File Name: | AI Chatbots with Python, LangChain, LangSmith & Streamlit |
Content Source: | https://www.udemy.com/course/ai-chatbots-with-python-langchain-langsmith-streamlit |
Genre / Category: | Other Tutorials |
File Size : | 444.5 MB |
Publisher: | Prateek Sethi |
Updated and Published: | August 6, 2025 |
In this hands-on masterclass, you’ll learn how to design, build real-world AI chatbots using Python, LangChain, Streamlit, and modern LLM platforms like OpenAI and Ollama. 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 with memory, LLM switching, persistent conversation history and LangSmith-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 clients
Key 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 placeholders
What 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 cloud
DOWNLOAD LINK: AI Chatbots with Python, LangChain, LangSmith & Streamlit
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