RAG Model for Document Retrieval and Generation

This project implements a Retrieval-Augmented Generation (RAG) model that uses a directory containing text files as documents for information retrieval and generation. The model combines retrieval and generation capabilities to answer questions based on the provided documents.

Table of Contents

• Introduction
• Features
• Installation
• Usage
  • Data Preparation
  • Creating Embeddings
  • Saving Dataset and Index
  • Loading Dataset and Index
  • Retrieval and Generation
• Example
• Troubleshooting
• Contributing
• License

Introduction

The RAG model leverages the power of retrieval and generation to provide informative responses based on a given set of documents. The model first retrieves relevant documents from a corpus and then generates a coherent response based on the retrieved information. This project demonstrates how to set up, train, and use a RAG model with a custom document corpus.

Features

• Document Retrieval: Efficiently retrieve relevant documents from a custom corpus.
• Response Generation: Generate coherent and relevant responses based on retrieved documents.
• Flexible and Customizable: Easily adapt the model to different types of documents and queries.

Installation

To get started with this project, you need to install the required packages. You can do this using pip:

pip install transformers datasets sentence-transformers torch

Ensure you have a compatible version of Python installed (Python 3.6 or higher is recommended).

Usage

Data Preparation

1. Prepare Your Documents: Collect all your text documents and place them in a directory. Each file should contain text data that the model will use for retrieval and generation.

Creating Embeddings

2. Create Embeddings: Use the DPRQuestionEncoder model to create embeddings for your documents. Embeddings are numerical representations of the documents, which enable efficient retrieval based on similarity.

Saving Dataset and Index

3. Save Dataset and Index: Convert your documents and their embeddings into a dataset format that can be saved to disk. Additionally, create an index using Faiss for fast document retrieval.

Loading Dataset and Index

4. Load the Dataset and Index: Load the saved dataset and index back into memory when you need to perform retrieval and generation. This step ensures that the model has access to the document embeddings for similarity search.

Retrieval and Generation

5. Retrieve and Generate Responses: Implement the function to retrieve documents based on a query and generate a response using the RAG model. This involves encoding the query, retrieving relevant documents, and generating a response using the retrieved context.

Example

To test the model, you can run a query to see how the RAG model retrieves and generates responses based on your document corpus. For instance, you might query "What is the content of the documents?" and observe the model's generated response.

Troubleshooting

• Dimension Mismatch: Ensure that the embeddings for both queries and documents have the same dimensions.
• Missing Keys: Ensure that all necessary keys (e.g., context_input_ids, context_attention_mask, doc_scores) are correctly extracted from the retrieval results.
• Inappropriate Responses: Implement content checks to filter out inappropriate responses. For instance, you can add checks to ensure the response does not contain any irrelevant or inappropriate content.

Contributing

Contributions are welcome! If you have any improvements or bug fixes, please open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.