from openai import OpenAI
client = OpenAI(
api_key="eyJ0eXxx", # 此处传token，不带Bearer
base_url="https://chat.intern-ai.org.cn/api/v1/",
)
completion = client.chat.completions.create(
model="intern-s1",
messages=[
{
"role": "user",
"content": "写一个关于独角兽的睡前故事，一句话就够了。"
}
]
)
print(completion.choices[0].message.content)
from openai import OpenAI
client = OpenAI(
api_key="eyJ0eXxx", # 此处传token，不带Bearer
base_url="https://chat.intern-ai.org.cn/api/v1/",
)
response = client.chat.completions.create(
model="intern-s1",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "图片里有什么？"},
{
"type": "image_url",
"image_url": {
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
},
},
],
}
],
extra_body={"thinking_mode": True},
)
print(response.choices[0].message.content)
import base64
from openai import OpenAI
client = OpenAI(
api_key="eyJ0eXxx", # 此处传token，不带Bearer
base_url="https://chat.intern-ai.org.cn/api/v1/",
)
# Function to encode the image
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
# Path to your image
image_path = "/root/share/intern.jpg"
# Getting the Base64 string
base64_image = encode_image(image_path)
completion = client.chat.completions.create(
model="intern-s1",
messages=[
{
"role": "user",
"content": [
{ "type": "text", "text": "图片里有什么?" },
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}",
},
},
],
}
],
)
print(completion.choices[0].message.content)
模型使用工具
Openai 格式
from openai import OpenAI
client = OpenAI(
api_key="",
base_url="https://chat.intern-ai.org.cn/api/v1/",
)
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current temperature for a given location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and country e.g. Bogotá, Colombia"
}
},
"required": [
"location"
],
"additionalProperties": False
},
"strict": True
}
}]
completion = client.chat.completions.create(
model="intern-s1",
messages=[{"role": "user", "content": "What is the weather like in Paris today?"}],
tools=tools
)
print(completion.choices[0].message.tool_calls)
Python 原生调用
import requests
import json
# API 配置
API_KEY = "eyJ0exxxxQ"
BASE_URL = "https://chat.intern-ai.org.cn/api/v1/"
ENDPOINT = f"{BASE_URL}chat/completions"
# 定义天气查询工具
WEATHER_TOOLS = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "获取指定城市或坐标的当前温度（摄氏度）",
"parameters": {
"type": "object",
"properties": {
"latitude": {"type": "number", "description": "纬度"},
"longitude": {"type": "number", "description": "经度"}
},
"required": ["latitude", "longitude"],
"additionalProperties": False
},
"strict": True
}
}]
def get_weather(latitude, longitude):
"""
获取指定坐标的天气信息
Args:
latitude: 纬度
longitude: 经度
Returns:
当前温度（摄氏度）
"""
try:
# 调用开放气象API
response = requests.get(
f"https://api.open-meteo.com/v1/forecast?latitude={latitude}&longitude={longitude}¤t=temperature_2m,wind_speed_10m&hourly=temperature_2m,relative_humidity_2m,wind_speed_10m"
)
data = response.json()
temperature = data['current']['temperature_2m']
return f"{temperature}"
except Exception as e:
return f"获取天气信息时出错: {str(e)}"
def make_api_request(messages, tools=None):
"""发送API请求"""
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}
payload = {
"model": "intern-s1",
"messages": messages,
"temperature": 0.7
}
if tools:
payload["tools"] = tools
payload["tool_choice"] = "auto"
try:
response = requests.post(ENDPOINT, headers=headers, json=payload, timeout=30)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
print(f"API请求失败: {e}")
return None
def main():
# 初始消息 - 巴黎的坐标
messages = [{"role": "user", "content": "请查询当前北京的温度"}]
print("🌤️ 正在查询天气...")
# 第一轮API调用
response = make_api_request(messages, WEATHER_TOOLS)
if not response:
return
assistant_message = response["choices"][0]["message"]
# 检查工具调用
if assistant_message.get("tool_calls"):
print("🔧 执行工具调用...")
print("tool_calls:",assistant_message.get("tool_calls"))
messages.append(assistant_message)
# 处理工具调用
for tool_call in assistant_message["tool_calls"]:
function_name = tool_call["function"]["name"]
function_args = json.loads(tool_call["function"]["arguments"])
tool_call_id = tool_call["id"]
if function_name == "get_weather":
latitude = function_args["latitude"]
longitude = function_args["longitude"]
weather_result = get_weather(latitude, longitude)
print(f"温度查询结果: {weather_result}°C")
# 添加工具结果
tool_message = {
"role": "tool",
"content": weather_result,
"tool_call_id": tool_call_id
}
messages.append(tool_message)
# 第二轮API调用获取最终答案
final_response = make_api_request(messages)
if final_response:
final_message = final_response["choices"][0]["message"]
print(f"✅ 最终回答: {final_message['content']}")
else:
print(f"直接回答: {assistant_message.get('content', 'No content')}")
if __name__ == "__main__":
main()
stream=True,打开流式传输
from openai import OpenAI
client = OpenAI(
api_key="eyxxxx",
base_url="https://chat.intern-ai.org.cn/api/v1/",
)
stream = client.chat.completions.create(
model="intern-s1",
messages=[
{
"role": "user",
"content": "Say '1 2 3 4 5 6 7' ten times fast.",
},
],
stream=True,
)
# 只打印逐字输出的内容
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True) # 逐字输出，不换行
通过extra_body={"thinking_mode": True}打开思考模式
from openai import OpenAI
client = OpenAI(
api_key="eyxxA", # 此处传token，不带Bearer
base_url="https://chat.intern-ai.org.cn/api/v1/",
)
completion = client.chat.completions.create(
model="intern-s1",
messages=[
{
"role": "user",
"content": "写一个关于独角兽的睡前故事，一句话就够了。"
}
],
extra_body={"thinking_mode": True,},
)
print(completion.choices[0].message)
科学能力
from getpass import getpass
from openai import OpenAI
api_key = getpass("请输入 API Key（输入不可见）：")
client = OpenAI(
api_key=api_key, # 此处传token，不带Bearer
base_url="https://chat.intern-ai.org.cn/api/v1/",
)
response = client.chat.completions.create(
model="intern-s1",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "这道题选什么"},
{
"type": "image_url",
"image_url": {
"url": "https://pic1.imgdb.cn/item/68d24759c5157e1a882b2505.jpg",
},
},
],
}
],
extra_body={"thinking_mode": True,},
)
print(response.choices[0].message.content)
感受：响应较慢。
MCP
- 外部数据获取：连接并处理来自各种外部源的数据
- 文件系统操作：具备完整的文件创建、读取、修改和删除能力，实现一个命令行版本的 cursor。
git clone https://github.com/fak111/mcp_tutorial.git
cd mcp_tutorial
bash install.sh
cd mcp-client
cp .env.example .env
文件系统服务
cd mcp-client
source .venv/bin/activate
uv run client_fixed.py ../mcp-server/filesystem/dist/index.js ../
