【LangChain系列】第七篇：工作流（链）简介及实践 - AI创想

from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain
# Initialize the language model
llm_model = "gpt-3.5-turbo"
llm = ChatOpenAI(temperature=0.9, model=llm_model)
# Create a prompt template
prompt = ChatPromptTemplate.from_template("What is the best name to describe a company that makes {product}?")
# Combine the LLM and prompt into an LLMChain
chain = LLMChain(llm=llm, prompt=prompt)
# Run the chain on some input data
product = "Queen Size Sheet Set"
chain_output = chain.invoke({
product})
print(chain_output)
# Output
# {'product': {'Queen Size Sheet Set'}, 'text': 'Royal Slumber Bedding Co.'}

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from langchain.chains import SimpleSequentialChain
# Initialize the language model
llm = ChatOpenAI(temperature=0.9, model=llm_model)
# Prompt template 1: Suggest a company name
first_prompt = ChatPromptTemplate.from_template("What is the best name to describe a company that makes {product}?")
chain_one = LLMChain(llm=llm, prompt=first_prompt)
# Prompt template 2: Generate a company description
second_prompt = ChatPromptTemplate.from_template("Write a 20-word description for the following company: {company_name}")
chain_two = LLMChain(llm=llm, prompt=second_prompt)
# Create the SimpleSequentialChain
overall_simple_chain = SimpleSequentialChain(chains=[chain_one, chain_two], verbose=True)
# Run the chain on some input data
product = "Queen Size Sheet Set"
chain_output = overall_simple_chain.invoke(product)
print(chain_output)
# Output
# {'input': 'Queen Size Sheet Set',
# 'output': 'Regal Comfort Linens provides luxurious and stylish bedding options to ensure a comfortable and elegant sleep experience for customers.'}

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from langchain.chains import SequentialChain
# Initialize the language model
llm = ChatOpenAI(temperature=0.9, model=llm_model)
# Prompt template 1: Translate review to English
first_prompt = ChatPromptTemplate.from_template("Translate the following review to English:\n\n{Review}")
chain_one = LLMChain(llm=llm, prompt=first_prompt, output_key="English_Review")
# Prompt template 2: Summarize the review in one sentence
second_prompt = ChatPromptTemplate.from_template("Can you summarize the following review in 1 sentence:\n\n{English_Review}")
chain_two = LLMChain(llm=llm, prompt=second_prompt, output_key="summary")
# Prompt template 3: Detect the language of the review
third_prompt = ChatPromptTemplate.from_template("What language is the following review:\n\n{Review}")
chain_three = LLMChain(llm=llm, prompt=third_prompt, output_key="language")
# Prompt template 4: Generate a follow-up response
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_four = LLMChain(llm=llm, prompt=fourth_prompt, output_key="followup_message")
# Create the SequentialChain
overall_chain = SequentialChain(
chains=[chain_one, chain_two, chain_three, chain_four],
input_variables=["Review"],
output_variables=["English_Review", "summary", "followup_message"],
verbose=True,
)
# Run the chain on some input data
review = "Je trouve le goût médiocre. La mousse ne tient pas, c'est bizarre. J'achète les mêmes dans le commerce et le goût est bien meilleur...\nVieux lot ou contrefaçon !?"
chain_output = overall_chain.invoke(review)
print(chain_output)
# Output
# Entering new SequentialChain chain...
# Finished chain.
# {'English_Review': "I find the taste mediocre. The foam doesn't hold, it's "
# 'weird. I buy the same ones in stores and the taste is much '
# 'better... \n'
# 'Old batch or counterfeit!?',
# 'Review': "Je trouve le goût médiocre. La mousse ne tient pas, c'est bizarre. "
# "J'achète les mêmes dans le commerce et le goût est bien "
# 'meilleur...\n'
# 'Vieux lot ou contrefaçon !?',
# 'followup_message': "Je suis désolé(e) d'apprendre que vous avez trouvé le "
# "goût du produit médiocre. Il est possible qu'il s'agisse "
# "d'un lot ancien ou contrefait, comme vous l'avez "
# "suggéré. Il est important de s'assurer de la qualité des "
# 'produits que nous consommons. Avez-vous envisagé de '
# 'contacter le fabricant pour clarifier la situation ? '
# "J'espère que votre prochaine expérience d'achat sera "
# 'plus satisfaisante. Merci de partager votre avis.',
# 'summary': 'The reviewer found the taste of the product mediocre and '
# 'different from what they usually buy in stores, suggesting that '
# 'it may be an old batch or counterfeit.'}

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from langchain.chains.router import MultiPromptChain
from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.prompts import PromptTemplate
# Define specialized prompt templates
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}"""
# Create prompt info dictionaries
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,
},
]
# Initialize the language model
llm = ChatOpenAI(temperature=0, model=llm_model)
# Create destination chains (LLMChains) for each prompt
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
# Define a default chain for inputs that don't match any specialized prompt
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 string snippet enclosed by triple backticks a JSON object formatted to look like below:
{
"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}
REMEMBER: If destination name not there input the question
<< INPUT >>
{input}
<< OUTPUT (remember to include the ```
json
```)>>"""
# Create the router prompt template
router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(destinations=destinations_str) # (a prompt template for the router to use)
router_prompt = PromptTemplate(template=router_template, input_variables=["input"], output_parser=RouterOutputParser())
router_chain = LLMRouterChain.from_llm(llm, router_prompt)
# Create the MultiPromptChain
chain = MultiPromptChain(
router_chain=router_chain,
destination_chains=destination_chains,
default_chain=default_chain,
verbose=True,
)
# Run the chain on some input data
physics_question = "What is black body radiation?"
chain_output = chain.invoke(physics_question)
print(chain_output)
# Output
# Entering new MultiPromptChain chain...
# physics: {'input': 'What is black body radiation?'}
# Finished chain.
# {'input': 'What is black body radiation?',
# 'text': "Black body radiation is the electromagnetic radiation emitted by a perfect absorber of radiation, known as a black body. A black body absorbs all incoming radiation and emits radiation across the entire electromagnetic spectrum. The spectrum of black body radiation is continuous and follows a specific distribution known as Planck's law. This phenomenon is important in understanding the behavior of objects at different temperatures and is a key concept in the field of thermal physics."}
math_question = "what is 2 + 2"
chain_output = chain.invoke(math_question )
print(chain_output)
# Output
# Entering new MultiPromptChain chain...
# math: {'input': 'what is 2 + 2'}
# Finished chain.
# {'input': 'what is 2 + 2', 'text': 'The answer to 2 + 2 is 4.'}
biology_question = "Why does every cell in our body contain DNA?"
chain_output = chain.invoke(biology_question )
print(chain_output)
# Output
# Entering new MultiPromptChain chain...
# None: {'input': 'Why does every cell in our body contain DNA?'}
# Finished chain.
# {'input': 'Why does every cell in our body contain DNA?',
# 'text': 'Every cell in our body contains DNA because DNA carries the genetic information that determines the characteristics and functions of an organism. DNA contains the instructions for building and maintaining an organism, including the proteins that are essential for cell function and structure. This genetic information is passed down from parent to offspring and is essential for the growth, development, and functioning of all cells in the body. Having DNA in every cell ensures that the genetic information is preserved and can be used to carry out the necessary processes for life.'}

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