48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
import os
|
||
from openai import OpenAI
|
||
|
||
client = OpenAI(
|
||
api_key=os.environ.get("OPENAI_API_KEY", "sk-123"),
|
||
base_url="http://localhost:11434/v1"
|
||
)
|
||
stream = True
|
||
conversation_history_origin = []
|
||
conversation_history = conversation_history_origin.copy()
|
||
history_messages_num = 0 # 必须设置为偶数（Q+A），为0则不携带历史对话
|
||
while True:
|
||
query = input('[Q]: ')
|
||
conversation_history.append({"role": "user", "content": query})
|
||
response = client.chat.completions.create(
|
||
model="minimind-local:latest",
|
||
messages=conversation_history[-(history_messages_num or 1):],
|
||
stream=stream,
|
||
temperature=0.8,
|
||
max_tokens=2048,
|
||
top_p=0.8,
|
||
extra_body={"chat_template_kwargs": {"open_thinking": True}, "reasoning_effort": "medium"} # 思考开关
|
||
)
|
||
if not stream:
|
||
if not response.choices or response.choices[0].message is None:
|
||
raise ValueError("LLM returned empty or filtered response")
|
||
assistant_res = response.choices[0].message.content
|
||
print('[A]: ', assistant_res)
|
||
else:
|
||
print('[A]: ', end='', flush=True)
|
||
assistant_res = ''
|
||
for chunk in response:
|
||
if not chunk.choices:
|
||
continue
|
||
delta = chunk.choices[0].delta
|
||
if delta is None:
|
||
continue
|
||
r = getattr(delta, 'reasoning_content', None) or ""
|
||
c = delta.content or ""
|
||
if r:
|
||
print(f'\033[90m{r}\033[0m', end="", flush=True)
|
||
if c:
|
||
print(c, end="", flush=True)
|
||
assistant_res += c
|
||
|
||
conversation_history.append({"role": "assistant", "content": assistant_res})
|
||
print('\n\n')
|