Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Asimov serves as a fundamental platform for AI-search and vector-search, allowing developers to upload various content sources such as documents and logs, which it then automatically chunks and embeds, making them accessible through a single API for enhanced semantic search, filtering, and relevance for AI applications. By streamlining the management of vector databases, embedding pipelines, and re-ranking systems, it simplifies the process of ingestion, metadata parameterization, usage monitoring, and retrieval within a cohesive framework. With features that support content addition through a REST API and the capability to conduct semantic searches with tailored filtering options, Asimov empowers teams to create extensive search functionalities with minimal infrastructure requirements. The platform efficiently manages metadata, automates chunking, handles embedding, and facilitates storage solutions like MongoDB, while also offering user-friendly tools such as a dashboard, usage analytics, and smooth integration capabilities. Furthermore, its all-in-one approach eliminates the complexities of traditional search systems, making it an indispensable tool for developers aiming to enhance their applications with advanced search capabilities.
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
Integrations
Gemini
No
Gemini Enterprise
No
Gemini Enterprise Agent Platform
No
Google AI Studio
No
Mimasa AI
No
MongoDB
Yes
Python
No
Integrations
Gemini
Yes
Gemini Enterprise
Yes
Gemini Enterprise Agent Platform
Yes
Google AI Studio
Yes
Mimasa AI
Yes
MongoDB
No
Python
Yes
Pricing Details
$20 per month
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)
Yes
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
Asimov
Country
United States
Website
www.asimov.mov/
Vendor Details
Company Name
Founded
1998
Country
United States
Website
blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/