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 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 application 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.

Requirements
Basic Python programming experience is required.
You should be able to sign up to OpenAI API with a valid phone number.

Description
Master LangChain, Pinecone, and OpenAI. Build hands-on generative LLM-powered applications with LangChain.

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, and OpenAI.

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 is LangChain?

LangChain is an open-source framework that allows developers working with AI to combine large language models (LLMs) like GPT-4 with external sources of computation and data. It makes it easy to build and deploy AI applications that are both scalable and performant.

It also facilitates entry into the AI field for individuals from diverse backgrounds and enables the deployment of AI as a service.

In this course, we'll go over LangChain components, LLM wrappers, Chains, and Agents. We'll dive deep into embeddings and vector databases such as Pinecone.

This will be a learning-by-doing experience. We'll build together, step-by-step, line-by-line, real-world LLM applications with Python, LangChain, and OpenAI.

We will develop an LLM-powered question-answering application using LangChain, Pinecone, and OpenAI for custom or private documents. This opens up an infinite number of practical use cases.

We will also build a summarization system, which is a valuable tool for anyone who needs to summarize large amounts of text. This includes students, researchers, and business professionals.

I will continue to add new projects that solve different problems. This course, and the technologies it covers, will always be under development and continuously updated.