Principle:Openai Openai python Embedding Result Processing

Knowledge Sources	OpenAI Embeddings Guide openai-python
Domains	NLP, Embeddings, Data_Processing
Last Updated	2026-02-15 00:00 GMT

Overview

A data extraction and computation pattern for consuming embedding vectors and computing similarity metrics for downstream tasks.

Description

Embedding result processing handles extraction of float vectors from the API response and their use in downstream computations. The CreateEmbeddingResponse contains indexed embedding objects preserving input order, usage statistics, and the model identifier. Vectors are used for cosine similarity computation, dot product, clustering, and as features for classification models.

Usage

Use this principle after embedding generation to extract vectors and compute similarities. Use numpy for efficient vector operations when working with many embeddings.

Theoretical Basis

# Extract vectors preserving order
vectors = [item.embedding for item in sorted(response.data, key=lambda x: x.index)]

# Common downstream operations
similarity = cosine_similarity(v1, v2)     # Semantic similarity
distances = pairwise_distances(vectors)    # Clustering input
features = np.array(vectors)               # ML feature matrix

# Token usage tracking
cost_estimate = response.usage.total_tokens * price_per_token

Related Pages

Implemented By

• Implementation:Openai_Openai_python_Embedding_Response_Model