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| import os | |
| from dotenv import load_dotenv | |
| from langchain_google_genai import GoogleGenerativeAIEmbeddings | |
| from langchain.evaluation import load_evaluator | |
| from langchain.evaluation import EmbeddingDistance | |
| from app.modules.crud_question_test.models.crud_question_tests import get_all_question_tests, get_question_test_url_by_description | |
| from app.configs.database import firebase_bucket | |
| # Import API key | |
| load_dotenv() | |
| # Define the google api key | |
| os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY') | |
| GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY") | |
| # Setting model embedding | |
| embedding_model = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=GOOGLE_API_KEY) | |
| gemini_evaluator = load_evaluator("embedding_distance", distance_metric=EmbeddingDistance.COSINE, embeddings=embedding_model) | |
| def compare_vector(vector_extract, vector_des): | |
| maxnimun_value = 2 | |
| for item in vector_des: | |
| two_object = (vector_extract, item) | |
| x = gemini_evaluator.evaluate_strings(prediction=two_object[0], reference=two_object[1]) | |
| if x.get('score') < maxnimun_value: | |
| maxnimun_value = x.get('score') | |
| des_item_choose = item | |
| if maxnimun_value == 2: | |
| return False | |
| elif maxnimun_value < 0.3: | |
| return des_item_choose | |
| else: | |
| return False | |
| def download_question_test(question_test_url): | |
| # check folder exist | |
| if not os.path.exists('data/question_tests'): | |
| os.makedirs('data/question_tests') | |
| # download file from firebase storage using "gs://" link | |
| name_bucket = question_test_url.split(f"gs://{firebase_bucket.name}/")[1] | |
| blob = firebase_bucket.blob(name_bucket) | |
| blob.download_to_filename(f'data/question_tests/{name_bucket}') | |
| return True | |
| def get_question_test(text): | |
| all_question_tests = get_all_question_tests() | |
| value_in_des = [] | |
| for item in all_question_tests: | |
| value_in_des.append(item['question_tests_description']) | |
| des_item_choose = compare_vector(text, value_in_des) | |
| if des_item_choose == False: | |
| return "No question test found" | |
| else: | |
| question_test_url = get_question_test_url_by_description(des_item_choose) | |
| if download_question_test(question_test_url): | |
| return True | |
| else: | |
| return False | |