Add eval/scoring/datasetio API providers to distribution templates & UI developer guide (#564)

# What does this PR do?

- add /eval, /scoring, /datasetio API providers to distribution
templates
- regenerate build.yaml / run.yaml files
- fix `template.py` to take in list of providers instead of only first
one
- override memory provider as faiss default for all distro (as only 1
memory provider is needed to start basic flow, chromadb/pgvector need
additional setup step).
```
python llama_stack/scripts/distro_codegen.py
```

- updated README to start UI via conda builds. 

## Test Plan

```
python llama_stack/scripts/distro_codegen.py
```

- Use newly generated `run.yaml` to start server
```
llama stack run ./llama_stack/templates/together/run.yaml
```
<img width="1191" alt="image"
src="https://github.com/user-attachments/assets/62f7d179-0cd0-427c-b6e8-e087d4648f09">


#### Registration
```
❯ 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 datasets list
┏━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ identifier ┃ provider_id ┃ metadata                                ┃ type    ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│ mmlu       │ huggingface │ {'path': 'llamastack/evals', 'name':    │ dataset │
│            │             │ 'evals__mmlu__details', 'split':        │         │
│            │             │ 'train'}                                │         │
└────────────┴─────────────┴─────────────────────────────────────────┴─────────┘
```

```
❯ llama-stack-client datasets register \
--dataset-id "simpleqa" \
--provider-id "huggingface" \
--url "https://huggingface.co/datasets/llamastack/evals" \
--metadata '{"path": "llamastack/evals", "name": "evals__simpleqa", "split": "train"}' \
--schema '{"input_query": {"type": "string"}, "expected_answer": {"type": "string", "chat_completion_input": {"type": "string"}}}'
❯ llama-stack-client datasets list
┏━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ identifier ┃ provider_id ┃ metadata                                                      ┃ type    ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│ mmlu       │ huggingface │ {'path': 'llamastack/evals', 'name': 'evals__mmlu__details',  │ dataset │
│            │             │ 'split': 'train'}                                             │         │
│ simpleqa   │ huggingface │ {'path': 'llamastack/evals', 'name': 'evals__simpleqa',       │ dataset │
│            │             │ 'split': 'train'}                                             │         │
└────────────┴─────────────┴───────────────────────────────────────────────────────────────┴─────────┘
```

```
❯ llama-stack-client eval_tasks register \
> --eval-task-id meta-reference-mmlu \
> --provider-id meta-reference \
> --dataset-id mmlu \
> --scoring-functions basic::regex_parser_multiple_choice_answer
❯ llama-stack-client eval_tasks register \
--eval-task-id meta-reference-simpleqa \
--provider-id meta-reference \
--dataset-id simpleqa \
--scoring-functions llm-as-judge::405b-simpleqa
❯ llama-stack-client eval_tasks list
┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┓
┃ dataset_id ┃ identifier       ┃ metadata ┃ provider_id    ┃ provider_resour… ┃ scoring_functio… ┃ type      ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━┩
│ mmlu       │ meta-reference-… │ {}       │ meta-reference │ meta-reference-… │ ['basic::regex_… │ eval_task │
│ simpleqa   │ meta-reference-… │ {}       │ meta-reference │ meta-reference-… │ ['llm-as-judge:… │ eval_task │
└────────────┴──────────────────┴──────────┴────────────────┴──────────────────┴──────────────────┴───────────┘
```

#### Test with UI
```
streamlit run app.py
```

## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.

llama_stack/distribution/ui/README.md

@@ -1,16 +1,41 @@
-# LLama Stack UI
+# (Experimental) LLama Stack UI
 
-[!NOTE] This is a work in progress.
+## Docker Setup
 
-## Prerequisite
-- Start up Llama Stack Server
-```
-llama stack run
-```
+:warning: This is a work in progress.
 
-## Running Streamlit App
+## Developer Setup
+
+1. Start up Llama Stack API server. More details [here](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html).
 
 ```
+llama stack build --template together --image-type conda
+
+llama stack run together
+```
+
+2. (Optional) Register datasets and eval tasks as resources. If you want to run pre-configured evaluation flows (e.g. Evaluations (Generation + Scoring) Page).
+
+```bash
+$ 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"}}}'
+```
+
+```bash
+$ llama-stack-client eval_tasks register \
+--eval-task-id meta-reference-mmlu \
+--provider-id meta-reference \
+--dataset-id mmlu \
+--scoring-functions basic::regex_parser_multiple_choice_answer
+```
+
+3. Start Streamlit UI
+
+```bash
 cd llama_stack/distribution/ui
 pip install -r requirements.txt
 streamlit run app.py