sdks/python/apache_beam/ml/rag/ingestion/postgres_common.py

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import json
from dataclasses import dataclass
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Literal
from typing import Optional
from typing import Tuple
from typing import Type
from typing import Union

from apache_beam.ml.rag.types import Chunk


def chunk_embedding_fn(chunk: Chunk) -> str:
  """Convert embedding to PostgreSQL array string.

    Formats dense embedding as a PostgreSQL-compatible array string.
    Example: [1.0, 2.0] -> '{1.0,2.0}'

    Args:
        chunk: Input Chunk object.

    Returns:
        str: PostgreSQL array string representation of the embedding.

    Raises:
        ValueError: If chunk has no dense embedding.
    """
  if chunk.embedding is None or chunk.embedding.dense_embedding is None:
    raise ValueError(f'Expected chunk to contain embedding. {chunk}')
  return '{' + ','.join(str(x) for x in chunk.embedding.dense_embedding) + '}'


@dataclass
class ColumnSpec:
  """Specification for mapping Chunk fields to SQL columns for insertion.

    Defines how to extract and format values from Chunks into database columns,
    handling the full pipeline from Python value to SQL insertion.

    The insertion process works as follows:
    - value_fn extracts a value from the Chunk and formats it as needed
    - The value is stored in a NamedTuple field with the specified python_type
    - During SQL insertion, the value is bound to a ? placeholder

    Attributes:
        column_name: The column name in the database table.
        python_type: Python type for the NamedTuple field that will hold the
            value. Must be compatible with must be compatible with
            :class:`~apache_beam.coders.row_coder.RowCoder`.
        value_fn: Function to extract and format the value from a Chunk.
            Takes a Chunk and returns a value of python_type.
        sql_typecast: Optional SQL type cast to append to the ? placeholder.
            Common examples:
            - "::float[]" for vector arrays
            - "::jsonb" for JSON data

    Examples:
        Basic text column (uses standard JDBC type mapping):
        >>> ColumnSpec.text(
        ...     column_name="content",
        ...     value_fn=lambda chunk: chunk.content.text
        ... )
        # Results in: INSERT INTO table (content) VALUES (?)

        Vector column with explicit array casting:
        >>> ColumnSpec.vector(
        ...     column_name="embedding",
        ...     value_fn=lambda chunk: '{' +
        ...         ','.join(map(str, chunk.embedding.dense_embedding)) + '}'
        ... )
        # Results in: INSERT INTO table (embedding) VALUES (?::float[])
        # The value_fn formats [1.0, 2.0] as '{1.0,2.0}' for PostgreSQL array

        Timestamp from metadata with explicit casting:
        >>> ColumnSpec(
        ...     column_name="created_at",
        ...     python_type=str,
        ...     value_fn=lambda chunk: chunk.metadata.get("timestamp"),
        ...     sql_typecast="::timestamp"
        ... )
        # Results in: INSERT INTO table (created_at) VALUES (?::timestamp)
        # Allows inserting string timestamps with proper PostgreSQL casting

    Factory Methods:
        text: Creates a text column specification (no type cast).
        integer: Creates an integer column specification (no type cast).
        float: Creates a float column specification (no type cast).
        vector: Creates a vector column specification with float[] casting.
        jsonb: Creates a JSONB column specification with jsonb casting.
    """
  column_name: str
  python_type: Type
  value_fn: Callable[[Chunk], Any]
  sql_typecast: Optional[str] = None

  @property
  def placeholder(self) -> str:
    """Get SQL placeholder with optional typecast."""
    return f"?{self.sql_typecast or ''}"

  @classmethod
  def text(
      cls, column_name: str, value_fn: Callable[[Chunk], Any]) -> 'ColumnSpec':
    """Create a text column specification."""
    return cls(column_name, str, value_fn)

  @classmethod
  def integer(
      cls, column_name: str, value_fn: Callable[[Chunk], Any]) -> 'ColumnSpec':
    """Create an integer column specification."""
    return cls(column_name, int, value_fn)

  @classmethod
  def float(
      cls, column_name: str, value_fn: Callable[[Chunk], Any]) -> 'ColumnSpec':
    """Create a float column specification."""
    return cls(column_name, float, value_fn)

  @classmethod
  def vector(
      cls,
      column_name: str,
      value_fn: Callable[[Chunk], Any] = chunk_embedding_fn) -> 'ColumnSpec':
    """Create a vector column specification."""
    return cls(column_name, str, value_fn, "::float[]")

  @classmethod
  def jsonb(
      cls, column_name: str, value_fn: Callable[[Chunk], Any]) -> 'ColumnSpec':
    """Create a JSONB column specification."""
    return cls(column_name, str, value_fn, "::jsonb")


class ColumnSpecsBuilder:
  """Builder for :class:`.ColumnSpec`'s with chainable methods."""
  def __init__(self):
    self._specs: List[ColumnSpec] = []

  @staticmethod
  def with_defaults() -> 'ColumnSpecsBuilder':
    """Add all default column specifications."""
    return (
        ColumnSpecsBuilder().with_id_spec().with_embedding_spec().
        with_content_spec().with_metadata_spec())

  def with_id_spec(
      self,
      column_name: str = "id",
      python_type: Type = str,
      convert_fn: Optional[Callable[[str], Any]] = None,
      sql_typecast: Optional[str] = None) -> 'ColumnSpecsBuilder':
    """Add ID :class:`.ColumnSpec` with optional type and conversion.

        Args:
            column_name: Name for the ID column (defaults to "id")
            python_type: Python type for the column (defaults to str)
            convert_fn: Optional function to convert the chunk ID
                       If None, uses ID as-is
            sql_typecast: Optional SQL type cast

        Returns:
            Self for method chaining

        Example:
            >>> builder.with_id_spec(
            ...     column_name="doc_id",
            ...     python_type=int,
            ...     convert_fn=lambda id: int(id.split('_')[1])
            ... )
        """
    def value_fn(chunk: Chunk) -> Any:
      value = chunk.id
      return convert_fn(value) if convert_fn else value

    self._specs.append(
        ColumnSpec(
            column_name=column_name,
            python_type=python_type,
            value_fn=value_fn,
            sql_typecast=sql_typecast))
    return self

  def with_content_spec(
      self,
      column_name: str = "content",
      python_type: Type = str,
      convert_fn: Optional[Callable[[str], Any]] = None,
      sql_typecast: Optional[str] = None) -> 'ColumnSpecsBuilder':
    """Add content :class:`.ColumnSpec` with optional type and conversion.

      Args:
          column_name: Name for the content column (defaults to "content")
          python_type: Python type for the column (defaults to str)
          convert_fn: Optional function to convert the content text
                      If None, uses content text as-is
          sql_typecast: Optional SQL type cast

      Returns:
          Self for method chaining

      Example:
          >>> builder.with_content_spec(
          ...     column_name="content_length",
          ...     python_type=int,
          ...     convert_fn=len  # Store content length instead of content
          ... )
      """
    def value_fn(chunk: Chunk) -> Any:
      if chunk.content.text is None:
        raise ValueError(f'Expected chunk to contain content. {chunk}')
      value = chunk.content.text
      return convert_fn(value) if convert_fn else value

    self._specs.append(
        ColumnSpec(
            column_name=column_name,
            python_type=python_type,
            value_fn=value_fn,
            sql_typecast=sql_typecast))
    return self

  def with_metadata_spec(
      self,
      column_name: str = "metadata",
      python_type: Type = str,
      convert_fn: Optional[Callable[[Dict[str, Any]], Any]] = None,
      sql_typecast: Optional[str] = "::jsonb") -> 'ColumnSpecsBuilder':
    """Add metadata :class:`.ColumnSpec` with optional type and conversion.

      Args:
          column_name: Name for the metadata column (defaults to "metadata")
          python_type: Python type for the column (defaults to str)
          convert_fn: Optional function to convert the metadata dictionary
                      If None and python_type is str, converts to JSON string
          sql_typecast: Optional SQL type cast (defaults to "::jsonb")

      Returns:
          Self for method chaining

      Example:
          >>> builder.with_metadata_spec(
          ...     column_name="meta_tags",
          ...     python_type=list,
          ...     convert_fn=lambda meta: list(meta.keys()),
          ...     sql_typecast="::text[]"
          ... )
      """
    def value_fn(chunk: Chunk) -> Any:
      if convert_fn:
        return convert_fn(chunk.metadata)
      return json.dumps(
          chunk.metadata) if python_type == str else chunk.metadata

    self._specs.append(
        ColumnSpec(
            column_name=column_name,
            python_type=python_type,
            value_fn=value_fn,
            sql_typecast=sql_typecast))
    return self

  def with_embedding_spec(
      self,
      column_name: str = "embedding",
      convert_fn: Optional[Callable[[List[float]], Any]] = None
  ) -> 'ColumnSpecsBuilder':
    """Add embedding :class:`.ColumnSpec` with optional conversion.

      Args:
          column_name: Name for the embedding column (defaults to "embedding")
          convert_fn: Optional function to convert the dense embedding values
                      If None, uses default PostgreSQL array format

      Returns:
          Self for method chaining

      Example:
          >>> builder.with_embedding_spec(
          ...     column_name="embedding_vector",
          ...     convert_fn=lambda values: '{' + ','.join(f"{x:.4f}"
          ...       for x in values) + '}'
          ... )
      """
    def value_fn(chunk: Chunk) -> Any:
      if chunk.embedding is None or chunk.embedding.dense_embedding is None:
        raise ValueError(f'Expected chunk to contain embedding. {chunk}')
      values = chunk.embedding.dense_embedding
      if convert_fn:
        return convert_fn(values)
      return '{' + ','.join(str(x) for x in values) + '}'

    self._specs.append(
        ColumnSpec.vector(column_name=column_name, value_fn=value_fn))
    return self

  def with_sparse_embedding_spec(
      self,
      column_name: str = "sparse_embedding",
      conv_fn: Optional[Callable[[Tuple[List[int], List[float]]], Any]] = None
  ) -> 'ColumnSpecsBuilder':
    """Add sparse embedding :class:`.ColumnSpec` with optional conversion.

      Args:
          column_name: Name for the sparse embedding column
            (defaults to "sparse_embedding")
          conv_fn: Optional function to convert the sparse embedding tuple
                      If None, converts to PostgreSQL-compatible JSON format

      Returns:
          Self for method chaining

      Example:
          >>> builder.with_sparse_embedding_spec(
          ...     column_name="sparse_vector",
          ...     convert_fn=lambda sparse: dict(zip(sparse[0], sparse[1]))
          ... )
      """
    def value_fn(chunk: Chunk) -> Any:
      if chunk.embedding is None or chunk.embedding.sparse_embedding is None:
        raise ValueError(f'Expected chunk to contain sparse embedding. {chunk}')
      sparse_embedding = chunk.embedding.sparse_embedding
      if conv_fn:
        return conv_fn(sparse_embedding)
      # Default: convert to dict format for JSON storage.
      indices, values = sparse_embedding
      return json.dumps(dict(zip(indices, values)))

    self._specs.append(
        ColumnSpec.jsonb(column_name=column_name, value_fn=value_fn))
    return self

  def add_metadata_field(
      self,
      field: str,
      python_type: Type,
      column_name: Optional[str] = None,
      convert_fn: Optional[Callable[[Any], Any]] = None,
      default: Any = None,
      sql_typecast: Optional[str] = None) -> 'ColumnSpecsBuilder':
    """""Add a :class:`.ColumnSpec` that extracts and converts a field from
        chunk metadata.

        Args:
            field: Key to extract from chunk metadata
            python_type: Python type for the column (e.g. str, int, float)
            column_name: Name for the column (defaults to metadata field name)
            convert_fn: Optional function to convert the extracted value to
                      desired type. If None, value is used as-is
            default: Default value if field is missing from metadata
            sql_typecast: Optional SQL type cast (e.g. "::timestamp")

        Returns:
            Self for chaining

        Examples:

            Simple string field:
            >>> builder.add_metadata_field("source", str)

            Integer with default:

            >>> builder.add_metadata_field(
            ...     field="count",
            ...     python_type=int,
            ...     column_name="item_count",
            ...     default=0
            ... )

            Float with conversion and default:

            >>> builder.add_metadata_field(
            ...     field="confidence",
            ...     python_type=intfloat,
            ...     convert_fn=lambda x: round(float(x), 2),
            ...     default=0.0
            ... )

            Timestamp with conversion and type cast:

            >>> builder.add_metadata_field(
            ...     field="created_at",
            ...     python_type=intstr,
            ...     convert_fn=lambda ts: ts.replace('T', ' '),
            ...     sql_typecast="::timestamp"
            ... )
        """
    name = column_name or field

    def value_fn(chunk: Chunk) -> Any:
      value = chunk.metadata.get(field, default)
      if value is not None and convert_fn is not None:
        value = convert_fn(value)
      return value

    spec = ColumnSpec(
        column_name=name,
        python_type=python_type,
        value_fn=value_fn,
        sql_typecast=sql_typecast)

    self._specs.append(spec)
    return self

  def add_custom_column_spec(self, spec: ColumnSpec) -> 'ColumnSpecsBuilder':
    """Add a custom :class:`.ColumnSpec` to the builder.

    Use this method when you need complete control over the :class:`.ColumnSpec`
    , including custom value extraction and type handling.

    Args:
        spec: A :class:`.ColumnSpec` instance defining the column name, type,
            value extraction, and optional SQL type casting.

    Returns:
        Self for method chaining

    Examples:
        Custom text column from chunk metadata:

        >>> builder.add_custom_column_spec(
        ...     ColumnSpec.text(
        ...         name="source_and_id",
        ...         value_fn=lambda chunk: \
        ...             f"{chunk.metadata.get('source')}_{chunk.id}"
        ...     )
        ... )
    """
    self._specs.append(spec)
    return self

  def build(self) -> List[ColumnSpec]:
    """Build the final list of column specifications."""
    return self._specs.copy()


@dataclass
class ConflictResolution:
  """Specification for how to handle conflicts during insert.

    Configures conflict handling behavior when inserting records that may
    violate unique constraints.

    Attributes:
        on_conflict_fields: Field(s) that determine uniqueness. Can be a single
            field name or list of field names for composite constraints.
        action: How to handle conflicts - either "UPDATE" or "IGNORE".
            UPDATE: Updates existing record with new values.
            IGNORE: Skips conflicting records.
        update_fields: Optional list of fields to update on conflict. If None,
            all non-conflict fields are updated.

    Examples:
        Simple primary key:

        >>> ConflictResolution("id")

        Composite key with specific update fields:

        >>> ConflictResolution(
        ...     on_conflict_fields=["source", "timestamp"],
        ...     action="UPDATE",
        ...     update_fields=["embedding", "content"]
        ... )

        Ignore conflicts:

        >>> ConflictResolution(
        ...     on_conflict_fields="id",
        ...     action="IGNORE"
        ... )
    """
  on_conflict_fields: Union[str, List[str]]
  action: Literal["UPDATE", "IGNORE"] = "UPDATE"
  update_fields: Optional[List[str]] = None

  def maybe_set_default_update_fields(self, columns: List[str]):
    if self.action != "UPDATE":
      return
    if self.update_fields is not None:
      return

    conflict_fields = ([self.on_conflict_fields] if isinstance(
        self.on_conflict_fields, str) else self.on_conflict_fields)
    self.update_fields = [col for col in columns if col not in conflict_fields]

  def get_conflict_clause(self) -> str:
    """Get conflict clause with update fields."""
    conflict_fields = [self.on_conflict_fields] \
      if isinstance(self.on_conflict_fields, str) \
        else self.on_conflict_fields

    if self.action == "IGNORE":
      conflict_fields_string = f"({', '.join(conflict_fields)})" \
        if len(conflict_fields) > 0 else ""
      return f"ON CONFLICT {conflict_fields_string} DO NOTHING"

    # update_fields should be set by query builder before this is called
    assert self.update_fields is not None, \
      "update_fields must be set before generating conflict clause"
    updates = [f"{field} = EXCLUDED.{field}" for field in self.update_fields]
    return f"ON CONFLICT " \
      f"({', '.join(conflict_fields)}) DO UPDATE SET {', '.join(updates)}"