LlamaIndex for RAG
LlamaIndex is a framework specifically designed for building RAG and data‑augmented LLM applications. It emphasises indexing and retrieval, with powerful query engines and data connectors.
LlamaIndex is data‑centric: focus on indexing, retrieval, and structured queries.
Basic RAG Pipeline with LlamaIndex
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
# Load documents
documents = SimpleDirectoryReader("data").load_data()
# Build index (chunks, embeds, stores)
index = VectorStoreIndex.from_documents(documents)
# Query engine
query_engine = index.as_query_engine(llm=OpenAI(model="gpt-3.5-turbo"))
response = query_engine.query("What is RAG?")
print(response)Key Features
- Data connectors: Load from PDFs, databases, APIs, Notion, Slack, etc.
- Index types: Vector, summary, keyword, tree, knowledge graph.
- Retrievers: Vector, BM25, custom.
- Query engines: Simple, router, sub‑question, custom.
- Chat engines: Conversational memory.
Router Query Engine
Routes queries to different indexes based on content (e.g., summarisation vs. detailed retrieval).
from llama_index.core.tools import QueryEngineTool
from llama_index.core.query_engine import RouterQueryEngine
tools = [QueryEngineTool.from_defaults(summary_engine, name="summary"), ...]
router = RouterQueryEngine.from_defaults(tools)When to Use LlamaIndex
Best for complex indexing, structured data, and when you need advanced query routing. Simpler API than LangChain for basic RAG.
Two Minute Drill
- LlamaIndex is data‑centric, built for RAG.
- `VectorStoreIndex.from_documents()` builds index.
- `as_query_engine()` creates a Q&A interface.
- Router query engine handles multiple indexes.
Need more clarification?
Drop us an email at career@quipoinfotech.com