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.
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.
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.
Scans candidate chunks using exact token matching, term frequency (TF-IDF 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.
Sub-20ms Latency
Eliminating network hops to remote vector APIs yields instant responses. Local queries against Wikipedia or code repositories resolve in ~15ms on modern Apple Silicon or NVMe storage.
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.