Building RAG Systems: From Basics to Production

RAG: Give LLMs Long-Term Memory

What is RAG?

Retrieval-Augmented Generation combines:

1. Information retrieval (search)
2. Large language models (generation)

Result: LLMs that can access and reason over your specific documents.

Why RAG?

• Up-to-date info: LLMs trained on old data
• Private data: Your docs not in training data
• Reduced hallucinations: Ground responses in facts
• Cost-effective: vs fine-tuning for every update
• Transparency: Can cite sources

RAG Architecture

1. Document Processing

• Load documents (PDF, TXT, HTML, etc.)
• Split into chunks (typically 500-1000 tokens)
• Create embeddings for each chunk
• Store in vector database

2. Query Processing

• User asks a question
• Convert question to embedding
• Search vector DB for similar chunks
• Retrieve top-k most relevant (k=3-5)

3. Generation

• Combine retrieved chunks with question
• Send to LLM with prompt template
• LLM generates answer based on context
• Return answer with sources

Key Components

Embedding Models

Indian Options:

• Sarvam AI Embeddings: Best for Indian languages
• OpenAI text-embedding-3-small: Good quality, $0.02/1M tokens
• Open-source options:
  • bge-large-en-v1.5 (Chinese, but works well)
  • e5-large-v2 (Microsoft, free)
  • instructor-large (versatile)

Vector Databases

Free/Cheap options for India:

• Qdrant: Open-source, easy to use
• Weaviate: Good for hybrid search
• ChromaDB: Simple, runs locally
• Pinecone: Managed, free tier 100K vectors
• Supabase pgvector: If already using Supabase

LangChain vs LlamaIndex

• LangChain: More features, steeper curve
• LlamaIndex: Focused on RAG, easier start
• Both: Good documentation, active community

Advanced RAG Techniques

1. Chunking Strategies

• Fixed-size: Simple, 500 tokens
• Semantic: Split on topics/paragraphs
• Sliding window: Overlap for context
• Hierarchical: Summaries + details

2. Retrieval Methods

• Dense retrieval: Vector similarity
• Sparse retrieval: BM25/TF-IDF
• Hybrid: Combine both (best results)
• Re-ranking: Use cross-encoder

3. Query Transformation

• Query expansion: Add related terms
• Hypothetical answers: Generate expected answer first
• Multi-query: Ask multiple ways

Production Considerations

• Caching: Cache embeddings and common queries
• Monitoring: Track retrieval quality and latency
• Cost optimization: Batch operations, use cheaper models
• Security: Ensure proper access controls

Build Your First RAG System

Weekend project: Build a RAG chatbot for your documents

1. Install LangChain/LlamaIndex
2. Set up ChromaDB locally
3. Use OpenAI embeddings (free trial)
4. Upload 5-10 PDFs
5. Build simple Streamlit UI
6. Deploy on Streamlit Cloud (free)

Resources

• LangChain RAG Tutorial
• LlamaIndex Quickstart
• Pinecone Learning Center
• AI Jason RAG tutorials (YouTube)