Principle:Mistralai Client python Embedding Response Processing

Knowledge Sources	Mistral AI Embeddings Mistral Client Python
Domains	NLP, Embeddings
Last Updated	2026-02-15 14:00 GMT

Overview

A response extraction pattern that navigates embedding API response structures to retrieve vector representations and usage metadata.

Description

Embedding Response Processing extracts the embedding vectors and metadata from the EmbeddingResponse object returned by the embeddings API. The response contains a data list where each element has an embedding field (the vector as a list of floats) and an index indicating which input it corresponds to. The response also contains usage statistics.

Usage

Use this principle after calling client.embeddings.create() to extract vectors for downstream tasks (search indexing, clustering, similarity computation).

Theoretical Basis

Embedding response structure:

• data: List of EmbeddingResponseData objects, one per input text
• data[i].embedding: List[float] — the dense vector representation
• data[i].index: int — maps back to the input index
• usage: Token consumption for billing and monitoring

Related Pages

Implemented By

• Implementation:Mistralai_Client_python_EmbeddingResponse