AI Chatbots with Python, LangChain, LangSmith & Streamlit

AI-Powered Chatbots with Python, LangChain, LangSmith & Streamlit

What you'll learn
- AI & LLM Foundations
- LangChain Core Skills
- Building with Streamlit
- Building a Scalable Chatbot
- Testing and Observability (with LangSmith)

Requirements
- Basic Python Knowledge (Required)
- Familiarity with the Command Line (Helpful)
- Curiosity About Chatbots and Generative AI

Description
Build Real-World AI Chatbots with Python, LangChain & Streamlit

From Zero to Production-Ready Conversational AI using LLMs, LangGraph, LangSmith& StreamlitAre you ready to build powerful, intelligent chatbots using cutting-edge AI tools?

In this hands-on masterclass, you’ll learn how to design, buildreal-world AI chatbotsusingPython, LangChain, Streamlit, and modern LLM platforms likeOpenAIandOllama. Whether you're a developer, data scientist, or AI enthusiast—this course will teach you everything you need to create production-ready AI assistants from scratch.

You’ll go far beyond the basics. By the end, you’ll have built a full-stack chatbot withmemory,LLM switching,persistent conversation historyandLangSmith-powered observability and debugging.What You’ll Build

A memory-enabled chatbot with a modern Streamlit UI

Multi-provider LLM support (OpenAI & Ollama)

Persistent chat history using SQLite

Fully traceable workflows using LangSmith

A ready-to-deploy conversational assistant for your business or clientsKey Technologies Covered

Python– Core scripting for AI Chatbot

LangChain– Prompting, chains, memory, parsing

Streamlit– Frontend chat UI and user interaction

LangSmith– Debugging, tracing, observability

OpenAI & Ollama– Cloud and local LLM integration

SQLChatMessageHistory– Long-term memory via SQLite

Prompt engineering– System + human messages and history placeholdersWhat You Will Learn

How to design a modular and scalable LLM chatbot

How to build Streamlit chat UIs

How to use LangChain’s memory components and prompt templates

How to switch between OpenAI and Ollama models on the fly

How to structure LangChain chains using RunnableLambda and RunnableWithMessageHistory

How to trace, debug, and compare model outputs using LangSmith

How to store and retrieve chat history from a SQL database

How to deploy your chatbot locally or to the cloudWho This Course Is For

Python developers exploring Generative AI and LLMs

Full-stack or backend developers integrating chatbots into apps

QA & automation engineers building smart validation tools

Data scientists wanting to build LLM-based assistants

Indie hackers and startup founders creating AI-powered tools

Students and career switchers entering the AI engineering spacePrerequisites

Basic knowledge of Python (functions, loops, data structures)

Familiarity with running Python scripts and installing packages

No prior LangChain or LLM experience required – everything is explained from scratch!By the end of this course, you'll have the skills and confidence to build intelligent, scalable AI chatbots that feel truly interactive—and production-grade.

Who this course is for:
- SDETs, QA & Automation Engineers
- Python Developers
- AI & ML Enthusiasts
More Info