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Set up the embedding model for semantic search. model2vec-rs downloads models automatically; use when build fails or model download issues occur.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Work with the SQLite database layer including FTS5, sqlite-vec, and schema operations. Use for database queries, indexing issues, or storage debugging.
Run and debug Rust tests for glhf. Use when running tests, fixing test failures, or adding new test cases.
Implement and debug semantic/vector search with model2vec-rs and sqlite-vec. Use for embedding generation, vector queries, hybrid search, or RRF fusion.
| name | setup-environment |
| description | Set up the embedding model for semantic search. model2vec-rs downloads models automatically; use when build fails or model download issues occur. |
glhf uses model2vec-rs with Potion-base-32M for embeddings. The model downloads automatically on first use.
No manual setup required:
cargo build --release
./target/release/glhf index
The embedding model (~130MB) will download to the HuggingFace cache on first run.
| Property | Value |
|---|---|
| Model | minishlab/potion-base-32M |
| Dimensions | 512 |
| Size | ~130MB |
| Cache Location | ~/.cache/huggingface/ |
# Run embedding tests
cargo test embed -- --ignored
| Error | Solution |
|---|---|
Failed to load model | Check internet connection, model will auto-download |
No space left | Clear HuggingFace cache: rm -rf ~/.cache/huggingface/ |
Slow first run | Normal - model downloads once, then cached |
For text-only search (faster indexing, no model download):
glhf index --skip-embeddings
This enables FTS5 search but disables semantic/hybrid modes.