Google RAG File Search - How It Works

Google offers two RAG file search solutions: Gemini API File Search (developer-friendly) and Vertex AI RAG Engine (enterprise-grade).

Gemini API File Search Tool

Announced November 7, 2025, this is a fully managed RAG system built directly into the Gemini API.

Architecture Overview

┌─────────────┐    ┌──────────────┐    ┌─────────────────┐    ┌────────────┐
│   Upload    │───▶│   Chunking   │───▶│   Embedding     │───▶│  Storage   │
│   Files     │    │   Strategy   │    │ gemini-001-emb  │    │ (3x size)  │
└─────────────┘    └──────────────┘    └─────────────────┘    └────────────┘
                                                                     │
┌─────────────┐    ┌──────────────┐    ┌─────────────────┐           │
│  Response   │◀───│   Generate   │◀───│    Retrieve     │◀──────────┘
│ + Citations │    │   (Gemini)   │    │  (Vector Search)│
└─────────────┘    └──────────────┘    └─────────────────┘

Step-by-Step Process

1. Data Ingestion

• Upload files to a File Search Store (corpus)
• Supported formats: PDF, DOCX, XLSX, PPTX, JSON, XML, TXT, Markdown, HTML, 100+ code file types
• Max file size: 100 MB per document
• Storage recommendation: Keep stores under 20 GB for optimal latency

2. Chunking

Documents are automatically broken into smaller pieces:

# Customizable chunking config
chunking_config = {
    "max_tokens_per_chunk": 200,   # Size of each chunk
    "max_overlap_tokens": 20       # Overlap between chunks
}

• Why chunking? LLMs have context limits; smaller chunks enable precise retrieval
• Overlap ensures context isn’t lost at chunk boundaries
• Default strategy is optimized, but customizable for specific use cases

3. Embedding Generation

Each chunk is converted to a 3072-dimensional vector using:

• Model: gemini-embedding-001
• Performance: Leads the MTEB (Massive Text Embedding Benchmark) Multilingual leaderboard
• Embeddings capture semantic meaning, not just keywords

4. Indexing & Storage

• Embeddings stored in optimized vector database
• Storage uses ~3x original file size (embeddings + metadata)
• Supports metadata filtering (key-value pairs for selective search)

5. Retrieval (at Query Time)

When a user asks a question:

1. Query → Embedding: User’s question converted to vector using same embedding model
2. Similarity Search: System finds chunks with most similar embeddings (cosine similarity)
3. Semantic matching: Finds relevant info even with different wording than source

Query: "What are the quarterly earnings?"
Matches: "Q3 revenue was $2.5B..." (even though "earnings" ≠ "revenue")

6. Generation

• Retrieved chunks injected as context into the prompt
• Gemini generates grounded response
• Built-in citations point to specific document sections used

Configuration Options

Pricing (Gemini API)

Limitations

• Cannot choose/tune embedding models
• Limited chunking strategies (no custom parsers)
• Cannot inspect embeddings or similarity scores
• Cannot customize ranking/reranking
• No fine-grained control over retrieval algorithm

Vertex AI RAG Engine (Enterprise)

For production enterprise workloads with more control.

Key Differences from Gemini File Search

Architecture Components

1. Data Ingestion: Local files, Cloud Storage, Google Drive
2. Data Transformation: Customizable chunking (size, overlap, strategy)
3. Embedding: Multiple model options
4. Indexing: Creates a “corpus” optimized for search
5. Retrieval: Configurable backends (Vertex AI Search, Pinecone, etc.)
6. Generation: Choose from 100+ LLMs in Model Garden

Vertex AI Search Integration

For high-volume applications:

• Handles large data volumes with low latency
• Improved performance and scalability
• Native integration with RAG Engine

When to Use Which

Graph RAG Support

Does Google File Search Support Graph RAG?

No. Gemini File Search is purely vector-based RAG. It does not build or traverse knowledge graphs.

Google’s GraphRAG Solution: Spanner Graph

Google provides a separate reference architecture for GraphRAG using Spanner Graph + Vertex AI.

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                     DATA INGESTION                              │
├─────────────────────────────────────────────────────────────────┤
│  Cloud Storage → Pub/Sub → Cloud Run → LLMGraphTransformer      │
│                                              ↓                  │
│                                    Extract entities &           │
│                                    relationships                │
│                                              ↓                  │
│                                    Spanner Graph                │
│                                    (graph + embeddings)         │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│                     QUERY SERVING                               │
├─────────────────────────────────────────────────────────────────┤
│  User Query → Embedding → Vector Search → Graph Traversal       │
│                                              ↓                  │
│                                    Retrieve connected           │
│                                    entities & relationships     │
│                                              ↓                  │
│                                    Vertex AI Ranking API        │
│                                              ↓                  │
│                                    Gemini summarizes            │
└─────────────────────────────────────────────────────────────────┘

Key Components

1. LLMGraphTransformer (LangChain): Extracts entities and relationships from text using Gemini
2. Spanner Graph: Stores both knowledge graph and vector embeddings
3. Hybrid Retrieval: Combines vector similarity + graph traversal
4. Vertex AI Ranking API: Filters results by semantic relevance

Vector RAG vs GraphRAG

When to Use GraphRAG

• Data has rich relationships (org charts, supply chains, research papers)
• Queries require multi-hop reasoning (“Who manages the person who wrote X?”)
• Need to understand entity connections across documents
• Standard RAG returns isolated facts without context

References

• Gemini API File Search Documentation
• Google Blog: Introducing File Search Tool
• Vertex AI RAG Engine Overview
• Vertex AI RAG Engine Blog
• Analytics Vidhya: Gemini API File Search
• Towards Data Science: Google’s File Search Tool
• GraphRAG with Spanner Graph Architecture