Implementation:Explodinggradients Ragas OpenAIEmbeddings Provider

Knowledge Sources	Explodinggradients_Ragas
Domains	Embeddings, OpenAI
Last Updated	2026-02-10 00:00 GMT

Overview

OpenAI embeddings provider with batch optimization and automatic sync/async client detection.

Description

OpenAIEmbeddings extends BaseRagasEmbedding to provide text embeddings using the OpenAI Python client. It supports both sync and async clients with automatic detection, tracks usage analytics, and defaults to the text-embedding-3-small model.

Usage

Use this provider when working directly with OpenAI's embedding API. Requires a pre-configured OpenAI client.

Code Reference

Source Location

• Repository: Explodinggradients_Ragas
• File: src/ragas/embeddings/openai_provider.py
• Lines: 10-149

Signature

class OpenAIEmbeddings(BaseRagasEmbedding):
    PROVIDER_NAME = "openai"
    REQUIRES_CLIENT = True
    DEFAULT_MODEL = "text-embedding-3-small"

    def __init__(
        self,
        client: Any,
        model: str = "text-embedding-3-small",
        cache: Optional[CacheInterface] = None,
    ) -> None:
        ...

Import

from ragas.embeddings.openai_provider import OpenAIEmbeddings

I/O Contract

Inputs

Name	Type	Required	Description
client	Any	Yes	Pre-configured OpenAI client (sync or async)
model	str	No	Embedding model name (default "text-embedding-3-small")

Outputs

Name	Type	Description
embed_text returns	List[float]	Embedding vector
embed_texts returns	List[List[float]]	Batch embedding vectors

Usage Examples

from openai import OpenAI
from ragas.embeddings.openai_provider import OpenAIEmbeddings

client = OpenAI()
embeddings = OpenAIEmbeddings(client=client, model="text-embedding-3-small")
vector = embeddings.embed_text("What is machine learning?")
print(len(vector))