LlamaIndex

What is LlamaIndex?

LlamaIndex is a data framework for connecting your own data with large language models (LLMs). It acts as a bridge between data sources — APIs, PDFs, documents, SQL databases, and more — and LLM applications, so those applications can access and reason over your data. It serves both individual developers on GitHub and enterprises looking for a managed solution.

Top Features of LlamaIndex

1. Data Ingestion: Load data from over 160 different sources and formats, including unstructured, semi-structured, and structured data such as APIs, PDFs, documents, and SQL databases.
2. Data Indexing: Store and organize your data, with connections to vector stores, document stores, graph stores, and SQL database providers.
3. Query Interface: Ask questions against your own data and get LLM responses informed by that knowledge.
4. End-User Application Development: Build applications such as chatbots, knowledge agents, and tools for structured analysis.
5. Flexible Data Integration: Supports unstructured, structured, and semi-structured data.

What is LlamaIndex used for?

• Connecting various data formats for use with LLM applications
• Storing and indexing data across different project types
• Running prompts that return responses enriched with your data
• Building chatbots, knowledge agents, and structured analytics tools

Who is LlamaIndex for?

• Developers
• Enterprises

Getting Started

1. Data Integration: Load data from over 160 sources and formats — unstructured, semi-structured, or structured.
2. Data Indexing: Store and index your data, connecting it with vector, document, graph, and SQL database providers.
3. Querying: Orchestrate LLM workflows over your data, from simple prompt chains to advanced Retrieval-Augmented Generation (RAG) and agents.
4. Evaluation: Measure retrieval quality and LLM response quality with built-in modules, and integrate with observability partners.
5. Language Support: LlamaIndex works with both Python and TypeScript.