NVIDIA NeMo Retriever vs. voyage-code-3 Comparison


NVIDIA NeMo Retriever NVIDIA	voyage-code-3 MongoDB	+	+
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About NVIDIA NeMo Retriever is a collection of microservices for building multimodal extraction, reranking, and embedding pipelines with high accuracy and maximum data privacy. It delivers quick, context-aware responses for AI applications like advanced retrieval-augmented generation (RAG) and agentic AI workflows. As part of the NVIDIA NeMo platform and built with NVIDIA NIM, NeMo Retriever allows developers to flexibly leverage these microservices to connect AI applications to large enterprise datasets wherever they reside and fine-tune them to align with specific use cases. NeMo Retriever provides components for building data extraction and information retrieval pipelines. The pipeline extracts structured and unstructured data (e.g., text, charts, tables), converts it to text, and filters out duplicates. A NeMo Retriever embedding NIM converts the chunks into embeddings and stores them in a vector database, accelerated by NVIDIA cuVS, for enhanced performance and speed of indexing.	About Voyage AI introduces voyage-code-3, a next-generation embedding model optimized for code retrieval. It outperforms OpenAI-v3-large and CodeSage-large by an average of 13.80% and 16.81% on a suite of 32 code retrieval datasets, respectively. It supports embeddings of 2048, 1024, 512, and 256 dimensions and offers multiple embedding quantization options, including float (32-bit), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8). With a 32 K-token context length, it surpasses OpenAI's 8K and CodeSage Large's 1K context lengths. Voyage-code-3 employs Matryoshka learning to create embeddings with a nested family of various lengths within a single vector. This allows users to vectorize documents into a 2048-dimensional vector and later use shorter versions (e.g., 256, 512, or 1024 dimensions) without re-invoking the embedding model.
Platforms Supported Windows Mac Linux Cloud On-Premises iPhone iPad Android Chromebook	Platforms Supported Windows Mac Linux Cloud On-Premises iPhone iPad Android Chromebook
Audience Enterprise developers and data scientists searching for a tool to build scalable, high-accuracy AI applications	Audience AI researchers and developers in search of a solution providing an embedding model for code retrieval
Support Phone Support 24/7 Live Support Online	Support Phone Support 24/7 Live Support Online
API Offers API	API Offers API
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing No information available. Free Version Free Trial	Pricing No information available. Free Version Free Trial
Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software	Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software
Training Documentation Webinars Live Online In Person	Training Documentation Webinars Live Online In Person
Company Information NVIDIA Founded: 1993 United States developer.nvidia.com/nemo-retriever	Company Information MongoDB Founded: 2007 United States blog.voyageai.com/2024/12/04/voyage-code-3/
Alternatives AI-Q NVIDIA Blueprint NVIDIA	Alternatives voyage-3-large MongoDB
NVIDIA NeMo Guardrails NVIDIA	Cohere Embed Cohere
BGE	Voyage AI MongoDB
NVIDIA NeMo NVIDIA	voyage-4-large Voyage AI
Voyage AI MongoDB View All	Codestral Embed Mistral AI View All
Categories Embedding Models Reranking Models	Categories Embedding Models

Integrations Elasticsearch Milvus NVIDIA NIM NVIDIA NeMo Qdrant Vespa Weaviate View All 2 Integrations	Integrations Elasticsearch Milvus NVIDIA NIM NVIDIA NeMo Qdrant Vespa Weaviate View All 5 Integrations
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