该篇是吴恩达关于基于 LangChain 的大语言模型应用开发系列教程的第四部分，主要介绍 LangChain 中的链（Chain），包括多种链类型的概念、使用场景和具体操作示例。

1.链的概念：链通常将大型语言模型（LLM）与提示相结合，能对文本或其他数据执行一系列操作，可同时处理多个输入。在LangChain中，链是一系列模型，它们被连接在一起以完成一个特定的目标。聊天机器人应用程序的链实例可能涉及使用LLM来理解用户输入，使用内存组件来存储过去的交互，以及使用决策组件来创建相关响应。
LangChain的chain模块是其框架中用于构建智能对话和任务式应用的核心组件之一，主要负责流程控制和数据传递。以下是chain模块的一些详细介绍：
流程控制：Chains是LangChain中的核心流程控制单元，它们负责串联不同的组件和步骤，定义应用程序的执行逻辑。
数据传递：Chains可以传递上下文和数据，使得不同的模块之间能够共享信息。
组合与嵌套：Chains支持嵌套和组合，可以构建复杂的流程，例如顺序执行、条件判断和循环等。
可重用性：Chains可以被定义为可重用的模块，在不同的应用场景中复用。
灵活性：LangChain支持多种类型的Chains，如简单链、索引链、对话链等，以满足不同的需求。

链的创建与组合：

单一链：开发者可以创建一个包含特定功能的单一链，例如文本预处理、模型推理等。
自定义链：利用内置的基础链类，开发者可以自定义链的输入、输出和处理逻辑。
顺序组合：将多个链按照执行顺序串联起来，前一个链的输出作为下一个链的输入。
并行组合：同时执行多个链，将它们的输出合并或选择性地使用。
嵌套链：在一个链的内部调用另一个链，实现更复杂的流程控制

核心链类型：

LLMChain：与大型语言模型（LLMs）直接交互的链，用于生成和理解自然语言
SimpleSequentialChain：一个简单的顺序执行链，用于按顺序执行一系列步骤
SequentialChain：一个顺序链，可以包含多个步骤，每个步骤可以是另一个链
RouterChain：用于智能路由决策，根据输入决定执行哪个链
TransformChain：用于数据处理，可以对输入数据进行转换或处理
通过这些链的组合和嵌套，LangChain框架能够实现复杂的自然语言处理应用程序，提供高度的扩展性和可维护性

2.LLM 链：是基础且强大的链类型。需导入 OpenAI 模型、聊天提示模板和 LLM 链；初始化语言模型和提示，将两者结合形成链。以产品名称生成公司名称为例，输入产品描述，通过链的运行，可得到对应公司名称。
3.顺序链 Sequential Chains

• 简单序列链 （SimpleSequentialChain）：用于按顺序运行一系列链，每个子链只接受一个输入并返回一个输出。如先根据产品生成公司名称，再根据公司名称生成描述，前一个链的输出作为后一个链的输入。

import warnings
warnings.filterwarnings('ignore')
import os
from langchain.chat_models import ChatOpenAI # model
from langchain.prompts import ChatPromptTemplate # prompt
from langchain.chains import LLMChain # chain
llm = ChatOpenAI(
    openai_api_base="https://dashscope.aliyuncs.com/compatible-mode/v1",
    openai_api_key=os.getenv("DASHSCOPE_API_KEY"),
    model_name="qwen-plus",	# 模型名称
    temperature=0.9
)
import pandas as pd
df = pd.read_csv('Data.csv')
df.head()

prompt = ChatPromptTemplate.from_template(
    "What is the best name to describe \
    a company that makes {product}?"
)
chain = LLMChain(llm=llm, prompt=prompt)
product = "Queen Size Sheet Set"
chain.run(product)
chain.invoke(product)

from langchain.chains import SimpleSequentialChain
llm = ChatOpenAI(
    openai_api_base="https://dashscope.aliyuncs.com/compatible-mode/v1",
    openai_api_key=os.getenv("DASHSCOPE_API_KEY"),
    model_name="qwen-plus",	# 模型名称
    temperature=0.9
)
# prompt template 1
first_prompt = ChatPromptTemplate.from_template(
    "What is the best name to describe \
    a company that makes {product}?"
)

# Chain 1
chain_one = LLMChain(llm=llm, prompt=first_prompt)

# prompt template 2
second_prompt = ChatPromptTemplate.from_template(
    "Write a 20 words description for the following \
    company:{company_name}"
)
# chain 2
chain_two = LLMChain(llm=llm, prompt=second_prompt)
overall_simple_chain = SimpleSequentialChain(chains=[chain_one, chain_two],
                                             verbose=True
                                            )
                                            
overall_simple_chain.invoke(product)

普通序列链（SequentialChain）：可处理多个输入和输出的情况。创建多个链，如翻译评论、总结评论、检测评论语言、根据总结和语言生成回应，要确保各链输入键和输出键精确匹配。运行时，输入评论数据，可得到翻译后的评论、评论摘要以及用原始语言生成的回应等结果。

from langchain.chains import SequentialChain
llm = ChatOpenAI(
    openai_api_base="https://dashscope.aliyuncs.com/compatible-mode/v1",
    openai_api_key=os.getenv("DASHSCOPE_API_KEY"),
    model_name="qwen-plus",	# 模型名称
    temperature=0.9
)

# prompt template 1: translate to english
first_prompt = ChatPromptTemplate.from_template(
    "Translate the following review to english:"
    "\n\n{Review}"
)
# chain 1: input= Review and output= English_Review
chain_one = LLMChain(llm=llm, prompt=first_prompt, 
                     output_key="English_Review"
                    )
second_prompt = ChatPromptTemplate.from_template(
    "Can you summarize the following review in 1 sentence:"
    "\n\n{English_Review}"
)
# chain 2: input= English_Review and output= summary
chain_two = LLMChain(llm=llm, prompt=second_prompt, 
                     output_key="summary"
                    )
                    
# prompt template 3: translate to english
third_prompt = ChatPromptTemplate.from_template(
    "What language is the following review:\n\n{Review}"
)
# chain 3: input= Review and output= language
chain_three = LLMChain(llm=llm, prompt=third_prompt,
                       output_key="language"
                      )

# prompt template 4: follow up message
fourth_prompt = ChatPromptTemplate.from_template(
    "Write a follow up response to the following "
    "summary in the specified language:"
    "\n\nSummary: {summary}\n\nLanguage: {language}"
)
# chain 4: input= summary, language and output= followup_message
chain_four = LLMChain(llm=llm, prompt=fourth_prompt,
                      output_key="followup_message"
                     )
                     
# overall_chain: input= Review 
# and output= English_Review,summary, followup_message
overall_chain = SequentialChain(
    chains=[chain_one, chain_two, chain_three, chain_four],
    input_variables=["Review"],
    output_variables=["English_Review", "summary","language","followup_message"],
    verbose=True
)

review = df.Review[5]
overall_chain(review)

4.路由链（多提示链）（Router Chain）：适用于根据输入内容将其路由到特定子链的场景。定义不同主题的提示模板，导入多提示链、LLM 路由器链和路由输出解析器；创建目的地链和默认链，定义路由模板，构建路由器链并组合形成总体链。提问时，若问题属于特定主题（如物理、数学等），会被路由到相应子链；若不属于任何子链相关主题，则会被传递到默认链。

physics_template = """You are a very smart physics professor. \
You are great at answering questions about physics in a concise\
and easy to understand manner. \
When you don't know the answer to a question you admit\
that you don't know.

Here is a question:
{input}"""


math_template = """You are a very good mathematician. \
You are great at answering math questions. \
You are so good because you are able to break down \
hard problems into their component parts, 
answer the component parts, and then put them together\
to answer the broader question.

Here is a question:
{input}"""

history_template = """You are a very good historian. \
You have an excellent knowledge of and understanding of people,\
events and contexts from a range of historical periods. \
You have the ability to think, reflect, debate, discuss and \
evaluate the past. You have a respect for historical evidence\
and the ability to make use of it to support your explanations \
and judgements.

Here is a question:
{input}"""


computerscience_template = """ You are a successful computer scientist.\
You have a passion for creativity, collaboration,\
forward-thinking, confidence, strong problem-solving capabilities,\
understanding of theories and algorithms, and excellent communication \
skills. You are great at answering coding questions. \
You are so good because you know how to solve a problem by \
describing the solution in imperative steps \
that a machine can easily interpret and you know how to \
choose a solution that has a good balance between \
time complexity and space complexity. 

Here is a question:
{input}"""

prompt_infos = [
    {
        "name": "physics", 
        "description": "Good for answering questions about physics", 
        "prompt_template": physics_template
    },
    {
        "name": "math", 
        "description": "Good for answering math questions", 
        "prompt_template": math_template
    },
    {
        "name": "History", 
        "description": "Good for answering history questions", 
        "prompt_template": history_template
    },
    {
        "name": "computer science", 
        "description": "Good for answering computer science questions", 
        "prompt_template": computerscience_template
    }
]

from langchain.chains.router import MultiPromptChain
from langchain.chains.router.llm_router import LLMRouterChain,RouterOutputParser
from langchain.prompts import PromptTemplate
llm = ChatOpenAI(
    openai_api_base="https://dashscope.aliyuncs.com/compatible-mode/v1",
    openai_api_key=os.getenv("DASHSCOPE_API_KEY"),
    model_name="qwen-plus",    # 模型名称
    temperature=0.9
)
destination_chains = {}
for p_info in prompt_infos:
    name = p_info["name"]
    prompt_template = p_info["prompt_template"]
    prompt = ChatPromptTemplate.from_template(template=prompt_template)
    chain = LLMChain(llm=llm, prompt=prompt)
    destination_chains[name] = chain  
    
destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
destinations_str = "\n".join(destinations)

default_prompt = ChatPromptTemplate.from_template("{input}")
default_chain = LLMChain(llm=llm, prompt=default_prompt)

MULTI_PROMPT_ROUTER_TEMPLATE = """Given a raw text input to a \
language model select the model prompt best suited for the input. \
You will be given the names of the available prompts and a \
description of what the prompt is best suited for. \
You may also revise the original input if you think that revising\
it will ultimately lead to a better response from the language model.

<< FORMATTING >>
Return a markdown code snippet with a JSON object formatted to look like:
“”“
{{{{
    "destination": string \ name of the prompt to use or "DEFAULT"
    "next_inputs": string \ a potentially modified version of the original input
}}}}
”“”

REMEMBER: "destination" MUST be one of the candidate prompt \
names specified below OR it can be "DEFAULT" if the input is not\
well suited for any of the candidate prompts.
REMEMBER: "next_inputs" can just be the original input \
if you don't think any modifications are needed.

<< CANDIDATE PROMPTS >>
{destinations}

<< INPUT >>
{{input}}

<< OUTPUT (remember to wrap the output with ```json (output)```)>>"""

router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(
    destinations=destinations_str
)
router_prompt = PromptTemplate(
    template=router_template,
    input_variables=["input"],
    output_parser=RouterOutputParser(),
)

router_chain = LLMRouterChain.from_llm(llm, router_prompt)

chain = MultiPromptChain(router_chain=router_chain, 
                         destination_chains=destination_chains, 
                         default_chain=default_chain, verbose=True
                        )
                        
chain.invoke("What is black body radiation?")
chain.invoke("what is 2 + 2")
chain.invoke("Why does every cell in our body contain DNA?")