LangChain Tutorial (Python) #4: Chat with Documents using Retrieval Chains
Channel: Leon van Zyl
Date: 2024-01-25

#openai #langchain

Retrieval chains allow us to connect our AI-application to external data sources to improve question answering.  This is one of the most important skills to have as an AI developer, and in this video we will break down the process step-by-step.
We will cover Document Loaders, Text Splitters, Embeddings, Vector Stores and Retrieval using the Retrieval Chain.

☕ Buy me a coffee:
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📑 Useful Links:
Langchain Python docs: https://python.langchain.com/docs/get_started/introduction
Source Code: https://github.com/leonvanzyl/langchain-python-tutorial

💬 Chat with Like-Minded Individuals on Discord:
https://discord.gg/VwHZzbNawh

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🕒 TIMESTAMPS:
00:00 - Intro
01:31 - Project setup
03:26 - Retrieval Explained (RAG)
04:29 - Context
04:56 - Langchain Document
06:31 - Documents Chain
07:55 - Adding Document Loader
10:09 - Text Splitter and Chunking
13:04 - Intro to Vector Stores
14:09 - Embeddings
15:06 - Creating the vector store
16:34 - Retrieval Chain
19:21 - Testing the Retrieval Chain
19:56 - Retriever K value