RAG with LlamaIndex - Document Loaders and Vector Index Types

LlamaIndex provides a high-level RAG framework that handles document loading, chunking, embedding, indexing, and querying with minimal boilerplate code. ShopMax India can use LlamaIndex to rapidly prototype and deploy RAG pipelines without writing low-level vector store and retrieval code. LlamaIndex's document loaders support PDFs, CSVs, web pages, and databases - covering all the product documentation sources ShopMax India uses.

LlamaIndex organizes RAG into three building blocks: Document (raw input), Node (chunked unit stored in the index), and Index (the searchable data structure). The VectorStoreIndex is the most common index type and uses embeddings for semantic search. The query engine wraps the index with retrieval and synthesis logic, returning answers with source attribution. LlamaIndex integrates with Anthropic Claude as the LLM and OpenAI or HuggingFace for embeddings via its Settings object.

The following example builds a LlamaIndex RAG pipeline for ShopMax India product documentation. Documents are loaded from text, indexed with Claude as the LLM, and queried through LlamaIndex's query engine with automatic source citation.

It gives the following output,

Q: What is the return policy for Sony headphones?
A: The Sony WH-1000XM5 has a 7-day return policy and accepts opened box returns.

Q: Which products are available in Delhi?
A: The Dell XPS 15 9530 laptop is available in Delhi, Mumbai, and Bangalore. Pan-India delivery is available for the Samsung Galaxy S24 Ultra, which also covers Delhi.

Q: What camera specs does the Samsung Galaxy S24 Ultra have?
A: The Samsung Galaxy S24 Ultra has a 200MP main camera and a 12MP ultrawide camera.

For ShopMax India, use LlamaIndex's SimpleDirectoryReader to automatically load and index all product PDF spec sheets from a folder, eliminating manual document preparation. Switch to ChromaVectorStore for persistent storage so the index survives service restarts. Enable response_mode='compact' in the query engine to reduce token usage by merging retrieved nodes before synthesis. Use the RetrieverQueryEngine with a custom postprocessor to apply metadata filters - for example, restricting results to products available in the customer's city based on their profile.