Getting Started - Guardrails AI

In this notebook, we will go through the basics of creating a RAIL spec and using Guardrails to enforce it. Our goal is to extract detailed patient information from a medical record. We will use a simple example to demonstrate how Guardrails can be used to enforce a RAIL spec. As an example, we will use the following medical record: We want our extracted information to contain the following fields: To get started, install the guardrails package with pip. At the heart of Guardrails is the RAIL spec. RAIL is a flavor of XML (standing for Reliable AI markup Language) that describes the expected structure and type of the output of the LLM, the quality criteria for the output to be valid and corrective actions to be taken if the output is invalid. A RAIL spec is composed of 3 main components: We start by creating the output schema. The output schema is a RAIL spec that describes the expected structure and type of the output of the LLM. As mentioned earlier, we want our extracted information

In this notebook, we will go through the basics of creating a RAIL spec and using Guardrails to enforce it. Our goal is to extract detailed patient information from a medical record. We will use a simple example to demonstrate how Guardrails can be used to enforce a RAIL spec. As an example, we will use the following medical record: We want our extracted information to contain the following fields: To get started, install the guardrails package with pip. At the heart of Guardrails is the RAIL spec. RAIL is a flavor of XML (standing for Reliable AI markup Language) that describes the expected s