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import os

import pymongo

import warnings

from sentence_transformers import SentenceTransformer

# Suppress all warnings (including FutureWarnings)

warnings.simplefilter(action="ignore", category=FutureWarning)

# Suppress Transformers & PyTorch warnings via environment variables

os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "1"

os.environ["PYTORCH_NO_ADVISORY_WARNINGS"] = "1"

# MongoDB connection

client = pymongo.MongoClient("")

db = client.vector_tests

# Load sentence transformer model

model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

# Manually access the tokenizer and set clean_up_tokenization_spaces to False

if hasattr(model, "tokenizer"):

model.tokenizer.clean_up_tokenization_spaces = False

# List of documents

docs = [

"The students studied for their exams.",

"Studying hard, the students prepared for their exams.",

"The chef cooked a delicious meal.",

"The chef cooked the chicken with the vegetables.",

"Known for its power and aggression, Mike Tyson's boxing style was feared by many."

]

# Print sentences

print(docs)

# Insert documents into MongoDB

for doc in docs:

doc_vector = model.encode(doc).tolist()

result_doc = {

'sentence': doc,

'vectorEmbedding': doc_vector

}

result = db.vectors_demo_1.insert_one(result_doc)

print(result)