import os
import openai
from dotenv import load_dotenv, find_dotenv

# 读取本地/项目的环境变量。

# find_dotenv()寻找并定位.env文件的路径
# load_dotenv()读取该.env文件，并将其中的环境变量加载到当前的运行环境中  
# 如果你设置的是全局的环境变量，这行代码则没有任何作用。
_ = load_dotenv(find_dotenv())

# 获取环境变量 OPENAI_API_KEY
openai.api_key = os.environ['OPENAI_API_KEY']

from openai import OpenAI
def get_completion(prompt, model="gpt-3.5-turbo"):
    
    messages = [{"role": "user", "content": prompt}]
    client = OpenAI()

    response = client.chat.completions.create(
        model=model,
        messages=messages,
        temperature=0, 
    )
    return response.choices[0].message.content

# 中文
get_completion("1+1是什么？")

'1+1等于2。'

# 英文
get_completion("What is 1+1?")

'1+1 equals 2.'

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!
"""

# 美式英语 + 平静、尊敬的语调
style = """American English \
in a calm and respectful tone
"""

# 要求模型根据给出的语调进行转化
prompt = f"""Translate the text \
that is delimited by triple backticks 
into a style that is {style}.
text: ```{customer_email}```
"""

print(prompt)

Translate the text that is delimited by triple backticks 
into a style that is American English in a calm and respectful tone
.
text: ```
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!
```

response = get_completion(prompt)

response

"Ah, I'm really frustrated that my blender lid flew off and splattered my kitchen walls with smoothie! And to make matters worse, the warranty doesn't cover the cost of cleaning up my kitchen. I could really use your help right now, friend."

# 普通话 + 平静、尊敬的语调
style = """正式普通话 \
用一个平静、尊敬的语调
"""

# 非正式用语
customer_email = """   
阿，我很生气，\
因为我的搅拌机盖掉了，\
把奶昔溅到了厨房的墙上！\
更糟糕的是，保修不包括打扫厨房的费用。\
我现在需要你的帮助，伙计！
"""

# 要求模型根据给出的语调进行转化
prompt = f"""把由三个反引号分隔的文本\
翻译成一种{style}风格。
文本: ```{customer_email}```
"""

print(prompt)

把由三个反引号分隔的文本翻译成一种正式普通话 用一个平静、尊敬的语调
风格。
文本: ```   
阿，我很生气，因为我的搅拌机盖掉了，把奶昔溅到了厨房的墙上！更糟糕的是，保修不包括打扫厨房的费用。我现在需要你的帮助，伙计！
```

response = get_completion(prompt)

response

'尊敬的先生/女士，\n\n我感到非常不安，因为我的搅拌机盖子掉了，导致奶昔溅到了厨房的墙上！更糟糕的是，保修不包括清洁厨房的费用。我现在需要您的帮助，朋友！感谢您的理解和支持。'

from langchain_community.chat_models.openai import ChatOpenAI

# 这里我们将参数temperature设置为0.0，从而减少生成答案的随机性。
# 如果你想要每次得到不一样的有新意的答案，可以尝试调整该参数。
chat = ChatOpenAI(temperature=0)
chat

prompt = f"""Translate the text \
that is delimited by triple backticks 
into a style that is {style}.
text: ```{customer_email}```
"""

template_string = """Translate the text \
that is delimited by triple backticks \
into a style that is {style}. \
text: ```{text}```
"""

from langchain.prompts.chat import ChatPromptTemplate
prompt_template = ChatPromptTemplate.from_template(template_string)

print(prompt_template.messages[0].prompt)

input_variables=['style', 'text'] template='Translate the text that is delimited by triple backticks into a style that is {style}. text: ```{text}```\n'

print(prompt_template.messages[0].prompt.input_variables)

['style', 'text']

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)

print(type(customer_messages))
print(type(customer_messages[0]))

<class 'list'>
<class 'langchain_core.messages.human.HumanMessage'>

print(customer_messages[0])

content="Translate 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 don't cover the cost of cleaning up me kitchen. I need yer help right now, matey!\n```\n"

# 中文提示
from langchain.prompts import ChatPromptTemplate

template_string = """把由三个反引号分隔的文本\
翻译成一种{style}风格。\
文本: ```{text}```
"""
prompt_template = ChatPromptTemplate.from_template(template_string)

customer_style = """正式普通话 \
用一个平静、尊敬的语气
"""

customer_email = """
阿，我很生气，\
因为我的搅拌机盖掉了，\
把奶昔溅到了厨房的墙上！\
更糟糕的是，保修不包括打扫厨房的费用。\
我现在需要你的帮助，伙计！
"""

customer_messages = prompt_template.format_messages(
                    style=customer_style,
                    text=customer_email)


print(customer_messages[0])

content='把由三个反引号分隔的文本翻译成一种正式普通话 用一个平静、尊敬的语气\n风格。文本: ```\n阿，我很生气，因为我的搅拌机盖掉了，把奶昔溅到了厨房的墙上！更糟糕的是，保修不包括打扫厨房的费用。我现在需要你的帮助，伙计！\n```\n'

customer_response = chat.invoke(customer_messages, temperature=0.0)

print(customer_response.content)

非常抱歉听到您的困扰。我理解您的不快，因为搅拌机盖掉了，导致奶昔溅到了厨房的墙上。更令人不安的是，保修不包括清洁厨房的费用。现在我需要您的帮助，朋友。让我们一起解决这个问题。感谢您的理解和支持。

print(customer_response.content)

非常抱歉听到您的困扰。我理解您的不快，因为搅拌机盖掉了，导致奶昔溅到了厨房的墙上。更令人不安的是，保修不包括清洁厨房的费用。现在我需要您的帮助，朋友。让我们一起解决这个问题。感谢您的理解和支持。

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)

把由三个反引号分隔的文本翻译成一种a polite tone that speaks in English Pirate风格。文本: ```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_response = chat(service_messages)
print(service_response.content)

/Users/lta/anaconda3/envs/cookbook/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `BaseChatModel.__call__` was deprecated in langchain-core 0.1.7 and will be removed in 0.3.0. Use invoke instead.
  warn_deprecated(

Ahoy there, me hearty customer! The warranty be not coverin' the cleanin' expenses for yer galley because 'tis yer own fault for misusin' yer blender by forgettin' to put the lid on afore startin' the blender. Tough luck, matey! Fare thee well!

service_reply = """嘿，顾客， \
保修不包括厨房的清洁费用， \
因为您在启动搅拌机之前 \
忘记盖上盖子而误用搅拌机, \
这是您的错。 \
倒霉！ 再见！
"""

service_style_pirate = """\
一个有礼貌的语气 \
使用正式的普通话 \
"""
service_messages = prompt_template.format_messages(
    style=service_style_pirate,
    text=service_reply)

print(service_messages[0].content)

把由三个反引号分隔的文本翻译成一种一个有礼貌的语气 使用正式的普通话 风格。文本: ```嘿，顾客， 保修不包括厨房的清洁费用， 因为您在启动搅拌机之前 忘记盖上盖子而误用搅拌机, 这是您的错。 倒霉！ 再见！
```

service_response = chat(service_messages)
print(service_response.content)

尊敬的顾客，根据我们的保修政策，很遗憾告知您，厨房清洁费用不在保修范围内。这是因为在使用搅拌机之前，您忘记盖上盖子而导致搅拌机误用，这属于您的责任范围。非常抱歉给您带来不便。祝您好运，再见。

# 英文版
prompt = """ Your task is to determine if the student's solution is correct or not

    To solve the problem do the following:
    - First, workout your own solution to the problem
    - Then compare your solution to the student's solution 
    and evaluate if the sudtent's solution is correct or not.
    ...
    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 acutal solution \
    just calculated:
    ```
    yes or no
    ```
    Student grade
    ```
    correct or incorrect
    ```
    
    Question:
    ```
    {question}
    ```
    Student's solution:
    ```
    {student's solution}
    ```
    Actual solution:
    
    """

# 中文版
prompt = """ 你的任务是判断学生的解决方案是正确的还是不正确的

要解决该问题，请执行以下操作：
 - 首先，制定自己的问题解决方案
 - 然后将您的解决方案与学生的解决方案进行比较
 并评估学生的解决方案是否正确。
...
使用下面的格式:

问题:
```
问题文本
```
学生的解决方案:
```
学生的解决方案文本
```
实际解决方案:
```
...
制定解决方案的步骤以及您的解决方案请参见此处
```
学生的解决方案和实际解决方案是否相同 \
只计算：
```
是或者不是
```
学生的成绩
```
正确或者不正确
```

问题:
```
{question}
```
学生的解决方案:
```
{student's solution}
```
实际解决方案:

"""

{
  "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)

input_variables=['text'] messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['text'], template='For the following text, extract the following information:\n\ngift: Was the item purchased as a gift for someone else? Answer True if yes, False if not or unknown.\n\ndelivery_days: How many days did it take for the product to arrive? If this information is not found, output -1.\n\nprice_value: Extract any sentences about the value or price,and output them as a comma separated Python list.\n\nFormat the output as JSON with the following keys:\ngift\ndelivery_days\nprice_value\n\ntext: {text}\n'))]

messages = prompt_template.format_messages(text=customer_review)

chat = ChatOpenAI(temperature=0.0)
response = chat(messages)
print(response.content)

{
    "gift": true,
    "delivery_days": 2,
    "price_value": ["It's slightly more expensive than the other leaf blowers out there"]
}

type(response.content)

str

response.content.get('gift')

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[43], line 1
----> 1 response.content.get('gift')

AttributeError: 'str' object has no attribute 'get'

from langchain.prompts import ChatPromptTemplate

customer_review = """\
这款吹叶机非常神奇。 它有四个设置：\
吹蜡烛、微风、风城、龙卷风。 \
两天后就到了，正好赶上我妻子的\
周年纪念礼物。 \
我想我的妻子会喜欢它到说不出话来。 \
到目前为止，我是唯一一个使用它的人，而且我一直\
每隔一天早上用它来清理草坪上的叶子。 \
它比其他吹叶机稍微贵一点，\
但我认为它的额外功能是值得的。
"""

review_template = """\
对于以下文本，请从中提取以下信息：

礼物：该商品是作为礼物送给别人的吗？ \
如果是，则回答 是的；如果否或未知，则回答 不是。

交货天数：产品需要多少天\
到达？ 如果没有找到该信息，则输出-1。

价钱：提取有关价值或价格的任何句子，\
并将它们输出为逗号分隔的 Python 列表。

使用以下键将输出格式化为 JSON：
礼物
交货天数
价钱

文本: {text}
"""

prompt_template = ChatPromptTemplate.from_template(review_template)
print(prompt_template)

input_variables=['text'] messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['text'], template='对于以下文本，请从中提取以下信息：\n\n礼物：该商品是作为礼物送给别人的吗？ 如果是，则回答 是的；如果否或未知，则回答 不是。\n\n交货天数：产品需要多少天到达？ 如果没有找到该信息，则输出-1。\n\n价钱：提取有关价值或价格的任何句子，并将它们输出为逗号分隔的 Python 列表。\n\n使用以下键将输出格式化为 JSON：\n礼物\n交货天数\n价钱\n\n文本: {text}\n'))]

messages = prompt_template.format_messages(text=customer_review)

chat = ChatOpenAI(temperature=0.0)
response = chat(messages)
print(response.content)

{
    "礼物": "是的",
    "交货天数": 2,
    "价钱": ["它比其他吹叶机稍微贵一点"]
}

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)

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)

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.
}
```

messages = prompt.format_messages(text=customer_review, format_instructions=format_instructions)

print(messages[0].content)

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: 这款吹叶机非常神奇。 它有四个设置：吹蜡烛、微风、风城、龙卷风。 两天后就到了，正好赶上我妻子的周年纪念礼物。 我想我的妻子会喜欢它到说不出话来。 到目前为止，我是唯一一个使用它的人，而且我一直每隔一天早上用它来清理草坪上的叶子。 它比其他吹叶机稍微贵一点，但我认为它的额外功能是值得的。


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.
}
```

response = chat(messages)
print(response.content)

```json
{
	"gift": true,
	"delivery_days": 2,
	"price_value": "它比其他吹叶机稍微贵一点"
}
```

output_dict = output_parser.parse(response.content)
output_dict

{'gift': True, 'delivery_days': 2, 'price_value': '它比其他吹叶机稍微贵一点'}

type(output_dict)

dict

output_dict.get('delivery_days')

2

# 中文
review_template_2 = """\
对于以下文本，请从中提取以下信息：：

礼物：该商品是作为礼物送给别人的吗？
如果是，则回答 是的；如果否或未知，则回答 不是。

交货天数：产品到达需要多少天？ 如果没有找到该信息，则输出-1。

价钱：提取有关价值或价格的任何句子，并将它们输出为逗号分隔的 Python 列表。

文本: {text}

{format_instructions}
"""

from langchain.output_parsers import ResponseSchema
from langchain.output_parsers import StructuredOutputParser

gift_schema = ResponseSchema(name="礼物",
                             description="这件物品是作为礼物送给别人的吗？\
                            如果是，则回答 是的，\
                            如果否或未知，则回答 不是。")

delivery_days_schema = ResponseSchema(name="交货天数",
                                      description="产品需要多少天才能到达？\
                                      如果没有找到该信息，则输出-1。")

price_value_schema = ResponseSchema(name="价钱",
                                    description="提取有关价值或价格的任何句子，\
                                    并将它们输出为逗号分隔的 Python 列表")


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)

The output should be a markdown code snippet formatted in the following schema, including the leading and trailing "```json" and "```":

```json
{
	"礼物": string  // 这件物品是作为礼物送给别人的吗？                            如果是，则回答 是的，                            如果否或未知，则回答 不是。
	"交货天数": string  // 产品需要多少天才能到达？                                      如果没有找到该信息，则输出-1。
	"价钱": string  // 提取有关价值或价格的任何句子，                                    并将它们输出为逗号分隔的 Python 列表
}
```

messages = prompt.format_messages(text=customer_review, format_instructions=format_instructions)
print(messages[0].content)

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: 这款吹叶机非常神奇。 它有四个设置：吹蜡烛、微风、风城、龙卷风。 两天后就到了，正好赶上我妻子的周年纪念礼物。 我想我的妻子会喜欢它到说不出话来。 到目前为止，我是唯一一个使用它的人，而且我一直每隔一天早上用它来清理草坪上的叶子。 它比其他吹叶机稍微贵一点，但我认为它的额外功能是值得的。


The output should be a markdown code snippet formatted in the following schema, including the leading and trailing "```json" and "```":

```json
{
	"礼物": string  // 这件物品是作为礼物送给别人的吗？                            如果是，则回答 是的，                            如果否或未知，则回答 不是。
	"交货天数": string  // 产品需要多少天才能到达？                                      如果没有找到该信息，则输出-1。
	"价钱": string  // 提取有关价值或价格的任何句子，                                    并将它们输出为逗号分隔的 Python 列表
}
```

response = chat(messages)
print(response.content)

```json
{
	"礼物": "是的",
	"交货天数": "两天",
	"价钱": "它比其他吹叶机稍微贵一点"
}
```

output_dict = output_parser.parse(response.content)
output_dict

{'礼物': '是的', '交货天数': '两天', '价钱': '它比其他吹叶机稍微贵一点'}

from langchain.docstore.wikipedia import Wikipedia
from langchain_community.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, Tool, AgentExecutor
from langchain.agents.react.base import DocstoreExplorer

docstore=DocstoreExplorer(Wikipedia())
tools = [
  Tool(
    name="Search",
    func=docstore.search,
    description="Search for a term in the docstore.",
  ),
  Tool(
    name="Lookup",
    func=docstore.lookup,
    description="Lookup a term in the docstore.",
  )
]

# 使用大语言模型
llm = ChatOpenAI(
  model_name="gpt-3.5-turbo",
  temperature=0,
)

# 初始化ReAct代理
react = initialize_agent(tools, llm, agent="react-docstore", verbose=True)
agent_executor = AgentExecutor.from_agent_and_tools(
  agent=react.agent,
  tools=tools,
  verbose=True,
)


question = "Author David Chanoff has collaborated with a U.S. Navy admiral who served as the ambassador to the United Kingdom under which President?"
agent_executor.run(question)

/Users/lta/anaconda3/envs/cookbook/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The class `DocstoreExplorer` was deprecated in LangChain 0.1.0 and will be removed in 0.3.0
  warn_deprecated(
/Users/lta/anaconda3/envs/cookbook/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The function `initialize_agent` was deprecated in LangChain 0.1.0 and will be removed in 0.3.0. Use Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. instead.
  warn_deprecated(
/Users/lta/anaconda3/envs/cookbook/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `Chain.run` was deprecated in langchain 0.1.0 and will be removed in 0.3.0. Use invoke instead.
  warn_deprecated(


> Entering new AgentExecutor chain...
Thought: I need to search David Chanoff, find the U.S. Navy admiral he collaborated with, and then find out which President the admiral served as the ambassador to the United Kingdom under.
Action: Search[David Chanoff]
Observation: David Chanoff is a noted author of non-fiction work. His work has typically involved collaborations with the principal protagonist of the work concerned. His collaborators have included; Augustus A. White, Joycelyn Elders, Đoàn Văn Toại, William J. Crowe, Ariel Sharon, Kenneth Good and Felix Zandman. He has also written about a wide range of subjects including literary history, education and foreign for The Washington Post, The New Republic and The New York Times Magazine. He has published more than twelve books.
Thought:David Chanoff has collaborated with U.S. Navy admiral William J. Crowe. I need to search William J. Crowe and find out which President he served as the ambassador to the United Kingdom under.
Action: Search[William J. Crowe]
Observation: William James Crowe Jr. (January 2, 1925 – October 18, 2007) was a United States Navy admiral and diplomat who served as the 11th chairman of the Joint Chiefs of Staff under Presidents Ronald Reagan and George H. W. Bush, and as the ambassador to the United Kingdom and Chair of the Intelligence Oversight Board under President Bill Clinton.
Thought:William J. Crowe served as the ambassador to the United Kingdom under President Bill Clinton. So the answer is Bill Clinton. 
Action: Finish[Bill Clinton]

> Finished chain.

'Bill Clinton'

第二章模型，提示和输出解释器¶

一、设置OpenAI API Key¶

二、直接使用OpenAI¶

2.1 计算1+1¶

2.2 用美式英语表达海盗邮件¶

2.3 中文版¶

三、通过LangChain使用OpenAI¶

3.1 模型¶

3.2 提示模板¶

3.2.1 使用LangChain提示模版¶

1️⃣ 构造提示模版字符串¶

2️⃣ 构造LangChain提示模版¶

3️⃣ 使用模版得到客户消息提示¶

4️⃣ 调用chat模型转换客户消息风格¶

5️⃣ 使用模版得到回复消息提示¶

6️⃣ 调用chat模型转换回复消息风格¶

3.2.2 中文版¶

3.2.2 为什么需要提示模版¶

3.3 输出解析器¶

3.3.1 如果没有输出解析器¶

1️⃣ 构造提示模版字符串¶

2️⃣ 构造langchain提示模版¶

3️⃣ 使用模版得到提示消息¶

4️⃣ 调用chat模型提取信息¶

📝 分析与总结¶

3.3.2 中文版¶

3.3.3 LangChain输出解析器¶

1️⃣ 构造提示模版字符串¶

2️⃣ 构造langchain提示模版¶

🔥 构造输出解析器¶

3️⃣ 使用模版得到提示消息¶

4️⃣ 调用chat模型提取信息¶

5️⃣ 使用输出解析器解析输出¶

📝 分析与总结¶

3.3.4 中文版¶

四、补充材料¶

4.1 链式思考推理(ReAct)¶

第二章 模型，提示和输出解释器¶

一、设置OpenAI API Key¶

二、直接使用OpenAI¶

2.1 计算1+1¶

2.2 用美式英语表达海盗邮件¶

2.3 中文版¶

三、通过LangChain使用OpenAI¶

3.1 模型¶

3.2 提示模板¶

3.2.1 使用LangChain提示模版¶

1️⃣ 构造提示模版字符串¶

2️⃣ 构造LangChain提示模版¶

3️⃣ 使用模版得到客户消息提示¶

4️⃣ 调用chat模型转换客户消息风格¶

5️⃣ 使用模版得到回复消息提示¶

6️⃣ 调用chat模型转换回复消息风格¶

3.2.2 中文版¶

3.2.2 为什么需要提示模版¶

3.3 输出解析器¶

3.3.1 如果没有输出解析器¶

1️⃣ 构造提示模版字符串¶

2️⃣ 构造langchain提示模版¶

3️⃣ 使用模版得到提示消息¶

4️⃣ 调用chat模型提取信息¶

📝 分析与总结¶

3.3.2 中文版¶

3.3.3 LangChain输出解析器¶

1️⃣ 构造提示模版字符串¶

2️⃣ 构造langchain提示模版¶

🔥 构造输出解析器¶

3️⃣ 使用模版得到提示消息¶

4️⃣ 调用chat模型提取信息¶

5️⃣ 使用输出解析器解析输出¶

📝 分析与总结¶

3.3.4 中文版¶

四、补充材料¶

4.1 链式思考推理(ReAct)¶

第二章模型，提示和输出解释器¶