Local RAG Assistant

Overview

The Local RAG Assistant is a compact, fully local document intelligence system for private question-answering over custom document collections. It combines document ingestion, embedding-based retrieval, and Ollama-powered inference — containerized with Docker and exposed through a REST API.

Built as a supporting project demonstrating end-to-end local AI infrastructure without hosted inference dependencies.

Problem

Many environments require AI systems that operate entirely within local infrastructure — without external data transmission, hosted APIs, or third-party inference dependencies.

• Need for private AI with no external data transmission
• Offline operation in restricted or air-gapped environments
• Sensitive document access without third-party API dependency
• Reproducible deployments across development and production

Solution

I built an end-to-end RAG pipeline with local inference, vector retrieval, and REST API integration — Dockerized for reproducible deployment using Ollama and local embedding models.

• Document ingestion and chunking pipeline for retrieval context windows
• Local embedding generation and vector store for similarity search
• Context assembly with ranked chunk selection for LLM prompts
• Ollama-powered local LLM response generation
• REST API layer for programmatic RAG access
• Docker containerization for reproducible deployment

Engineering Highlights

• Retrieval-Augmented Generation pipeline with fully local inference

• Vector search with embedding-based similarity retrieval

• Docker containerization for reproducible deployment

• REST API integration for programmatic document intelligence

Technology Stack

• Python
• Docker
• Ollama
• RAG
• REST API
• Vector Search
• LLMs