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Description

Meii AI stands at the forefront of AI innovations, providing specialized Large Language Models that can be customized using specific organizational data and can be securely hosted in private or cloud environments. Our AI methodology, rooted in Retrieval Augmented Generation (RAG), effectively integrates Embedded Models and Semantic Search to deliver tailored and insightful responses to conversational inquiries, catering specifically to enterprise needs. With a blend of our distinct expertise and over ten years of experience in Data Analytics, we merge LLMs with Machine Learning algorithms to deliver exceptional solutions designed for mid-sized enterprises. We envision a future where individuals, businesses, and governmental entities can effortlessly utilize advanced technology. Our commitment to making AI universally accessible drives our team to continuously dismantle the barriers that separate machines from human interaction, fostering a more connected and efficient world. This mission not only reflects our dedication to innovation but also underscores the transformative potential of AI in diverse sectors.

Description

Voyage AI has unveiled voyage-code-3, an advanced embedding model specifically designed to enhance code retrieval capabilities. This innovative model achieves superior performance, surpassing OpenAI-v3-large and CodeSage-large by averages of 13.80% and 16.81% across a diverse selection of 32 code retrieval datasets. It accommodates embeddings of various dimensions, including 2048, 1024, 512, and 256, and provides an array of embedding quantization options such as float (32-bit), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8). With a context length of 32 K tokens, voyage-code-3 exceeds the limitations of OpenAI's 8K and CodeSage Large's 1K context lengths, offering users greater flexibility. Utilizing an innovative approach known as Matryoshka learning, it generates embeddings that feature a layered structure of varying lengths within a single vector. This unique capability enables users to transform documents into a 2048-dimensional vector and subsequently access shorter dimensional representations (such as 256, 512, or 1024 dimensions) without the need to re-run the embedding model, thus enhancing efficiency in code retrieval tasks. Additionally, voyage-code-3 positions itself as a robust solution for developers seeking to improve their coding workflow.

API Access

Has API No 

API Access

Has API No 

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Integrations

Elasticsearch No 
Milvus No 
Qdrant No 
Vespa No 

Integrations

Elasticsearch Yes 
Milvus Yes 
Qdrant Yes 
Vespa Yes 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App Yes 
iPad App Yes 
Android App Yes 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook 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 

Customer Support

Business Hours Yes 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

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

Types of Training

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

Types of Training

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

Vendor Details

Company Name

Meii AI

Founded

2024

Country

India

Website

www.meii.ai/

Vendor Details

Company Name

MongoDB

Founded

2007

Country

United States

Website

blog.voyageai.com/2024/12/04/voyage-code-3/

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