metadata
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
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 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.