README.md

---
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.