97c002ac88
Implement a new endpoint that converts natural language input into structured tasks using an LLM. Features include: - LLM service abstraction with support for OpenAI and Google Gemini - Dependency injection pattern for easy provider switching - Robust error handling and response formatting - Integration with existing user authentication and task creation - Fallback to mock LLM service for testing or development
44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
"""
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Pydantic schemas for the Chat-to-Tasks feature.
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"""
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from typing import List, Optional
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from pydantic import BaseModel, Field
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from app.schemas.task import TaskRead
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class ChatInput(BaseModel):
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"""Schema for chat input from user."""
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message: str = Field(
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...,
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description="Natural language input describing tasks to be created",
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min_length=3,
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max_length=2000,
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)
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class ChatProcessingError(BaseModel):
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"""Schema for error details when processing chat."""
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error_type: str = Field(..., description="Type of error encountered")
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error_detail: str = Field(..., description="Detailed error information")
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class ChatResponse(BaseModel):
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"""Schema for chat response with parsed tasks."""
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original_message: str = Field(..., description="Original user message")
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tasks: List[TaskRead] = Field(
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default_factory=list,
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description="Tasks extracted from the message",
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)
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processing_successful: bool = Field(
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default=True,
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description="Indicates if processing was successful",
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)
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error: Optional[ChatProcessingError] = Field(
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default=None,
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description="Error details if processing was not successful",
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) |