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About

Gemini Embedding models, including the newer Gemini Embedding 2, are part of Google’s Gemini AI ecosystem and are designed to convert text, phrases, sentences, and code into numerical vector representations that capture their semantic meaning. Unlike generative models that produce new content, the embedding model transforms input data into dense vectors that represent meaning in a mathematical format, allowing computers to compare and analyze information based on conceptual similarity rather than exact wording. These embeddings enable applications such as semantic search, recommendation systems, document retrieval, clustering, classification, and retrieval-augmented generation pipelines. The model can process input in more than 100 languages and supports up to 2048 tokens per request, allowing it to embed longer pieces of text or code while maintaining strong contextual understanding.

About

Jina Reranker v2 is a state-of-the-art reranker designed for Agentic Retrieval-Augmented Generation (RAG) systems. It enhances search relevance and RAG accuracy by reordering search results based on deeper semantic understanding. It supports over 100 languages, enabling multilingual retrieval regardless of the query language. It is optimized for function-calling and code search, making it ideal for applications requiring precise function signatures and code snippet retrieval. Jina Reranker v2 also excels in ranking structured data, such as tables, by understanding the downstream intent to query structured databases like MySQL or MongoDB. With a 6x speedup over its predecessor, it offers ultra-fast inference, processing documents in milliseconds. The model is available via Jina's Reranker API and can be integrated into existing applications using platforms like Langchain and LlamaIndex.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

AI developers and data engineers who need a high-performance embedding model to convert text or code into semantic vectors for search, retrieval, and AI applications

Audience

Developers and data scientists interested in a solution to enhance relevance and performance

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Google
Founded: 1998
United States
blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/

Company Information

Jina
Founded: 2020
United States
jina.ai/reranker/

Alternatives

Alternatives

txtai

txtai

NeuML
RankLLM

RankLLM

Castorini

Categories

Categories

Integrations

Amazon Web Services (AWS)
Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google AI Studio
Google Cloud Platform
LangChain
LlamaIndex
Microsoft Azure
MongoDB
MySQL
Python

Integrations

Amazon Web Services (AWS)
Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google AI Studio
Google Cloud Platform
LangChain
LlamaIndex
Microsoft Azure
MongoDB
MySQL
Python
Claim Gemini Embedding 2 and update features and information
Claim Gemini Embedding 2 and update features and information
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Claim Jina Reranker and update features and information