Embed content
Turn product docs, support articles, and media assets into searchable vectors.
Search & RAG
Alibaba retrieval models for grounded AI.
Explore embedding and reranking routes for search, retrieval augmented generation, product discovery, and knowledge-base workflows.
Retrieval flow
From documents to better Qwen answers.
Use embeddings and reranking to improve context quality before generation.
Retrieve candidates
Match a user query to the most relevant chunks before sending context to Qwen.
Rerank results
Score candidate passages so the final answer uses the strongest source material.
3 routes
Search & RAG models
Embedding and rerank routes with input planning, estimates, and Alibaba Cloud access notes.
Alibaba Cloud
Qwen3 Rerank
Qwen3 Rerank is the Alibaba reranking route for improving retrieval quality after initial search or embedding recall. Nfero connects it to Search and RAG workflows.
Best for: Search and retrieval-augmented generation
QwenRerankingTerms apply
- Availability
- Guide and examples
- Estimated standard demo
- Confirm with provider
Alibaba Cloud
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.
Best for: Search and retrieval-augmented generation
EmbeddingEmbeddingTerms apply
- Availability
- Guide and examples
- Estimated standard demo
- Confirm with provider
Alibaba Cloud
Tongyi Embedding Vision Plus
Tongyi Embedding Vision Plus is Alibaba's multimodal embedding route for image and text retrieval. Nfero positions it for visual search, catalog retrieval, and grounded multimodal applications.
Best for: Search and retrieval-augmented generation
EmbeddingEmbeddingTerms apply
- Availability
- Guide and examples
- Estimated standard demo
- Confirm with provider
Open-source proof
Show the open-source side of Alibaba retrieval.
ViDoRAG and zvec help explain visual-document RAG and local vector search beside Alibaba embedding and rerank routes.