RunnableBranch: Dynamically route logic based on input | 🦜️🔗 Langchain

基于输入的动态路由逻辑，通过上一步的输出选择下一步操作，允许创建非确定性链。路由保证路由间的结构和连贯。

有以下两种方法执行路由

1、通过RunnableLambda （推荐该方法）

2、RunnableBranch

from langchain_community.chat_models import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplatemodel=ChatOpenAI(model="gpt-3.5-turbo",temperature=0)
chain = (PromptTemplate.from_template("""根据用户的问题, 把它归为关于 `LangChain`, `OpenAI`, or `Other`.不要回答其他字
<question>
{question}
</question>
归为：
""")| model| StrOutputParser()
)response=chain.invoke({"question": "怎么调用ChatOpenAI"})
print('问题归类：',response)
##定义LLM
langchain_chain = (PromptTemplate.from_template("""你是一个langchain专家. \开头用 "langchain有人告诉我"回答问题. \Question: {question}Answer:""")| model
)
openai_chain = (PromptTemplate.from_template("""你是一个OpenAI专家. \开头用 "OpenAI有人告诉我"回答问题. \Question: {question}Answer:""")| model
)
general_chain = (PromptTemplate.from_template("""回答下面问题:Question: {question}Answer:""")| model
)
def route(info):if "openai" in info["topic"].lower():return openai_chainelif "langchain" in info["topic"].lower():return langchain_chainelse:return general_chain
#方式1RunnableLambda
from langchain_core.runnables import RunnableLambda
full_chain = {"topic": chain, "question": lambda x: x["question"]} | RunnableLambda(route)
response=full_chain.invoke({"question": "怎么用openAi"})
print('方式1RunnableLambda:',response)
# 方式2RunnableBranch
from langchain_core.runnables import RunnableBranch
branch = RunnableBranch((lambda x: "anthropic" in x["topic"].lower(), openai_chain),(lambda x: "langchain" in x["topic"].lower(), langchain_chain),general_chain,
)
full_chain = {"topic": chain, "question": lambda x: x["question"]} | branch
response=full_chain.invoke({"question": "怎么用lanGchaiN?"})
print('方式2RunnableBranch:',response)