feat(api): simplify client imports (#1687)

# What does this PR do?
closes #1554 

## Test Plan
test_agents.py

docs/source/building_applications/agent.md

@@ -14,7 +14,7 @@ Agents are configured using the `AgentConfig` class, which includes:
 - **Safety Shields**: Guardrails to ensure responsible AI behavior
 
 ```python
-from llama_stack_client.lib.agents.agent import Agent
+from llama_stack_client import Agent
 
 
 # Create the agent
@@ -44,14 +44,14 @@ Each interaction with an agent is called a "turn" and consists of:
 - **Output Message**: The agent's response
 
 ```python
-from llama_stack_client.lib.agents.event_logger import EventLogger
+from llama_stack_client import AgentEventLogger
 
 # Create a turn with streaming response
 turn_response = agent.create_turn(
     session_id=session_id,
     messages=[{"role": "user", "content": "Tell me about Llama models"}],
 )
-for log in EventLogger().log(turn_response):
+for log in AgentEventLogger().log(turn_response):
     log.print()
 ```
 ###  Non-Streaming

docs/source/building_applications/agent_execution_loop.md

@@ -67,9 +67,7 @@ sequenceDiagram
 Each step in this process can be monitored and controlled through configurations. Here's an example that demonstrates monitoring the agent's execution:
 
 ```python
-from llama_stack_client import LlamaStackClient
-from llama_stack_client.lib.agents.agent import Agent
-from llama_stack_client.lib.agents.event_logger import EventLogger
+from llama_stack_client import LlamaStackClient, Agent, AgentEventLogger
 from rich.pretty import pprint
 
 # Replace host and port
@@ -113,7 +111,7 @@ response = agent.create_turn(
 )
 
 # Monitor each step of execution
-for log in EventLogger().log(response):
+for log in AgentEventLogger().log(response):
     log.print()
 
 # Using non-streaming API, the response contains input, steps, and output.

docs/source/building_applications/evals.md

@@ -23,9 +23,7 @@ In this example, we will show you how to:
 
 ##### Building a Search Agent
 ```python
-from llama_stack_client import LlamaStackClient
-from llama_stack_client.lib.agents.agent import Agent
-from llama_stack_client.lib.agents.event_logger import EventLogger
+from llama_stack_client import LlamaStackClient, Agent, AgentEventLogger
 
 client = LlamaStackClient(base_url=f"http://{HOST}:{PORT}")
 
@@ -54,7 +52,7 @@ for prompt in user_prompts:
         session_id=session_id,
     )
 
-    for log in EventLogger().log(response):
+    for log in AgentEventLogger().log(response):
         log.print()
 ```
 

docs/source/building_applications/rag.md

@@ -55,11 +55,11 @@ chunks_response = client.vector_io.query(
 A better way to ingest documents is to use the RAG Tool. This tool allows you to ingest documents from URLs, files, etc. and automatically chunks them into smaller pieces.
 
 ```python
-from llama_stack_client.types import Document
+from llama_stack_client import RAGDocument
 
 urls = ["memory_optimizations.rst", "chat.rst", "llama3.rst"]
 documents = [
-    Document(
+    RAGDocument(
         document_id=f"num-{i}",
         content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
         mime_type="text/plain",
@@ -86,7 +86,7 @@ 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.lib.agents.agent import Agent
+from llama_stack_client import Agent
 
 # Create agent with memory
 agent = Agent(
@@ -140,9 +140,9 @@ response = agent.create_turn(
 
 You can print the response with below.
 ```python
-from llama_stack_client.lib.agents.event_logger import EventLogger
+from llama_stack_client import AgentEventLogger
 
-for log in EventLogger().log(response):
+for log in AgentEventLogger().log(response):
     log.print()
 ```
 

docs/source/building_applications/tools.md

@@ -189,7 +189,7 @@ group_tools = client.tools.list_tools(toolgroup_id="search_tools")
 ## Simple Example: Using an Agent with the Code-Interpreter Tool
 
 ```python
-from llama_stack_client.lib.agents.agent import Agent
+from llama_stack_client import Agent
 
 # Instantiate the AI agent with the given configuration
 agent = Agent(