LangChain Mastery:Develop LLM Apps with LangChain & Pinecone
LangChain Mastery:Develop LLM Apps with LangChain & Pinecone
Step-by-Step LLM App Development using LangChain, Pinecone, OpenAI and Gemini. Make production-ready apps with Python.
Product Brand: Udemy
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Udemy Coupon Code for LangChain Mastery:Develop LLM Apps with LangChain & Pinecone Course. Step-by-Step LLM App Development using LangChain, Pinecone, OpenAI and Gemini. Make production-ready apps with Python.
Created by Andrei Dumitrescu, Crystal Mind Academy | 10.5 hours on-demand video course
LangChain Mastery Course Overview
LangChain Mastery:Develop LLM Apps with LangChain & Pinecone
Fully updated in 2024 for the latest versions of LangChain, OpenaAI, Google’s Gemini and Pinecone. Master LangChain, Pinecone, OpenAI and Google’s Gemini. Build hands-on generative LLM-powered applications with LangChain. Create powerful web-based front-ends for your generative apps using Streamlit.
The AI revolution is here and it will change the world! In a few years, the entire society will be reshaped by artificial intelligence. By the end of this course, you will have a solid understanding of the fundamentals of LangChain, Pinecone, OpenAI and Google’s Gemini Pro and Pro Vision. You’ll also be able to create modern front-ends using Streamlit in pure Python.
This LangChain course is the 2nd part of “OpenAI API with Python Bootcamp”. It is not recommended for complete beginners as it requires some essential Python programming experience. Currently, the effort, knowledge, and money of major technology corporations worldwide are being invested in AI. In this course, you’ll learn how to build state-of-the-art LLM-powered applications with LangChain.
What you’ll 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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Who this course is for
- Python programmers who want to build LLM-Powered Applications using LangChain, Pinecone and OpenAI.
- Any technical person interested in the most disruptive technology of this decade.
- Any programmer interested in AI.
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Taught by Andrei Dumitrescu
