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86 lines
2.4 KiB
Python
86 lines
2.4 KiB
Python
# Agenten Plattform
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#
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# (c) 2024 Magnus Bender
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# Institute of Humanities-Centered Artificial Intelligence (CHAI)
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# Universitaet Hamburg
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# https://www.chai.uni-hamburg.de/~bender
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#
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# source code released under the terms of GNU Public License Version 3
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# https://www.gnu.org/licenses/gpl-3.0.txt
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"""
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This represents the basic types used for representing extracted information from the data.
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The types are implemented using [pydantic](https://docs.pydantic.dev/).
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It provides validation, allow JSON serialization and works well with [FastAPI](https://fastapi.tiangolo.com/) which is used internally for the http request between the agents and the management.
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**This is work in progress!**
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"""
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from typing import List, Any, Dict
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from pydantic import BaseModel
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class ExtractionSchema(BaseModel):
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"""
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This is the basic class used as superclass for all extracted information from data items.
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For all the `ExtractionSchema` is is required that the data can be serialized to json.
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Thus, mostly only default data types like `int, str, bool, list, dict, tuple` also including `ExtractionSchema` and `RiddleInformation` can be used here!
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"""
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class ExtractedContent(ExtractionSchema):
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"""
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An extracted content item.
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"""
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type : str
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"""
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The type, as a string, the actual string will depend on the extraction agent.
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"""
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content : str | Any
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"""
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The extracted content
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"""
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class ExtractedPositions(ExtractionSchema):
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"""
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A position (like time, coordinates, ...) where something was extracted (each position should belong to a content item).
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"""
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type : str
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"""
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The type, as a string, the actual string will depend on the extraction agent.
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"""
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position : str | int | Any
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"""
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The position, will also depend on the extraction agent.
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"""
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description : str | Any = None
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"""
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An optional description for more details.
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"""
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class ExtractedData(ExtractionSchema):
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"""
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Contains the extracted items from a data file.
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"""
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contents : List[ExtractedContent] = []
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"""
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The extracted contents (i.e., transcriptions etc.), each item here should belong a position item at the same index.
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"""
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positions : List[ExtractedPositions] = []
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"""
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The positions of extracted contents, each item here should belong a content item at the same index.
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"""
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other : Dict[str, Any] = {}
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"""
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Possibly more data. Use a keywords (depending on agent) and store the data there.
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"""
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