LlamaIndex

https://llamaindex.ai

Introduction

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

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

Basic Usage

1. Setting up

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

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

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

3. Querying

# Create query engine
query_engine = index.as_query_engine()

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

Key Concepts

1. Data Connectors

LlamaIndex provides various data connectors to load your data:

File readers (PDF, Word, Text, etc.)
Database connectors
API connectors
Web scrapers

2. Data Indexes

Different types of indexes available:

Vector Store Index
List Index
Tree Index
Keyword Table Index

3. Query Types

Natural language queries
Structured queries
Hybrid queries

Advanced Features

Data Agents: Automated reasoning over your data
Evaluation Framework: Tools to evaluate response quality
Customization: Custom prompts, embeddings, and node parsers
Streaming Support: Real-time response streaming
Multi-modal: Support for text, images, and other data types

Best Practices

Choose appropriate index structures based on your use case
Implement proper error handling
Monitor token usage
Use caching when possible
Consider chunking strategies for large documents

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