Build Managed RAG from Scratch with Gemini API File Search (Deep Dive & Demo) скачать в хорошем качестве

Build Managed RAG from Scratch with Gemini API File Search (Deep Dive & Demo)

Learn how to build Managed RAG (Retrieval-Augmented Generation) applications directly through the Gemini API using the new File Search tool. Disclaimer: The views and opinions expressed in this video are my own and do not necessarily reflect the official policy or position of any past or present employer. In this deep dive, I go beyond the broad architecture to show you exactly what happens under the hood, automating the complex steps of document ingestion, chunking, and retrieval. If you are a developer looking to streamline your AI workflows without managing complex vector databases, this video is for you. 🚀 In this video, you will learn: Traditional RAG vs. Gemini File Search: How Gemini automates the "hard parts" of RAG (chunking, tokenization, embedding creation). Live Demo: A step-by-step guide to creating a File Store, uploading documents, and indexing 50+ page PDFs in seconds. Advanced Configurations: How to control chunking strategies and add custom metadata via the API. Pricing & Limits: A breakdown of the generous pricing (free storage/queries) and enterprise-grade limits (up to 1TB). Supported Formats: Using PDFs, SQL, Microsoft Office files, and more with Gemini. 🔗 Resources Mentioned: Gemini API Documentation: https://ai.google.dev/gemini-api/docs... ⏱️ Timestamps: 0:00 - Introduction to Gemini File Search API 0:52 - Traditional RAG Architecture vs. Managed RAG 1:45 - Demo: Creating a File Store 2:15 - Uploading Files & Automated Indexing 2:48 - Advanced Chunking & Custom Metadata 3:30 - Live Querying & Retrieval Test 4:15 - Code Walkthrough (Python & JavaScript) 5:00 - Supported File Types (PDF, SQL, etc.) 5:35 - Rate Limits & Storage Capacity (1TB) 6:15 - Pricing Model (Free Storage & Querying) 6:45 - Conclusion 💡 Key Takeaways: The Gemini File Search tool abstracts away the complexity of building RAG systems. Unlike manual pipelines where you manage the embeddings and vector search, Gemini handles the lifecycle of your data securely within your Google Cloud tenant. With support for up to 1TB of data and free query-time embeddings, it is a game-changer for building lightweight to mid-sized AI applications. ❤️ Found this helpful? If this deep dive added value to your workflow, please Like, Comment, and Subscribe for more practical AI tutorials and Google Cloud insights!