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What is Nomic Embed?

Nomic Embed is an extensive suite of open-source, high-performance embedding models designed for various applications, including multilingual text handling, multimodal content integration, and code analysis. Among these models, Nomic Embed Text v2 utilizes a Mixture-of-Experts (MoE) architecture that adeptly manages over 100 languages with an impressive 305 million active parameters, providing rapid inference capabilities. In contrast, Nomic Embed Text v1.5 offers adaptable embedding dimensions between 64 and 768 through Matryoshka Representation Learning, enabling developers to balance performance and storage needs effectively. For multimodal applications, Nomic Embed Vision v1.5 collaborates with its text models to form a unified latent space for both text and image data, significantly improving the ability to conduct seamless multimodal searches. Additionally, Nomic Embed Code demonstrates superior embedding efficiency across multiple programming languages, proving to be an essential asset for developers. This adaptable suite of models not only enhances workflow efficiency but also inspires developers to approach a wide range of challenges with creativity and innovation, thereby broadening the scope of what they can achieve in their projects.

What is Jina Reranker?

Jina Reranker v2 emerges as a sophisticated reranking solution specifically designed for Agentic Retrieval-Augmented Generation (RAG) frameworks. By utilizing advanced semantic understanding, it enhances the relevance of search outcomes and the precision of RAG systems via efficient result reordering. This cutting-edge tool supports over 100 languages, rendering it a flexible choice for multilingual retrieval tasks regardless of the query's language. It excels particularly in scenarios involving function-calling and code searches, making it invaluable for applications that require precise retrieval of function signatures and code snippets. Moreover, Jina Reranker v2 showcases outstanding capabilities in ranking structured data, such as tables, by effectively interpreting the intent behind queries directed at structured databases like MySQL or MongoDB. Boasting an impressive sixfold increase in processing speed compared to its predecessor, it guarantees ultra-fast inference, allowing for document processing in just milliseconds. Available through Jina's Reranker API, this model integrates effortlessly into existing applications and is compatible with platforms like Langchain and LlamaIndex, thus equipping developers with a potent tool to elevate their retrieval capabilities. Additionally, this versatility empowers users to streamline their workflows while leveraging state-of-the-art technology for optimal results.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Baseten
Go
Google Cloud Platform
Java
JavaScript
LangChain
LlamaIndex
Microsoft Azure
MongoDB
MySQL
PHP
Python
Ruby

Integrations Supported

Amazon Web Services (AWS)
Baseten
Go
Google Cloud Platform
Java
JavaScript
LangChain
LlamaIndex
Microsoft Azure
MongoDB
MySQL
PHP
Python
Ruby

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
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

Nomic

Company Location

United States

Company Website

www.nomic.ai/embed

Company Facts

Organization Name

Jina

Date Founded

2020

Company Location

United States

Company Website

jina.ai/reranker/

Categories and Features

Categories and Features

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