Structured Data Extraction

LLMs are capable of ingesting large amounts of unstructured data and returning it in structured formats, and LlamaIndex is set up to make this easy.

Using LlamaIndex, you can get an LLM to read natural language and identify semantically important details such as names, dates, addresses, and figures, and return them in a consistent structured format regardless of the source format.

This can be especially useful when you have unstructured source material like chat logs and conversation transcripts.

Once you have structured data you can send them to a database, or you can parse structured outputs in code to automate workflows.

Core Guides

Quickstart

The simplest way to perform structured extraction is with our LLM classes. Take a look at the following starter resources: - Simple Guide to Structured Outputs

There are also relevant sections for our LLM guides: OpenAI, Anthropic, and Mistral.

In-depth Guides

For a more comprehensive overview of structured data extraction with LlamaIndex, including lower-level modules, check out the following guides. Check out our standalone lower-level modules like Pydantic programs or as part of a RAG pipeline. We also have standalone output parsing modules that you can use yourself with an LLM / prompt.

• Structured Outputs
• Pydantic Program
• Output Parsing

We also have multi-modal structured data extraction. Check it out.

Misc Examples

Some additional miscellaneous examples highlighting use cases:

• Extracting names and locations from descriptions of people
• Extracting album data from music reviews
• Extracting information from emails