One of the most common use-cases for an LLM application is to answer questions about a set of documents. LlamaIndex has rich support for many forms of question & answering.
Types of question answering use cases
Q&A has all sorts of sub-types, such as:
What to do
Semantic search: finding data that matches not just your query terms, but your intent and the meaning behind your question. This is sometimes known as “top k” search.
Summarization: condensing a large amount of data into a short summary relevant to your current question
Where to search
Over documents: LlamaIndex can pull in unstructured text, PDFs, Notion and Slack documents and more and index the data within them.
Over structured data: if your data already exists in a SQL database, as JSON or as any number of other structured formats, LlamaIndex can query the data in these sources.
How to search
Combine multiple sources: is some of your data in Slack, some in PDFs, some in unstructured text? LlamaIndex can combine queries across an arbitrary number of sources and combine them.
Route across multiple sources: given multiple data sources, your application can first pick the best source and then “route” the question to that source.
Multi-document queries: some questions have partial answers in multiple data sources which need to be questioned separately before they can be combined