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

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# What does this PR do?
closes #1554 

## Test Plan
test_agents.py
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ehhuang 2025-03-20 10:15:49 -07:00 • committed by GitHub
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commit ea6a4a14ce
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11 changed files with 40 additions and 58 deletions
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10
docs/source/building_applications/rag.md
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@ -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()
```