vector-memory-hack

name

vector-memory-hack

description

Fast semantic search for AI agent memory files using TF-IDF and SQLite. Enables instant context retrieval from MEMORY.md or any markdown documentation. Use when the agent needs to (1) Find relevant context before starting a task, (2) Search through large memory files efficiently, (3) Retrieve specific rules or decisions without reading entire files, (4) Enable semantic similarity search instead of keyword matching. Lightweight alternative to heavy embedding models - zero external dependencies, <10ms search time.

Vector Memory Hack

Ultra-lightweight semantic search for AI agent memory systems. Find relevant context in milliseconds without heavy dependencies.

Why Use This?

Problem: AI agents waste tokens reading entire MEMORY.md files (3000+ tokens) just to find 2-3 relevant sections.

Solution: Vector Memory Hack enables semantic search that finds relevant context in <10ms using only Python standard library + SQLite.

Benefits:

• ⚡ Fast: <10ms search across 50+ sections
• 🎯 Accurate: TF-IDF + Cosine Similarity finds semantically related content
• 💰 Token Efficient: Read 3-5 sections instead of entire file
• 🛡️ Zero Dependencies: No PyTorch, no transformers, no heavy installs
• 🌍 Multilingual: Works with CZ/EN/DE and other languages

Quick Start

1. Index your memory file

python3 scripts/vector_search.py --rebuild

2. Search for context

# Using the CLI wrapper
vsearch "backup config rules"

# Or directly
python3 scripts/vector_search.py --search "backup config rules" --top-k 5