LlamaIndex
Open-source project
LlamaIndex is an open-source data framework that helps developers connect large language models to external data sources, primarily by building indexes for retrieval-augmented generation (RAG) applications.
Definition
LlamaIndex is an open-source data framework that helps developers connect large language models to external data sources, primarily by building indexes for retrieval-augmented generation (RAG) applications.
Overview
LlamaIndex provides tools to ingest data from documents, databases, APIs, and other sources, transform it into embeddings stored in a vector database, and retrieve the most relevant chunks of that data to feed into an LLM at query time. This retrieval step is the core mechanism behind retrieval-augmented generation, which lets language models answer questions grounded in data they were not originally trained on. LlamaIndex offers a range of indexing strategies and query engines suited to different data structures, and it is commonly used alongside LangChain for building the broader application logic around an LLM, or with agent frameworks like LangGraph to give agents access to external knowledge. It is a common building block in courses on Retrieval-Augmented Generation.
Key Features
- Data ingestion connectors for documents, databases, and APIs
- Indexing and chunking strategies for large document sets
- Integration with vector databases for semantic retrieval
- Query engines for retrieval-augmented generation
- Compatibility with multiple LLM providers