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Pass a Pydantic or Zod schema and get JSON that matches it.
vlm-1 accepts a custom schema in addition to pre-defined domains.
Define fields, types, and validation rules with Pydantic or Zod.
Pass the schema as json_schema on GenerationConfig.
The response matches that schema.
from typing import Literal
from pydantic import BaseModel, Field
class ImagePrediction(BaseModel):
label: Literal["tv", "document", "other"] = Field(..., title="Class label for the image.")
caption: str = Field(..., title="Caption for the image.")
from PIL import Image
from vlmrun.client import VLMRun
from vlmrun.client.types import PredictionResponse, GenerationConfig
# Initialize the client
client = VLMRun(api_key="your-api-key")
# Process the image with the custom schema
image = Image.open("path/to/image.jpg")
prediction: PredictionResponse = client.image.generate(
images=[image],
domain="image.caption",
config=GenerationConfig(
json_schema=ImagePrediction.model_json_schema()
)
)
response_dict = prediction.response.model_dump()
try:
image_prediction: ImagePrediction = ImagePrediction.model_validate(response_dict)
print(f"Image classified as: {image_prediction.label}")
print(f"Caption: {image_prediction.caption}")
except ValidationError as e:
print(f"Validation error: {e}")