metal-kompanion/docs/embeddings-and-storage.md

Embedding Backends & Storage Options

Embeddings

• Local: Ollama + llama.cpp gguf (e.g., nomic-embed-text, all-MiniLM-gguf).
• Remote: OpenAI text-embedding-3-small/large; SentenceTransformers via Python service.

Abstraction

IEmbedder interface with embed(texts: string[], model?: string) -> {model, vectors}.

Storage

• SQLite + FAISS (local, simple)
• Postgres + pgvector (robust, SQL)
• Qdrant (fast ANN, tags)
• Chroma (lightweight)

Abstraction

IVectorStore with upsert, query, delete, supports namespace, metadata filter and TTL.

Selection Matrix

• If offline-first: SQLite+FAISS + local embedder.
• If multi-host: Postgres+pgvector or Qdrant.

Next Steps

• Implement IEmbedder adapters (Ollama, OpenAI).
• Implement IVectorStore adapters (SQLite+FAISS, pgvector, Qdrant).
• Wire to MCP tools in kom.memory.v1.