Why I started building NeuroHealth — my AI-powered personal knowledge platform

About the Author:

I am a data platforms and digital transformation professional with a strong personal interest in fitness, health, and sports science. Fitness has been my hobby for many years, during which I completed formal training and successfully passed exams in health, physical activity, and exercise methodologies.

At the same time, I have long been passionate about exploring AI technologies, especially LLMs and semantic search systems. These two interests — health & fitness on the one hand, and AI/ML on the other — have inspired the development of NeuroHealth, an experimental personal knowledge platform that combines structured health information with advanced AI-driven search and retrieval capabilities.

NeuroHealth will leverage various Large Language Models (LLMs) — not limited to OpenAI — to ensure flexibility, privacy, and adaptability for different use cases and deployment scenarios.

The Problem:

Every year I read and watch dozens of lectures, research papers, and courses on health, fitness, and well-being. And every time I face the same problem — there is no easy way to store, structure, and retrieve this valuable knowledge when I really need it. Notes get lost, files are forgotten, and searching inside documents is slow and inefficient.

As a fitness and health enthusiast, this became a personal challenge to solve. As a data architect and AI technology explorer — this became a technical opportunity.

The Idea:

That’s why I started building NeuroHealth — an AI-powered personal knowledge platform for health and fitness content.

What makes it special?

Based on semantic search and RAG (Retrieval-Augmented Generation) principles;
Uses vector database (Qdrant) and LLMs to find the right knowledge fast — not just keywords;
Designed to integrate with various LLM models (not limited to OpenAI — flexibility and privacy matter);
Built on FastAPI and PostgreSQL — scalable and cloud-ready.

The Architecture (first draft):

Lecture/Doc → Text Parser → Metadata + Sections → Qdrant Vectors

                            ↓                     ↑

                   PostgreSQL            FastAPI + LLM

                                  |

                              User Query

Tech stack:

Python (FastAPI, Pydantic)
PostgreSQL (for structured metadata)
Qdrant (semantic vector search)
OpenAI LLM (for RAG pipeline)
Docker (optional, for deployment)

Why these technologies?
Qdrant — best open-source vector search DB, ideal for local personal data;

FastAPI — lightweight, fast backend for API and LLM orchestration;

PostgreSQL — simple but reliable metadata store;

OpenAI (LLM) — for natural language Q&A over personal content;

RAG approach — gives precise, context-based answers instead of generic hallucinations.

What’s next?
In upcoming weeks I’ll share my progress:

Qdrant setup;

First FastAPI endpoints;
RAG pipeline working demo;
Open discussion: AI Agents for health recommendations?

If you are into RAG, LLM, or personal AI — let’s connect. I’m open to feedback, ideas and collaboration!

DEV Community