Building a Resilient Local RAG Backend Engine for Hacktoberfest 2026

Description and use

A local RAG backend using FastAPI, PostgreSQL pgvector, and Ollama llama3.2 was built to keep data private and run offline.

Problem

Sensitive data was being sent to third‑party cloud APIs when querying large language models, compromising privacy and data ownership.

Outcome

The system enables low‑latency, privacy‑preserving RAG queries locally, satisfying Hacktoberfest 2026 requirements.

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 sensitive data to third‑party cloud APIs.

Source

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

Source