mteb-retrieve

Semantic search with embeddings encodes documents and queries into vector representations, then ranks them by cosine similarity. Load your documents, encode them using an embedding model like sentence-transformers, compute cosine similarities between the query and all documents, and retrieve the top-k most similar results for your task.

Sentence-transformers models are designed for encoding text into dense vectors optimized for semantic similarity tasks. Choose a model based on your domain and language requirements, then use it consistently to encode both documents and queries so similarity comparisons are meaningful across your entire dataset.

Encoding converts raw text into vector embeddings using a pre-trained model. Load sentence-transformers, pass your documents and query text through the model to generate embeddings, then store or cache the document vectors for fast similarity computation and retrieval without re-encoding on every query.

Yes, cosine similarity measures the angular distance between embedding vectors and directly indicates semantic relevance. Compute cosine similarity between your query embedding and each document embedding, then sort and retrieve the top-k documents with the highest similarity scores for your ranking needs.

Inspect the top-k retrieved results and their similarity scores to verify semantic alignment with your query. Review the cosine similarity values and examine document content to ensure the embedding model captures the intended meaning, then adjust your model or parameters if results drift from expected relevance.

Yes, this retrieval workflow aligns with MTEB (Massive Text Embedding Benchmark) evaluation tasks. Load benchmark datasets, encode documents and queries with your chosen embedding model, compute cosine similarities, retrieve top-k results, and validate performance against benchmark metrics to assess your retrieval system.