Video, Audio, PDF, Images
AI Models & Assistants / Knowledge library
gemini embedding 2 preview
Gemini family endpoint. Consult the linked model catalog for exact modalities, thinking settings, lifecycle and limits.
Why learn gemini embedding 2 preview?
Offers the documented Google model capabilities through hosted.
The problem it solves
Provides model inference for supported documented inputs.
Where it came from
gemini embedding 2 preview: Gemini family endpoint. Consult the linked model catalog for exact modalities, thinking settings, lifecycle and limits. Published aliases: gemini-embedding-2-preview. Input: consult source. Output: consult source. Checked 2026-10-13. Model capabilities are distinct from tools available in a particular agent.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain what gemini embedding 2 preview does.
- Identify a use case for gemini embedding 2 preview.
- Describe a limitation of gemini embedding 2 preview.
- Name a compatible tool for gemini embedding 2 preview.
- Compare gemini embedding 2 preview against an alternative.
Documentation and sources
- Primary source / model card (ai.google.dev)
Catalog research notes
- See the AI Workbench for source dates, pricing evidence and configuration details.
Connect the ideas
Built on
Real World Example
Unified Cross-Modal Retrieval for Media Archives
Digital media archives and content management platforms that need to search across diverse file types.
The business problem
Traditional search systems rely on metadata tags, which fail when users search for content using a different modality, such as finding a video clip by describing its visual content or searching for a specific audio segment using a text query.
How this topic solves it
Gemini Embedding 2 Preview enables the mapping of text, images, video, audio, and PDFs into a single, unified embedding space, allowing for semantic retrieval where a text query can successfully match non-text media assets.
- Ingest diverse media assets including video, audio, and PDF documents into the storage system.
- Pass each asset through the Gemini Embedding 2 Preview endpoint to generate high-dimensional vector representations.
- Store the resulting vectors in a vector database to maintain the unified semantic space.
- Convert user search queries into vectors using the same embedding model to perform similarity searches across the unified index.
Explains how various media inputs are transformed into a shared vector space for retrieval.
Multimodal embedding endpoint
Unified semantic index
Text input
- Media Assets → generates vector → Gemini Embedding 2
- Gemini Embedding 2 → stores embedding → Vector Database
- User Query → embeds query → Gemini Embedding 2
- Gemini Embedding 2 → queries similarity → Vector Database
Expected business outcome
Users can perform intuitive, cross-modal searches, retrieving relevant video or audio segments based on descriptive text queries without relying on manual tagging.
Limitations and checks
As a preview model, Gemini Embedding 2 Preview may have more restrictive rate limits compared to stable models, which should be monitored via API response headers.