automation

Automated Weather Updates with Signal and FastAPI

Explore a Signal-based weather assistant using FastAPI, LangChain, and Docker for automated updates and queries.

Shipped January 2026

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A Signal-based assistant integrated with a structured weather service for automated weather updates and queries. This project combines FastAPI, LangChain, and Docker to deliver weather information via Signal messaging.

Features

  • FastAPI-based weather-service providing daily weather forecasts and JSON responses.
  • Signal assistant (assistant-core) capable of parsing direct weather commands and natural language queries.
  • Structured LangChain agent for handling weather queries.
  • Background cron job for daily weather notifications.
  • Dockerized microservices communicating over a shared Docker network.

Tech Stack

  • Python
  • FastAPI
  • LangChain (structured chat agent)
  • Docker
  • Signal API (for messaging integration)

Getting Started

Prerequisites

  • Docker and Docker Compose installed
  • Access to Signal API credentials/configuration

Installation & Running

  1. Clone the repository:
git clone https://github.com/justin-napolitano/signal-weather-stack-tool.git
cd signal-weather-stack-tool
  1. Build and start the Docker containers:
docker-compose up --build

  1. The services will run on the assistant-net Docker network, with:

    • weather-service exposing /today?city=&state= endpoint
    • assistant-core exposing /inbox for Signal message handling

  2. Configure environment variables such as CRON_SCHEDULE and NOTIFY_URL as needed.

Project Structure

  • index.md: Project documentation and overview.
  • weather-service: FastAPI app providing weather data and daily forecast notifications.
  • assistant-core: Signal message handler that routes weather queries and sends responses.
  • notifier-gateway: HTTP gateway forwarding messages to Signal.

Future Work / Roadmap

  • Complete and extend documentation and usage examples.
  • Add support for more natural language commands and additional weather data.
  • Improve error handling and retry mechanisms for gateway communication.
  • Enhance scheduling flexibility and configuration management.
  • Integrate more advanced LangChain agents or alternative LLM tools.

For detailed usage and architecture, refer to the index.md file within the repository.

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