import os
import sys
import json
from openai import OpenAI
client = OpenAI(
base_url="https://aihubmix.com/v1",
api_key=os.environ.get("AIHUBMIX_API_KEY", "sk-***"),
)
# ── Tool definition & mock execution ─────────────────────────
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather for a location",
"parameters": {
"type": "object",
"properties": {"location": {"type": "string", "description": "City name"}},
"required": ["location"]
}
}
}]
WEATHER_DB = {
"boston": {"temperature": "45°F (7°C)", "condition": "rainy", "humidity": "85%", "wind": "15 mph NE"},
"tokyo": {"temperature": "72°F (22°C)", "condition": "sunny", "humidity": "45%", "wind": "5 mph S"},
}
def execute_tool(name: str, args: dict) -> str:
if name == "get_weather":
key = next((k for k in WEATHER_DB if k in args.get("location", "").lower()), None)
return json.dumps(WEATHER_DB.get(key, {"temperature": "65°F", "condition": "clear"}))
return "{}"
# ── Stream response collector ────────────────────────────────
def stream_and_collect(turn: int, **kwargs):
"""Stream response, print thinking/content in real-time, accumulate reasoning_details/tool_calls."""
rd = {} # accumulated reasoning_details
content = "" # accumulated response text
tc_map = {} # accumulated tool_calls (by index)
cur = "none" # current output section: none / thinking / content
stream = client.chat.completions.create(stream=True, **kwargs)
for chunk in stream:
if not chunk.choices:
continue
delta = chunk.choices[0].delta
# ── Handle thinking ──
rd_delta = getattr(delta, "reasoning_details", None)
if rd_delta and isinstance(rd_delta, dict):
for k, v in rd_delta.items():
if k == "type":
rd[k] = v
elif isinstance(v, str):
rd[k] = rd.get(k, "") + v
elif v is not None:
rd[k] = v
# Print thinking chunks in real-time
thinking_chunk = rd_delta.get("thinking", "")
if thinking_chunk:
if cur != "thinking":
cur = "thinking"
label = "Interleaved Thinking" if turn > 1 else "Thinking"
sys.stdout.write(f"\n[{label}] ")
sys.stdout.write(thinking_chunk)
sys.stdout.flush()
# ── Handle content ──
if delta.content:
if cur != "content":
if cur == "thinking":
sys.stdout.write("\n")
cur = "content"
sys.stdout.write("\n[Response] ")
sys.stdout.write(delta.content)
sys.stdout.flush()
content += delta.content
# ── Handle tool_calls ──
for tc in delta.tool_calls or []:
i = tc.index
if i not in tc_map:
tc_map[i] = {"id": "", "type": "function",
"function": {"name": "", "arguments": ""}}
if tc.id:
tc_map[i]["id"] = tc.id
if tc.function:
tc_map[i]["function"]["name"] += tc.function.name or ""
tc_map[i]["function"]["arguments"] += tc.function.arguments or ""
# End current output section
if cur in ("thinking", "content"):
sys.stdout.write("\n")
tool_calls = [tc_map[i] for i in sorted(tc_map)] if tc_map else None
return {
"content": content or None,
"reasoning_details": rd or None,
"tool_calls": tool_calls,
}
# ── Multi-turn conversation loop ─────────────────────────────
messages = [
{"role": "user", "content": "What's the weather like in Boston? Then recommend what to wear."}
]
turn = 0
while True:
turn += 1
print(f"\n── Turn {turn} ──")
result = stream_and_collect(
turn,
model="claude-sonnet-4-5",
messages=messages,
tools=tools,
extra_body={"reasoning": {"max_tokens": 2000}},
)
# Print tool calls
if result["tool_calls"]:
for tc in result["tool_calls"]:
print(f"[Tool Call: {tc['function']['name']}] {tc['function']['arguments']}")
# Build assistant message, preserve reasoning_details (critical!)
assistant_msg = {"role": "assistant", "content": result["content"]}
if result["tool_calls"]:
assistant_msg["tool_calls"] = result["tool_calls"]
if result["reasoning_details"]:
assistant_msg["reasoning_details"] = result["reasoning_details"] # pass back unmodified
messages.append(assistant_msg)
# No tool_calls means conversation is done
if not result["tool_calls"]:
break
# Execute tools and append results to messages
for tc in result["tool_calls"]:
args = json.loads(tc["function"]["arguments"])
tool_result = execute_tool(tc["function"]["name"], args)
print(f"[Tool Result: {tc['function']['name']}] {tool_result}")
messages.append({"role": "tool", "tool_call_id": tc["id"], "content": tool_result})
