Local RAG: Sovereign Semantic Retrieval Without Cloud Vector Stores

Embed, index, and query proprietary PDFs, codebase repositories, and internal knowledge bases entirely on your local workstation. Zero document vectors or query tokens are transmitted over external networks.

Quick Answer: What is Local RAG?

Local Retrieval-Augmented Generation (RAG) indexes private documents and codebases into on-device vector embeddings (384-dimensional dense vectors) and performs dual-stage semantic search (vector cosine candidate selection + exact lexical reranking) entirely on your workstation. This eliminates cloud vector database fees, eliminates API latency, and prevents proprietary documents from ever leaking to third-party endpoints.

How Does the Dual-Stage Hybrid Retrieval Pipeline Work?

Dual-Stage Hybrid Retrieval Pipeline

Standard single-stage vector databases suffer from semantic drift and miss exact identifier matches. FineTuneMyAI employs a high-precision two-stage pipeline.

STAGE 1: Dense Vector Candidate Searchtop_k = 20-50

Calculates normalized cosine similarity between the query embedding and pre-indexed 384-dimensional chunk vectors. Rapidly prunes millions of tokens down to the top semantically relevant candidate chunks.

STAGE 2: Lexical Re-Rankingrerank_k = 3-5

Scans candidate chunks using exact token matching, term frequency (TF-IDF lexical density), and boundary alignment. Elevates exact technical terms, function names, and legal clauses to the top context window.

Zero Cloud Indexing

Vector indices are written directly to ./data/rag/indexes/ on your local disk. No third-party SaaS vector databases receive your documents.

Low-Latency On-Device Search

Eliminating remote network roundtrips delivers fast retrieval. Benchmarked at ~12–28ms on Apple Silicon (M3, 16GB unified memory, NVMe) for corpora up to 25,000 chunks (384-d / 768-d), scaling predictably without cloud latency spikes.

Intelligent Chunk Presets

Pre-tuned chunking strategies for source code (preserving AST scopes), technical documentation (headers and code blocks), and narrative text.

Build your local vector index in seconds

Test semantic search and retrieval accuracy in our dedicated Local RAG Studio.

Open Local RAG Studio