Embedding Generation CLI

This tool generates vector embeddings for OMOP CDM concepts and stores them in the configured embedding backend.

At present, the production CLI path is PostgreSQL-oriented and stores embeddings in Postgres/pgvector-backed model tables. It specifically targets concepts that do not yet have embeddings and processes them in batches.

Supported Models

Currently supported are only Ollama models

Prerequisites

Installation: install the PostgreSQL backend dependencies:

pip install "omop-emb[postgres]"

Database: Postgres implementation of OMOP CDM. See omop-graph documentation for information how to setup.
Environment: OMOP_DATABASE_URL must be exported or existing in the .env file (e.g., postgresql://user:pass@localhost:5432/omop).
Connectivity: Access to an OpenAI-compatible embeddings endpoint. Currently only Ollama supported.

Backend Scope

omop-emb now defines a backend abstraction layer for both PostgreSQL and FAISS-style storage. The current add-embeddings CLI still targets the PostgreSQL backend path.

`add-embeddings`

Usage

omop-emb add-embeddings --api-base <URL> --api-key <KEY> [OPTIONS]

where [OPTIONS] are optional arguments that can be specified as described below.

Command Options

Option	Short	Type	Default	Description
`--api-base`		`String`	Required	Base URL for the embedding API service.
`--api-key`		`String`	Required	API key for the embedding API provider.
`--index-type`		`IndexType`	`FLAT`	The storage index for the embeddings for retrieval. Currently supported: `FLAT`.
`--batch-size`	`-b`	`Integer`	`100`	Number of concepts to process in each chunk.
`--model`	`-m`	`String`	`text-embedding-3-small`	Name of the embedding model to use for generating vectors.
`--backend`		`Literal['pgvector', 'faiss']`	`None`	Embedding backend to use (can be replaced by `OMOP_EMB_BACKEND` env var). Requires the respective backend installed using `pip install omop-emb[pgvector or faiss]`
`--faiss-base-dir`		`String`	`None`	Optional base directory for FAISS backend storage.
`--standard-only`		`Boolean`	`False`	If set, only generate embeddings for OMOP standard concepts (`standard_concept = 'S'`).
`--vocabulary`		`List[String]`	`None`	Filter to embed concepts only from specific OMOP vocabularies.
`--num-embeddings`	`-n`	`Integer`	`None`	Limit the number of concepts processed (useful for testing).