8000 Merge pull request #115 from u2d-ai/0.0.110 · u2d-ai/msaDocModels@427c7c5 · GitHub
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Merge pull request #115 from u2d-ai/0.0.110
feat: add svcEngineUtils models, update pyproject.toml
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‎docs/release-notes.md

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# msaDocModels Release Notes
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## Possible future features:
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# 0.0.110
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- add svcEngineUtils models
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# 0.0.109
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‎msaDocModels/__init__.py

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import glob
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from os.path import basename, dirname, isfile, join
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version = "0.0.109"
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version = "0.0.110"
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__author__ = "Stefan Welcker"
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__copyright__ = "Copyright 2022, U2D.ai"
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__license__ = "MIT"

‎msaDocModels/sdu.py

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text_extraction_statistics: Union[StatisticsDocument, StatisticsPages]
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class UtilsRunEmbeddedPythonCodeInput(BaseModel):
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"""
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Represents the input data for running embedded Python code within a document.
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Attributes:
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document_id: optional uuid for document.
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code: The Python code to be executed.
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vars: A dictionary representing variables for code execution.
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return_vars: A list of keys from local variables which need to return back.
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"""
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document_id: Optional[UUID4] = None
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code: str
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vars: Dict[str, Any] = {}
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return_vars: List[str] = []
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class UtilsRunEmbeddedPythonCodeDTO(BaseModel):
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"""
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Represents the output data of the run_code function.
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Attributes:
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vars: A dictionary containing the modified provided variables after code execution.
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"""
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vars: Dict[str, Any]
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class UtilsJsonProps(BaseModel):
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"""
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Represents the input data for nfs_writer.
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Attributes:
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file_path: path to file.
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json_payload: json payload.
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"""
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file_path: str
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json_payload: str
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class CleanDocumentInput(BaseDocumentInput):
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"""
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Model that contains input data to clean text from document.

‎pyproject.toml

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[tool.poetry]
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name = "msaDocModels"
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version = "0.0.109"
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version = "0.0.110"
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description = "MSA Document Pydantic Models and Schemas, used to store Parser, NLP, NLU and AI results for processed documents"
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authors = ["Stefan Welcker"]
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readme = "README.md"

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