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襄阳市建设工程质量监督站网站,怎么做招聘网站,广东住房和城乡建设厅网站,请问有重庆有做网站吗1. 学习背景 在LangChain for LLM应用程序开发中课程中#xff0c;学习了LangChain框架扩展应用程序开发中语言模型的用例和功能的基本技能#xff0c;遂做整理为后面的应用做准备。视频地址#xff1a;基于LangChain的大语言模型应用开发构建和评估高

1. 先准备尝试调用O…1. 学习背景 在LangChain for LLM应用程序开发中课程中学习了LangChain框架扩展应用程序开发中语言模型的用例和功能的基本技能遂做整理为后面的应用做准备。视频地址基于LangChain的大语言模型应用开发构建和评估高

2. 先准备尝试调用OpenAI API 本实验基于jupyternotebook进行。 2.1先安装openai包、langchain包 !pip install openai !pip install langchain2.2 尝试调用openai包 import openai# 此处需要提前准备好可使用的openai KEY openai.api_key XXXX openai.base_url XXXXdef get_completion(prompt, model gpt-3.5-turbo):messages [{role: user, content: prompt}]response openai.chat.completions.create(model model,messages messages,temperature 0,)return response.choices[0].message.content get_completion(What is 11?)输出结果 1 1 equals 2.3.尝试用API解决邮件对话问题 3.1 邮件内容和风格 customer_email
   Arrr, I be fuming that me blender lid
   flew off and splattered me kitchen walls
   with smoothie! And to make matters worse,
   the warranty dont cover the cost of
   cleaning up me kitchen. I need yer help
   right now, matey! style American English
   in a calm and respectful tone3.2 构造成prompt prompt fTranslate the text
   that is delimited by triple backticks
   into a style that is {style}. text: {customer_email}prompt输出如下 Translate the text that is delimited by triple backticks into a style that is American English in a calm and respectful tone\n. \ntext: \nArrr, I be fuming that me blender lid flew off and splattered me kitchen walls with smoothie! And to make matters worse,the warranty dont cover the cost of cleaning up me kitchen. I need yer help right now, matey!\n\n3.3 使用上述prompt得到答案 response get_completion(prompt) response输出如下 I must express my frustration that my blender lid unexpectedly came off and caused my kitchen walls to be covered in smoothie splatters! And unfortunately, the warranty does not cover the cleaning costs of my kitchen. I kindly request your immediate assistance, my friend.4. 尝试用langchain解决 4.1 用langchain调用API from langchain.chat_models import ChatOpenAI chat ChatOpenAI(api_key XXXX,base_url XXXX,temperature0.0) print(chat)输出如下 ChatOpenAI(clientopenai.resources.chat.completions.Completions object at 0x7f362ab4f340, async_clientopenai.resources.chat.completions.AsyncCompletions object at 0x7f362aba9d80, temperature0.0, openai_api_keysk-gGSeHiJn09Ydl6Q1655eCf128b3a42XXXXXXXXXXXXXX, openai_api_baseXXXX, openai_proxy)4.2 构造prompt模板 注意和3.2的区别一个用了f“”“”“一个直接”“”“”。 template_string Translate the text
   that is delimited by triple backticks
   into a style that is {style}.
   text: {text} customer_style American English in a calm and respectful tonecustomer_email
   Arrr, I be fuming that me blender lid
   flew off and splattered me kitchen walls
   with smoothie! And to make matters worse,
   the warranty dont cover the cost of
   cleaning up me kitchen. I need yer help
   right now, matey!4.3 调用ChatPromptTemplate from langchain.prompts import ChatPromptTemplate

   将构造的prompt模板化

   prompt_template ChatPromptTemplate.from_template(template_string)

   模板中的占位符填充的参数

   customer_messages prompt_template.format_messages(style customer_style,text customer_email ) print(type(customer_messages)) print(customer_messages[0])输出如下 class list contentTranslate the text that is delimited by triple backticks into a style that is American English in a calm and respectful tone\n. text: \nArrr, I be fuming that me blender lid flew off and splattered me kitchen walls with smoothie! And to make matters worse, the warranty dont cover the cost of cleaning up me kitchen. I need yer help right now, matey!\n\n4.4 使用LLM解决问题

   Call the LLM to translate to the style of the customer message

   customer_response chat(customer_messages) print(customer_response.content)输出如下 Oh man, I m really frustrated that my blender lid flew off and made a mess of my kitchen walls with smoothie! And on top of that, the warranty doesnt cover the cost of cleaning up my kitchen. I could really use your help right now, buddy!5. 调用langchain对邮件回复 5.1定义回复的prompt service_reply Hey there customer,
   the warranty does not cover
   cleaning expenses for your kitchen
   because its your fault that
   you misused your blender
   by forgetting to put the lid on before
   starting the blender.
   Tough luck! See ya! service_style_pirate
   a polite tone
   that speaks in English Pirate

   继续使用前面定义的prompt_template占位符用参数填充

   service_messages prompt_template.format_messages(style service_style_pirate,text service_reply)print(service_messages[0].content)输出如下 Translate the text that is delimited by triple backticks into a style that is a polite tone that speaks in English Pirate. text: Hey there customer, the warranty does not cover cleaning expenses for your kitchen because its your fault that you misused your blender by forgetting to put the lid on before starting the blender. Tough luck! See ya!5.2 使用LLM解决问题 service_response chat(service_messages) print(service_response.content)输出如下 Ahoy there, me heartie! Unfortunately, the warranty be not coverin the cost of cleanin yer kitchen, as tis yer own fault for misusin yer blender by forgettin to put on the lid afore startin the blendin. Aye, tis a tough break indeed! Fare thee well, matey!至此我们就完成了使用langchain去实现prompt的构造、转换和调用。

3. 用langchain转化回答为JSON格式 6.1 构造模板

   顾客对产品的评论

   customer_review
   This leaf blower is pretty amazing. It has four settings:
   candle blower, gentle breeze, windy city, and tornado.
   It arrived in two days, just in time for my wifes
   anniversary present.
   I think my wife liked it so much she was speechless.
   So far Ive been the only one using it, and Ive been
   using it every other morning to clear the leaves on our lawn.
   Its slightly more expensive than the other leaf blowers
   out there, but I think its worth it for the extra features.

   顾客意见形成模板

   review_template
   For the following text, extract the following information:gift: Was the item purchased as a gift for someone else?
   Answer True if yes, False if not or unknown.delivery_days: How many days did it take for the product
   to arrive? If this information is not found, output -1.price_value: Extract any sentences about the value or price,
   and output them as a comma separated Python list.Format the output as JSON with the following keys: gift delivery_days price_valuetext: {text} from langchain.prompts import ChatPromptTemplate

   构造模板占位符信息用prompt填充

   prompt_template ChatPromptTemplate.from_template(review_template) messages prompt_template.format_messages(textcustomer_review)

   调用LLM输入为prompt

   response chat(messages) print(response.content)输出如下 {gift: true,delivery_days: 2,price_value: Its slightly more expensive than the other leaf blowers out there, but I think its worth it for the extra features. }6.2 构造合适的prompt print(type(response.content))输出如下 str可以看到输出内容是字符串类型的为了方便处理数据我们需要的是JSON格式因此还需要进行转化。 from langchain.output_parsers import ResponseSchema from langchain.output_parsers import StructuredOutputParsergift_schema ResponseSchema(namegift, descriptionWas the item purchased as a gift for someone else? Answer True if yes, False if not or unknown.) delivery_days_schema ResponseSchema(namedelivery_days, descriptionHow many days did it take for the product to arrive? If this information \is not found, output -1.) price_value_schema ResponseSchema(nameprice_value, descriptionExtract any sentences about the value or price, and output them as a comma \separated Python list.)response_schemas [gift_schema, delivery_days_schema,price_value_schema]

   构造转换器

   output_parser StructuredOutputParser.from_response_schemas(response_schemas) format_instructions output_parser.get_format_instructions() print(format_instructions)输出如下 The output should be a markdown code snippet formatted in the following schema, including the leading and trailing json and :json {gift: string // Was the item purchased as a gift for someone else? Answer True if yes, False if not or unknown.delivery_days: string // How many days did it take for the product to arrive? If this information is not found, output -1.price_value: string // Extract any sentences about the value or price, and output them as a comma separated Python list. }LLM会根据构造的prompt进行回答生成最终的回答结果。接着构造完整的prompt review_template_2
   For the following text, extract the following information:gift: Was the item purchased as a gift for someone else?
   Answer True if yes, False if not or unknown.delivery_days: How many days did it take for the product
   to arrive? If this information is not found, output -1.price_value: Extract any sentences about the value or price,
   and output them as a comma separated Python list.text: {text}{format_instructions} prompt ChatPromptTemplate.from_template(templatereview_template_2) messages prompt.format_messages(textcustomer_review, format_instructionsformat_instructions) print(messages[0].content)输出如下 For the following text, extract the following information:gift: Was the item purchased as a gift for someone else? Answer True if yes, False if not or unknown.delivery_days: How many days did it take for the productto arrive? If this information is not found, output -1.price_value: Extract any sentences about the value or price,and output them as a comma separated Python list.text: This leaf blower is pretty amazing. It has four settings:candle blower, gentle breeze, windy city, and tornado. It arrived in two days, just in time for my wifes anniversary present. I think my wife liked it so much she was speechless. So far Ive been the only one using it, and Ive been using it every other morning to clear the leaves on our lawn. Its slightly more expensive than the other leaf blowers out there, but I think its worth it for the extra features.The output should be a markdown code snippet formatted in the following schema, including the leading and trailing json and :json {gift: string // Was the item purchased as a gift for someone else? Answer True if yes, False if not or unknown.delivery_days: string // How many days did it take for the product to arrive? If this information is not found, output -1.price_value: string // Extract any sentences about the value or price, and output them as a comma separated Python list. }6.3 使用LLM解决问题 response chat(messages) print(response.content)输出如下 json {gift: True,delivery_days: 2,price_value: Its slightly more expensive than the other leaf blowers out there, but I think its worth it for the extra features. }进行格式转换 output_dict output_parser.parse(response.content) print(output_dict)输出如下 {gift: True, delivery_days: 2, price_value: Its slightly more expensive than the other leaf blowers out there, but I think its worth it for the extra features.}接下来查看输出类型 type(output_dict)输出如下 dict接下来就可以愉快的使用输出数据了。 总的来说langchain对于格式化输出和prompt构造拥有较好的效果可以很好使用。