How to Implement a RAG System Using GPT-4o

GPT-4o, OpenAI’s latest flagship model, combines the power of a large language model (LLM) with extensive information and a specific custom knowledge base. In this guide, we'll walk you through setting up and implementing a Retrieval-Augmented Generation (RAG) system using GPT-4o.

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What is a RAG?

RAG is a framework that allows developers to integrate retrieval approaches with generative AI, combining the strengths of a large language model with a vast amount of information. This method enhances the model's ability to provide accurate and context-specific responses by accessing a custom knowledge base.


Getting Started: Setting Up Your OpenAI Account and API Key

To begin, you’ll need to create an OpenAI account. Visit the OpenAI Platform signup page and complete the registration form. After submitting your email and password, you’ll receive an activation email from OpenAI. If you don't see it in your inbox, check your spam folder or resend the verification email.

Once your account is verified, you can generate your API token. Navigate to the "API Keys" section in the left menu. If it’s your first time setting up your account, you’ll need to verify your phone number. After verification, you can create a new secret key. Remember to store this key securely, as it will only be displayed once.



Using Langchain's Approach on Talkdai/Dialog

What is Langchain?

Langchain is a framework that enables users to work with LLM models using chains—a concept that combines a prompt, an LLM model, and other extensible features. Langchain supports OpenAI and other LLM models, making it accessible to developers worldwide for creating advanced AI applications


What is Talkdai/Dialog?

Talkdai/Dialog, or simply Dialog, is an application designed to help users deploy LLM agents easily. It allows developers to deploy LLMs without needing extensive DevOps knowledge, enabling them to get started in less than a day.


Setting Up Dialog

Clone the Repository: In your terminal, navigate to your desired folder and clone the Dialog repository.

Add Required Files:

  • .env File: Copy the .env.sample file to the root directory and modify it with your OpenAI API key.
  • Prompt File: Define your prompt settings in a prompt.toml file (or any name you prefer).
  • CSV File: Prepare a CSV file with your specific knowledge base content. This CSV should have columns for category, subcategory, question, and content.


Configuring the Environment

.env File

Copy and modify the .env.sample file with your specific data, including your OpenAI API key.


Prompt.toml File

This file contains settings for the model, including temperature and other parameters, as well as the initial prompt that guides the agent's behavior.


Knowledge Base CSV

The CSV file should include:

  • Category: The category of the knowledge.
  • Subcategory: The subcategory of the knowledge.
  • Question: The question or title that the content addresses.
  • Content: The detailed content that will be used to generate responses.

Running the Application

With your environment set up, you can now run your application:


docker-compose up --build

Once the logs indicate "Application startup complete," open your browser and navigate to http://localhost:8000. Access the /ask endpoint, input your query in JSON format, and receive a response from GPT-4o.



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