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ease
features
design
support

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Description

Cohere's Embed stands out as a premier multimodal embedding platform that effectively converts text, images, or a blend of both into high-quality vector representations. These vector embeddings are specifically tailored for various applications such as semantic search, retrieval-augmented generation, classification, clustering, and agentic AI. The newest version, embed-v4.0, introduces the capability to handle mixed-modality inputs, permitting users to create a unified embedding from both text and images. It features Matryoshka embeddings that can be adjusted in dimensions of 256, 512, 1024, or 1536, providing users with the flexibility to optimize performance against resource usage. With a context length that accommodates up to 128,000 tokens, embed-v4.0 excels in managing extensive documents and intricate data formats. Moreover, it supports various compressed embedding types such as float, int8, uint8, binary, and ubinary, which contributes to efficient storage solutions and expedites retrieval in vector databases. Its multilingual capabilities encompass over 100 languages, positioning it as a highly adaptable tool for applications across the globe. Consequently, users can leverage this platform to handle diverse datasets effectively while maintaining performance efficiency.

Description

Pinecone Rerank V0 is a cross-encoder model specifically designed to enhance precision in reranking tasks, thereby improving enterprise search and retrieval-augmented generation (RAG) systems. This model processes both queries and documents simultaneously, enabling it to assess fine-grained relevance and assign a relevance score ranging from 0 to 1 for each query-document pair. With a maximum context length of 512 tokens, it ensures that the quality of ranking is maintained. In evaluations based on the BEIR benchmark, Pinecone Rerank V0 stood out by achieving the highest average NDCG@10, surpassing other competing models in 6 out of 12 datasets. Notably, it achieved an impressive 60% increase in performance on the Fever dataset when compared to Google Semantic Ranker, along with over 40% improvement on the Climate-Fever dataset against alternatives like cohere-v3-multilingual and voyageai-rerank-2. Accessible via Pinecone Inference, this model is currently available to all users in a public preview, allowing for broader experimentation and feedback. Its design reflects an ongoing commitment to innovation in search technology, making it a valuable tool for organizations seeking to enhance their information retrieval capabilities.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Cohere
Airbyte
Amazon Bedrock
Amazon Web Services (AWS)
Anyscale
Apify
Context Data
Estuary Flow
HoneyHive
Instill
Llama
Matillion
New Relic
Nuclia
Pinecone
Redpanda
TwelveLabs
Unstructured
Vercel
Voyage AI

Integrations

Cohere
Airbyte
Amazon Bedrock
Amazon Web Services (AWS)
Anyscale
Apify
Context Data
Estuary Flow
HoneyHive
Instill
Llama
Matillion
New Relic
Nuclia
Pinecone
Redpanda
TwelveLabs
Unstructured
Vercel
Voyage AI

Pricing Details

$0.47 per image
Free Trial
Free Version

Pricing Details

$25 per month
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

Cohere

Founded

2019

Country

Canada

Website

cohere.com/embed

Vendor Details

Company Name

Pinecone

Founded

2019

Country

United States

Website

www.pinecone.io/blog/pinecone-rerank-v0-announcement/

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