L-4 Step-by-Step Guide to Building a ChatGPT Clone
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- Опубликовано: 9 фев 2025
- In this step-by-step guide, I'll show you how to build your own ChatGPT clone using LangChain, OpenAI API, and Streamlit. Whether you're a beginner or an experienced developer, this video will walk you through the entire process, from setting up your environment to deploying your chatbot.
Code Repository:
Check out the code on GitHub: github.com/Aar...
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In this video, you'll learn:
How to set up and configure LangChain.
How to integrate the OpenAI API for chatbot functionalities.
How to use Streamlit to create a user-friendly interface.
#chatgpt #llms #generativeai #openai #openaiapi #languagemodels #langchain #gpt #gpt3
One more video, packed with all the information, explained in simple terms. 👏
Thanks!
Thank you so much for the great and simple explanation, Mam. Simply great with the latest trends . God Bless you
My pleasure 😊
We can not thank you more than praying for you madam , God Almighty will continue blessing you the great job you are rendering through this your RUclips channel
Thank you so much
Your videos are exceptional
I appreciate that!
Very impressive video maam
Thanks a lot
Thank you for this tutorial!
You are welcome!
Good content.
Glad you enjoyed it
Mam, please make videos on rag and fine tunning
It will be my next video. Working on it.
Mam Please make video on Fine tunning and make one end to project on fine tunning please
Sure
Can you share deployment also. I'm trying to deploy on streamlit but some cuda issues and error
Sure, I will cover that too soon.
@@CodeWithAarohi waiting
Hi @Scoopsie-12b Need your help. Could u please tell how did u deploy?
Mam, after completing the Playlist of Gen AI, what all project we can build, some idea, I mean if you can make a video on roadmap for Gen AI regarding projects and all, that would be helpful as well. I mean how can we land with a job? Regards
I will do a video on it once I finish the GenAI playlist.
@CodeWithAarohi okay Mam
Ma'am, in your application, you are simply appending the user message and the response from the model to the session state. During the next user input, we pass this to the model to maintain the context window. But what if we have a long conversation? How can we pass all the messages to the model? Will it give an error as we will exceed the token limit (context window of the LLM)? Please explain.
btw Thanks ma'am your explanation is supeerrrbbb :)
I will cover it in my upcoming videos