What is RAG?
Retrieval-Augmented Generation (RAG) is the most important pattern for building production AI applications. It connects LLMs to your own data, enabling accurate answers based on your documents, databases, and knowledge bases.
Why LlamaIndex?
LlamaIndex is the leading framework for RAG applications. It handles document ingestion, chunking, embedding, retrieval, and response generation with minimal code.
Quick Start
pip install llama-index
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
# Load documents
documents = SimpleDirectoryReader("data").load_data()
# Create index
index = VectorStoreIndex.from_documents(documents)
# Query
query_engine = index.as_query_engine()
response = query_engine.query("What is this document about?")
print(response)
Key Concepts
Document Ingestion
LlamaIndex handles PDFs, Word docs, web pages, databases, and more. Documents are automatically parsed and chunked into manageable pieces.
Embeddings
Documents are converted to vector embeddings for semantic search. You can use OpenAI, Hugging Face, or local embedding models.
Retrieval Strategies
Choose from simple similarity search, hybrid search (keyword + semantic), or agent-based retrieval for complex queries.
Advanced Features
- Multi-document queries across different data sources
- Structured data extraction from unstructured documents
- Chat history and conversation memory
- Custom retrieval strategies for specific use cases