About this video

• Video Title: Google Changed RAG Forever with NEW Gemini File Search Tool
• Channel: AI with Surya
• Speakers: Surya
• Duration: 00:08:47

Overview

This video introduces and demonstrates Google's new Gemini API file search tool, which simplifies the process of integrating private data with large language models (LLMs) through Retrieval Augmented Generation (RAG). The presenter explains the challenges of traditional RAG implementation and highlights how the new tool automates complex steps, making it more accessible, faster, and cost-effective for developers.

Key takeaways

• Gemini File Search Tool: Google's update to the Gemini API introduces a file search tool that simplifies RAG, making it scalable and affordable.
• RAG Explained: RAG connects LLMs to external knowledge to ground responses in facts, akin to an "open book test" for the AI.
• Traditional RAG Challenges: Building a RAG system from scratch involves complex, multi-stage processes like semantic chunking, embedding, vector indexing, and retrieval, which are engineering headaches.
• File Search Automation: The Gemini file search tool automates the entire RAG pipeline, from indexing to querying, into a simple API call.
• Process Overview: The tool involves an offline indexing phase (chunking, embedding, storing) and a real-time querying phase where the model retrieves relevant information from the indexed data to generate precise answers.
• Implementation Simplicity: The tool can be implemented in three main steps: creating a file store, uploading files, and querying.
• Key Features: The tool automatically handles citations and supports numerous file types.
• Breakthroughs: The tool offers faster development speed, significantly reduces the cost barrier (free data storage, free embedding and query time, minimal indexing fee), and maintains powerful capabilities.

The three main steps to implement the Gemini file search tool are:

1. Creating the file store: This is the initial setup of a place to store your files.
2. Uploading and importing a file: This involves adding your documents to the created file store, which kicks off the offline indexing process.
3. Querying: This step triggers the real-time querying process where you ask questions and the model generates answers based on the indexed files.

The Gemini file search tool differs significantly from traditional RAG implementations in terms of complexity and cost:

Complexity:

• Traditional RAG: Involves a complex, multi-stage engineering process including sourcing and chunking documents, creating embeddings using separate models, managing a vector database, and building retrieval and ranking systems. This is described as an "infrastructure headache."
• Gemini File Search Tool: Automates the entire RAG pipeline into a simple, elegant API call. It hides the underlying complexity, handling steps like semantic chunking, embedding, and vector indexing automatically.

Cost:

• Traditional RAG: Can be expensive due to the infrastructure required for managing databases, embedding models, and the overall development effort.
• Gemini File Search Tool: Significantly reduces cost barriers. Data storage is free, creating embeddings at query time is free, and there is only a tiny one-time fee for indexing files. Users only pay standard Gemini rates for the context used in the final answer.

The two key phases of the file search tool's process are:

1. Offline Indexing Process: This is where the heavy lifting occurs. When a file is uploaded, it's automatically broken into chunks, converted into numerical representations (embeddings) that capture semantic meaning, and then these embeddings are stored in a specialized database. This entire flow from document to database is a one-time indexing step.
2. Real-Time Querying Process: When a user asks a question, Gemini first determines if external knowledge is needed. If so, it generates optimized search queries, converts them into embeddings, and searches the database. The most relevant text chunks are then fed back to the Gemini model to generate a precise, grounded answer.

The three major breakthroughs enabled by the Gemini file search tool are:

1. Speed of Development: Because it's accessible through an API and automates complex processes, developing applications using this tool is significantly faster.
2. Cost Barrier Reduction: Data storage is free, embedding creation at query time is free, and there's only a small one-time indexing fee. This makes powerful RAG capabilities much more affordable.
3. Power and Simplicity: Developers don't sacrifice functionality for ease of use. The tool still supports numerous file types and provides advanced capabilities like citations automatically, all within a simple interface.