| --- |
| title: Ray Peat Learning Embeddings |
| description: Dense vector embeddings for Ray Peat bioenergetic corpus, generated using sentence-transformers/all-mpnet-base-v2 |
| --- |
| |
| # Ray Peat Learning Embeddings |
|
|
| This dataset contains dense vector embeddings for the Ray Peat bioenergetic corpus, designed for semantic search and retrieval-augmented generation (RAG) applications. |
|
|
| ## Dataset Contents |
|
|
| - **embeddings_20250728_221826.npy**: Dense vector embeddings (768-dimensional) |
| - **metadata_20250728_221826.json**: Metadata and text content for each embedding |
| - **ray_peat_embeddings_20250728_221825.pkl**: Complete embeddings in pickle format |
| - **checkpoint.json**: Processing checkpoint information |
| - **embedding_report.json**: Generation statistics and metrics |
| |
| ## Technical Details |
| |
| - **Embedding Model**: sentence-transformers/all-mpnet-base-v2 |
| - **Dimensions**: 768 |
| - **Generation Date**: July 28, 2025 |
| - **Total Embeddings**: ~18,500+ text segments |
| - **Source**: Ray Peat bioenergetic articles and materials |
| |
| ## Usage |
| |
| ### Download Embeddings |
| ```python |
| from huggingface_hub import snapshot_download |
| import numpy as np |
| import json |
| |
| # Download all files |
| local_dir = snapshot_download( |
| repo_id="abanwild/peatlearn-embeddings", |
| repo_type="dataset" |
| ) |
| |
| # Load embeddings |
| embeddings = np.load(f"{local_dir}/embeddings_20250728_221826.npy") |
| |
| # Load metadata |
| with open(f"{local_dir}/metadata_20250728_221826.json", "r") as f: |
| metadata = json.load(f) |
| |
| print(f"Loaded {len(embeddings)} embeddings with {embeddings.shape[1]} dimensions") |
| ``` |
| |
| ### Integration with PeatLearn |
| This dataset is designed to work with the [PeatLearn](https://github.com/thewildofficial/PeatLearn) project. The embeddings enable semantic search across Ray Peat's bioenergetic research. |
| |
| ## License |
| This dataset is released under the same license as the PeatLearn project. |
| |
