Quickbot Document Search Template is a powerful document search application built with Google Cloud's Agent Builder. It features a user-friendly frontend interface and a robust backend API to deliver efficient search capabilities.

Overview

This project allows users to search through documents indexed by Google Cloud's Vertex AI Search (formerly Generative AI App Builder). It's designed with a decoupled frontend and backend architecture, suitable for scalable deployments.

Demo

Here's how the Document Search Template provides answers from your documents:

Prerequisites

Before you begin, ensure you have the following installed:

Docker and Docker Compose v2: Essential for the containerized deployment.
- Verify your Docker Compose version with docker compose version. If you have an older docker-compose (with a hyphen), you might need to upgrade to use docker compose in the commands.
Google Cloud SDK (gcloud CLI): Required for manual backend setup, authentication, and interaction with Google Cloud services.
Python 3.x: For backend development (if not using Docker).
Node.js and npm (or yarn): For frontend development (if not using Docker).

Getting Started

You have two main options to get the application running:

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

This is the simplest way to get the entire application (frontend and backend) up and running! You just need to run docker compose up and you will be all set! But in order to do that, you may need to authenticate with gcloud. See the next steps:

Ensure Docker and Docker Compose v2 are installed and running.
Authenticate with Google Cloud for Datastore/Engine Creation: If you intend for the application (specifically the backend running in Docker) to create or interact with Google Cloud Datastores and Search Engines, you need to provide Google Cloud credentials to the Docker container. The recommended way for local development is using Application Default Credentials (ADC). Run the following commands in your local terminal:
```
gcloud auth application-default login
gcloud config set project <your-project-id>
gcloud auth application-default set-quota-project <your-project-id>

# Verify your configuration
gcloud auth list
gcloud config list project
```
This will create or update ADC on your local machine. The docker-compose.yml file is typically configured to mount these local credentials into the backend container, allowing it to authenticate.

Windows Users: The path to ADC might differ on Windows. You may need to adjust the commented-out volume mount paths for gcloud credentials in the docker-compose.yml file to ensure the backend container can access them.
Build Docker Images (with Datastore Setup Choice): The Docker setup includes a build-time argument IS_FIRST_DEPLOYMENT to facilitate initial Google Cloud Vertex AI Search datastore and engine creation.
- Automatic Default Setup (with Public Data): If you're running this for the first time and want a quick start with sample data (and have authenticated as per step 2), build the Docker images with the IS_FIRST_DEPLOYMENT argument set to "True":
```
docker compose build --build-arg IS_FIRST_DEPLOYMENT="True"
```
  This configuration will automatically create a default datastore and search engine in your Google Cloud project, populated with public documents from Alphabet. These documents are sourced from Google's official guide for trying Enterprise Search with unstructured data.
- Custom Setup or Existing Datastore: If you do not set IS_FIRST_DEPLOYMENT="True" during the build (or set it to any other value, or omit it), the application will not attempt to create the default datastore. Instead, you'll need to:
  - Configure the backend with environment variables to connect to an existing datastore/engine.
  - Provide specific environment variables that instruct the backend on how to create a custom datastore/engine if desired. Refer to the "Environment Variables" section for backend configuration details. If you are using this path, you can build without the extra argument:
```
docker compose build
```
Run the application: After building the images (with or without the IS_FIRST_DEPLOYMENT arg as per your choice), start the services:
```
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.

A. Backend Setup

Navigate to the backend/ directory.
```
cd backend
```
Create a virtual environment and install dependencies:
```
# Check if you are already in an environment
pip -V

# If not, create and activate (for Linux/macOS)
python3 -m venv .venv
source .venv/bin/activate

# Install requirements
pip3 install -r requirements.txt
```
VS Code Tip: If VS Code doesn't recognize your virtual environment, press Ctrl + Shift + P (or Cmd + Shift + P on Mac), type "Python: Select Interpreter", choose "Enter interpreter path...", and then find and select .venv/bin/python inside your backend directory.

Setup Google Cloud (gcloud) credentials: Ensure you're authenticated and your project is configured correctly.

gcloud auth login # Login with your user account
gcloud config set project <your-project-id>

# For services using Application Default Credentials (ADC) locally
gcloud auth application-default login
# Optionally, set a quota project for ADC if not inherited
gcloud auth application-default set-quota-project <your-project-id>

# Verify configuration
gcloud auth list
gcloud config list project

Configure Environment Variables: Backend configuration is managed via environment variables. Create a .local.env file in the backend/ directory (you can copy from .local.env.example if one exists). This file should be added to .gitignore.
- For Mac/Windows (or zsh console on Linux): Source the variables directly (from the backend/ directory):
```
. ./.local.env
```
- For Linux (bash): Open backend/.venv/bin/activate and append the export commands from your backend/.local.env file after the PATH export section. For example:
```
# ... existing activate script content ...
_OLD_VIRTUAL_PATH="$PATH"
PATH="$VIRTUAL_ENV/bin:$PATH"
export PATH

# Quickbot env variables (copied from .local.env)
export ENVIRONMENT="development"
export FRONTEND_URL="http://localhost:4200"
export GCP_PROJECT_ID="<your-project-id>"
export BIG_QUERY_DATASET="your_bq_dataset_for_search_tuning"
# ... other necessary variables for datastore, engine, etc. ...
```
Verify the variables are set by running env in your activated terminal.
Run the setup script (if applicable): This script might perform initial configurations like database setup.
```
# from the backend/ directory
python3 setup.py
```

Run the backend application:

# from the backend/ directory
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 for API endpoints). 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)

document-search-using-agent-builder/
├── backend/                # Python backend (FastAPI/Uvicorn)
│   ├── .venv/              # Python virtual environment (gitignored)
│   ├── .local.env          # Local environment variables (gitignored)
│   ├── main.py             # Main application file
│   ├── requirements.txt    # Backend dependencies
│   ├── setup.py            # Backend setup script
│   └── README.md           # Backend-specific instructions
├── frontend/               # TypeScript frontend (Angular)
│   ├── node_modules/       # Node.js dependencies (gitignored)
│   ├── src/                # Frontend source code
│   ├── package.json        # Frontend dependencies and scripts
│   ├── tsconfig.json       # TypeScript configuration
│   └── README.md           # Frontend-specific instructions
├── 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 typically defined in backend/.local.env.
- When running with Docker, these variables are usually passed into the backend container via the docker-compose.yml file (often referencing a .env file at the root or backend/ directory).
- Key variables include:
  - GCP_PROJECT_ID: Your Google Cloud Project ID.
  - ENVIRONMENT: Application environment (e.g., development, production).
  - FRONTEND_URL: URL of the frontend application (e.g., http://localhost:4200).
  - BIG_QUERY_DATASET: (If used) Name of the BigQuery dataset for search tuning analytics.
  - Variables for Vertex AI Search: DATA_STORE_ID, ENGINE_ID, LOCATION_ID, etc., especially if not using the IS_FIRST_DEPLOYMENT="True" Docker build arg for automatic setup.
- Consult backend/README.md or backend/.local.env.example 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
pip install pylint black
# or pip 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 your_module_name

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

README.md

Quickbot - Document Search using Agent Builder