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# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
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#    http://www.apache.org/licenses/LICENSE-2.0
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"""Core types for RAG pipelines.

This module contains the core dataclasses used throughout the RAG pipeline
implementation. The primary type is EmbeddableItem, which represents any
content that can be embedded and stored in a vector database.

Types:
  - Content: Container for embeddable content
  - Embedding: Vector embedding with optional metadata
  - EmbeddableItem: Universal container for embeddable content
  - Chunk: Alias for EmbeddableItem (backward compatibility)
"""

import uuid
from dataclasses import dataclass
from dataclasses import field
from typing import Any
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
from typing import Union


@dataclass
class Content:
  """Container for embeddable content.

  Args:
      text: Text content to be embedded.
      image: Image as bytes or path/URI
          (e.g., 'gs://bucket/img.jpg').
  """
  text: Optional[str] = None
  image: Optional[Union[bytes, str]] = None


@dataclass
class Embedding:
  """Represents vector embeddings with optional metadata.

  Args:
      dense_embedding: Dense vector representation.
      sparse_embedding: Optional sparse vector representation for hybrid search.
  """
  dense_embedding: Optional[List[float]] = None
  sparse_embedding: Optional[Tuple[List[int], List[float]]] = None


@dataclass
class EmbeddableItem:
  """Universal container for embeddable content.

  Represents any content that can be embedded and stored in a vector database.
  Use factory methods for convenient construction, or construct directly with
  a Content object.

  Examples:
      Text (via factory):
          item = EmbeddableItem.from_text(
              "hello world", metadata={'src': 'doc'})

      Image (via factory):
          item = EmbeddableItem.from_image(
              'gs://bucket/img.jpg')

      Text (direct, equivalent to old Chunk usage):
          item = EmbeddableItem(
              content=Content(text="hello"), index=3)

  Args:
      content: The content to embed.
      id: Unique identifier.
      index: Position within source document (for chunking use cases).
      metadata: Additional metadata (e.g., document source, language).
      embedding: Embedding populated by the embedding step.
  """
  content: Content
  id: str = field(default_factory=lambda: str(uuid.uuid4()))
  index: int = 0
  metadata: Dict[str, Any] = field(default_factory=dict)
  embedding: Optional[Embedding] = None

  @classmethod
  def from_text(
      cls,
      text: str,
      *,
      id: Optional[str] = None,
      index: int = 0,
      metadata: Optional[Dict[str, Any]] = None,
  ) -> 'EmbeddableItem':
    """Create an EmbeddableItem with text content.

    Args:
        text: The text content to embed
        id: Unique identifier (auto-generated if not provided)
        index: Position within source document (for chunking)
        metadata: Additional metadata
    """
    return cls(
        content=Content(text=text),
        id=id or str(uuid.uuid4()),
        index=index,
        metadata=metadata or {},
    )

  @classmethod
  def from_image(
      cls,
      image: Union[bytes, str],
      *,
      id: Optional[str] = None,
      metadata: Optional[Dict[str, Any]] = None,
  ) -> 'EmbeddableItem':
    """Create an EmbeddableItem with image content.

    Args:
        image: Image bytes or path/URI (e.g. GCS path)
        id: Unique identifier (auto-generated if not
            provided)
        metadata: Additional metadata
    """
    return cls(
        content=Content(image=image),
        id=id or str(uuid.uuid4()),
        metadata=metadata or {},
    )

  @property
  def dense_embedding(self) -> Optional[List[float]]:
    return self.embedding.dense_embedding if self.embedding else None

  @property
  def sparse_embedding(self) -> Optional[Tuple[List[int], List[float]]]:
    return self.embedding.sparse_embedding if self.embedding else None

  @property
  def content_string(self) -> str:
    """Returns storable string content for ingestion.

    Falls back through content fields in priority order:
    text > image URI.
    """
    if self.content.text is not None:
      return self.content.text
    if isinstance(self.content.image, str):
      return self.content.image
    raise ValueError(
        f'EmbeddableItem does not contain storable string content'
        f' (text or image URI). {self}')


# Backward compatibility alias. Existing code using Chunk continues to work
# unchanged since Chunk IS EmbeddableItem.
Chunk = EmbeddableItem