ractogateway.rag.embedders

RAG embedding providers.

class ractogateway.rag.embedders.BaseEmbedder[source]

Bases: ABC

Embed a list of texts into dense float vectors.

All embedders implement both sync embed() and async aembed() variants. The dimension of returned vectors is declared via the dimension property (-1 if unknown until the first call).

property dimension: int

Dimensionality of the embedding vectors.

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

abstractmethod 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.

abstractmethod async aembed(texts)[source]

Async variant of embed().

Return type:

list[list[float]]

class ractogateway.rag.embedders.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]]

class ractogateway.rag.embedders.OpenAIEmbedder(model='text-embedding-3-small', *, api_key=None, base_url=None, dimensions=None, batch_size=256)[source]

Bases: BaseEmbedder

Embed texts using the OpenAI Embeddings API.

Parameters:

  • model (str) – OpenAI embedding model (default "text-embedding-3-small").

  • api_key (str | None) – OpenAI API key. Falls back to OPENAI_API_KEY env var.

  • base_url (str | None) – Custom base URL (Azure OpenAI or proxy).

  • dimensions (int | None) – Override output dimensionality (supported for text-embedding-3-*).

  • 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]]

class ractogateway.rag.embedders.VoyageEmbedder(model='voyage-3', *, api_key=None, input_type='document', batch_size=128)[source]

Bases: BaseEmbedder

Embed texts using the Voyage AI API.

Voyage AI embeddings are optimised for Anthropic Claude RAG pipelines and are the recommended choice when using Claude as the generation LLM.

Parameters:

  • model (str) – Voyage model name (default "voyage-3").

  • api_key (str | None) – Voyage API key. Falls back to VOYAGE_API_KEY env var.

  • input_type (str | None) – "query" for queries, "document" for documents to index. Using the correct type improves retrieval quality.

  • batch_size (int) – Maximum 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]]