Gemini Embedding 2

Today Google released Gemini Embedding 2 — the first embedding model that natively handles text, images, video, audio, and PDFs in a single unified embedding space. This is a game-changer for anyone building RAG applications, semantic search, or multimodal AI apps.

Until now, building a search system that could handle both images and text required separate embedding models and complex orchestration. Gemini Embedding 2 changes that: one model, one embedding space, all modalities.

What Makes It Different

Previous embedding models like OpenAI's text-embedding-3 or Google's text-embedding-004 only handled text. For images, you needed CLIP. For audio, something else entirely. Each modality lived in its own vector space, making cross-modal search impossible without complex pipelines.

Gemini Embedding 2 solves this by training a single model across all modalities from the ground up. The result:

• Search an image collection with text queries — "find screenshots showing error messages"
• Search text docs with image queries — upload a diagram, find related documentation
• Build RAG over video/audio content — query meeting recordings, podcasts, tutorials
• Interleaved input — embed text + images together in a single request

Model Specifications

Here's what you're working with:

Output Dimensions

Choose your embedding size based on your needs:

• 3072 — Default, highest quality
• 1536 — Balanced quality/cost
• 768 — Smallest, fastest retrieval

Smaller dimensions reduce storage costs and speed up similarity searches, with a modest tradeoff in retrieval quality. For most applications, 1536 is the sweet spot.

Quick Start: Python Example

Here's how to embed text, images, and audio in a single request:

client = genai.Client()

with open("example.png", "rb") as f:
    image_bytes = f.read()

with open("sample.mp3", "rb") as f:
    audio_bytes = f.read()

result = client.models.embed_content(
    model="gemini-embedding-2-preview",
    contents=[
        "What is the meaning of life?",
        types.Part.from_bytes(
            data=image_bytes,
            mime_type="image/png",
        ),
        types.Part.from_bytes(
            data=audio_bytes,
            mime_type="audio/mpeg",
        ),
    ],
)

The model returns embeddings for each content item. All embeddings live in the same vector space, so you can directly compare a text embedding to an image embedding using cosine similarity.

Specifying Output Dimensions

To use smaller embeddings, add the output_dimensionality parameter:

result = client.models.embed_content(
    model="gemini-embedding-2-preview",
    contents=["Your text here"],
    config={"output_dimensionality": 768}
)

Practical Use Cases for Vibe Coders

Here's where this gets exciting for builders:

Example: Building a Multimodal RAG System

Let's say you're building a coding assistant that can reference your project's documentation, screenshots, and tutorial videos. Here's the flow:

1. Index your content: Embed all docs, images, and video clips with Gemini Embedding 2
2. Store in a vector DB: Use ChromaDB, Weaviate, or Pinecone
3. Query multimodally: User asks "how do I fix this error?" + attaches screenshot
4. Retrieve relevant context: Find matching docs, similar error screenshots, and tutorial segments
5. Generate answer: Pass retrieved context to Claude or GPT for the response

Previously this required stitching together 3+ different models. Now it's one API call.

Integrations

Gemini Embedding 2 works with the tools you're already using:

• LangChain — Native integration for RAG pipelines
• LlamaIndex — Multimodal indexing and retrieval
• Weaviate — Vector database with multimodal support
• ChromaDB — Local vector store for prototyping
• Pinecone — Managed vector database at scale
• Vertex AI Vector Search — Google Cloud native option

When to Use This

Considerations

• Preview model: gemini-embedding-2-preview — expect iteration before GA
• Video limit: 120 seconds max — chunk longer videos
• PDF limit: 6 pages — split larger documents
• Cost: Pricing not yet announced for GA — factor this into production planning

What This Means for AI Apps

Multimodal embeddings represent a step change in what's possible with semantic search and RAG. Instead of building separate pipelines for each content type, you can now:

• Build unified search experiences across all your content
• Create AI assistants that truly understand context — text, images, and audio together
• Reduce infrastructure complexity and maintenance burden

For vibe coders building AI-powered apps, this is a significant unlock. The barrier to multimodal AI just got a lot lower.

Key Takeaways

• First natively multimodal embedding model — text, images, video, audio, PDFs in one space
• Model name: gemini-embedding-2-preview
• Output dimensions: 3072 (default), 1536, or 768
• Interleaved input: Combine text + images in one request
• Integrations: LangChain, LlamaIndex, Weaviate, ChromaDB, Pinecone
• Use cases: RAG, semantic search, classification, content recommendation

Learn more in the official Google announcement.