apache/beam · error · TypeError

Unexpected keyword arguments: {', '.join(kwargs)}

Error message

Unexpected keyword arguments: {', '.join(kwargs)}

What it means

The BigQuery RAG ingestion SchemaConfig __init__ accepts known keyword arguments plus the deprecated chunk_to_dict_fn alias; any remaining unexpected kwargs raise a TypeError listing them. This guards against silently ignored misnamed options.

Source

Thrown at sdks/python/apache_beam/ml/rag/ingestion/bigquery.py:76

      ...       {'name': 'source_url', 'type': 'STRING'}
      ...     ]
      ...   },
      ...   embeddable_to_dict_fn=lambda item: {
      ...       'id': item.id,
      ...       'embedding': item.embedding.dense_embedding,
      ...       'source_url': item.metadata.get('url')
      ...   }
      ... )
    """
    self.schema = schema
    if 'chunk_to_dict_fn' in kwargs:
      warnings.warn(
          "chunk_to_dict_fn is deprecated, use embeddable_to_dict_fn",
          DeprecationWarning,
          stacklevel=2)
      embeddable_to_dict_fn = kwargs.pop('chunk_to_dict_fn')
    if kwargs:
      raise TypeError(f"Unexpected keyword arguments: {', '.join(kwargs)}")
    if embeddable_to_dict_fn is None:
      raise TypeError("SchemaConfig requires embeddable_to_dict_fn")
    self.embeddable_to_dict_fn = embeddable_to_dict_fn


class BigQueryVectorWriterConfig(VectorDatabaseWriteConfig):
  def __init__(
      self,
      write_config: dict[str, Any],
      *,  # Force keyword arguments
      schema_config: Optional[SchemaConfig] = None):
    """Configuration for writing vectors to BigQuery using managed transforms.
    
    Supports both default schema (id, embedding, content, metadata columns) and
    custom schemas through SchemaConfig.

    Example with default schema:
      >>> config = BigQueryVectorWriterConfig(

View on GitHub (pinned to 12126d8942)

Solutions

1. Remove or correct the unexpected keyword arguments listed in the message
2. Move write-related options (table etc.) into BigQueryVectorWriterConfig/write_config
3. Replace legacy chunk_to_dict_fn with embeddable_to_dict_fn (the only accepted alias)

Example fix

// before
SchemaConfig(embeddable_to_dct_fn=fn)  # typo -> unexpected kwarg
// after
SchemaConfig(embeddable_to_dict_fn=fn)

ALLOWED = {'embeddable_to_dict_fn', 'chunk_to_dict_fn'}
unknown = set(kwargs) - ALLOWED
assert not unknown, f'unexpected SchemaConfig kwargs: {unknown}'

def valid_schema_config_kwargs(kwargs: dict) -> bool:
    return not (set(kwargs) - {'embeddable_to_dict_fn', 'chunk_to_dict_fn'})

try:
    sc = SchemaConfig(**opts)
except TypeError as e:
    if str(e).startswith('Unexpected keyword arguments'): retry_with_cleaned_kwargs(opts)
    else: raise

When it happens

Trigger: Calling SchemaConfig(...) with keyword arguments other than embeddable_to_dict_fn / metadata_fn-style accepted params, or the legacy chunk_to_dict_fn.

Common situations: Typos like embeddable_to_dict_func; passing writer options (table, project) into SchemaConfig instead of BigQueryVectorWriterConfig; old code still passing removed parameters.

Understand the failure class

Background: "must be a positive integer", "cannot be empty", "invalid argument": how invalid-argument errors work across open-source libraries — this error's family across 33 libraries.

Related errors

• %s: gcs_location must be of type string or ValueProvider; go
• %s: table must be of type string; got a callable instead
• Table schema must be of the type bigquery.TableSchema
• Unexpected schema argument: %s.
• Item {item.id} missing embedding

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/92ec4be791069cb7. Report an issue: GitHub.