What it is
A wrapper around BERT-style models specialized for producing fixed-size sentence embeddings. The standard for building dense retrieval, clustering, deduplication and semantic similarity.
How Vaaani uses it
- Vector indexes for RAG (paired with Pinecone, Qdrant or Weaviate)
- De-duplicating support tickets and FAQs
- Building 'similar items' / recommendation features
- Multilingual semantic search across mixed-script corpora
Why it makes the cut
Cheaper than calling OpenAI embeddings on millions of docs, and you control the model. The default embedding layer for self-hosted Vaaani RAG.
Sample code
from sentence_transformers import SentenceTransformer m = SentenceTransformer("all-mpnet-base-v2") emb = m.encode(["Vaaani builds AI workers", "Custom chatbots and automation"]) # 768-dim vectors, ready for Pinecone
Related in the Vaaani stack
Have a project that needs Sentence-Transformers?
30-min discovery call. You describe the busywork; I map it to an AI worker and a budget.