rag.embed

Backward-compatibility shim. Embeddings has moved to ffai.core.embeddings.

class Embeddings(model='mistral/mistral-embed', api_key=None, api_base=None, cache_enabled=True, cache_size=256, device='cpu', **kwargs)[source]

Bases: object

Compute text embeddings using local or remote models.

Supports local sentence-transformer models (prefix local/) and remote providers via LiteLLM (e.g. mistral/mistral-embed, openai/text-embedding-3-small). Results are optionally cached in an LRU-style in-memory cache.

Parameters:

model (str) – Model identifier. Use local/<name> for local sentence-transformer models, or <provider>/<model> for remote APIs.
api_key (str | None) – API key for the remote provider. If None, the key is read from a provider-specific environment variable.
api_base (str | None) – Optional custom API base URL.
cache_enabled (bool) – Whether to cache embedding results in memory.
cache_size (int) – Maximum number of entries in the embedding cache.
device (str) – Torch device for local models (e.g. "cpu", "cuda").
**kwargs (Any) – Extra keyword arguments forwarded to the LiteLLM embedding call.

async aembed(texts)[source]

Compute embeddings for one or more texts asynchronously.

Parameters:: texts (str | list[str]) – A single text string or a list of text strings.
Returns:: List of embedding vectors, one per input text.
Return type:: list[list[float]]

async aembed_single(text)[source]

Compute the embedding for a single text asynchronously.

Parameters:: text (str) – The text to embed.
Returns:: The embedding vector.
Return type:: list[float]

embed(texts)[source]

Compute embeddings for one or more texts synchronously.

Parameters:: texts (str | list[str]) – A single text string or a list of text strings.
Returns:: List of embedding vectors, one per input text.
Return type:: list[list[float]]

embed_single(text)[source]

Compute the embedding for a single text synchronously.

Parameters:: text (str) – The text to embed.
Returns:: The embedding vector.
Return type:: list[float]

clear_cache()[source]

Clear the embedding cache.

Returns:: Number of entries that were in the cache before clearing.
Return type:: int

cache_stats()[source]

Return statistics about the embedding cache.

Returns:: Dictionary with keys enabled, max_size, and entries.
Return type:: dict[str, Any]

static cosine_similarity(a, b)[source]

Compute cosine similarity between two vectors.

Parameters:

a (list[float]) – First vector.
b (list[float]) – Second vector.

Returns:

Cosine similarity score in the range [-1, 1]. Returns 0.0 if either vector has zero magnitude.

Return type:

float

property provider: str

property is_local: bool