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Velocira local development

Docker Desktop is the supported local runtime. The repository-root compose.yml starts the complete application: PostgreSQL, Qdrant, the AI service, the Spring API, and the Next.js frontend.

Start the complete stack

  1. Install and start Docker Desktop.

  2. Copy .env.example to .env, then replace the three required secret placeholders: POSTGRES_PASSWORD, JWT_SECRET, and AI_SERVICE_SHARED_SECRET.

  3. From the repository root, run:

    docker compose up --build --wait

Open http://localhost:3000. Docker waits until each service is healthy. The first backend image build downloads Maven dependencies and can take several minutes on a new machine; following builds reuse Docker's build cache.

Useful commands

# See service health and published ports
docker compose ps

# Follow all service logs
docker compose logs --follow

# Stop services while retaining database and Qdrant data
docker compose down

# Stop services and remove local Docker volumes (deletes local database/vector data)
docker compose down --volumes

The published defaults are frontend 3000, API 8080, AI service 8000, PostgreSQL 5432, and Qdrant 6333/6334. Change them in .env if they conflict with another local service.

Always run Compose from the repository root so the single compose.yml file and its matching .env are used together.

Default project flow

The first-use path is intentionally brief: create a project, describe the idea, and select Generate project. The server derives a title and sensible project defaults, records a canonical brief, and starts a durable background project-plan job. The workspace translates worker stages into plain-language progress, preserves the original brief on failure, supports safe retry/cancellation, and lets users request a focused update after the first result. Detailed discovery, sources, formal SRS controls, and documentation-package exports remain under Advanced project details.

About

Velocira is a full-stack web application that accepts a user's project description as input and uses AI/ML models to generate comprehensive, production-ready documentation outputs including SRS documents, Use Case diagrams, ERDs, API structures, architecture proposals, and implementation roadmaps.

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