Using Knowledge Base for Retrieval-Augmented Generation (RAG)

Agent can implement Retrieval-Augmented Generation (RAG) by integrating knowledge bases, enabling it to retrieve relevant information from knowledge bases before generating responses, thereby improving the accuracy and timeliness of answers.

Adding Knowledge Bases in Agent

RAG is an AI solution that combines knowledge retrieval with large language model generation capabilities. It retrieves relevant information from knowledge bases before the model generates responses, inputting the retrieval results along with user questions to the large language model, thereby improving the accuracy and timeliness of responses.

Developers can switch to the Knowledge Base tab in the Agent visual editor, click the Add Knowledge Base button, select the target knowledge base element in the popup, and click the Confirm button. Agent supports adding multiple knowledge bases, each of which can be enabled or disabled, with the default being enabled. When a knowledge base is no longer needed, developers can click the delete button on the right to remove it from the Agent.

There is also a Let Large Language Model Decide Whether to Use switch, which is enabled by default. When enabled, it is in non-mandatory mode; when disabled, it is in mandatory mode. In mandatory mode, Agent will first query the knowledge base using the user input content before the initial request to the large language model, using the results as contextual supplement and enhancement. If developers disable it, the large language model will decide on its own whether to query and how to query the knowledge base. Regardless of whether Let Large Language Model Decide Whether to Use is enabled or disabled, developers can design knowledge base usage strategies in system prompts to guide the large language model's use of knowledge bases.

For knowledge base creation, please refer to AI Knowledge Base.