Building a Resilient Local RAG Backend Engine for Hacktoberfest 2026

Описание и применение

Создан локальный RAG‑бэкенд, использующий FastAPI, PostgreSQL с pgvector и Ollama для llama3.2, обеспечивающий приватность данных без обращения к внешним API.

Задача

Develop a local-first Retrieval-Augmented Generation backend that runs open-weight models offline to preserve data ownership and privacy.

Результат

The system enables low‑latency, privacy‑preserving RAG queries without external API calls.

Рабочий процесс

• Clone the repository
• Install Python dependencies
• Start Docker Compose to launch PostgreSQL and Ollama
• Run the FastAPI server with Uvicorn

Доказательства

For the Hacktoberfest 2026 DEV Challenge, I built a zero‑dependency, local‑first Retrieval‑Augmented Generation (RAG) backend engine designed to run open‑weight models completely offline.

Источник

An asynchronous FastAPI backend paired with PostgreSQL (pgvector) and Ollama (llama3.2). The system allows querying local AI models with vector context retrieval without sending data to third‑party APIs.

Источник

Running open‑weight models like llama3.2 locally gives full data ownership and privacy. By keeping embeddings in pgvector and processing prompts locally via Ollama, sensitive data never leaves the developer's workstation.

Источник