Context formatting and injection in LangChain - Mini Project: Build & Apply

Context Formatting and Injection with LangChain

📖 Scenario: You are building a simple chatbot that answers questions about a product catalog. You want to prepare the context by formatting product details and then inject this context into your LangChain prompt template.

🎯 Goal: Create a dictionary with product details, format this data into a context string, and inject it into a LangChain prompt template to prepare for generating answers.

📋 What You'll Learn

Create a dictionary called product_info with exact keys and values

Create a string variable called context that formats the product details

Create a LangChain PromptTemplate called template that uses the context variable

Create a prompt by formatting the template with the context

💡 Why This Matters

🌍 Real World

Formatting and injecting context is essential when building chatbots or AI assistants that need to provide answers based on specific data.

💼 Career

Many AI and software development jobs require preparing and managing prompt templates with dynamic context for language models.

1

Create the product information dictionary

Create a dictionary called product_info with these exact entries: 'name': 'Smartphone X', 'price': '$799', 'features': '5G, OLED display, 128GB storage'.

LangChain

# Create the product_info dictionary with name, price, and features
# Your code here

Need a hint?

Use curly braces {} to create a dictionary with the exact keys and values.

2

Format the product information into a context string

Create a string variable called context that formats the product details from product_info into this exact format: "Product: Smartphone X\nPrice: $799\nFeatures: 5G, OLED display, 128GB storage" using an f-string.

LangChain

product_info = {
    'name': 'Smartphone X',
    'price': '$799',
    'features': '5G, OLED display, 128GB storage'
}
# Create the context string using an f-string
# Your code here

Need a hint?

Use an f-string with newline characters \n to format the string exactly.

3

Create a LangChain PromptTemplate using the context

Import PromptTemplate from langchain.prompts. Then create a PromptTemplate called template with input_variables=['context'] and template="Here is the product info:\n{context}\nPlease answer the question.".

LangChain

product_info = {
    'name': 'Smartphone X',
    'price': '$799',
    'features': '5G, OLED display, 128GB storage'
}

context = f"Product: {product_info['name']}\nPrice: {product_info['price']}\nFeatures: {product_info['features']}"

# Import PromptTemplate and create the template variable
# Your code here

Need a hint?

Use from langchain.prompts import PromptTemplate and create the template with the exact input_variables and template string.

4

Format the prompt with the context string

Create a variable called prompt by calling template.format(context=context) to inject the formatted context into the prompt template.

LangChain

from langchain.prompts import PromptTemplate

product_info = {
    'name': 'Smartphone X',
    'price': '$799',
    'features': '5G, OLED display, 128GB storage'
}

context = f"Product: {product_info['name']}\nPrice: {product_info['price']}\nFeatures: {product_info['features']}"

template = PromptTemplate(input_variables=['context'], template="Here is the product info:\n{context}\nPlease answer the question.")

# Format the prompt with the context
# Your code here

Need a hint?

Call template.format() with the context variable to create the final prompt string.