A local UI package for turning markdown or text chunk zips into embeddings
Project description
embedding
A local UI package for turning a zip of .md or .txt chunks into embedding vectors.
What it does
- launches with the
embeddingcommand - lets you choose an embedding model from a dropdown or type a custom model name
- reads a zip of
.mdor.txtfiles - creates one embedding vector per file
- exports a zip with:
embedding_summary.jsonembedding_manifest.csv*_embeddings.jsonl(optional)*_embeddings.csv(optional)*_embeddings.npz(optional)
Install
pip install embedding
Run
embedding
Suggested input
Use a zip produced after your chunking step, such as the recursive chunk zip that contains many small .md chunk files.
Suggested output use
jsonlfor readable records and pipelinescsvfor spreadsheet-style inspectionnpzfor loading embeddings directly into NumPy / Python
Notes
- This package creates embeddings from local text files using
sentence-transformersmodels. - It does not call an LLM by itself.
- It stores one vector per input chunk file.
Ownership note
The package metadata and copyright notice are set to Wenxi Wang. You should still verify PyPI package-name availability, trademark questions, and any legal or patent issues yourself before publishing.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file embeddin-0.1.0-py3-none-any.whl.
File metadata
- Download URL: embeddin-0.1.0-py3-none-any.whl
- Upload date:
- Size: 7.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
2c4205c13f300dfadcb44eb2a265aede03b1047559bf7fb056d348778170aba0
|
|
| MD5 |
c03d0fa7af49c088478e21e36b3109a0
|
|
| BLAKE2b-256 |
0b56b9224dad58662c335c1a49be8ad5b9d2716258fe3a223b5aea55ce1c9ddf
|