Welcome to VLM Run Hub, a comprehensive repository of pre-defined Pydantic schemas for extracting structured data from unstructured visual domains such as images, videos, and documents. Designed for Vision Language Models (VLMs) and optimized for real-world use cases, VLM Run Hub simplifies the integration of visual ETL into your workflows.
Image JSON { "issuing_state" : " MT " , "license_number" : " 0812319684104 " , "first_name" : " Brenda " , "middle_name" : " Lynn " , "last_name" : " Sample " , "address" : { "street" : " 123 MAIN STREET " , "city" : " HELENA " , "state" : " MT " , "zip_code" : " 59601 " }, "date_of_birth" : " 1968-08-04 " , "gender" : " F " , "height" : " 5'06 \" " , "weight" : 150.0 , "eye_color" : " BRO " , "issue_date" : " 2015-02-15 " , "expiration_date" : " 2023-08-04 " , "license_class" : " D " }
💡 Motivation
While vision models like OpenAI’s GPT-4o and Anthropic’s Claude Vision excel in exploratory tasks like "chat with images," they often lack practicality for automation and integration, where strongly-typed, validated outputs are crucial.
The Structured Outputs API (popularized by GPT-4o, Gemini) addresses this by constraining LLMs to return data in precise, strongly-typed formats such as Pydantic models. This eliminates complex parsing and validation, ensuring outputs conform to expected types and structures. These schemas can be nested and include complex types like lists and dictionaries, enabling seamless integration with existing systems while leveraging the full capabilities of the model.
🧰 Why use this hub of pre-defined Pydantic schemas?
📚 Easy to use: Pydantic is a well-understood and battle-tested data model for structured data.
Pydantic is a well-understood and battle-tested data model for structured data. 🔋 Batteries included: Each schema in this repo has been validated across real-world industry use cases—from healthcare to finance to media—saving you weeks of development effort.
Each schema in this repo has been validated across real-world industry use cases—from healthcare to finance to media—saving you weeks of development effort. 🔍 Automatic Data-validation: Built-in Pydantic validation ensures your extracted data is clean, accurate, and reliable, reducing errors and simplifying downstream workflows.
Built-in Pydantic validation ensures your extracted data is clean, accurate, and reliable, reducing errors and simplifying downstream workflows. 🔌 Type-safety: With Pydantic’s type-safety and compatibility with tools like mypy and pyright , you can build composable, modular systems that are robust and maintainable.
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