LlamaIndex (formerly GPT Index) is a data framework designed to help you build LLM applications over your private data. It provides a simple interface to connect your data with Large Language Models (LLMs), enabling you to:

Ingest and index your existing data
Create knowledge-based applications
Structure your data for various LLM tasks

Features

Data Connectors: Import data from various sources (PDFs, APIs, databases, etc.)
Data Indexes: Efficiently structure and store your data
Query Interface: Natural language querying of your data
Application Integrations: Easy integration with LangChain and other frameworks
Flexible LLM Support: Works with OpenAI, Anthropic, Hugging Face models, and more

Installation

bash
pip install llama-index-core llama-index-llms-openai

Basic Usage

1. Setting up

python
import os
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core import Settings
from llama_index.llms.openai import OpenAI

# Set your OpenAI API key
os.environ['OPENAI_API_KEY'] = 'your-api-key'

# Configure settings
Settings.llm = OpenAI(model="gpt-3.5-turbo")

2. Loading Documents

python
# Load documents from a directory
documents = SimpleDirectoryReader('data').load_data()

# Create an index
index = VectorStoreIndex.from_documents(documents)

3. Querying

python
# Create query engine
query_engine = index.as_query_engine()

# Ask questions
response = query_engine.query("What information do you have about X?")
print(response)