RAG

Table of Contents

1. Introduction
2. Architecture
3. Modules
   • LlamaHandler
4. Installation
5. Usage
6. Running the Application

Introduction

This project is designed to extract and process data from PDF files. The processed data is stored in vector databases, allowing for efficient retrieval and question-answering using large language models (LLMs).

Architecture

The architecture of the project consists of three main engines:

Persistence Engine

1. PDF Files: The process starts with the input PDF files.
2. all-mpnet Model: Text data is embedded using the all-mpnet model, creating a representation of the textual data.
3. Text Store & Image Store: The embeddings are stored in respective vector databases for text.

Retrieval Engine

1. Query: The user provides a query.
2. all-mpnet Model: The query is embedded using the all-mpnet model.
3. Vector Search (Text): A search is conducted in the text store to retrieve the top K relevant text embeddings.

Answer Engine

1. Prompt Formation: The retrieved text informations are combined to form a prompt.
2. Mistral LLM: The prompt is processed using the Mistal LLM to generate an answer.
3. Answer: The final answer is returned to the user.

Project Structure

├── app.py
├── data
│   ├── index
│   ├── pdf
│   ├── readme_images
├── src
│   ├── config
│   │   ├── config.py
│   ├── llama_handler
│   │   ├── llama_handler.py
│   ├── prompts
│   │   ├── prompts.py
├── static
│   ├── styles.css
├── templates
│   ├── index.html
├── requirements.txt
├── README.md

Modules

LlamaHandler

The LlamaHandler class handles the embedding and retrieval of data, as well as generating answers using LLMs. It performs the following tasks:

• Embeds text using pre-trained models.
• Stores embeddings in vector databases.
• Retrieves relevant data based on a query.
• Generates context for the answer engine.
• Uses Mistal LLM to provide answers.

Installation

Dependencies

To install the necessary dependencies, run:

pip install -r requirements.txt

Ollama Setup

Download Ollama from here Ollama

Run the following code after installing Ollama

ollama run mistral

Usage

Running the Application

To start the Flask application, run:

python app.py

Navigate to http://localhost:5000 in your web browser to access the application.

Processing PDFs

Add Pdf files at this location ./data/pdf

To process the PDFs and persist the data, send a POST request to the /persist endpoint:

curl -X POST http://localhost:5000/persist

Asking Questions

To ask a question and receive an answer, send a POST request to the /answer endpoint with the question in the request body

curl -X POST -H "Content-Type: application/json" -d '{"question": "Your question here"}' http://localhost:5000/answer

UI

This how the interactive UI looks