fix playground for v1 (#799)

# What does this PR do?

- update playground callsites for v1 api changes

## Test Plan

```
cd llama_stack/distribution/ui
streamlit run app.py
```


https://github.com/user-attachments/assets/eace11c6-600a-42dc-b4e7-6948a706509f




## Sources

Please link relevant resources if necessary.


## 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/page/distribution/datasets.py

@@ -14,6 +14,6 @@ def datasets():
     datasets_info = {
         d.identifier: d.to_dict() for d in llama_stack_api.client.datasets.list()
     }
-
-    selected_dataset = st.selectbox("Select a dataset", list(datasets_info.keys()))
-    st.json(datasets_info[selected_dataset], expanded=True)
+    if len(datasets_info) > 0:
+        selected_dataset = st.selectbox("Select a dataset", list(datasets_info.keys()))
+        st.json(datasets_info[selected_dataset], expanded=True)

llama_stack/distribution/ui/page/distribution/eval_tasks.py

@@ -16,7 +16,8 @@ def eval_tasks():
         d.identifier: d.to_dict() for d in llama_stack_api.client.eval_tasks.list()
     }
 
-    selected_eval_task = st.selectbox(
-        "Select an eval task", list(eval_tasks_info.keys()), key="eval_task_inspect"
-    )
-    st.json(eval_tasks_info[selected_eval_task], expanded=True)
+    if len(eval_tasks_info) > 0:
+        selected_eval_task = st.selectbox(
+            "Select an eval task", list(eval_tasks_info.keys()), key="eval_task_inspect"
+        )
+        st.json(eval_tasks_info[selected_eval_task], expanded=True)

llama_stack/distribution/ui/page/distribution/providers.py

@@ -10,11 +10,17 @@ from modules.api import llama_stack_api
 
 def providers():
     st.header("🔍 API Providers")
-    apis_providers_info = llama_stack_api.client.providers.list()
-    # selected_api = st.selectbox("Select an API", list(apis_providers_info.keys()))
-    for api in apis_providers_info.keys():
+    apis_providers_lst = llama_stack_api.client.providers.list()
+    api_to_providers = {}
+    for api_provider in apis_providers_lst:
+        if api_provider.api in api_to_providers:
+            api_to_providers[api_provider.api].append(api_provider)
+        else:
+            api_to_providers[api_provider.api] = [api_provider]
+
+    for api in api_to_providers.keys():
         st.markdown(f"###### {api}")
-        st.dataframe([p.to_dict() for p in apis_providers_info[api]], width=500)
+        st.dataframe([x.to_dict() for x in api_to_providers[api]], width=500)
 
 
 providers()

llama_stack/distribution/ui/page/playground/chat.py

@@ -121,7 +121,7 @@ if prompt := st.chat_input("Example: What is Llama Stack?"):
         if stream:
             for chunk in response:
                 if chunk.event.event_type == "progress":
-                    full_response += chunk.event.delta
+                    full_response += chunk.event.delta.text
                 message_placeholder.markdown(full_response + "▌")
             message_placeholder.markdown(full_response)
         else:

llama_stack/distribution/ui/page/playground/rag.py

@@ -44,14 +44,21 @@ def rag_chat_page():
                 ]
 
                 providers = llama_stack_api.client.providers.list()
+                memory_provider = None
+
+                for x in providers:
+                    if x.api == "memory":
+                        memory_provider = x.provider_id
+
                 llama_stack_api.client.memory_banks.register(
                     memory_bank_id=memory_bank_name,  # Use the user-provided name
                     params={
+                        "memory_bank_type": "vector",
                         "embedding_model": "all-MiniLM-L6-v2",
                         "chunk_size_in_tokens": 512,
                         "overlap_size_in_tokens": 64,
                     },
-                    provider_id=providers["memory"][0].provider_id,
+                    provider_id=memory_provider,
                 )
 
                 # insert documents using the custom bank name
@@ -69,9 +76,6 @@ def rag_chat_page():
             "Select Memory Banks",
             memory_banks,
         )
-        memory_bank_configs = [
-            {"bank_id": bank_id, "type": "vector"} for bank_id in selected_memory_banks
-        ]
 
         available_models = llama_stack_api.client.models.list()
         available_models = [
@@ -133,14 +137,13 @@ def rag_chat_page():
         sampling_params={
             "strategy": strategy,
         },
-        tools=[
-            {
-                "type": "memory",
-                "memory_bank_configs": memory_bank_configs,
-                "query_generator_config": {"type": "default", "sep": " "},
-                "max_tokens_in_context": 4096,
-                "max_chunks": 10,
-            }
+        toolgroups=[
+            dict(
+                name="builtin::memory",
+                args={
+                    "memory_bank_ids": [bank_id for bank_id in selected_memory_banks],
+                },
+            )
         ],
         tool_choice="auto",
         tool_prompt_format="json",
@@ -179,7 +182,7 @@ def rag_chat_page():
             retrieval_response = ""
             for log in EventLogger().log(response):
                 log.print()
-                if log.role == "memory_retrieval":
+                if log.role == "tool_execution":
                     retrieval_response += log.content.replace("====", "").strip()
                     retrieval_message_placeholder.info(retrieval_response)
                 else: