Llama Index — Data Framework for LLM Applications

What is LlamaIndex?

LlamaIndex is a data framework that connects LLMs to your data. It handles the entire RAG pipeline — data ingestion from 160+ sources, chunking, embedding, indexing, retrieval, and response synthesis. While LangChain focuses on chains and agents, LlamaIndex focuses on making your data queryable by LLMs. The two are complementary and often used together.

Answer-Ready: LlamaIndex is a data framework for LLM applications. Handles RAG end-to-end: 160+ data connectors, automatic chunking/embedding, multiple index types, and query engines. Used for production document Q&A, chatbots, and knowledge bases. LlamaCloud for managed RAG. 38k+ GitHub stars.

Best for: Teams building document Q&A and RAG applications. Works with: OpenAI, Claude, any LLM; 20+ vector stores. Setup time: Under 3 minutes.

Core Features

1. Data Connectors (160+)

# Local files
from llama_index.core import SimpleDirectoryReader
docs = SimpleDirectoryReader("./data").load_data()

# Web
from llama_index.readers.web import SimpleWebPageReader
docs = SimpleWebPageReader().load_data(["https://docs.example.com"])

# Database
from llama_index.readers.database import DatabaseReader
reader = DatabaseReader(uri="postgresql://...")
docs = reader.load_data(query="SELECT * FROM articles")

# APIs: Notion, Slack, Google Drive, GitHub, Confluence, etc.

2. Index Types

3. Query Engines

# Simple Q&A
query_engine = index.as_query_engine()

# Chat (with memory)
chat_engine = index.as_chat_engine()

# With reranking
from llama_index.postprocessor.cohere_rerank import CohereRerank
query_engine = index.as_query_engine(
    node_postprocessors=[CohereRerank(top_n=3)],
)

# Sub-question decomposition
from llama_index.core.query_engine import SubQuestionQueryEngine
query_engine = SubQuestionQueryEngine.from_defaults(query_engine_tools=[...])

4. Agents

from llama_index.core.agent import ReActAgent
from llama_index.core.tools import QueryEngineTool

tools = [
    QueryEngineTool.from_defaults(query_engine=policy_engine, name="policy", description="Company policies"),
    QueryEngineTool.from_defaults(query_engine=product_engine, name="product", description="Product documentation"),
]

agent = ReActAgent.from_tools(tools)
response = agent.chat("What is the return policy for electronics?")

5. Evaluation

from llama_index.core.evaluation import FaithfulnessEvaluator, RelevancyEvaluator

faithfulness = FaithfulnessEvaluator()
relevancy = RelevancyEvaluator()

result = faithfulness.evaluate_response(query="...", response=response)
print(f"Faithful: {result.passing}")

LlamaIndex vs LangChain

FAQ

Q: Can I use Claude with LlamaIndex? A: Yes, set llm = Anthropic(model="claude-sonnet-4-20250514") as the LLM backend.

Q: What is LlamaCloud? A: Managed RAG infrastructure by LlamaIndex. Handles parsing, indexing, and retrieval as a service.

Q: Can I use it with LangChain? A: Yes, LlamaIndex query engines can be used as LangChain tools. They are complementary.