Chain that interprets a prompt and executes python code to do math.
Abstract base class for creating structured sequences of calls to components.
Chains should be used to encode a sequence of calls to components like models, document retrievers, other chains, etc., and provide a simple interface to this sequence.
Chain to run queries against LLMs.
This class is deprecated. See below for an example implementation using LangChain runnables:
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
model = OpenAI()
chain = prompt | model | StrOutputParser()
chain.invoke("your adjective here")Chain that interprets a prompt and executes python code to do math.
This class is deprecated. See below for a replacement implementation using LangGraph. The benefits of this implementation are:
Install LangGraph with:
pip install -U langgraphimport math
from typing import Annotated, Sequence
from langchain_core.messages import BaseMessage
from langchain_core.runnables import RunnableConfig
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.graph import END, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt.tool_node import ToolNode
import numexpr
from typing_extensions import TypedDict
@tool
def calculator(expression: str) -> str:
"""Calculate expression using Python's numexpr library.
Expression should be a single line mathematical expression
that solves the problem.