AI Agents Know Where to Look: A Cross Knowledge Base Search Architecture

AI has been all the rage lately, and after seeing the variety of applications people are creating, I wanted to give it a shot myself! In this article, we’ll explore an architecture that uses AI agents to create a multi-knowledge base QnA chatbot. We’ll combine multiple agents for selector logic, summarization, aggregation, and refining questions based on conversation history. This will allow us to create a chatbot that can handle questions across multiple knowledge bases while keeping track of the context from previous interactions. By the end of the article, you’ll have a better understanding of the immense potential of AI agents in making complex tasks more manageable and unlocking valuable insights from various information sources. If the article is too long, here’s a sneak peek of the final architecture: <img alt="" src="https://miro.medium.com/v2/resize:fit:700/1*QGQSkSdDK-h2TT8muLmrqQ.png" style="height:746px; width:700px" /> Context-aware chatbot with cross-knowledge base search <h1>The Idea and the Problem</h1> I’ve already implemented a basic chatbot with a question-semantic search that essentially allows the app to talk to a specific piece of text. You can check out the helpful <a href="https://platform.openai.com/docs/tutorials" rel="noopener ugc nofollow" target="_blank">OpenAI tutorial here</a>! It’s surprisingly good — I can talk to a large piece of text about any detail it has! <a href="https://betterprogramming.pub/ai-agents-know-where-to-look-a-simple-cross-knowledge-base-search-architecture-60b3c6a9179b">Read More</a>