Merge pull request #12 from codefuse-ai/modelcache_localDB_dev

Adding Startup Modes

Author: peng3307165

README.md

@@ -29,16 +29,23 @@ Codefuse-ModelCache
 ### Introduction
 Codefuse-ModelCache is a semantic cache for large language models (LLMs). By caching pre-generated model results, it reduces response time for similar requests and improves user experience. <br />This project aims to optimize services by introducing a caching mechanism. It helps businesses and research institutions reduce the cost of inference deployment, improve model performance and efficiency, and provide scalable services for large models.  Through open-source, we aim to share and exchange technologies related to large model semantic cache.
 ## Quick Deployment
+The project's startup scripts are divided into flask4modelcache.py and flask4modelcache_demo.py.
+- flask4modelcache_demo.py is a quick test service that embeds sqlite and faiss, and users do not need to be concerned about database-related matters.
+- flask4modelcache.py is the normal service that requires configuration of mysql and milvus database services.
 ### Dependencies
 
 - Python version: 3.8 and above
 - Package Installation
 ```shell
 pip install requirements.txt 
 ```
-### Environment Configuration
-Before starting the service, the following environment configurations should be performed:
+### Service Startup
+#### Demo Service Startup
+1. Download the embedding model bin file from the following address: [https://huggingface.co/shibing624/text2vec-base-chinese/tree/main](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main). Place the downloaded bin file in the model/text2vec-base-chinese folder.
+2. Start the backend service using the flask4modelcache_dome.py script.
 
+#### Normal Service Startup
+Before starting the service, the following environment configurations should be performed:
 1. Install the relational database MySQL and import the SQL file to create the data tables. The SQL file can be found at: reference_doc/create_table.sql
 2. Install the vector database Milvus.
 3. Add the database access information to the configuration files: 

README_CN.md

@@ -28,17 +28,24 @@ Codefuse-ModelCache
 ## 项目简介
 Codefuse-ModelCache 是一个开源的大模型语义缓存系统，通过缓存已生成的模型结果，降低类似请求的响应时间，提升用户体验。该项目从服务优化角度出发，引入缓存机制，在资源有限和对实时性要求较高的场景下，帮助企业和研究机构降低推理部署成本、提升模型性能和效率、提供规模化大模型服务。我们希望通过开源，分享交流大模型语义Cache的相关技术。
 ## 快速部署
+项目中启动服务脚本分为flask4modelcache.py 和 flask4modelcache_demo.py，其中：
+
+- flask4modelcache_demo.py 为快速测试服务，内嵌了sqlite和faiss，用户无需关心数据库相关事宜。
+- flask4modelcache.py 为正常服务，需用户具备mysql和milvus等数据库服务。
 ### 环境依赖
 
 - python版本: 3.8及以上
 - 依赖包安装：
 ```shell
 pip install requirements.txt 
 ```
+### 服务启动
+#### Demo服务启动
+- 离线模型bin文件下载， 参考地址：[https://huggingface.co/shibing624/text2vec-base-chinese/tree/main](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main)，并将下载的bin文件，放到 model/text2vec-base-chinese 文件夹中。
+- 执行flask4modelcache_demo.py脚本即可启动。
 
-### 环境配置
+#### 正常服务启动
 在启动服务前，应该进行如下环境配置：
-
 1. 安装关系数据库 mysql， 导入sql创建数据表，sql文件: reference_doc/create_table.sql
 2. 安装向量数据库milvus
 3. 在配置文件中添加数据库访问信息，配置文件为：