Introduction to LangChain: Connecting LLMs with Real Data

Large Language Models (LLMs) like GPT-4 are incredibly powerful. But by default, they operate in a vacuum. They don’t know your internal tools, databases, or real-time APIs. That’s where LangChain comes in. It gives LLMs access to real data and actions — turning them into practical assistants that can answer company-specific questions, summarize documents, generate SQL queries, and much more.

LangChain is a framework that makes it easier to build applications that combine LLMs with external tools. Whether you’re building a chatbot, automation workflow, or internal search engine, LangChain gives you the building blocks to do it fast.

What Is LangChain?

LangChain is an open-source framework built in Python and JavaScript. It simplifies the process of connecting LLMs like GPT-4 with other components such as:

• Data sources (files, websites, databases)
• APIs (Stripe, Google Search, Slack, etc.)
• Memory systems (to hold conversational context)
• Agents (for decision-making)
• Chains (custom pipelines)

Rather than manually engineering each step in a prompt-engineering workflow, LangChain lets you plug pieces together in a modular way.

Why Use LangChain?

LLMs on their own are stateless and isolated. They can't remember things between calls, fetch live data, or take actions unless you wrap that behavior in code.

LangChain gives you prebuilt abstractions for:

• Injecting dynamic data into prompts
• Storing memory across sessions
• Handling multi-step workflows
• Routing inputs to the right tool
• Parsing complex outputs

If you’ve ever felt limited by raw API calls to OpenAI, LangChain opens up the next level.

LangChain Use Cases

Here are some practical examples where LangChain shines:

• Internal Search Assistant: Ask questions about company wikis, PDFs, or SQL data
• Chatbots with memory: Keep track of conversation context and personalize replies
• Agent-based automation: Let the model decide when to call APIs, browse websites, or write files
• Document summarizers: Automatically read and summarize documents on upload
• Report generators: Combine real-time data from APIs into human-readable summaries

Installing LangChain

If you're using Python, start with:

pip install langchain openai

For JavaScript or TypeScript developers:

npm install langchain openai

You’ll also need an OpenAI API key or similar key for whatever model provider you're using.

Basic Example: Text Completion

Let’s look at a basic LangChain example that uses OpenAI to complete a prompt.

from langchain.llms import OpenAI

llm = OpenAI(temperature=0.7)

response = llm("Tell me a joke about developers.")
print(response)

This is similar to calling the OpenAI API directly, but now you can easily add chains, memory, or output parsing on top.

Using Chains

Chains are sequences of steps that process and enrich data. The simplest example is a PromptTemplate chain:

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

template = PromptTemplate.from_template("Translate '{text}' to French.")
chain = LLMChain(llm=llm, prompt=template)

print(chain.run("I love coding"))

This creates a reusable prompt format and passes your input into it.

Working with Tools and Agents

LangChain agents are smart entities that decide which tools to use based on the user input. You can define tools like Google Search, calculator functions, or file readers — and the agent will invoke them intelligently.

from langchain.agents import load_tools, initialize_agent
from langchain.agents import AgentType

tools = load_tools(["serpapi", "llm-math"], llm=llm)

agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)

response = agent.run("What's the capital of France squared?")
print(response)

Here, the agent breaks down the question and uses the appropriate tool: search for the capital, then square the result.

Using Memory in Chatbots

By default, LLMs forget everything after each request. LangChain lets you add memory so that chatbots can remember previous questions, tone, or facts mentioned earlier.

from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain

memory = ConversationBufferMemory()
conversation = ConversationChain(llm=llm, memory=memory)

print(conversation.predict(input="My name is Karan."))
print(conversation.predict(input="What is my name?"))

The second message will correctly respond with “Karan,” even though you didn’t include it in the prompt again.

Retrieval-Augmented Generation (RAG)

One of the most important use cases is combining LLMs with your own data. For example, answering questions based on PDF files, Markdown docs, or database records.

LangChain uses vector stores to index your data. When a user asks something, the relevant chunks are fetched and passed into the prompt.

Here’s a simplified flow:

1. Split your documents into chunks
2. Generate embeddings for each chunk
3. Store them in a vector database (like FAISS, Pinecone, or Chroma)
4. On each question, search the index and pass results to the LLM

This is how apps like ChatGPT for PDFs or Notion AI work under the hood.

Popular Integrations

LangChain supports a large list of integrations:

Component	Examples
Vector Stores	FAISS, Pinecone, Weaviate, Chroma
Document Loaders	PDF, Markdown, CSV, Notion, Web URLs
LLM Providers	OpenAI, Anthropic, Cohere, Hugging Face
Tools/Agents	Google Search, Wolfram Alpha, Zapier, Shell

LangChain vs DIY

You can always build these workflows manually by calling the OpenAI API, parsing outputs, adding memory, and chaining together steps with your own logic. But LangChain saves you time by handling:

• Token management
• Prompt formatting
• Retry logic
• Agent routing
• Memory context

It abstracts the boilerplate so you can focus on building features, not infrastructure.

Frontend Options

If you want to build a UI on top of LangChain, combine it with tools like:

• Streamlit (for quick dashboards)
• Next.js or React (for full-stack apps)
• Gradio or Flask (for prototyping APIs)

LangChain is backend-agnostic — it just exposes Python or JavaScript functions that you can wrap in any interface you want.