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

# What does this PR do?
closes #1554 

## Test Plan
test_agents.py

docs/getting_started.ipynb

@@ -1203,7 +1203,7 @@
         }
       ],
       "source": [
-        "from llama_stack_client.lib.inference.event_logger import EventLogger\n",
+        "from llama_stack_client import InferenceEventLogger\n",
         "\n",
         "message = {\"role\": \"user\", \"content\": \"Write me a sonnet about llama\"}\n",
         "print(f'User> {message[\"content\"]}', \"green\")\n",
@@ -1215,7 +1215,7 @@
         ")\n",
         "\n",
         "# Print the tokens while they are received\n",
-        "for log in EventLogger().log(response):\n",
+        "for log in InferenceEventLogger().log(response):\n",
         "    log.print()\n"
       ]
     },
@@ -1632,8 +1632,7 @@
         }
       ],
       "source": [
-        "from llama_stack_client.lib.agents.agent import Agent\n",
-        "from llama_stack_client.lib.agents.event_logger import EventLogger\n",
+        "from llama_stack_client import Agent, AgentEventLogger\n",
         "from termcolor import cprint\n",
         "\n",
         "agent = Agent(\n",
@@ -1659,7 +1658,7 @@
         "        ],\n",
         "        session_id=session_id,\n",
         "    )\n",
-        "    for log in EventLogger().log(response):\n",
+        "    for log in AgentEventLogger().log(response):\n",
         "        log.print()\n"
       ]
     },
@@ -1808,14 +1807,12 @@
       ],
       "source": [
         "import uuid\n",
-        "from llama_stack_client.lib.agents.agent import Agent\n",
-        "from llama_stack_client.lib.agents.event_logger import EventLogger\n",
+        "from llama_stack_client import Agent, AgentEventLogger, RAGDocument\n",
         "from termcolor import cprint\n",
-        "from llama_stack_client.types import Document\n",
         "\n",
         "urls = [\"chat.rst\", \"llama3.rst\", \"memory_optimizations.rst\", \"lora_finetune.rst\"]\n",
         "documents = [\n",
-        "    Document(\n",
+        "    RAGDocument(\n",
         "        document_id=f\"num-{i}\",\n",
         "        content=f\"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}\",\n",
         "        mime_type=\"text/plain\",\n",
@@ -1858,7 +1855,7 @@
         "        messages=[{\"role\": \"user\", \"content\": prompt}],\n",
         "        session_id=session_id,\n",
         "    )\n",
-        "    for log in EventLogger().log(response):\n",
+        "    for log in AgentEventLogger().log(response):\n",
         "        log.print()"
       ]
     },
@@ -1969,7 +1966,7 @@
         }
       ],
       "source": [
-        "from llama_stack_client.types.agents.turn_create_params import Document\n",
+        "from llama_stack_client import Document\n",
         "\n",
         "codex_agent = Agent(\n",
         "    client, \n",
@@ -2891,8 +2888,7 @@
       ],
       "source": [
         "# NBVAL_SKIP\n",
-        "from llama_stack_client.lib.agents.agent import Agent\n",
-        "from llama_stack_client.lib.agents.event_logger import EventLogger\n",
+        "from llama_stack_client import Agent, AgentEventLogger\n",
         "from termcolor import cprint\n",
         "\n",
         "agent = Agent(\n",
@@ -2918,7 +2914,7 @@
         "        ],\n",
         "        session_id=session_id,\n",
         "    )\n",
-        "    for log in EventLogger().log(response):\n",
+        "    for log in AgentEventLogger().log(response):\n",
         "        log.print()\n"
       ]
     },
@@ -2993,8 +2989,7 @@
         }
       ],
       "source": [
-        "from llama_stack_client.lib.agents.agent import Agent\n",
-        "from llama_stack_client.lib.agents.event_logger import EventLogger\n",
+        "from llama_stack_client import Agent, AgentEventLogger\n",
         "\n",
         "agent = Agent(\n",
         "    client, \n",
@@ -3021,7 +3016,7 @@
         "        session_id=session_id,\n",
         "    )\n",
         "\n",
-        "    for log in EventLogger().log(response):\n",
+        "    for log in AgentEventLogger().log(response):\n",
         "        log.print()\n"
       ]
     },

docs/notebooks/Llama_Stack_Agent_Workflows.ipynb

@@ -47,9 +47,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_stack_client import LlamaStackClient\n",
+    "from llama_stack_client import LlamaStackClient, Agent\n",
     "from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n",
-    "from llama_stack_client.lib.agents.agent import Agent\n",
     "from rich.pretty import pprint\n",
     "import json\n",
     "import uuid\n",

docs/notebooks/Llama_Stack_RAG_Lifecycle.ipynb

@@ -34,10 +34,8 @@
     }
    ],
    "source": [
-    "from llama_stack_client import LlamaStackClient\n",
+    "from llama_stack_client import LlamaStackClient, Agent\n",
     "from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n",
-    "from llama_stack_client.types.agent_create_params import AgentConfig\n",
-    "from llama_stack_client.lib.agents.agent import Agent\n",
     "from rich.pretty import pprint\n",
     "import json\n",
     "import uuid\n",

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(

docs/source/getting_started/index.md

@@ -197,9 +197,7 @@ import os
 import uuid
 from termcolor import cprint
 
-from llama_stack_client.lib.agents.agent import Agent
-from llama_stack_client.lib.agents.event_logger import EventLogger
-from llama_stack_client.types import Document
+from llama_stack_client import Agent, AgentEventLogger, RAGDocument
 
 
 def create_http_client():
@@ -225,7 +223,7 @@ client = (
 # Documents to be used for RAG
 urls = ["chat.rst", "llama3.rst", "memory_optimizations.rst", "lora_finetune.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",
@@ -284,7 +282,7 @@ for prompt in user_prompts:
         messages=[{"role": "user", "content": prompt}],
         session_id=session_id,
     )
-    for log in EventLogger().log(response):
+    for log in AgentEventLogger().log(response):
         log.print()
 ```