获取openapikey

#!pip install python-dotenv

#!pip install openai

import osimport openai
​
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
openai.api_key = os.environ['OPENAI_API_KEY']

# account for deprecation of LLM model
import datetime
# Get the current date
current_date = datetime.datetime.now().date()
​
# Define the date after which the model should be set to "gpt-3.5-turbo"
target_date = datetime.date(2024, 6, 12)
​
# Set the model variable based on the current date
if current_date > target_date:llm_model = "gpt-3.5-turbo"
else:llm_model = "gpt-3.5-turbo-0301"

Chat API : OpenAI

调用openapi

def get_completion(prompt, model=llm_model):messages = [{"role": "user", "content": prompt}]response = openai.ChatCompletion.create(model=model,messages=messages,temperature=0, )return response.choices[0].message["content"]
​get_completion("What is 1+1?")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 don't cover the cost of \
cleaning up me kitchen. I need yer help \
right now, matey!
"""
style = """American English \
in a calm and respectful tone
"""prompt = f"""Translate the text \
that is delimited by triple backticks 
into a style that is {style}.
text: ```{customer_email}```
"""
​
print(prompt)
response = get_completion(prompt)response

deepseek

import requests
import json# DeepSeek API 配置
DEEPSEEK_API_KEY = "替换为您的实际API密钥"  # 替换为您的实际API密钥
DEEPSEEK_API_URL = "https://api.deepseek.com/v1/chat/completions"def get_deepseek_completion(prompt, model="deepseek-chat"):"""使用 DeepSeek API 获取回复"""headers = {"Authorization": f"Bearer {DEEPSEEK_API_KEY}","Content-Type": "application/json"}payload = {"model": model,"messages": [{"role": "user", "content": prompt}],"temperature": 0}try:response = requests.post(DEEPSEEK_API_URL, headers=headers, json=payload)response.raise_for_status()  # 检查HTTP错误result = response.json()return result['choices'][0]['message']['content']except requests.exceptions.RequestException as e:print(f"API请求错误: {e}")return Noneexcept (KeyError, IndexError) as e:print(f"解析响应错误: {e}")return Nonecustomer_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 don't cover the cost of \
cleaning up me kitchen. I need yer help \
right now, matey!
"""style = """American English \
in a calm and respectful tone
"""prompt = f"""Translate the text \
that is delimited by triple backticks 
into a style that is {style}.
text: ```{customer_email}```
"""# 获取并打印翻译结果
translation = get_deepseek_completion(prompt)
if translation:print("\n翻译结果:")print(translation)

OpenAI 方式​​	​​DeepSeek 方式​​
openai.ChatCompletion.create()	requests.post()发送HTTP请求
直接返回对象	解析JSON响应

LangChain+openapi

Let's try how we can do the same using LangChain.

#!pip install --upgrade langchain

from langchain.chat_models import ChatOpenAI# To control the randomness and creativity of the generated
# text by an LLM, use temperature = 0.0
chat = ChatOpenAI(temperature=0.0, model=llm_model)template_string = """Translate the text \
that is delimited by triple backticks \
into a style that is {style}. \
text: ```{text}```
"""from langchain.prompts import ChatPromptTemplate
​
prompt_template = ChatPromptTemplate.from_template(template_string)customer_style = """American English \
in a calm and respectful tone
"""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 don't cover the cost of \
cleaning up me kitchen. I need yer help \
right now, matey!
"""customer_messages = prompt_template.format_messages(style=customer_style,text=customer_email)# Call the LLM to translate to the style of the customer message
customer_response = chat(customer_messages)
print(customer_response.content)service_reply = """Hey there customer, \
the warranty does not cover \
cleaning expenses for your kitchen \
because it's 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\
"""service_messages = prompt_template.format_messages(style=service_style_pirate,text=service_reply)
​
print(service_messages[0].content)
service_response = chat(service_messages)
print(service_response.content)

Langchain+deepseek

from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate# 配置 DeepSeek AI
DEEPSEEK_API_KEY = " # 替换为您的实际API密钥" 
DEEPSEEK_API_BASE = "https://api.deepseek.com/v1"# 创建 DeepSeek 聊天模型
chat = ChatOpenAI(temperature=0.0,model="deepseek-chat",  # DeepSeek 模型名称openai_api_key=DEEPSEEK_API_KEY,openai_api_base=DEEPSEEK_API_BASE
)# 翻译模板
template_string = """Translate the text \
that is delimited by triple backticks \
into a style that is {style}. \
text: ```{text}```
"""# 创建提示模板
prompt_template = ChatPromptTemplate.from_template(template_string)# 第一部分：客户邮件翻译
customer_style = """American English in a calm and respectful tone"""
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 don't cover the cost of \
cleaning up me kitchen. I need yer help \
right now, matey!
"""# 格式化消息
customer_messages = prompt_template.format_messages(style=customer_style,text=customer_email
)# 调用 DeepSeek 翻译
customer_response = chat(customer_messages)
print("客户邮件翻译结果:")
print(customer_response.content)
print("\n" + "-"*50 + "\n")# 第二部分：服务回复翻译
service_reply = """Hey there customer, \
the warranty does not cover \
cleaning expenses for your kitchen \
because it's 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"""# 格式化消息
service_messages = prompt_template.format_messages(style=service_style_pirate,text=service_reply
)# 打印格式化后的提示
print("服务回复提示内容:")
print(service_messages[0].content)
print("\n" + "-"*50 + "\n")# 调用 DeepSeek 翻译
service_response = chat(service_messages)
print("服务回复翻译结果:")
print(service_response.content)

Output Parsers

{"gift": False,"delivery_days": 5,"price_value": "pretty affordable!"
}
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 wife's \
anniversary present. \
I think my wife liked it so much she was speechless. \
So far I've been the only one using it, and I've been \
using it every other morning to clear the leaves on our lawn. \
It's slightly more expensive than the other leaf blowers \
out there, but I think it's worth it for the extra features.
"""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 ChatPromptTemplateprompt_template = ChatPromptTemplate.from_template(review_template)
print(prompt_template)
messages = prompt_template.format_messages(text=customer_review)
chat = ChatOpenAI(temperature=0.0, model=llm_model)
response = chat(messages)
print(response.content)
type(response.content)
# You will get an error by running this line of code 
# because'gift' is not a dictionary
# 'gift' is a string
response.content.get('gift')

Parse the LLM output string into a Python dictionary

from langchain.output_parsers import ResponseSchema
from langchain.output_parsers import StructuredOutputParser
gift_schema = ResponseSchema(name="gift",description="Was the item purchased\as a gift for someone else? \Answer True if yes,\False if not or unknown.")
delivery_days_schema = ResponseSchema(name="delivery_days",description="How many days\did it take for the product\to arrive? If this \information is not found,\output -1.")
price_value_schema = ResponseSchema(name="price_value",description="Extract 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]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
format_instructions = output_parser.get_format_instructions()
print(format_instructions)
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(template=review_template_2)messages = prompt.format_messages(text=customer_review, format_instructions=format_instructions)
print(messages[0].content)
response = chat(messages)
print(response.content)
output_dict = output_parser.parse(response.content)
type(output_dict)
output_dict.get('delivery_days')

参考：LangChain for LLM Application Development - DeepLearning.AI