1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
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)
|
