abanwild/peatlearn-embeddings

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.