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update:reademe knowledge init

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@ -103,24 +103,6 @@ As the knowledge base is currently the most significant user demand scenario, we
2. Custom addition of knowledge bases
3. Various usage scenarios such as constructing knowledge bases through plugin capabilities and web crawling. Users only need to organize the knowledge documents, and they can use our existing capabilities to build the knowledge base required for the large model.
Create your own knowledge base:
1.Place personal knowledge files or folders in the pilot/datasets directory.
2.Run the knowledge repository script in the tools directory.
```
python tools/knowledge_init.py
--vector_name : your vector store name default_value:default
--append: append mode, True:append, False: not append default_value:False
```
3.Add the knowledge base in the interface by entering the name of your knowledge base (if not specified, enter "default") so you can use it for Q&A based on your knowledge base.
Note that the default vector model used is text2vec-large-chinese (which is a large model, so if your personal computer configuration is not enough, it is recommended to use text2vec-base-chinese). Therefore, ensure that you download the model and place it in the models directory.
### LLMs Management
@ -191,9 +173,27 @@ $ python pilot/server/webserver.py
Notice: the webserver need to connect llmserver, so you need change the .env file. change the MODEL_SERVER = "http://127.0.0.1:8000" to your address. It's very important.
## Usage Instructions
We provide a user interface for Gradio, which allows you to use DB-GPT through our user interface. Additionally, we have prepared several reference articles (written in Chinese) that introduce the code and principles related to our project.
- [LLM Practical In Action Series (1) — Combined Langchain-Vicuna Application Practical](https://medium.com/@cfqcsunny/llm-practical-in-action-series-1-combined-langchain-vicuna-application-practical-701cd0413c9f)
####Create your own knowledge repository:
1.Place personal knowledge files or folders in the pilot/datasets directory.
2.Run the knowledge repository script in the tools directory.
```
python tools/knowledge_init.py
--vector_name : your vector store name default_value:default
--append: append mode, True:append, False: not append default_value:False
```
3.Add the knowledge repository in the interface by entering the name of your knowledge repository (if not specified, enter "default") so you can use it for Q&A based on your knowledge base.
Note that the default vector model used is text2vec-large-chinese (which is a large model, so if your personal computer configuration is not enough, it is recommended to use text2vec-base-chinese). Therefore, ensure that you download the model and place it in the models directory.
## Acknowledgement
The achievements of this project are thanks to the technical community, especially the following projects:

32
README.zh.md
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@ -110,22 +110,6 @@ DB-GPT基于 [FastChat](https://github.com/lm-sys/FastChat) 构建大模型运
用户只需要整理好知识文档，即可用我们现有的能力构建大模型所需要的知识库能力。
打造属于你的知识库：
1、将个人知识文件或者文件夹放入pilot/datasets目录中
2、在tools目录执行知识入库脚本
```
python tools/knowledge_init.py
--vector_name : your vector store name default_value:default
--append: append mode, True:append, False: not append default_value:False
```
3、在界面上新增知识库输入你的知识库名（如果没指定输入default）,就可以根据你的知识库进行问答
注意，这里默认向量模型是text2vec-large-chinese(模型比较大，如果个人电脑配置不够建议采用text2vec-base-chinese),因此确保需要将模型download下来放到models目录中
### 大模型管理能力
@ -196,6 +180,22 @@ $ python webserver.py
2. [大模型实战系列(2) —— DB-GPT 阿里云部署指南](https://zhuanlan.zhihu.com/p/629467580)
3. [大模型实战系列(3) —— DB-GPT插件模型原理与使用](https://zhuanlan.zhihu.com/p/629623125)
####打造属于你的知识库：
1、将个人知识文件或者文件夹放入pilot/datasets目录中
2、在tools目录执行知识入库脚本
```
python tools/knowledge_init.py
--vector_name : your vector store name default_value:default
--append: append mode, True:append, False: not append default_value:False
```
3、在界面上新增知识库输入你的知识库名（如果没指定输入default）,就可以根据你的知识库进行问答
注意，这里默认向量模型是text2vec-large-chinese(模型比较大，如果个人电脑配置不够建议采用text2vec-base-chinese),因此确保需要将模型download下来放到models目录中。
## 感谢
项目取得的成果，需要感谢技术社区，尤其以下项目。