Easiest Way to use RAG

Quick Overview

The video demonstrates how to implement Retrieval Augmented Generation (RAG) using the Gemini API's File Search tool, walking through setting up a project, uploading documents like PDFs, configuring ranking strategies, and querying the system to retrieve specific answers, as well as showing integration with Clerk for multi-tenancy user management and Firebase Firestore for data storage.

Key Points: Google introduced File Search in the Gemini API, enabling RAG systems to retrieve information from user-uploaded files (JSON, JS, PDF, DOCX, TXT) via an embedding model (e.g., 'gemini-001-embedding-004'). The File Search API has rate limits based on user tier, with the Free tier limited to 1 GB of total project file search stores, while Tier 1 allows 10 GB. The implementation involves creating a file search store, uploading files (like 'DeepSeekV3.pdf'), and using the store as a tool in the generatecontent API call. The system architecture includes an offline indexing process (Documents -> File Storage -> Embedding Model -> Database) and a real-time querying process involving Gemini deciding whether to use external knowledge. The custom application demonstrated integrates Clerk for authentication and multi-tenancy (organizations) and Firebase Firestore for storing user data. Advanced configuration options for file indexing include choosing a Ranking Strategy (Semantic, Keyword, Hybrid) and Chunking Strategy (Automatic or Manual with custom chunk size/overlap). The total training cost for DeepSeek-V3 was $5.576 million, broken down into Pre-Training ($5.538 million), Context Extension ($0.238 million), and Post-Training ($0.01 million).

Context: This video tutorial explains how to build smarter Retrieval Augmented Generation (RAG) systems using the Gemini API's File Search feature, which allows the LLM to ground its answers in custom documents. The presenter, Muhammad Farooq (aka Prompt Engineering), builds a multi-tenant SaaS application using this technology, integrating it with authentication via Clerk and data storage using Firebase Firestore to demonstrate a complete, production-ready RAG solution.