QuickBot Website Search Template is a powerful website search application. It features a user-friendly frontend interface and a robust backend API that leverages a custom Google Cloud Function for its Retrieval Augmented Generation (RAG) capabilities.

Overview

This project allows users to perform intelligent searches over website content. The backend uses a custom Google Cloud Function (e.g., create-intent-index) for processing website data, generating embeddings, and managing the search index. This README guides you through setting up the necessary cloud infrastructure (primarily the Cloud Function) and then running both the frontend and backend components.

Demo

See the Website Search Template in action, learning from a live website:

Prerequisites

Before you begin, ensure you have the following installed:

Google Cloud SDK (gcloud CLI): Essential for authentication, deploying the Cloud Function, and interacting with Google Cloud services.
Docker and Docker Compose v2: Recommended for running the frontend and backend services in a containerized environment.
- Verify your Docker Compose version with docker compose version.
Python 3.10+: Required if you choose the manual setup path for the backend.
Node.js and npm (or yarn): Required if you choose the manual setup path for the frontend.

Getting Started

The setup process involves two main phases:

Initial Cloud Setup: Deploying the essential Google Cloud Function.
Running the Application: Choosing between Docker Compose or manual setup for the frontend and backend.

Phase 1: Initial Cloud Setup (Required for All Run Options)

This phase ensures the core cloud infrastructure, specifically the RAG Cloud Function, is deployed.

1. Configure Google Cloud (`gcloud`)

Authenticate with gcloud and set your default project. This account will be used to deploy the Cloud Function and for the application to interact with other GCP services.

# Log in with your Google account
gcloud auth login

# Set your active Google Cloud project
gcloud config set project <your-gcp-project-id>

# Set up Application Default Credentials (ADC) for local development
# This allows your local application to authenticate as you.
gcloud auth application-default login
gcloud auth application-default set-quota-project <your-gcp-project-id>

# Verify your configuration
gcloud auth list
gcloud config list project

Replace <your-gcp-project-id> with your actual Google Cloud Project ID.

2. Deploy the RAG Cloud Function (e.g., `create-intent-index`)

The backend relies on this Cloud Function for its RAG capabilities.

Function Details (Example):

Name: create-intent-index (you can choose another name, but be consistent)
Source Location (relative to project root): functions/create-intent
Entry Point: create_intent_index
Runtime: python311 (or as specified in the function's requirements.txt)
Region: Choose a GCP region (e.g., us-central1)

Deployment Steps:

Ensure you are in the project root directory.
Deploy the Cloud Function using gcloud: Replace <your-chosen-function-name>, <your-gcp-project-id>, and <your-chosen-region> with your actual values.
```
gcloud functions deploy <your-chosen-function-name> \
  --project=<your-gcp-project-id> \
  --gen2 \
  --runtime=python311 \
  --region=<your-chosen-region> \
  --source=./functions/create-intent \
  --entry-point=create_intent_index \
  --trigger-http \
  --allow-unauthenticated \
  --memory=1Gi \
  --timeout=3600s
```
- Note: --allow-unauthenticated is used here for development simplicity. For production, you should configure IAM to secure your Cloud Function and grant specific invoker permissions.
- The deployment process might take a few minutes.
Retrieve the Deployed Function's URL and Name: After successful deployment, you need the HTTP trigger URL and the exact name you used.
```
# Get the URL
gcloud run services describe <your-chosen-function-name> \
  --project=<your-gcp-project-id> \
  --region=<your-chosen-region> \
  --format="value(status.url)"
```
Important: Carefully note down the <your-chosen-function-name> you used and the FUNCTION_URL output by the command above. You will need these for the environment variable configuration in the next phase.

3. Prepare Environment Variable Values

After deploying the Cloud Function, you will have the following critical pieces of information:

Your <your-gcp-project-id>
The <your-chosen-region> for the Cloud Function
The <your-chosen-function-name>
The FUNCTION_URL (retrieved in the previous step)

You will need to use these values to configure your application.

For Docker Compose: You will directly edit the docker-compose.yml file.
For Manual Setup: You will create a backend/.local.env file.

4. Run Backend Setup Script (e.g., for BigQuery)

The backend might have a setup script (e.g., backend/setup.py) to initialize resources like BigQuery tables. Run this after configuring your gcloud and ensuring the necessary environment variables (like GCP_PROJECT_ID, BIG_QUERY_DATASET) are available in your shell.

Temporarily set environment variables if not using a sourced .env file for this step:

export GCP_PROJECT_ID="<your-gcp-project-id>"
export BIG_QUERY_DATASET="quickbot_website_search_bq" # Or your chosen name
# ... any other vars needed by setup.py

Run the script:
```
python3 backend/setup.py
```

Phase 2: Running the Application

After completing Phase 1, choose one of the following options:

Option 1: Using Docker Compose (Recommended for Quick Start)

This option runs the backend and frontend services within Docker containers. It assumes the Cloud Function is already deployed (Phase 1).

Ensure Docker and Docker Compose v2 are installed and running.
Navigate to the project root directory.

Configure docker-compose.yml: Open your docker-compose.yml file. Locate the environment section for your backend service (and frontend service, if applicable). You will need to set the environment variables directly here using the values obtained in "Phase 1".

Example docker-compose.yml backend service snippet:

services:
  backend:
    # ... build context, Dockerfile ...
    environment:
      - GCP_PROJECT_ID=<your-gcp-project-id>
      - GCP_REGION=<your-chosen-region>
      - FUNCTION_URL=<function_url_retrieved_in_phase_1_step_2>
      - BIG_QUERY_DATASET=quickbot_website_search_bq # Or your chosen name
      - ENVIRONMENT=development
      - FRONTEND_URL=http://localhost:4200 # Or the frontend container's address
      # - GOOGLE_APPLICATION_CREDENTIALS=/root/.config/gcloud/application_default_credentials.json # If mounting ADC
    # volumes:
      # - ~/.config/gcloud/application_default_credentials.json:/root/.config/gcloud/application_default_credentials.json:ro # For ADC
    ports:
      - "8080:8080" # Or your backend port
  # frontend:
  #   ...
  #   environment:
  #     - REACT_APP_API_URL=http://localhost:8080/api # Example for frontend

Replace the placeholder values (e.g., <your-gcp-project-id>) with your actual values.

Build Docker Images:
```
docker compose build
```
Run the application:
```
docker compose up
```
The frontend should typically be available at http://localhost:4200 (or as configured) and the backend API at http://localhost:8080.

Option 2: Manual Setup (for Development and Customization)

Follow these steps if you prefer to run the frontend and backend services manually on your local machine. This requires the Cloud Function to be deployed (Phase 1).

A. Backend Setup

Navigate to the backend/ directory.
```
cd backend
```

Create a virtual environment and install dependencies (if not already done):

python3 -m venv .venv
source .venv/bin/activate
pip3 install -r requirements.txt

Configure Environment Variables for Manual Run: Create a .local.env file in the backend/ directory using the values from "Phase 1". Example backend/.local.env:

GCP_PROJECT_ID="<your-gcp-project-id>"
GCP_REGION="<your-chosen-region>"
FUNCTION_URL="<function_url_retrieved_in_phase_1_step_2>"
ENVIRONMENT="development"
FRONTEND_URL="http://localhost:4200"
BIG_QUERY_DATASET="quickbot_website_search_bq"

Source the local environment variables:
- For Mac/Windows (or zsh console on Linux):
```
. ./.local.env
```
- For Linux (bash): Modify backend/.venv/bin/activate to include exports from .local.env (see previous README examples for how to do this). Then re-activate. Verify with env.

Run the backend application:

# from the backend/ directory, with .venv active and .local.env sourced
uvicorn main:app --reload --port 8080

B. Frontend Setup

(These instructions assume a typical TypeScript/Angular frontend. Adjust as necessary based on your frontend/README.md.)

Navigate to the frontend/ directory.
```
cd frontend
```
Install dependencies:
```
npm install
```
Environment Variables (if applicable): The frontend might require its own environment configuration (e.g., via a .env file or Angular's environment.ts files to point to the backend API at http://localhost:8080). Check the frontend/ directory or its README.md for specific instructions.
Run the frontend application:
```
npm start
# Or, for many Angular projects:
# ng serve
```
The application will typically be available at http://localhost:4200.

Project Structure (highlighting important parts)

website-search-using-custom-rag/
├── backend/                # Python backend (FastAPI/Uvicorn)
│   ├── .venv/              # Python virtual environment (gitignored)
│   ├── .local.env          # Local environment variables for manual run (gitignored)
│   ├── main.py             # Main application file
│   ├── requirements.txt    # Backend dependencies
│   ├── setup.py            # Backend setup script (e.g., for BigQuery)
│   └── README.md           # Backend-specific instructions
├── frontend/               # TypeScript frontend (e.g., Angular)
│   ├── node_modules/       # Node.js dependencies (gitignored)
│   ├── src/                # Frontend source code
│   ├── package.json        # Frontend dependencies and scripts
│   └── README.md           # Frontend-specific instructions
├── functions/              # Source code for Google Cloud Functions
│   └── create-intent/      # Example: Source for the RAG Cloud Function
│       └── ...
├── docker-compose.yml      # Docker Compose configuration for all services
└── README.md               # This file: Root project README

Environment Variables

Configuration for both frontend and backend is primarily managed through environment variables.

Backend:
- When running manually, backend environment variables are defined in backend/.local.env.
- When running with Docker, these variables are set directly in the environment section of the backend service in docker-compose.yml.
- Key variables include:
  - GCP_PROJECT_ID: Your Google Cloud Project ID.
  - GCP_REGION: The GCP region where your Cloud Function is deployed.
  - FUNCTION_URL: The HTTP trigger URL of your deployed RAG Cloud Function.
  - BIG_QUERY_DATASET: (If used) Name of the BigQuery dataset.
  - ENVIRONMENT: Application environment (e.g., development, production).
  - FRONTEND_URL: URL of the frontend application (e.g., http://localhost:4200) for CORS.
- Consult backend/.local.env.example (if one exists) or the backend's configuration logic for a complete list.
Frontend:
- Frontend environment variables (e.g., API endpoint URLs) are usually managed within the frontend's build system (e.g., Angular's environment.ts files or a .env file in the frontend/ directory).
- Consult frontend/README.md for specific details.

Code Styling & Commit Guidelines

To maintain code quality and consistency across the project:

TypeScript (Frontend): We follow the Angular Coding Style Guide by leveraging Google's TypeScript Style Guide using gts. This includes a formatter, linter, and automatic code fixer.
Python (Backend): We adhere to the Google Python Style Guide, using tools like pylint and black for linting and formatting.
Commit Messages: We suggest following Angular's Commit Message Guidelines to create clear and descriptive commit messages.

Frontend (TypeScript with `gts`)

(Assumes setup within the frontend/ directory)

Initialize gts (if not already done in the project): Navigate to frontend/ and run:
```
npx gts init
```
This will set up gts and create necessary configuration files (like tsconfig.json). Ensure your tsconfig.json (or a related gts config file like .gtsrc) includes an extension for gts defaults, typically:
```
{
  "extends": "./node_modules/gts/tsconfig-google.json"
  // ... other configurations
}
```
Check for linting issues: (This assumes a lint script is defined in frontend/package.json, e.g., "lint": "gts lint")
```
# from frontend/ directory
npm run lint
```
Fix linting issues automatically (where possible): (This assumes a fix script is defined in frontend/package.json, e.g., "fix": "gts fix")
```
# from frontend/ directory
npm run fix
```

Backend (Python with `pylint` and `black`)

(Assumes setup within the backend/ directory and its virtual environment activated)

Ensure Dependencies are Installed: Add pylint and black to your backend/requirements.txt file if not already present:
```
pylint
black
```
Then install them within your virtual environment:
```
# from backend/ directory, with .venv activated
pip3 install pylint black
# or pip3 install -r requirements.txt
```
Configure pylint: It's recommended to have a .pylintrc file in your backend/ directory to configure pylint rules. You can generate one if it doesn't exist:
```
# from backend/ directory
pylint --generate-rcfile > .pylintrc
```
Customize this file according to your project's needs and the Google Python Style Guide.

Check for linting issues with pylint: Navigate to the backend/ directory and run:

# from backend/ directory
pylint .
# Or specify modules/packages: pylint src/ your_module_name

Format code with black: To automatically format all Python files in the backend/ directory and its subdirectories:
```
# from backend/ directory
python3 -m black . --line-length=80
```

README.md

QuickBot - Website Search using Custom RAG