Looking to build Gen AI applications that integrate with Vertex AI PaLM Foundational models? Would you like to use utilize standard frameworks like Python Flask, Streamlit, Gradio to host these applications on Google Cloud? You have come to the right place.
The Sample Applications listed are provided as application templates that you can use. The key goal of these applications is to get you started quickly and help you understand how you can integrate the Vertex PaLM API and the necessary commands to deploy these applications to Google Cloud.
You can go through the various applications and pick an application or two that interests you. Click on any of the applications to see detailed documentation, sample template and instructions to deploy on Google Cloud.
We provide instructions for setting up your environment in Cloud Shell. Before you run any of the sample applications, ensure that you have followed the instructions in SETUP.md.
| Requirement | Application Name | Technologies Used |
|---|---|---|
| Develop a chat application using FlutterFlow and Vertex AI PaLM API model. | Medium Post | FlutterFlow, Flutter, Cloud Functions v2, Python |
| Develop a chat application using Python Flask framework and Vertex AI PaLM API model. | chat-flask-cloudrun | Cloud Run, Python Flask |
| Develop a chat application using Gradio framework and Vertex AI PaLM API model. | chat-gradio | Cloud Run, Gradio, Python |
| Develop a chat application using Streamlit framework and Vertex AI PaLM API model. | chat-streamlit | Cloud Run, Streamlit, Python |
| Provide an API for the Vertex AI PaLM Code Model for your client applications. | code-predict-cloudfunction | Cloud Functions v2, Python |
| Provide an API for the Vertex AI PaLM Text Model for your client applications. | 1. text-predict-cloudfunction - Python 2. text-predict-cloudfunction - Java 3. text-predict-cloudfunction - LangChain |
Cloud Functions v2, Python, Java, LangChain Python, langchain4j |
| Provide an API for the Vertex AI PaLM Chat Model using langchain4j for your client applications. | chat-predict-cloudfunction-java | Cloud Functions v2, Java, langchain4j |
| Develop an Event-Driven application that processes uploaded files and summarizes their content. If you are looking for a detailed summarization solution with reference architecture, refer to our Jump Start Solution - Generative AI Document Summarization. | summarization-gcs-cloudfunction | Cloud Functions v2, Cloud Storage, Python |
| Develop a Slack Slash Command that helps summarize text for the user. | summarization-slack | Cloud Functions v2, Python |
| Develop a Apps Script function to summarize text inside a Google Doc. | summarization-appsscript | Cloud Functions v2, Python, Apps Script |
| Develop a Chrome Extension that works with Vertex AI PaLM Text Model. | wordlookup-chromeextension | Cloud Functions v2, Python, HTML, CSS, JavaScript |
| Sample Applications using PaLM Developers API | palm-api-apps | Applications built using PaLM Developers API https://developers.generativeai.google/ |
If you are conducting a Gen AI Hackathon or making some of the foundational models available to a larger set of developers in your organization, you would probably want to control access to the APIs and put a API Gateway/Proxy in front of those models. Google Cloud provides Apigee that you can use. Check out this blog post that goes into the details on:
- Why you would want to control access to your foundational models?
- Advantages of an API proxy that manages access to your foundational models.
- Step by Step guide to setup Apigee API Proxy.
If you would prefer not to use Cloud Shell and would like to utilize a Developer IDE like VS Code, we are provided support for importing and running/deploying these applications within your IDE environment.
Cloud Code for VS Code provides IDE support for the full development cycle of Kubernetes and Cloud Run applications, from creating a cluster to running your finished application. We are providing the entire list of applications in the form of custom applications that you can import directly into VS Code in which you have configured Cloud Code.
Assuming that you have Visual Studio Code and the Cloud Code plugin setup, click on the Cloud Code link in the status bar.
- Click on New Application
- Select Custom Application
- When asked for the Git Repository URL, enter the URL of this repository: https://github.com/rominirani/genai-apptemplates-googlecloud
- You will shown all the projects. Select one of your choice.
- Complete the rest of the steps to import the projects into Visual Studio Code.
Check the screencast below:
