RAG-Powered Chatbot

Overview

This tutorial shows you how to build a Retrieval-Augmented Generation (RAG) chatbot that answers user questions by searching your knowledge base and using the results to generate accurate, context-aware responses.

How It Works

Step 1: Set Up Your Knowledge Base

First, create a folder for your documentation and ingest your files.

Create a Folder

const response = await fetch('https://api.Rayrift.com/v1/folders', {
  method: 'POST',
  headers: {
    'Authorization': 'sk_live_...',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    name: "Product Manuals",
    description: "User guides and documentation"
  })
});

const { id: folderId } = await response.json();
console.log('Folder ID:', folderId);

Ingest Documents

Upload your PDF manuals, FAQs, or other documentation:

// Upload a PDF file
const formData = new FormData();
formData.append('file', pdfFile);
formData.append('payload', JSON.stringify({
  folder_id: folderId
}));

const uploadResponse = await fetch('https://api.Rayrift.com/v1/documents', {
  method: 'POST',
  headers: {
    'Authorization': 'sk_live_...'
  },
  body: formData
});

const document = await uploadResponse.json();
console.log('Document uploaded:', document.id);

Step 2: Implement the Search Function

Create a function that searches your knowledge base:

async function searchKnowledgeBase(query, folderId) {
  const response = await fetch('https://api.Rayrift.com/v1/search', {
    method: 'POST',
    headers: {
      'Authorization': 'sk_live_...',
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      query: query,
      limit: 5,
      folder_id: folderId
    })
  });

  const { results } = await response.json();
  return results;
}

Step 3: Build the Chatbot Logic

Combine search results with an LLM to generate answers:

async function answerQuestion(userQuestion, folderId) {
  // Step 1: Search the knowledge base
  const searchResults = await searchKnowledgeBase(userQuestion, folderId);
  
  // Step 2: Build context from search results
  const context = searchResults
    .map((hit, index) => `[${index + 1}] ${hit.text}`)
    .join('\n\n');
  
  // Step 3: Create prompt for LLM
  const prompt = `You are a helpful assistant. Answer the user's question using only the information provided in the context below. If the context doesn't contain enough information, say so.

Context:
${context}

User Question: ${userQuestion}

Answer:`;

  // Step 4: Call your LLM (OpenAI, Anthropic, etc.)
  const llmResponse = await callLLM(prompt);
  
  return {
    answer: llmResponse,
    sources: searchResults.map(hit => ({
      text: hit.text.substring(0, 100) + '...',
      score: hit.score
    }))
  };
}

// Example LLM integration (using OpenAI)
async function callLLM(prompt) {
  const response = await fetch('https://api.openai.com/v1/chat/completions', {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${process.env.OPENAI_API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      model: 'gpt-4',
      messages: [{ role: 'user', content: prompt }],
      temperature: 0.7
    })
  });

  const data = await response.json();
  return data.choices[0].message.content;
}

Step 4: Create the Chat Interface

Build a simple chat interface:

// Express.js example
app.post('/api/chat', async (req, res) => {
  try {
    const { message, folderId } = req.body;
    
    const result = await answerQuestion(message, folderId);
    
    res.json({
      answer: result.answer,
      sources: result.sources
    });
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

Complete Example

Here’s a complete Node.js/Express implementation:

const express = require('express');
const fetch = require('node-fetch');
const app = express();

app.use(express.json());

const Rayrift_API_KEY = process.env.Rayrift_API_KEY;
const Rayrift_API_URL = 'https://api.Rayrift.com/v1';
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;

async function searchRayrift(query, folderId) {
  const response = await fetch(`${Rayrift_API_URL}/search`, {
    method: 'POST',
    headers: {
      'Authorization': Rayrift_API_KEY,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      query,
      limit: 5,
      folder_id: folderId
    })
  });
  
  const data = await response.json();
  return data.results || [];
}

async function generateAnswer(question, context) {
  const prompt = `Answer the following question using the provided context. If the context doesn't contain enough information, say so.

Context:
${context}

Question: ${question}

Answer:`;

  const response = await fetch('https://api.openai.com/v1/chat/completions', {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${OPENAI_API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      model: 'gpt-4',
      messages: [{ role: 'user', content: prompt }]
    })
  });

  const data = await response.json();
  return data.choices[0].message.content;
}

app.post('/chat', async (req, res) => {
  const { message, folderId } = req.body;
  
  try {
    // Search knowledge base
    const results = await searchRayrift(message, folderId);
    
    // Build context
    const context = results.map(r => r.text).join('\n\n');
    
    // Generate answer
    const answer = await generateAnswer(message, context);
    
    res.json({
      answer,
      sources: results.map(r => ({
        snippet: r.text.substring(0, 150),
        score: r.score
      }))
    });
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

app.listen(3000, () => {
  console.log('Chatbot server running on port 3000');
});

Best Practices

Improve Search Quality: Use specific folder IDs to scope searches to relevant documentation.

Handle Edge Cases: Check if search results are empty and provide appropriate fallback responses.

Show Sources: Display source snippets to users so they can verify information.

Rate Limiting: Implement rate limiting on your chat endpoint to prevent abuse.

Next Steps

Learn about Automated Content Analysis to keep your knowledge base up to date
Explore the Search API for advanced query options
Check out Folder Management for organizing large knowledge bases

Getting started

Tutorials

Overview

How It Works

Step 1: Set Up Your Knowledge Base

Create a Folder

Ingest Documents

Step 2: Implement the Search Function

Step 3: Build the Chatbot Logic

Step 4: Create the Chat Interface

Complete Example

Best Practices

Next Steps

​Overview

​How It Works

​Step 1: Set Up Your Knowledge Base

​Create a Folder

​Ingest Documents

​Step 2: Implement the Search Function

​Step 3: Build the Chatbot Logic

​Step 4: Create the Chat Interface

​Complete Example

​Best Practices

​Next Steps

Overview

How It Works

Step 1: Set Up Your Knowledge Base

Create a Folder

Ingest Documents

Step 2: Implement the Search Function

Step 3: Build the Chatbot Logic

Step 4: Create the Chat Interface

Complete Example

Best Practices

Next Steps