Make LlamaStackLibraryClient work correctly (#581)

This PR does a few things:

- it moves "direct client" to llama-stack repo instead of being in the
llama-stack-client-python repo
- renames it to `LlamaStackLibraryClient`
- actually makes synchronous generators work 
- makes streaming and non-streaming work properly

In many ways, this PR makes things finally "work"

## Test Plan

See a `library_client_test.py` I added. This isn't really quite a test
yet but it demonstrates that this mode now works. Here's the invocation
and the response:

```
INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct python llama_stack/distribution/tests/library_client_test.py ollama
```


![image](https://github.com/user-attachments/assets/17d4e116-4457-4755-a14e-d9a668801fe0)

llama_stack/distribution/tests/library_client_test.py

@@ -0,0 +1,103 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import argparse
+import os
+
+from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
+from llama_stack_client.lib.agents.agent import Agent
+from llama_stack_client.lib.agents.event_logger import EventLogger as AgentEventLogger
+from llama_stack_client.lib.inference.event_logger import EventLogger
+from llama_stack_client.types import UserMessage
+from llama_stack_client.types.agent_create_params import AgentConfig
+
+
+def main(config_path: str):
+    client = LlamaStackAsLibraryClient(config_path)
+    client.initialize()
+
+    models = client.models.list()
+    print("\nModels:")
+    for model in models:
+        print(model)
+
+    if not models:
+        print("No models found, skipping chat completion test")
+        return
+
+    model_id = models[0].identifier
+    response = client.inference.chat_completion(
+        messages=[UserMessage(content="What is the capital of France?", role="user")],
+        model_id=model_id,
+        stream=False,
+    )
+    print("\nChat completion response (non-stream):")
+    print(response)
+
+    response = client.inference.chat_completion(
+        messages=[UserMessage(content="What is the capital of France?", role="user")],
+        model_id=model_id,
+        stream=True,
+    )
+
+    print("\nChat completion response (stream):")
+    for log in EventLogger().log(response):
+        log.print()
+
+    print("\nAgent test:")
+    agent_config = AgentConfig(
+        model=model_id,
+        instructions="You are a helpful assistant",
+        sampling_params={
+            "strategy": "greedy",
+            "temperature": 1.0,
+            "top_p": 0.9,
+        },
+        tools=(
+            [
+                {
+                    "type": "brave_search",
+                    "engine": "brave",
+                    "api_key": os.getenv("BRAVE_SEARCH_API_KEY"),
+                }
+            ]
+            if os.getenv("BRAVE_SEARCH_API_KEY")
+            else []
+        ),
+        tool_choice="auto",
+        tool_prompt_format="json",
+        input_shields=[],
+        output_shields=[],
+        enable_session_persistence=False,
+    )
+    agent = Agent(client, agent_config)
+    user_prompts = [
+        "Hello",
+        "Which players played in the winning team of the NBA western conference semifinals of 2024, please use tools",
+    ]
+
+    session_id = agent.create_session("test-session")
+
+    for prompt in user_prompts:
+        response = agent.create_turn(
+            messages=[
+                {
+                    "role": "user",
+                    "content": prompt,
+                }
+            ],
+            session_id=session_id,
+        )
+
+        for log in AgentEventLogger().log(response):
+            log.print()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("config_path", help="Path to the config YAML file")
+    args = parser.parse_args()
+    main(args.config_path)