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What is Neum AI?

No company wants to engage with customers using information that is no longer relevant. Neum AI empowers businesses to keep their AI solutions informed with precise and up-to-date context. Thanks to its pre-built connectors compatible with various data sources, including Amazon S3 and Azure Blob Storage, as well as vector databases like Pinecone and Weaviate, you can set up your data pipelines in a matter of minutes. You can further enhance your data processing by transforming and embedding it through integrated connectors for popular embedding models such as OpenAI and Replicate, in addition to leveraging serverless functions like Azure Functions and AWS Lambda. Additionally, implementing role-based access controls ensures that only authorized users can access particular vectors, thereby securing sensitive information. Moreover, you have the option to integrate your own embedding models, vector databases, and data sources for a tailored experience. It is also beneficial to explore how Neum AI can be deployed within your own cloud infrastructure, offering you greater customization and control. Ultimately, with these advanced features at your disposal, you can significantly elevate your AI applications to facilitate outstanding customer interactions and drive business success.

What is Gemini Embedding?

The first text model of the Gemini Embedding, referred to as gemini-embedding-001, has officially launched and is accessible through both the Gemini API and Gemini Enterprise Agent Platform, having consistently held its top spot on the Massive Text Embedding Benchmark Multilingual leaderboard since its initial trial in March, thanks to its exceptional performance in retrieval, classification, and multiple embedding tasks, outperforming both legacy Google models and those from other external developers. Notably, this versatile model supports over 100 languages and features a maximum input limit of 2,048 tokens, employing the cutting-edge Matryoshka Representation Learning (MRL) technique, which enables developers to choose from output dimensions of 3072, 1536, or 768 for optimal quality, efficiency, and performance. Users can easily access this model through the well-known embed_content endpoint in the Gemini API. This transition process is designed for a smooth user experience, minimizing any impact on existing workflows and ensuring continuity in operations. The launch of this model represents a significant step forward in the field of text embeddings, paving the way for even more advancements in multilingual applications.

Media

Media

Integrations Supported

AWS Lambda
Amazon S3
Azure Blob Storage
Azure Functions
ChatGPT
GPT-3
GPT-3.5
GPT-4
Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google AI Studio
OpenAI
Python
Replicate

Integrations Supported

AWS Lambda
Amazon S3
Azure Blob Storage
Azure Functions
ChatGPT
GPT-3
GPT-3.5
GPT-4
Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google AI Studio
OpenAI
Python
Replicate

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$0.15 per 1M input tokens
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Neum AI

Company Website

www.neum.ai/

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

developers.googleblog.com/en/gemini-embedding-available-gemini-api/

Categories and Features

Categories and Features

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