How to Use the Gemini API with Python - Build a Customizable AI Chatbot 

Thank you! I’m happy you found the video useful. There will be many more coming.

That’s great! Learning to code is challenging but very rewarding. I hope you will find my future videos helpful as well 😊

Thanks. Happy to hear it was useful.

You’re welcome! Let me know if there is any specific content that you would be interested in seeing.

Thanks! I appreciate the comment. Let me know what other content you would like to see.

@@aarondunn-zt7ev It will be great if you use GEMNI API and made a chatbot, which will use custom datasheet from me.

To run your chatbot on Google Cloud Run, you'll need to containerize your application using Docker, then deploy it. This involves creating a Dockerfile to package your code and dependencies, pushing the image to Google Container Registry, and deploying it via the Google Cloud Console. You can follow Google's Cloud Run quickstart guide (cloud.google.com/run/docs/quickstarts ) for detailed steps. If there's interest, I could create a detailed tutorial video on this process in the future.

@@aarondunn-zt7ev that would be great if you could make such a tutorial. I have tried to put it on cloud run but my understanding is lacking. I keep getting a "service Unavailable" message on my run url. Im also not sure if I need to get a secret key etc. (my program runs fine from the console, but fails from the cloud run url). Anyway, great tutorials and I have subscribed :)

Yes, definitely! Gemini 1.5 is multimodal and can take images, audio and video in the prompt with text. I am working on a video on Gemini for vision / images right now and will let you know when it’s posted.

@@aarondunn-zt7ev okay, ty

I just uploaded my video on Gemini for vision. Check it out here: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-XcMcNBZawAU.htmlsi=6p93NAlB6o9DePnz

There are 2 approaches you can take. One is to just put all your data inside the prompt. This is possible nowadays even with big datasets as models now have huge context windows (up to 2 million tokens for Gemini 1.5 Pro). However, adding too much data to the prompt can be costly and even result in lower quality answers. The other approach is RAG (Retrieval Augmented Generation) which extracts smaller chunks of your dataset that are similar to your input query / question and then uses that data to produce an answer. This is much more efficient and when done properly may even result in better outputs. With either method, you would include in the prompt an instruction to only consider the data you provided when generating the response. This doesn’t work 100% of the time but overall it’s pretty reliable. I put out a video a little while back that demonstrated RAG for a chatbot and how to restrict the model to the data you provide. Check out the video here: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-cpm28hEvGAA.htmlsi=qAX6wM0ytI_VSu7v

You would select whatever project you want your Gemini code associated with. If you don’t have any Google Cloud projects then you should be able to select a Default option. Let me know if this doesn’t work and I’ll take a closer look.

I choose the generative language client and there is a key error every time

In the model give a system instruction like, (system instruction= "Give response only from the file uploaded and not from outside the pdf or csv file provided" ) . This will limit it to answer only from the knowledge base given, feel free to customise your system instructions.

There are 2 approaches you can take. One is to just put all your data inside the prompt. This is possible nowadays even with big datasets as models now have huge context windows (up to 2 million tokens for Gemini 1.5 Pro). However, adding too much data to the prompt can be costly and even result in lower quality answers. The other approach is RAG (Retrieval Augmented Generation) which extracts smaller chunks of your dataset that are similar to your input query / question and then uses that data to produce an answer. This is much more efficient and when done properly may even result in better outputs. With either method, you would include in the prompt an instruction to only consider the data you provided when generating the response. This doesn’t work 100% of the time but overall it’s pretty reliable. I put out a video a little while back that demonstrated RAG for a chatbot and how to restrict the model to the data you provide. Check out the video here: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-cpm28hEvGAA.htmlsi=qAX6wM0ytI_VSu7v

I don’t have any experience creating Facebook apps, but I can look to do some more research on it. From what I’ve discovered so far, to add a Gemini-powered chatbot to your Facebook page, you'll need to integrate it using Facebook Messenger's API. This involves setting up a Facebook Developer account, creating a Facebook app, and configuring a webhook to handle messages. You'd then connect the Gemini API to process and respond to these messages. It would be an interesting project and video!