Privacy-Focused RAG System: Building a Personal AI Assistant with Local LLM & Vector Search

This is a multimodal-capable personal AI assistant system powered by a local LLM (Gemma 4 26b). Designed as a “Private AI with Personal Memory,” it features long-term memory implementation using a high-speed vector search engine and session isolation across multiple devices. The lightweight backend, built with Python and FastAPI, ensures responsive performance in home server environments.

1. High-Speed “Long-Term Memory” via sqlite-vec

We transformed past conversations into a database of 1024-dimensional vector data. By adopting the latest “sqlite-vec” extension, we enabled similarity-based episode retrieval in just milliseconds within a lightweight SQLite environment. This RAG (Retrieval-Augmented Generation) architecture injects relevant context into the LLM rather than simply displaying chat history.

2. Optimized Thread Isolation & Session Management

We transitioned from single-stream history to a thread-isolated architecture based on unique Chat IDs. The DB schema was optimized so each thread maintains independent short-term memory (chat history) and long-term memory (vector data). This provides a practical user experience where multiple topics can be discussed in parallel without memory confusion.

3. Multimodal Analysis & Integrated Emotion Estimation

The system supports Vision capabilities, allowing for interactions based on image content. In parallel with response generation, an “emotion estimation logic” automatically determines the AI’s expression and tone across four stages (happy, sad, angry, normal). We pursued high interactivity by integrating dynamic TTS (Text-to-Speech) synchronization.

4. Docker × FastAPI for Maximum Development Efficiency

Using the asynchronous Python framework FastAPI and Docker infrastructure, we eliminated environment discrepancies between development and production (Home Server/NUC). Process management via PM2 ensures high availability and crash recovery, even under the heavy computational loads typical of LLM processing.

Estimated Pricing & Lead Time (AI System Development Package)

Requirement Definition & LLM Selection (Local Optimization)	$335.00
Backend Development (FastAPI / Asynchronous Implementation)	$665.00
RAG & Memory Systems (sqlite-vec / Vector DB Setup)	$535.00
Multimodal & Voice Integration (Vision / TTS Integration)	$400.00
Deployment & Infrastructure (Docker / PM2 / Nginx)	$200.00
Total Estimate (Tax Incl.)	$2,135.00
Estimated Turnaround	Approx. 150 hours

【Core Responsibilities】

AI Architectural Design: Designing memory-persistent RAG systems using local LLMs.
Backend Development: Constructing multimodal API endpoints via FastAPI.
Database Design: SQLite schema design with integrated vector search and query optimization.
Infrastructure Setup: Building Docker container foundations and ensuring process persistence with PM2.

【Tech Stack】

Gemma 4 26b (Local LLM) / Python 3.x / FastAPI / SQLite (sqlite-vec) / Ollama / Docker / PM2 / JavaScript (Next.js) / Text-to-Speech (TTS)

*To ensure privacy, the demo site for this system is not open to the public.