Build an LLM-powered Q&A App using LangChain, OpenAI and Python

2h 38m 22s
English
Paid

WHAT IS THIS PROJECT?

LLMs like GPT are great at answering questions about data they've been trained on...but what if you want to ask it questions about data it hasn't been trained on? For example, maybe you want to ask them about information from after their training cut-off date, or information from non-public documents? One of the best ways to do this is inputting the information, even large amounts of information such books and documents, into the model. And that's exactly what this project will teach you from scratch!

In this project you'll learn how to build state-of-the-art LLM-powered applications with LangChain, Pinecone, OpenAI, and Python! We'll build together, step-by-step, line-by-line. This will be a learning-by-doing experience.

WHY IS THIS PROJECT AWESOME?

This is a portfolio project. It requires about 3 hours to both learn LangChain and build the Q&A application.

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. LangChain is a great entry point into the AI field for individuals from diverse backgrounds and enables the deployment of AI as a service. It has a virtually infinite number of practical use cases.

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# Title Duration
1 Project Demo 05:25
2 Introduction to LangChain 07:16
3 Setting Up The Environment: LangChain, Pinecone, and Python-dotenv 11:02
4 LLM Models (Wrappers): GPT-3 06:14
5 ChatModels: GPT-3.5-Turbo and GPT-4 04:42
6 Prompt Templates 05:11
7 Simple Chains 05:50
8 Sequential Chains 08:08
9 Introduction to LangChain Agents 04:01
10 LangChain Agents in Action 05:29
11 Short Recap of Embeddings 01:53
12 Introduction to Vector Databases 06:58
13 Splitting and Embedding Text Using LangChain 09:20
14 Inserting the Embeddings into a Pinecone Index 07:54
15 Asking Questions (Similarity Search) 07:54
16 Project Introduction 06:09
17 Loading Your Custom (Private) PDF Documents 07:28
18 Loading Different Document Formats 05:13
19 Public and Private Service Loaders 04:38
20 Chunking Strategies and Splitting the Documents 06:39
21 Embedding and Uploading to a Vector Database (Pinecone) 11:18
22 Asking and Getting Answers 10:34
23 Adding Memory (Chat History) 09:06

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