1. What is LangChain?
LangChain is an open-source framework designed to build powerful applications powered by Large Language Models (LLMs) like OpenAI’s GPT, Google’s Gemini, Claude, Mistral, Cohere, and more.
While LLMs can generate impressive results on their own, LangChain lets you chain multiple components together, such as prompts, memory, tools, retrievers, and agents to build context-aware, data-connected, and task-specific applications.
LangChain focuses on composability, meaning you can build pipelines (called chains) by connecting small modular parts like building blocks.
2. Why Use LangChain?
LLMs by themselves are stateless — they respond to a single prompt without memory or tools. LangChain helps you:
- Add memory to keep track of conversations
- Connect with external data sources (databases, documents, APIs)
- Use tools like search engines, calculators, or web scraping
- Structure prompts consistently
- Build complex multi-step workflows
LangChain is especially powerful when building:
- Chatbots
- RAG (Retrieval-Augmented Generation) systems
- Autonomous agents
- Document question answering systems
- Data analysis apps
3. LangChain Architecture Overview
LangChain is modular, and the core components include:
| Component | Description |
|---|---|
| LLMs | Interfaces to different language models (OpenAI, Gemini, etc.) |
| Prompts | Templates that define the structure of input |
| Chains | Sequential logic combining LLMs, prompts, memory, output parsers |
| Memory | Stores conversation history or state |
| Tools | External functionalities the agent can use (e.g., calculator, search) |
| Retrievers | Brings data from documents, vector stores, etc. |
| Agents | Decision-makers that choose which tool to use for a task |
4. Installation
You can install LangChain and the required LLM providers using pip:
pip install langchain pip install langchain-openai pip install langchain-google-genai pip install python-dotenv
If you are using Jupyter Notebook:
pip install ipython
5. How LangChain Works (Simplified)
LangChain applications work by chaining together several building blocks — like LLMs, prompts, memory, and tools — into a structured workflow. The real strength of LangChain is how it organizes these parts into a modular and composable architecture.
Here’s a step-by-step breakdown of how LangChain works:
5.1 Define a Prompt Template
What is it?
A prompt template is a reusable format that tells the LLM how to behave and what information to expect. Instead of writing raw prompts every time, you define a template that dynamically fills in user inputs.
Example:
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant that answers user queries."),
("human", "Question: {question}")
])
Explanation:
"system": Provides initial instructions to the LLM (like setting behavior)."human": The part that inserts actual user input dynamically using{question}.
Why it matters:
Prompt templates ensure consistency and allow you to abstract away the formatting of input data to the LLM.
5.2 Initialize the LLM (Language Model)
What is it?
This is where you connect LangChain to a specific large language model like:
- OpenAI GPT
- Google Gemini (via
langchain-google-genai) - Claude
- HuggingFace
Example:
from langchain_google_genai import ChatGoogleGenerativeAI
llm = ChatGoogleGenerativeAI(
model="gemini-1.5-pro",
temperature=0.7,
max_tokens=512
)
Explanation:
model: Specifies which model to use (e.g.,gemini-1.5-pro)temperature: Controls randomness (0 = deterministic, 1 = creative)max_tokens: Sets maximum response length
5.3 (Optional) Add an Output Parser
What is it?
LangChain includes output parsers that convert raw LLM responses into structured formats like strings, lists, or dictionaries.
Example:
from langchain_core.output_parsers import StrOutputParser output_parser = StrOutputParser()
Why it matters:
Many LLMs return verbose or raw text; parsers help clean and structure the output before using or displaying it.
5.4 Build a Chain (The Core Pipeline)
What is it?
This is where you connect all the components together using the pipe operator |. It represents data flow: input passes through the prompt → LLM → output parser.
Example:
chain = prompt | llm | output_parser
How it works under the hood:
- Takes your input dictionary (e.g.,
{'question': 'What is LangChain?'}) - Uses the prompt to create a formatted message
- Sends the prompt to the LLM
- Receives the LLM’s raw output
- Passes that to the output parser
- Returns clean text as the final output
Chaining allows modular design — you can plug in or swap components easily.
5.5 Invoke the Chain with Input
What is it?
Once the chain is built, you use .invoke() to pass in real user input and get the final response.
Example:
response = chain.invoke({"question": "What is LangChain?"})
print(response)
What happens internally:
- The input is inserted into the prompt
- Prompt is passed to the Gemini model
- LLM returns a response
- Parser cleans the output
- You get the final answer
5.6 (Optional) Integrate with a User Interface
While the above steps cover backend logic, you can integrate this chain into:
- A Streamlit interface
- A FastAPI or Flask backend
- A Jupyter notebook
- A React frontend via API