Personal RAG Chatbot
Personal Project · Full-Stack Developer
Personal RAG Chatbot
The Personal RAG Chatbot is a custom-built retrieval-augmented generation system designed to turn my personal notes and documents into a conversational knowledge base.
By combining embeddings, vector search, and an LLM backend, it allows natural language queries that return precise, contextually relevant answers from my own data.
Key Features:
- Embeddings & Vector Search: Uses OpenAI embeddings stored in a vector database for lightning-fast semantic retrieval.
- RAG Pipeline: Integrates a retrieval layer with GPT-based generation for accurate, source-grounded responses.
- FastAPI Backend: Handles data ingestion, chunking, and retrieval with a robust API layer.
- Dockerized Setup: Fully containerized for local development and cloud deployment.
- Automated Data Sync: GitHub Actions pipeline to refresh and re-embed notes whenever the knowledge base changes.
Workflow:
The system ingests my personal notes (Markdown, PDFs, and text files), chunks them into semantically meaningful segments, generates vector embeddings, and stores them in a vector database. When a query is made, the chatbot retrieves the top matching segments and uses them as context for a GPT-based model, ensuring the answer is relevant and grounded in my own information.
This project merges my skills in AI, backend engineering, and automation into a single, highly practical tool I use daily.
Joshua Fields — full portfolio
