How was logging and error handling implemented? What embedding models were used?

Logging in Python is usually done using the built-in logging module, which allows configuring log levels (DEBUG, INFO, WARNING, ERROR, CRITICAL), message formats, and output destinations (files, console, remote services).

Error handling is implemented through try-except constructs, where exceptions can be caught, logged, and further actions can be decided.

Example of logging and error handling:

import logging

logging.basicConfig(level=logging.INFO, filename='app.log', format='%(asctime)s - %(levelname)s - %(message)s')

try:
    result = 10 / 0
except ZeroDivisionError as e:
    logging.error(f'Division error: {e}')
else:
    logging.info(f'Result: {result}')

Regarding embedding models, in the context of Python and machine learning, models like Word2Vec, GloVe, FastText, or transformers like BERT are often used to convert text into vector representations. The choice of model depends on the task: search, classification, text generation.

Example of using BERT to obtain embeddings:

from transformers import BertModel, BertTokenizer
import torch

 tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
 model = BertModel.from_pretrained('bert-base-uncased')

text = "Sample text for embedding"
inputs = tokenizer(text, return_tensors='pt')
outputs = model(**inputs)
embeddings = outputs.last_hidden_state.mean(dim=1)  # averaging over tokens

Thus, logging and error handling ensure stability and ease of debugging, while embedding models enable working with the semantics of text.