GoogleEmbedding

Gemini Embedding 001

gemini-embedding-001

Gemini-Embedding-001 is Google's high-quality text embedding model designed for semantic understanding and retrieval tasks. It converts text into dense vector representations optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. The model emphasizes strong multilingual performance, high semantic accuracy, and efficient embedding generation, making it well suited for large-scale knowledge indexing and production retrieval pipelines.

Context: 128K tokens
Endpoint

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Pricing

Input$0.075 / 1M

Output$0.30 / 1M

Cache Write (5m)$0.075 / 1M

Cache Write (1h)$0.075 / 1M

Cache Read$0.075 / 1M

Quick Start

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Endpoint

python

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-001",    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-001",#     messages=[{"role": "user", "content": "Hello!"}],#     extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )

Supported Parameters

API docs

Common4 params

modelinputencoding_formatdimensions

Extended1 param

user

Cursor IDE Model IDs

Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.

gemini-embedding-001

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