LocalMind
Personal Project
Local RAG Intelligence
The Challenge
Building a privacy-first RAG system that runs entirely on local hardware while maintaining the accuracy and speed of cloud-based solutions.
The Solution
Implemented a high-speed incremental indexer and hybrid search pipeline that optimizes local compute resources while leveraging Gemini for high-quality grounding.
What It Does
LocalMind indexes your local filesystem and provides a chat interface to query your documents. It uses semantic and keyword search to retrieve relevant context from PDFs, code, notes, and data files, providing answers grounded in actual local content with full citations.
How It Works
Scans directories using SHA-256 for incremental updates. Parses diverse file formats (AST-based for Python, regex for TS/JS). Generates recursive chunks with overlap. Stores embeddings in a local ChromaDB instance. Executes hybrid search using Reciprocal Rank Fusion (RRF) and passes top context to Gemini for final generation.
Process Flow
Key Innovations
Technologies Used
Performance Metrics
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