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104 lines
2.9 KiB
Markdown
104 lines
2.9 KiB
Markdown
# Playground UI
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```{note}
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Playground UI is currently experimental and subject to change. We welcome feedback and contributions to help improve it.
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```
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Llama Stack Playground UI is an simple interface aims to:
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- Showcase **capabilities** and **concepts** of Llama Stack in an interactive environment
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- Demo **end-to-end** application code to help users get started to build their own applications
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- Provide an **UI** to help users inspect and analyze Llama Stack API providers and resources
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## Key Features
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#### Playground
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Interactive pages for users to play with and explore Llama Stack API capabilities.
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##### Chatbot
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```{eval-rst}
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.. video:: https://github.com/user-attachments/assets/6ca617e8-32ca-49b2-9774-185020ff5204
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:nocontrols:
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:autoplay:
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:playsinline:
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:muted:
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:loop:
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:width: 100%
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```
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- **Chat**: Chat with Llama models
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- **RAG**: Uploading documents to memory_banks and chat with RAG agent
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##### Evaluations
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```{eval-rst}
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.. video:: https://github.com/user-attachments/assets/6cc1659f-eba4-49ca-a0a5-7c243557b4f5
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:nocontrols:
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:autoplay:
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:playsinline:
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:muted:
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:loop:
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:width: 100%
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```
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- **Evaluations (Scoring)**: Run evaluations on your AI application datasets
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```{eval-rst}
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.. video:: https://github.com/user-attachments/assets/345845c7-2a2b-4095-960a-9ae40f6a93cf
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:nocontrols:
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:autoplay:
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:playsinline:
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:muted:
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:loop:
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:width: 100%
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```
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- **Evaluations (Generation + Scoring)**: Use pre-registered evaluation tasks to evaluate an model or agent candidate
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##### Inspect
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```{eval-rst}
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.. video:: https://github.com/user-attachments/assets/01d52b2d-92af-4e3a-b623-a9b8ba22ba99
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:nocontrols:
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:autoplay:
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:playsinline:
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:muted:
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:loop:
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:width: 100%
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```
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- **Inspect** Llama Stack API providers and resources (models, datasets, memory_banks, eval_tasks, etc).
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## Starting the Playground UI
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To start the Playground UI, run the following commands:
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1. Start up the Llama Stack API server
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```bash
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llama stack build --template together --image-type conda
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llama stack run together
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```
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2. (Optional) Register datasets and eval tasks as resources. If you want to run pre-configured evaluation flows (e.g. Evaluations (Generation + Scoring) Page).
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```bash
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$ llama-stack-client datasets register \
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--dataset-id "mmlu" \
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--provider-id "huggingface" \
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--url "https://huggingface.co/datasets/llamastack/evals" \
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--metadata '{"path": "llamastack/evals", "name": "evals__mmlu__details", "split": "train"}' \
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--schema '{"input_query": {"type": "string"}, "expected_answer": {"type": "string", "chat_completion_input": {"type": "string"}}}'
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```
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```bash
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$ llama-stack-client eval_tasks register \
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--eval-task-id meta-reference-mmlu \
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--provider-id meta-reference \
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--dataset-id mmlu \
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--scoring-functions basic::regex_parser_multiple_choice_answer
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```
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3. Start Streamlit UI
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```bash
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cd llama_stack/distribution/ui
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pip install -r requirements.txt
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streamlit run app.py
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```
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