llama-stack-mirror/llama_stack/distribution/ui
Sébastien Han dc94433072
feat(pre-commit): enhance pre-commit hooks with additional checks (#2014)
# 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>
2025-04-30 11:35:49 -07:00
..
modules fix: add tavily_search option to playground api (#1909) 2025-04-09 15:56:41 +02:00
page fix: tools page on playground resets agent after every interaction (#2044) 2025-04-28 23:13:27 +02:00
__init__.py move playground ui to llama-stack repo (#536) 2024-11-26 22:04:21 -08:00
app.py feat: Add tools page to playground (#1904) 2025-04-09 15:26:52 +02:00
Containerfile fix: Playground Container Issue (#1868) 2025-04-09 11:45:15 +02:00
README.md chore: simplify running the demo UI (#1907) 2025-04-09 11:22:29 -07:00
requirements.txt feat(pre-commit): enhance pre-commit hooks with additional checks (#2014) 2025-04-30 11:35:49 -07:00

(Experimental) LLama Stack UI

Docker Setup

⚠️ This is a work in progress.

Developer Setup

  1. Start up Llama Stack API server. More details here.
llama stack build --template together --image-type conda

llama stack run together
  1. (Optional) Register datasets and eval tasks as resources. If you want to run pre-configured evaluation flows (e.g. Evaluations (Generation + Scoring) Page).
llama-stack-client datasets register \
--dataset-id "mmlu" \
--provider-id "huggingface" \
--url "https://huggingface.co/datasets/llamastack/evals" \
--metadata '{"path": "llamastack/evals", "name": "evals__mmlu__details", "split": "train"}' \
--schema '{"input_query": {"type": "string"}, "expected_answer": {"type": "string", "chat_completion_input": {"type": "string"}}}'
llama-stack-client benchmarks register \
--eval-task-id meta-reference-mmlu \
--provider-id meta-reference \
--dataset-id mmlu \
--scoring-functions basic::regex_parser_multiple_choice_answer
  1. Start Streamlit UI
uv run --with ".[ui]" streamlit run llama_stack/distribution/ui/app.py

Environment Variables

Environment Variable Description Default Value
LLAMA_STACK_ENDPOINT The endpoint for the Llama Stack http://localhost:8321
FIREWORKS_API_KEY API key for Fireworks provider (empty string)
TOGETHER_API_KEY API key for Together provider (empty string)
SAMBANOVA_API_KEY API key for SambaNova provider (empty string)
OPENAI_API_KEY API key for OpenAI provider (empty string)