Install Required Libraries:
bash
pip install sentence-transformers numpy
Prepare Your Text File:
Ensure you have a text file with the path you provide when running the script.
Run the Script:
Run the script from the command line, providing the path to your text file:
bash
python script_name.py path_to_your_text_file.txt
Explanation:
Reading the Text:
The read_text function reads the entire content of the file into a string.
Chunking the Text:
The chunk_text function splits the text into chunks of a specified size with optional overlap.
Generating Embeddings:
The generate_embeddings function uses a pre-trained model from Hugging Face to generate embeddings for each chunk.
Finding Most Similar Chunks:
The find_most_similar function computes the cosine similarity between all pairs of embeddings and identifies the top 2 most similar chunks for each chunk.
Printing Embeddings and Similar Chunks:
The embeddings are printed directly, and the indices of the most similar chunks are printed for each chunk.
Notes:
Adjust Parameters:
You can adjust chunk_size, overlap, model_name, and top_k in the main function as needed.
Model Selection:
The model 'sentence-transformers/all-MiniLM-L6-v2' is used by default for generating embeddings. You can choose other models from Hugging Face's model hub.
Handling Large Files:
For very large files, consider processing the text in larger chunks or using more efficient methods to manage memory usage.
for the large-scale indexing, see proposal at https://www.reddit.com/r/LocalLLaMA/comments/1gwopap/project_idea_continuous_indexing_all_of_github/