从LLMChain迁移

LLMChain 将提示词模板、大型语言模型和输出解析器组合成一个类。

切换到LCEL实现的一些优势包括：

• 内容和参数的清晰性。遗留的LLMChain包含一个默认的输出解析器和其他选项。
• 更容易的流式处理。LLMChain仅通过回调支持流式处理。
• 如果需要，更容易访问原始消息输出。LLMChain仅通过参数或回调暴露这些。

%pip install --upgrade --quiet langchain-openai

import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass()

传统

Details

<!--IMPORTS:[{"imported": "LLMChain", "source": "langchain.chains", "docs": "https://python.langchain.com/api_reference/langchain/chains/langchain.chains.llm.LLMChain.html", "title": "Migrating from LLMChain"}, {"imported": "ChatPromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html", "title": "Migrating from LLMChain"}, {"imported": "ChatOpenAI", "source": "langchain_openai", "docs": "https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html", "title": "Migrating from LLMChain"}]-->
from langchain.chains import LLMChain
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_messages(
    [("user", "Tell me a {adjective} joke")],
)

legacy_chain = LLMChain(llm=ChatOpenAI(), prompt=prompt)

legacy_result = legacy_chain({"adjective": "funny"})
legacy_result

{'adjective': 'funny',
 'text': "Why couldn't the bicycle stand up by itself?\n\nBecause it was two tired!"}

请注意，LLMChain 默认返回一个 dict，其中包含来自 StrOutputParser 的输入和输出，因此要提取输出，您需要访问 "text" 键。

legacy_result["text"]

"Why couldn't the bicycle stand up by itself?\n\nBecause it was two tired!"

LCEL

Details

<!--IMPORTS:[{"imported": "StrOutputParser", "source": "langchain_core.output_parsers", "docs": "https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html", "title": "Migrating from LLMChain"}, {"imported": "ChatPromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html", "title": "Migrating from LLMChain"}, {"imported": "ChatOpenAI", "source": "langchain_openai", "docs": "https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html", "title": "Migrating from LLMChain"}]-->
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_messages(
    [("user", "Tell me a {adjective} joke")],
)

chain = prompt | ChatOpenAI() | StrOutputParser()

chain.invoke({"adjective": "funny"})

'Why was the math book sad?\n\nBecause it had too many problems.'

如果您想模仿 LLMChain 中输入和输出的 dict 打包，可以使用 RunnablePassthrough.assign ，例如：

<!--IMPORTS:[{"imported": "RunnablePassthrough", "source": "langchain_core.runnables", "docs": "https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html", "title": "Migrating from LLMChain"}]-->
from langchain_core.runnables import RunnablePassthrough

outer_chain = RunnablePassthrough().assign(text=chain)

outer_chain.invoke({"adjective": "funny"})

{'adjective': 'funny',
 'text': 'Why did the scarecrow win an award? Because he was outstanding in his field!'}

下一步

请参阅 本教程 以获取有关使用提示词模板、大型语言模型和输出解析器的更多详细信息。

查看 LCEL 概念文档 以获取更多背景信息。