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