Python SDK

from euriai import EuriaiClient

client = EuriaiClient(api_key="YOUR_EURI_API_KEY")

# Chat completion
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=100
)
print(response.choices[0].message.content)

# Simple prompt
euriai --api_key YOUR_EURI_API_KEY --prompt "Tell me a joke"

# Streaming
euriai --api_key YOUR_EURI_API_KEY --prompt "Stream a fun fact" --stream

# List models
euriai --models

from euriai.langchain_embed import EuriaiEmbeddings

embeddings = EuriaiEmbeddings(
    api_key="YOUR_EURI_API_KEY",
    model="text-embedding-3-small"
)

result = embeddings.embed_query("What is machine learning?")
print(len(result))  # 1536 dimensions

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_EURI_API_KEY",
    base_url="https://api.euron.one/api/v1/euri"
)

# Chat
response = client.chat.completions.create(
    model="gemini-2.5-flash",
    messages=[{"role": "user", "content": "Explain Docker in one paragraph."}]
)
print(response.choices[0].message.content)

# Embeddings
embedding = client.embeddings.create(
    model="text-embedding-3-small",
    input="Hello world"
)
print(embedding.data[0].embedding[:5])

# Image generation
image = client.images.generate(
    model="gemini-3-pro-image-preview",
    prompt="A cat wearing sunglasses",
    n=1
)
print(image.data[0].url)

# Speech-to-text
with open("audio.mp3", "rb") as f:
    transcript = client.audio.transcriptions.create(
        model="whisper-large-v3-turbo",
        file=f
    )
print(transcript.text)