LLamaIndex includes engines that enable you to design domain-specific, context-augmented LLM applications and support tasks like data ingestion, vector indexing and natural language question-answering on documents, to name a few. Our teams used LlamaIndex to build a retrieval-augmented generation (RAG) pipeline that automates document ingestion, indexes document embeddings and queries these embeddings based on user input. Using LlamaHub, you can extend or customize LlamaIndex modules to suit your needs and build, for example, LLM applications with your preferred LLMs, embeddings and vector store providers.
LlamaIndex is a data framework designed to facilitate the integration of private or domain-specific data with large language models (LLMs). It offers tools for ingesting data from diverse sources — including APIs, databases and PDFs — and structures this data into a format that LLMs can easily consume. Through various types of "engines," LlamaIndex enables natural language interactions with this structured data, making it accessible for applications ranging from query-based retrieval to conversational interfaces. Similar to LangChain, LlamaIndex’s goal is to accelerate development with LLMs, but it takes more of a data framework approach.
