This is a template repository for building applications using microservices architecture. It includes a basic frontend, backend, an LLM serving engine (e.g., vLLM), and nginx as a reverse proxy. This template is designed to help you quickly set up and deploy applications that use large language models (LLMs) along with traditional web services.

• Frontend: Responsible for the frotnend part of the application.
• Backend: Handles the business logic and communicates with the LLM server.
• LLM Server: A serving engine (such as vLLM) that provides API endpoints for large language model inference.
• Nginx: Acts as a reverse proxy to route traffic between the frontend and backend.

├── frontend/ # Source code for the frontend application
├── backend/ # Source code for the backend application
├── models/ # Directory for storing models (e.g., huggingface checkpoints)
├── nginx.conf # Nginx configuration file
├── docker-compose.yml # Docker Compose configuration for orchestrating services

Ensure you have the following installed:

• Docker
• Docker Compose

1. Clone the repository:

   git clone https://github.com/alex0dd/llm-app-microservices-template.git
   cd llm-app-microservices-template

2. Configure the environment:

   • Download a huggingface checkpoint of the model (e.g., Meta-Llama-3.1-8B-Instruct)
   • Place your the checkpoint directory in the models/ directory.
   • Ensure the nginx.conf file is correctly configured (especially the llm_server section).

3. Build and start the services:

   With vLLM:

   docker-compose up --build

   With ollama:

   docker compose -f docker-compose-ollama.yaml up --build
   docker compose -f docker-compose-ollama.yaml up --build --force-recreate --remove-orphans

   This will build and start the following services:

   • nginx: Reverse proxy server
   • frontend: The web frontend, accessible via http://localhost
   • backend: The backend API server, accessible via http://localhost/api
   • llm_server: LLM serving engine, available for backend use, but not exposed to the public.

4. Access the application:

   • Frontend: http://localhost
   • Backend API: http://localhost/api

• The frontend sends requests to the backend.

• The backend exposes the LLM via an API, communicates with the llm_server for model inference.

• nginx handles routing for public requests (i.e., requests to the frontend and backend), but it does not expose the LLM server directly to the outside world.

1. Frontend: Modify the source code in the frontend/ folder according to your frontend framework.
2. Backend: Implement your business logic in the backend/ folder. Ensure it properly communicates with the LLM server.
3. LLM Model: Replace the model in models/ with the one you want to use (e.g., Meta-Llama-3.1-8B-Instruct).
4. LLM Server: Replace the llm_server section in the docker-compose.yaml file with other servers such as ollama.

This project is licensed under the MIT License.

Feel free to fork this repository and make contributions.