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import random |
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from fastapi import APIRouter, Depends |
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from fastapi.responses import JSONResponse |
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from src.api_models import ( |
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ResponseGuessWord, ResponseSemanticCalculation, |
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RequestSemanticCalculation, ResponseMessage, |
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SemanticCalculation |
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) |
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from src.setting import AVAILABLE_WORDS, CFG |
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from src.vector_db import VectorDatabaseHandler |
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router = APIRouter() |
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DEFAULT_RESPONSES = { |
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500: {"description": "Internal Server Error", "model": ResponseMessage}, |
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} |
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@router.get( |
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"/v1/service/status", |
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response_model=ResponseMessage, |
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responses={**DEFAULT_RESPONSES}, |
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description="Description: The endpoint is used to check the service status.", |
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tags=["Service Status"] |
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) |
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async def status() -> ResponseMessage: |
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"""Health endpoint.""" |
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return ResponseMessage(message="Success.") |
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@router.get( |
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"/v1/service/get_guess_word", |
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response_model=ResponseGuessWord, |
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responses={**DEFAULT_RESPONSES}, |
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description="Description: The endpoint is used to get a random word from the list of available words.", |
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tags=["Get Word"] |
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) |
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async def get_guess_word() -> ResponseGuessWord: |
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try: |
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guess_word = random.choices(AVAILABLE_WORDS, k=1)[0] |
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except Exception as e: |
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return JSONResponse(status_code=500, content={"message": str(e)}) |
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return ResponseGuessWord(word=guess_word) |
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@router.get( |
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"/v1/service/semantic_calculation", |
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response_model=ResponseSemanticCalculation, |
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responses={**DEFAULT_RESPONSES}, |
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description="Description: The endpoint is used to calculate the semantic similarity between the guessed word \ |
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and the supposed word.", |
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tags=["Semantic Analysis"] |
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) |
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async def semantic_calculation( |
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request: RequestSemanticCalculation = Depends(RequestSemanticCalculation) |
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) -> ResponseGuessWord: |
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supposed_word = request.supposed_word |
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guessed_word = request.guessed_word |
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if supposed_word not in AVAILABLE_WORDS: |
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return ResponseSemanticCalculation( |
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word_exist=False, |
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metadata=None |
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) |
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vector_db = VectorDatabaseHandler( |
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db_path=CFG.db.folder_path, |
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table_name=CFG.db.table_name, |
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metrics_cfg=CFG.db.metrics |
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) |
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try: |
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result = vector_db(guessed_word, supposed_word) |
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except Exception as e: |
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return JSONResponse(status_code=500, content={"message": str(e)}) |
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return ResponseSemanticCalculation( |
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word_exist=True, |
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metadata=SemanticCalculation(**result) |
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) |
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