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| from pydantic import BaseModel, Field | |
| class QAQuery(BaseModel): | |
| """ | |
| Schema for the question-answering input. | |
| """ | |
| context: str = Field( | |
| ..., | |
| example="The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, whose company designed and built the tower.", | |
| description="The text passage or document that contains the information needed to answer the question." | |
| ) | |
| question: str = Field( | |
| ..., | |
| example="Who designed the Eiffel Tower?", | |
| description="The question to be answered based on the provided context." | |
| ) | |
| class Config: | |
| schema_extra = { | |
| "example": { | |
| "context": "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, whose company designed and built the tower.", | |
| "question": "Who designed the Eiffel Tower?" | |
| } | |
| } | |
| class QAResponse(BaseModel): | |
| """ | |
| Schema for the question-answering output. | |
| """ | |
| answer: str = Field( | |
| ..., | |
| example="Gustave Eiffel", | |
| description="The answer to the question based on the provided context." | |
| ) | |
| class Config: | |
| schema_extra = { | |
| "example": { | |
| "answer": "Gustave Eiffel" | |
| } | |
| } |