深入解析 LangChain 的核心理念与实际应用

from langchain.prompts import PromptTemplate

# 创建一个模板，其中包含占位符变量
template = "`Translate the following text to {language}: {text}`"
prompt = PromptTemplate(
    input_variables=["language", "text"],
    template=template
)

# 使用模板生成实际的提示内容
formatted_prompt = prompt.format(language="French", text="Hello, how are you?")
print(formatted_prompt)
# 输出: `Translate the following text to French: Hello, how are you?`

from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatOpenAI

# 定义模型和提示模板
llm = ChatOpenAI(model_name="gpt-4")
prompt = PromptTemplate(input_variables=["product"],
                         template="`What are the benefits of {product}?`")

# 构建一个 LLM 链
chain = LLMChain(llm=llm, prompt=prompt)

# 执行链任务
result = chain.run("solar panels")
print(result)

from langchain.tools import Tool
from langchain.chains import LLMChain

# 自定义工具
search_tool = Tool(
    name="search",
    func=lambda query: f"`Simulated search result for: {query}`",
    description="Performs a web search"
)

# 使用工具链实现多步骤任务
from langchain.agents import initialize_agent
agent = initialize_agent(tools=[search_tool], llm=ChatOpenAI(model_name="gpt-4"), agent="zero-shot-react-description")
response = agent.run("Who won the FIFA World Cup in 2022?")
print(response)

from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
from langchain.chat_models import ChatOpenAI

# 创建一个记忆对象
memory = ConversationBufferMemory()

# 创建一个带记忆的对话链
conversation = ConversationChain(llm=ChatOpenAI(model_name="gpt-4"), memory=memory)

# 多轮对话
print(conversation.predict(input="Hi, what's your name?"))
print(conversation.predict(input="Can you remind me what I just asked?"))

from langchain.chains import SequentialChain
from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory

# 定义多个子任务的提示模板
summary_prompt = PromptTemplate(
    input_variables=["text"],
    template="`Summarize the following text in one sentence: {text}`"
)
translation_prompt = PromptTemplate(
    input_variables=["summary", "language"],
    template="`Translate the following summary to {language}: {summary}`"
)

# 定义模型
llm = ChatOpenAI(model_name="gpt-4")

# 构建链
summary_chain = LLMChain(llm=llm, prompt=summary_prompt)
translation_chain = LLMChain(llm=llm, prompt=translation_prompt)

# 使用 SequentialChain 组织任务
chain = SequentialChain(
    chains=[summary_chain, translation_chain],
    input_variables=["text", "language"],
    output_variables=["translation"]
)

# 运行多步骤任务
result = chain.run({"text": "LangChain is a powerful tool for building LLM-based applications.", "language": "Chinese"})
print(result)