Gemini Embedding 2
gemini-embedding-2-previewGemini Embedding 2 is Google's advanced text embedding model designed for high-accuracy semantic representation across large-scale retrieval and understanding tasks. It converts text into dense vector embeddings optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. Built for production use, it offers strong multilingual support, improved semantic similarity accuracy, and efficient embedding generation, making it well suited for large knowledge indexing pipelines and enterprise-scale retrieval applications.
- Context
- 8.2K tokens
- Endpoint
Service Status
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Pricing
Quick Start
Select an endpoint and copy a working example for this model.
from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="gemini-embedding-2-preview", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="gemini-embedding-2-preview",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )Supported Parameters
API docsmodelinputencoding_formatdimensionsuserCursor IDE Model IDs
Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
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