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Description
Belva's AiDB is an innovative database optimized for artificial intelligence, specifically tailored to enhance large language models by automatically generating relational maps that improve the model's intelligence with every new input, all while utilizing fewer context tokens and yielding superior outcomes without requiring additional tuning. With just 15 lines of code, you can establish a knowledge base that boosts AI capabilities, minimizes context token consumption, and easily adapts to increasing demands. The setup for AiDB takes only 5 minutes, making it a more efficient choice than custom retrieval-augmented generation systems. One API key is all you need to harness the power of AiDB. Transitioning to AiDB allows your language models to achieve more with minimal coding. At Belva, we have redefined the way artificial intelligence interacts with data. Thanks to our innovative indexing and relational mapping techniques, traditional context windows become almost unnecessary. By incorporating AiDB into your technology stack, you will witness remarkable improvements in your AI's performance. If your AI relies on or requires a knowledge base, AiDB is an essential addition. Enhanced efficiency translates to reduced resource wastage as you scale up operations, making AiDB an indispensable tool for modern AI solutions.
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.
API Access
Has API
API Access
Has API
Integrations
Amazon Bedrock
Amazon OpenSearch Service
Amazon S3
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Belva Engineer
Integrations
Amazon Bedrock
Amazon OpenSearch Service
Amazon S3
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Belva Engineer
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
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
Belva
Country
United States
Website
www.belva.ai/aidb
Vendor Details
Company Name
Amazon
Founded
1994
Country
United States
Website
aws.amazon.com/s3/features/vectors/