Auto-fixing malformed output in LangChain - Mini Project: Build & Apply

Auto-fixing Malformed Output with LangChain

📖 Scenario: You are building a chatbot using LangChain that sometimes returns answers with formatting errors. You want to automatically fix these malformed outputs to improve user experience.

🎯 Goal: Create a LangChain chain that takes a user question, generates an answer, and then uses a fixer chain to correct any formatting mistakes in the output.

📋 What You'll Learn

Create a dictionary called data with a key question and value 'What is the capital of France?'

Create a variable called fix_threshold and set it to 0.7

Create a LangChain LLMChain called answer_chain that uses an LLM to answer the question from data

Create a LangChain LLMChain called fixer_chain that takes the output of answer_chain and fixes malformed formatting

💡 Why This Matters

🌍 Real World

Chatbots and AI assistants often produce outputs with formatting issues. Automatically fixing these improves clarity and user trust.

💼 Career

Understanding how to chain LLM calls and fix outputs is useful for AI developers building robust conversational agents.

1

Set up the initial data dictionary

Create a dictionary called data with a key question and value 'What is the capital of France?'

LangChain

# Create the data dictionary with the question
# Your code here

Need a hint?

Use curly braces to create a dictionary and assign the key 'question' with the exact string.

2

Add a fix threshold variable

Create a variable called fix_threshold and set it to 0.7

LangChain

data = {'question': 'What is the capital of France?'}
# Create fix_threshold variable
# Your code here

Need a hint?

Just assign the number 0.7 to the variable fix_threshold.

3

Create the answer chain

Create a LangChain LLMChain called answer_chain that uses an LLM to answer the question from data

LangChain

data = {'question': 'What is the capital of France?'}
fix_threshold = 0.7
# Create answer_chain using LLMChain
# Your code here

Need a hint?

Use OpenAI LLM with temperature 0 and a prompt template that takes 'question' as input.

4

Create the fixer chain to auto-fix malformed output

Create a LangChain LLMChain called fixer_chain that takes the output of answer_chain and fixes malformed formatting

LangChain

from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

llm = OpenAI(temperature=0)
prompt = PromptTemplate(input_variables=['question'], template='Answer the question: {question}')
answer_chain = LLMChain(llm=llm, prompt=prompt)
fix_threshold = 0.7
data = {'question': 'What is the capital of France?'}
# Create fixer_chain to fix malformed output
# Your code here

Need a hint?

Create a new prompt template that takes 'text' and asks to fix formatting errors, then create an LLMChain with it.