Google Gemini

from openai import OpenAI

client = OpenAI(
    api_key="your-truefoundry-api-key",
    base_url="{GATEWAY_BASE_URL}",
)

response = client.chat.completions.create(
    model="gemini-main/gemini-2.5-flash",
    messages=[{"role": "user", "content": "What is TrueFoundry in one line?"}],
)
print(response.choices[0].message.content)

response = client.audio.speech.create(
    model="gemini-main/gemini-2.5-pro-preview-tts",
    voice="alloy",
    input="Hello from TrueFoundry. The AI gateway makes multi-provider routing simple.",
)

with open("tts.wav", "wb") as f:
    f.write(response.read())

doc = client.embeddings.create(
    model="gemini-main/gemini-embedding-2",
    input="TrueFoundry is an AI gateway that unifies access to multiple LLM providers.",
    extra_body={"task_type": "RETRIEVAL_DOCUMENT"},
)
query = client.embeddings.create(
    model="gemini-main/gemini-embedding-2",
    input="What does TrueFoundry do?",
    extra_body={"task_type": "RETRIEVAL_QUERY"},
)
print("dim:", len(doc.data[0].embedding))

import base64

response = client.images.generate(
    model="gemini-main/gemini-3.1-flash-image",
    prompt="A minimalist isometric illustration of a cloud with a lightning bolt, flat colors.",
    size="1024x1024",
    n=1,
)

item = response.data[0]
if getattr(item, "b64_json", None):
    image_bytes = base64.b64decode(item.b64_json)
else:
    import requests
    image_bytes = requests.get(item.url, timeout=60).content

with open("generated.png", "wb") as f:
    f.write(image_bytes)

import base64
from PIL import Image as PILImage, ImageDraw

mask_img = PILImage.new("L", (1024, 1024), 0)
ImageDraw.Draw(mask_img).rectangle((700, 0, 1024, 300), fill=255)
mask_img.save("mask.png", format="PNG")

with open("generated.png", "rb") as img_f, open("mask.png", "rb") as mask_f:
    response = client.images.edit(
        model="gemini-main/gemini-3.1-flash-image",
        image=img_f,
        mask=mask_f,
        prompt="Paint a bright yellow sun in the top-right corner.",
        n=1,
    )

item = response.data[0]
if getattr(item, "b64_json", None):
    image_bytes = base64.b64decode(item.b64_json)
else:
    import requests
    image_bytes = requests.get(item.url, timeout=60).content

with open("edited.png", "wb") as f:
    f.write(image_bytes)

import json, uuid
from openai import OpenAI

FINETUNE_BASE_MODEL = "gemini-2.5-flash"

with open("finetune_training.jsonl", "w") as f:
    for topic, haiku in [
        ("the sun",   "Bright orb in the sky"),
        ("a river",   "Silver thread of life"),
        ("the moon",  "Pale night-watcher gleams"),
        ("a forest",  "Tall trees stand in green"),
        ("the ocean", "Vast blue stretching wide"),
    ]:
        f.write(json.dumps({"messages": [
            {"role": "user", "content": f"What is {topic}?"},
            {"role": "assistant", "content": haiku},
        ]}) + "\n")

ft_client = OpenAI(
    api_key="your-truefoundry-api-key",
    base_url="{GATEWAY_BASE_URL}",
    default_headers={
        "x-tfy-provider-name": "gemini-main",
        "x-tfy-provider-model": FINETUNE_BASE_MODEL,
        "x-tfy-file-purpose": "fine-tune",
    },
)

with open("finetune_training.jsonl", "rb") as f:
    ft_file = ft_client.files.create(file=f, purpose="fine-tune")

ft_job = ft_client.fine_tuning.jobs.create(
    training_file=ft_file.id,
    model=f"gemini-main/{FINETUNE_BASE_MODEL}",
    suffix=f"gemini-ft-{uuid.uuid4().hex[:6]}",
    extra_body={"hyperparameters": {"n_epochs": 2}},
)
print(f"created: {ft_job.id}  status={ft_job.status}")

# Retrieve status (queued, running, succeeded, failed, cancelled)
ft_job = ft_client.fine_tuning.jobs.retrieve(ft_job.id)
print(f"status: {ft_job.status}")