-7b

2.2.4 更改配置文件

茴香豆的所有功能开启和模型切换都可以通过 config.ini 文件进行修改，默认参数如下：

[feature_store]
# `feature_store.py` use this throttle to distinct `good_questions` and `bad_questions`
reject_throttle = -1.0
# text2vec model, support local relative path, huggingface repo and URL.
# for example:
#  "maidalun1020/bce-embedding-base_v1"
#  "BAAI/bge-m3"
#  "https://api.siliconflow.cn/v1/embeddings"
embedding_model_path = "maidalun1020/bce-embedding-base_v1"

# reranker model, support list:
#  "maidalun1020/bce-reranker-base_v1"
#  "BAAI/bge-reranker-v2-minicpm-layerwise"
#  "https://api.siliconflow.cn/v1/rerank"
reranker_model_path = "maidalun1020/bce-reranker-base_v1"

# if using `siliconcloud` API as `embedding_model_path` or `reranker_model_path`, give the token
api_token = ""
api_rpm = 1000
work_dir = "workdir"

[web_search]
engine = "serper"
# web search engine support ddgs and serper
# For ddgs, see https://pypi.org/project/duckduckgo-search
# For serper, check https://serper.dev/api-key to get a free API key
serper_x_api_key = "YOUR-API-KEY-HERE"
domain_partial_order = ["arxiv.org", "openai.com", "pytorch.org", "readthedocs.io", "nvidia.com", "stackoverflow.com", "juejin.cn", "zhuanlan.zhihu.com", "www.cnblogs.com"]
save_dir = "logs/web_search_result"

[llm]
enable_local = 1
enable_remote = 0
# hybrid llm service address
client_url = "http://127.0.0.1:8888/inference"

[llm.server]
# local LLM configuration
# support "internlm/internlm2-chat-7b", "internlm2_5-7b-chat" and "qwen/qwen-7b-chat-int8"
# support local path, for example
# local_llm_path = "/path/to/your/internlm2_5"

local_llm_path = "internlm/internlm2_5-7b-chat"
local_llm_max_text_length = 3000
# llm server listen port
local_llm_bind_port = 8888

# remote LLM service configuration
# support "gpt", "kimi", "deepseek", "zhipuai", "step", "internlm", "xi-api" and "alles-apin"
# support "siliconcloud", see https://siliconflow.cn/zh-cn/siliconcloud
# xi-api and alles-apin is chinese gpt proxy
# for internlm, see https://internlm.intern-ai.org.cn/api/document

remote_type = "kimi"
remote_api_key = "YOUR-API-KEY-HERE"
# max text length for remote LLM.
# use 128000 for kimi, 192000 for gpt/xi-api, 16000 for deepseek, 128000 for zhipuai, 40000 for internlm2
remote_llm_max_text_length = 128000
# openai API model type, support model list:
# "auto" for kimi. To save money, we auto select model name by prompt length.
# "auto" for step to save money, see https://platform.stepfun.com/
# "gpt-4-0613" for gpt/xi-api,
# "deepseek-chat" for deepseek,
# "glm-4" for zhipuai,
# "gpt-4-1106-preview" for alles-apin or OpenAOE
# "internlm2-latest" for internlm
# for example "alibaba/Qwen1.5-110B-Chat", see https://siliconflow.readme.io/reference/chat-completions-1
remote_llm_model = "auto"
# request per minute
rpm = 500

[coreference_resolution]
base_url = 'http://127.0.0.1:9999/v1'
api_key = 'token-abc123'

[worker]
# enable web search or not
enable_web_search = 1
# enable search enhancement or not
enable_sg_search = 0
# enable coreference resolution in `PreprocNode`
enable_cr = 0
save_path = "logs/work.txt"

[worker.time]
enable = 0
start = "00:00:00"
end = "23:59:59"
has_weekday = 1

[sg_search]
# download `src` from https://github.com/sourcegraph/src-cli#installation
binary_src_path = "/usr/local/bin/src"
src_access_token = "YOUR-SRC-ACCESS-TOKEN"

# add your repo here, we just take opencompass and lmdeploy as example
[sg_search.opencompass]
github_repo_id = "open-compass/opencompass"
introduction = "用于评测大型语言模型（LLM）. 它提供了完整的开源可复现的评测框架，支持大语言模型、多模态模型的一站式评测，基于分布式技术，对大参数量模型亦能实现高效评测。评测方向汇总为知识、语言、理解、推理、考试五大能力维度，整合集纳了超过70个评测数据集，合计提供了超过40万个模型评测问题，并提供长文本、安全、代码3类大模型特色技术能力评测。"
# introduction = "For evaluating Large Language Models (LLMs). It provides a fully open-source, reproducible evaluation framework, supporting one-stop evaluation for large language models and multimodal models. Based on distributed technology, it can efficiently evaluate models with a large number of parameters. The evaluation directions are summarized in five capability dimensions: knowledge, language, understanding, reasoning, and examination. It integrates and collects more than 70 evaluation datasets, providing in total over 400,000 model evaluation questions. Additionally, it offers evaluations for three types of capabilities specific to large models: long text, security, and coding."

[sg_search.lmdeploy]
github_repo_id = "internlm/lmdeploy"
introduction = "lmdeploy 是一个用于压缩、部署和服务 LLM（Large Language Model）的工具包。是一个服务端场景下，transformer 结构 LLM 部署工具，支持 GPU 服务端部署，速度有保障，支持 Tensor Parallel，多并发优化，功能全面，包括模型转换、缓存历史会话的 cache feature 等. 它还提供了 WebUI、命令行和 gRPC 客户端接入。"
# introduction = "lmdeploy is a toolkit for compressing, deploying, and servicing Large Language Models (LLMs). It is a deployment tool for transformer-structured LLMs in server-side scenarios, supporting GPU server-side deployment, ensuring speed, and supporting Tensor Parallel along with optimizations for multiple concurrent processes. It offers comprehensive features including model conversion, cache features for caching historical sessions and more. Additionally, it provides access via WebUI, command line, and gRPC clients."
# add your repo here, we just take opencompass and lmdeploy as example

[sg_search.mmpose]
github_repo_id = "open-mmlab/mmpose"
introduction = "MMPose is an open-source toolbox for pose estimation based on PyTorch"

[sg_search.mmdetection]
github_repo_id = "open-mmlab/mmdetection"
introduction = "MMDetection is an open source object detection toolbox based on PyTorch."

[sg_search.huixiangdou]
github_repo_id = "internlm/huixiangdou"
introduction = "茴香豆是一个基于 LLM 的群聊知识助手。设计拒答、响应两阶段 pipeline 应对群聊场景，解答问题同时不会消息泛滥。"

[sg_search.xtuner]
github_repo_id = "internlm/xtuner"
introduction = "XTuner is an efficient, flexible and full-featured toolkit for fine-tuning large models."

[sg_search.mmyolo]
github_repo_id = "open-mmlab/mmyolo"
introduction = "OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc."

[sg_search.Amphion]
github_repo_id = "open-mmlab/Amphion"
introduction = "Amphion is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development."

[sg_search.mmcv]
github_repo_id = "open-mmlab/mmcv"
introduction = "MMCV is a foundational library for computer vision research and it provides image/video processing, image and annotation visualization, image transformation, various CNN architectures and high-quality implementation of common CPU and CUDA ops"

[frontend]
# chat group assistant type, support "lark_group", "wechat_personal", "wechat_wkteam" and "none"
# for "lark_group", open https://open.feishu.cn/document/home/introduction-to-custom-app-development/self-built-application-development-process to create one
# for "wechat_personal", read ./docs/add_wechat_group_zh.md to setup gateway
# for "wkteam", see https://wkteam.cn/
type = "none"

# for "lark", it is chat group webhook url, send reply to group, for example "https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxxxxxxxx"
# for "lark_group", it is the url to fetch chat group message, for example "http://101.133.161.20:6666/fetch", `101.133.161.20` is your own public IPv4 addr
# for "wechat_personal", it is useless
webhook_url = "https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxxxxxxxx"

# when a new group chat message is received, should it be processed immediately or wait for 18 seconds in case the user hasn't finished speaking?
# support "immediate"
message_process_policy = "immediate"

执行下面的命令更改配置文件，让茴香豆使用本地模型：

sed -i '9s#.*#embedding_model_path = "/root/models/bce-embedding-base_v1"#' /root/huixiangdou/config.ini
sed -i '15s#.*#reranker_model_path = "/root/models/bce-reranker-base_v1"#' /root/huixiangdou/config.ini
sed -i '43s#.*#local_llm_path = "/root/models/internlm2-chat-7b"#' /root/huixiangdou/config.ini

也可以用编辑器手动修改，文件位置为 /root/huixiangdou/config.ini。

注意

配置文件默认的模型和下载好的模型相同。如果不修改地址为本地模型地址，茴香豆将自动从 huggingface hub 拉取模型。如果选择拉取模型的方式，需要提前在命令行中运行 huggingface-cli login 命令，验证 huggingface 权限。

2.3 知识库创建

修改完配置文件后，就可以进行知识库的搭建，本次教程选用的是茴香豆和 MMPose 的文档，利用茴香豆搭建一个茴香豆和 MMPose 的知识问答助手。

conda activate huixiangdou

cd /root/huixiangdou && mkdir repodir

git clone https://github.com/internlm/huixiangdou --depth=1 repodir/huixiangdou
git clone https://github.com/open-mmlab/mmpose    --depth=1 repodir/mmpose

# Save the features of repodir to workdir, and update the positive and negative example thresholds into `config.ini`
mkdir workdir
python3 -m huixiangdou.service.feature_store

在 huixiangdou 文件加下创建 repodir 文件夹，用来储存知识库原始文档。再创建一个文件夹 workdir 用来存放原始文档特征提取到的向量知识库。

知识库创建成功后会有一系列小测试，检验问题拒答和响应效果，如图所示，关于“mmpose 安装”的问题，测试结果可以很好的反馈相应答案和对应的参考文件，但关于“std：：vector 使用”的问题，因为属于 C++ 范畴，不再在知识库范围内，测试结果显示拒答，说明我们的知识助手工作正常。

和 Web 版一样，本地版也可以通过编辑正反例来调整茴香豆的拒答和响应，正例位于 /root/huixiangdou/resource/good_questions.json 文件夹中，反例位于/root/huixiangdou/resource/bad_questions.json。

需要注意的是，每次更新原始知识文档和正反例，都需要重新运行 python3 -m huixiangdou.service.feature_store 命令进行向量知识库的重新创建和应答阈值的更新。

配置中可见，在运行过一次特征提取后，茴香豆的阈值从 -1.0 更新到了 0.33。 配置文件中的 work_dir 参数指定了特征提取后向量知识库存放的位置。如果有多个知识库快速切换的需求，可以通过更改该参数实现。

2.4 测试知识助手

2.4.1 命令行运行

运行下面的命令，可以用命令行对现有知识库问答助手进行测试：

conda activate huixiangdou
cd /root/huixiangdou
python3 -m huixiangdou.main --standalone

通过命令行的方式可以看到对话的结果以及中间的过程，便于我们确认知识库是否覆盖需求，正反例是否合理。

2.4.2 Gradio UI 界面测试

茴香豆也用 gradio 搭建了一个 Web UI 的测试界面，用来测试本地茴香豆助手的效果。

本节课程中，茴香豆助手搭建在远程服务器上，因此需要先建立本地和服务器之间的透传，透传默认的端口为 7860，在本地机器命令行中运行如下命令：

ssh -CNg -L 7860:127.0.0.1:7860 root@ssh.intern-ai.org.cn -p <你的ssh端口号>

在运行茴香豆助手的服务器端，输入下面的命令，启动茴香豆 Web UI：

conda activate huixiangdou
cd /root/huixiangdou
python3 -m huixiangdou.gradio

在本地浏览器中输入 127.0.0.1:7860 打开茴香豆助手测试页面.
助手的效果。

本节课程中，茴香豆助手搭建在远程服务器上，因此需要先建立本地和服务器之间的透传，透传默认的端口为 7860，在本地机器命令行中运行如下命令：

ssh -CNg -L 7860:127.0.0.1:7860 root@ssh.intern-ai.org.cn -p <你的ssh端口号>

在运行茴香豆助手的服务器端，输入下面的命令，启动茴香豆 Web UI：

conda activate huixiangdou
cd /root/huixiangdou
python3 -m huixiangdou.gradio

在本地浏览器中输入 127.0.0.1:7860 打开茴香豆助手测试页面.