feat: ReACT agent example

examples/agents/hello.py

@@ -33,7 +33,9 @@ def main(host: str, port: int):
         print(f"Available shields found: {available_shields}")
 
     available_models = [
-        model.identifier for model in client.models.list() if model.model_type == "llm"
+        model.identifier
+        for model in client.models.list()
+        if model.model_type == "llm" and "405B" not in model.identifier
     ]
     if not available_models:
         print(colored("No available models. Exiting.", "red"))

examples/agents/react_agent.py

@@ -0,0 +1,72 @@
+# 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 uuid
+
+import fire
+
+from llama_stack_client import LlamaStackClient
+from llama_stack_client.lib.agents.client_tool import client_tool
+from llama_stack_client.lib.agents.event_logger import EventLogger
+from llama_stack_client.lib.agents.react.agent import ReActAgent
+
+
+@client_tool
+def torchtune(query: str = "torchtune"):
+    """
+    Answer information about torchtune.
+
+    :param query: The query to use for querying the internet
+    :returns: Information about torchtune
+    """
+    dummy_response = """
+            torchtune is a PyTorch library for easily authoring, finetuning and experimenting with LLMs.
+
+            torchtune provides:
+
+            PyTorch implementations of popular LLMs from Llama, Gemma, Mistral, Phi, and Qwen model families
+            Hackable training recipes for full finetuning, LoRA, QLoRA, DPO, PPO, QAT, knowledge distillation, and more
+            Out-of-the-box memory efficiency, performance improvements, and scaling with the latest PyTorch APIs
+            YAML configs for easily configuring training, evaluation, quantization or inference recipes
+            Built-in support for many popular dataset formats and prompt templates
+    """
+    return dummy_response
+
+
+def main():
+    client = LlamaStackClient(
+        base_url="http://localhost:8321",
+    )
+
+    model = "meta-llama/Llama-3.1-8B-Instruct"
+    print(type(torchtune))
+    agent = ReActAgent(
+        client=client,
+        model=model,
+        builtin_toolgroups=["builtin::websearch"],
+        client_tools=[torchtune],
+    )
+
+    session_id = agent.create_session(f"ttest-session-{uuid.uuid4().hex}")
+
+    response = agent.create_turn(
+        messages=[{"role": "user", "content": "What's the current time in new york?"}],
+        session_id=session_id,
+        stream=True,
+    )
+    for log in EventLogger().log(response):
+        log.print()
+
+    response2 = agent.create_turn(
+        messages=[{"role": "user", "content": "What is torchtune?"}],
+        session_id=session_id,
+        stream=True,
+    )
+    for log in EventLogger().log(response2):
+        log.print()
+
+
+if __name__ == "__main__":
+    fire.Fire(main)