LangChain Mastery: Build GenAI Apps with LangChain &Pinecone
Step-by-Step Approach to LangChain and Pinecone for GenAI with LLMs. Develop Real-World LLM-Powered Apps with Python
4.64 (3451 reviews)
20,408
students
10.5 hours
content
Mar 2025
last update
$84.99
regular price
What you will learn
How to Use LangChain, Pinecone, and OpenAI to Build LLM-Powered Applications.
Learn about LangChain components, including LLM wrappers, prompt templates, chains, and agents.
Learn about using multimodal Google's Gemini Pro Vision
How to integrate Google's Gemini Pro and Pro Vision AI models with LangChain
Learn about the different types of chains available in LangChain, such as stuff, map_reduce, refine, and LangChain agents.
Acquire a solid understanding of embeddings and vector data stores.
Learn how to use embeddings and vector data stores to improve the performance of your LangChain applications.
Deep Dive into Pinecone.
Learn about Pinecone Indexes and Similarity Search.
Project: Build an LLM-powered question-answering app with a modern web-based front-end for custom or private documents.
Project: Build a summarization system for large documents using various methods and chains: stuff, map_reduce, refine, or LangChain Agents.
This will be a Learning-by-Doing Experience. We'll Build Together, Step-by-Step, Line-by-Line, Real-World Applications (including front-ends using Streamlit).
You'll learn how to create web interfaces (front-ends) for your LLM and generative AI apps using Streamlit.
Streamlit: main concepts, widgets, session state, callbacks.
Learn how to use Jupyter AI efficiently.
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udemy ID
6/5/2023
course created date
6/27/2023
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