LangChain Memory Tutorial | Building a ChatGPT Clone in Python

In this LangChain tutorial, we will explore the powerful capabilities of LangChain memory and build a ChatGPT clone that is available all the time and works in the terminal. LangChain is a framework for developing applications powered by language models, allowing us to create data-aware and agentic applications that can interact with their environment.

Links:
🐱 Github: https://github.com/alejandro-ao/chagpt-cli-python
🦜 Langchain Buffer Memory: https://python.langchain.com/en/latest/modules/memory/types/buffer.html
🦜 Langchain Entity Memory: https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html

In this project-based tutorial, we will cover LangChain memory and its various types, including the LangChain Conversation Buffer Memory and Conversation Entity Memory. We will also provide three different LangChain memory examples, using the ChatGPT API with Python.

To build our ChatGPT clone, we will be using the OpenAI Chatbot GPT and Python OpenAI to create a natural language processing engine. We will explore the fundamentals of machine learning and large language models, allowing us to create a robust chatbot that can interact with users in a conversational manner.

Whether you are new to LangChain or an experienced developer, this tutorial will provide you with the skills and knowledge needed to create powerful language model applications.

So, join us in this LangChain memory module tutorial and learn how to create a chatbot that can interact with users in real-time, providing an immersive conversational experience. Don’t miss this opportunity to learn from a LangChain expert and take your language model development skills to the next level!

Timestamps:

0:00 Intro
1:10 Setup
4:35 Create Chat
13:07 Hand-made Memory
20:15 Buffer Memory
26:36 Entity Memory
34:00 Create Bash Alias
38:01 Conclusion