Simplify component init interface (#819)

Removes the need to having to define `**kwargs` when the user defines
the component `__init__` function. This was needed before to pass the
the `produces` and `consumes` to some read/write components without the
user having to explicitly define them. This was resolved by removing
those two arguments from the component signature.

Tested on a pipeline with load/write components.

README.md

@@ -190,7 +190,7 @@ from fondant.component import PandasTransformComponent
 
 class ExampleComponent(PandasTransformComponent):
 
-    def __init__(self, *, argument1, argument2, **kwargs) -> None:
+    def __init__(self, *, argument1, argument2) -> None:
         """
         Args:
             argumentX: An argument passed to the component

components/caption_images/src/main.py

@@ -76,7 +76,6 @@ def __init__(
         model_id: str,
         batch_size: int,
         max_new_tokens: int,
-        **kwargs,
     ):
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
         logger.info(f"Device: {self.device}")

components/chunk_text/src/main.py

@@ -43,7 +43,6 @@ def __init__(
         chunk_strategy: t.Optional[str],
         chunk_kwargs: t.Optional[dict],
         language_text_splitter: t.Optional[str],
-        **kwargs,
     ):
         """
         Args:
@@ -59,7 +58,6 @@ def __init__(
             https://python.langchain.com/docs/modules/data_connection/document_transformers/
             code_splitter
             for more information on supported languages.
-            kwargs: Unhandled keyword arguments passed in by Fondant.
         """
         self.chunk_strategy = chunk_strategy
         self.chunk_kwargs = chunk_kwargs

components/crop_images/src/main.py

@@ -35,13 +35,11 @@ def __init__(
         *,
         cropping_threshold: int,
         padding: int,
-        **kwargs,
     ) -> None:
         """
         Args:
             cropping_threshold (int): threshold parameter used for detecting borders
             padding (int): padding for the image cropping.
-            kwargs: Unhandled keyword arguments passed in by Fondant.
         """
         self.cropping_threshold = cropping_threshold
         self.padding = padding

components/download_images/src/main.py

@@ -32,7 +32,6 @@ def __init__(
         resize_only_if_bigger: bool,
         min_image_size: int,
         max_aspect_ratio: float,
-        **kwargs,
     ):
         """Component that downloads images from a list of URLs and executes filtering and resizing.
 
@@ -47,7 +46,6 @@ def __init__(
             resize_only_if_bigger: If True, resize only if image is bigger than image_size.
             min_image_size: Minimum size of the images.
             max_aspect_ratio: Maximum aspect ratio of the images.
-            kwargs: Unhandled keyword arguments passed in by Fondant
 
         Returns:
             Dask dataframe

components/embed_images/src/main.py

@@ -71,7 +71,6 @@ def __init__(
         *,
         model_id: str,
         batch_size: int,
-        **kwargs,
     ):
         """
         Args:

components/embed_text/src/main.py

@@ -29,7 +29,6 @@ def __init__(
         model: str,
         api_keys: dict,
         auth_kwargs: dict,
-        **kwargs,
     ):
         to_env_vars(api_keys)
 

components/evaluate_ragas/src/main.py

@@ -15,17 +15,14 @@ def __init__(
         llm_class_name: str,
         llm_kwargs: dict,
         produces: t.Dict[str, t.Any],
-        **kwargs,
     ) -> None:
         """
         Args:
             llm_module_name: Module from which the LLM is imported. Defaults to
              langchain.chat_models
             llm_class_name: Name of the selected llm. Defaults to ChatOpenAI
-            module: Module from which the LLM is imported. Defaults to langchain.llms
             llm_kwargs: Arguments of the selected llm
             produces: RAGAS metrics to compute.
-            kwargs: Unhandled keyword arguments passed in by Fondant.
         """
         self.llm = self.extract_llm(
             llm_module_name=llm_module_name,

components/filter_image_resolution/src/main.py

@@ -18,13 +18,11 @@ def __init__(
         *,
         min_image_dim: int,
         max_aspect_ratio: float,
-        **kwargs,
     ) -> None:
         """
         Args:
             min_image_dim: minimum image dimension.
             max_aspect_ratio: maximum aspect ratio.
-            kwargs: Unhandled keyword arguments passed in by Fondant.
         """
         self.min_image_dim = min_image_dim
         self.max_aspect_ratio = max_aspect_ratio

components/filter_language/src/main.py

@@ -46,12 +46,11 @@ def is_language(self, row):
 class LanguageFilterComponent(PandasTransformComponent):
     """Component that filter columns based on provided language."""
 
-    def __init__(self, *, language, **kwargs):
+    def __init__(self, *, language):
         """Setup language filter component.
 
         Args:
             language: Only keep text passages which are in the provided language.
-            kwargs: Unhandled keyword arguments passed in by Fondant
         """
         self.lang_detector = LanguageIdentification(language)
 

components/filter_text_length/src/main.py

@@ -11,13 +11,12 @@
 class FilterTextLengthComponent(PandasTransformComponent):
     """A component that filters out text based on their length."""
 
-    def __init__(self, *, min_characters_length: int, min_words_length: int, **kwargs):
+    def __init__(self, *, min_characters_length: int, min_words_length: int):
         """Setup component.
 
         Args:
             min_characters_length: minimum number of characters
             min_words_length: minimum number of words.
-            kwargs: Unhandled keyword arguments passed in by Fondant
         """
         self.min_characters_length = min_characters_length
         self.min_words_length = min_words_length

components/generate_minhash/src/main.py

@@ -33,12 +33,11 @@ def compute_minhash(shingles: list) -> np.ndarray:
 class MinHashGeneratorComponent(PandasTransformComponent):
     """Component generates minhashes of text."""
 
-    def __init__(self, *, shingle_ngram_size: int, **kwargs):
+    def __init__(self, *, shingle_ngram_size: int):
         """Setup component.
 
         Args:
             shingle_ngram_size: Defines size of ngram used for the shingle generation.
-            kwargs: Unhandled keyword arguments passed in by Fondant
         """
         self.shingle_ngram_size = shingle_ngram_size
 

components/index_aws_opensearch/src/main.py

@@ -22,7 +22,6 @@ def __init__(
         use_ssl: Optional[bool],
         verify_certs: Optional[bool],
         pool_maxsize: Optional[int],
-        **kwargs,
     ):
         session = boto3.Session()
         credentials = session.get_credentials()
@@ -35,7 +34,6 @@ def __init__(
             verify_certs=verify_certs,
             connection_class=RequestsHttpConnection,
             pool_maxsize=pool_maxsize,
-            **kwargs,
         )
         self.create_index(index_body)
 

components/index_qdrant/src/main.py

@@ -26,7 +26,6 @@ def __init__(
         host: Optional[str] = None,
         path: Optional[str] = None,
         force_disable_check_same_thread: bool = False,
-        **kwargs,
     ):
         """Initialize the IndexQdrantComponent with the component parameters."""
         self.client = QdrantClient(

components/index_weaviate/src/main.py

@@ -23,7 +23,6 @@ def __init__(
         additional_headers: t.Optional[dict],
         vectorizer: t.Optional[str],
         module_config: t.Optional[dict],
-        **kwargs,
     ):
         self.client = weaviate.Client(
             url=weaviate_url,

components/load_from_csv/src/main.py

@@ -4,7 +4,6 @@
 import dask.dataframe as dd
 import pandas as pd
 from fondant.component import DaskLoadComponent
-from fondant.core.schema import Field
 
 logger = logging.getLogger(__name__)
 
@@ -13,13 +12,11 @@ class CSVReader(DaskLoadComponent):
     def __init__(
         self,
         *,
-        produces: t.Dict[str, Field],
         dataset_uri: str,
         column_separator: str,
         column_name_mapping: t.Optional[dict],
         n_rows_to_load: t.Optional[int],
         index_column: t.Optional[str],
-        **kwargs,
     ) -> None:
         """
         Args:
@@ -33,14 +30,12 @@ def __init__(
             index_column: Column to set index to in the load component,
                 if not specified a default globally unique index will
                 be set.
-            kwargs: Unhandled keyword arguments passed in by Fondant.
         """
         self.dataset_uri = dataset_uri
         self.column_separator = column_separator
         self.column_name_mapping = column_name_mapping
         self.n_rows_to_load = n_rows_to_load
         self.index_column = index_column
-        self.produces = produces
 
     def get_columns_to_keep(self) -> t.List[str]:
         # Only read required columns

components/load_from_files/src/main.py

@@ -290,7 +290,7 @@ def get_filesystem(path_uri: str) -> fsspec.spec.AbstractFileSystem | None:
 class LoadFromFiles(DaskLoadComponent):
     """Component that loads datasets from files."""
 
-    def __init__(self, *_, directory_uri: str, **kwargs) -> None:
+    def __init__(self, *_, directory_uri: str) -> None:
         self.directory_uri = directory_uri
 
     def load(self) -> dd.DataFrame:

components/load_from_hf_hub/src/main.py

@@ -6,7 +6,6 @@
 import dask.dataframe as dd
 import pandas as pd
 from fondant.component import DaskLoadComponent
-from fondant.core.schema import Field
 
 logger = logging.getLogger(__name__)
 
@@ -17,17 +16,14 @@ class LoadFromHubComponent(DaskLoadComponent):
     def __init__(
         self,
         *,
-        produces: t.Dict[str, Field],
         dataset_name: str,
         column_name_mapping: t.Optional[dict],
         image_column_names: t.Optional[list],
         n_rows_to_load: t.Optional[int],
         index_column: t.Optional[str],
-        **kwargs,
     ) -> None:
         """
         Args:
-            produces: The schema the component should produce
             dataset_name: name of the dataset to load.
             column_name_mapping: Mapping of the consumed hub dataset to fondant column names
             image_column_names: A list containing the original hub image column names. Used to
@@ -36,14 +32,12 @@ def __init__(
               testing pipeline runs on a small scale.
             index_column: Column to set index to in the load component, if not specified a default
                 globally unique index will be set.
-            kwargs: Unhandled keyword arguments passed in by Fondant.
         """
         self.dataset_name = dataset_name
         self.column_name_mapping = column_name_mapping
         self.image_column_names = image_column_names
         self.n_rows_to_load = n_rows_to_load
         self.index_column = index_column
-        self.produces = produces
 
     def get_columns_to_keep(self) -> t.List[str]:
         # Only read required columns

components/load_from_parquet/src/main.py

@@ -6,7 +6,6 @@
 import dask.dataframe as dd
 import pandas as pd
 from fondant.component import DaskLoadComponent
-from fondant.core.schema import Field
 
 logger = logging.getLogger(__name__)
 
@@ -17,12 +16,10 @@ class LoadFromParquet(DaskLoadComponent):
     def __init__(
         self,
         *,
-        produces: t.Dict[str, Field],
         dataset_uri: str,
         column_name_mapping: t.Optional[dict],
         n_rows_to_load: t.Optional[int],
         index_column: t.Optional[str],
-        **kwargs,
     ) -> None:
         """
         Args:
@@ -33,13 +30,11 @@ def __init__(
               testing pipeline runs on a small scale.
             index_column: Column to set index to in the load component, if not specified a default
                 globally unique index will be set.
-            kwargs: Unhandled keyword arguments passed in by Fondant.
         """
         self.dataset_uri = dataset_uri
         self.column_name_mapping = column_name_mapping
         self.n_rows_to_load = n_rows_to_load
         self.index_column = index_column
-        self.produces = produces
 
     def get_columns_to_keep(self) -> t.List[str]:
         # Only read required columns

components/load_from_pdf/src/main.py

@@ -7,25 +7,21 @@
 import fsspec as fs
 import pandas as pd
 from fondant.component import DaskLoadComponent
-from fondant.core.schema import Field
 
 logger = logging.getLogger(__name__)
 
 
 class PDFReader(DaskLoadComponent):
     def __init__(
         self,
-        produces: t.Dict[str, Field],
         *,
         pdf_path: str,
         n_rows_to_load: t.Optional[int] = None,
         index_column: t.Optional[str] = None,
         n_partitions: t.Optional[int] = None,
-        **kwargs,
     ) -> None:
         """
         Args:
-            produces: The schema the component should produce
             pdf_path: Path to the PDF file
             n_rows_to_load: optional argument that defines the number of rows to load.
                 Useful for testing pipeline runs on a small scale.
@@ -34,9 +30,7 @@ def __init__(
             n_partitions: Number of partitions of the dask dataframe. If not specified, the number
                 of partitions will be equal to the number of CPU cores. Set to high values if
                 the data is large and the pipeline is running out of memory.
-            kwargs: Unhandled keyword arguments passed in by Fondant.
         """
-        self.produces = produces
         self.pdf_path = pdf_path
         self.n_rows_to_load = n_rows_to_load
         self.index_column = index_column

components/normalize_text/src/main.py

@@ -52,7 +52,6 @@ def __init__(
         normalize_lines: bool,
         do_lowercase: bool,
         remove_punctuation: bool,
-        **kwargs,
     ):
         self.remove_additional_whitespaces = remove_additional_whitespaces
         self.apply_nfc = apply_nfc

components/resize_images/src/main.py

@@ -11,7 +11,7 @@
 class ResizeImagesComponent(PandasTransformComponent):
     """Component that resizes images based on a given width and height."""
 
-    def __init__(self, *, resize_width: int, resize_height: int, **kwargs) -> None:
+    def __init__(self, *, resize_width: int, resize_height: int) -> None:
         self.resize_width = resize_width
         self.resize_height = resize_height
 

components/retrieve_from_weaviate/src/main.py

@@ -18,14 +18,12 @@ def __init__(
         hybrid_query: t.Optional[str],
         hybrid_alpha: t.Optional[float],
         rerank: bool,
-        **kwargs,
     ) -> None:
         """
         Args:
             weaviate_url: An argument passed to the component.
             class_name: Name of class to query
             top_k: Amount of context to return.
-            kwargs: Unhandled keyword arguments passed in by Fondant.
             additional_config: Additional configuration passed to the weaviate client.
             additional_headers: Additional headers passed to the weaviate client.
             hybrid_query: The hybrid query to be used for retrieval. Optional parameter.

components/retrieve_laion_by_embedding/src/main.py

@@ -20,7 +20,6 @@ def __init__(
         num_images: int,
         aesthetic_score: int,
         aesthetic_weight: float,
-        **kwargs,
     ) -> None:
         """
 
@@ -30,7 +29,6 @@ def __init__(
                 between 0 and 9.
             aesthetic_weight: weight of the aesthetic embedding to add to the query,
                 between 0 and 1.
-            kwargs: Unhandled keyword arguments passed in by Fondant.
         """
         self.client = ClipClient(
             url="https://knn.laion.ai/knn-service",