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Google Cloud Vertex AI Chatbot

This repository contains code and instructions on creating a simple chatbot using Langchain w/ Google Cloud's Vertex AI platform and the Gemini 1.5 Pro model.

Prerequisites

  • A Google Cloud Platform (GCP) account with billing enabled.
  • Python 3.9 or later installed on your local machine.
  • A text editor or IDE of your choice.

Setup

  1. Create a Google Cloud Project:
  • If you don't have an existing project, create one in the Google Cloud Console.
  1. Enable the Vertex AI API:
  • Navigate to the Vertex AI section in the console and enable the API if it's not already enabled.
  1. Create a Service Account:
  • Go to the IAM & Admin > Service Accounts section.
  • Create a new service account.
  • Grant the "Vertex AI User" role to the service account.
  • Download the JSON key file for the service account.
  1. Set Environment Variables:
  • Create a .env file in the root directory of this repository.
  • Add the following line, replacing path/to/your/keyfile.json with the actual path to your downloaded key file:
GOOGLE_APPLICATION_CREDENTIALS="path/to/your/keyfile.json"

Installation

  1. Clone the Repository:
git clone https://github.com/brainiakk/langchain-gemini-test.git
cd langchain-gemini-test
  1. Install Dependencies:
pip install -r requirements.txt

Running the Chatbot

  1. Run the Python Script:
python -m main
  1. Interact with the Chatbot:
  • The script will start a loop and prompt you for input.
  • Type your query and press Enter.
  • The chatbot will respond using the Gemini Pro model.

Code Explanation

  • load_dotenv(): This line loads environment variables from the .env file, including the path to your service account key file.
  • llm = ChatVertexAI(...): This line initializes the ChatVertexAI object from the vertexai.preview.language_models library. It specifies the Gemini Pro model and a temperature value for controlling the randomness of the responses.
  • response = llm.invoke(text): This line sends the user's input (text) to the Vertex AI API for processing by the Gemini Pro model.
  • print(response.content): This line prints the model's response to the console.

Notes

  • The code provided is a basic example and can be extended with additional features.
  • Refer to the Vertex AI documentation for more advanced usage and customization options.
  • Remember to replace path/to/your/keyfile.json with the actual path to your service account key file.

Disclaimer

This code is provided for educational purposes only.