This document outlines the upcoming tasks for the development and implementation of Retrieval-Augmented Generation (RAG) models. The focus is on augmenting RAG models to handle various data formats and sources effectively.
- Develop a RAG model to handle complex PDF documents, such as resumes and reports.
- Implement text extraction from diverse PDF layouts.
- Ensure the model comprehends structured and unstructured data within PDFs.
- Enable RAG to query and retrieve information from various databases.
- Integrate RAG with both SQL and NoSQL databases.
- Address database schema variations and query optimization.
- Enhance RAG models to extract information from websites.
- Deal with content from dynamically generated and diverse website architectures.
- Adapt to changes in website layouts and formats.
- Develop a RAG model specifically for the Arabic language.
- Handle complexities of Arabic script and grammar.
- Ensure cultural and contextual relevance in content generation.
- Create a versatile RAG model for any text-based document.
- Develop a flexible preprocessing pipeline for different document types.
- Maintain accuracy and relevance in retrieval across various formats.
- Utilize NLP Frameworks such as Transformers and Hugging Face.
- Employ OCR Tools like Tesseract and others.
- Manage databases using tools like SQL and MongoDB.
- Use Web Scraping Tools including Beautiful Soup and Selenium for data extraction.
The aim is to expand RAG capabilities to enhance automation and optimize content generation and data processing tasks in diverse applications.
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