Example of creating text embeddings using Gensim

word2vec.py

# Prerequisite: 
# Download the word2vec-google-news-300 model from Hugging Face
# https://huggingface.co/fse/word2vec-google-news-300/tree/main
# Download word2vec-google-news-300.model and word2vec-google-news-300.model.vectors.npy
# Place them in the same folder as this file.

from gensim.models import KeyedVectors

# Load the KeyedVectors model
model_path = "word2vec-google-news-300.model"
model = KeyedVectors.load(model_path)

def search_string_to_vector(search_string, model):
    words = search_string.lower().split()
    vectors = [model[word] for word in words if word in model]

    if vectors:
        search_vector = sum(vectors) / len(vectors)
        return search_vector
    else:
        return None

# Example usage
search_string = "Hello World!"
print("Search string:", search_string)

search_vector = search_string_to_vector(search_string, model)

if search_vector is not None:
    print("Search vector:", search_vector)
    print("Vector length:", len(search_vector))
else:
    print("No vector representation available for the search string.")