Core concepts

This chapter will guide you through the core concepts of Ragu.

Users

Users are the people who interact with Ragu. Users come with roles attached, which determine the level of access they have to the platform.

Ragu administrators have the highest level of access, and are the only users who can access the back office. Administrators are responsible for setting up the various different components of the platform, such as workflows, agents and knowledge bases.

Ragu users are the people who can utilise the platform to assists them in the various workflows administrators set up.

All Ragu users belong to specific groups. A user's groups dictate what agents or collections they can access.

Workflow

Workflows can be thought of as any process that can be quantized into steps.

For example, the creation and finalization of travel orders, submitting JIRA hours, inventory management, can all be thought of as workflows that have clear indications of when they start, what steps are required to perform them, and when they end.

A chat can also be thought of as a workflow. Unlike structured workflows tailored for specific tasks, chats do not have a clear indication of when they end. This makes them useful for processes such as onboarding employees or customer support. With chats users can finish these processes in a conversational manner instead of scraping through company documentation.

Agents

Every workflow consists of agents. Agents are large language models (LLMs) that have a specific context associated with them. An agent's context instructs its LLM on how it should behave and what it should expect from users. If you've ever customized ChatGPT before (told it your interests, what is should call you), you have worked with contexts before.

An agent can also have tools associated with them. Tools allow agents to integrate with other systems. They can transform agents from regular chat bots into problem solving powerhouses.

Any workflow can have an arbitrary number of agents associated with it, however in this guide we will focus on the simplest workflow - chats. A chat workflow has a single agent whose context is enriched with a knowledge base.

Knowledge base

A knowledge base consists of one or several collections that contain documents.

Both collections and documents are stand-alone entities that are managed independently of agents.

By having standalone knowledge bases, Ragu administrators have the ability to assign them to agents as they see fit with the click of a button.

Collections

Collections, as the name implies, are collections of documents that are available to an agent when it's asked to perform some task.

Whenever you assign a collection to an agent, you can instruct the agent on how to use the data it obtains from it at conversation time.

Documents

Documents are the basic building blocks of a collection, and in turn, knowledge bases. Currently, Ragu supports most textual document types.