These are the core concepts that Superagent uses to create LLM Agents. It contains practical examples as well as links to papers or sources.
memory
as well as a document
which gives the Agent the possibility to remember previous messages and do Question/Answering.
References:
Document
to an Agent
.
Documents are files, such as PDF, TXT, images, Markdown etc. that can be ingested by passing a publicly available URL
to the Superagent API.
Superagent splits these documents into smaller chunks and stores them in a vector database for use downstream.
This approach has some downsides when working with tabular data. We are looking into how to best approach this problem.
Agents
using prompts.
A prompt is piece of text that gives context to the LLM. It can contain instructions on how the Agent should act, input variables used for injecting data into the prompt.
Common examples of input_variables are human_input
, question
and chat_history
for in context memory.