sentence-transformers
Category: knowledge-and-rag · License: MIT · Version: 1.0.0
Tags: Sentence Transformers, Embeddings, Semantic Similarity, RAG, Multilingual, Multimodal, Pre-Trained Models, Clustering, Semantic Search, Production
When to use it
Generates sentence, text, and image embeddings locally with the Python sentence-transformers (SBERT) library, using pre-trained Hugging Face models such as all-MiniLM-L6-v2, all-mpnet-base-v2, and multilingual variants. Covers encoding, cosine similarity, semantic search, batch encoding, fine-tuning, and LangChain/LlamaIndex integration. Use when building embeddings for RAG, running semantic search or similarity scoring, clustering or classifying text, embedding multilingual text without an API, or fine-tuning an embedding model on domain data. For API-based or managed embeddings, use OpenAI or Cohere Embed instead.Example requests
Ask your agent in plain language, for example:- “How do I get embeddings for a list of sentences without calling an API?”
- “I have abstracts.csv with about 40k paper abstracts in a ‘abstract’ column. I want to build a semantic search so I can type a query and get the top 10 most similar abstracts. I’m on a laptop with no GPU. Can you set that up with all-mpnet-base-v2 and tell me how long it will take?”
- “I have about 3,000 customer support tickets in German, French and English. I want to group them into topics automatically, and I can’t send the text to any external service because of privacy rules. What should I do?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole knowledge-and-rag category with /plugin install knowledge-and-rag@research-agent-skills.