feat(service): support en language

Author: tpoisonooo

.gitignore

@@ -12,3 +12,4 @@ pk/
 badcase.txt
 config.bak
 config.ini
+resource/prompt.txt

README.md

@@ -1,125 +1,126 @@
 # HuixiangDou: Overcoming Group Chat Scenarios with LLM-based Technical Assistance
-
-<small> 简体中文 | [English](README_en.md) </small>
+<small> [简体中文](README_zh.md) | English </small>
 
 [![GitHub license](https://img.shields.io/badge/license-GPL--3--Clause-brightgreen.svg)](./LICENSE)
 ![CI](https://img.shields.io/github/actions/workflow/status/internml/huixiangdou/lint.yml?branch=master)
 
-“茴香豆”是一个基于 LLM 的领域特定知识助手。特点：
-1. 应对群聊这类复杂场景，解答用户问题的同时，不会消息泛滥
-2. 提出一套解答技术问题的算法 pipeline
-3. 部署成本低，只需要 LLM 模型满足 4 个 trait 即可解答大部分用户问题，见[技术报告](./resource/HuixiangDou.pdf)
+"HuixiangDou" is a domain-specific knowledge assistant based on the LLM. Features:
+1. Deal with complex scenarios like group chats, answer user questions without causing message flooding.
+2. Propose an algorithm pipeline for answering technical questions.
+3. Low deployment cost, only need the LLM model to meet 4 traits can answer most of the user's questions, see [technical report](./resource/HuixiangDou.pdf).
+
+View [HuixiangDou inside](./huixiangdou-inside.md).
 
-查看[茴香豆已运行在哪些场景](./huixiangdou-inside.md)。
+# 🔥 Run
 
-# 🔥 运行
+We will take lmdeploy & mmpose as examples to explain how to deploy the knowledge assistant to Feishu group chat.
 
-我们将以 lmdeploy & mmpose 为例，介绍如何把知识助手部署到飞书群
+## STEP1. Establish Topic Feature Repository
 
-## STEP1. 建立话题特征库
-```bash
-# 下载聊天话题
+```shell
+# Download chat topics
 mkdir repodir
 git clone https://github.com/openmmlab/mmpose --depth=1 repodir/mmpose
 git clone https://github.com/internlm/lmdeploy --depth=1 repodir/lmdeploy
 
-# 建立特征库
-cd HuixiangDou && mkdir workdir # 创建工作目录
-python3 -m pip install -r requirements.txt # 安装依赖
-python3 service/feature_store.py repodir workdir # 把 repodir 的特征保存到 workdir
+# Establish feature repository
+cd HuixiangDou && mkdir workdir # Create working directory
+python3 -m pip install -r requirements.txt # Install dependencies
+python3 service/feature_store.py repodir workdir # Save features from repodir to workdir
 ```
-运行结束后，茴香豆能够区分应该处理哪些用户话题，哪些闲聊应该拒绝。请编辑 [good_questions](./resource/good_questions.json) 和 [bad_questions](./resource/bad_questions.json)，尝试自己的领域知识（医疗，金融，电力等）。
-
-```bash
-# 接受技术话题
-process query: mmdeploy 现在支持 mmtrack 模型转换了么
-process query: 有啥中文的 text to speech 模型吗?
-# 拒绝闲聊
-reject query: 今天中午吃什么？
-reject query: 茴香豆是怎么做的
+
+After running, HuixiangDou can distinguish which user topics should be dealt with and which chitchats should be rejected. Please edit [good_questions](./resource/good_questions.json) and [bad_questions](./resource/bad_questions.json), and try your own domain knowledge (medical, finance, electricity, etc.).
+
+```shell
+# Accept technical topics
+process query: Does mmdeploy support mmtrack model conversion now?
+process query: Are there any Chinese text to speech models?
+# Reject chitchat
+reject query: What to eat for lunch today?
+reject query: How to make HuixiangDou?
 ```
 
-## STEP2. 运行基础版技术助手
+## STEP2. Run Basic Technical Assistant
 
-**配置免费 TOKEN**
+**Configure free TOKEN**
 
-茴香豆使用了搜索引擎，点击 [serper 官网](https://serper.dev/api-key)获取限额 WEB_SEARCH_TOKEN，填入 `config.ini`
+HuixiangDou uses a search engine. Click on the [serper official website](https://serper.dev/api-key) to obtain a quota-limited WEB_SEARCH_TOKEN and fill it in `config.ini`.
 
-```bash
+```shell
 # config.ini
 ..
 [web_search]
 x_api_key = "${YOUR-X-API-KEY}"
 ..
 ```
 
-**测试问答效果**
+**Test Q&A Effect**
 
-请保证 GPU 显存超过 20GB（如 3090 及以上），若显存较低请按 FAQ 修改。
+Please ensure that the GPU memory is over 20GB (such as 3090 or above). If the memory is low, please modify it according to the FAQ.
 
-首次运行将自动下载配置中的 internlm2-7B 和 text2vec-large-chinese，请保证网络畅通。
+The first run will automatically download the configuration of internlm2-7B and text2vec-large-chinese, please ensure network connectivity.
 
-  * **非 docker 用户**。如果你**不**使用 docker 环境，可以一次启动所有服务。
-    ```bash
+  * **Non-docker users**. If you **don't** use docker environment, you can start all services at once.
+    ```shell
     # standalone
     python3 main.py workdir --standalone
     ..
-    ErrorCode.SUCCESS, 请教下视频流检测 跳帧  造成框一闪一闪的  有好的优化办法吗
-    1. 帧率控制和跳帧策略是优化视频流检测性能的关键，但需要注意跳帧对检测结果的影响。
-    2. 多线程处理和缓存机制可以提高检测效率，但需要注意检测结果的稳定性。
-    3. 使用滑动窗口的方式可以减少跳帧和缓存对检测结果的影响。
+    ErrorCode.SUCCESS, Could you please advise if there is any good optimization method for video stream detection flickering caused by frame skipping?
+    1. Frame rate control and frame skipping strategy are key to optimizing video stream detection performance, but you need to pay attention to the impact of frame skipping on detection results.
+    2. Multithreading processing and caching mechanism can improve detection efficiency, but you need to pay attention to the stability of detection results.
+    3. The use of sliding window method can reduce the impact of frame skipping and caching on detection results.
     ```
 
-  * **docker 用户**。如果你正在使用 docker，`HuixiangDou` 的 Hybrid LLM Service 需要分离部署。
-    ```bash
-    # 启动 LLM 服务
+  * **Docker users**. If you are using docker, HuixiangDou's Hybrid LLM Service needs to be deployed separately.
+    ```shell
+    # Start LLM service
     python3 service/llm_server_hybride.py
     ```
-    打开新终端，把 host IP 配置进 `config.ini`，运行
+    Open a new terminal, configure the host IP in `config.ini`, run
 
-    ```bash
+    ```shell
     # config.ini
     [llm]
     ..
-    client_url = "http://10.140.24.142:39999/inference" # 举例
+    client_url = "http://10.140.24.142:39999/inference" # example
 
     python3 main.py workdir
     ```
 
-## STEP3.集成到飞书[可选]
+## STEP3. Integrate into Feishu [Optional]
 
-点击[创建飞书自定义机器人](https://open.feishu.cn/document/client-docs/bot-v3/add-custom-bot)，获取回调 WEBHOOK_URL，填写到 config.ini
+Click [Create a Feishu Custom Robot](https://open.feishu.cn/document/client-docs/bot-v3/add-custom-bot) to get the WEBHOOK_URL callback, and fill it in the config.ini.
 
-```bash
+```shell
 # config.ini
 ..
 [frontend]
 type = "lark"
 webhook_url = "${YOUR-LARK-WEBHOOK-URL}"
 ```
 
-运行。结束后，技术助手的答复将发送到飞书群。
-```bash
+Run. After it ends, the technical assistant's reply will be sent to the Feishu group chat.
+```shell
 python3 main.py workdir
 ```
 <img src="./resource/figures/lark-example.png" width="400">
 
-如果还需要读取飞书群消息，见[飞书开发者广场-添加应用能力-机器人](https://open.feishu.cn/app?lang=zh-CN)。
+If you still need to read Feishu group messages, see [Feishu Developer Square - Add Application Capabilities - Robots](https://open.feishu.cn/app?lang=zh-CN).
 
-## STEP4.高精度配置[可选]
-为了进一步提升助手的答复体验，以下特性，开启得越多越好。
+## STEP4. High Accuracy Method [Optional]
+To further enhance the experience of the assistant's response, the more features you turn on, the better.
 
-1. 使用更高精度 local LLM
+1. Use higher accuracy local LLM
 
-    把 config.ini 中的`llm.local` 模型调整为 `internlm2-20B`
-    此选项效果显著，但需要更大的 GPU 显存。
+    Adjust the `llm.local` model in config.ini to `internlm2-20B`.
+    This option has a significant effect, but requires more GPU memory.
 
 2. Hybrid LLM Service
 
-    对于支持 openai 接口的 LLM 服务，茴香豆可以发挥它的 Long Context 能力。
-    以 kimi 为例，以下是 `config.ini` 配置示例：
+    For LLM services that support the openai interface, HuixiangDou can utilize its Long Context ability.
+    Using Kimi as an example, below is an example of `config.ini` configuration:
 
-    ```bash
+    ```shell
     # config.ini
     [llm.server]
     ..
@@ -129,83 +130,88 @@ python3 main.py workdir
     remote_llm_max_text_length = 128000
     remote_llm_model = "moonshot-v1-128k"
     ```
-    我们同样支持 gpt API。注意此特性会增加响应耗时和运行成本。
+    We also support the gpt API. Note that this feature will increase response time and operating costs.
 
-3. repo 搜索增强
+3. Repo search enhancement
 
-    此特性适合处理疑难问题，需要基础开发能力调整 prompt。
+    This feature is suitable for handling difficult questions and requires basic development capabilities to adjust the prompt.
 
-    * 点击 [sourcegraph-account-access](https://sourcegraph.com/users/tpoisonooo/settings/tokens) 获取 token
+    * Click [sourcegraph-account-access](https://sourcegraph.com/users/tpoisonooo/settings/tokens) to get token
 
-        ```bash
+        ```shell
         # open https://github.com/sourcegraph/src-cli#installation
         curl -L https://sourcegraph.com/.api/src-cli/src_linux_amd64 -o /usr/local/bin/src && chmod +x /usr/local/bin/src
 
-        # 把 token 填入 config.ini
+        # Fill the token into config.ini
         [sg_search]
         ..
         src_access_token = "${YOUR_ACCESS_TOKEN}"
         ```
-    
-    * 编辑 repo 的名字和简介，我们以 opencompass 为例
 
-        ```bash
+    * Edit the name and introduction of the repo, we take opencompass as an example
+
+        ```shell
         # config.ini
         # 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）.."
+        introduction = "Used for evaluating large language models (LLM) .."
         ```
-    
-    * 使用 `python3 service/sg_search.py` 单测，返回内容应包含 opencompass 源码和文档
-  
-       ```bash
+
+    * Use `python3 -m service.sg_search` for unit test, the returned content should include opencompass source code and documentation
+
+       ```shell
        python3 service/sg_search.py
        ..
        "filepath": "opencompass/datasets/longbench/longbench_trivia_qa.py",
        "content": "from datasets import Dataset..
        ```
 
-    运行 `main.py`，茴香豆将在合适的时机，启用搜索增强。
+    Run `main.py`, HuixiangDou will enable search enhancement when appropriate.
+
+4. Tune Parameters
 
+    It is often unavoidable to adjust parameters with respect to business scenarios.
+    * Refer to [data.json](./tests/data.json) to add real data, run [test_intention_prompt.py](./tests/test_intention_prompt.py) to get suitable prompts and thresholds, and update them into [worker](./service/worker.py).
+    * Adjust the [number of search results](./service/worker.py) based on the maximum length supported by the model.
 
 # 🛠️ FAQ 
 
-1. 如何接入其他 IM ？
-    * 微信。企业微信请查看[企业微信应用开发指南](https://developer.work.weixin.qq.com/document/path/90594)；对于个人微信，我们已向微信团队确认暂无 API，须自行搜索学习
-    * 钉钉。参考[钉钉开放平台-自定义机器人接入](https://open.dingtalk.com/document/robots/custom-robot-access)
+1. How to access other IMs?
+    * WeChat. For Enterprise WeChat, see [Enterprise WeChat Application Development Guide](https://developer.work.weixin.qq.com/document/path/90594) ; for personal WeChat, we have confirmed with the WeChat team that there is currently no API, you need to search and learn by yourself.
+    * DingTalk. Refer to [DingTalk Open Platform-Custom Robot Access](https://open.dingtalk.com/document/robots/custom-robot-access)
 
-2. 机器人太高冷/太嘴碎怎么办？
+2. What if the robot is too cold/too chatty?
 
-    * 把真实场景中，把应该回答的问题填入`resource/good_questions.json`，应该拒绝的填入`resource/bad_questions.json`
-    * 调整 `repodir` 中的主题内容，确保底库的 markdown 文档不包含场景无关内容
+    * Fill in the questions that should be answered in the real scenario into `resource/good_questions.json`, and fill the ones that should be rejected into `resource/bad_questions.json`.
+    * Adjust the theme content in `repodir` to ensure that the markdown documents in the main library do not contain irrelevant content.
 
-    重新执行 `service/feature_store.py`，更新阈值和特征库
+    Re-run `service/feature_store.py` to update thresholds and feature libraries.
 
-3. 启动正常，但运行期间显存 OOM 怎么办？
+3. Launch is normal, but out of memory during runtime?
 
-    基于 transformers 结构的 LLM 长文本需要更多显存，此时需要对模型做 kv cache 量化，如 [lmdeploy 量化说明](https://github.com/InternLM/lmdeploy/blob/main/docs/en/kv_int8.md)。然后使用 docker 独立部署 Hybrid LLM Service。
+    LLM long text based on transformers structure requires more memory. At this time, kv cache quantization needs to be done on the model, such as [lmdeploy quantization description](https://github.com/InternLM/lmdeploy/blob/main/docs/en/kv_int8.md). Then use docker to independently deploy Hybrid LLM Service.
 
-4. 如何接入其他 local LLM/ 接入后效果不理想怎么办？
+4. How to access other local LLM / After access, the effect is not ideal?
 
-    * 打开 [hybrid llm service](./service/llm_server_hybrid.py)，增加新的 LLM 推理实现
-    * 参照 [test_intention_prompt 和测试数据](./tests/test_intention_prompt.py)，针对新模型调整 prompt 和阈值，更新到 [worker.py](./service/worker.py)
+    * Open [hybrid llm service](./service/llm_server_hybrid.py), add a new LLM inference implementation.
+    * Refer to [test_intention_prompt and test data](./tests/test_intention_prompt.py), adjust prompt and threshold for the new model, and update them into [worker.py](./service/worker.py).
 
-5. 响应太慢/请求总是失败怎么办？
+5. What if the response is too slow/request always fails?
 
-    * 参考 [hybrid llm service](./service/llm_server_hybrid.py) 增加指数退避重传
-    * local LLM 替换为 [lmdeploy](https://github.com/internlm/lmdeploy) 等推理框架，而非原生的 huggingface/transformers
-      
-5. GPU 显存太低怎么办？
+    * Refer to [hybrid llm service](./service/llm_server_hybrid.py) to add exponential backoff and retransmission.
+    * Replace local LLM with an inference framework such as [lmdeploy](https://github.com/internlm/lmdeploy), instead of the native huggingface/transformers.
 
-    此时无法运行 local LLM，只能用 remote LLM 配合 text2vec 执行 pipeline。请确保 `config.ini` 只使用 remote LLM，关闭 local LLM
+6. What if the GPU memory is too low?
 
-# 📝 引用
-```bash
+    At this time, it is impossible to run local LLM, and only remote LLM can be used in conjunction with text2vec to execute the pipeline. Please make sure that `config.ini` only uses remote LLM and turn off local LLM.
+
+# 📝 Citation
+```shell
 @misc{2023HuixiangDou,
     title={HuixiangDou: Overcoming Group Chat Scenarios with LLM-based Technical Assistance},
     author={HuixiangDou Contributors},
     howpublished = {\url{https://github.com/internlm/huixiangdou}},
     year={2023}
 }
-```
+```