Implementation of new pipeline interface

components/caption_images/README.md

@@ -7,13 +7,11 @@ This component captions images using a BLIP model from the Hugging Face hub
 
 **This component consumes:**
 
-- images
-    - data: binary
+- images_data: binary
 
 **This component produces:**
 
-- captions
-    - text: string
+- captions_text: string
 
 ### Arguments
 

components/caption_images/fondant_component.yaml

@@ -5,16 +5,12 @@ tags:
   - Image processing
 
 consumes:
-  images:
-    fields:
-      data:
-        type: binary
+  images_data:
+    type: binary
 
 produces:
-  captions:
-    fields:
-      text:
-        type: utf8
+  captions_text:
+    type: utf8
 
 args:
   model_id:

components/caption_images/src/main.py

@@ -90,7 +90,7 @@ def __init__(
         self.max_new_tokens = max_new_tokens
 
     def transform(self, dataframe: pd.DataFrame) -> pd.DataFrame:
-        images = dataframe["images"]["data"]
+        images = dataframe["images_data"]
 
         results: t.List[pd.Series] = []
         for batch in np.split(
@@ -112,4 +112,4 @@ def transform(self, dataframe: pd.DataFrame) -> pd.DataFrame:
                 ).T
                 results.append(captions)
 
-        return pd.concat(results).to_frame(name=("captions", "text"))
+        return pd.concat(results).to_frame(name=("captions_text"))

components/chunk_text/README.md

@@ -11,14 +11,12 @@ consists of the id of the original document followed by the chunk index.
 
 **This component consumes:**
 
-- text
-    - data: string
+- text_data: string
 
 **This component produces:**
 
-- text
-    - data: string
-    - original_document_id: string
+- text_data: string
+- text_original_document_id: string
 
 ### Arguments
 

components/chunk_text/fondant_component.yaml

@@ -10,18 +10,14 @@ tags:
   - Text processing
 
 consumes:
-  text:
-    fields:
-      data:
-        type: string
+  text_data:
+    type: string
 
 produces:
-  text:
-    fields:
-      data:
-        type: string
-      original_document_id:
-        type: string
+  text_data:
+    type: string
+  text_original_document_id:
+    type: string
 
 args:
   chunk_size:

components/chunk_text/src/main.py

@@ -38,7 +38,7 @@ def __init__(
     def chunk_text(self, row) -> t.List[t.Tuple]:
         # Multi-index df has id under the name attribute
         doc_id = row.name
-        text_data = row[("text", "data")]
+        text_data = row[("text_data")]
         docs = self.text_splitter.create_documents([text_data])
         return [
             (doc_id, f"{doc_id}_{chunk_id}", chunk.page_content)
@@ -63,9 +63,4 @@ def transform(self, dataframe: pd.DataFrame) -> pd.DataFrame:
         )
         results_df = results_df.set_index("id")
 
-        # Set multi-index column for the expected subset and field
-        results_df.columns = pd.MultiIndex.from_product(
-            [["text"], results_df.columns],
-        )
-
         return results_df

components/chunk_text/tests/chunk_text_test.py

@@ -7,7 +7,7 @@ def test_transform():
     """Test chunk component method."""
     input_dataframe = pd.DataFrame(
         {
-            ("text", "data"): [
+            ("text_data"): [
                 "Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Aenean commodo",
                 "ligula eget dolor. Aenean massa. Cum sociis natoque penatibus et magnis dis",
                 "parturient montes, nascetur ridiculus mus. Donec quam felis, ultricies nec,",
@@ -25,8 +25,8 @@ def test_transform():
 
     expected_output_dataframe = pd.DataFrame(
         {
-            ("text", "original_document_id"): ["a", "a", "a", "b", "b", "c", "c"],
-            ("text", "data"): [
+            ("text_original_document_id"): ["a", "a", "a", "b", "b", "c", "c"],
+            ("text_data"): [
                 "Lorem ipsum dolor sit amet, consectetuer",
                 "amet, consectetuer adipiscing elit. Aenean",
                 "elit. Aenean commodo",

components/download_images/README.md

@@ -14,15 +14,13 @@ from the img2dataset library.
 
 **This component consumes:**
 
-- images
-    - url: string
+- images_url: string
 
 **This component produces:**
 
-- images
-    - data: binary
-    - width: int32
-    - height: int32
+- images_data: binary
+- images_width: int32
+- images_height: int32
 
 ### Arguments
 

components/download_images/fondant_component.yaml

@@ -13,21 +13,17 @@ tags:
   - Image processing
 
 consumes:
-  images:
-    fields:
-      url:
-        type: string
+  images_url:
+    type: string
 
 produces:
-  images:
-    fields:
-      data:
-        type: binary
-      width:
-        type: int32
-      height:
-        type: int32
-    additionalFields: false
+  images_data:
+    type: binary
+  images_width:
+    type: int32
+  images_height:
+    type: int32
+#    additionalFields: false
 
 args:
   timeout:
@@ -53,7 +49,7 @@ args:
     description: Resize mode to use. One of "no", "keep_ratio", "center_crop", "border".
     type: str
     default: 'border'
-  resize_only_if_bigger: 
+  resize_only_if_bigger:
     description: If True, resize only if image is bigger than image_size.
     type: bool
     default: False

components/download_images/src/main.py

@@ -119,7 +119,7 @@ async def download_dataframe() -> None:
             images = await asyncio.gather(
                 *[
                     self.download_and_resize_image(id_, url, semaphore=semaphore)
-                    for id_, url in zip(dataframe.index, dataframe["images"]["url"])
+                    for id_, url in zip(dataframe.index, dataframe["images_url"])
                 ],
             )
             results.extend(images)
@@ -134,8 +134,5 @@ async def download_dataframe() -> None:
 
         results_df = results_df.dropna()
         results_df = results_df.set_index("id", drop=True)
-        results_df.columns = pd.MultiIndex.from_product(
-            [["images"], results_df.columns],
-        )
 
         return results_df

components/download_images/tests/test_component.py

@@ -45,7 +45,7 @@ def test_transform(respx_mock):
 
     input_dataframe = pd.DataFrame(
         {
-            ("images", "url"): urls,
+            "images_url": urls,
         },
         index=pd.Index(ids, name="id"),
     )
@@ -55,9 +55,9 @@ def test_transform(respx_mock):
     resized_images = [component.resizer(io.BytesIO(image))[0] for image in images]
     expected_dataframe = pd.DataFrame(
         {
-            ("images", "data"): resized_images,
-            ("images", "width"): [image_size] * len(ids),
-            ("images", "height"): [image_size] * len(ids),
+            "images_data": resized_images,
+            "images_width": [image_size] * len(ids),
+            "images_height": [image_size] * len(ids),
         },
         index=pd.Index(ids, name="id"),
     )

components/embed_images/README.md

@@ -7,13 +7,11 @@ Component that generates CLIP embeddings from images
 
 **This component consumes:**
 
-- images
-    - data: binary
+- images_data: binary
 
 **This component produces:**
 
-- embeddings
-    - data: list<item: float>
+- embeddings_data: list<item: float>
 
 ### Arguments
 

components/embed_images/fondant_component.yaml

@@ -2,21 +2,17 @@ name: Embed images
 description: Component that generates CLIP embeddings from images
 image: fndnt/embed_images:dev
 tags:
-   - Image processing
+  - Image processing
 
 consumes:
-  images:
-      fields:
-        data:
-          type: binary
+  images_data:
+    type: binary
 
 produces:
-  embeddings:
-    fields:
-      data:
-        type: array
-        items:
-          type: float32
+  embeddings_data:
+    type: array
+    items:
+      type: float32
 
 args:
   model_id:

components/embed_images/src/main.py

@@ -90,7 +90,7 @@ def __init__(
         self.batch_size = batch_size
 
     def transform(self, dataframe: pd.DataFrame) -> pd.DataFrame:
-        images = dataframe["images"]["data"]
+        images = dataframe["images_data"]
 
         results: t.List[pd.Series] = []
         for batch in np.split(
@@ -110,4 +110,4 @@ def transform(self, dataframe: pd.DataFrame) -> pd.DataFrame:
                 ).T
                 results.append(embeddings)
 
-        return pd.concat(results).to_frame(name=("embeddings", "data"))
+        return pd.concat(results).to_frame(name=("embeddings_data"))

components/embed_text/README.md

@@ -7,14 +7,12 @@ Component that generates embeddings of text passages.
 
 **This component consumes:**
 
-- text
-    - data: string
+- text_data: string
 
 **This component produces:**
 
-- text
-    - data: string
-    - embedding: list<item: float>
+- text_data: string
+- text_embedding: list<item: float>
 
 ### Arguments
 

components/embed_text/fondant_component.yaml

@@ -5,21 +5,17 @@ tags:
   - Text processing
 
 consumes:
-  text:
-    fields:
-      data:
-        type: string
+  text_data:
+    type: string
 
 produces:
-  text:
-    fields:
-      data:
-        type: string
-      embedding:
-        type: array
-        items:
-          type: float32
-
+  text_data:
+    type: string
+  text_embedding:
+    type: array
+    items:
+      type: float32
+
 args:
   model_provider:
     description: |

components/embed_text/src/main.py

@@ -65,7 +65,7 @@ def get_embeddings_vectors(self, texts):
         return self.embedding_model.embed_documents(texts.tolist())
 
     def transform(self, dataframe: pd.DataFrame) -> pd.DataFrame:
-        dataframe[("text", "embedding")] = self.get_embeddings_vectors(
-            dataframe[("text", "data")],
+        dataframe["text_embedding"] = self.get_embeddings_vectors(
+            dataframe["text_data"],
         )
         return dataframe

components/embedding_based_laion_retrieval/README.md

@@ -9,13 +9,11 @@ used to find images similar to the embedded images / captions.
 
 **This component consumes:**
 
-- embeddings
-    - data: list<item: float>
+- embeddings_data: list<item: float>
 
 **This component produces:**
 
-- images
-    - url: string
+- images_url: string
 
 ### Arguments
 

components/embedding_based_laion_retrieval/fondant_component.yaml

@@ -7,19 +7,15 @@ tags:
   - Data retrieval
 
 consumes:
-  embeddings:
-    fields:
-      data:
-        type: array
-        items:
-          type: float32
+  embeddings_data:
+    type: array
+    items:
+      type: float32
 
 produces:
-  images:
-    fields:
-      url:
-        type: string
-  additionalSubsets: false
+  images_url:
+    type: string
+# additionalFields: false
 
 args:
   num_images: