Text Embeddings & Semantic Search
Turn sentences into vectors and fetch nearest by meaning
WHAT THIS CAPABILITY MEANS
Encodes a piece of text into a fixed-length vector so that semantically similar texts sit close together, then retrieves related items by nearest-neighbour lookup. Output is a ranked result list (item ids and scores), not prose. Unlike question answering it only finds possibly relevant material; it does not compose the answer.
How it is done
The mainstream design is a two-tower model: queries and documents are compressed by encoders, and training pushes true pairs above random negatives, typically with contrastive learning and large batches. At query time all document vectors are pre-computed into an index and an approximate nearest-neighbour search returns the top-k in milliseconds. Sparse and dense representations are often mixed to combine keyword hits with semantic recall.
Representative products
6Pinecone
2019A managed vector database for similarity search
Hugging Face Hub
2016The open clearing house for models and datasets
Transformers
2018One API to load and train pretrained models
Together API
2022An inference API for open models
Replicate
2019Run community-uploaded models through an API
Perplexity
2022Answers built as it searches, each one backed by sources
Organizations involved
Typical uses
- Search over enterprise documents and code
- The recall stage of RAG pipelines
- Deduplication, clustering and topic discovery
- Recommendation and similar-content entry points
How it is evaluated
- Recall@k
- Whether the top-k contain all relevant documents
- nDCG
- Discounted cumulative gain that accounts for rank position
- MRR
- Mean reciprocal rank of the first relevant result
Limits and hard parts
- Semantic similarity is not relevance: near neighbours may merely share wording
- Cross-domain or cross-lingual use degrades noticeably without adaptation
- Chunking long documents severs context, and answers at chunk boundaries are easily missed
Concepts behind it
Word Embeddings
Turning words into coordinates — synonyms land near each other, and meaning becomes something you can add and subtract
Vectors & Vector Spaces
AI turns everything — words, images, sounds, users — into one thing: a list of numbers
Retrieval-Augmented Generation
Rather than cramming knowledge into parameters, leave it outside and look it up on demand — an open-book exam instead of a closed-book one