AI Document Analysis Service (DocAI)

A Go microservice that ingests documents (PDF, TXT), extracts their text, and utilizes Large Language Models (LLMs) via OpenRouter to generate summaries and extract key metadata.

📋 Prerequisites

Ensure you have the following installed on your local machine:

• Docker & Docker Compose
• Go (for local development)

⚙️ Configuration

1. Clone the repository:

   git clone <repo-url>
   cd docai

2. Environment Setup: Copy the example environment file (.env.example):

   cp .env.example .env

   Note: Update OPENROUTER_API_KEY in .env if you wish to test actual LLM analysis.

🏃‍♂️ Getting Started

We use a Makefile to orchestrate workflows.

Start the Application

make start-app

This runs migrations, creates a minio bucket and runs the server The server will start at http://localhost:8080.

Other Commands

For a list of all available commands, run:

make help

📖 API Documentation

The API is documented using OpenAPI 3.0.

• Swagger UI: Accessible at http://localhost:8080/swagger/ when the server is running.
• Spec File: Located at docs/swagger.yaml.

🧪 Testing

The project includes end-to-end integration tests.

make test

CI

CI runs make test-ci on every push using .github/workflows/ci.yml. It spins up Postgres and MinIO containers for tests.

Tip

Secrets Configuration: To fully enable integration tests, add your .env variables (e.g., OPEN_ROUTER_KEY) to GitHub Secrets. The workflow automatically populates the runner's .env file from these secrets.