RAG System for Local LLM
A complete RAG pipeline that runs entirely on local hardware. Documents are indexed using FAISS with hybrid search (dense + sparse retrieval), and inference is handled by Ollama-based local models. No data leaves the machine.
Tech Stack & Infrastructure
The Problem
Organizations with sensitive documents can't use cloud-based AI services due to data privacy and compliance requirements.
The Solution
A fully local RAG system with document ingestion, chunking, embedding, hybrid retrieval, and LLM inference — all running on-premise without internet access.
Architecture Overview
A pipeline that ingests documents, chunks them, embeds them locally using SentenceTransformers, and stores them in FAISS. Ollama handles LLM inference.
Engineering Decisions
Opted for fully local embedding and inference to guarantee zero data leakage for enterprise clients.
Key Tradeoffs
Local inference requires significant hardware resources (GPUs) on-premise compared to calling a cloud API.
Core Challenges
Tuning the hybrid search weights (dense vs. keyword) to yield the most relevant context for the LLM.
Results & Impact
Achieved high-accuracy document retrieval and Q&A without any data ever leaving the local network.
Future Roadmap
Implement advanced RAG techniques like re-ranking models and query expansion.