langchain学习笔记（十）
Bind runtime args | 🦜️🔗 Langchain
1、有时，我们希望使用常量参数调用Runnable序列中的Runnable，这些参数不是序列中前一个Runnable的输出的一部分，也不是用户的输入，这时可以用Runnable.bind()
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
prompt = ChatPromptTemplate.from_messages([("system","用代数符号写出下面的方程，然后求解。 格式EQUATION:...换行SOLUTION:...换行",),("human", "{equation_statement}"),]
)
model = ChatOpenAI(temperature=0)
runnable = ({"equation_statement": RunnablePassthrough()} | prompt | model | StrOutputParser()
)
print(runnable.invoke("x的三次方加7等于12"))
# 使用model.bind，此处限制输入某些字
runnable = ({"equation_statement": RunnablePassthrough()}| prompt| model.bind(stop="SOLUTION")| StrOutputParser()
)
print('model.bind:',runnable.invoke("x的三次方加7等于12"))2、通过bind给openAI模型绑定openAI函数、openAI工具
注意以下的方程求解需要GPT4才能给出正确答案，奇怪的是上面不用bind function的gpt3.5可以回答正确
注意name的key不能为中文
function = {"name": "solver","description": "Formulates and solves an equation","parameters": {"type": "object","properties": {"equation": {"type": "string","description": "The algebraic expression of the equation",},"solution": {"type": "string","description": "The solution to the equation",},},"required": ["equation", "solution"],},
}
prompt = ChatPromptTemplate.from_messages([("system","Write out the following equation using algebraic symbols then solve it.",),("human", "{equation_statement}"),]
)
model=model="gpt-3.5-turbo".bind(function_call={"name": "solver"}, functions=[function]
)
runnable = {"equation_statement": RunnablePassthrough()} | prompt | model
print(runnable.invoke("x raised to the third plus seven equals 12"))