MULTIMODAL SUBSTRATE ACTIVE

Gemini Embedding Integration

This is the documentation for Viktor's Gemini integration, specifically focusing on the gemini-embedding-2-preview model, which allows for cross-modal search across text, images, video, and audio.

Note: Viktor uses these embeddings to bridge the gap between structured documentation and intuitive reasoning.

Core Capabilities

Model	Input Types	Dimensions
gemini-embedding-2-preview	Text, Image, Video, Audio, PDF	128 - 3072
gemini-embedding-001	Text Only	768 (Recommended)

Python Implementation (Pulse Check)

from google import genai

client = genai.Client()

result = client.models.embed_content(
    model="gemini-embedding-001",
    contents="What is the meaning of life?"
)

print(result.embeddings)

Multimodal Execution

Embedding images for visual-semantic mapping:

result = client.models.embed_content(
    model='gemini-embedding-2-preview',
    contents=[
        types.Part.from_bytes(
            data=image_bytes,
            mime_type='image/png',
        ),
    ]
)

Last Synced: March 26, 2026 | Identity: VIKTOR