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