MODELS

nomic-embed-text-v2-moe

by Nomic AI

multilingualnomic-ai

Overview

Open-source multilingual Mixture-of-Experts text embedding model for retrieval and RAG.

Details

nomic-embed-text-v2-moe is a Nomic AI multilingual Mixture-of-Experts text embedding model. The supplied official sources describe it as open-source and intended for retrieval and RAG, with official weights, code, paper, and a Hugging Face model card. Nomic’s API reference also documents a text-embedding endpoint, POST /v1/embedding/text, with task_type values including search_document, search_query, classification, and clustering.

When to Use

Use for evaluating multilingual text embeddings for retrieval or RAG workflows. Use when you want an open-source embedding model with official weights and code available from Nomic AI. Use Nomic’s hosted embedding API when your workflow needs documented task types such as search_document search_query classification or clustering.

Getting Started

Review the Hugging Face model card for official weights and model usage details.
Read Nomic’s launch/blog documentation for model context and intended retrieval/RAG use cases.
Inspect the nomic-ai/contrastors GitHub repository for the official code source.
If using hosted inference
review Nomic’s POST /v1/embedding/text API reference and supported task_type values.
Check Nomic’s pricing page before relying on the hosted Developer API in production.

Key Features

•Multilingual text embedding model.
•Mixture-of-Experts embedding architecture
•according to Nomic’s product page/source metadata.
•Positioned for retrieval and RAG use cases.
•Official Hugging Face model card with weights.
•Official code repository linked from Nomic AI.
•Hosted text-embedding API documentation with task types for search
•classification
•and clustering.

Capabilities

•text-embedding
•multilingual-embeddings
•semantic-search
•retrieval
•rag
•classification
•clustering

Last updated Jun 22, 2026