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
126 lines
4.7 KiB
Python
126 lines
4.7 KiB
Python
"""
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Router for the chat-to-tasks functionality.
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"""
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import logging
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from fastapi import APIRouter, Depends, HTTPException
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from sqlalchemy.orm import Session
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from app.api import deps
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from app.crud import task as task_crud
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from app.models.user import User
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from app.schemas.chat import ChatInput, ChatResponse, ChatProcessingError
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from app.schemas.task import TaskCreate, TaskRead
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from app.services.llm_service import LLMService, get_llm_service
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from app.db.session import get_db
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# Set up logger
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logger = logging.getLogger(__name__)
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router = APIRouter()
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@router.post("/chat-to-tasks", response_model=ChatResponse)
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async def create_tasks_from_chat(
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chat_input: ChatInput,
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db: Session = Depends(get_db),
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current_user: User = Depends(deps.get_current_active_user),
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llm_service: LLMService = Depends(get_llm_service),
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):
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"""
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Convert natural language chat input into one or more task objects.
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This endpoint:
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1. Takes the user's natural language input
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2. Sends it to an LLM for processing
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3. Parses the LLM's response into TaskCreate objects
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4. Creates the tasks in the database
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5. Returns the created tasks
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All tasks are associated with the authenticated user.
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"""
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if not chat_input.message or len(chat_input.message.strip()) < 3:
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raise HTTPException(
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status_code=400,
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detail="Message must be at least 3 characters long",
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)
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# Initialize response
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response = ChatResponse(original_message=chat_input.message)
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try:
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# Process the chat message with the LLM service
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logger.info(f"Processing chat input: {chat_input.message[:50]}...")
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llm_tasks_data = await llm_service.chat_to_tasks(chat_input.message)
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if not llm_tasks_data:
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logger.warning("LLM returned no tasks")
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response.processing_successful = False
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response.error = ChatProcessingError(
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error_type="parsing_error",
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error_detail="No tasks could be extracted from your message",
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)
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return response
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# Convert LLM response to TaskCreate objects and create in DB
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created_tasks = []
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for task_data in llm_tasks_data:
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try:
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# Map LLM response fields to TaskCreate schema
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# Handle different field names or formats that might come from the LLM
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task_create_data = {
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"title": task_data.get("title", "Untitled Task"),
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"description": task_data.get("description", ""),
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"priority": task_data.get("priority", "medium").lower(),
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}
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# Handle due_date if present
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if due_date := task_data.get("due_date"):
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if due_date != "null" and due_date is not None:
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task_create_data["due_date"] = due_date
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# Map status if present (convert "pending" to "todo" if needed)
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if status := task_data.get("status"):
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if status.lower() == "pending":
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task_create_data["status"] = "todo"
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else:
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task_create_data["status"] = status.lower()
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# Create TaskCreate object and validate
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task_in = TaskCreate(**task_create_data)
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# Create task in database with current user as owner
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db_task = task_crud.task.create_with_owner(
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db=db, obj_in=task_in, user_id=current_user.id
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)
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# Add created task to response
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created_tasks.append(TaskRead.model_validate(db_task))
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except Exception as e:
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logger.error(f"Error creating task: {e}")
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# Continue with other tasks if one fails
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continue
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if not created_tasks:
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# If no tasks were successfully created
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response.processing_successful = False
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response.error = ChatProcessingError(
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error_type="creation_error",
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error_detail="Could not create any tasks from your message",
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)
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else:
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# Add created tasks to response
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response.tasks = created_tasks
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return response
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except Exception as e:
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logger.exception(f"Error in chat-to-tasks endpoint: {e}")
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response.processing_successful = False
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response.error = ChatProcessingError(
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error_type="processing_error",
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error_detail=f"An error occurred while processing your request: {str(e)}",
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)
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return response |