How to Build a Mobile RAG Application in React Native

How do you build a mobile RAG application in React Native?

Build a mobile RAG app by implementing vector embeddings, local storage with SQLite or Realm, and semantic search. Combine retrieved context with LLM prompts for grounded responses. AI Mobile Launcher's RAG Pack includes OpenAI embeddings, chunking strategies, vector storage, and retrieval—all pre-configured for mobile deployment.

Retrieval Augmented Generation (RAG) enables AI apps to provide accurate, context-aware responses by retrieving relevant information before generating. This is essential for apps dealing with private data, documentation, or specialized knowledge bases.

What is RAG and why use it in mobile apps?

RAG solves a fundamental AI limitation—hallucination—by grounding responses in actual data:

• Accuracy - Responses based on your actual data, not model training
• Privacy - Keep sensitive data local on device
• Freshness - Update knowledge without retraining models
• Domain Expertise - Specialized knowledge for your industry
• Cost Efficiency - Smaller context windows, lower API costs

What architecture does a mobile RAG system need?

A complete mobile RAG implementation requires these components:

// RAG Architecture Overview
src/
├── features/
│   └── rag/
│       ├── services/
│       │   ├── embeddingService.ts    # Generate embeddings
│       │   ├── chunkingService.ts     # Split documents
│       │   ├── vectorStore.ts         # Store/search vectors
│       │   └── retrievalService.ts    # Semantic search
│       ├── hooks/
│       │   ├── useRAG.ts              # Main RAG hook
│       │   └── useDocumentLoader.ts   # Load documents
│       └── types/
│           └── rag.types.ts
├── database/
│   └── vectorDB.ts                    # SQLite vector storage
└── api/
    └── embeddings.api.ts              # OpenAI embeddings API

How do you generate embeddings in React Native?

Convert text to vector embeddings for semantic search:

// services/embeddingService.ts
import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

interface EmbeddingResult {
  text: string;
  embedding: number[];
}

export async function generateEmbedding(text: string): Promise<number[]> {
  const response = await openai.embeddings.create({
    model: 'text-embedding-3-small', // Cheaper, good for mobile
    input: text,
  });
  
  return response.data[0].embedding;
}

export async function generateBatchEmbeddings(
  texts: string[]
): Promise<EmbeddingResult[]> {
  const response = await openai.embeddings.create({
    model: 'text-embedding-3-small',
    input: texts,
  });
  
  return texts.map((text, i) => ({
    text,
    embedding: response.data[i].embedding,
  }));
}

// Cost: ~$0.02 per 1M tokens with text-embedding-3-small

How do you store vectors locally on mobile?

Use SQLite with a vector similarity extension or implement cosine similarity manually:

// database/vectorStore.ts
import * as SQLite from 'expo-sqlite';

interface VectorDocument {
  id: string;
  content: string;
  embedding: number[];
  metadata: Record<string, any>;
}

export class MobileVectorStore {
  private db: SQLite.SQLiteDatabase;
  
  async initialize() {
    this.db = await SQLite.openDatabaseAsync('vectors.db');
    await this.db.execAsync(`
      CREATE TABLE IF NOT EXISTS documents (
        id TEXT PRIMARY KEY,
        content TEXT NOT NULL,
        embedding TEXT NOT NULL,
        metadata TEXT
      );
      CREATE INDEX IF NOT EXISTS idx_documents_id ON documents(id);
    `);
  }
  
  async addDocument(doc: VectorDocument): Promise<void> {
    await this.db.runAsync(
      'INSERT OR REPLACE INTO documents (id, content, embedding, metadata) VALUES (?, ?, ?, ?)',
      [doc.id, doc.content, JSON.stringify(doc.embedding), JSON.stringify(doc.metadata)]
    );
  }
  
  async search(queryEmbedding: number[], limit: number = 5): Promise<VectorDocument[]> {
    // Fetch all documents (for small datasets)
    // For large datasets, use approximate nearest neighbor libraries
    const results = await this.db.getAllAsync<any>('SELECT * FROM documents');
    
    // Calculate cosine similarity for each
    const scored = results.map(doc => ({
      ...doc,
      embedding: JSON.parse(doc.embedding),
      metadata: JSON.parse(doc.metadata || '{}'),
      score: cosineSimilarity(queryEmbedding, JSON.parse(doc.embedding)),
    }));
    
    // Return top matches
    return scored
      .sort((a, b) => b.score - a.score)
      .slice(0, limit);
  }
}

function cosineSimilarity(a: number[], b: number[]): number {
  let dotProduct = 0;
  let normA = 0;
  let normB = 0;
  
  for (let i = 0; i < a.length; i++) {
    dotProduct += a[i] * b[i];
    normA += a[i] * a[i];
    normB += b[i] * b[i];
  }
  
  return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
}

How do you implement RAG retrieval and generation?

Combine semantic search with LLM generation:

// hooks/useRAG.ts
import { useState, useCallback } from 'react';
import { generateEmbedding } from '../services/embeddingService';
import { vectorStore } from '../database/vectorStore';

interface RAGResponse {
  answer: string;
  sources: Array<{ id: string; content: string; score: number }>;
}

export function useRAG() {
  const [isLoading, setIsLoading] = useState(false);

  const query = useCallback(async (question: string): Promise<RAGResponse> => {
    setIsLoading(true);
    
    try {
      // 1. Generate embedding for the question
      const queryEmbedding = await generateEmbedding(question);
      
      // 2. Search for relevant documents
      const relevantDocs = await vectorStore.search(queryEmbedding, 5);
      
      // 3. Build context from retrieved documents
      const context = relevantDocs
        .map(doc => doc.content)
        .join('\n\n---\n\n');
      
      // 4. Generate answer with context
      const response = await fetch('/api/chat', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({
          messages: [
            {
              role: 'system',
              content: `Answer based on the following context. If the answer isn't in the context, say so.\n\nContext:\n${context}`,
            },
            { role: 'user', content: question },
          ],
        }),
      });
      
      const data = await response.json();
      
      return {
        answer: data.content,
        sources: relevantDocs.map(doc => ({
          id: doc.id,
          content: doc.content.slice(0, 200) + '...',
          score: doc.score,
        })),
      };
    } finally {
      setIsLoading(false);
    }
  }, []);

  return { query, isLoading };
}

What are the best practices for mobile RAG?

• Chunk Wisely - Split documents into 500-1000 token chunks with overlap for context
• Cache Embeddings - Generate embeddings once, store locally for reuse
• Limit Vector Count - Keep under 10,000 vectors for mobile performance
• Use Hybrid Search - Combine vector search with keyword matching
• Show Sources - Display retrieved documents to build user trust

People Also Ask

Build RAG Apps with AI Mobile Launcher

For Developers: AI Mobile Launcher's RAG Pack includes document chunking, embedding generation, vector storage, semantic search, and LLM integration—all optimized for mobile deployment.

For Founders: Need a knowledge-based AI app for your business? Contact CasaInnov to build your custom RAG mobile application.