AI Agent that Chats to a Database!

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In this second video of the series, I implement Natural Language Query (NLQ) in an AI ecommerce chatbot, to improve the accuracy and relevancy of catalog retrieval.

I utilize the BotPress platform to develop the prototype (Link below)

In the video, I deploy the AI chatbot on a dummy Magento instance. That being said, this type of bot would work just as well on an ecommerce platform, such as Shopify, BigCommerce, WooCommerce, SquareSpace, SalesForce etc.

I utilize Supabase as a remote Postgres database that can be queried via SQL. I also use Postman to test API calls and the OpenAI playground to iterate on system prompts.

🛠️ Tools & Platforms Used:

Timestamps
00:00 - Intro
00:40 - What is NLQ?
01:28 - NLQ vs Vector DB
03:32 - End to End Flow
05:54 - Catalog & Database
06:53 - Querying Rest API
10:42 - Integrating into Chatbot
12:14 - Testing the NLQ Bot
17:52 - Challenges & Considerations
19:47 - Review & Next Steps

👋 Connect with Us: Have any questions or want to discuss more about AI automation? Drop your thoughts in the comments below or join our community to collaborate with other automation enthusiasts!
  1. Daniel Walsh
  2. ecommerce
  3. chatbot
  4. ai
  5. nlq
  6. sql
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Комментарии
Автор

Thank you Daniel. This is a very useful video that clarifies your first one. There are very few if any at all videos on chatbot security measures. It might be an idea to dedicate a video on the topic of securing your chatbot. All the best!

myworld
Автор

This is great video and thanks for sharing this invaluable information, I was wondering:
1. Do you keep the context of the conversation as long as the user keeps chatting with the bot?. And If so, could that make it slower?
2. Is there a way to make the chatbot work faster?. Say reducing/consolidating the steps, using a different model, review the js code?
Thanks again!

alexisayala