Key takeaways
LLMMathChain
is a utility chain in LangChain that enhances the mathematical capabilities of large language models (LLMs) by generating and executing Python code to solve complex math problems.To implement
LLMMathChain
in Python, we need to import necessary modules, create an OpenAI instance, define the chain components, and execute queries while tracking token usage.
In LangChain, chains are fundamental building blocks designed to process input by using a specific combination of elements. These elements, known as links, can be either primitives or other chains. Primitives include prompts, LLMs (large language models), utilities, or other chains. A chain essentially acts as a pipeline, performing a set of operations on an input and returning a result. This can range from a prompt-based pass through an LLM to the application of a Python function to text.
Chains are categorized into three types.
These are specific-purpose chains used to extract a particular answer from an LLM. They have a very narrow purpose and are ready to use right out of the box. Utility chains are usually straightforward and designed for specific tasks, like LLMMathChain
, which enhances an LLM’s ability to solve mathematical problems.
These chains serve as building blocks for other chains but are not used on their own directly. Generic chains provide foundational functionalities that can be combined in various ways to create more complex and specialized chains. They’re essentially components that can be assembled in different configurations to achieve a wide range of tasks.
These are more specialized and are not covered in the basic explanation. They’re designed for tasks that involve combining and processing information from multiple documents.
LLMMathChain
LLMMathChain
is a utility chain in LangChain that enhances the mathematical capabilities of large language models (LLMs). It overcomes LLMs’ inherent limitations in performing mathematical calculations.
When encountering a complex mathematical query, LLMMathChain
does not attempt to solve it directly. Instead, it generates Python code that can compute the solution. This code is then executed in a Python environment to obtain an accurate answer.
We utilize LLMMathChain
to solve complex math problems using LLM. To implement LLMMathChain
in Python, we typically begin by importing the necessary modules from the LangChain library. We utilize LLMMathChain
to solve complex math problems using an LLM. Next, we define the components of our chain. In our demonstration example, we’re using two chain components: the prompt and the LLM. Each component is responsible for a specific task in the NLP process.
LLMMathChain
primarily uses natural language understanding (NLU) to comprehend math problems. It breaks down text, identifies key elements, and constructs mathematical equations. Additional NLP techniques like text summarization and question answering might be used for complex problems.
from langchain import OpenAIfrom langchain.chains import LLMMathChainfrom langchain.callbacks import get_openai_callbackllm = OpenAI(temperature=0,openai_api_key="OpenAI-API-Key")def count_tokens(chain, query):with get_openai_callback() as cb:result = chain.invoke(query)print(result)print(f'Spent a total of {cb.total_tokens} tokens')return resultllm_math = LLMMathChain(llm=llm)count_tokens(llm_math, "What is 13 raised to the .2423 power?")
Note: Make sure to replace
OpenAI_API_Key
with your actual OpenAI API key.
Lines 1–3: We import the necessary modules like OpenAI
, get_openai_callback
and LLMMathChain
from LangChain. LLMMathChain
specializes in mathematical operations.
Lines 5–8: We create an instance of the OpenAI
class with a specified "OpenAI-API-Key"
. The temperature
parameter is set to 0
, which typically means the model will generate more deterministic and less random responses.
Line 10–15: We define a function that takes a chain
and a query
as arguments. The function is designed to execute a query using a specified chain and track the number of tokens used in the process.
Lines 11–12: We use a context manager to initiate a callback for tracking token usage. We then execute the query using the invoke()
method of the provided chain. This method sends the query to the chain for processing.
Lines 13–15: We print the query result
and the total number of tokens spent processing the query.
Line 17: We initialize an instance of LLMMathChain
using the previously created OpenAI
object.
Line 18: We call the count_tokens()
function with the llm_math
chain and a mathematical query. This executes the query, prints the result, and shows the total tokens used.