DEV Community: Nitisha

LangChain Components

Nitisha — Wed, 17 Jun 2026 02:22:10 +0000

What are LangChain Components?

LangChain Components are the individual building blocks provided by LangChain that work together to create AI-powered applications. Each component performs a specific task, such as generating responses, managing memory, retrieving information, using tools, or processing documents.

1.Models**:Core interfaces through which U interact with AI models.
This component standardizes the interface to communicate with different LLM's using their API's.

2.Prompts: Input provided to LLM are called prompts.
This component provides a way to manage and optimize the input given to LLMs to get the best possible output.

Types of Prompts

Dynamic and Reusable: Templates that let you swap variables (like names or data) without rewriting the core instruction.
Role-based : Assigning a specific persona to the AI (e.g., "You are an expert coder") to guide the style of its response. 3.** Few-Shot Prompting** : Providing a few examples of input and output to help the AI understand the pattern you want it to follow.

3.Chains:Used to build pipelines.

Parallel Chains: Run multiple models or processes at the same time to gather information, which is then combined into a single output.

Conditional Chains: Use logic to determine the next step in the process. For example, an AI agent can analyze feedback and decide between different paths (like sending a "Thank You" or an "Email" based on the result).

4.Indexes: Connect your application to external knowledge such as PDFs, websites, or databases.

Doc Loader: Imports your data from various sources.
Text Splitter: Breaks large documents into smaller, manageable pieces.
Vector Store: Saves those pieces in a way the AI can easily search.
Retrievers: The mechanism used to fetch the relevant data when a question is asked.

5.Memory: "LLM API calls are "stateless."
This means the model does not remember past conversations made via API.
With the LangChain Memory component, it can store conversations, allowing the AI to keep track of context over time.

6.Agents: Agents use LLMs to decide which actions to take and in what order.It is like chatbot with superpower.
Instead of just responding, an Agent acts as a controller that uses tools (like search engines or calculators) to complete a task.

Types of Agents

Action Agents: They execute a task, observe the result, and repeat until finished.
Plan-and-Execute Agents: They create a step-by-step plan for complex problems before performing each action.
ReAct Agents: They use a loop of "Reasoning" and "Acting" to think through a task, pick a tool, and adjust based on observations.

Check the previous blog on LangChain Introduction.

Nitisha

Jun 15

What is LangChain? A Detailed Explanation

#langchain #llm #detailed

2 min read

What is LangChain? A Detailed Explanation

Nitisha — Mon, 15 Jun 2026 11:11:49 +0000

LangChain is a powerful framework that solves a major headache in AI development: vendor lock-in.

Why LangChain?
When you build an application by calling an LLM's API directly (like OpenAI's GPT or Google's Gemini), your code becomes tightly coupled to that specific provider. If you decide to switch models ,perhaps to save money, improve performance, or move to an open-source model you often have to rewrite the entire "plumbing" of your application, from how prompts are formatted to how responses are parsed.

How LangChain Solves This
LangChain acts as a standardized interface (or abstraction layer). Instead of your code talking directly to a specific LLM, it talks to LangChain's generic "LLM" component.

Think of it like an electrical plug. Instead of hard-wiring your appliance to the wall, you use a universal outlet. Whether the power comes from a coal plant, a solar farm, or a wind turbine (the different LLM providers), your appliance stays the same.

Without LangChain:

# Direct API: Rigid and hard to change
from openai import OpenAI

client = OpenAI(api_key="your_key")

def ask_model(question):
    # This structure is specific only to OpenAI
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": question}]
    )
    return response.choices[0].message.content

The "Magic" of the Standard Interface :

In LangChain, every model provider (OpenAI, Anthropic, Google, Llama) implements the same standard methods. Because they all "speak the same language" to your code

You don't change your logic: Your core application code—the part that manages memory, prompt templates, and chains—remains identical.
Minimal configuration changes: To switch from GPT-4 to Claude or a local Llama model, you typically only need to change one line of initialization code where you define the model object
Rapid Experimentation: You can test three different models against your application in minutes just by swapping the model name in your configuration, rather than refactoring your entire codebase.

The Power of Abstraction
When you use LangChain, you are using abstractions. An abstraction simplifies complex code by representing a process as a named component

Prompt Templates: Instead of hard-coding strings, you use a template object that stays the same regardless of which LLM you use.
Unified Methods: You use functions like .invoke() or .stream() for every model. Under the hood, LangChain translates these into the specific API calls required by the provider you selected.

With LangChain

from langchain_openai import ChatOpenAI

# You only change this object to swap models (e.g., to Anthropic or Llama)
llm = ChatOpenAI(model="gpt-4", api_key="your_key")

def ask_model(question):
    # .invoke() works the same way regardless of the model chosen
    response = llm.invoke(question)
    return response.content

For more info:

Home - Docs by LangChain

docs.langchain.com

Check this for next imporant topic: LangChain Components

Nitisha

Jun 17

LangChain Components

#langchain #langchaincomponents #llm #beginners

2 min read

I know this was a bit of a long read, but I'm confident that by now you have a solid understanding of what LangChain is and why it's so useful.
If you found this blog helpful, please leave a like and let me know in the comments if the explanation was easy to understand. 🚀

AI • LLMs • LangChain • Open Source

Nitisha — Mon, 15 Jun 2026 05:08:23 +0000

Nitisha

Jun 14

Learning in Public: My Daily Journey into LLMs, LangChain, and Open Source.

#langchain #opensource #llm #machinelearning

2 min read

Learning in Public: My Daily Journey into LLMs, LangChain, and Open Source.

Nitisha — Sun, 14 Jun 2026 12:05:08 +0000

Hi everyone! 👋

I’m Nitisha, a postgraduate MCA student based in Bengaluru, and I am officially starting my journey of "Learning in Public."

My goal is to master Artificial Intelligence and Data Science, specifically focusing on NLP, LLMs, and LangChain. I’ve decided to document everything here—the wins, the bugs, the research, and the breakthroughs.

What to expect from this space:

If you’re also starting your journey or are interested in AI development, hit the Follow button! Here is what I’ll be sharing:

📝 TIL (Today I Learned): Quick, bite-sized insights on the concepts I'm exploring.
💻 Code & Snippets: Actual implementation of my projects and experiments.
🤖 Agent Sessions: Live looks at my coding sessions and agent workflows.
🚀 Project Updates: Progress reports on my open-source contributions.
📸 Visuals & Notes: Sketches, diagrams, and resources that help me understand complex topics.

I believe that the best way to learn is to teach what we know and share what we’re learning. I’m currently a beginner, and I’m looking forward to growing with all of you.

🤝 Let’s Grow Together

Starting with my next post, I’ll be sharing my raw, real-time learning notes on LangChain. These notes will be exactly how I create them during my sessions—honest, unpolished, and very much a work in progress!

I truly believe that we can reach great heights by learning from each other. To make this even better, I’d love to know what you’re interested in.

Check :

Nitisha

Jun 15

What is LangChain? A Detailed Explanation

#langchain #llm #detailed

2 min read

Do follow along on my journey—hit that follow button! I’m looking forward to your feedback and cooperation as we navigate this AI landscape together. Stay tuned for the next update, and let’s learn something new.