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features
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Description

Amazon S3 Vectors is the pioneering cloud object storage solution that inherently accommodates the storage and querying of vector embeddings at a large scale, providing a specialized and cost-efficient storage option for applications such as semantic search, AI-driven agents, retrieval-augmented generation, and similarity searches. It features a novel “vector bucket” category in S3, enabling users to classify vectors into “vector indexes,” store high-dimensional embeddings that represent various forms of unstructured data such as text, images, and audio, and perform similarity queries through exclusive APIs, all without the need for infrastructure provisioning. In addition, each vector can include metadata, such as tags, timestamps, and categories, facilitating attribute-based filtered queries. Notably, S3 Vectors boasts impressive scalability; it is now widely accessible and can accommodate up to 2 billion vectors per index and as many as 10,000 vector indexes within a single bucket, while ensuring elastic and durable storage with the option of server-side encryption, either through SSE-S3 or optionally using KMS. This innovative approach not only simplifies managing large datasets but also enhances the efficiency and effectiveness of data retrieval processes for developers and businesses alike.

Description

Create your RAG system using a more straightforward approach than options such as LangChain, enabling you to select from an extensive array of hosted and remote services for vector databases, datasets, Large Language Models (LLMs), and application integrations. Leverage SciPhi to implement version control for your system through Git and deploy it from any location. SciPhi's platform is utilized internally to efficiently manage and deploy a semantic search engine that encompasses over 1 billion embedded passages. The SciPhi team will support you in the embedding and indexing process of your initial dataset within a vector database. After this, the vector database will seamlessly integrate into your SciPhi workspace alongside your chosen LLM provider, ensuring a smooth operational flow. This comprehensive setup allows for enhanced performance and flexibility in handling complex data queries.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon Bedrock Yes 
Amazon OpenSearch Service Yes 
Amazon S3 Yes 
Amazon SageMaker Unified Studio Yes 
Amazon Web Services (AWS) Yes 
Docker No 
Git No 
Mimasa AI Yes 

Integrations

Amazon Bedrock No 
Amazon OpenSearch Service No 
Amazon S3 No 
Amazon SageMaker Unified Studio No 
Amazon Web Services (AWS) No 
Docker Yes 
Git Yes 
Mimasa AI No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$249 per month
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 Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person No 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/s3/features/vectors/

Vendor Details

Company Name

SciPhi

Website

www.sciphi.ai/

Product Features

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