forked from phoenix-oss/llama-stack-mirror

History

ehhuang ca2910d27a docs: update test_agents to use new Agent SDK API (#1402 ) # Summary: new Agent SDK API is added in https://github.com/meta-llama/llama-stack-client-python/pull/178 Update docs and test to reflect this. Closes https://github.com/meta-llama/llama-stack/issues/1365 # Test Plan: ```bash py.test -v -s --nbval-lax ./docs/getting_started.ipynb LLAMA_STACK_CONFIG=fireworks \ pytest -s -v tests/integration/agents/test_agents.py \ --safety-shield meta-llama/Llama-Guard-3-8B --text-model meta-llama/Llama-3.1-8B-Instruct ```		2025-03-06 15:21:12 -08:00
..
modules	build: format codebase imports using ruff linter (#1028 )	2025-02-13 10:06:21 -08:00
page	docs: update test_agents to use new Agent SDK API (#1402 )	2025-03-06 15:21:12 -08:00
__init__.py	move playground ui to llama-stack repo (#536 )	2024-11-26 22:04:21 -08:00
app.py	Fix precommit check after moving to ruff (#927 )	2025-02-02 06:46:45 -08:00
README.md	chore: Make README code blocks more easily copy pastable (#1420 )	2025-03-05 09:11:01 -08:00
requirements.txt	[llama stack ui] add native eval & inspect distro & playground pages (#541 )	2024-12-04 09:47:09 -08:00

README.md

(Experimental) LLama Stack UI

Docker Setup

⚠️ This is a work in progress.

Developer Setup

Start up Llama Stack API server. More details here.

llama stack build --template together --image-type conda

llama stack run together

(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

Start Streamlit UI

cd llama_stack/distribution/ui
pip install -r requirements.txt
streamlit run app.py