How to Use Gemini Embedding 2: Complete Beginner Guide
By Braincuber Team
Published on April 20, 2026
Building search and retrieval systems used to mean either translating everything into text or combining a vision model and a text encoder that were trained separately. While this is useful for broad use cases, we can easily miss deeper connections between text and imagery.
What You will Learn:
- Understanding Gemini Embedding 2 and native multimodality
- Key features including Matryoshka Representation Learning
- Setting up environment and API key
- Generating multimodal embeddings
- Best practices for migration from legacy models
- Real-world use cases in RAG and search systems
What Is Gemini Embedding 2?
Gemini Embedding 2 is Google latest embedding model designed for multimodal encoding. The Google genai Python API allows developers to use the gemini-embedding-2-preview model.
At a high level, embedding models convert data into numerical vectors that capture meaning. Historically, these models focused on text. Gemini Embedding 2 expands that scope so developers can work with multiple data types using a single model.
The core value proposition is simple: now we can index, compare, and search across different media format without building separate pipelines for each one.
The Shift to Native Multimodality
Older systems handled multimodal data in a roundabout way. Audio had to be transcribed. Videos needed captions. Images required tagging.
Each step added latency, extra labor, and introduced potential loss of meaning. All this effort was so that we could map everything to a single, shared vector space that was often text-based.
Gemini Embedding 2 removes that extra layer. It maps text, images, video, audio, and PDFs into a shared vector space from the start. In practice, this means a video showing a crowded city street can live near the text "urban traffic" in vector space.
Gemini Embedding 2 Key Features
Large Text Context
Supports up to 8,192 tokens, which is enough for long documents or detailed records.
Native Audio and Video
Handles up to two minutes of video or audio without requiring transcription.
Interleaved Inputs
Accepts combinations of text and media in a single request, producing a unified embedding.
Multilingual Coverage
Works across more than 100 languages, enabling cross-language search without translation pipelines.
The Technical Advantages of Gemini Embedding 2
One of the standout features in Gemini Embedding 2 is how it uses Matryoshka Representation Learning (MRL). The concept is pretty elegant: the embedding is structured so the most critical information gets front-loaded into the vector.
While the full vector outputs at 3,072 dimensions, MRL lets developers cleanly truncate that down to much smaller sizes, like 768 or even 256 dimensions. You get the flexibility to store smaller vectors, which drastically cuts down costs and speeds up retrieval, all without taking a massive hit to accuracy.
MRL Benefits
MRL gives you the flexibility to optimize storage and retrieval speed by truncating vectors without retraining models or overhauling your pipeline.
A Shared Semantic Space Across Modalities
MRL is great, but the way this model handles multimodal alignment at scale is where things get really interesting. Essentially, it creates a unified semantic space across all data types.
Instead of building separate silos for different format, the model is trained to cluster similar concepts together.
A voice memo, a photograph, and a written paragraph will all map to the same mathematical neighborhood if they are conveying the exact same idea.
Skipping the Translation Step
If you look at traditional retrieval pipelines, they usually rely on intermediate transformations. You have to transcribe an audio file or generate a caption for an image before you can actually search it. Every time you do that, you compress the original data and inevitably introduce noise.
Gemini Embedding 2 bypasses this entirely by embedding raw audio and video directly. Without that middleman, there is practically zero information loss.
Capturing Context with Mixed Inputs
Another massive advantage comes into play when you combine different data types, for example, text and an image, into a single embedding call. The model actually learns the relationship between those inputs during inference.
Take an e-commerce product listing, for example. Instead of treating the product photo and the written description as isolated pieces of data, the model fuses them into a single, highly contextualized vector.
How to Get Started Using Gemini Embedding 2
Set Up Your Environment
Create an API key through Google AI Studio and install the Python SDK.
pip install -U google-genai
Once that is set up, set your API key as an environment variable called GEMINI_API_KEY. You can do this either within the project by using a .env file or through your system environment variable manager.
Generate Multimodal Embeddings
Create embeddings from text, images, or combined inputs.
from google import genai
from google.genai import types
client = genai.Client()
with open("sample.png","rb") as f:
image_bytes = f.read()
response = client.models.embed_content(
model="gemini-embedding-2-preview",
contents=[
"A photo of a vintage typewriter",
types.Part.from_bytes(
data=image_bytes,
mime_type="image/jpeg"
)
]
)
print(response.embeddings)
This produces a single vector that represents both the text and the image together.
Best Practices for Migration
Follow these tips when moving from legacy embedding models.
| Practice | Description |
|---|---|
| Re-index your data | Existing vectors are not compatible with the new model. |
| Benchmark retrieval quality | Test real queries to confirm improvements for your use case. |
| Start with a subset | Migrate a smaller dataset first to validate storage and retrieval behavior. |
Real-World Use Cases for Unified Vector Spaces
Advancing Retrieval-Augmented Generation (RAG)
Most RAG systems today rely on text embeddings. With Gemini Embedding 2, you can extend this to multimodal agentic RAG systems.
For example, a support assistant could retrieve a diagram from a PDF, translate an audio transcription, or do actions described in a short video clip instead of only parsing texts and emails. This leads to a wider variety of use cases using a singular model instead of multiple different models and agents.
Streamlining Cross-Modal Search and Classification
Organizations often store large amounts of unstructured data, such as images, recordings, and documents. Most of it is either hard to search or the records are poorly kept.
With a shared embedding space, you can query that data using natural language. A search like "whiteboard sketches of system architecture" can surface relevant images or meeting recordings without manual tagging.
Final Thoughts
Gemini Embedding 2 simplifies a problem that used to require multiple systems and complex model architecture. By supporting text, images, audio, and video in a single model, it reduces both engineering overhead and operational complexity.
If you are building search, recommendation systems, or RAG pipelines, this is worth exploring. The biggest advantage is not just better performance, it is this small revolution in how we parse information for our systems.
Frequently Asked Questions
What is the main difference between Gemini Embedding 2 and older models?
Older models like text-embedding-004 were text-only. If you wanted to search videos or images, you had to transcribe or tag them first. Gemini Embedding 2 is natively multimodal, meaning it understands text, images, audio, video, and PDFs directly within the same mathematical space without any intermediate steps.
What are the limits for non-text inputs like video and audio?
In the current preview, you can embed up to 120 seconds of video and up to 80 seconds of native audio per request. If you have longer files, the best practice is to chunk them into segments to create a searchable semantic timeline.
How much does Gemini Embedding 2 cost?
Text, image, and video inputs cost $0.25 per 1 million tokens. Native audio is slightly more expensive at $0.50 per 1 million tokens because it is more computationally intensive to process sound waves directly.
Can Gemini Embedding 2 handle multi-page documents?
Yes, it can directly embed PDFs up to 6 pages long. For longer documents, you should split the PDF into 6-page chunks and index them individually.
What is Matryoshka Representation Learning (MRL)?
MRL structures embeddings so the most critical information is front-loaded, allowing you to truncate vectors from 3,072 down to 768 or 256 dimensions without significant accuracy loss. This reduces storage costs and speeds up retrieval.
Need Help with AI Implementation?
Our experts can help you implement Gemini Embedding 2 and build multimodal search or RAG systems. Get a free consultation to discuss your project requirements.