Build a RAG System with Claude & ChatGPT APIs

What We’re Building

In this tutorial, we will build a Retrieval-Augmented Generation (RAG) system that combines the capabilities of Claude and ChatGPT APIs. This system will efficiently fetch relevant data from a document repository and generate contextually rich responses using advanced language models.

The final application will allow users to input queries, retrieve pertinent information from a pre-indexed document set, and use AI models to generate comprehensive answers. This integrated approach offers robust performance in applications requiring high accuracy and relevance, such as customer support systems and research assistants.

Setup and Installation

We will begin by setting up our development environment and installing necessary libraries. This involves setting up Node.js and installing the required SDKs for accessing the APIs.

npm install openai anthropic dotenv

Next, we’ll configure our environment variables to securely store our API keys. Create a .env file in your project root and add the following variables:

OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
  

Step 1: Setting Up the Document Repository

First, we need to establish a document repository that our RAG system can query. We’ll use a simple JSON file to simulate this repository. Ensure the data is structured for quick access and relevance scoring.

const fs = require('fs');
const documents = JSON.parse(fs.readFileSync('documents.json', 'utf8'));

function getRelevantDocuments(query) {
  // Simple keyword matching for relevance
  return documents.filter(doc => doc.text.includes(query));
}

module.exports = { getRelevantDocuments };
  

This code reads a JSON file containing our documents and filters them based on the query. The getRelevantDocuments function will be used later to fetch relevant documents for any given query.

Step 2: Integrating Claude and ChatGPT APIs

Next, we’ll set up the integration with Claude and ChatGPT APIs to process and generate responses. This involves configuring both APIs and establishing a connection to send and receive data.

require('dotenv').config();
const { OpenAI } = require('openai');
const { Anthropic } = require('anthropic');

const openAIClient = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const anthropicClient = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });

async function generateResponseWithClaude(prompt) {
  const response = await anthropicClient.chat.completions.create({
    model: "claude-3-5-sonnet-20241022",
    messages: [{ role: "user", content: prompt }]
  });
  return response.data.choices[0].message.content;
}

async function generateResponseWithChatGPT(prompt) {
  const response = await openAIClient.chat.completions.create({
    model: "gpt-4o",
    messages: [{ role: "user", content: prompt }]
  });
  return response.data.choices[0].message.content;
}

module.exports = { generateResponseWithClaude, generateResponseWithChatGPT };
  

This code establishes connections to both APIs using the modern SDKs. It defines functions to send a prompt to each service and receive a generated response, which will be used to produce the final answer.

Step 3: Building the Query Handling Logic

We’ll now build the core logic to handle user queries. This involves retrieving relevant documents and using our integrated AI functions to generate a cohesive response.

const { getRelevantDocuments } = require('./documentRepository');
const { generateResponseWithClaude, generateResponseWithChatGPT } = require('./aiIntegrations');

async function handleUserQuery(query) {
  const relevantDocs = getRelevantDocuments(query);
  const combinedContext = relevantDocs.map(doc => doc.text).join('\n');
  const prompt = `Based on these documents:\n${combinedContext}\nAnswer the following question: ${query}`;

  const claudeResponse = await generateResponseWithClaude(prompt);
  const chatGPTResponse = await generateResponseWithChatGPT(prompt);

  return {
    claude: claudeResponse,
    chatGPT: chatGPTResponse
  };
}

module.exports = { handleUserQuery };
  

This function combines document retrieval and AI processing to form a complete query handling mechanism. It gathers context from relevant documents and sends it to both Claude and ChatGPT for response generation, allowing you to compare or combine their outputs as needed.

Testing Your Implementation

Once the setup is complete, it’s crucial to test your application to ensure everything is functioning as expected. You can create a simple script to simulate user queries and verify the responses.

const { handleUserQuery } = require('./queryHandler');

(async () => {
  const query = "How does the RAG system work?";
  const responses = await handleUserQuery(query);

  console.log("Claude's response:", responses.claude);
  console.log("ChatGPT's response:", responses.chatGPT);
})();
  

Running this script should output responses from both Claude and ChatGPT, allowing you to assess their quality and relevance based on the provided document context.

What to Build Next

• Integrate a user interface using React to make the RAG system interactive and user-friendly.
• Add a feedback mechanism to improve the relevance and quality of the responses based on user input.
• Implement advanced natural language processing techniques to enhance document retrieval accuracy.