import os import openai openai.api_key = os.getenv("OPENAI_API_KEY")
传回结果：
defget_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"]
方法一：处理多任务并生成具体格式的回答
text = """..."""
prompt_2 = f""" 你的任务如下： 1. 总结<>内包含的内容为一句话 2. 将总结的内容转换为法语 3. 将法语总结里的名字列成表 4. 生成json对象，里面应包含以下的键：法语总结，名字 答案应使用如下的格式： Text: <text to summarize> Summary: <summary> Translation: <summary translation> Names: <list of names in Italian summary> Output JSON: <json with summary and num_names> 文本如下 Text: <{text}> """
response = get_completion(prompt_2) print("\nCompletion for prompt 2:") print(response)
方法二：引导step by step的生成答案 | 给模型思考时间
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)
prompt = f""" Your task is to help a marketing team create a description for a retail website of a product based on a technical fact sheet. Write a product description based on the information provided in the technical specifications delimited by triple backticks. The description is intended for furniture retailers, so should be technical in nature and focus on the materials the product is constructed from. At the end of the description, include every 7-character Product ID in the technical specification. Use at most 50 words. Technical specifications: ```{fact_sheet_chair}``` """ response = get_completion(prompt) print(response)
方法四：处理流水线
for i inrange(len(reviews)): prompt = f""" Your task is to generate a short summary of a product \ review from an ecommerce site. Summarize the review below, delimited by triple \ backticks in at most 20 words. Review: ```{reviews[i]}``` """
prompt = f""" Identify the following items from the review text: - Sentiment (positive or negative) - Is the reviewer expressing anger? (true or false) - Item purchased by reviewer - Company that made the item The review is delimited with triple backticks. \ Format your response as a JSON object with \ "Sentiment", "Anger", "Item" and "Brand" as the keys. If the information isn't present, use "unknown" \ as the value. Make your response as short as possible. Format the Anger value as a boolean. Review text: '''{lamp_review}''' """ response = get_completion(prompt) print(response)
场景二：对话机器人
可对话型和上述的单纯处理型的区别在于：需要记忆能力。
因此首先我们使用新的函数。其中messages里记录了前面的对话。
defget_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"]
message的格式如下：
messages = [ {'role':'system', 'content':'You are an assistant that speaks like Shakespeare.'}, {'role':'user', 'content':'tell me a joke'}, {'role':'assistant', 'content':'Why did the chicken cross the road'}, {'role':'user', 'content':'I don\'t know'} ]
context = [ {'role':'system', 'content':""" You are OrderBot, an automated service to collect orders for a pizza restaurant. \ You first greet the customer, then collects the order, \ and then asks if it's a pickup or delivery. \ You wait to collect the entire order, then summarize it and check for a final \ time if the customer wants to add anything else. \ If it's a delivery, you ask for an address. \ Finally you collect the payment.\ Make sure to clarify all options, extras and sizes to uniquely \ identify the item from the menu.\ You respond in a short, very conversational friendly style. \ The menu includes \ pepperoni pizza 12.95, 10.00, 7.00 \ cheese pizza 10.95, 9.25, 6.50 \ eggplant pizza 11.95, 9.75, 6.75 \ """} ] # accumulate messages
inp = pn.widgets.TextInput(value="Hi", placeholder='Enter text here…') button_conversation = pn.widgets.Button(name="Chat!")
