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LangChain之案例分析

simon
1
2025-10-16
1. 执行多个chain
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.chains import SimpleSequentialChain

# location 链
llm = OpenAI(temperature=1)
template = """Your job is to come up with a classic dish from the area that the users suggests.
% USER LOCATION
{user_location}

YOUR RESPONSE:
"""
prompt_template = PromptTemplate(input_variables=["user_location"], template=template)
location_chain = LLMChain(llm=llm, prompt=prompt_template)

# meal 链
template = """Given a meal, give a short and simple recipe on how to make that dish at home.
% MEAL
{user_meal}

YOUR RESPONSE:
"""
prompt_template = PromptTemplate(input_variables=["user_meal"], template=template)
meal_chain = LLMChain(llm=llm, prompt=prompt_template)

# 通过 SimpleSequentialChain 串联起来，第一个答案会被替换第二个中的user_meal，然后再进行询问
overall_chain = SimpleSequentialChain(chains=[location_chain, meal_chain], verbose=True)
review = overall_chain.run("Rome")
2. 结构化输出
from langchain.output_parsers import StructuredOutputParser, ResponseSchema
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI

llm = OpenAI(model_name="text-davinci-003")

# 告诉他我们生成的内容需要哪些字段，每个字段类型式啥
response_schemas = [
    ResponseSchema(name="bad_string", description="This a poorly formatted user input string"),
    ResponseSchema(name="good_string", description="This is your response, a reformatted response")
]

# 初始化解析器
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)

# 生成的格式提示符
# {
#	"bad_string": string  // This a poorly formatted user input string
#	"good_string": string  // This is your response, a reformatted response
#}
format_instructions = output_parser.get_format_instructions()

template = """
You will be given a poorly formatted string from a user.
Reformat it and make sure all the words are spelled correctly

{format_instructions}

% USER INPUT:
{user_input}

YOUR RESPONSE:
"""

# 将我们的格式描述嵌入到 prompt 中去，告诉 llm 我们需要他输出什么样格式的内容
prompt = PromptTemplate(
    input_variables=["user_input"],
    partial_variables={"format_instructions": format_instructions},
    template=template
)

promptValue = prompt.format(user_input="welcom to califonya!")
llm_output = llm(promptValue)

# 使用解析器进行解析生成的内容
output_parser.parse(llm_output)
3. 爬取网页并输出JSON数据
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains import LLMRequestsChain, LLMChain

llm = OpenAI(model_name="gpt-3.5-turbo", temperature=0)

template = """在 >>> 和 <<< 之间是网页的返回的HTML内容。
网页是新浪财经A股上市公司的公司简介。
请抽取参数请求的信息。

>>> {requests_result} <<<
请使用如下的JSON格式返回数据
{{
  "company_name":"a",
  "company_english_name":"b",
  "issue_price":"c",
  "date_of_establishment":"d",
  "registered_capital":"e",
  "office_address":"f",
  "Company_profile":"g"

}}
Extracted:"""

prompt = PromptTemplate(
    input_variables=["requests_result"],
    template=template
)

chain = LLMRequestsChain(llm_chain=LLMChain(llm=llm, prompt=prompt))
inputs = {
  "url": "https://vip.stock.finance.sina.com.cn/corp/go.php/vCI_CorpInfo/stockid/600519.phtml"
}

response = chain(inputs)
print(response['output'])
4. 自定义agent中所使用的工具
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.tools import BaseTool
from langchain.llms import OpenAI
from langchain import LLMMathChain, SerpAPIWrapper

llm = OpenAI(temperature=0)

# 初始化搜索链和计算链
search = SerpAPIWrapper()
llm_math_chain = LLMMathChain(llm=llm, verbose=True)

# 创建一个功能列表，指明这个 agent 里面都有哪些可用工具，agent 执行过程可以看必知概念里的 Agent 那张图
tools = [
    Tool(
        name = "Search",
        func=search.run,
        description="useful for when you need to answer questions about current events"
    ),
    Tool(
        name="Calculator",
        func=llm_math_chain.run,
        description="useful for when you need to answer questions about math"
    )
]

# 初始化 agent
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

# 执行 agent
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
5. 使用Memory实现一个带记忆的对话机器人
from langchain.memory import ChatMessageHistory
from langchain.chat_models import ChatOpenAI

chat = ChatOpenAI(temperature=0)

# 初始化 MessageHistory 对象
history = ChatMessageHistory()

# 给 MessageHistory 对象添加对话内容
history.add_ai_message("你好！")
history.add_user_message("中国的首都是哪里？")

# 执行对话
ai_response = chat(history.messages)
print(ai_response)
6. 使用 Hugging Face 模型

使用 Hugging Face 模型之前，需要先设置环境变量：

import os
os.environ['HUGGINGFACEHUB_API_TOKEN'] = ''

这里，使用Hugging Face 模型有两种方式：

  1. 使用在线的 Hugging Face 模型

  2. 下载 Hugging Face 模型后本地使用：适用于服务器无法联网或者需要本地微调模型的情况。

下面来详细介绍两种使用方法：

（1）使用在线的 Hugging Face 模型：

from langchain import PromptTemplate, HuggingFaceHub, LLMChain

template = """Question: {question}
Answer: Let's think step by step."""

prompt = PromptTemplate(template=template, input_variables=["question"])
llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature":0, "max_length":64})
llm_chain = LLMChain(prompt=prompt, llm=llm)

question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
print(llm_chain.run(question))

将 Hugging Face 模型直接拉到本地使用：

from langchain import PromptTemplate, LLMChain
from langchain.llms import HuggingFacePipeline
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSeq2SeqLM

model_id = 'google/flan-t5-large'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

pipe = pipeline(
    "text2text-generation",
    model=model,
    tokenizer=tokenizer,
    max_length=100
)

local_llm = HuggingFacePipeline(pipeline=pipe)
print(local_llm('What is the capital of France? '))


template = """Question: {question} Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])

llm_chain = LLMChain(prompt=prompt, llm=local_llm)
question = "What is the capital of England?"
print(llm_chain.run(question))

将模型拉到本地使用的好处：

  • 训练模型

  • 可以使用本地的 GPU

  • 有些模型无法在 Hugging Face 运行

7. 通过自然语言执行SQL命令

我们通过 SQLDatabaseToolkit 或者 SQLDatabaseChain 都可以实现执行SQL命令的操作：

from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI

db = SQLDatabase.from_uri("sqlite:///../notebooks/Chinook.db")
toolkit = SQLDatabaseToolkit(db=db)

agent_executor = create_sql_agent(
    llm=OpenAI(temperature=0),
    toolkit=toolkit,
    verbose=True
)

agent_executor.run("Describe the playlisttrack table")
8. 单次问答
import os
os.environ["OPENAI_API_KEY"] = '你的api key'

from langchain.llms import OpenAI

llm = OpenAI(model_name="text-davinci-003", max_tokens=1024)
llm("怎么评价人工智能")
9. 通过 Google 搜索并返回答案

借助 Serpapi 来进行实现，Serpapi 提供了 google 搜索的 api 接口。首先需要我们到 Serpapi官网（https://serpapi.com/）上注册一个用户，并复制他给我们生成 api key。然后我们需要像上面的 openai api key 一样设置到环境变量里面去。

import os
os.environ["OPENAI_API_KEY"] = '你的api key'
os.environ["SERPAPI_API_KEY"] = '你的api key'

from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.llms import OpenAI
from langchain.agents import AgentType

# 加载 OpenAI 模型
llm = OpenAI(temperature=0, max_tokens=2048) 

 # 加载 serpapi 工具
tools = load_tools(["serpapi"])

# 如果搜索完想再计算一下可以这么写
# tools = load_tools(['serpapi', 'llm-math'], llm=llm)

# 如果搜索完想再让他再用python的print做点简单的计算，可以这样写
# tools=load_tools(["serpapi","python_repl"])

# 工具加载后都需要初始化，verbose 参数为 True，会打印全部的执行详情
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

# 运行 agent
agent.run("What's the date today? What great events have taken place today in history?")

  • zero-shot-react-description: 根据工具的描述和请求内容的来决定使用哪个工具 （最常用）

  • react-docstore: 使用 ReAct 框架和 docstore 交互, 使用Search 和Lookup 工具, 前者用来搜, 后者寻找term, 举例: Wipipedia 工具

  • self-ask-with-search 此代理只使用一个工具: Intermediate Answer, 它会为问题寻找事实答案(指的非 gpt 生成的答案, 而是在网络中,文本中已存在的), 如 Google search API 工具

  • conversational-react-description: 为会话设置而设计的代理, 它的prompt会被设计的具有会话性, 且还是会使用 ReAct 框架来决定使用来个工具, 并且将过往的会话交互存入内存

10. 对超长文本进行总结
from langchain.document_loaders import UnstructuredFileLoader
from langchain.chains.summarize import load_summarize_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain import OpenAI

# 导入文本
loader = UnstructuredFileLoader("/content/sample_data/data/lg_test.txt")
# 将文本转成 Document 对象
document = loader.load()
print(f'documents:{len(document)}')

# 初始化文本分割器
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size = 500,
    chunk_overlap = 0
)

# 切分文本
split_documents = text_splitter.split_documents(document)
print(f'documents:{len(split_documents)}')

# 加载 llm 模型
llm = OpenAI(model_name="text-davinci-003", max_tokens=1500)

# 创建总结链
chain = load_summarize_chain(llm, chain_type="refine", verbose=True)

# 执行总结链，（为了快速演示，只总结前5段）
chain.run(split_documents[:5])


11. 构建本地知识库问答机器人
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain import OpenAI
from langchain.document_loaders import DirectoryLoader
from langchain.chains import RetrievalQA

# 加载文件夹中的所有txt类型的文件
loader = DirectoryLoader('/content/sample_data/data/', glob='**/*.txt')
# 将数据转成 document 对象，每个文件会作为一个 document
documents = loader.load()

# 初始化加载器
text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
# 切割加载的 document
split_docs = text_splitter.split_documents(documents)

# 初始化 openai 的 embeddings 对象
embeddings = OpenAIEmbeddings()
# 将 document 通过 openai 的 embeddings 对象计算 embedding 向量信息并临时存入 Chroma 向量数据库，用于后续匹配查询
docsearch = Chroma.from_documents(split_docs, embeddings)

# 创建问答对象
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(), return_source_documents=True)
# 进行问答
result = qa({"query": "科大讯飞今年第一季度收入是多少？"})
print(result)


12. 构建向量索引数据库

Chroma

from langchain.vectorstores import Chroma

# 持久化数据
docsearch = Chroma.from_documents(documents, embeddings, persist_directory="D:/vector_store")
docsearch.persist()

# 加载数据
docsearch = Chroma(persist_directory="D:/vector_store", embedding_function=embeddings)

Pinecone

Pinecone是一个在线的向量数据库。所以，我可以第一步依旧是注册，然后拿到对应的 api key。

Pinecone网址：https://app.pinecone.io/
免费版如果索引14天不使用会被自动清除。

from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import DirectoryLoader
from langchain.vectorstores import Chroma, Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain

import pinecone

# 初始化 pinecone
pinecone.init(
  api_key="你的api key",
  environment="你的Environment"
)

loader = DirectoryLoader('/content/sample_data/data/', glob='**/*.txt')
# 将数据转成 document 对象，每个文件会作为一个 document
documents = loader.load()

# 初始化加载器
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)
# 切割加载的 document
split_docs = text_splitter.split_documents(documents)

index_name="liaokong-test"

# 持久化数据
# docsearch = Pinecone.from_texts([t.page_content for t in split_docs], embeddings, index_name=index_name)

# 加载数据
docsearch = Pinecone.from_existing_index(index_name,embeddings)

query = "科大讯飞今年第一季度收入是多少？"
docs = docsearch.similarity_search(query, include_metadata=True)

llm = OpenAI(temperature=0)
chain = load_qa_chain(llm, chain_type="stuff", verbose=True)
chain.run(input_documents=docs, question=query)
13. 使用GPT3.5模型构建油管频道问答机器人
import os

from langchain.document_loaders import YoutubeLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import ChatVectorDBChain, ConversationalRetrievalChain

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
  ChatPromptTemplate,
  SystemMessagePromptTemplate,
  HumanMessagePromptTemplate
)

# 加载 youtube 频道
loader = YoutubeLoader.from_youtube_url('https://www.youtube.com/watch?v=Dj60HHy-Kqk')
# 将数据转成 document
documents = loader.load()

# 初始化文本分割器
text_splitter = RecursiveCharacterTextSplitter(
  chunk_size=1000,
  chunk_overlap=20
)

# 分割 youtube documents
documents = text_splitter.split_documents(documents)

# 初始化 openai embeddings
embeddings = OpenAIEmbeddings()

# 将数据存入向量存储
vector_store = Chroma.from_documents(documents, embeddings)
# 通过向量存储初始化检索器
retriever = vector_store.as_retriever()

system_template = """
Use the following context to answer the user's question.
If you don't know the answer, say you don't, don't try to make it up. And answer in Chinese.
-----------
{question}
-----------
{chat_history}
"""

# 构建初始 messages 列表，这里可以理解为是 openai 传入的 messages 参数
messages = [
  SystemMessagePromptTemplate.from_template(system_template),
  HumanMessagePromptTemplate.from_template('{question}')
]

# 初始化 prompt 对象
prompt = ChatPromptTemplate.from_messages(messages)


# 初始化问答链
qa = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0.1,max_tokens=2048),retriever,condense_question_prompt=prompt)


chat_history = []
while True:
  question = input('问题：')
  # 开始发送问题 chat_history 为必须参数,用于存储对话历史
  result = qa({'question': question, 'chat_history': chat_history})
  chat_history.append((question, result['answer']))
  print(result['answer'])
14. 用 OpenAI 连接万种工具

结合使用 zapier 来实现将万种工具连接起来

zapier官网：https://zapier.com/l/natural-language-actions

from langchain.llms import OpenAI
from langchain.agents import initialize_agent
from langchain.agents.agent_toolkits import ZapierToolkit
from langchain.utilities.zapier import ZapierNLAWrapper


llm = OpenAI(temperature=.3)
zapier = ZapierNLAWrapper()
toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
agent = initialize_agent(toolkit.get_tools(), llm, agent="zero-shot-react-description", verbose=True)

# 我们可以通过打印的方式看到我们都在 Zapier 里面配置了哪些可以用的工具
for tool in toolkit.get_tools():
  print (tool.name)
  print (tool.description)
  print ("\n\n")

agent.run('请用中文总结最后一封"******@qq.com"发给我的邮件。并将总结发送给"******@qq.com"')

动物装饰