Agents

Agents are the main way for your users to interact with knowledge bases in Ragu. They are essentially LLMs with some additional plumbing to enrich their context whenever you prompt them.

Metadata

Each agent has a name, description, and language. None of these settings influence the agent's behavior and exist solely for display purposes. They are intended to hint to users what an agent's purpose is, as well as the expected language of the agent.

Model

Each agent has a model. This is the LLM that will be used by the agent to answer questions. An LLM can receive general instructions on how to behave. We call these general instructions the agent's context.

The context is always sent as a system message to the LLM. This is a special kind of message the LLM will treat differently from chat messages and will use it to guide its behavior.

The context is written in second tense, as though you are referring directly to the agent. The bulk of an agent's behaviour is defined by its context.

The temperature of an agent's LLM is a value between 0 and 1 that controls the "creativity" of the agent's output. The higher the value, the more unhinged the agent will be. You usually want to keep this at a low value since high temperatures can make the agent start to make things up, or in other words, hallucinate.

Instructions

If you created your first agent you might have seen some instruction settings that we overlooked for the sake of brevity. These instructions are intented for the agent's underlying LLM and they instruct it on how to behave in specific situations.

All instructions have defaults. There is no need to set them explicitly unless you wish to fully customize the agent's behaviour.

The available instructions are as follows:

• Prompt instruction Gives the agent additional directives for answering prompts. Sent alongside every prompt as part of the user message and instructs the LLM on what to do with the prompt.

• Language instruction Gives the agent additional directives for answering prompts in a specific language. Sent alongside the context in the system message.

• Title instruction Gives the agent additional directives for generating titles. Whenever a conversation is started, the initial user prompt will be used to generate a title for it. This instruction can be used to customise the way the title is generated.

• Summary instruction Gives the agent additional directives for generating summaries. Whenever a conversation becomes too large, the agent will use this instruction to generate a summary of it. The summary will be used to replace the current conversation history. The summary will inevitably omit some information, but will keep the context size under control.

Message evaluation

Every message sent by an agent can be evaluated. You can use these evaluations to reason about an agent's performance.