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OmopEmb

OmopEmb Adapter

OmopEmbAdapter wraps omop-emb to provide embedding index queries. It supports two storage backends and two query modes.

Storage backends

Backend	Config	Description
`sqlitevec`	`db_path: /path/to/index.db`	File-based; zero external dependencies
`pgvector`	`db_url: postgresql://...`	PostgreSQL; scales to larger indexes

Query modes

Neighbour lookup (`embedding_neighbours`)

Fetches the existing vector for a concept ID from the index, then finds the nearest neighbours by similarity. Does not require an API key — all data is in the index.

On-the-fly search (`embedding_search`)

Encodes the query string using a live embedding model (api_base + api_key), then searches the index for similar concepts with optional domain, vocabulary, standard, and active-status filtering.

Raw encoding (`embedding_encode`)

Encodes one free-text string using the configured query embedding model and returns the raw vector. This is primarily intended as a building block for higher-level ACP workflows.

`index_status` response

{
  "available": true,
  "backend_type": "pgvector",
  "models": [
    {
      "model_name": "qwen3-embedding:0.6b",
      "provider": "OLLAMA",
      "dimensions": 1024,
      "index_type": "FLAT",
      "concept_count": 438924
    }
  ]
}

Lazy backend initialisation

The underlying backend object is created on first use (not at server startup). If the backend is unavailable, index_status returns "available": false with an empty models list rather than crashing.