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

3. Use results in your workflow

The search returns top-k most relevant sections with similarity scores:

1. [0.288] Auto-Backup System
   Script: /root/.openclaw/workspace/scripts/backup-config.sh
   ...

2. [0.245] Security Rules
   Never send emails without explicit user consent...

How It Works

MEMORY.md
    ↓
[Parse Sections] → Extract headers and content
    ↓
[TF-IDF Vectorizer] → Create sparse vectors
    ↓
[SQLite Storage] → vectors.db
    ↓
[Cosine Similarity] → Find top-k matches

Technology Stack:

• Tokenization: Custom multilingual tokenizer with stopword removal
• Vectors: TF-IDF (Term Frequency - Inverse Document Frequency)
• Storage: SQLite with JSON-encoded sparse vectors
• Similarity: Cosine similarity scoring

Commands

Rebuild Index

python3 scripts/vector_search.py --rebuild

Parses MEMORY.md, computes TF-IDF vectors, stores in SQLite.

Incremental Update

python3 scripts/vector_search.py --update

Only processes changed sections (hash-based detection).

Search

python3 scripts/vector_search.py --search "your query" --top-k 5

Statistics

python3 scripts/vector_search.py --stats

Integration for Agents

Required step before every task:

# Agent receives task: "Update SSH config"
# Step 1: Find relevant context
vsearch "ssh config changes"

# Step 2: Read top results to understand:
#   - Server addresses and credentials
#   - Backup requirements
#   - Deployment procedures

# Step 3: Execute task with full context

Configuration

Edit these variables in scripts/vector_search.py:

MEMORY_PATH = Path("/path/to/your/MEMORY.md")
VECTORS_DIR = Path("/path/to/vectors/storage")
DB_PATH = VECTORS_DIR / "vectors.db"

Customization

Adding Stopwords

Edit the stopwords set in _tokenize() method for your language.

Changing Similarity Metric

Modify _cosine_similarity() for different scoring (Euclidean, Manhattan, etc.)

Batch Processing

Use rebuild() for full reindex, update() for incremental changes.

Performance

| Metric | Value |

|--------|-------|

| Indexing Speed | ~50 sections/second |

| Search Speed | <10ms for 1000 vectors |

| Memory Usage | ~10KB per section |

| Disk Usage | Minimal (SQLite + JSON) |

Comparison with Alternatives

| Solution | Dependencies | Speed | Setup | Best For |

|----------|--------------|-------|-------|----------|

| Vector Memory Hack | Zero (stdlib only) | <10ms | Instant | Quick deployment, edge cases |

| sentence-transformers | PyTorch + 500MB | ~100ms | 5+ min | High accuracy, offline capable |

| OpenAI Embeddings | API calls | ~500ms | API key | Best accuracy, cloud-based |

| ChromaDB | Docker + 4GB RAM | ~50ms | Complex | Large-scale production |

When to use Vector Memory Hack:

• ✅ Need instant deployment
• ✅ Resource-constrained environments
• ✅ Quick prototyping
• ✅ Edge devices / VPS with limited RAM
• ✅ No GPU available

When to use heavier alternatives:

• Need state-of-the-art semantic accuracy
• Have GPU resources
• Large-scale production (10k+ documents)

File Structure

vector-memory-hack/
├── SKILL.md                  # This file
└── scripts/
    ├── vector_search.py      # Main Python module
    └── vsearch               # CLI wrapper (bash)

Example Output

$ vsearch "backup config rules" 3

Search results for: 'backup config rules'

1. [0.288] Auto-Backup System
   Script: /root/.openclaw/workspace/scripts/backup-config.sh
   Target: /root/.openclaw/backups/config/
   Keep: Last 10 backups
   
2. [0.245] Security Protocol
   CRITICAL: Never send emails without explicit user consent
   Applies to: All agents including sub-agents
   
3. [0.198] Deployment Checklist
   Before deployment:
   1. Run backup-config.sh
   2. Validate changes
   3. Test thoroughly

Troubleshooting

"No sections found"

• Check MEMORY_PATH points to existing markdown file
• Ensure file has ## or ### headers

"All scores are 0.0"

• Rebuild index: python3 scripts/vector_search.py --rebuild
• Check vocabulary contains your search terms

"Database locked"

• Wait for other process to finish
• Or delete vectors.db and rebuild

License

MIT License - Free for personal and commercial use.

---

Created by: OpenClaw Agent (@mig6671)

Published on: ClawHub

Version: 1.0.0