feat: Updated playground rag to use session id for persistent conversation (#1870)

# What does this PR do?

This PR updates the [playground RAG
example](llama_stack/distribution/ui/page/playground/rag.py) so that the
agent is able to use its builtin conversation history. Here we are using
streamlit's `cache_resource` functionality to prevent the agent from
re-initializing after every interaction as well as storing its
session_id in the `session_state`. This allows the agent in the RAG
example to behave more closely to how it works using the python-client
directly.

[//]: # (If resolving an issue, uncomment and update the line below)
Closes #1869 

## Test Plan

Without these changes, if you ask it "What is 2 + 2"? followed by the
question "What did I just ask?" It will provide an obviously incorrect
answer.

With these changes, you can ask the same series of questions and it will
provide the correct answer.

[//]: # (## Documentation)

Signed-off-by: Michael Clifford <mcliffor@redhat.com>

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

@@ -4,6 +4,8 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+import uuid
+
 import streamlit as st
 from llama_stack_client import Agent, AgentEventLogger, RAGDocument
 
@@ -102,8 +104,8 @@ def rag_chat_page():
 
         # Add clear chat button to sidebar
         if st.button("Clear Chat", use_container_width=True):
-            st.session_state.messages = []
-            st.rerun()
+            st.session_state.clear()
+            st.cache_resource.clear()
 
     # Chat Interface
     if "messages" not in st.session_state:
@@ -123,23 +125,31 @@ def rag_chat_page():
     else:
         strategy = {"type": "greedy"}
 
-    agent = Agent(
-        llama_stack_api.client,
-        model=selected_model,
-        instructions=system_prompt,
-        sampling_params={
-            "strategy": strategy,
-        },
-        tools=[
-            dict(
-                name="builtin::rag/knowledge_search",
-                args={
-                    "vector_db_ids": list(selected_vector_dbs),
-                },
-            )
-        ],
-    )
-    session_id = agent.create_session("rag-session")
+    @st.cache_resource
+    def create_agent():
+        return Agent(
+            llama_stack_api.client,
+            model=selected_model,
+            instructions=system_prompt,
+            sampling_params={
+                "strategy": strategy,
+            },
+            tools=[
+                dict(
+                    name="builtin::rag/knowledge_search",
+                    args={
+                        "vector_db_ids": list(selected_vector_dbs),
+                    },
+                )
+            ],
+        )
+
+    agent = create_agent()
+
+    if "agent_session_id" not in st.session_state:
+        st.session_state["agent_session_id"] = agent.create_session(session_name=f"rag_demo_{uuid.uuid4()}")
+
+    session_id = st.session_state["agent_session_id"]
 
     # Chat input
     if prompt := st.chat_input("Ask a question about your documents"):