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LlamaIndex

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Introduction

AI data framework for knowledge management

Added on: Mar 12, 2026

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LlamaIndex Product Information

LlamaIndex Overview

LlamaIndex is an open-source data framework for building AI applications that connects large language models to external data sources enabling developers to create powerful retrieval augmented generation systems.

This product stands out with features such as:

  • Data Connectors: Connect LLMs to any external data source
  • RAG Framework: Build retrieval augmented generation pipelines
  • Index Types: Multiple indexing strategies for different use cases
  • Query Engine: Flexible querying over indexed data collections
  • Open Source: Free to use with active community support

How to Use Llamaindex

Get started in a few simple steps

1

Install LlamaIndex

Install LlamaIndex in your Python environment using pip and import it into your project.

2

Connect Your Data

Use data connectors to load documents from files, databases, APIs, or other external sources.

3

Build Your Index

Create an appropriate index type for your use case to make your data queryable by LLMs.

4

Query Your Data

Use the query engine to ask natural language questions and receive answers from your indexed data.


LlamaIndex's Core Features in Detail

Powerful features from LlamaIndex

Data Connector Library

Provides connectors for hundreds of data sources making it easy to integrate any external data with LLMs.

Flexible Indexing

Offers multiple index types optimized for different retrieval patterns and use case requirements.

Active Community

Benefits from a large active open source community continuously adding connectors and improving capabilities.


LlamaIndex Use Cases

Discover how LlamaIndex can benefit different users

AI Developers

Build RAG-powered applications that ground LLM responses in your specific proprietary data sources.

Data Engineers

Create pipelines that connect organizational data to AI systems for intelligent retrieval and question answering.

Researchers

Experiment with advanced retrieval and indexing strategies for AI systems using a flexible open framework.