Research From AI Files

# WGSL Screen‑Driven Compute — RAG Seed Pack

This pack contains 12 WGSL examples (with metadata) ready to embed into AnythingLLM backed by LanceDB.

## Files
- `wgsl_rag_seed.jsonl` — one JSON object per line with fields:
  - id, slug, title, category, difficulty, tags, summary, body_markdown, wgsl_code, io, dependencies

## Suggested Workspace Setup (AnythingLLM + LanceDB)

1. Create a new **Workspace** named “GVPIE WGSL RAG”.
2. Add **Data Source → Files** and upload `wgsl_rag_seed.jsonl`.
3. Ingestion settings (recommended):
   - Chunking: **By document** (disable aggressive chunking for code)
   - Max tokens per chunk: **1500–2000**
   - Overlap: **0–50 tokens**
   - Metadata extraction: **enabled** (title, category, tags, difficulty)
4. After embedding, try queries like:
   - “r32float storage texture identity node”
   - “convolution kernel from row strip”
   - “temporal EMA previous frame feedback”
   - “screen‑driven ring buffer queue”
5. Retrieval prompts (pattern):
   - *“Give me the WGSL code only for {topic}, include I/O bindings.”*
   - *“Summarize the protocol and return a short explanation plus the code fence.”*

## Tips
- Prefer **semantic** search over keyword-only.
- Use the `tags` and `category` metadata as filters to narrow recall.
- When generating new examples, re‑use the same metadata schema so future embeddings remain consistent.

Happy hacking!