reasoning works for both toolcall and response

1 files changed, 50 insertions, 0 deletions

diff --git a/src/agents/02-sandbox/works.py b/src/agents/02-sandbox/works.py
new file mode 100644
index 0000000..d40c3aa
--- /dev/null
+++ b/src/agents/02-sandbox/works.py
@@ -0,0 +1,50 @@
+import json
+from typing import List
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_ollama import ChatOllama
+
+tool_def = {
+    "type": "function",
+    "function": {
+        "name": "validate_user",
+        "description": "Validate user using historical addresses.\\n\\n    Args:\\n        user_id (int): the user ID.\\n        addresses (List[str]): Previous addresses as a list of strings.",
+        "parameters": {
+            "type": "object",
+            "required": ["user_id", "addresses"],
+            "properties": {
+                "addresses": {"type": "array", "items": {"type": "string"}, "description": ""},
+                "user_id": {"type": "integer", "description": ""},
+            },
+        },
+    },
+}
+
+system_prompt = f"""# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{json.dumps(tool_def)}
+</tools>
+
+For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
+<tool_call>
+{{"name": "<function-name>", "arguments": "<args-json-object>"}}
+</tool_call>"""
+
+llm = ChatOllama(
+    model="qwen3:1.7b",
+    temperature=0,
+)
+
+messages = [
+    SystemMessage(content=system_prompt),
+    HumanMessage(content="Could you validate user 123? They previously lived at 123 Fake St in Boston MA and 234 Pretend Boulevard in Houston TX.Think before you call any tools"),
+]
+
+result = llm.invoke(messages)
+
+print("--- Full AIMessage Result ---")
+print(result)
+