获取openapikey
#!pip install python-dotenv
#!pip install openai
import os
import 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 None
except (KeyError, IndexError) as e:
print(f"解析响应错误: {e}")
return None
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}```
"""
# 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 |
|---|---|
|
|
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_value
text: {text}
"""
from langchain.prompts import ChatPromptTemplate
prompt_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