LangChain Tutorial for Beginners: Step-by-Step Guide with Python Examples

LangChain Tutorial for Beginners: Your First Steps with Python

Hello there, future AI builder! Have you ever wondered how smart computer programs, like the chatbots you talk to, are made? Many of them use powerful AI brains called Large Language Models, or LLMs. This langchain python tutorial beginners step-by-step guide will show you how to connect your Python code to these amazing brains using a special tool called LangChain.

LangChain helps you make your computer programs really smart. It acts like a helper, making it easier to build apps that understand language and talk back. By the end of this guide, you will be able to create your own simple AI tools with Python. We will cover everything from setting up your computer to building your first AI helper.

Getting Started: Your Python Setup

Before we jump into LangChain, we need to make sure your computer is ready. LangChain works with Python, a popular programming language. If you don’t have Python installed, you can get it from the official Python website. Just pick the latest stable version for your computer.

You’ll also need a code editor, like VS Code or PyCharm. These programs help you write and organize your code neatly. They make coding much easier and more fun. This initial Python setup for LangChain is super important.

Creating a Virtual Environment

Imagine you have many toy projects, and each project needs specific tools. A virtual environment is like a special box for each project. It keeps all the tools and libraries for one project separate from others. This way, different projects don’t get mixed up.

This virtual environment creation helps avoid conflicts between different versions of libraries. It’s a really good habit for any Python developer. Let’s create one for our LangChain journey.

Here are the steps to create a virtual environment:

1. Open your computer’s terminal or command prompt. This is where you type commands.
2. Go to the folder where you want to keep your LangChain project. You can use the cd command, like cd Documents/my_langchain_project. If you don’t have a folder yet, make one with mkdir my_langchain_project.
3. Once you are in your project folder, type this command to create a virtual environment:

python -m venv venv

This command creates a new folder named venv inside your project folder. This venv folder contains everything your isolated environment needs. It’s like unwrapping a new toolbox just for this project.

Now, you need to “activate” this special box so your computer uses its tools. The command to activate it depends on your computer’s operating system.

For Windows:

.\venv\Scripts\activate

For macOS/Linux:

source venv/bin/activate

You will know it’s activated because (venv) will appear at the start of your terminal line. This means you are now working inside your special project box. This virtual environment creation is a crucial Python setup for LangChain step.

Installing LangChain and Other Dependencies

Now that our special project box is active, it’s time to put the tools inside it. We need to install LangChain itself, plus a way to talk to the powerful AI models. For this langchain python tutorial beginners step-by-step, we’ll use OpenAI models.

OpenAI makes some of the most popular LLMs, like the one powering ChatGPT. We’ll install the LangChain library and the OpenAI library together. This dependency installation is straightforward.

In your activated virtual environment (where you see (venv)), type this command:

pip install langchain langchain-openai python-dotenv

Let’s break down what these parts do. pip is Python’s package installer, which helps you get new libraries. langchain is the main library we’ll use for our AI projects. langchain-openai is a special part of LangChain that helps it talk to OpenAI’s models. Finally, python-dotenv helps us manage secret keys safely.

After you press Enter, pip will download and install these libraries. It might take a moment, so be patient. Once it’s done, you’re ready for the next step in our Python setup for LangChain.

Setting Up Your OpenAI API Key

To talk to OpenAI’s powerful models, you need a special “key.” This key is like a password that tells OpenAI who you are and allows your program to use their services. You can get an API key from the OpenAI website after creating an account. Please note that using OpenAI’s models usually costs a small amount of money, based on how much you use them.

Never share your API key with anyone or put it directly into your code. This is very important for security. Instead, we’ll use a .env file to keep it secret. This is where python-dotenv comes in handy.

1. In your project folder (the same one where venv is), create a new file named .env. Make sure it’s exactly .env, with the dot at the beginning.
2. Open this .env file with your code editor.
3. Inside the .env file, add this line, replacing YOUR_OPENAI_API_KEY with your actual key:

OPENAI_API_KEY=YOUR_OPENAI_API_KEY

Save the .env file. Now, your Python programs can safely load this key without it being visible in your code. This is a very important Python setup for LangChain step for working with LLMs.

Your First LangChain Script: Talking to an AI

You’ve done the setup, and now the fun begins! We’re going to write our very first Python script that uses LangChain to talk to an AI. This will be a simple “Hello, AI!” moment for you. We’ll ask the AI a question and get a response.

This example will show you the basic steps of working with LLMs through LangChain. It’s like sending a text message to a very smart friend. You write your message, send it, and wait for their reply.

The Simplest Interaction: LLMs

An LLM, or Large Language Model, is the core brain of our AI. It’s a computer program trained on a huge amount of text. This training allows it to understand questions, generate human-like text, and even write poems or code. LangChain helps us easily interact with these powerful models.

Let’s create a file named simple_llm.py in your project folder. Open this file in your code editor and type the following code:

# simple_llm.py

# First, we need to load our secret API key
from dotenv import load_dotenv
load_dotenv() # This loads variables from our .env file

# Now, we import the necessary parts from LangChain
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

# Let's create an instance of our AI model
# We tell LangChain to use the OpenAI Chat model
# The model="gpt-3.5-turbo" specifies which OpenAI model to use.
# You can try "gpt-4" if you have access and want a more powerful model.
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)

# Now, let's ask our AI a question!
# We put our question inside a HumanMessage object.
# This tells the AI that a human is speaking.
question = "Tell me a fun fact about the universe."
messages = [HumanMessage(content=question)]

# Send the question to the AI and get a response
print(f"Asking the AI: '{question}'")
response = llm.invoke(messages)

# Print out what the AI said!
print("\nAI says:")
print(response.content)

Let’s break down this first Python script step-by-step.

1. from dotenv import load_dotenv; load_dotenv(): This line finds your .env file and loads the OPENAI_API_KEY into your program’s memory. It keeps your key safe.
2. from langchain_openai import ChatOpenAI: This imports the specific part of LangChain that connects to OpenAI’s chat models.
3. from langchain_core.messages import HumanMessage: This helps us format our questions properly for the AI. It signals that the message comes from a person.
4. llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7): Here, we create our AI brain. We specify gpt-3.5-turbo as the model. The temperature setting controls how creative or random the AI’s answers are; a lower number makes it more focused, a higher number makes it more imaginative.
5. question = "Tell me a fun fact about the universe.": This is the text of your question.
6. messages = [HumanMessage(content=question)]: We wrap our question in a HumanMessage. This is a standard way to send messages to chat models.
7. response = llm.invoke(messages): This is the magic line! We “invoke” the LLM, meaning we send our question to it. The AI processes it and sends back a response.
8. print(response.content): Finally, we print out the AI’s answer. The actual text of the answer is stored in response.content. This is how you handle responses from the LLM.

To run this first Python script:

1. Save the simple_llm.py file.
2. Make sure your virtual environment is still active ((venv) in your terminal).
3. In your terminal, type: python simple_llm.py

You should see your question printed, and then the AI’s fun fact about the universe! Congratulations, you’ve just completed your first Python script using LangChain and an LLM. This is a big step in our langchain python tutorial beginners step-by-step.

Understanding Prompts

In the example above, “Tell me a fun fact about the universe” was our prompt. A prompt is simply the instruction or question you give to the AI. It’s how you tell the AI what you want it to do. The quality of your prompt greatly affects the quality of the AI’s answer.

Think of prompts as giving clear instructions to a very smart but literal assistant. If you ask a vague question, you might get a vague answer. If you ask a specific, well-thought-out question, you’ll likely get a better response. Crafting good prompts is a skill you’ll develop over time when working with LLMs.

LangChain helps you manage and create prompts easily. Instead of just sending raw text, you can use PromptTemplates. These are like fill-in-the-blank templates for your questions. Let’s see how a PromptTemplate works.

# prompt_example.py

from dotenv import load_dotenv
load_dotenv()

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# Initialize the LLM
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)

# Create a prompt template
# We use placeholders like {topic} and {style} that we can fill in later.
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant that writes short, engaging summaries."),
        ("human", "Write a {style} summary about {topic}."),
    ]
)

# Now, we combine the prompt and the LLM into a "chain"
# We'll learn more about chains soon, but this is a simple way to connect them.
chain = prompt | llm

# Let's ask the AI for a summary about space in a simple style
topic1 = "the history of the internet"
style1 = "child-friendly"
response1 = chain.invoke({"topic": topic1, "style": style1})
print(f"Child-friendly summary of {topic1}:")
print(response1.content)
print("-" * 30)

# Let's try another topic and style
topic2 = "quantum physics"
style2 = "one-sentence explanation"
response2 = chain.invoke({"topic": topic2, "style": style2})
print(f"One-sentence explanation of {topic2}:")
print(response2.content)

In this prompt_example.py script:

1. ChatPromptTemplate.from_messages: We define our prompt using a list of messages. “System” messages give the AI overall instructions or a persona. “Human” messages are what you, the user, would say.
2. {topic} and {style}: These are placeholders. We fill them in when we use the prompt.
3. chain = prompt | llm: This line connects our prompt template to the LLM. The | symbol is a simple way to “pipe” the output of one step into the next.
4. chain.invoke({"topic": topic1, "style": style1}): When we invoke the chain, we provide values for topic and style. The template then builds the full prompt, and sends it to the LLM.

Using PromptTemplates is a powerful way to make your AI interactions reusable and flexible. This is a core concept in working with LLMs effectively.

Building Chains: Making AI Do More

So far, you’ve seen how to ask an AI a single question. But what if you want the AI to do several things in a specific order? Or maybe use its answer from one step to inform the next? That’s where “chains” come in! Chain creation examples are at the heart of building more complex AI applications.

What are Chains?

Think of a chain like a series of connected steps or tasks. Each step takes an input, does something, and then passes its output to the next step. In LangChain, a chain is a sequence of calls to components, such as LLMs, prompt templates, or other chains. They let you combine different parts of LangChain to create more complex and powerful applications.

Chains help you design workflows for your AI. For example, you might have one part of the chain generate ideas, and another part then takes those ideas and writes a summary. This makes your AI applications much more capable than just asking a single question. Understanding chains is key for this langchain python tutorial beginners step-by-step.

Simple Chain Creation Examples

Let’s build a simple chain that combines a PromptTemplate and an LLM. We already touched upon this in the prompt example, but let’s make it clearer.

The most basic chain takes your input, formats it using a prompt, sends it to the LLM, and then gives you the LLM’s answer. This is often called an LLMChain when using older LangChain syntax, or simply a pipeline using the new “LangChain Expression Language” (LCEL), which is what we used before with the | symbol. LCEL is the modern and simpler way for chain creation examples.

Let’s create a file called simple_chain.py:

# simple_chain.py

from dotenv import load_dotenv
load_dotenv()

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

# 1. Initialize the LLM
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)

# 2. Define our prompt template
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are an expert at simplifying complex topics."),
        ("human", "Explain {topic} to a 10-year-old."),
    ]
)

# 3. Define an output parser
# This helps us get a clean string output from the AI's response object.
output_parser = StrOutputParser()

# 4. Create the chain using LCEL
# The input goes to the prompt, then to the LLM, then its output is parsed.
chain = prompt | llm | output_parser

# Now, let's use our chain to explain a topic
topic_to_explain = "photosynthesis"
print(f"Asking the AI to explain '{topic_to_explain}' to a 10-year-old...")
response = chain.invoke({"topic": topic_to_explain})

# Print the clear string response
print("\nAI explains:")
print(response)

# Let's try another topic
another_topic = "the internet"
print(f"\nAsking the AI to explain '{another_topic}' to a 10-year-old...")
response2 = chain.invoke({"topic": another_topic})
print("\nAI explains:")
print(response2)

In this simple_chain.py:

1. StrOutputParser(): This is a helpful component that takes the AI’s structured response and just extracts the plain text content. It makes handling responses easier.
2. chain = prompt | llm | output_parser: This line clearly shows the flow. Your topic input first goes to the prompt. The prompt then creates a full message for the llm. The llm generates a response. Finally, the output_parser takes that response and gives you just the text.

Run this script: python simple_chain.py. You’ll see two clear explanations suitable for a child. This example demonstrates how simple chain creation examples can be, making it easy for beginners to start working with LLMs.

Sequential Chains: Step-by-Step Thinking

What if you need the AI to do multiple, linked steps? For instance, first generate a list of ideas, and then for each idea, write a short description. This is where SequentialChains or more complex LCEL patterns are powerful. While SequentialChain is an older concept, LCEL allows you to build powerful sequential workflows very easily.

Let’s create a chain that first comes up with some product names and then, in a second step, suggests a slogan for each name.

Create a file named sequential_chain_example.py:

# sequential_chain_example.py

from dotenv import load_dotenv
load_dotenv()

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

# Initialize the LLM
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)
output_parser = StrOutputParser()

# --- First Chain: Generate Product Names ---
name_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a creative product namer."),
        ("human", "Generate 3 unique names for a new product that is a {product_type}."),
    ]
)
name_chain = name_prompt | llm | output_parser

# --- Second Chain: Generate Slogans for a Product Name ---
slogan_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a catchy slogan creator."),
        ("human", "For the product named '{product_name}', suggest a short and catchy slogan. Make it only one slogan."),
    ]
)
slogan_chain = slogan_prompt | llm | output_parser

# --- Combine them into a larger workflow using LCEL ---
# We use a custom function to process the output of the first chain
# before passing it to the second.

def process_names_for_slogans(names_string, product_type):
    # Split the names string into individual names (assuming comma or newline separated)
    # This might need refinement depending on how the LLM formats the names
    names_list = [name.strip() for name in names_string.split('\n') if name.strip()]
    if not names_list: # Fallback if LLM doesn't return names as expected
        names_list = [name.strip() for name in names_string.split(',') if name.strip()]

    results = []
    for name in names_list:
        # For each name, we'll store its product type and the name itself
        # This structure allows us to pass 'product_name' to the slogan_chain
        results.append({"product_type": product_type, "product_name": name})
    return results

from langchain_core.runnables import RunnablePassthrough, RunnableLambda

# This defines the overall workflow:
# 1. Take initial input (product_type)
# 2. Pass it to name_chain to get product names
# 3. Process these names using our custom function
# 4. For each processed name, run the slogan_chain
# 5. Collect all slogans and format them nicely

full_workflow = (
    RunnablePassthrough.assign(names_output=name_chain)
    | RunnableLambda(lambda x: process_names_for_slogans(x['names_output'], x['product_type']))
    | RunnableLambda(lambda names_and_types: [
        {"product_name": item["product_name"], "slogan": slogan_chain.invoke({"product_name": item["product_name"]})}
        for item in names_and_types
    ])
)


# Let's run the full workflow
product_type_input = "smart pet feeder"
print(f"Generating names and slogans for a '{product_type_input}'...")

# The invoke method takes the initial input for the whole workflow
final_result = full_workflow.invoke({"product_type": product_type_input})

print("\n--- Final Product Ideas ---")
for item in final_result:
    print(f"Product Name: {item['product_name']}")
    print(f"Slogan: {item['slogan']}")
    print("-" * 20)

This sequential_chain_example.py is a bit more advanced but shows the power of chain creation examples:

1. name_chain: This chain takes a product_type and generates a list of names.
2. slogan_chain: This chain takes a product_name and generates a slogan.
3. full_workflow: This combines everything.
   • RunnablePassthrough.assign(names_output=name_chain): This step first runs the name_chain to get the product names and adds them to the input dictionary under names_output.
   • RunnableLambda(lambda x: process_names_for_slogans(x['names_output'], x['product_type'])): This is a custom function that takes the names generated by the first chain, processes them into a list, and prepares them for the next step.
   • The final RunnableLambda then iterates through each generated product name and runs the slogan_chain for each one, collecting all the slogans.

Run this script (python sequential_chain_example.py), and you will see the AI first generate product names, and then create a slogan for each of them. This is a powerful demonstration of chain creation examples and handling responses between different steps.

Memory: Helping AI Remember

Imagine talking to someone who forgets everything you said a moment ago. That would be a strange conversation! Most of our interactions with AIs, especially chatbots, require them to remember previous turns in a conversation. This is where “memory” comes in.

LangChain provides ways to give your AI applications memory. This means the AI can remember past messages, questions, and answers within a single conversation. Memory is crucial for building natural and helpful conversational AI. Without memory, every interaction would be like starting a brand new conversation.

There are different kinds of memory in LangChain, but for beginners, ConversationBufferMemory is a great place to start. It simply remembers all the messages in a conversation. Let’s see an example.

Create a file named memory_example.py:

# memory_example.py

from dotenv import load_dotenv
load_dotenv()

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.memory import ConversationBufferMemory
from langchain_core.runnables import RunnablePassthrough, RunnableLambda
from langchain_core.messages import AIMessage, HumanMessage

# Initialize the LLM
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)

# Initialize memory. This will store our conversation history.
memory = ConversationBufferMemory(return_messages=True, memory_key="chat_history")

# Our prompt now needs a special placeholder for the chat history
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a friendly chatbot that helps users learn about AI. Your name is Buddy."),
        MessagesPlaceholder(variable_name="chat_history"), # This is where the memory will be inserted
        ("human", "{input}"),
    ]
)

# A chain to save the conversation to memory after each turn
# We need a way to combine the current input with the chat history from memory
# and then pass it to the LLM, and finally update the memory.
# This requires a slightly more complex chain for memory management.

# Let's define the components of our chain
# First, load history from memory
# Second, format the input to include history and current message
# Third, run the LLM
# Fourth, save the current turn to memory

def get_chat_history(inputs):
    # This function retrieves chat history from the memory
    return memory.load_memory_variables({})["chat_history"]

# This creates our chain with memory
# The 'RunnablePassthrough' helps pass along the 'input' for the LLM
# The 'get_chat_history' function fetches the history
# The 'prompt' then builds the full message including history and new input
# The 'llm' processes it, and then we save the interaction.
conversational_chain = (
    RunnablePassthrough.assign(
        chat_history=RunnableLambda(get_chat_history)
    )
    | prompt
    | llm
)

# Function to interact with the chain and update memory
def chat_with_buddy(user_message):
    # Invoke the chain with the current message
    response = conversational_chain.invoke({"input": user_message})
    
    # Save the human message and AI response to memory
    memory.save_context(
        {"input": user_message},
        {"output": response.content}
    )
    return response.content

print("Hi! I'm Buddy, your AI learning assistant. Ask me anything about AI!")

print(f"Buddy: {chat_with_buddy('What is a Large Language Model?')}")
print(f"Buddy: {chat_with_buddy('Can you give me a simpler explanation?')}")
print(f"Buddy: {chat_with_buddy('What's your name again?')}")
print(f"Buddy: {chat_with_buddy('How can LangChain help me use them?')}")

In memory_example.py:

1. ConversationBufferMemory(return_messages=True, memory_key="chat_history"): We create a memory object. return_messages=True makes it return message objects, and memory_key is the name of the variable where history will be stored in our prompt.
2. MessagesPlaceholder(variable_name="chat_history"): This is added to our ChatPromptTemplate. It tells the prompt to insert the content of chat_history (which comes from our memory) at this spot.
3. get_chat_history(inputs): This small helper function simply loads the existing chat history from our memory object.
4. conversational_chain: This chain structure ensures that:
   • The chat_history is loaded.
   • The prompt combines the system message, the loaded chat_history, and your new input.
   • The llm generates a response.
5. memory.save_context(...): After each turn, we manually save both the user’s message and the AI’s response to the memory object. This updates the chat_history for the next interaction.

Run python memory_example.py. You’ll see that “Buddy” remembers previous parts of the conversation. When you ask for a “simpler explanation,” it remembers you’re still talking about Large Language Models. And when you ask its name again, it recalls it from its initial system prompt. Memory is essential for building engaging and useful chatbots. This is an important step in langchain python tutorial beginners step-by-step for conversational apps.

Agents: Letting AI Choose Tools

Sometimes, an AI needs to do more than just generate text. What if it needs to search the internet, do math, or look up information in a database? That’s where “agents” come in. Agents are like smart AI supervisors that can decide which “tools” to use to achieve a goal.

Imagine your AI has a toolbox. Each tool does a specific job, like a calculator or a web search engine. An agent is the AI that looks at your request, decides which tool (or tools) it needs, uses them, and then gives you the answer. This ability to choose and use tools makes agents incredibly powerful for working with LLMs on complex tasks.

For beginners, the concept is simple: give the AI a goal, and it will figure out how to use available tools to reach that goal. The agent uses an LLM to “reason” about which tool to use.

Let’s look at a simple example where an agent can perform a web search. We’ll need a search tool for this. For web search, we can use Tavily Search API. You’ll need to sign up for a free API key at Tavily AI. Once you have your key, add it to your .env file:

TAVILY_API_KEY=YOUR_TAVILY_API_KEY

Create a file named agent_example.py:

# agent_example.py

from dotenv import load_dotenv
load_dotenv()

from langchain_openai import ChatOpenAI
from langchain.agents import create_react_agent, AgentExecutor
from langchain import hub
from langchain_core.tools import Tool
from langchain_community.tools.tavily_research import TavilySearchResults

# Initialize the LLM
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)

# --- Define the Tools our Agent can use ---
# Here, our agent will have access to a web search tool.
# The 'TavilySearchResults' tool allows the agent to perform internet searches.
search_tool = TavilySearchResults(max_results=3) # We'll get up to 3 search results

# The list of tools our agent has access to
tools = [
    Tool(
        name="Tavily Search",
        func=search_tool.run,
        description="A search engine optimized for LLMs to answer questions about current events or general knowledge. Input should be a search query."
    )
]

# --- Create the Agent ---
# We'll use a pre-built prompt from LangChain Hub for our agent.
# This prompt guides the LLM on how to act as an agent and use tools.
prompt = hub.pull("hwchase17/react")

# Create the agent itself
# 'create_react_agent' is a standard way to make an agent that "reasons and acts"
agent = create_react_agent(llm, tools, prompt)

# --- Run the Agent ---
# AgentExecutor is what actually runs the agent, managing its actions and tool calls.
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True)

print("Hello! I am an AI agent with web search capabilities.")

# Ask the agent a question that requires a search
question1 = "What is the capital of France?"
print(f"\nUser: {question1}")
response1 = agent_executor.invoke({"input": question1})
print(f"Agent: {response1['output']}")

# Ask a more complex question
question2 = "Who won the last soccer World Cup and where was it held?"
print(f"\nUser: {question2}")
response2 = agent_executor.invoke({"input": question2})
print(f"Agent: {response2['output']}")

# Ask a question that the agent should just know (no search needed)
question3 = "What color is the sky on a clear day?"
print(f"\nUser: {question3}")
response3 = agent_executor.invoke({"input": question3})
print(f"Agent: {response3['output']}")

In agent_example.py:

1. TavilySearchResults(): This creates our web search tool. It lets the agent look things up online.
2. tools = [...]: We put all the tools the agent can use into a list. Here, it’s just our search tool.
3. prompt = hub.pull("hwchase17/react"): LangChain Hub is like a shared library of pre-made prompts. We’re using a common agent prompt called “react” (Reasoning and Acting). This prompt tells the LLM how to think step-by-step and use its tools.
4. agent = create_react_agent(llm, tools, prompt): This line creates our agent. It needs an LLM to do its thinking, the list of tools it can use, and the prompt to guide its behavior.
5. agent_executor = AgentExecutor(...): This is the engine that runs the agent. verbose=True is helpful because it shows you the agent’s “thought process” in the terminal, showing when it decides to use a tool and what it’s thinking.

Run python agent_example.py. You’ll see the agent’s internal monologue: it thinks about your question, decides it needs to use the “Tavily Search” tool, executes the search, reads the results, and then formulates an answer. For question3, it might just answer directly without searching because it’s common knowledge. This is a powerful demonstration of working with LLMs and tools. You can read more about agents in our Advanced LangChain Concepts post.

Advanced Topics (Briefly for beginners)

This langchain python tutorial beginners step-by-step covered the essentials. As you become more comfortable, you’ll find even more powerful features in LangChain. Here are a couple of ideas for what you might explore next.

Working with Different LLMs

We’ve used OpenAI’s gpt-3.5-turbo model in this tutorial. However, LangChain supports many other LLMs! You can connect to models from Google (like Gemini), Anthropic (like Claude), or even open-source models that you can run on your own computer. This flexibility means you can choose the best LLM for your specific needs and budget. Switching models often just means importing a different class (e.g., from langchain_google_genai import ChatGoogleGenerativeAI) and initializing it similarly.

Loading Documents

Most real-world AI applications need to work with your own data. Imagine having an AI that can answer questions about your company’s documents, your personal notes, or a huge book. LangChain has powerful tools to help you “load documents” from various sources (PDFs, websites, text files, databases). It can then split these documents into smaller pieces and store them in a special database called a “vector store.” This process, known as Retrieval-Augmented Generation (RAG), allows your AI to “look up” relevant information from your documents before answering your questions, making its responses much more accurate and specific. You can explore this further in our Building a Q&A Bot with LangChain guide.

Error Basics: What to Do When Things Go Wrong

Even experienced programmers run into errors. Don’t worry, it’s a normal part of learning! When your first Python script or a more complex chain creation example doesn’t work, here are some error basics tips:

• Read the Error Message: Python error messages can look scary, but they contain valuable clues. Look for the last few lines, which often point to the exact file and line number where the problem occurred.
• Common LangChain Errors:
  • AuthenticationError: This usually means your OpenAI API key is missing or incorrect. Double-check your .env file and make sure the key is correctly set as OPENAI_API_KEY=....
  • RateLimitError: You might be sending too many requests to the AI too quickly, or you’ve hit your usage limit. Wait a bit and try again, or check your OpenAI account dashboard.
  • AttributeError or ModuleNotFoundError: You might have forgotten to pip install a required library (like langchain-openai or python-dotenv). Ensure you installed everything in your activated virtual environment.
  • Internet Connection: Ensure your computer is connected to the internet, as most LLMs are cloud-based.
• Use print() statements: If you’re unsure what data is being passed around in your chain, add print() statements to see the values of variables at different steps.
• verbose=True for Agents: As shown in the agent example, setting verbose=True in AgentExecutor can give you amazing insights into what the agent is thinking, which helps in debugging its decisions.
• Google It: Copy and paste the exact error message into a search engine. Chances are, someone else has encountered the same problem, and you’ll find solutions or explanations.

Debugging is a skill that gets better with practice. Don’t get discouraged!

Organizing Your LangChain Code

As your LangChain projects grow, keeping your code neat and organized becomes very important. Good code organization makes your projects easier to understand, maintain, and share with others. This is a crucial skill even for langchain python tutorial beginners step-by-step.

Best Practices for Code Organization

Here are some simple tips for keeping your LangChain projects tidy:

1. Project Structure:
   • Keep your .env file at the root of your project.
   • Put your Python scripts (.py files) in a logical structure. For small projects, putting them directly in the root is fine. For larger ones, you might have src/ for source code, data/ for data files, etc.
   • Always keep your venv folder within your project directory.
2. Separate Concerns:
   • Don’t put all your code in one giant file. For example, if you have many different chains, put each chain’s definition in its own function or even its own file.
   • You could have a models.py for initializing LLMs, prompts.py for all your prompt templates, and chains.py for defining your various chains.
3. Use Functions:
   • Wrap reusable pieces of code in functions. If you find yourself copying and pasting code, it’s a good candidate for a function.
   • For instance, a function create_qa_chain(llm) could return a configured question-answering chain.
4. Comments:
   • Add comments to your code to explain what complex parts do. Imagine a future “you” (or someone else) trying to understand your code.
   • Good comments clarify why you did something, not just what you did (the code usually shows what).
5. Meaningful Names:
   • Use clear and descriptive names for your variables, functions, and files. Instead of x or temp, use user_question or summarization_chain.
6. requirements.txt:
   • After installing all your dependencies, create a requirements.txt file. This lists all the libraries your project needs. Someone else can then easily install everything by running pip install -r requirements.txt.
   • To create it: pip freeze > requirements.txt (make sure your virtual environment is active).
   • This is essential for code organization and sharing example projects.

By following these simple code organization tips, your LangChain projects will be much easier to manage as they grow more complex.

Your Own LangChain Projects: Ideas for Practice

You’ve learned the basic building blocks of LangChain. Now it’s time to put your knowledge into practice! The best way to learn is by doing. Here are some example projects you can try to build, expanding on what you’ve learned in this langchain python tutorial beginners step-by-step.

Simple Example Projects to Get Started

1. An Idea Generator:
   • Concept: Create an AI that generates creative ideas based on a topic you provide.
   • How to build: Use a ChatPromptTemplate with a variable for topic. The system message could instruct the AI to be a “creative brainstorming partner.”
   • Enhancement: Make it generate 5 ideas, and then pick the best one. This could use a sequential chain.
   • Skills practiced: first Python script, working with LLMs, chain creation examples.
2. Basic Chatbot (with Memory):
   • Concept: Build a simple chatbot that remembers your conversation.
   • How to build: Expand on the memory_example.py script. Let the user type multiple questions and have the AI respond.
   • Enhancement: Give your chatbot a specific personality (e.g., “a grumpy but helpful librarian”). Add this to the system message in your prompt.
   • Skills practiced: handling responses, memory, code organization.
3. Restaurant Review Summarizer:
   • Concept: Feed the AI a long restaurant review, and have it summarize the key pros and cons.
   • How to build: Use a ChatPromptTemplate to instruct the AI to act as a “review analyzer.” Pass the review text as input.
   • Enhancement: Extract specific entities like the food mentioned, atmosphere, and service ratings. This might involve more advanced prompt engineering.
   • Skills practiced: chain creation examples, handling responses.
4. Simple Translator:
   • Concept: Create an AI that translates text from one language to another.
   • How to build: Your prompt could be: “Translate the following {text_to_translate} from {source_language} to {target_language}.”
   • Enhancement: Allow the AI to detect the source_language automatically if not provided by the user (this would be a multi-step chain).
   • Skills practiced: working with LLMs, chain creation examples.
5. Question-Answering Bot (RAG without Documents yet):
   • Concept: Ask a question, and if the AI doesn’t know, it uses a search tool to find the answer.
   • How to build: This would be an agent using the TavilySearchResults tool, similar to our agent_example.py but with a general question-answering prompt.
   • Enhancement: Add more tools, perhaps a calculator tool for math questions. You can learn more about this in our Building a Q&A Bot with LangChain post.
   • Skills practiced: agent creation, tool usage, error basics.

These example projects are fantastic ways to solidify your understanding. Start simple, then gradually add more features.

Where to Go Next

You’ve taken a fantastic step-by-step journey through LangChain with Python. To continue your learning:

• LangChain Documentation: The official LangChain documentation is an excellent resource. It’s comprehensive and kept up-to-date.
• LangChain GitHub: Explore the LangChain GitHub repository to see how the library is built and to find more examples.
• Community: Join online communities, forums, or Discord channels dedicated to LangChain and AI. Learning from others and sharing your own example projects is incredibly rewarding.

Remember, building with AI is a journey of continuous learning. Keep experimenting, keep building, and have fun!

Conclusion

You’ve reached the end of this langchain python tutorial beginners step-by-step guide! You’ve learned how to set up your Python environment, install LangChain, and write your first Python script to talk to an AI. We explored the power of working with LLMs, built chain creation examples to make AI do more, and even touched upon memory and agents.

LangChain is a powerful framework that simplifies building intelligent applications. With the foundation you’ve built here, you’re well-equipped to start your own AI projects. Keep practicing with the example projects and exploring the vast capabilities of LangChain. The world of AI is waiting for your creativity!