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FunctionCalling的单一函数调用
天气预报查询（今天长沙的天气如何？）
1 import json
2 import requests
3 from openai import OpenAI
4
5 client = OpenAI()
6
7 location = "长沙"
8
9 def get_current_weather(city):
10 url = "https://restapi.amap.com/v3/weather/weatherInfo?key=0f219ddb5f23d95ea1731fe653f906a3&city={city}".format(city=city)
11 response = requests.get(url)
12 result = eval(response.text)["lives"][0]
13 weather_info = {
14 "location": city,
15 "weather": result["weather"],
16 "temperature": result["temperature"],
17 "time": result["reporttime"]
18 }
19 return json.dumps(weather_info, ensure_ascii=False)
20
21 messages = []
22 messages.append({"role":"system", "content":"你是一个查询天气的机器人，你需要根据用户提供的地址来回答当地的天气情况"})
23 messages.append({"role":"user", "content": f"""今天{location}的天气如何？"""})
24 tools = [{
25 "type":"function",
26 "function": {
27 "name":"get_current_weather",
28 "description": "获取给定位置的当前天气",
29 "parameters": {
30 "type": "object",
31 "properties": {
32 "location": {
33 "type": "string",
34 "description": "城市或区，例如长沙"
35 }
36 },
37 "required":["location"]
38 }
39 }
40 }]
41
42 response = client.chat.completions.create(
43 model = "gpt-3.5-turbo",
44 messages = messages,
45 tools = tools
46 )
47
48 messages.append(response.choices[0].message)
49 print(messages)
50 function_name = response.choices[0].message.tool_calls[0].function.name
51 print(function_name)
52 function_id = response.choices[0].message.tool_calls[0].id
53 print(function_id)
54 messages.append({
55 "tool_call_id": function_id,
56 "role": "tool",
57 "name": function_name,
58 "content": get_current_weather(location)
59 })
60 print(messages)
61
62 response = client.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
63 print(response.choices[0].message.content)
View Code
运行结果
Function call的多函数调用
查询学校课程对应的老师（帮我查询北京大学的中国历史课程是哪位老师(teacher)。）
1 import json
2 from openai import OpenAI
3 client = OpenAI()
4 tools = [{
5 "type": "function",
6 "function": {
7 "name": "get_class_number",
8 "description": "根据学校、课程查询上课编号",
9 "parameters": {
10 "type": "object",
11 "properties": {
12 "school": {
13 "description": "学校",
14 "type": "string"
15 },
16 "course": {
17 "description": "课程",
18 "type": "string"
19 }
20 },
21 "required": ["school", "course"]
22 }
23 }
24 }, {
25 "type": "function",
26 "function": {
27 "name": "get_course_teacher",
28 "description": "查询某课程的老师",
29 "parameters": {
30 "type": "object",
31 "properties": {
32 "class_number": {
33 "description": "上课编号",
34 "type": "string"
35 }
36 },
37 "required": ["class_number"]
38 },
39 }
40 }]
41
42 def get_class_number(school: str, course: str):
43 class_number = {
44 "清华大学": {
45 "高等数学": "MATH101",
46 "线性代数": "MATH102",
47 },
48 "北京大学": {
49 "大学英语": "ENG201",
50 "中国历史": "HIST202",
51 }
52 }
53 return {"class_number": class_number[school][course]}
54
55 def get_course_teacher(class_number: str):
56 course_teacher_mapping = {
57 "MATH101": "张老师",
58 "MATH102": "李老师",
59 "ENG201": "王老师",
60 "HIST202": "赵老师",
61 }
62 teacher = course_teacher_mapping.get(class_number)
63 return {"teacher": teacher}
64
65 messages = []
66 messages = [
67 {
68 "role": "system",
69 "content": "你是一位高效的教育助手，现在需要查询某高校的老师名称。"
70 },
71 {
72 "role": "user",
73 "content": "帮我查询北京大学的中国历史课程是哪位老师(teacher)。"
74 }
75 ]
76
77 # 第一次调用
78 first_response = client.chat.completions.create(
79 model="gpt-3.5-turbo",
80 messages=messages,
81 tools=tools,
82 tool_choice="auto",
83 )
84 print(first_response.choices[0].message)
85
86 messages.append(first_response.choices[0].message)
87
88 first_function = {}
89 if first_response.choices[0].message.tool_calls:
90 tool_call = first_response.choices[0].message.tool_calls[0]
91 args = tool_call.function.arguments
92 if tool_call.function.name == "get_class_number":
93 first_function = get_class_number(**json.loads(args))
94 if tool_call.function.name == "get_course_teacher":
95 first_function = get_course_teacher(**json.loads(args))
96
97 print(first_function)
98
99 tool_call = first_response.choices[0].message.tool_calls[0]
100 messages.append({
101 "role": "tool",
102 "tool_call_id": tool_call.id,
103 "content": str(json.dumps(first_function)),
104 "name": tool_call.function.name
105 })
106 print("***" * 40)
107 print(messages)
108 print("***" * 40)
109
110 second_response = client.chat.completions.create(
111 model="gpt-3.5-turbo",
112 messages=messages,
113 tools=tools,
114 tool_choice="auto"
115 )
116 print(second_response.choices[0].message)
117
118 messages.append(second_response.choices[0].message)
119 second_function = {}
120 if second_response.choices[0].message.tool_calls:
121 tool_call = second_response.choices[0].message.tool_calls[0]
122 args = tool_call.function.arguments
123 if tool_call.function.name == "get_class_number":
124 second_function = get_class_number(**json.loads(args))
125 if tool_call.function.name == "get_course_teacher":
126 second_function = get_course_teacher(**json.loads(args))
127
128 print(second_function)
129 tool2_call = second_response.choices[0].message.tool_calls[0]
130 # 将函数的结果添加到messages中，继续送入模型问答
131 messages.append(
132 {
133 "role": "tool",
134 "tool_call_id": tool2_call.id,
135 "content": str(json.dumps(second_function)),
136 "name":tool2_call.function.name
137 }
138 )
139
140 last_response = client.chat.completions.create(
141 model="gpt-3.5-turbo",
142 messages=messages,
143 tools=tools,
144 tool_choice="auto",
145 )
146 print(last_response.choices[0].message.content)
View Code
运行结果
FunctionCalling调用SQL
查询一下最高工资的员工姓名及对应的工资
import json
import pymysql
from openai import OpenAI
client = OpenAI()
def connect_database(query):
conn = pymysql.connect(
host="localhost",
port=3306,
user="root",
password="123456",
database="school",
charset="utf8mb4",
)
cursor = conn.cursor()
cursor.execute(query)
result = cursor.fetchall()
cursor.close()
conn.close()
return result
messages = []
messages.append({"role": "system", "content": "通过针对业务数据库生成 SQL 查询来回答用户的问题"})
messages.append({"role": "user", "content": "查询一下最高工资的员工姓名及对应的工资"})
response = client.chat.completions.create(
messages=messages,
model="gpt-3.5-turbo",
tools=[
{
"type": "function",
"function": {
"name": "connect_database",
"description": "使用此函数回答业务问题，要求输出是一个SQL查询语句",
"parameters": {
"type": "object",
"properties": {
"sql": {
"type": "string",
"description": f"SQL查询提取信息以回答用户的问题。"
f"查询应该以纯文本返回，而不是JSON。"
f"数据库的表为 emp 表。字段有 id,name,salary"
f"查询应该只包含MySQL支持的语法。",
}
},
"required": ["sql"],
},
}
}
]
)
print("tool calls", response.choices[0].message.tool_calls[0])
messages.append(response.choices[0].message)
function_name = response.choices[0].message.tool_calls[0].function.name
function_id = response.choices[0].message.tool_calls[0].id
function_response = connect_database(
json.loads(
response.choices[0].message.tool_calls[0].function.arguments
).get("sql")
)
print("dbResult", function_response)
messages.append({
"role": "tool",
"tool_call_id": function_id,
"name": function_name,
"content": str(function_response),
})
last_response = client.chat.completions.create(
messages=messages,
model="gpt-3.5-turbo",
)
print(last_response.choices[0].message.content)
View Code
运行效果
