LangChain architecture overview - Mini Project: Build & Apply

LangChain Architecture Overview

📖 Scenario: You are building a simple LangChain setup to understand its architecture. LangChain helps connect language models with data and tools in a structured way.

🎯 Goal: Create a basic LangChain architecture example with these parts: a language model, a prompt template, a chain that connects them, and a final call to run the chain.

📋 What You'll Learn

Create a variable called llm that holds an OpenAI language model instance with model name 'text-davinci-003'.

Create a variable called prompt that holds a PromptTemplate with input variable 'name' and template 'Hello, {name}!'

Create a variable called chain that holds an LLMChain using the llm and prompt variables.

Call the chain.run method with the argument name='Alice'.

💡 Why This Matters

🌍 Real World

LangChain is used to build applications that combine language models with data sources and tools, like chatbots, question answering, and automation.

💼 Career

Understanding LangChain architecture helps developers create advanced AI-powered applications that integrate language models effectively.

1

Set up the language model

Import OpenAI from langchain.llms and create a variable called llm that holds an OpenAI instance with model_name='text-davinci-003'.

LangChain

# Import OpenAI and create llm with model_name='text-davinci-003'
# Your code here

Hint

Use OpenAI(model_name='text-davinci-003') to create the language model.

2

Create the prompt template

Import PromptTemplate from langchain.prompts and create a variable called prompt that holds a PromptTemplate with input_variables=['name'] and template='Hello, {name}!'.

LangChain

from langchain.llms import OpenAI

llm = OpenAI(model_name='text-davinci-003')
# Import PromptTemplate and create prompt with input_variables=['name'] and template='Hello, {name}!'
# Your code here

Hint

Use PromptTemplate(input_variables=['name'], template='Hello, {name}!') to create the prompt.

3

Create the chain

Import LLMChain from langchain.chains and create a variable called chain that holds an LLMChain using the llm and prompt variables.

LangChain

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

llm = OpenAI(model_name='text-davinci-003')
prompt = PromptTemplate(input_variables=['name'], template='Hello, {name}!')
# Import LLMChain and create chain using llm and prompt
# Your code here

Hint

Use LLMChain(llm=llm, prompt=prompt) to create the chain.

4

Run the chain

Call the chain.run method with the argument name='Alice' to get the output.

LangChain

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

llm = OpenAI(model_name='text-davinci-003')
prompt = PromptTemplate(input_variables=['name'], template='Hello, {name}!')
chain = LLMChain(llm=llm, prompt=prompt)
# Run the chain with name='Alice'
# Your code here

Hint

Use chain.run(name='Alice') to run the chain.

Practice

1. What is the main purpose of a chain in LangChain architecture?

easy

A. To link different steps like prompts and LLMs to build language apps

B. To store data permanently in a database

C. To create user interfaces for language models

D. To train new language models from scratch

Solution

1. Step 1: Understand the role of chains

   Chains connect parts like prompt templates and language models to form a workflow.

2. Step 2: Identify the main purpose

   Chains help organize and link these steps to build smart language apps easily.

3. Final Answer:

   To link different steps like prompts and LLMs to build language apps -> Option A

4. Quick Check:

   Chains link steps = D [OK]

Hint: Chains connect prompts and models to build apps [OK]

Common Mistakes:

• Thinking chains store data permanently
• Confusing chains with UI components
• Assuming chains train models

2. Which of the following is the correct way to create a prompt template in LangChain?

easy

A. PromptTemplate.create("Hello, {name}!")

B. PromptTemplate("Hello, {name}!")

C. PromptTemplate(template="Hello, {name}!")

D. PromptTemplate.new(template="Hello, {name}!")

Solution

1. Step 1: Recall PromptTemplate syntax

   PromptTemplate requires a named argument 'template' with the string pattern.

2. Step 2: Match syntax to options

   Only PromptTemplate(template="Hello, {name}!") uses PromptTemplate(template="...") correctly.

3. Final Answer:

   PromptTemplate(template="Hello, {name}!") -> Option C

4. Quick Check:

   Named 'template' argument = A [OK]

Hint: Use named 'template' argument to create PromptTemplate [OK]

Common Mistakes:

• Passing template string without argument name
• Using non-existent create() or new() methods
• Confusing positional and keyword arguments

3. Given this code snippet, what will be the output?

from langchain import PromptTemplate, LLMChain

prompt = PromptTemplate(template="What is the capital of {country}?")
chain = LLMChain(prompt=prompt)
result = chain.run(country="France")
print(result)

medium

A. "Paris"

B. An error because LLM is missing

C. "What is the capital of France?"

D. "France"

Solution

1. Step 1: Analyze the code components

   The chain is created with a prompt but no LLM (language model) is provided.

2. Step 2: Understand LangChain requirements

   LLMChain needs an LLM to generate answers; missing it causes an error.

3. Final Answer:

   An error because LLM is missing -> Option B

4. Quick Check:

   LLM missing causes error = B [OK]

Hint: LLMChain needs an LLM instance to run [OK]

Common Mistakes:

• Assuming chain.run returns prompt text
• Expecting output without LLM
• Confusing prompt template with output

4. Identify the error in this LangChain code snippet:

from langchain import PromptTemplate, LLMChain

prompt = PromptTemplate(template="Say hello to {name}")
chain = LLMChain(prompt=prompt, llm=None)
output = chain.run(name="Alice")
print(output)

medium

A. LLMChain requires a valid LLM, not None

B. PromptTemplate syntax is incorrect

C. run() method does not accept arguments

D. Missing import for LLM class

Solution

1. Step 1: Check PromptTemplate usage

   PromptTemplate is correctly created with a template string.

2. Step 2: Check LLMChain initialization

   LLMChain requires a valid LLM object; passing None causes failure.

3. Final Answer:

   LLMChain requires a valid LLM, not None -> Option A

4. Quick Check:

   LLM must be valid, not None = A [OK]

Hint: LLMChain needs a real LLM instance, not None [OK]

Common Mistakes:

• Thinking run() can't take arguments
• Assuming PromptTemplate syntax is wrong
• Missing imports but not causing this error

5. You want to build a LangChain app that asks a user's name, then uses an LLM to greet them. Which architecture correctly links these parts?

hard

A. Create a PromptTemplate and run it directly without an LLMChain

B. Create an LLMChain without a prompt and run it with user input

C. Create an LLM instance and call it directly without prompt or chain

D. Create a PromptTemplate for the question, then an LLMChain with that prompt and an LLM, then run the chain with user input

Solution

1. Step 1: Understand LangChain app structure

   PromptTemplate creates the question, LLMChain links prompt and LLM to generate answers.

2. Step 2: Identify correct linking

   Create a PromptTemplate for the question, then an LLMChain with that prompt and an LLM, then run the chain with user input correctly creates prompt, then LLMChain with prompt and LLM, then runs with input.

3. Final Answer:

   Create a PromptTemplate for the question, then an LLMChain with that prompt and an LLM, then run the chain with user input -> Option D

4. Quick Check:

   Prompt + LLM in chain = C [OK]

Hint: Chain = prompt + LLM + run with input [OK]

Common Mistakes:

• Trying to run prompt alone without chain
• Using LLM without prompt or chain
• Skipping linking steps in LangChain