Building a Resilient Local RAG Backend Engine for Hacktoberfest 2026

Description and use

A local RAG backend built with FastAPI, PostgreSQL pgvector, and Ollama llama3.2 that keeps all data and inference on the developer’s machine, preserving privacy.

Problem

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

Outcome

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

Workflow

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

Evidence

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.

Source

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.

Source

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.

Source