LangChain architecture overview

Introduction

LangChain helps you build smart apps that use language models easily. It organizes parts so you can connect and control them well.

When you want to create a chatbot that can remember past talks.

When you need to combine language models with other tools like search or databases.

When building apps that process and understand text step-by-step.

When you want to manage complex language tasks by breaking them into smaller parts.

When you want to reuse and connect different language-related components smoothly.

Syntax

LangChain

from langchain import LLMChain, PromptTemplate, OpenAI

# Create a prompt template
prompt = PromptTemplate(input_variables=["topic"], template="Write a short story about {topic}.")

# Create a language model instance
llm = OpenAI(temperature=0.7)

# Combine prompt and model into a chain
story_chain = LLMChain(llm=llm, prompt=prompt)

# Run the chain
result = story_chain.run({"topic": "a friendly robot"})
print(result)

LangChain uses chains to connect prompts and models.

Each part has a clear role: prompt templates create questions, LLMs answer, chains link them.

Examples

This example makes the model say hello to a given name.

LangChain

from langchain import LLMChain, PromptTemplate, OpenAI

prompt = PromptTemplate(input_variables=["name"], template="Say hello to {name}.")
llm = OpenAI(temperature=0)
chain = LLMChain(llm=llm, prompt=prompt)
print(chain.run({"name": "Alice"}))

This example shows a simple conversation that remembers what you said before.

LangChain

from langchain import ConversationChain, OpenAI

llm = OpenAI(temperature=0)
conversation = ConversationChain(llm=llm)
print(conversation.predict(input="Hi!"))

Sample Program

This program asks the language model to share a fun fact about an animal you choose. It shows how LangChain connects prompts and models simply.

LangChain

from langchain import LLMChain, PromptTemplate, OpenAI

# Define a prompt template with a variable
prompt = PromptTemplate(input_variables=["animal"], template="Describe a fun fact about a {animal}.")

# Create an OpenAI language model instance
llm = OpenAI(temperature=0)

# Create a chain that links the prompt and the model
fact_chain = LLMChain(llm=llm, prompt=prompt)

# Run the chain with input
output = fact_chain.run({"animal": "dolphin"})
print(output)

Important Notes

LangChain breaks down language tasks into parts you can connect and reuse.

Chains can be simple or complex, depending on your app needs.

Using prompt templates helps keep your questions clear and easy to change.

Summary

LangChain organizes language model apps into parts called chains.

Prompt templates create questions, LLMs answer, and chains link them.

This makes building smart language apps easier and clearer.

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