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Local AI Platform
A self-hosted AI application designed to demonstrate production-grade backend engineering, local LLM integration, retrieval-augmented generation (RAG), observability, security, and reliability.
The project emphasizes measurable behavior, reproducible deployments, automated testing, and documented architectural trade-offs.
Project Goals
- Build and expose a well-defined API for local LLM inference.
- Integrate retrieval-augmented generation with source-grounded responses.
- Measure latency, throughput, errors, and retrieval quality.
- Implement structured logging, metrics, and distributed tracing.
- Apply authentication, authorization, input validation, and secure configuration.
- Test failure handling, retries, idempotency, and recovery.
- Automate testing and deployment.
- Document architectural decisions and operational procedures.
Technology Stack
Planned technologies:
- Runtime: Node.js
- Language: TypeScript
- API framework: Fastify
- Validation: Zod
- Local inference: Ollama
- Database and retrieval: PostgreSQL with pgvector
- Containers: Docker and Docker Compose
- Testing: Automated unit, integration, and failure-injection tests
- Observability: OpenTelemetry and compatible monitoring tools
- CI/CD: Automated build, test, and deployment workflows
Technologies will be introduced incrementally as project requirements justify them.
Current Status
Status: Initial development
The initial milestone is to establish a working API with health and readiness endpoints, then integrate local model inference and baseline performance measurements.
Features listed in the project goals are planned capabilities, not claims that they have already been implemented.
Getting Started
Prerequisites
- Node.js and npm
- Git
Additional dependencies, including Ollama and PostgreSQL, will be introduced as the project evolves.
Installation
Clone the repository:
git clone <repository-url>
cd local-ai-platform
Install dependencies:
npm ci
Development
The development server and available scripts will be documented here as they are implemented.
Architecture
The initial architecture will consist of an HTTP API and a local model inference service.
Subsequent iterations will introduce retrieval, persistence, observability, security controls, and deployment automation.
Architecture diagrams and significant design decisions will be maintained alongside the code.
Testing and Quality
The project will progressively introduce:
- Automated unit and integration tests
- API contract validation
- Performance benchmarks
- Retrieval and answer-quality evaluations
- Security regression tests
- Failure-recovery tests
Performance and quality claims will be backed by reproducible measurements.
Security
- Never commit credentials, access tokens, private keys, or production data.
- Keep local configuration and secrets outside version control.
- Validate untrusted input and enforce authorization at the API boundary.
- Treat retrieved documents and model-generated output as untrusted.
- Do not expose the API publicly without appropriate authentication and security controls.
Report suspected vulnerabilities privately to the repository owner.
Documentation
As the project develops, documentation will cover:
- Local setup and deployment
- Architecture decision records
- Performance and evaluation results
- Threat models and security controls
- Operational runbooks and recovery procedures
- Known limitations and future improvements