Build AI Workflows Using LangChain (Python Library) | AI Engineering Framework

Library: LangChain Install: pip install langchain openai Basic AI Call ❌ from openai import OpenAI client = OpenAI() response = client.chat.completions.create( model="gpt-4.1-mini", messages=[{"role":"user","content":"Explain machine learning"}] ) print(response.choices[0].message.content) LangChain Workflow ✅ from langchain.prompts import PromptTemplate from langchain.chat_models import ChatOpenAI from langchain.chains import LLMChain template = "Explain {topic} in simple terms." prompt = PromptTemplate( input_variables=["topic"], template=template ) llm = ChatOpenAI() chain = LLMChain(llm=llm, prompt=prompt) print(chain.run("machine learning")) LangChain builds AI pipelines. LangChain is one of the most popular frameworks used to build applications powered by large language models. Instead of calling AI models directly, LangChain helps engineers create structured workflows that combine: Prompts AI models Tools Memory Data sources This enables developers to build more advanced AI systems such as: AI agents Chatbots Knowledge assistants RAG systems Automation pipelines LangChain simplifies complex AI workflows and provides modular components that developers can combine to build scalable AI applications. 🧰 TOOLS & TECHNOLOGIES USED Programming Python 3.10+ Libraries LangChain OpenAI SDK Concepts Prompt templates Chains AI workflows Agent frameworks Optional Tools Vector databases (FAISS, Pinecone) Streamlit Docker 📁 PROJECT FOLDER STRUCTURE langchain_project/ │ ├── chains/ │ └── simple_chain.py │ ├── prompts/ │ └── templates.py │ ├── agents/ │ └── ai_agent.py │ ├── requirements.txt └── README.md 🧠 STEP-BY-STEP IMPLEMENTATION 🔹 STEP 1: Install LangChain pip install langchain openai This installs the core framework. 🔹 STEP 2: Create Prompt Template from langchain.prompts import PromptTemplate template = "Explain {topic} in simple terms." Prompt templates allow reusable prompts. 🔹 STEP 3: Define Input Variables promp