LangChain & GenAI Applications
Build Powerful LLM-Powered Apps with LangChain
What is LangChain?
LangChain is an open-source framework that helps developers build powerful applications using Large Language Models (LLMs) like GPT, Claude, or PaLM. It makes it easier to combine LLMs with data sources, memory, tools, and workflows.
Why use LangChain?
Key Components of LangChain
| Component | Purpose |
| Prompt Templates | Predefined LLM instructions |
| LLMs | Interface to models like GPT, Claude |
| Memory | Maintain context between interactions |
| Chains | Sequential/logic flows of components |
| Agents | Use tools + LLMs for smart decision-making |
| Document Loaders | Load files (PDF, CSV, YouTube, etc.) |
| Retrievers | Search chunks from documents/databases |
Getting Started with LangChain
Install LangChain
| pip install langchain
pip install openai # or another LLM provider |
Basic LLM Usage
| from langchain.llms import OpenAI
llm = OpenAI(temperature=0.7) response = llm(“What is Generative AI?”) print(response) |
Using Chains: Connect Prompts + LLMs
| from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
prompt = PromptTemplate( input_variables=[“topic”], template=”Explain {topic} like I’m 5 years old.” )
chain = LLMChain(llm=llm, prompt=prompt) print(chain.run(“machine learning”)) |
Add Memory to Chatbots
| from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
memory = ConversationBufferMemory() chat = ConversationChain(llm=llm, memory=memory)
chat.run(“Hi, my name is Tania.”) chat.run(“What is my name?”) |
The bot will remember your name across conversations!
Working with Documents (PDF, CSV, etc.)
| from langchain.document_loaders import PyPDFLoader
from langchain.vectorstores import FAISS from langchain.embeddings import OpenAIEmbeddings from langchain.chains import RetrievalQA
# Load and embed documents loader = PyPDFLoader(“report.pdf”) docs = loader.load()
# Create vector store db = FAISS.from_documents(docs, OpenAIEmbeddings()) retriever = db.as_retriever()
# Build QA chain qa = RetrievalQA.from_chain_type(llm=llm, retriever=retriever) qa.run(“What is the summary of the report?”) |
Real-Life GenAI Applications Using LangChain
| Application | Description |
| Chat with PDFs | Ask questions about long documents |
| Marketing Content Generator | Generate ads, slogans, product descriptions |
| AI Tutors | Personalized education with memory and feedback |
| Search Assistants | Combine LLM with real-time web search |
| Legal/Policy Assistants | Analyze large legal documents with citations |
| Coding Assistants | Integrate LLMs with IDEs and API docs |
Learning Outcomes
By the end of this module, you will be able to:
Next Steps
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