LlamaIndex Has a Free API — Connect LLMs to Your Data

#ai #llamaindex #rag #python

LlamaIndex is a data framework for LLM applications. It handles ingesting, indexing, and querying your data with LLMs — the easiest way to build RAG.

What Is LlamaIndex?

LlamaIndex connects LLMs to your data sources. Load documents, build an index, query with natural language.

Features:

160+ data connectors (PDF, Notion, Slack, databases)
Multiple index types (vector, keyword, knowledge graph)
Built-in RAG pipeline
Agents and tools
Streaming support

Quick Start

pip install llama-index

5-Line RAG

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# Load documents
documents = SimpleDirectoryReader("data").load_data()

# Build index
index = VectorStoreIndex.from_documents(documents)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("What is the refund policy?")
print(response)

Advanced: Custom Pipeline

from llama_index.core import VectorStoreIndex
from llama_index.readers.web import SimpleWebPageReader
from llama_index.llms.openai import OpenAI

# Load web pages
docs = SimpleWebPageReader().load_data(["https://docs.example.com/api"])

# Custom LLM
llm = OpenAI(model="gpt-4o", temperature=0)

# Build and query
index = VectorStoreIndex.from_documents(docs)
engine = index.as_query_engine(llm=llm)
result = engine.query("How do I authenticate?")