Data, information, and knowledge are terms that are often confused when mentioned together. Simply put, these three words have a progressively deeper relationship, with knowledge being the most abstract and requiring a high degree of stability and reliability in its content.
We all know that both traditional machine learning models and large language models require a vast amount of data during the training process, and we always hope that this data is of high quality and reliability. Although many existing large language models already include an impressive collection of data and instructions, similar to how no human can become omniscient and omnipotent, these models often have "knowledge" blind spots.
By loading additional knowledge content, agents can learn and master parts of the knowledge that their original LLMs are not proficient in. Just like humans learn knowledge, agents can make themselves more knowledgeable through this method.
agentUniverse defines a standard knowledge format, which includes various knowledge data loading methods, and connections to diverse knowledge storage systems. You can define any form of knowledge data into standard knowledge components for agents and other components to use.
By now, you should have a basic understanding of the design principles behind knowledge components. In the next section, we will introduce you to the standard definitions of knowledge components, how to customize and create your own knowledge, and how to utilize knowledge.