LangChain Tutorial – Build Powerful AI Applications with Language Models

Learn how to use LangChain to build AI applications with language models. This beginner-friendly tutorial covers chains, agents, tools, and integrations for chatbots, question answering, and generative AI workflows.

1. Introduction

LangChain is a framework for building applications with large language models (LLMs).

  1. Helps developers chain together LLM calls, prompts, and tools.
  2. Supports chatbots, question answering, summarization, and generative AI pipelines.
  3. Integrates with OpenAI, Hugging Face, and other LLM providers.

2. Installation


pip install langchain
pip install openai

3. Basic Usage

3.1 Simple LLM Chain


from langchain import OpenAI, LLMChain
from langchain.prompts import PromptTemplate

# Define prompt template
template = "Summarize the following text: {text}"
prompt = PromptTemplate(input_variables=["text"], template=template)

# Initialize OpenAI LLM
llm = OpenAI(model_name="text-davinci-003")

# Create chain
chain = LLMChain(llm=llm, prompt=prompt)

# Run chain
output = chain.run("LangChain makes building AI applications easier and faster.")
print(output)

3.2 Using Agents with Tools


from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI

# Example tool
tools = [Tool(name="Search", func=lambda x: f"Searching for {x}", description="Fake search tool")]

# Initialize agent
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)

# Run agent
agent.run("Find information about LangChain.")

4. Features

  1. Chains: Combine multiple LLM calls in a sequence.
  2. Agents: Use LLMs with reasoning, tools, and actions.
  3. Memory: Maintain conversation history or context.
  4. Integrations: Works with APIs, databases, and external tools.
  5. Prompts: Template-driven prompt management for consistent outputs.

5. Best Practices

  1. Start with simple chains, then expand to agents.
  2. Use prompt templates for clarity and consistency.
  3. Integrate memory carefully to manage context effectively.
  4. Test thoroughly to avoid unintended outputs from the AI.
  5. Combine LangChain with Hugging Face or OpenAI models for full workflow.

6. Outcome

After learning LangChain, beginners will be able to:

  1. Build LLM-powered applications with chains and agents.
  2. Implement chatbots, question answering, and text generation pipelines.
  3. Integrate LLMs with tools, APIs, and databases.
  4. Create scalable and maintainable generative AI workflows.