forked from phoenix-oss/llama-stack-mirror

History

ehhuang c8a20b8ed0 feat: allow specifying specific tool within toolgroup (#1239 ) Summary: E.g. `builtin::rag::knowledge_search` Test Plan: ``` LLAMA_STACK_CONFIG=fireworks pytest -s -v tests/client-sdk/agents/ --safety-shield meta-llama/Llama-Guard-3-8B ```		2025-02-26 14:07:05 -08:00
..
modules	build: format codebase imports using ruff linter (#1028 )	2025-02-13 10:06:21 -08:00
page	feat: allow specifying specific tool within toolgroup (#1239 )	2025-02-26 14:07:05 -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	fix!: update eval-tasks -> benchmarks (#1032 )	2025-02-13 16:40:58 -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