Project Overview
This project implements a Retrieval-Augmented Generation (RAG) method for creating a question-answering system. It utilizes the LLaMA 3 language model in conjunction with LangChain and Ollama packages to process PDFs, convert them into text, create embeddings, and then store the output in a database.
1. PDF to Text Conversion
Definition: This module handles the conversion of uploaded PDF files into plain text format. It forms the basis for further processing like chunking and embedding.
2. Text Chunking and Embedding
Definition: The extracted text is divided into manageable chunks, and embeddings are generated using the LLaMA 3 model. This step ensures that the information is stored in a format that is easy to retrieve and query.
3. Template-Based Prompting
Definition: Templates are created to guide the model's response generation based on user prompts. This structured approach helps in retrieving relevant information from the text embeddings.
4. Chain Creation
Definition: A chain of operations is built to handle the end-to-end process of input query processing, information retrieval, and response generation using the LLaMA 3 model.
5. Question-Answering and Output Storage
Definition: The system answers user questions by retrieving relevant information from the embeddings and templates. The output is then stored in a database for future reference.
6. Technologies and Tools
Definition: This section lists the technologies and tools used in the project, including Python, LangChain, Ollama, LLaMA 3, and relevant libraries for PDF processing and database storage.
7. Installation and Setup
Definition: Instructions on how to set up the project locally, including environment setup, package installations, and running the system.
8. Usage Guide
Definition: A guide on how to use the system, including how to upload PDFs, enter prompts, and retrieve answers.
9. Future Enhancements
Definition: A roadmap for potential future improvements, such as adding support for more file types, improving the chunking algorithm, or integrating additional models.