mirror of https://github.com/meta-llama/llama-stack.git synced 2025-06-29 11:24:19 +00:00

History

Ilya Kolchinsky 40f41af2f7 feat: Add a direct (non-agentic) RAG option to the Playground RAG page (#1940 ) # What does this PR do? This PR makes it possible to switch between agentic and non-agentic RAG when running the respective Playground page. When non-agentic RAG is selected, user queries are answered by directly querying the vector DB, augmenting the prompt, and sending the extended prompt to the model via Inference API. ## Test Plan - Launch the Playground and go to the RAG page; - Select the vector DB ID; - Adjust other configuration parameters if necessary; - Set the radio button to Agent-based RAG; - Send a message to the chat; - The query will be answered by an agent using the knowledge search tool as indicated by the output; - Click the 'Clear Chat' button to make it possible to switch modes; - Send a message to the chat again; - This time, the query will be answered by the model directly as can be deduced from the reply.		2025-04-11 10:16:10 -07:00
..
modules	fix: add tavily_search option to playground api (#1909 )	2025-04-09 15:56:41 +02:00
page	feat: Add a direct (non-agentic) RAG option to the Playground RAG page (#1940 )	2025-04-11 10:16:10 -07:00
__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	chore: simplify running the demo UI (#1907 )	2025-04-09 11:22:29 -07:00

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

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)