本文正在参加 人工智能创作者扶持方案
众所周知,ChatGPT能够帮助研制人员编写或许Debug程序代码,可是在执行过程中,ChatGPT会将程序代码的一些相关文字解说和代码段混合着回来,如此,研制人员还需要自己进行编辑和张贴操作,功率上差强人意,本次咱们企图将ChatGPT直接嵌入到代码事务中,让ChatGPT生成能够直接运转的代码。
ChatGPT的片面答复问题
首要,咱们向ChatGPT提出一个简单的代码需求:
能够看到,就像上文所描绘的那样,ChatGPT会将文字描绘和代码片段混合着回来,其实对于真实的需求者来说,文字描绘自身是不用要的,由于假如发问者不知道什么是布隆过滤器,也就不会提出布隆过滤器相关的代码需求。
再看ChatGPT回来的布隆过滤器代码:
import hashlib
class BloomFilter:
def __init__(self, size, hash_count):
self.size = size
self.hash_count = hash_count
self.bit_array = [False] * size
def add(self, string):
for seed in range(self.hash_count):
result = hashlib.sha256((string + str(seed)).encode()).hexdigest()
index = int(result, 16) % self.size
self.bit_array[index] = True
def __contains__(self, string):
for seed in range(self.hash_count):
result = hashlib.sha256((string + str(seed)).encode()).hexdigest()
index = int(result, 16) % self.size
if not self.bit_array[index]:
return False
return True
大体上,没有毛病。可是片面性太强,什么是片面性?便是ChatGPT其实不是站在需求者的视角来编写代码,而是站在自己的角度上,它没有考虑事务的上下文联系,也便是类和办法命名、办法参数、以及参数类型或许回来值以及类型,这些东西是否符合需求者当时的代码事务。
当然,这并不是ChatGPT的错,主要是输入的问题描绘不行清晰和详细,但假如每次都需要将代码事务逻辑转化为文字告诉ChatGPT,又有些画蛇添足,狗尾续貂之感。
根据事务配置ChatGPT
那么怎样将ChatGPT融入事务代码?首要创立Openai接入函数:
import openai
openai.api_key = "apikey"
def generate_code(func, docstring):
init_prompt = "You are a Python expert who can implement the given function."
definition = f"def {func}"
prompt = f"Read this incomplete Python code:\n```python\n{definition}\n```"
prompt += "\n"
prompt += f"Complete the Python code that follows this instruction: '{docstring}'. Your response must start with code block '```python'."
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
temperature=0,
max_tokens=1024,
top_p=1,
messages=[
{
"role": "system",
"content": init_prompt,
},
{
"role": "user",
"content": prompt,
},
],
)
codeblock = response.choices[0].message.content
code = next(filter(None, codeblock.split("```python"))).rsplit("```", 1)[0]
code = code.strip()
return code
诀窍便是提前设置好引导词:
init_prompt = "You are a Python expert who can implement the given function."
definition = f"def {func}"
prompt = f"Read this incomplete Python code:\n```python\n{definition}\n```"
prompt += "\n"
prompt += f"Complete the Python code that follows this instruction: '{docstring}'. Your response must start with code block '```python'."
这儿咱们提前设置两个参数func和docstring,也便是函数名和功用描绘,要求ChatGPT严厉按照参数的输入来回来代码,现在运转函数:
if __name__ == '__main__':
print(generate_code("test","Sum two numbers"))
程序回来:
➜ chatgpt_write_code /opt/homebrew/bin/python3.10 "/Users/liuyue/wodfan/work/chatgpt_write_code/chatgpt_write_code.p
y"
def test(a, b):
return a + b
如此一来,ChatGPT就不会回来废话,而是直接交给咱们能够运转的代码。
装修器调用ChatGPT
事实上,函数调用环节也能够省掉,咱们能够使用Python装修器的闭包原理,直接将所界说函数的参数和描绘传递给ChatGPT,随后再直接运转被装修的函数,提高功率:
import inspect
from functools import wraps
def chatgpt_code(func):
@wraps(func)
def wrapper(*args, **kwargs):
signature = f'{func.__name__}({", ".join(inspect.signature(func).parameters)}):'
docstring = func.__doc__.strip()
code = generate_code(signature, docstring)
print(f"generated code:\n```python\n{code}\n```")
exec(code)
return locals()[func.__name__](*args, **kwargs)
return wrapper
将办法界说好之后,使用根据ChatGPT的装修器:
if __name__ == '__main__':
@chatgpt_code
def sum_two(num1,num2):
"""
Sum two numbers.
"""
print(sum_two(1,2))
程序回来:
➜ chatgpt_write_code /opt/homebrew/bin/python3.10 "/Users/liuyue/wodfan/work/chatgpt_write_code/chatgpt_write_code.p
y"
sum_two(num1, num2):
generated code:
def sum_two(num1, num2):
"""
Sum two numbers.
"""
return num1 + num2
3
直接将事务逻辑和运转成果悉数回来。
那么现在,回到开篇的关于布隆过滤器的问题:
if __name__ == '__main__':
@chatgpt_code
def bloom(target:str,storage:list):
"""
Use a Bloom filter to check if the target is in storage , Just use this func , no more class
"""
print(bloom("你好",["你好","Helloworld"]))
程序回来:
➜ chatgpt_write_code /opt/homebrew/bin/python3.10 "/Users/liuyue/wodfan/work/chatgpt_write_code/chatgpt_write_code.p
y"
generated code:
def bloom(target, storage):
# Initialize the Bloom filter with all zeros
bloom_filter = [0] * len(storage)
# Hash the target and set the corresponding bit in the Bloom filter to 1
for i in range(len(storage)):
if target in storage[i]:
bloom_filter[i] = 1
# Check if all the bits corresponding to the target are set to 1 in the Bloom filter
for i in range(len(storage)):
if target in storage[i] and bloom_filter[i] == 0:
return False
return True
True
➜ chatgpt_write_code
丝滑流通,和事务衔接得天衣无缝,拉链般重合,不需要挑挑拣拣,也不用复制张贴。
结语
毫无疑问,ChatGPT确然是神兵利器,吹毛可断,无坚不摧。但东西虽好,也需要看在谁的手里,所谓东西无高低,功力有高深,类比的话,假如倚天剑握在三岁孩童手中,不只毫无增益,还可能伤其自身,可是握在峨眉掌门灭绝师太手里,那就能够横扫千军如卷席了,那才能体现大宗匠的手法。最终,奉上项目代码,与众乡亲同飨:github.com/zcxey2911/chatgptapi_write_code