EmbeddingGemma 2 Studio

from sentence_transformers import SentenceTransformer
from PIL import Image
import numpy as np

# 1. Load Google DeepMind EmbeddingGemma 2 (740M modular parameters)
model = SentenceTransformer("google/embeddinggemma-2-740m", trust_remote_code=True)

# 2. Asymmetric retrieval: Prefix search queries with 'task: search_query | '
query_text = "task: search_query | a golden retriever playing on sunny beach"
query_embedding = model.encode(query_text)

# 3. Multimodal ingestion: Target image is mapped into identical 768d vector space
target_image = Image.open("sample_photo.jpg")
image_embedding = model.encode(target_image)

# 4. Optional Matryoshka Representation Learning (MRL) truncation to 256 dimensions
query_256 = query_embedding[:256]
image_256 = image_embedding[:256]

# 5. Compute cosine similarity
query_norm = query_256 / np.linalg.norm(query_256)
image_norm = image_256 / np.linalg.norm(image_256)
similarity = float(np.dot(query_norm, image_norm))

print(f"Cosine Similarity (256d MRL): {similarity:.4f}")

import { pipeline, env } from '@huggingface/transformers';

// Enable WebGPU device and multi-threaded WebAssembly
env.backends.onnx.wasm.numThreads = 4;

// 1. Initialize feature-extraction pipeline with browser WebGPU
const embedder = await pipeline('feature-extraction', 'google/embeddinggemma-2-740m', {
  device: 'webgpu',
  dtype: 'fp16',
});

// 2. Compute text query embedding directly on user hardware (zero backend latency)
const queryOutput = await embedder('task: search_query | vintage brown leather jacket', {
  pooling: 'mean',
  normalize: true,
});

// 3. Extract 768-dimensional float array
const queryVector = Array.from(queryOutput.data);

// 4. Truncate with Matryoshka (e.g. 128 dimensions for hyper-fast vector cache)
const mrl128Vector = queryVector.slice(0, 128);

console.log('On-device 128d vector computed:', mrl128Vector.length);

# 1. Pull EmbeddingGemma 2 locally via Ollama
ollama pull embeddinggemma-2:740m

# 2. Run local embedding calculation via CLI
ollama run embeddinggemma-2 "task: search_query | high efficiency sorting algorithms"

# 3. Call local REST API from your backend app
curl http://localhost:11434/api/embeddings -d '{
  "model": "embeddinggemma-2:740m",
  "prompt": "task: search_query | microservices event driven architecture"
}'

# Hugging Face Inference Endpoints / OpenRouter API Call
curl https://api-inference.huggingface.co/models/google/embeddinggemma-2-740m \
  -H "Authorization: Bearer $HF_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": {
      "source_sentence": "task: search_query | cute golden retriever puppy on beach",
      "sentences": [
        "A playful dog near the ocean waves",
        "A red sports car on race track"
      ]
    }
  }'