sdks/python/apache_beam/ml/rag/embeddings/base.py

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"""RAG-specific embedding adapters.

This module provides adapters for extracting content from
EmbeddableItem instances and mapping embeddings back. Adapters
are used by EmbeddingsManager to support various input types.
"""

from collections.abc import Sequence

from apache_beam.ml.rag.types import EmbeddableItem
from apache_beam.ml.rag.types import Embedding
from apache_beam.ml.transforms.base import EmbeddingTypeAdapter


def create_text_adapter(
) -> EmbeddingTypeAdapter[EmbeddableItem, EmbeddableItem]:
  """Creates adapter for text content embedding.

  Works with any EmbeddableItem that has text content
  (content.text). Extracts text for embedding and maps
  results back as Embedding objects.

  Returns:
      EmbeddingTypeAdapter configured for text embedding
  """
  return EmbeddingTypeAdapter(
      input_fn=_extract_text, output_fn=_add_embedding_fn)


# Backward compatibility alias.
create_rag_adapter = create_text_adapter


def _extract_text(items: Sequence[EmbeddableItem]) -> list[str]:
  """Extract text from items for embedding."""
  texts = []
  for item in items:
    if not item.content.text:
      raise ValueError(
          f"Expected text content in {type(item).__name__} {item.id}, "
          "got None")
    texts.append(item.content.text)
  return texts


def _add_embedding_fn(
    items: Sequence[EmbeddableItem],
    embeddings: Sequence[list[float]]) -> list[EmbeddableItem]:
  """Create Embeddings from items and embedding vectors."""
  for item, embedding in zip(items, embeddings):
    item.embedding = Embedding(dense_embedding=embedding)
  return list(items)