https://github.com/sonny-thomas/bottega-agent
About AI Chatbot using LangChain's LangGraph agentic workflow & Anthropic Claude 3.5 sonnet
https://github.com/sonny-thomas/bottega-agent
anthropic claude-3-5-sonnet langgraph python
Last synced: 7 months ago
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About AI Chatbot using LangChain's LangGraph agentic workflow & Anthropic Claude 3.5 sonnet
- Host: GitHub
- URL: https://github.com/sonny-thomas/bottega-agent
- Owner: sonny-thomas
- Created: 2025-02-27T03:04:44.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-27T03:25:26.000Z (8 months ago)
- Last Synced: 2025-02-27T04:24:46.329Z (8 months ago)
- Topics: anthropic, claude-3-5-sonnet, langgraph, python
- Language: Python
- Homepage:
- Size: 369 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Restaurant AI Assistant
Welcome to the Restaurant AI Assistant, an intelligent chatbot designed to enhance the dining experience for customers of Bottega Restaurant.
## Table of Contents
- [Features](#features)
- [Demo](#demo)
- [Installation](#installation)
- [Usage](#usage)
- [Technologies Used](#technologies-used)
- [Contributing](#contributing)
- [License](#license)
## Features
- **Menu Information**: Fetch details about menu items, including descriptions and prices.
- **Order Management**: Place orders, modify cart contents, and check order status.
- **Personalization**: See your previous order, get recommendations from AI.
## Demo
## Set Up
Enter your API keys in the env file
ANTHROPIC_API_KEY (https://console.anthropic.com/)
TWILIO_ACCOUNT_SID (https://console.twilio.com/)
TWILIO_AUTH_TOKEN (https://console.twilio.com/)
twilio_phone_number
restaurant_phone_number
(Recommended) Setup LangChain API for tracing and monitoring (https://smith.langchain.com)
LANGCHAIN_API_KEY
LANGCHAIN_TRACING_V2 = True
LANGCHAIN_PROJECT = "Project Name of Choice"
STRIPE_SECRET_KEY
## Usage
1. Start the Flask server:
```
python app.py
```
2. Open your web browser and navigate to `http://localhost:5000`
3. Start interacting with the AI Assistant to explore menu items, place orders, or get assistance with your dining experience.
## Technologies Used
- Python
- Flask
- SQLite
- LangChain + LangGraph
- Anthropic Claude Sonnet 3.5
- React (for the frontend)
- Twilio API for text messaging feature
- Stripe API for payments
## Contributing
We welcome contributions to improve the Restaurant AI Assistant. Please follow these steps:
1. Fork the repository
2. Create a new branch (`git checkout -b feature/amazing-feature`)
3. Make your changes
4. Commit your changes (`git commit -m 'Add some amazing feature'`)
5. Push to the branch (`git push origin feature/amazing-feature`)
6. Open a Pull Request
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.
---
# Deploying Aramex Chatbot to Google Cloud Platform
## Prerequisites
- Mac M1 computer
- Docker Desktop installed and running
- Google Cloud SDK installed
- A Google Cloud Platform account with a project set up
- Git repository with the Aramex chatbot code
## Step 1: Prepare the Local Environment
1. Open Terminal and navigate to your project directory.
2. Ensure you have a `.env` file in your project root with all necessary environment variables:
```
ANTHROPIC_API_KEY=your_anthropic_api_key
TWILIO_ACCOUNT_SID=your_twilio_account_sid
TWILIO_AUTH_TOKEN=your_twilio_auth_token
LANGCHAIN_API_KEY=your_langchain_api_key
LANGCHAIN_TRACING_V2=true
LANGCHAIN_PROJECT=ARABOT_DEMO_1
twilio_phone_number=your_twilio_phone_number
restaurant_phone_number=your_restaurant_phone_number
```
3. Update the `app.py` to use the correct port:
```python
if __name__ == '__main__':
app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 8080)))
```
## Step 2: Create and Configure Dockerfile
Create a `Dockerfile` in your project root with the following content:
```dockerfile
FROM --platform=linux/amd64 python:3.11-slim
WORKDIR /app
COPY build ./build
COPY app.py .
COPY requirements.txt .
COPY customer_chatbot_new.db .
COPY customer_chatbot_new_memory.db .
COPY .env .
RUN apt-get update && apt-get install -y --no-install-recommends \
gcc \
&& rm -rf /var/lib/apt/lists/*
RUN pip install --no-cache-dir -r requirements.txt
EXPOSE 8080
CMD ["python", "app.py"]
```
## Step 3: Build and Push Docker Image
1. Enable Docker BuildKit:
```bash
export DOCKER_BUILDKIT=1
```
2. Create a new builder instance:
```bash
docker buildx create --name mybuilder --use
```
3. Build and push the Docker image:
```bash
docker buildx build --platform linux/amd64 -t gcr.io/[PROJECT-ID]/aramex-chatbot:latest . --push
```
Replace `[PROJECT-ID]` with your Google Cloud Project ID.
## Step 4: Deploy to Google Cloud Platform
1. Authenticate with Google Cloud:
```bash
gcloud auth login
gcloud config set project [PROJECT-ID]
```
2. Enable required GCP services:
```bash
gcloud services enable run.googleapis.com containerregistry.googleapis.com
```
3. Deploy to Cloud Run:
```bash
gcloud run deploy aramex-chatbot \
--image gcr.io/[PROJECT-ID]/aramex-chatbot:latest \
--platform managed \
--region [REGION] \
--allow-unauthenticated \
--port 8080
```
Replace `[REGION]` with your desired region (e.g., `us-central1`).
## Step 4: Verify Deployment
1. After successful deployment, Cloud Run will provide a URL where your application is hosted.
2. Open the provided URL in a web browser to verify that your Aramex chatbot is running correctly.
3. Test the chatbot functionality to ensure it's working as expected in the cloud environment.
## Troubleshooting
- If you encounter issues with the build process, try removing the existing builder and creating a new one:
```bash
docker buildx rm mybuilder
docker buildx create --name mybuilder --use
```
- If environment variables are not being recognized, double-check that they are correctly set in the Cloud Run console or through the `gcloud` command.
- Monitor the application logs in the Google Cloud Console for any runtime errors or issues.
## Maintenance
- To update your application, make changes to your local code, rebuild the Docker image with a new tag, and redeploy using the same `gcloud run deploy` command with the new image tag.
- Regularly update your dependencies and base Docker image to ensure you have the latest security patches.
For any questions or support, please contact sonnythomas0618@gmail.com