Privacy settings

Privacy and GDPR notice

Nfero uses essential storage for language preferences and demo access controls. With your consent, Google Analytics and lightweight usage events help improve model demos and outbound subscription routing. Live demo prompts may be sent to configured Alibaba-hosted model endpoints.

Embedding

Text Embedding V4

Text Embedding V4 is Alibaba's text embedding route for semantic indexing and retrieval. Nfero uses it as the foundation page for Search and RAG planning.

EmbeddingEmbeddingDemo preview
AvailabilityDemo preview
AccessDemo preview
RegionSingapore-first

Retrieval planning

Plan a grounded AI workflow

Alibaba Cloud access

Use this route to design search, embedding, and reranking flows before enabling production access in Alibaba Cloud.

1

Input

Prepare text, image, or document content for vector search and scoring.

2

Retrieve

Use embeddings to collect candidate context for a user query.

3

Rerank

Apply Qwen reranking to prioritize the best source passages.

Nfero may earn commission if you later purchase through a qualifying link, at no extra cost to you.

Model playbook

What teams use it for

Retrieval demo

Search relevance

Rank model pages for a developer searching for the right model route.

Embedding slot

Vector match

Embed model descriptions and cluster them by task, cost, and hosted availability.

Rerank slot

Rerank result

Rerank model candidates by reasoning, vision, and compatibility.

Usage, access, and limitsTerms apply

Usage terms

Terms apply. Confirm usage terms in the customer's Alibaba agreement before production launch.

Hosted access

This route is ready for discovery, pricing estimates, and input planning while live access is prepared.

Model limits

Confirm final limits in Alibaba Cloud before production use.

Developer implementationAPI examples
curl
curl -X POST https://nfero.com/api/models/text-embedding-v4/run \
  -H 'content-type: application/json' \
  -d '{"texts":["Nfero Model Hub","Alibaba Model Studio"],"normalize":true}'
JavaScript
await fetch('/api/models/text-embedding-v4/run', {
  method: 'POST',
  headers: { 'content-type': 'application/json' },
  body: JSON.stringify({
  "texts": [
    "Nfero Model Hub",
    "Alibaba Model Studio"
  ],
  "normalize": true
})
});
Python
import requests
requests.post('https://nfero.com/api/models/text-embedding-v4/run', json={
  "texts": [
    "Nfero Model Hub",
    "Alibaba Model Studio"
  ],
  "normalize": true
})