Large language models are typically trained on public data and require significant computational resources. As a result, they often lack up-to-date or domain-specific knowledge. To address this, Retrieval-Augmented Generation (RAG) technology is used. RAG matches user questions with the most relevant external data and uses that content as context for model responses.
What Is the AgentBuilder Knowledge Base?
- AgentBuilder provides a visual, user-friendly interface for managing personal or team knowledge bases.
- Knowledge bases can be quickly integrated into AI applications to enhance their capabilities.
- You can prepare and upload:
- Long text documents (TXT, Markdown, DOCX, HTML, JSONL, PDF)
- Structured data (CSV, Excel, etc.)
Example Scenario
- If your company wants to build an AI customer service assistant using existing product documentation, simply upload the documents to the AgentBuilder knowledge base and create a conversational application. This process, which once took weeks, is now fast and easy to maintain.
Knowledge Base and Documents
- In AgentBuilder, a knowledge base is a collection of documents.
- A knowledge base can be integrated into an application as a retrieval context.
- Documents can be uploaded by developers or team members, or synchronized from other data sources (each document usually corresponds to a single file in the data source).
This guide uses clear, concise language and removes redundant content. All steps use symbols, and the structure is optimized for easy understanding.