Overview
Encode documents with Hugging Face and search/summarize with the model of your choice.
Problem
Teams struggle to index docs consistently and choose the right embedding model.
Solution
AI Hub’s EmbeddingService batches vectors via HF Inference API and returns arrays ready for your index.
How it works
POST task=encode with your texts and model name. Persist vectors (id↔vector). At query time, retrieve top-k and pass to the LLM for grounded answers.
Who is this for
Developers
Knowledge Management
Support
Expected outcomes
- Fewer hallucinations via retrieval grounding
- Faster answers across internal documentation
Key metrics
Answer accuracy (human-rated)
Baseline
70 %
Target
90 %
Search latency (p95)
Baseline
900 ms
Target
250 ms
Gallery
Downloads & templates
Case studies
Support portal deflects tickets with RAG
Self-serve answers improved; deflection up 36%.
SaaS Enterprise APAC
Security impact
- Document text & vector representations · PII: none
Compliance
- GDPR (enterprise data processor role)
- SOC2
Availability & next steps
Pro
Enterprise