Chatting with 44K Fashion Products: LangChain Opportunities and Pitfalls

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  • Опубліковано 9 чер 2024
  • In this video, I’m going to uncover some LangChain pitfalls and opportunities by building a fashion e-commerce chatbot from scratch using generic building blocks with data from Hugging Face.
    Link to the code:
    colab.research.google.com/dri...
    ▬▬▬▬▬▬ V I D E O C H A P T E R S & T I M E S T A M P S ▬▬▬▬▬▬
    0:00 Introduction and overview
    0:48 AI and Headless Ecommerce
    2:20 Chatting with 44K fashion products
    5:15 What do we want to achieve?
    7:15 Pitfalls and opportunities
  • Наука та технологія

КОМЕНТАРІ • 22

  • @farzinhaddadpour7192
    @farzinhaddadpour7192 9 місяців тому

    I loved this video! Short and very informative! Thanks for the effort!

  • @aimademerich
    @aimademerich Місяць тому

    Phenomenal thank you 🙏🏽

  • @avidlearner8117
    @avidlearner8117 9 місяців тому +3

    Fantastic video. Anyone working on an actual product can relate to this. Yes, there's a ton of "how to" content, with live code that give incredible results. But as soon as you go beyond the demonstration, the real work starts and you actually change the way you see LLMs. They suddenly become mega functions and a lot less magical. Still! Plenty of stuff to discover. But one jey aspect I think is separating everything into agentic tools and having a lot of small schemas to map every little aspects so that the larger LLM can then have a complete picture of a lot of small elements. Thanks for sharing!

    • @BunniesAI
      @BunniesAI 9 місяців тому +1

      Agreed. Awesome video 🤩 I’m running an e-commerce store with 90.000 products (car parts and gear for cars). Finding the right part for the right car would be tremendously helpful in a conversational style (we have a car-database, but it’s not the interaction I think the customer wants). Getting past the “simple” stuff and into the nitty gritty would be awesome.

    • @rabbitmetrics
      @rabbitmetrics  9 місяців тому +1

      @NyeMedier2010 It sounds like a case where you might need to customize the BaseRetriever class used in the conversational chain. Appreciate the comment - thanks for watching!

    • @rabbitmetrics
      @rabbitmetrics  9 місяців тому +2

      Much appreciated! Thanks for watching. Will dive into the creation of good tools at some point.

    • @BunniesAI
      @BunniesAI 9 місяців тому

      @@rabbitmetrics I’m signing up for your community 🤩

    • @rabbitmetrics
      @rabbitmetrics  9 місяців тому

      @@BunniesAI We would be delighted to have you join

  • @haojiehang5863
    @haojiehang5863 5 місяців тому

    Hey there, wondering what tool is used for build the presentation, the animation and overall flow looks pretty good.

    • @rabbitmetrics
      @rabbitmetrics  4 місяці тому +1

      Thanks! I'm using Final Cut Pro for the "explainer part" of the videos.

  • @kumarj693
    @kumarj693 9 місяців тому +1

    I am not a Coder but I work as a Linux Engineer. Honestly, I didn't really understood the concepts that you explained completely, can I still join your rabbitmetrics course for learning AI & its use cases & be able to create my AI business where I can make extra bucks ? I am interested in learning AI & implement it but I have no idea where to start.

    • @rabbitmetrics
      @rabbitmetrics  9 місяців тому +1

      @kumarj693 Thanks for watching. The goal with the community is to bring together the tech and business side of AI and focus on solving problems. We're only starting up now, but non-coders have joined already.

  • @altered.thought
    @altered.thought 9 місяців тому +2

    doesn't open ai function calling solve the structured response issue? as per this tutorial -> ua-cam.com/video/8eCZeFhvyGE/v-deo.html

    • @rabbitmetrics
      @rabbitmetrics  9 місяців тому +1

      Yes, that would one way to solve it but it ties you to OpenAI. I will revisit this issue in later videos. Thanks for watching

  • @Poprollable
    @Poprollable 9 місяців тому

    Causal ai is the solution. In the meantime, knowledge graphs.