ractogateway.rag.embedders.google_embedder

Google Gemini embedding provider.

Install with: pip install ractogateway[google]

class ractogateway.rag.embedders.google_embedder.GoogleEmbedder(model='text-embedding-004', *, api_key=None, task_type=None, batch_size=100)[source]

Bases: BaseEmbedder

Embed texts using the Google Gemini Embeddings API.

Parameters:

model (str) – Gemini embedding model (default "text-embedding-004").
api_key (str | None) – Gemini API key. Falls back to GEMINI_API_KEY env var.
task_type (str | None) – Gemini task type hint (e.g. "RETRIEVAL_DOCUMENT", "RETRIEVAL_QUERY"). None lets the API decide.
batch_size (int) – Maximum number of texts per API call.

property dimension: int

Dimensionality of the embedding vectors.

Returns -1 if not known until after the first call.

embed(texts)[source]

Embed texts synchronously.

Parameters:: texts (list[str]) – Non-empty list of strings to embed.
Return type:: list[list[float]]
Returns:: list[list[float]] – One embedding vector per input text, in the same order.

async aembed(texts)[source]

Async variant of embed().

Return type:: list[list[float]]