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ease
features
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support

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Description

BilberryDB is a robust vector-database solution tailored for enterprises, aimed at facilitating the development of AI applications that can manage various types of multimodal data, such as images, video, audio, 3D models, tabular data, and text, all within a single unified framework. It delivers rapid similarity search and retrieval through the use of embeddings, supports few-shot or no-code workflows that empower users to establish effective search and classification functionalities without the necessity for extensive labeled datasets, and provides a developer SDK, including TypeScript, alongside a visual builder to assist non-technical users. The platform prioritizes quick query responses in under a second, enabling the effortless integration of different data types and the swift launch of apps enhanced with vector-search capabilities ("Deploy as an App"), allowing organizations to develop AI-powered systems for search, recommendations, classification, or content discovery without the need to construct their own infrastructure from the ground up. Furthermore, its comprehensive features make it an ideal choice for companies looking to leverage AI technology efficiently and effectively.

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

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Elasticsearch
Milvus
Qdrant
TypeScript
Vespa
Weaviate

Integrations

Elasticsearch
Milvus
Qdrant
TypeScript
Vespa
Weaviate

Pricing Details

Free
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

BilberryDB

Founded

2016

Country

France

Website

bilberrydb.com

Vendor Details

Company Name

MongoDB

Founded

2007

Country

United States

Website

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

Product Features

Product Features

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