《ChatGPT Prompt Engineering for Developers》学习笔记

import openai

openai.api_key = 'xxx'

def get_completion(prompt, model="gpt-3.5-turbo"):
    messages = [{"role": "user", "content": prompt}]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0, # this is the degree of randomness of the model's output
    )
    return response.choices[0].message["content"]

text = f"""
内容省略（可添加任意内容）
"""
prompt = f"""
Summarize the text delimited by triple backticks \
into a single sentence.
```{text}```
"""

response = get_completion(prompt)
print(response)

prompt = f"""
Generate a list of three made-up book titles along \
with their authors and genres. 
Provide them in JSON format with the following keys: 
book_id, title, author, genre.
"""

response = get_completion(prompt)
print(response)

text_1 = f"""
如何把大象放进冰箱？\
首先打开冰箱的门，\
然后把大象放进去，\
最后关上冰箱门。
"""
prompt = f"""
You will be provided with text delimited by triple quotes. 
If it contains a sequence of instructions, \
re-write those instructions in the following format:

Step 1 - ...
Step 2 - …
…
Step N - …

If the text does not contain a sequence of instructions, \
then simply write \"No steps provided.\"

```{text_1}```
"""

response = get_completion(prompt)
print("Completion for Text 1:")
print(response)

prompt = f"""
Your task is to answer in a consistent style.

<child>: Teach me about patience.

<grandparent>: The river that carves the deepest \
valley flows from a modest spring; the \
grandest symphony originates from a single note; \
the most intricate tapestry begins with a solitary thread.

<child>: Teach me about love.
"""

response = get_completion(prompt)
print(response)

prompt = f"""
Your task is to perform the following actions: 
1 - Summarize the following text delimited by 
  <> with 1 sentence.
2 - Translate the summary into Chinese.
3 - List each name in the Chinese summary.
4 - Output a json object that contains the 
  following keys: chinese_summary, num_names.

Use the following format:
Text: <text to summarize>
Summary: <summary>
Translation: <summary translation>
Names: <list of names in Chinese summary>
Output JSON: <json with summary and num_names>

Text: <{text}>
"""

response = get_completion(prompt)
print("\nCompletion for prompt 2:")
print(response)

prompt = f"""
Your task is to determine if the student's solution \
is correct or not.
To solve the problem do the following:
- First, work out your own solution to the problem. 
- Then compare your solution to the student's solution \ 
and evaluate if the student's solution is correct or not. 
Don't decide if the student's solution is correct until 
you have done the problem yourself.

Use the following format:
Question:
'''
question here
'''
Student's solution:
'''
student's solution here
'''
Actual solution:
'''
steps to work out the solution and your solution here
'''
Is the student's solution the same as actual solution \
just calculated:
'''
yes or no
'''
Student grade:
'''
correct or incorrect
'''

Question:
'''
I'm building a solar power installation and I need help \
working out the financials. 
- Land costs $100 / square foot
- I can buy solar panels for $250 / square foot
- I negotiated a contract for maintenance that will cost \
me a flat $100k per year, and an additional $10 / square \
foot
What is the total cost for the first year of operations \
as a function of the number of square feet.
'''
Student's solution:
'''
Let x be the size of the installation in square feet.
Costs:
1. Land cost: 100x
2. Solar panel cost: 250x
3. Maintenance cost: 100,000 + 100x
Total cost: 100x + 250x + 100,000 + 100x = 450x + 100,000
'''
Actual solution:
"""

response = get_completion(prompt)
print(response)

Let x be the size of the installation in square feet.

Costs:
1. Land cost: 100x
2. Solar panel cost: 250x
3. Maintenance cost: 100,000 + 10x

Total cost: 100x + 250x + 100,000 + 10x = 360x + 100,000

Is the student's solution the same as actual solution just calculated:
No

Student grade:
Incorrect

prompt = f"""
Your task is to generate a short summary of a product \
review from an ecommerce site to give feedback to the \
Shipping deparmtment. 

Summarize the review below in Chinese, delimited by triple 
backticks, in at most 30 words, and focusing on any aspects \
that mention shipping and delivery of the product. 

Review: '''{prod_review}'''
"""

response = get_completion(prompt)
print(response)

prompt = f"""
Your task is to extract relevant information from \
a product review from an ecommerce site to give \
feedback to the Shipping department. The extracted result \
should be translated into Chinese.

From the review below delimited by triple quotes \
extract the information relevant to shipping and \
delivery. Limit to 30 words. 

Review: '''{prod_review}'''
"""

response = get_completion(prompt)
print(response)

prompt = f"""
What is the sentiment of the following product review, 
which is delimited with triple backticks?

Give your answer as a single word, either "positive" \
or "negative".

Review text: '''{lamp_review}'''
"""

response = get_completion(prompt)
print(response)

prompt = f"""
Identify a list of emotions that the writer of the \
following review is expressing. Include no more than \
five items in the list. Format your answer as a list of \
lower-case words separated by commas.

Review text: '''{lamp_review}'''
"""

response = get_completion(prompt)
print(response)

prompt = f"""
Is the writer of the following review expressing anger?\
The review is delimited with triple backticks. \
Give your answer as either yes or no.

Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)

prompt = f"""
Identify the following items from the review text: 
- Item purchased by reviewer
- Company that made the item

The review is delimited with triple backticks. \
Format your response as a JSON object with \
"Item" and "Brand" as the keys. 
If the information isn't present, use "unknown" \
as the value.
Make your response as short as possible.
  
Review text: '''{lamp_review}'''
"""

response = get_completion(prompt)
print(response)

prompt = f"""
Determine five topics that are being discussed in the \
following text, which is delimited by triple backticks.

Make each item one or two words long. 

Format your response as a list of items separated by commas.

Text sample: '''{story}'''
"""

response = get_completion(prompt)
print(response)

topic_list = [
    "nasa", "local government", "engineering", 
    "employee satisfaction", "federal government"
]

prompt = f"""
Determine whether each item in the following list of \
topics is a topic in the text below, which
is delimited with triple backticks.

Give your answer as list with 0 or 1 for each topic.\

List of topics: {", ".join(topic_list)}

Text sample: '''{story}'''
"""

response = get_completion(prompt)
print(response)

prompt = f"""
Translate the following  text to French and Spanish
and English pirate: \
'''I want to order a basketball'''
"""

response = get_completion(prompt)
print(response)

prompt = f"""
Tell me which language this is: 
'''Combien coûte le lampadaire?'''
"""

response = get_completion(prompt)
print(response)

prompt = f"""
Translate the following from slang to a business letter: 
'Dude, This is Joe, check out this spec on this standing lamp.'
"""

response = get_completion(prompt)
print(response)

data_json = { "resturant employees" :[ 
    {"name":"Shyam", "email":"shyamjaiswal@gmail.com"},
    {"name":"Bob", "email":"bob32@gmail.com"},
    {"name":"Jai", "email":"jai87@gmail.com"}
]}

prompt = f"""
Translate the following python dictionary from JSON to an HTML \
table with column headers and title: {data_json}
"""

response = get_completion(prompt)
print(response)

text = f"""
Got this for my daughter for her birthday cuz she keeps taking \
mine from my room.  Yes, adults also like pandas too.  She takes \
it everywhere with her, and it's super soft and cute.  One of the \
ears is a bit lower than the other, and I don't think that was \
designed to be asymmetrical. It's a bit small for what I paid for it \
though. I think there might be other options that are bigger for \
the same price.  It arrived a day earlier than expected, so I got \
to play with it myself before I gave it to my daughter.
"""

prompt = f"proofread and correct this review: ```{text}```"
response = get_completion(prompt)
print(response)

from redlines import Redlines

diff = Redlines(text,response)
display(Markdown(diff.output_markdown))

prompt = f"""
proofread and correct this review. Make it more compelling. 
Ensure it follows APA style guide and targets an advanced reader. 
Output in markdown format.
Text: ```{text}```
"""

response = get_completion(prompt)
display(Markdown(response))

prompt = f"""
You are a customer service AI assistant.
Your task is to send an email reply in Chinese to a valued customer.
Given the customer email delimited by ```, \
Generate a reply to thank the customer for their review.
If the sentiment is positive or neutral, thank them for \
their review.
If the sentiment is negative, apologize and suggest that \
they can reach out to customer service. 
Make sure to use specific details from the review.
Write in a concise and professional tone.
Sign the email as `AI customer agent`.
Customer review: '''{review}'''
Review sentiment: {sentiment}
"""

response = get_completion(prompt)
print(response)

def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0):
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature, # this is the degree of randomness of the model's output
    )
#     print(str(response.choices[0].message))
    return response.choices[0].message["content"]

messages =  [  
{'role':'system', 'content':'You are friendly chatbot.'},
{'role':'user', 'content':'Hi, my name is Isa'},
{'role':'assistant', 'content': "Hi Isa! It's nice to meet you. \
Is there anything I can help you with today?"},
{'role':'user', 'content':'Yes, you can remind me, What is my name?'}  ]
response = get_completion_from_messages(messages, temperature=1)
print(response)

import streamlit as st
import openai

st.markdown('# ChatGPT demo')


def init_session():
    if 'round' not in st.session_state:
        print("重置轮数")
        st.session_state['round'] = 1

    if 'question' not in st.session_state:
        print("重置问题记录")
        st.session_state['question'] = []

    if 'answer' not in st.session_state:
        print("重置回答记录")
        st.session_state['answer'] = []


init_session()
code_input = st.text_input('请输入使用密钥')
system_input = st.text_input('请设定 ChatGPT 角色（用于生成符合角色的回答）', "你是一个有用的AI助手")
latest_input = st.text_area('请输入内容')

openai.api_key = code_input

if st.button('发送'):
    st.session_state['question'].append(latest_input)

    message_list = []
    dic_q_item = {"role": "system", "content": system_input}
    message_list.append(dic_q_item)
    for n in range(st.session_state['round']):
        # 第 n 轮，有 n 个问题，n - 1 个答案待组装
        if n - 1 >= 0:
            dic_a_item = {"role": "assistant", "content": st.session_state['answer'][n - 1]}
            message_list.append(dic_a_item)
        dic_q_item = {"role": "user", "content": st.session_state['question'][n]}
        message_list.append(dic_q_item)

    completion = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        max_tokens=1000,
        user="wzy",
        messages=message_list,
        temperature=0
    )

    answer = completion.choices[0].message['content']
    st.session_state['answer'].append(answer)

    col1, col2 = st.columns(2)

    for i in range(st.session_state['round']):
        with col1:
            st.text('User')
            st.write('  ' + st.session_state['question'][i])
            st.text(' ')
            st.write(' ')
        with col2:
            st.text(' ')
            st.write(' ')
            st.text('ChatGPT')
            st.write('  ' + st.session_state['answer'][i])

    st.session_state['round'] += 1

if st.button('清空对话记录'):
    st.session_state.clear()