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Building LLM-Powered Chatbots with LangChain: A Step-by-Step Tutorial

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Introduction to LangChain for Large Language Models

Large Language Models (LLMs) have revolutionized the field of natural language processing, enabling the creation of powerful applications like chatbots, question-answering systems, and text generation tools. However, building these applications from scratch can be challenging and time-consuming. This is where LangChain comes in - a Python library that makes it easier to develop applications powered by LLMs.

In this article, we'll dive into LangChain and explore how it can be used to build LLM-powered applications. We'll cover installation, key concepts, and provide code examples to help you get started.

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What is LangChain?

LangChain is an open-source library that provides a standard interface for working with LLMs. It abstracts away many of the complexities of interacting with LLMs directly, allowing developers to focus on building their applications.

Some key features of LangChain include:

  • Support for multiple LLM providers like OpenAI, Cohere, Hugging Face, etc.
  • Modular components like prompts, chains, agents, and memory
  • Utilities for working with documents, embeddings, and vector stores
  • Integration with other tools and frameworks

By leveraging LangChain, developers can quickly prototype and deploy LLM-powered applications without having to worry about the low-level details of LLM APIs.


To get started with LangChain, you'll need to install it using pip. It's recommended to create a virtual environment first:

python -m venv langchain-env
source langchain-env/bin/activate
pip install langchain

You'll also need to install the packages for the LLM provider you want to use. For example, to use OpenAI's models:

pip install openai

Make sure you have an API key for the provider you're using. You can set it as an environment variable:

export OPENAI_API_KEY=your_api_key_here

Key Concepts


Prompts are the instructions or context you provide to the LLM to guide its output. LangChain provides a PromptTemplate class to make it easy to create and use prompts.

Here's an example of creating a prompt template:

from langchain import PromptTemplate
template = """
You are an assistant that helps users write professional emails.
Given the following context:
Write a professional email response. Sign the email as "John".
prompt = PromptTemplate(

You can then use the prompt by passing in the input variables:

context = "I need to inform my boss that I'll be taking a vacation next week."

This will print out the completed prompt with the provided context.


LangChain provides a standard interface for interacting with different LLM providers through the LLM class.

Here's an example of using OpenAI's text-davinci-003 model:

from langchain.llms import OpenAI
llm = OpenAI(model_name="text-davinci-003")
result = llm("What is the capital of France?")

This will print out the model's response to the question.


Chains allow you to combine multiple components, like prompts and LLMs, to create more complex applications. LangChain provides several built-in chains, as well as the ability to create custom chains.

Here's an example of a simple sequential chain that takes in a prompt, passes it to an LLM, and then passes the LLM's output to a second prompt:

from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
first_prompt = PromptTemplate(
    template="What is a good name for a company that makes {product}?",
second_prompt = PromptTemplate(
    template="Write a catchphrase for the following company: {company_name}",
chain = LLMChain(llm=llm, prompt=first_prompt)
second_chain = LLMChain(llm=llm, prompt=second_prompt)
result = chain.run("colorful socks")
print(f"Company name: {result}")
result = second_chain.run(result)
print(f"Catchphrase: {result}")

This will generate a company name based on the product "colorful socks", and then generate a catchphrase for that company name.


Agents are systems that use LLMs to dynamically determine which actions to take based on the user's input. They can use tools to interact with the outside world and make decisions on what to do next.

Here's an example of a simple agent that uses a search tool and a calculator tool:

from langchain.agents import initialize_agent, Tool
from langchain.tools import BaseTool
from langchain.llms import OpenAI
def search_api(query):
    return "Search results for {query} would be returned here"
def calculator(expression):
    return eval(expression)
search_tool = Tool(
    description="Useful for searching the internet for information."
calculator_tool = Tool(
    description="Useful for doing math calculations."
tools = [search_tool, calculator_tool]
llm = OpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
result = agent.run("What is the population of Canada divided by the population of the United States?")

This agent will first search for the populations of Canada and the US, extract the numbers from the search results, and then use the calculator to divide them.


LangChain is a powerful library that makes it easier to develop applications powered by LLMs. By providing a standard interface and modular components, it allows developers to focus on building their applications rather than worrying about the details of LLM APIs.

In this article, we covered the basics of LangChain, including installation, key concepts like prompts, LLMs, chains, and agents, and provided code examples to help you get started.

To learn more about LangChain, check out the official documentation at https://langchain.readthedocs.io/ (opens in a new tab). You can also explore the examples directory in the GitHub repo for more advanced use cases and ideas.

As LLMs continue to advance, tools like LangChain will play an increasingly important role in making them accessible and easy to use for a wide range of applications. By leveraging LangChain, you can build powerful language-based applications and be at the forefront of this exciting field.

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