Principle:Groq Groq python Vector Extraction
| Knowledge Sources | |
|---|---|
| Domains | NLP, Embeddings, Data_Parsing |
| Last Updated | 2026-02-15 16:00 GMT |
Overview
The process of extracting numerical vector data and usage statistics from an embedding API response.
Description
Vector Extraction involves parsing the structured embedding response to obtain the actual float vectors and associated metadata. The response contains a list of embedding objects (one per input text), each with an embedding vector and an index indicating its position in the input array. Usage statistics provide token consumption data.
Usage
Use this principle after receiving an embedding response. Access vectors via response.data[i].embedding and usage via response.usage.
Theoretical Basis
# Abstract vector extraction
response = embed(texts)
vectors = [item.embedding for item in response.data]
tokens_used = response.usage.total_tokens
# vectors is now a list of float lists for downstream use
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