Getting Started with Gemini Embedding 2

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.

In this article, I’ll walk you through Gemini Embedding 2 and show how it removes that friction. You’ll learn what it is, why it matters, and how to start using it in real projects.

What Is Gemini Embedding 2?

Gemini Embedding 2 is Google’s 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 formats 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. 

The relationship is learned directly from the raw inputs, not inferred through intermediate text like an audio transcription. This shift simplifies system design and preserves more of the original context.

Gemini Embedding 2 Key Features

Let’s talk about some of the features that make Gemini Embedding 2 special:

• Large text context: Supports up to 8,192 tokens, which is enough for long documents or detailed records.
• Native audio and video support: 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.

These features reduce the need for separate preprocessing systems and simplify the overall architecture.

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. 

It's a huge benefit for performance tuning because you don't have to retrain models or overhaul your entire pipeline just to optimize your storage.

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 formats, 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're conveying the exact same idea. 

You no longer have to juggle modality-specific models or try to hack them together right before output, which makes ranking and downstream similarity search infinitely smoother.

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's practically zero information loss. 

If you're building semantic search for call recordings or trying to detect user intent in raw video clips, you aren't bottlenecked by what a text transcription model happened to catch.

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. 

When your embedding actually reflects the complete picture rather than fragmented parts, retrieval quality naturally goes up.

Dramatically simpler architecture

From an infrastructure standpoint, the simplicity here is hard to overstate. Relying on a single embedding model for every data type completely changes the math on how you build these systems.

Instead of maintaining a tangled web of specialized tools, you're looking at one indexing pipeline, a single similarity metric, and one vector database schema. It strips out a ton of operational overhead and makes scaling much less of a headache. 

Plus, if you want to experiment with a new data source later on, you don't have to rip out your existing architecture to make it work. You're finally free to design retrieval systems based on actual meaning, rather than constantly fighting against the limitations of your data types.

How to Get Started Using Gemini Embedding 2

Let’s go through a simple example of how we can use Gemini Embedding 2 even locally.

Setting up your environment and API key

Start by creating an API key through Google AI Studio. Then install the latest Python SDK in your Python environment:

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’s environment variable manager.

Generating your first multimodal embedding

Here is a simple example that creates an embedding from both text and an image:

from google import genai
from google.genai import types

client = genai.Client()

with open(“sample.png”,”rb”) as f:
    image_bytes = f.read()

# Example of an interleaved input, this has both the text and the image as part of a single vector
# Create multiple of these for separate encoding vectors
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 migrating from legacy models

If you are moving from older embedding models, keep a few things in mind:

• 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.

Taking an incremental approach reduces risk and makes it easier to compare results.

Real-World Use Cases for Unified Vector Spaces

Now that we know how to use Gemini Embedding 2, let's talk about how we can implement it in the real world.

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’s this small revolution in how we parse information for our systems. If you have more interest in how to use Gemini or AI more broadly, try checking out these other resources as well:

• Gemini 3.1
• Building with Gemini 3.1 Pro
• AI Engineering with Langchain
• RAG with Langchain