KloudMinds

Team Member:

• Student 1: Zhengyang Luo, [t0933775].
• Student 2: Ripeng Li, [t0933462]
• Student 3: Yihan Jin, [t0933695]
• Student 4: Yiliu Dong, [t0933287]

1. Project Overview

1.1 Project Description

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AI-driven cloud-native file management and R-A-G service

• Large storage space

• Fast retrieval (exact/fuzzy)

• AI reading analysis

1.2 System Architecture

1.2.1 General structure

1.2.2 Storage structure

1.2.3 RAG structure

1.2.4 Chat structure

1.3 Key Components

• backend: Backend, control the whole system, respond to frontend requests and send message to rabbitmq, based on springboot and Java.

• frontend: User interface, base on Vue.

• jiexi-server: Parse the file(docx, doc, pdf) to pure txt for further process and interact with MinIO, based on Java.

• retrieve-server: Vectorize and parallelly upload file chunks, based on Python and flask.

• chat-server: Provide chat service, based on Python and flask.

2. Environment Setup

2.1 Prerequisites

• Git
• Kubernetes
• AWS CLI
• helm
• Java
• Maven
• Python3 and libs
• Javascript
• Vue

2.2 Installation

If you are using a Linux distribution or MacOS, all these requirements can be installed using your package manager(zypper, homebrew, apt-get, pacman), just run as follow:

${your_package_manager} install kubenetes

3. Application Deployment

Note that this section has been split into separate instruction files so there is a readme.md in each subfolder of the project that gives very detailed deployment and debugging instructions.

Contents of each subfolder are as follow:

1. readme.md: Deployment documentation providing detailed deployment and debugging guidance.

2. yamls/*.yaml: Kubernetes configuration files

3. test/*.test: Test code

4. src: Source code of microservice

Please follow the order 1 -> 2 -> 3 for configuration and deployment:

1. AWS-configure, EFS

2. Minio, elastic-search, monitoring-logging, ndbmysql, rabbitmq，weaviate, retrieve-server, ai-chat-server, jiexi-server

3. backend, frontend

After deployment, you can log in and use the service by accessing the port exposed by the frontend.