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New Gemini Embedding model is now generally available on the Gemini API |
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| We're excited to announce that our new Gemini Embedding model, gemini-embedding-001, is now generally available and stable in the Gemini API. | The model ranks #1 on the Massive Text Embedding Benchmark (MTEB) leaderboard and is priced at $0.15 USD per million tokens. It supports 100+ languages and is optimized for a wide range of tasks, including semantic search, document retrieval, and classification. | You can generate text embeddings by using the embed_content method: |
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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)
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A key feature of gemini-embedding-001 is its flexible output dimension. While the default size is 3072 for maximum quality, you can truncate the embedding vector to smaller sizes, like 768 or 1536, to optimize for performance and storage costs, without needing to re-embed your content. | For advanced use cases, such as building a production-ready Retrieval-Augmented Generation (RAG) system, you can specify a task_type to generate embeddings optimized for your specific needs. | |
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| The Google AI Studio team |
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Building with multimodal AI | Discover how Gemini's multimodal understanding is unlocking a new class of vision-powered apps and truly context-aware software in the latest episode with Gemini's multimodal product lead, Ani Baddepudi. Watch the video |
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| You can get significantly higher request limits and flexible pay-as-you-go pricing with a paid tier. | |
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