Learn how to create Hybrid Search applications using Vertex AI Vertex Search to combine semantic searching with keyword search to return results based on both semantic meaning and keyword matching.
Learn how to build your own Retrieval-Augmented Generation (RAG) solutions for greater control and flexibility than out-of-the-box implementations. Create a custom RAG solution using Vertex AI APIs, vector stores, and the LangChain framework.
這堂課程會說明 BigQuery 中的檢索增強生成 (RAG) 解決方案,協助您減少 AI 幻覺。當中介紹的 RAG 工作流程包含建立嵌入項目、搜尋向量空間,以及生成更符合需求的答案。另外,這堂課程會解釋這些步驟背後的概念與原因,以及實際運用 BigQuery 實作的方法。完成課程之後,學員將學會使用 BigQuery,以及 Gemini 和嵌入模型等生成式 AI 模型,建立 RAG pipeline 來處理自己的 AI 幻覺應用實例。
This lab tests your ability to develop a real-world Generative AI Q&A solution using a RAG framework. You will use Firestore as a vector database and deploy a Flask app as a user interface to query a food safety knowledge base.