BGE Base EN v1.5 - Kyma API

Overview

768-dimension English embeddings — half the vector of bge-large-en at the same per-million rate, so an index built on it is half the storage and half the comparison cost. Retrieval quality gives up little on short chunks, which is what the 512-token window enforces anyway.

Specs

Field	Value
Model ID	`bge-base-en`
Best for	English retrieval where index size matters
Context window	512
Input modalities	Text
Output modalities	Text
Speed	Fast
Cost band	Cheap
Release stage	Stable

Pricing

	Per 1M tokens
Input	$0.00675
Output	$0.00

Example

from openai import OpenAI

client = OpenAI(base_url="https://kymaapi.com/v1", api_key="kyma-...")

response = client.embeddings.create(
    model="bge-base-en",
    input=["first document", "second document"],
)

Agent query example

Ask the API which models fit, instead of hardcoding an id:

curl "https://kymaapi.com/v1/models?recommended_for=search-documents&quality_tier=strong"

​Overview

​Specs

​Pricing

​Example

​Agent query example

Overview

Specs

Pricing

Example

Agent query example