Vector Memory Skill

This skill provides vector-based semantic memory storage using embeddings for intelligent recall by meaning.

When to Use

• You need semantic search (find memories by meaning, not keywords)

• You want to retrieve similar documents or conversations

• You're building an agent that needs context-aware memory

• You need to cluster or group related memories

Capabilities

• vstore: Store text with automatic embedding generation

• vsearch: Search memories by semantic similarity

• vdelete: Remove a memory by ID

• vlist: List all stored memories

• vsimilar: Find memories similar to a given ID

• vclear: Clear all memories

How It Works

• Text is converted to embeddings using OpenAI's API

• Embeddings are stored in JSON with metadata

• Search uses cosine similarity to find semantically related memories

• No external vector database required - pure JSON storage

Environment Variables

Required:

• OPENAI_API_KEY
• For generating embeddings

Optional:

• VECTOR_MEMORY_DIM
• Embedding dimensions (default: 1536 for text-embedding-ada-002)

Usage Examples

// Store a memory with semantic embedding vstore('Meeting notes: Discussed Q1 roadmap and budget allocation') // Returns: "Stored memory with ID: mem_abc123"

// Search by meaning (not keywords) vsearch('What did we talk about regarding money?') // Returns: Memories about budget, funding, financial discussions

// Find similar memories vsimilar('mem_abc123') // Returns: Semantically similar memories

// List all memories vlist() // Returns: List of stored memories with metadata

// Clear all vclear() // Returns: "Cleared all vector memories"

Features

• Semantic search:Find by meaning, not keywords

• Similarity scoring: Results ranked by relevance score

• Automatic embeddings: No manual vector generation needed

• Metadata support: Store timestamps and tags with memories

• Pure JSON: No external database dependencies