forked from phoenix-oss/llama-stack-mirror
# What does this PR do? Add several new pre-commit hooks to improve code quality and security: - no-commit-to-branch: prevent direct commits to protected branches like `main` - check-yaml: validate YAML files - detect-private-key: prevent accidental commit of private keys - requirements-txt-fixer: maintain consistent requirements.txt format and sorting - mixed-line-ending: enforce LF line endings to avoid mixed line endings - check-executables-have-shebangs: ensure executable scripts have shebangs - check-json: validate JSON files - check-shebang-scripts-are-executable: verify shebang scripts are executable - check-symlinks: validate symlinks and report broken ones - check-toml: validate TOML files mainly for pyproject.toml The respective fixes have been included. Signed-off-by: Sébastien Han <seb@redhat.com> |
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.. | ||
_static | ||
notebooks | ||
openapi_generator | ||
resources | ||
source | ||
zero_to_hero_guide | ||
conftest.py | ||
contbuild.sh | ||
dog.jpg | ||
getting_started.ipynb | ||
getting_started_llama4.ipynb | ||
getting_started_llama_api.ipynb | ||
license_header.txt | ||
make.bat | ||
Makefile | ||
readme.md | ||
requirements.txt |
Llama Stack Documentation
Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our ReadTheDocs page.
Render locally
pip install -r requirements.txt
cd docs
python -m sphinx_autobuild source _build
You can open up the docs in your browser at http://localhost:8000
Content
Try out Llama Stack's capabilities through our detailed Jupyter notebooks:
- Building AI Applications Notebook - A comprehensive guide to building production-ready AI applications using Llama Stack
- Benchmark Evaluations Notebook - Detailed performance evaluations and benchmarking results
- Zero-to-Hero Guide - Step-by-step guide for getting started with Llama Stack