Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Codestral Embed marks Mistral AI's inaugural venture into embedding models, focusing specifically on code and engineered for optimal code retrieval and comprehension. It surpasses other prominent code embedding models in the industry, including Voyage Code 3, Cohere Embed v4.0, and OpenAI’s large embedding model, showcasing its superior performance. This model is capable of generating embeddings with varying dimensions and levels of precision; for example, even at a dimension of 256 and int8 precision, it maintains a competitive edge over rival models. The embeddings are organized by relevance, enabling users to select the top n dimensions, which facilitates an effective balance between quality and cost. Codestral Embed shines particularly in retrieval applications involving real-world code data, excelling in evaluations such as SWE-Bench, which uses actual GitHub issues and their solutions, along with Text2Code (GitHub), which enhances context for tasks like code completion or editing. Its versatility and performance make it a valuable tool for developers looking to leverage advanced code understanding capabilities.
Description
NVIDIA NeMo Retriever is a suite of microservices designed for creating high-accuracy multimodal extraction, reranking, and embedding workflows while ensuring maximum data privacy. It enables rapid, contextually relevant responses for AI applications, including sophisticated retrieval-augmented generation (RAG) and agentic AI processes. Integrated within the NVIDIA NeMo ecosystem and utilizing NVIDIA NIM, NeMo Retriever empowers developers to seamlessly employ these microservices, connecting AI applications to extensive enterprise datasets regardless of their location, while also allowing for tailored adjustments to meet particular needs. This toolset includes essential components for constructing data extraction and information retrieval pipelines, adeptly extracting both structured and unstructured data, such as text, charts, and tables, transforming it into text format, and effectively removing duplicates. Furthermore, a NeMo Retriever embedding NIM processes these data segments into embeddings and stores them in a highly efficient vector database, optimized by NVIDIA cuVS to ensure faster performance and indexing capabilities, ultimately enhancing the overall user experience and operational efficiency. This comprehensive approach allows organizations to harness the full potential of their data while maintaining a strong focus on privacy and precision.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
GitHub
Yes
Mistral AI
Yes
Mistral Code
Yes
NVIDIA NIM
No
NVIDIA NeMo
No
Integrations
GitHub
No
Mistral AI
No
Mistral Code
No
NVIDIA NIM
Yes
NVIDIA NeMo
Yes
Pricing Details
No price information available.
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
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Mistral AI
Founded
2023
Country
United States
Website
mistral.ai/news/codestral-embed
Vendor Details
Company Name
NVIDIA
Founded
1993
Country
United States
Website
developer.nvidia.com/nemo-retriever