Workflow:Openai Openai python Embeddings Generation
| Knowledge Sources | |
|---|---|
| Domains | Embeddings, Semantic_Search, NLP, API_Integration |
| Last Updated | 2026-02-15 10:00 GMT |
Overview
End-to-end process for generating text embeddings using the OpenAI Embeddings API, converting text into dense vector representations for semantic search, clustering, and similarity comparisons.
Description
This workflow covers text embedding generation through the OpenAI Python SDK. The Embeddings API converts text strings into fixed-dimensional numerical vectors that capture semantic meaning. These vectors can be used for semantic search, text classification, clustering, recommendation systems, and anomaly detection. The SDK supports both single and batch text inputs, configurable encoding formats (float or base64), and optional dimensionality control. The SDK also provides lazy proxies for optional numpy and pandas dependencies to simplify vector processing workflows.
Usage
Execute this workflow when you need to convert text into vector representations for downstream machine learning or search tasks. This is appropriate when building semantic search engines, implementing RAG (Retrieval-Augmented Generation) pipelines, performing text similarity comparisons, clustering documents, or any application that requires numerical text representations for vector database storage or mathematical operations.
Execution Steps
Step 1: Client Initialization
Create an OpenAI or AsyncOpenAI client. The embeddings endpoint is accessed via client.embeddings.create(). No additional dependencies are required for basic embedding generation, though numpy is useful for vector operations.
Key considerations:
- Standard API key authentication applies
- Both sync and async clients support embedding creation
- Optional numpy integration for efficient vector handling
Step 2: Prepare Input Text
Prepare the text input as a single string or a list of strings for batch processing. Each string represents a document, sentence, or text chunk to be embedded. Pre-process text as needed (trimming, cleaning) and ensure inputs are within the model's token limits.
Key considerations:
- Batch multiple texts in a single request for efficiency
- The input can be a string or a list of strings
- Token limits vary by model; longer texts may need chunking
- Clean and normalize text for consistent embedding quality
Step 3: Generate Embeddings
Call client.embeddings.create() with the model name (e.g., text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002) and the input text. Optionally specify encoding_format (float or base64) and dimensions to control the output vector size. The response contains an array of embedding objects, each with an index and vector data.
Key considerations:
- text-embedding-3-small offers good performance at lower cost
- text-embedding-3-large provides higher quality at higher cost
- The dimensions parameter can reduce vector size for storage optimization
- Batch requests are more efficient than individual calls
Step 4: Process Embedding Results
Extract the embedding vectors from the response object. Each embedding.embedding is a list of floats representing the text vector. Store vectors in a vector database, compute similarity scores using cosine similarity, or use them as features for downstream ML tasks. The response also includes token usage statistics.
Key considerations:
- Cosine similarity is the standard metric for comparing embeddings
- Store vectors in a vector database (Pinecone, Weaviate, pgvector) for search
- The response includes usage.total_tokens for cost tracking
- Use numpy for efficient vector arithmetic when processing many embeddings