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Using Serverless Generative AI | Google Vertex AI
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- čas přidán 5. 08. 2024
- uild & deploy blazing-fast generative AI apps on Google Cloud without server management! This guide walks you through leveraging Vertex AI Studio and GCP Cloud Run to create serverless applications powered by cutting-edge generative models. Unleash serverless AI capabilities without infrastructure headaches. Get started today!
Resources:
Learn more about Vertex AI → goo.gle/4bKbYpD
Vertex AI Studio → goo.gle/44PvWNA
Cloud Run → goo.gle/3yxh5vm
Chapters:
0:00 - Intro
0:50 - Generative AI for Travel Advertisers
3:06 - Vertex AI Studio
5:01 - The code
7:31 - Recap
Watch more episodes of Serverless Expeditions → goo.gle/ServerlessExpeditions
Subscribe to Google Cloud Tech → goo.gle/GoogleCloudTech
#ServerlessAIExpeditions
Incredibly useful video to learn more about what's possible with vertex AI and cloud functions. Please keep them coming!
Glad to hear you found it useful!
OMG, I haven't seen the video yet but if it is what I feel it is, thank you, totally perfect timing.
Nice demo. One thing I like about this workflow is that you didn't need to create a "thing" in Vertex AI that must be tracked, versioned, deployed, kept up to date, etc. It appears you're using a pre-created model, where the only new thing being created to support the application you're making is a particular way of invoking a Vertex AI Python library (the prompt, temperature, etc).
For context, as a developer, I'm imagining all the "things" in Google Cloud I'd need to create to deploy a cloud function like this. It appears that it would be just the function itself (after first making sure the Vertex AI API is enabled in the GCP project), no need to make a model first and then reference that model from the cloud function code. The fewer things to track and deploy the better. I suppose I'd start making a custom model if I wasn't happy with the output from this any of the pre-created ones.
Good point! It takes significant effort to create and maintain your own model. If you can use a prebuilt model, you can get your app to market much sooner.
Thanks for sharing your thoughts. For "easy" use cases, pre-trained models are the fastest ones to build applications on. By "easy" I mean use cases that generate generic responses that are easy to validate or not harmful and threatening. In this application, "travel" is an easy use case because it generates generic information about certain places, I can use Google Maps API to validate if the response is accurate. For more complex use cases, you may want to fine-tune (add at least 1000 examples), build RAG, or build your own model. I'll write more details in my LinkedIn in a few week (look for "lantran25") -- hope to have you in the conversation.
Very good video, I got to know, just simply we can use VertexAI like this.. wow! expecting more serverless video for AI Usecases.
Thank for sharing👍
Please add image search option in youtube
Any chance to have the repo link for the code?? 😊
how did I know you weren't going to go into the part I was hoping to see!? I love how she says "the interesting part" about the part I could have guessed... I wanna know where the video comes from! LOL How are these place/slogan combos turned into video?! would love an addendum to address that leg of the process! PLS! :)
The code that generates the video doesn't have anything to do with AI, so we felt it was best to exclude it in the interest of time. But it uses the libraries PIL and textwrap. Hope that gives you something to work with!
Great Video! Is there a public repo with the source code?
Really nice, simple and useful video. Quick question: would that AI be able to read current websites and execute queries on them?
For an external application, you need to scrap the web content, and include these info as "context" in your prompt, then you can execute questions (you can search with keywords RAG and Langchain to see examples)
Hi,I got the verification error several while creating the free trail GCP account.i try with several mails and several ATM card it doesn't work with anytime.And the google doesn't provide the support for this kind of issues if anyone knows how to resolve the issues please ping me
This is fantastic! Could you please make text-music or text-text or text-image or text-video serverless generated ai apps with react native and then publish on gcp?
Thank you for the suggestion! If you're creating a React Native app (or any other app) you may find it easier if you split the problem into smaller ones. Create your client app separately from the backend. Let the client app call a REST API that you built on Cloud Run. Your Cloud Run service hits the Vertex API like we did in this video. The client/frontend and the backend don't need to know about each other, except for the API contract they share.
Hi Team, I have created a free trail account through net banking and it costed me a 1000 rs and the description said that it was refundable if I close the billing account. But when I clicked on close billing account, it stated that the billing account was charged 5900 and it may reflect under payments. I don't understand since my free credits were not over , how can they charge me with this. Please help me in this.. what should I do?
Google Cloud provides billing support. Do a web search for "google cloud billing support" and you will find it. If that doesn't work, take a screenshot of your cloud billing report, remove any personal info, and post it in the official sub-reddit for Google Cloud (r/googlecloud).
is there a repo to consult?
What is the reasoning behind transforming the model's response into JSON using Python string manipulation rather than asking the model to return JSON in the desired format?
Great question. I developed this solution on an early-stage initial model on GCP last year, so I used string manipulation. Recent models can understand the prompting better, and yes - you are correct, you can give an example of the output as part of the prompt and it will generate JSON string
# QUESTION:
How to generate structured datasets?
Here is a big difficulty you can help with.
# CONTEXT:
A chatbot can easily be created using Vertex AI Search and Conversation / DailogFlow CX agent. The agent in turn relies on having a good dataset. Uploading unstructured data to create a dataset is very problematic.
# OBJECTIVE:
To have a well-structured dataset the agent finds easy to get the correct responses from.
# ISSUE:
This Vertex AI Search and Conversation RAG embedding and indexing process works well on the Google sample data with respect to pixel phones, but badly or not at all when other unstructured data is used. The RAG process initially turns all the unstructured data into a soup which often turns the raw data into incomprehensible junk.
# NEEDED:
A streamlined method of using Generative AI to convert unstructured data (e.g. PDF or HTML files) into well-structured files (e.g. BigQuery)?
That is a great question and I like how you phrased it. It would be a great topic for a future video! I added it to the list of videos to create.
Excellent. Please don't delay in making the video. This dataset creation issue is blocking a lot of new applications from succeeding.@@TheMomander
Nice API key
That is nice..What if the model does not return predictable strings ..say it sends some link and asks you to go check yourself..or any other verbose text ..then the parsing code for video string generation would need human intervention? Oh yes I would love to know about serverless vector datastore and how to generate embeddings for a custom pdf…but I don’t want to pay constant vector db cost…only on demand .. Can we also have serverless rag etc..please do more videos on that… I appreciate your work - Shudh from Bangalore
To reduce the chance of answers that your code can't parse, lower the temperature like we did in the video. Thank you for your other suggestions!
You can also add string examples or json example output to the prompt (called multi-shot prompting).
@@lantrann-googleIs the multi-shot prompting idea supported in the Vertex AI UI that was shown in the video? And if so, would it be included in the generated code too?
@@user72974 Multi shot prompting means adding example output as part of a prompt, so the generated code will include it too. For example:
```
Provide 3 reasons to study Python
Example output:
Reason 1: Study Python because
Reason 2: Study Python because
Reason 3: Study Python because
```
You'll see the response follows the exact template in the prompt. Here is the response I got
Reason 1: Study Python because it is a versatile language that can be used for a wide range of applications, from web development to data science.
Reason 2: Study Python because it is a beginner-friendly language that is easy to learn and use.
Reason 3: Study Python because it is an in-demand language that can boost your career prospects.
@@user72974 Hi, yes, you can include multiple examples in the prompt, and these examples will be included in the generated code as part of a string for prompt. In my experience, 3 example is sufficient for an easy use case
But we want one click deployment with AI just description needed for the website design.
make a startup that automates these things under the hood :)
@@phaZZi6461 That's a very good product idea!