Lecture Transcript Notes

Introduction

• Recording started, Roman took over the presentation.
• Roman is sharing his screen to demonstrate project creation and usage in Google Cloud Console.
• Ensured all participants could see and hear clearly.

Creating a New Project in Google Cloud

• If you're new to Google Cloud, logged in and can't see any project, create a new one.
  • Click on the project dropdown menu in the top bar.
  • Click 'New Project', give it a name, and assign the necessary billing account.

Session Goals

• Objectives: Introduction to Gen AI, Vertex AI, and integrating these models into applications.
• Applications could be mobile, web, or command line tools needing API connectivity to Vertex AI's models.
• Importance of serverless tools for simplicity and operational ease.
  • Example tools: Google Cloud Functions, Cloud Run.

Setting Up The Environment

• Selected Google Cloud Console and the specific project for the session's code lab.
• Activated Cloud Shell, ensuring to be in the correct project (gcloud config list, gcloud config set project <project-id>).
• Set up environment variables for project ID and region.
  • export GCP_PROJECT=<project-id>
  • export GCP_REGION=us-central1
• Enabled necessary cloud services (e.g., Cloud Build, Cloud Functions).

Understanding the Code Lab

• Repository: gen-ai-app-templates-google-cloud
  • Contains various integration projects with Vertex AI (e.g., text prediction using cloud functions).
• Navigation: Into text-predict-cloud-function directory in the repository.
• Use Google Cloud Editor (based on VS Code) to examine the files (main.py, requirements.txt).

Main Code Explanation

• main.py: Handles the cloud function logic including retrieving and processing the prompt.
• requirements.txt: Lists necessary dependencies such as the functions framework and Google Cloud libraries.
• Explained how this code integrates with Vertex AI using a Python example.

Deploying The Cloud Function

• Used gcloud commands to deploy the function.
  • Example: gcloud functions deploy predict_text ...
  • Discussed various command parameters such as --runtime, --region, and environment variables.
• Viewing Deployment: Explained how to check deployment status via the Cloud Functions Console.
• Testing: Using console’s built-in tester and curl for API testing.

Performing Cleanups (Post-session)

• Optional: Deployments can be deleted via the Cloud Functions Console to avoid unwanted charges or issues.

Introduction to LangChain

• Brief overview: LangChain is a framework that simplifies integrations with multiple LLMs and data sources.
• Importance: Useful for complex applications requiring interactions with various LLMs and data sources.
• Contains abstractions for models, prompts, vector stores, etc.
• Demonstrated potential integration with Google Cloud and various other services.
• Highlighted LangChain’s use with Google documents and other non-Google services.

Example of LangChain Integration

• Showed an example of importing and using LangChain with Google’s Vertex AI models.
• Comparison: Using LangChain vs. direct API invocation for various LLMs.

Conclusion

• Reviewed session objectives and ensured the lab completion with participants.
• Explained submission requirements for verification (e.g., screenshots of the Cloud Function's details and curl command results).
• Encouraged participants to explore further integrations and use cases.