Principle:Groq Groq python Text Embedding Request
| Knowledge Sources | |
|---|---|
| Domains | NLP, Embeddings |
| Last Updated | 2026-02-15 16:00 GMT |
Overview
The process of converting text into dense vector representations using a neural embedding model hosted on a remote API.
Description
Text Embedding transforms text strings into fixed-length numerical vectors (embeddings) that capture semantic meaning. These vectors can be used for semantic search, clustering, classification, and retrieval-augmented generation (RAG). The embedding model maps similar texts to nearby points in the vector space.
Key aspects:
- Semantic representation: Text meaning is encoded in a dense vector
- Batch processing: Multiple texts can be embedded in a single API call
- Encoding formats: Vectors returned as float lists or base64-encoded strings
- Model selection: Different models produce different vector dimensions and qualities
Usage
Use this principle when you need vector representations of text for similarity search, RAG, clustering, or classification tasks. Embedding requests are synchronous and return immediately.
Theoretical Basis
Text embedding uses a neural encoder to map text to a fixed-dimensional vector space:
Failed to parse (syntax error): {\displaystyle \vec{v} = f_{embed}(\text{input\_text}) \in \mathbb{R}^d }
Where d is the embedding dimension (model-dependent) and f_embed is the neural encoder. Semantic similarity between texts is measured by cosine similarity: