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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.
Description
The Gemini API libraries offer official, production-ready SDKs from Google for utilizing the Gemini API in various widely-used programming languages. Google advises developers to utilize the Google GenAI SDK for their Gemini projects, as these libraries are crafted and supported by Google, featured in official documentation and examples, and are suitable for production environments. The available SDKs encompass Python, JavaScript/TypeScript, Go, Java, and C#, with convenient installation via standard package managers like pip for Google GenAI, npm for Google GenAI, Maven for Google GenAI, and dotnet for adding the Google GenAI package. These SDKs provide access to the most recent features of the Gemini API and are optimized for superior performance when handling Gemini models. Due to the lack of ongoing support for older libraries, Google strongly encourages transitioning to the new Google GenAI SDK for a more reliable development experience, ensuring that developers can leverage the best tools available for their needs. Moreover, adopting the latest SDK not only enhances performance but also aligns with future updates and improvements from Google.
API Access
Has API
API Access
Has API
Integrations
Gemini
Python
C#
Gemini Enterprise
Gemini Enterprise Agent Platform
Go
Google AI Studio
Java
JavaScript
Maven
Integrations
Gemini
Python
C#
Gemini Enterprise
Gemini Enterprise Agent Platform
Go
Google AI Studio
Java
JavaScript
Maven
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Founded
1998
Country
United States
Website
blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/
Vendor Details
Company Name
Founded
1998
Country
United States
Website
ai.google.dev/gemini-api/docs/libraries