Create Retrieval-Augmented Generation RAG application in Python From Scratch Ollama Llama LangChain

Science & Technology

Introduction

In this article, we will explore the process of building a prototype for a Retrieval-Augmented Generation (RAG) application in Python from the ground up. The focus will be on leveraging the Ollama framework, the Llama 3.1 large language model (LLM), and the LangChain Python framework.

Introduction

RAG applications combine traditional retrieval mechanisms with the generative capabilities of large language models. With a well-designed RAG system, complex queries can be answered efficiently by retrieving relevant information from a dataset, and then generating answers that are well-structured and comprehensible.

Real-World Application Overview

To demonstrate the effectiveness of the RAG application we will develop, we'll consider a case where we have payroll data represented in a table. This artificially generated table includes names, work hours, regular hourly rates, overtime hours, and overtime hourly rates.

For example, if we query, "Can you calculate the salary of Emily Gaus?" the model takes only a few seconds to process the information and provide the correct output, including a breakdown of calculations saved to a file. This allows us to validate the model's response against the original data.

In another scenario, we can use the application to analyze fictional biographies, such as that of "Alexus Gaus," an imaginary character. The model will be tasked with explaining Alexus's economic circumstances based purely on the provided biography. It should derive insights like his father’s occupation and the historical context, resulting in a well-reasoned answer.

RAG System Structure

A typical RAG application consists of five subsystems:

  1. Loading and Parsing Data: This subsystem loads and parses textual and tabular data into Python.
  2. Text Splitting: The loaded text is split into smaller chunks, increasing efficiency in indexing and storage.
  3. Embedding and Storing Data: Text chunks are transformed into numerical embeddings using models and stored in a retrievable database.
  4. Retrieval System: The retrieval subsystem searches the database based on user input and fetches relevant text chunks.
  5. Generation System: This subsystem uses a large language model to generate final answers based on the retrieved information.

Prerequisites

To follow along with the tutorial, you will need:

  • A computer with Python (tested with Python 3.12).
  • A GPU capable of running models (tested on Nvidia 39 GPU with 24 GB of VRAM).
  • The Ollama model framework installed on your system.

Installation Steps

  1. Install Ollama: Download and install the Ollama framework.
  2. Install Llama 3.1: Pull the model using Ollama commands.
  3. Install Nomic Embed Text: For text embeddings.
  4. Create Workspace and Virtual Environment: Set up a folder and a Python virtual environment.
  5. Install Required Python Libraries: Libraries include LangChain, PyPDF, ChromaDB, etc.

Coding the RAG Application

Code starts with importing necessary libraries, followed by defining model parameters. We will segment the process into various code blocks:

  1. Import Libraries (OS, LangChain, Ollama, adding that these libraries help facilitate document retrieval and embedding).
  2. Load and convert PDF files to usable text.
  3. Split the text into manageable chunks while retaining contextual overlap.
  4. Create embeddings for each chunk and store them in a retrievable database.
  5. Load models for both embedding and text generation.
  6. Implement retrieval and generation processes, integrating user queries and interfacing with the LLM for final responses.

Running the Application

After coding, the application is run through various queries to validate its effectiveness. Typical questions include salary calculations for employees or analyzing fictional biographies. The output is checked to ensure accuracy, which is reflected in the generated files.

Conclusion

By following the detailed steps outlined above, you can build a powerful RAG application in Python. Such applications are immensely beneficial for various data-driven tasks, enabling intelligent data retrieval and processing to yield insightful answers effectively.

Keywords

  • Retrieval-Augmented Generation
  • Python
  • Ollama
  • Llama 3.1
  • LangChain
  • Large Language Model
  • Text Embedding
  • Document Parsing

FAQ

What is RAG?
RAG stands for Retrieval-Augmented Generation, a method that combines information retrieval and generative language models to answer user queries.

What prerequisites are needed to build a RAG application?
You will need Python, a decent GPU, and the installation of the Ollama framework and related libraries.

Which libraries are used in this RAG application?
Libraries include LangChain, PyPDF, ChromaDB, and others for facilitating document handling and embedding.

How is the data processed in a RAG system?
Data is loaded, parsed, split into chunks, embedded, stored in a database, and then retrieved for generating responses using a large language model.

Can the RAG application handle various file types?
Yes, the implementation can process PDF files and potentially extend to other document formats with modifications.