Getting Started with RAG

Retrieval-Augmented Generation (RAG) is transforming how we build AI applications. Instead of relying solely on a language model's training data, RAG combines the power of information retrieval with generative AI.

What is RAG?

RAG is a technique that enhances AI responses by:

1. Retrieving relevant information from a knowledge base
2. Augmenting the prompt with this context
3. Generating accurate, grounded responses

This approach solves common LLM problems like hallucinations and outdated information.

Why Use RAG?

Accuracy

Your AI answers are grounded in actual documents, not just the model's training data.

Privacy

Keep sensitive data in your own database instead of fine-tuning models.

Up-to-date Information

Add new documents without retraining expensive models.

Cost-Effective

Use smaller models with retrieval instead of massive fine-tuned models.

How RAG Works

javascript
// 1. User asks a question
const question = "What is our refund policy?";

// 2. Search your documents
const relevantDocs = await search(question);

// 3. Build context from results
const context = relevantDocs.map(d => d.content).join('\n');

// 4. Generate answer with context
const answer = await llm.generate({
  prompt: `Context: ${context}\n\nQuestion: ${question}`
});

Building Your First RAG App

Step 1: Upload Documents

javascript
import { EasyRAG } from '@easyrag/sdk';

const client = new EasyRAG(apiKey);

await client.upload('my-dataset', [
  policies.pdf,
  documentation.docx,
  faq.md
]);

Step 2: Search

javascript
const results = await client.search(
  'my-dataset',
  'What is the refund policy?'
);

console.log(results.data); // Relevant chunks with scores

Step 3: Query

javascript
const answer = await client.query(
  'my-dataset',
  'What is the refund policy?'
);

console.log(answer.data.result); // AI-generated answer

Common Use Cases

Customer Support

Answer customer questions from your documentation automatically.

Internal Knowledge Base

Help employees find information across all company documents.

Research Assistant

Search through papers and generate literature reviews.

Legal Document Analysis

Find relevant clauses and precedents in contracts.

Best Practices

1. Chunk Your Documents Properly

javascript
await client.upload('dataset', file, {
  chunkSize: 500,      // Adjust based on your content
  chunkOverlap: 50     // Maintain context between chunks
});

2. Use Metadata for Filtering

javascript
await client.upload('dataset', file, {
  metadata: {
    'file.pdf': {
      department: 'legal',
      year: 2024,
      confidential: false
    }
  }
});

3. Filter Your Searches

javascript
const results = await client.search('dataset', question, {
  filters: [
    { key: 'department', match: { value: 'legal' } }
  ]
});

4. Stream Responses for Better UX

javascript
for await (const chunk of client.queryStream('dataset', question)) {
  if (chunk.delta) {
    process.stdout.write(chunk.delta);
  }
}

Measuring Success

Track these metrics:

• Retrieval Precision: Are the right documents being found?
• Answer Quality: Are the generated responses accurate?
• Response Time: How fast are queries processed?
• User Satisfaction: Are users finding what they need?

Next Steps

1. Sign up for EasyRAG
2. Upload your first documents
3. Try searching and querying
4. Build your first RAG-powered feature

Ready to get started? Check out our Quick Start Guide or explore the SDK documentation.

Resources

• Documentation
• API Reference
• SDK Guide

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Have questions? Reach out to us at support@easyrag.com