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Description

Gemini Decision 1 is a specialized AI model from Google designed to evaluate information and produce structured decisions instead of generating natural language responses. The model enables developers to ask specific questions about provided content and receive probabilistic answers that can guide automated processes. It supports several decision formats, including yes-or-no classifications, selections from predefined options, and scores based on established criteria. Each evaluation can include confidence measurements and probability distributions to help applications interpret the model's predictions. Gemini Decision 1 offers a context window of approximately 262,144 tokens, allowing it to examine lengthy documents and substantial amounts of contextual data. Applications can use the model to classify incoming requests, prioritize tasks, route information, and assess whether content meets specified requirements. Its structured outputs are particularly useful for AI agents that need to select actions or evaluate conditions within automated workflows. Developers can access the model through supported API services, including Google's Gemini API ecosystem and Vercel AI Gateway. By focusing on decision-oriented inference rather than open-ended text generation, Gemini Decision 1 provides a way to incorporate AI-powered classification and evaluation into software applications.

Description

Gemini Embedding models, which include the advanced Gemini Embedding 2, are integral to Google's Gemini AI framework and are specifically created to translate text, phrases, sentences, and code into numerical vector forms that encapsulate their semantic significance. In contrast to generative models that create new content, these embedding models convert input into dense vectors that mathematically represent meaning, facilitating the comparison and analysis of information based on conceptual relationships instead of precise wording. This functionality allows for various applications, including semantic search, recommendation systems, document retrieval, clustering, classification, and retrieval-augmented generation processes. Additionally, the model accommodates input in over 100 languages and can handle requests of up to 2048 tokens, enabling it to effectively embed longer texts or code while preserving a deep contextual understanding. Ultimately, the versatility and capability of the Gemini Embedding models play a crucial role in enhancing the efficacy of AI-driven tasks across diverse fields.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Gemini Yes 
Gemini platform Yes 
Google AI Studio Yes 
Gemini Enterprise No 
Google Cloud Platform Yes 
Mimasa AI No 
Python No 

Integrations

Gemini Yes 
Gemini platform Yes 
Google AI Studio Yes 
Gemini Enterprise Yes 
Google Cloud Platform No 
Mimasa AI Yes 
Python Yes 

Pricing Details

$0.10 per 1M tokens (input)
$0.10 per 1 million tokens (input), no charge for output tokens
Free Trial No 
Free Version Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

aistudio.google.com/docs/models/gemini-decision-1

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/

Product Features

Product Features

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