llamaindex

To connect LLMs to private data for retrieval-augmented generation, use a data framework to ingest documents, create vector indices, and query the indexed data using customizable query engines.

Ingesting documents from multiple data sources into a vector index is handled by data connectors supporting over 300 sources, creating efficient indices for fast data retrieval and natural language querying.

You can build autonomous agents that access your indexed documents to perform complex tasks, utilizing agent functionalities designed to use tools and execute advanced queries over your data.

The data framework supports different index types beyond standard vector indices, including list and tree indices, allowing flexible data structuring for efficient knowledge retrieval over private data.

To build a chatbot that answers questions from internal company documents, you need a data framework to load the files, create indices, and establish a query engine to retrieve information.

When using RAG capabilities for question-answering over external data sources, limitations depend on the efficiency of your chosen index type and the data connectors' compatibility with your sources.