# Perplexity — Text embeddings explained: how they work [+examples]

- Company: Perplexity (perplexity.ai)
- Announced: 2026-10-29
- Category: not stated
- Coverage: not counted
- Announcement: no
- Group: routine
- Source: https://www.perplexity.ai/hub/blog/text-embeddings
- Record: https://forck.live/items/29407-text-embeddings-explained-how-they-work-examples
- Subject: Perplexity

Perplexity's explainer walks through how text embeddings turn words, sentences and documents into vectors that machine learning systems can compare. It describes the pipeline an embedding model runs on new text: tokenization into tokens with vocabulary IDs, encoding and pooling into a vector, then similarity calculation using measures such as cosine similarity or dot product. The piece distinguishes word, sentence and document embeddings by how much text each single vector represents, and notes that traditional models like Word2Vec learn by predicting a word from its surrounding words while modern retrieval-focused models often use contrastive learning. It lists business uses including semantic search, text classification, clustering, retrieval-augmented generation, recommendation systems, named entity recognition and machine translation, citing Delivery Hero, MindCraft.ai, Doczy.ai, Ring, LinkedIn's JUDE system, Amazon Health Services and Transglot as examples.

## Evidence

Verbatim from https://www.perplexity.ai/hub/blog/text-embeddings:

> If you’re familiar with the basic concept of text embeddings but wondering how to put them to work, you’ve come to the right place. We’ll dissect vectors, tokenization, similarity calculation, go down the rabbit hole of evaluating embedding models, and show you real-world use cases.
> 
> What are text embeddings?
> 
> Text embeddings are numerical representations of words, sentences, or documents that capture their semantic meaning and relationships. They’re generated by embedding models that convert text into vectors — arrays of numbers that machine learning algorithms can process and compare.
> 
> ML engineers use text embeddings to help software applications recognize relationships between words and sentences, making it easier to search, categorize, and analyze large volumes of data.
> 
> Say you’ve got thousands of incoming support tickets daily. Customers report all imaginable problems, but you need to categorize and label them. One way to automate this process is to use a pretrained text embedding model to convert each ticket into a vector.
> 
> For example, two customers submit the following messages:
> 
> “I was charged twice for my subscription ”: [0.21, -0.47, 0.83, ...]

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