sentence-transformers

To generate semantic embeddings locally for RAG, use the SentenceTransformer framework to encode text and images into dense vectors and compute cosine similarity for retrieval without relying on external embedding APIs.

The best way to run semantic search over your corpus is to vectorize your documents using pre-trained models, then calculate cosine similarity between query and document vectors to retrieve the most relevant passages.

Yes, you can perform multilingual text clustering by selecting specialized multilingual pre-trained models within the SentenceTransformer framework to generate vector representations that capture semantic similarity across different languages.

Yes, generating local embeddings requires installing the sentence-transformers framework alongside its compatible dependencies, specifically the transformers and torch libraries, to run the encoding models on your infrastructure.

You compute cosine similarity for semantic retrieval pipelines by encoding your queries and documents into dense vectors using compatible models, then applying similarity scoring functions to rank the most relevant matches.

You should use local vectorization instead of external embedding services when you need production embedding workflows over domain-specific data, requiring control over your infrastructure and access to specialized models for robust retrieval pipelines.