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    genai-processors-pydantic

    genai-processors-pydantic

    mbeacom July 14, 2025
    6 copies 0 downloads

    The Pydantic Gemini Processor to be used with Gemini's genai-processors

    Agent Definition
    # genai-processors-pydantic
    
    [![PyPI version](https://img.shields.io/pypi/v/genai-processors-pydantic.svg)](https://pypi.org/project/genai-processors-pydantic/)
    [![Validation](https://github.com/mbeacom/genai-processors-pydantic/actions/workflows/validate.yml/badge.svg)](https://github.com/mbeacom/genai-processors-pydantic/actions/workflows/validate.yml)
    [![codecov](https://codecov.io/github/mbeacom/pydantic-gemini-processor/graph/badge.svg?token=9Ue94I4FEw)](https://codecov.io/github/mbeacom/pydantic-gemini-processor)
    [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](LICENSE)
    
    A Pydantic validator processor for Google's [genai-processors](https://github.com/google-gemini/genai-processors) framework.
    
    **Note:** This is an independent contrib processor that extends the genai-processors ecosystem.
    
    ## ⚠️ Important: Current Limitations & Roadmap
    
    This processor was developed based on feedback from the genai-processors maintainers. While functional and tested, it has known limitations in certain scenarios. See [MAINTAINER_FEEDBACK.md](MAINTAINER_FEEDBACK.md) for detailed analysis and our roadmap to address these challenges:
    
    * **Streaming**: Currently works best with complete JSON in single Parts
    * **Tool Integration**: Planned support for `genai_types.ToolResponse` Parts
    * **Multi-Model Validation**: Single-model design; multi-model support planned
    * **MIME Type Independence**: ✅ Already handles unmarked JSON Parts
    
    We're committed to addressing these limitations while maintaining a stable API.
    
    ## PydanticValidator
    
    The PydanticValidator is a PartProcessor that validates the JSON content of a ProcessorPart against a specified [Pydantic](https://docs.pydantic.dev/latest/) model. It provides a simple, declarative way to enforce data schemas and improve the robustness of your AI pipelines.
    
    ## Motivation
    
    In many AI applications, processors ingest data from external sources like user inputs or API calls. This data can be unpredictable or malformed. T

    Tags

    agentaiasynciogeminigemini-processorgenaigenerative-aihacktoberfestmultimodalprocessor

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