Gemini RAG: Multimodal RAG API

Key Concepts

• Multimodal Retrieval-Augmented Generation (RAG): A system that retrieves information from both text and visual data (images, charts, diagrams) using a shared embedding space.
• File Search Store: A managed vector database within the Gemini API that handles document chunking, embedding, and retrieval.
• Metadata Filtering: The ability to attach key-value pairs (e.g., department: legal) to documents to restrict search scopes during queries.
• Page-Level Citations: Grounding responses by referencing the specific page number within a source document.
• Shared Embedding Space: A technical architecture where text and images are converted into vectors within the same mathematical space, allowing for cross-modal search.

---

1. Overview of the Gemini API Update

Google has significantly upgraded the Gemini API’s "File Search" tool, transforming it from a text-only RAG pipeline into a fully multimodal system. This update allows developers to store and query PDFs, documents, and images simultaneously. The system automatically handles the complex pipeline of ingestion, chunking, and embedding, effectively replacing the need for hand-rolled RAG stacks.

2. The Five-Stage Pipeline

The system operates through a streamlined, automated process:

1. Ingest: Files are uploaded via the Files API or direct path.
2. Chunking: The service splits text into token-bound chunks and images into discrete tiles or page regions.
3. Embed: Gemini embedding models map both text and visual content into a shared vector space.
4. Storing: Data is indexed in the File Search Store, including any attached custom metadata.
5. Query: When a query is made, the model retrieves the top-K relevant chunks (across modalities) and generates a grounded response with citations.

3. Key Features and Capabilities

• Multimodal Search: Users can perform queries like "Show me the chart where revenue dipped," and the system will retrieve the actual image/chart rather than just text descriptions.
• Custom Metadata Filtering: Developers can tag documents with arbitrary labels (e.g., region: EU, modality: chart). During a query, the metadata_filter parameter can be used to narrow the search, ensuring the model only considers relevant document subsets.
• Page-Level Citations: The API now returns specific page numbers for retrieved chunks, providing transparency and traceability for the model's answers.

4. Implementation and Code Usage

The update maintains backward compatibility with existing File Search implementations.

• Metadata Attachment: Metadata is passed as a dictionary during the file upload process.
• Filtering: Metadata filters are applied within the file_search tool configuration.
• Example Workflow:
  • Text Query: Asking about "multi-head attention" retrieves specific pages from technical papers.
  • Cross-Modal Query: Asking about revenue dips retrieves visual charts and interprets the data points within them.
  • Synthesis: The model can synthesize information across multiple documents, though it is recommended to use metadata filters to ensure the model focuses on the correct source material.

5. Pricing and Technical Constraints

• Ingestion: Charged at standard Gemini embedding rates.
• Storage/Query: Vector storage and query-time embeddings are free.
• Limits: Files are capped at 100 MB each; the free tier provides 1 GB of total storage.
• Retention: Original files are stored for 48 hours, which is critical for re-embedding tasks.

6. Notable Perspectives

The presenter emphasizes that this update "closes most of the gap between this and a hand-rolled retrieval stack." By handling the vision pipeline internally, Google removes the need for developers to stitch together separate text and vision services. The ability to interpret diagrams and schematics directly—without needing to extract text—is highlighted as a major breakthrough for enterprise applications like insurance claims, engineering specs, and medical reports.

Synthesis

The Gemini API update represents a shift toward "all-in-one" RAG solutions. By integrating multimodal embedding and metadata-based filtering directly into the API, Google has simplified the development of complex, document-heavy AI applications. The most significant takeaway is the system's ability to treat visual data (charts, diagrams) with the same semantic weight as text, allowing for more accurate, grounded, and context-aware responses in enterprise environments.