// Use when onboarding to nuggetizer, ragnarok, rank_llm, or umbrela and you need development environment setup for one repo or several repos at once, including clone-if-needed, uv or pip installation paths, shared virtualenv reuse, and smoke tests.
| name | castorini-onboard |
| description | Use when onboarding to nuggetizer, ragnarok, rank_llm, or umbrela and you need development environment setup for one repo or several repos at once, including clone-if-needed, uv or pip installation paths, shared virtualenv reuse, and smoke tests. |
| metadata | {"version":"0.1.0","visibility":"public"} |
Unified development environment setup for the Castorini Python repos, whether the user wants one repo or several.
| Repo | PyPI Name | CLI Binary | GitHub |
|---|---|---|---|
| nuggetizer | nuggetizer | nuggetizer | castorini/nuggetizer |
| ragnarok | pyragnarok | ragnarok | castorini/ragnarok |
| rank_llm | rank-llm | rank-llm | castorini/rank_llm |
| umbrela | umbrela | umbrela | castorini/umbrela |
pyproject.toml with matching project name) → install that one.github.com:castorini)command -v uv
If present, use uv silently. If absent, ask the user once: install uv or proceed with pip.
.venv-shared in the current workspace root over repo-local .venv directories..venv-shared already exists in the current workspace root and reuse it if present.castorini/<repo>..venv-shared when it already exists in the workspace root.uv for sync and dependency-group support.pip only when uv is unavailable or the user explicitly asks.uv pip install \
-e './ragnarok[cloud,api]' \
-e './nuggetizer[api]' \
-e './umbrela[cloud,api]' \
-e './rank_llm[openai,api]'
This pulls OpenAI support for all four repos and API or uvicorn support for each repo from its own declared extras instead of relying on an indirect transitive install from another package.
doctor --output json smoke test after install.pre-commit install in each repository after a source install.pre-commit install
references/extras.md — Per-repo optional dependency stacksreferences/install-recipes.md — Per-repo clone, install, and smoke-test command sequencesuv sync --group dev understands dependency groups; pip install -e . does not. If you fall back to pip, install dev tools manually.ragnarok uses the package name pyragnarok on PyPI even though the repo and CLI command are ragnarok.rank_llm uses the package name and CLI binary rank-llm, while the repository and import package are spelled rank_llm.rank_llm supports openai and api as separate extras; use both when the user wants OpenAI-backed reranking plus HTTP serving helpers.umbrela only needs Java 21 for pyserini evaluation workflows, not for the default cloud-oriented development install..venv-shared, make sure it was created with a Python version compatible with the target repo instead of blindly reusing an older interpreter.Install and run Anserini quickly by downloading the latest fatjar from the official Maven Central repo instead of building from source. Use when users want fast setup, smoke tests, or command execution without Maven project compilation.
Use when serving Anserini retrieval together with any subset of rank_llm, ragnarok, nuggetizer, or umbrela over HTTP, especially for local port planning, direct request payload compatibility, curl or jq pipelines, or sequencing retrieval, reranking, generation, nugget creation, nugget assignment, and passage judging from an Anserini fatjar RestServer.
Use when building, debugging, or reviewing CLI commands across nuggetizer, ragnarok, rank_llm, or umbrela and you need the shared castorini.cli.v1 envelope, common introspection commands, artifact shapes, or cross-repo CLI consistency rules.
Use when coordinating an end-to-end Castorini pipeline across rank_llm, ragnarok, nuggetizer, and umbrela, especially for stage handoffs, JSONL compatibility, retrieval-to-answer evaluation flow, or reproducing a multi-stage experiment.
Use when publishing nuggetizer, ragnarok, rank_llm, or umbrela to PyPI or TestPyPI and you need the release sequence for version bumps, build checks, twine validation, TestPyPI dry-runs, or final production publishing.