build multi LLM quiz game using LangChain|Tutorial:102
GITHUB: https://github.com/ronidas39/LLMtutor...
TELEGRAM: https://t.me/ttyoutubediscussion
*00:00:20* - Introduction
Ronnie welcomes viewers to Total Technology Zone, tutorial 102 on building a multi-LLM quiz game using LangChain.
*00:01:01* - Tutorial Overview
Uses three LLMs: one for questions, one for answers, one for evaluation.
*00:02:00* - Channel Promotion
Encourages viewers to subscribe, like, and share.
*00:03:22* - Coding Begins
Imports LangChain and OpenAI modules.
Sets up the first LLM for generating questions.
*00:05:22* - Question Chain
Creates the question chain and response variable.
*00:06:43* - Answer LLM
Sets up the second LLM for answers.
Defines template and prompt for answers.
*00:07:50* - Answer Chain
Creates the answer chain and prints Q&A.
*00:08:57* - Evaluation LLM
Sets up the third LLM for evaluation.
Defines template and prompt for evaluation.
*00:10:03* - Evaluation Chain
Creates the evaluation chain and ensures correct process.
*00:11:52* - Running and Debugging
Demonstrates running the code and resolving issues.
*00:17:03* - Multi-Model Example
Shows using different LLMs for each task.
*00:18:55* - Conclusion
Plans for future tutorials on LangGraph and invites viewer suggestions.
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*00:00:20* - What's up, guys? This is Ronnie. Welcome back to our Channel, Total Technology Zone. This is tutorial 102, and today's topic will be how to build a multi-LLM quiz game using LangChain. I believe you guys are excited after watching the title slide, right? I'm also excited. I was thinking about what could be done for the next video, and this use case came across. I found this tutorial request on a freelancing website where someone asked if it's possible to use LLM to create games for school students or showcase different capabilities. So, that's what we will do today—build a quiz game using LangChain. First, we'll use one LLM to generate the questions, another to answer them, and a third to evaluate them. You can use the same LLM or different LLMs; it doesn't matter.
*00:01:01* - First, I'll show you the structure of the code, which you can extend later. Here's the source code link. Without wasting much time, let's start coding. But before that, I have a couple of requests. If you're watching my channel for the first time, please subscribe. I've posted over 100 videos before this, so if your goal is to work in AI, become an LLM developer, or learn LangChain, please check out my playlist from the beginning. Also, please like, share, and hit the notification bell to support my channel. Your support helps me grow and reach a larger audience.
*00:02:00* - Let's start coding. First, import the necessary modules from LangChain and OpenAI. We'll set up three LLMs: one for questions, one for answers, and one for evaluation.
*00:03:22* - Here's the code structure. We'll import the modules and set up the first LLM for generating questions.
*00:05:22* - We'll create the question chain and response variable. The template for the questions instructs the LLM to generate a question as if it were an experienced teacher.
*00:06:43* - Next, we'll set up the second LLM for answering questions. The template instructs the LLM to answer the question correctly and succinctly.
*00:07:50* - We'll create the answer chain and print both the question and the answer.
*00:08:57* - Now, we'll set up the third LLM for evaluation. The template for the evaluation instructs the LLM to assign marks based on the correctness of the answer, with a scale from 0 to 5.
*00:10:03* - We'll create the evaluation chain and ensure the process is correct.
*00:11:52* - Let's run and debug the code. We'll see the question generation, answering, and evaluation in action. Any issues will be resolved during this phase.
*00:17:03* - Here's a demonstration using different LLMs for each task. You can use GPT-4 for generating questions and answering, and GPT-3.5 for evaluation. This showcases the flexibility of using multiple models.
*00:18:55* - Conclusion. I hope you found this tutorial helpful. I'm planning future tutorials on LangGraph, which will help build multi-agent workflows seamlessly. Until then, I'll post more on multi-agent systems and use cases. If you have any suggestions or specific use cases you want me to cover, please let me know in the comments or via email. Thank you for watching, and see you in the next video.