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Description
The Exa API provides access to premier online content through an embeddings-focused search methodology.
By comprehending the underlying meaning of queries, Exa delivers results that surpass traditional search engines.
Employing an innovative link prediction transformer, Exa effectively forecasts connections that correspond with a user's specified intent.
For search requests necessitating deeper semantic comprehension, utilize our state-of-the-art web embeddings model tailored to our proprietary index, while for more straightforward inquiries, we offer a traditional keyword-based search alternative.
Eliminate the need to master web scraping or HTML parsing; instead, obtain the complete, clean text of any indexed page or receive intelligently curated highlights ranked by relevance to your query.
Users can personalize their search experience by selecting date ranges, specifying domain preferences, choosing a particular data vertical, or retrieving up to 10 million results, ensuring they find exactly what they need.
This flexibility allows for a more tailored approach to information retrieval, making it a powerful tool for diverse research needs.
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
Integrations
AccessOwl
Yes
AgentKey
Yes
Agnost AI
Yes
Anything
Yes
BrainyAI
Yes
Composio
Yes
Elasticsearch
No
Incredible
Yes
Metorial
Yes
Milvus
No
Integrations
AccessOwl
No
AgentKey
No
Agnost AI
No
Anything
No
BrainyAI
No
Composio
No
Elasticsearch
Yes
Incredible
No
Metorial
No
Milvus
Yes
Pricing Details
$100 per month
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
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
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
No
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
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Vendor Details
Company Name
Exa.ai
Country
United States
Website
exa.ai/
Vendor Details
Company Name
MongoDB
Founded
2007
Country
United States
Website
blog.voyageai.com/2024/12/04/voyage-code-3/