pre-commit fixes

docs/source/building_applications/rag.md

@@ -20,6 +20,11 @@ We may add more storage types like Graph IO in the future.
 Here's how to set up a vector database for RAG:
 
 ```python
+# Create http client
+from llama_stack_client import LlamaStackClient
+
+client = LlamaStackClient(base_url=f"http://localhost:{os.environ['LLAMA_STACK_PORT']}")
+
 # Register a vector db
 vector_db_id = "my_documents"
 response = client.vector_dbs.register(
@@ -81,15 +86,14 @@ results = client.tool_runtime.rag_tool.query(
 One of the most powerful patterns is combining agents with RAG capabilities. Here's a complete example:
 
 ```python
-from llama_stack_client.types.agent_create_params import AgentConfig
 from llama_stack_client.lib.agents.agent import Agent
 
-# Configure agent with memory
-agent_config = AgentConfig(
+# Create agent with memory
+agent = Agent(
+    client,
     model="meta-llama/Llama-3.3-70B-Instruct",
     instructions="You are a helpful assistant",
-    enable_session_persistence=False,
-    toolgroups=[
+    tools=[
         {
             "name": "builtin::rag/knowledge_search",
             "args": {
@@ -98,8 +102,6 @@ agent_config = AgentConfig(
         }
     ],
 )
-
-agent = Agent(client, agent_config)
 session_id = agent.create_session("rag_session")
 
 
@@ -122,7 +124,7 @@ response = agent.create_turn(
     ],
     documents=[
         {
-            "content": "https://raw.githubusercontent.com/example/doc.rst",
+            "content": "https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/memory_optimizations.rst",
             "mime_type": "text/plain",
         }
     ],
@@ -136,6 +138,14 @@ response = agent.create_turn(
 )
 ```
 
+You can print the response with below.
+```python
+from llama_stack_client.lib.agents.event_logger import EventLogger
+
+for log in EventLogger().log(response):
+    log.print()
+```
+
 ### Unregistering Vector DBs
 
 If you need to clean up and unregister vector databases, you can do so as follows: