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A Retrieval-Augmented Generation (RAG) model combines retrieval and generation techniques. It retrieves relevant documents using a retriever and generates responses with a generator. This hybrid approach improves the relevance and quality of the output, useful for tasks like question answering and conversational agents.

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RAG_MODEL-app Documentation

Overview

This documentation provides an overview of the RAG_MODEL-app, its structure, and its components.

Folder Structure

RAG_MODEL-app/
├── Api/
├── src/
├────── EXTRACTOR.py
├────── main.py
├── README.md
├── requirements.txt

Api/

This directory contains the API-related code and resources. The specifics of this directory's contents are as follows:

  • (Detail any specific files and their purposes here if available.)

src/EXTRACTOR.py

This script is responsible for the data extraction processes used by the RAG model.

src/main.py

The main entry point for running the RAG model application. This script likely orchestrates the overall workflow of the application.

README.md

A README file that provides an introduction and basic instructions for the RAG model application. Ensure to read this file first to understand how to set up and run the application.

requirements.txt

This file lists the Python dependencies required to run the RAG model application. Use this file to install the necessary packages using pip:

pip install -r requirements.txt

Getting Started

  1. Clone the Repository:

    git clone <repository-url>
    cd RAG_MODEL-app
  2. Install Dependencies:

    pip install -r requirements.txt
  3. Run the Application:

    python main.py

Detailed Descriptions

Api/

(Provide details on the specific contents of the Api/ directory if necessary.)

src/EXTRACTOR.py

This script handles the extraction of data required for the RAG model. It may include functions for data preprocessing, feature extraction, and other related tasks.

src/main.py

The central script for running the application. It may include the following:

  • Initialization of the model
  • Loading of data
  • Execution of the model
  • Output of results

README.md

This file provides an introduction to the application, including setup instructions, usage examples, and other relevant information.

requirements.txt

Lists all the Python libraries and versions required to run the application. Ensure to install all dependencies before running the application to avoid any missing package errors.


This is a basic template. If you need more detailed documentation or specific sections, please provide more details or the contents of specific files, and I can elaborate further.

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A Retrieval-Augmented Generation (RAG) model combines retrieval and generation techniques. It retrieves relevant documents using a retriever and generates responses with a generator. This hybrid approach improves the relevance and quality of the output, useful for tasks like question answering and conversational agents.

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