Gemini Embedding 2 is Google's embedding generation model that's ideal for complex retrieval and analytics tasks.
Gemini Embedding 2 accepts multimodal inputs to generate 3072-dimensional vectors. It accepts images, text, documents, audio, and video inputs and semantically maps the generated vectors into a unified semantic space. This lets you perform tasks, such as searching for an image based on a text description.
Gemini Embedding 2 introduces several features to optimize embedding quality and flexibility:
Custom task instructions: By specifying task instructions (for example,
task:code retrievalortask:search result) optimize the embeddings for the intended relationships and retrieve more accurate results for the specific goal.Adjustable result size: The model generates a 3072-dimensional float vector, by default. However, you can retrieve a smaller dimensional output by specifying the
output_dimensionalityparameter.Document OCR: Read OCR from document inputs.
Audio track extraction: Extract audio tracks from video inputs and interleave them with video frames.
For more information on how to use Gemini Embedding 2, see Get multimodal embeddings.
Try in Agent Platform (Preview) Deploy example app
| Model ID | gemini-embedding-2 |
|
|---|---|---|
| Supported inputs & outputs |
|
|
| Token limits |
|
|
| Maximum sequence length |
8,192 tokens |
|
| Output dimensions |
Up to 3,072 (with MRL support) |
|
| Consumption options |
|
|
| See Consumption options for more information. | ||
| Technical specifications | ||
| Images |
|
|
| Documents |
|
|
| Video |
|
|
| Audio |
|
|
| Parameter defaults |
|
|
| Supported regions | ||
|
Model availability |
|
|
| See Deployments and endpoints for more information. | ||
| Knowledge cutoff date | November 2025 | |
| Versions |
|
|
| Security controls | ||
| See Security controls for more information. | ||
| Supported languages | See Supported languages. | |
| Pricing | See Pricing. | |