diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index f5876f3b6..adfaf43b3 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -18,7 +18,6 @@ repos:
           "--exit-non-zero-on-fix",
         ]
 
-
   - repo: https://github.com/PyCQA/bandit
     rev: 1.7.4
     hooks:
@@ -55,4 +54,13 @@ repos:
           - types-jsonschema
           - types-PyYAML
           - types-requests
-        pass_filenames: false
\ No newline at end of file
+        pass_filenames: false
+
+  - repo: local
+    hooks:
+      - id: generate_component_readmes
+        name: Generate component READMEs
+        language: python
+        entry: python scripts/component_readme/generate_readme.py
+        files: ^components/.*/fondant_component.yaml
+        additional_dependencies: ["fondant"]
\ No newline at end of file
diff --git a/components/caption_images/README.md b/components/caption_images/README.md
index 83a78e706..b65ec6176 100644
--- a/components/caption_images/README.md
+++ b/components/caption_images/README.md
@@ -1,9 +1,51 @@
-# caption_images
+# Caption images
 
 ### Description
-This component captions inputted images using [BLIP](https://huggingface.co/docs/transformers/model_doc/blip).
+This component captions images using a BLIP model from the Hugging Face hub
 
-### **Inputs/Outputs**
+### Inputs / outputs
 
-See [`fondant_component.yaml`](fondant_component.yaml) for a more detailed description on all the input/output parameters. 
+**This component consumes:**
+- images
+  - data: binary
 
+**This component produces:**
+- captions
+  - text: string
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| model_id | str | Id of the BLIP model on the Hugging Face hub | Salesforce/blip-image-captioning-base |
+| batch_size | int | Batch size to use for inference | 8 |
+| max_new_tokens | int | Maximum token length of each caption | 50 |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+caption_images_op = ComponentOp.from_registry(
+    name="caption_images",
+    arguments={
+        # Add arguments
+        # "model_id": "Salesforce/blip-image-captioning-base",
+        # "batch_size": 8,
+        # "max_new_tokens": 50,
+    }
+)
+pipeline.add_op(caption_images_op, dependencies=[...])  #Add previous component as dependency
+```
+
+### Testing
+
+You can run the tests using docker with BuildKit. From this directory, run:
+```
+docker build . --target test
+```
diff --git a/components/caption_images/fondant_component.yaml b/components/caption_images/fondant_component.yaml
index 24a9f6815..9f12f6ef1 100644
--- a/components/caption_images/fondant_component.yaml
+++ b/components/caption_images/fondant_component.yaml
@@ -1,5 +1,5 @@
 name: Caption images
-description: Component that captions images using a model from the Hugging Face hub
+description: This component captions images using a BLIP model from the Hugging Face hub
 image: ghcr.io/ml6team/caption_images:dev
 
 consumes:
@@ -16,14 +16,14 @@ produces:
 
 args:
   model_id:
-    description: id of the model on the Hugging Face hub
+    description: Id of the BLIP model on the Hugging Face hub
     type: str
     default: "Salesforce/blip-image-captioning-base"
   batch_size:
-    description: batch size to use
+    description: Batch size to use for inference
     type: int
     default: 8
   max_new_tokens:
-    description: maximum token length of each caption
+    description: Maximum token length of each caption
     type: int
     default: 50
\ No newline at end of file
diff --git a/components/download_images/README.md b/components/download_images/README.md
index b759e8b56..e23779857 100644
--- a/components/download_images/README.md
+++ b/components/download_images/README.md
@@ -1,19 +1,70 @@
-# download_images
+# Download images
 
 ### Description
-This component takes in image URLs as input and downloads the images, along with some metadata (like their height and width).
-The images are stored in a new colum as bytes objects. This component also resizes the images using the [resizer](https://github.com/rom1504/img2dataset/blob/main/img2dataset/resizer.py) function from the img2dataset library.
+Component that downloads images from a list of URLs.
 
-If the component is unable to retrieve the image at a URL (for any reason), it will return `None` for that particular URL.
+This component takes in image URLs as input and downloads the images, along with some metadata 
+(like their height and width). The images are stored in a new colum as bytes objects. This 
+component also resizes the images using the 
+[resizer](https://github.com/rom1504/img2dataset/blob/main/img2dataset/resizer.py) function 
+from the img2dataset library.
 
-### **Inputs/Outputs**
 
-See [`fondant_component.yaml`](fondant_component.yaml) for a more detailed description on all the input/output parameters. 
+### Inputs / outputs
 
+**This component consumes:**
+- images
+  - url: string
+
+**This component produces:**
+- images
+  - data: binary
+  - width: int32
+  - height: int32
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| timeout | int | Maximum time (in seconds) to wait when trying to download an image, | 10 |
+| retries | int | Number of times to retry downloading an image if it fails. | / |
+| n_connections | int | Number of concurrent connections opened per process. Decrease this number if you are running into timeout errors. A lower number of connections can increase the success rate but lower the throughput. | 100 |
+| image_size | int | Size of the images after resizing. | 256 |
+| resize_mode | str | Resize mode to use. One of "no", "keep_ratio", "center_crop", "border". | border |
+| resize_only_if_bigger | bool | If True, resize only if image is bigger than image_size. | False |
+| min_image_size | int | Minimum size of the images. | / |
+| max_aspect_ratio | float | Maximum aspect ratio of the images. | inf |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+download_images_op = ComponentOp.from_registry(
+    name="download_images",
+    arguments={
+        # Add arguments
+        # "timeout": 10,
+        # "retries": 0,
+        # "n_connections": 100,
+        # "image_size": 256,
+        # "resize_mode": "border",
+        # "resize_only_if_bigger": "False",
+        # "min_image_size": 0,
+        # "max_aspect_ratio": "inf",
+    }
+)
+pipeline.add_op(download_images_op, dependencies=[...])  #Add previous component as dependency
+```
 
 ### Testing
 
 You can run the tests using docker with BuildKit. From this directory, run:
 ```
 docker build . --target test
-```
\ No newline at end of file
+```
diff --git a/components/download_images/fondant_component.yaml b/components/download_images/fondant_component.yaml
index bc3d0a647..4e329e79e 100644
--- a/components/download_images/fondant_component.yaml
+++ b/components/download_images/fondant_component.yaml
@@ -1,5 +1,13 @@
 name: Download images
-description: Component that downloads images based on URLs
+description: |
+  Component that downloads images from a list of URLs.
+  
+  This component takes in image URLs as input and downloads the images, along with some metadata 
+  (like their height and width). The images are stored in a new colum as bytes objects. This 
+  component also resizes the images using the 
+  [resizer](https://github.com/rom1504/img2dataset/blob/main/img2dataset/resizer.py) function 
+  from the img2dataset library.
+
 image: ghcr.io/ml6team/download_images:dev
 
 consumes:
@@ -21,7 +29,7 @@ produces:
 
 args:
   timeout:
-    description: Maximum time (in seconds) to wait when trying to download an image
+    description: Maximum time (in seconds) to wait when trying to download an image,
     type: int
     default: 10
   retries:
@@ -29,7 +37,10 @@ args:
     type: int
     default: 0
   n_connections:
-    description: Number of concurrent connections opened per process. Decrease this number if you are running into timeout errors. A lower number of connections can increase the success rate but lower the throughput.
+    description: |
+      Number of concurrent connections opened per process. Decrease this number if you are running 
+      into timeout errors. A lower number of connections can increase the success rate but lower 
+      the throughput.
     type: int
     default: 100
   image_size:
diff --git a/components/embed_images/README.md b/components/embed_images/README.md
index 126e6844b..c805533ca 100644
--- a/components/embed_images/README.md
+++ b/components/embed_images/README.md
@@ -1,9 +1,43 @@
 # Embed images
 
 ### Description
-This component takes images as input and embeds them using a CLIP model from Hugging Face.
-The embeddings are stored in a new colum as arrays of floats.
+Component that generates CLIP embeddings from images
 
-### **Inputs/Outputs**
+### Inputs / outputs
+
+**This component consumes:**
+- images
+  - data: binary
+
+**This component produces:**
+- embeddings
+  - data: list<item: float>
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| model_id | str | Model id of a CLIP model on the Hugging Face hub | openai/clip-vit-large-patch14 |
+| batch_size | int | Batch size to use when embedding | 8 |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+embed_images_op = ComponentOp.from_registry(
+    name="embed_images",
+    arguments={
+        # Add arguments
+        # "model_id": "openai/clip-vit-large-patch14",
+        # "batch_size": 8,
+    }
+)
+pipeline.add_op(embed_images_op, dependencies=[...])  #Add previous component as dependency
+```
 
-See [`fondant_component.yaml`](fondant_component.yaml) for a more detailed description on all the input/output parameters. 
\ No newline at end of file
diff --git a/components/embed_images/fondant_component.yaml b/components/embed_images/fondant_component.yaml
index d56868031..3c84e2e16 100644
--- a/components/embed_images/fondant_component.yaml
+++ b/components/embed_images/fondant_component.yaml
@@ -1,5 +1,5 @@
 name: Embed images
-description: Component that embeds images using CLIP
+description: Component that generates CLIP embeddings from images
 image: ghcr.io/ml6team/embed_images:dev
 
 consumes:
diff --git a/components/embedding_based_laion_retrieval/README.md b/components/embedding_based_laion_retrieval/README.md
new file mode 100644
index 000000000..3d18043dd
--- /dev/null
+++ b/components/embedding_based_laion_retrieval/README.md
@@ -0,0 +1,47 @@
+# Embedding based LAION retrieval
+
+### Description
+This component retrieves image URLs from LAION-5B based on a set of CLIP embeddings. It can be 
+used to find images similar to the embedded images / captions.
+
+
+### Inputs / outputs
+
+**This component consumes:**
+- embeddings
+  - data: list<item: float>
+
+**This component produces:**
+- images
+  - url: string
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| num_images | int | Number of images to retrieve for each prompt | / |
+| aesthetic_score | int | Aesthetic embedding to add to the query embedding, between 0 and 9 (higher is prettier). | 9 |
+| aesthetic_weight | float | Weight of the aesthetic embedding when added to the query, between 0 and 1 | 0.5 |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+embedding_based_laion_retrieval_op = ComponentOp.from_registry(
+    name="embedding_based_laion_retrieval",
+    arguments={
+        # Add arguments
+        # "num_images": 0,
+        # "aesthetic_score": 9,
+        # "aesthetic_weight": 0.5,
+    }
+)
+pipeline.add_op(embedding_based_laion_retrieval_op, dependencies=[...])  #Add previous component as dependency
+```
+
diff --git a/components/embedding_based_laion_retrieval/fondant_component.yaml b/components/embedding_based_laion_retrieval/fondant_component.yaml
index 0380ba526..23c5d60da 100644
--- a/components/embedding_based_laion_retrieval/fondant_component.yaml
+++ b/components/embedding_based_laion_retrieval/fondant_component.yaml
@@ -1,5 +1,7 @@
-name: LAION retrieval
-description: A component that retrieves image URLs from LAION-5B based on a set of CLIP embeddings
+name: Embedding based LAION retrieval
+description: |
+  This component retrieves image URLs from LAION-5B based on a set of CLIP embeddings. It can be 
+  used to find images similar to the embedded images / captions.
 image: ghcr.io/ml6team/embedding_based_laion_retrieval:dev
 
 consumes:
diff --git a/components/filter_comments/README.md b/components/filter_comments/README.md
new file mode 100644
index 000000000..dff4c6730
--- /dev/null
+++ b/components/filter_comments/README.md
@@ -0,0 +1,41 @@
+# Filter comments
+
+### Description
+Component that filters code based on the code to comment ratio
+
+### Inputs / outputs
+
+**This component consumes:**
+- code
+  - content: string
+
+**This component produces no data.**
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| min_comments_ratio | float | The minimum code to comment ratio | 0.1 |
+| max_comments_ratio | float | The maximum code to comment ratio | 0.9 |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+filter_comments_op = ComponentOp.from_registry(
+    name="filter_comments",
+    arguments={
+        # Add arguments
+        # "min_comments_ratio": 0.1,
+        # "max_comments_ratio": 0.9,
+    }
+)
+pipeline.add_op(filter_comments_op, dependencies=[...])  #Add previous component as dependency
+```
+
diff --git a/components/filter_image_resolution/README.md b/components/filter_image_resolution/README.md
new file mode 100644
index 000000000..3074d74af
--- /dev/null
+++ b/components/filter_image_resolution/README.md
@@ -0,0 +1,42 @@
+# Filter image resolution
+
+### Description
+Component that filters images based on minimum size and max aspect ratio
+
+### Inputs / outputs
+
+**This component consumes:**
+- images
+  - width: int32
+  - height: int32
+
+**This component produces no data.**
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| min_image_dim | int | Minimum image dimension | / |
+| max_aspect_ratio | float | Maximum aspect ratio | / |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+filter_image_resolution_op = ComponentOp.from_registry(
+    name="filter_image_resolution",
+    arguments={
+        # Add arguments
+        # "min_image_dim": 0,
+        # "max_aspect_ratio": 0.0,
+    }
+)
+pipeline.add_op(filter_image_resolution_op, dependencies=[...])  #Add previous component as dependency
+```
+
diff --git a/components/filter_line_length/README.md b/components/filter_line_length/README.md
new file mode 100644
index 000000000..340cf486f
--- /dev/null
+++ b/components/filter_line_length/README.md
@@ -0,0 +1,45 @@
+# Filter line length
+
+### Description
+Component that filters code based on line length
+
+### Inputs / outputs
+
+**This component consumes:**
+- code
+  - avg_line_length: double
+  - max_line_length: int32
+  - alphanum_fraction: double
+
+**This component produces no data.**
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| avg_line_length_threshold | int | Threshold for average line length to filter on | / |
+| max_line_length_threshold | int | Threshold for maximum line length to filter on | / |
+| alphanum_fraction_threshold | float | Alphanum fraction to filter on | / |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+filter_line_length_op = ComponentOp.from_registry(
+    name="filter_line_length",
+    arguments={
+        # Add arguments
+        # "avg_line_length_threshold": 0,
+        # "max_line_length_threshold": 0,
+        # "alphanum_fraction_threshold": 0.0,
+    }
+)
+pipeline.add_op(filter_line_length_op, dependencies=[...])  #Add previous component as dependency
+```
+
diff --git a/components/image_cropping/README.md b/components/image_cropping/README.md
index 1aa393ed2..31b8f8c00 100644
--- a/components/image_cropping/README.md
+++ b/components/image_cropping/README.md
@@ -1,16 +1,60 @@
-# Image cropping component
+# Image cropping
 
-## Description
-This component is based on the `TransformComponent` and is used to crop out image borders. This is typically useful when working with graphical images that have single-color borders (e.g. logos, icons, etc.).
+### Description
+This component crops out image borders. This is typically useful when working with graphical 
+images that have single-color borders (e.g. logos, icons, etc.).
 
-## Usage
-The component takes an image and calculates which color is most present in the border. It then crops the image in order to minimize this single-color border. The `padding` argument will add extra border to the image before cropping it, in order to avoid cutting off parts of the image.
-The resulting crop will always be square. If a crop is not possible, the component will return the original image.
+The component takes an image and calculates which color is most present in the border. It then 
+crops the image in order to minimize this single-color border. The `padding` argument will add 
+extra border to the image before cropping it, in order to avoid cutting off parts of the image.
+The resulting crop will always be square. If a crop is not possible, the component will return 
+the original image.
 
-## Examples
-Examples of image cropping by removing the single-color border. Left side is original image, right side is border-cropped image.
+#### Examples
+Examples of image cropping by removing the single-color border. Left side is original image, 
+right side is border-cropped image.
 
 ![Example of image cropping by removing the single-color border. Left side is original, right side is cropped image](../../docs/art/components/image_cropping/component_border_crop_1.png)
 ![Example of image cropping by removing the single-color border. Left side is original, right side is cropped image](../../docs/art/components/image_cropping/component_border_crop_0.png)
 
 
+### Inputs / outputs
+
+**This component consumes:**
+- images
+  - data: binary
+
+**This component produces:**
+- images
+  - data: binary
+  - width: int32
+  - height: int32
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| cropping_threshold | int | Threshold parameter used for detecting borders. A lower (negative) parameter results in a more performant border detection, but can cause overcropping. Default is -30 | -30 |
+| padding | int | Padding for the image cropping. The padding is added to all borders of the image. | 10 |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+image_cropping_op = ComponentOp.from_registry(
+    name="image_cropping",
+    arguments={
+        # Add arguments
+        # "cropping_threshold": -30,
+        # "padding": 10,
+    }
+)
+pipeline.add_op(image_cropping_op, dependencies=[...])  #Add previous component as dependency
+```
+
diff --git a/components/image_cropping/fondant_component.yaml b/components/image_cropping/fondant_component.yaml
index a4fedb9a4..65072471d 100644
--- a/components/image_cropping/fondant_component.yaml
+++ b/components/image_cropping/fondant_component.yaml
@@ -1,6 +1,21 @@
 name: Image cropping
-description: Component that removes single-colored borders around images and crops them appropriately
 image: ghcr.io/ml6team/image_cropping:dev
+description: |
+  This component crops out image borders. This is typically useful when working with graphical 
+  images that have single-color borders (e.g. logos, icons, etc.).
+  
+  The component takes an image and calculates which color is most present in the border. It then 
+  crops the image in order to minimize this single-color border. The `padding` argument will add 
+  extra border to the image before cropping it, in order to avoid cutting off parts of the image.
+  The resulting crop will always be square. If a crop is not possible, the component will return 
+  the original image.
+  
+  #### Examples
+  Examples of image cropping by removing the single-color border. Left side is original image, 
+  right side is border-cropped image.
+  
+  ![Example of image cropping by removing the single-color border. Left side is original, right side is cropped image](../../docs/art/components/image_cropping/component_border_crop_1.png)
+  ![Example of image cropping by removing the single-color border. Left side is original, right side is cropped image](../../docs/art/components/image_cropping/component_border_crop_0.png)
 
 consumes:
   images:
diff --git a/components/image_resolution_extraction/README.md b/components/image_resolution_extraction/README.md
new file mode 100644
index 000000000..2ec9e4b14
--- /dev/null
+++ b/components/image_resolution_extraction/README.md
@@ -0,0 +1,38 @@
+# Image resolution extraction
+
+### Description
+Component that extracts image resolution data from the images
+
+### Inputs / outputs
+
+**This component consumes:**
+- images
+  - data: binary
+
+**This component produces:**
+- images
+  - data: binary
+  - width: int32
+  - height: int32
+
+### Arguments
+
+This component takes no arguments.
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+image_resolution_extraction_op = ComponentOp.from_registry(
+    name="image_resolution_extraction",
+    arguments={
+        # Add arguments
+    }
+)
+pipeline.add_op(image_resolution_extraction_op, dependencies=[...])  #Add previous component as dependency
+```
+
diff --git a/components/language_filter/README.md b/components/language_filter/README.md
index ba9bd4636..1409e8c08 100644
--- a/components/language_filter/README.md
+++ b/components/language_filter/README.md
@@ -1,7 +1,45 @@
-# Language filter
+# Filter languages
 
-## Description
-This component is based on the `TransformComponent` and is used to filter a dataframe based on language. 
-It allows you to remove rows that do not match the provided language, thus providing a way to focus 
-on specific languages within your data.
+### Description
+A component that filters text based on the provided language.
 
+### Inputs / outputs
+
+**This component consumes:**
+- text
+  - data: string
+
+**This component produces no data.**
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| language | str | A valid language code or identifier (e.g., "en", "fr", "de"). | en |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+language_filter_op = ComponentOp.from_registry(
+    name="language_filter",
+    arguments={
+        # Add arguments
+        # "language": "en",
+    }
+)
+pipeline.add_op(language_filter_op, dependencies=[...])  #Add previous component as dependency
+```
+
+### Testing
+
+You can run the tests using docker with BuildKit. From this directory, run:
+```
+docker build . --target test
+```
diff --git a/components/language_filter/fondant_component.yaml b/components/language_filter/fondant_component.yaml
index d639a9a6e..b40f43a1f 100644
--- a/components/language_filter/fondant_component.yaml
+++ b/components/language_filter/fondant_component.yaml
@@ -1,5 +1,5 @@
 name: Filter languages
-description: A component that filters text based on the language.
+description: A component that filters text based on the provided language.
 image: ghcr.io/ml6team/filter_language:latest
 
 consumes:
diff --git a/components/load_from_files/README.md b/components/load_from_files/README.md
index 962baab36..89a857fd7 100644
--- a/components/load_from_files/README.md
+++ b/components/load_from_files/README.md
@@ -1,35 +1,48 @@
 # Load from files
 
-## Description
-This component is based on the `DaskLoadComponent` and is used to load dataset from files within a directory. 
-It allows you to load datasets which
-- Have files within a local data directory 
-- Have compressed files like .zip, gzip, tar or tar.gz within the data directory
-- Are hosted on remote locations like AWS S3 bucket, Azure's Blob storage or GCP's cloud storage
+### Description
+This component loads data from files in a local or remote (AWS S3, Azure Blob storage, GCS) 
+location. It supports the following formats: .zip, gzip, tar and tar.gz.
 
-And returns a dataframe with two columns
-- file_filename(containing the file name in string format)
-- file_content (containing the respective file content in bytes format)
 
-Here is an illustration of how to use this component in your pipeline
-on a local directory with zip files
+### Inputs / outputs
+
+**This component consumes no data.**
+
+**This component produces:**
+- file
+  - filename: string
+  - content: binary
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| directory_uri | str | Local or remote path to the directory containing the files | / |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
 
 ```python
-from fondant.pipeline import Pipeline, ComponentOp
+from fondant.pipeline import ComponentOp
 
-my_pipeline = Pipeline(
-    pipeline_name="my_pipeline",
-    base_path="./",  # TODO: update this
-    pipeline_description="This is my pipeline",
-)
 
-load_from_files = ComponentOp(
-    component_dir="components/load_from_files",
+load_from_files_op = ComponentOp.from_registry(
+    name="load_from_files",
     arguments={
-        "directory_uri": "./data.zip", # change this to your
-                                       # directory_uri, remote or local
-    },
+        # Add arguments
+        # "directory_uri": ,
+    }
 )
+pipeline.add_op(load_from_files_op, dependencies=[...])  #Add previous component as dependency
+```
+
+### Testing
 
-my_pipeline.add_op(load_from_files, dependencies=[])
-```
\ No newline at end of file
+You can run the tests using docker with BuildKit. From this directory, run:
+```
+docker build . --target test
+```
diff --git a/components/load_from_files/fondant_component.yaml b/components/load_from_files/fondant_component.yaml
index 2673e13db..6481c8e14 100644
--- a/components/load_from_files/fondant_component.yaml
+++ b/components/load_from_files/fondant_component.yaml
@@ -1,5 +1,7 @@
 name: Load from files
-description: Component that loads a dataset from files
+description: |
+  This component loads data from files in a local or remote (AWS S3, Azure Blob storage, GCS) 
+  location. It supports the following formats: .zip, gzip, tar and tar.gz.
 image: ghcr.io/ml6team/load_from_files:dev
 
 produces:
diff --git a/components/load_from_hf_hub/README.md b/components/load_from_hf_hub/README.md
new file mode 100644
index 000000000..a59f95540
--- /dev/null
+++ b/components/load_from_hf_hub/README.md
@@ -0,0 +1,47 @@
+# Load from hub
+
+### Description
+Component that loads a dataset from the hub
+
+### Inputs / outputs
+
+**This component consumes no data.**
+
+**This component produces:**
+- dummy_variable
+  - data: binary
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| dataset_name | str | Name of dataset on the hub | / |
+| column_name_mapping | dict | Mapping of the consumed hub dataset to fondant column names | / |
+| image_column_names | list | Optional argument, a list containing the original image column names in case the dataset on the hub contains them. Used to format the image from HF hub format to a byte string. | None |
+| n_rows_to_load | int | Optional argument that defines the number of rows to load. Useful for testing pipeline runs on a small scale | None |
+| index_column | str | Column to set index to in the load component, if not specified a default globally unique index will be set | None |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+load_from_hf_hub_op = ComponentOp.from_registry(
+    name="load_from_hf_hub",
+    arguments={
+        # Add arguments
+        # "dataset_name": ,
+        # "column_name_mapping": {},
+        # "image_column_names": "None",
+        # "n_rows_to_load": "None",
+        # "index_column": "None",
+    }
+)
+pipeline.add_op(load_from_hf_hub_op, dependencies=[...])  #Add previous component as dependency
+```
+
diff --git a/components/load_from_parquet/README.md b/components/load_from_parquet/README.md
new file mode 100644
index 000000000..57d826707
--- /dev/null
+++ b/components/load_from_parquet/README.md
@@ -0,0 +1,45 @@
+# Load from parquet
+
+### Description
+Component that loads a dataset from a parquet uri
+
+### Inputs / outputs
+
+**This component consumes no data.**
+
+**This component produces:**
+- dummy_variable
+  - data: binary
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| dataset_uri | str | The remote path to the parquet file/folder containing the dataset | / |
+| column_name_mapping | dict | Mapping of the consumed dataset | None |
+| n_rows_to_load | int | Optional argument that defines the number of rows to load. Useful for testing pipeline runs on a small scale | None |
+| index_column | str | Column to set index to in the load component, if not specified a default globally unique index will be set | None |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+load_from_parquet_op = ComponentOp.from_registry(
+    name="load_from_parquet",
+    arguments={
+        # Add arguments
+        # "dataset_uri": ,
+        # "column_name_mapping": "None",
+        # "n_rows_to_load": "None",
+        # "index_column": "None",
+    }
+)
+pipeline.add_op(load_from_parquet_op, dependencies=[...])  #Add previous component as dependency
+```
+
diff --git a/components/minhash_generator/README.md b/components/minhash_generator/README.md
new file mode 100644
index 000000000..95766e907
--- /dev/null
+++ b/components/minhash_generator/README.md
@@ -0,0 +1,47 @@
+# MinHash generator
+
+### Description
+A component that generates minhashes of text.
+
+### Inputs / outputs
+
+**This component consumes:**
+- text
+  - data: string
+
+**This component produces:**
+- text
+  - minhash: list<item: uint64>
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| shingle_ngram_size | int | Define size of ngram used for the shingle generation | 3 |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+minhash_generator_op = ComponentOp.from_registry(
+    name="minhash_generator",
+    arguments={
+        # Add arguments
+        # "shingle_ngram_size": 3,
+    }
+)
+pipeline.add_op(minhash_generator_op, dependencies=[...])  #Add previous component as dependency
+```
+
+### Testing
+
+You can run the tests using docker with BuildKit. From this directory, run:
+```
+docker build . --target test
+```
diff --git a/components/pii_redaction/README.md b/components/pii_redaction/README.md
index 39183017d..347a27aa4 100644
--- a/components/pii_redaction/README.md
+++ b/components/pii_redaction/README.md
@@ -1,13 +1,56 @@
-## PII detection and redaction
+# PII redaction
 
-This component detects and redacts Personal Identifiable Information (PII) from code. Redaction means that sensitive data is replaced by random data.
+### Description
+This component detects and redacts Personal Identifiable Information (PII) from code. 
+Redaction means that sensitive data is replaced by random data.
 
-The code is based on the PII removal code used as part of the [BigCode project](https://github.com/bigcode-project/bigcode-dataset/tree/main/pii).
+The code is based on the PII removal code used as part of the 
+[BigCode project](https://github.com/bigcode-project/bigcode-dataset/tree/main/pii).
 
-### PII detection
+#### PII detection
 
-The component detects emails, IP addresses and API/SSH keys in text datasets (in particular datasets of source code). Regexes are used for emails and IP addresses (they are adapted from [BigScience PII pipeline](https://github.com/bigscience-workshop/data-preparation/tree/main/preprocessing/training/02_pii)). The [`detect-secrets`](https://github.com/Yelp/detect-secrets) package is used for finding secrets keys. Additionally filters are implemented on top to reduce the number of false positives, using the [gibberish-detector](https://github.com/domanchi/gibberish-detector) package.
+The component detects emails, IP addresses and API/SSH keys in text datasets (in particular 
+datasets of source code). Regexes are used for emails and IP addresses (they are adapted from 
+[BigScience PII pipeline](https://github.com/bigscience-workshop/data-preparation/tree/main/preprocessing/training/02_pii)). 
+The [`detect-secrets`](https://github.com/Yelp/detect-secrets) package is used for finding 
+secrets keys. Additionally filters are implemented on top to reduce the number of false 
+positives, using the [gibberish-detector](https://github.com/domanchi/gibberish-detector) package.
 
-### PII redaction
+#### PII redaction
+
+PII is replaced by random data which is stored in the `replacements.json` file.
+A component that detects and redacts Personal Identifiable Information (PII) from 
+code.
+
+
+### Inputs / outputs
+
+**This component consumes:**
+- code
+  - content: string
+
+**This component produces:**
+- code
+  - content: string
+
+### Arguments
+
+This component takes no arguments.
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+pii_redaction_op = ComponentOp.from_registry(
+    name="pii_redaction",
+    arguments={
+        # Add arguments
+    }
+)
+pipeline.add_op(pii_redaction_op, dependencies=[...])  #Add previous component as dependency
+```
 
-PII is replaced by random data which is stored in the `replacements.json` file.
\ No newline at end of file
diff --git a/components/pii_redaction/fondant_component.yaml b/components/pii_redaction/fondant_component.yaml
index 11d1166b7..b64f11fa3 100644
--- a/components/pii_redaction/fondant_component.yaml
+++ b/components/pii_redaction/fondant_component.yaml
@@ -1,5 +1,25 @@
 name: PII redaction
-description: A component that detects and redacts Personal Identifiable Information (PII) from code.
+description: |
+  This component detects and redacts Personal Identifiable Information (PII) from code. 
+  Redaction means that sensitive data is replaced by random data.
+  
+  The code is based on the PII removal code used as part of the 
+  [BigCode project](https://github.com/bigcode-project/bigcode-dataset/tree/main/pii).
+  
+  #### PII detection
+  
+  The component detects emails, IP addresses and API/SSH keys in text datasets (in particular 
+  datasets of source code). Regexes are used for emails and IP addresses (they are adapted from 
+  [BigScience PII pipeline](https://github.com/bigscience-workshop/data-preparation/tree/main/preprocessing/training/02_pii)). 
+  The [`detect-secrets`](https://github.com/Yelp/detect-secrets) package is used for finding 
+  secrets keys. Additionally filters are implemented on top to reduce the number of false 
+  positives, using the [gibberish-detector](https://github.com/domanchi/gibberish-detector) package.
+  
+  #### PII redaction
+  
+  PII is replaced by random data which is stored in the `replacements.json` file.
+  A component that detects and redacts Personal Identifiable Information (PII) from 
+  code.
 image: ghcr.io/ml6team/pii_redaction:dev
 
 consumes:
diff --git a/components/prompt_based_laion_retrieval/README.md b/components/prompt_based_laion_retrieval/README.md
index ef6c68464..a7467ead4 100644
--- a/components/prompt_based_laion_retrieval/README.md
+++ b/components/prompt_based_laion_retrieval/README.md
@@ -1,9 +1,52 @@
-# prompt_based_laion_retrieval
+# LAION retrieval
 
 ### Description
-This component retrieves image URLs from the [LAION-5B dataset](https://laion.ai/blog/laion-5b/) based on text prompts. The retrieval itself is done based on CLIP embeddings similarity between the prompt sentences and the captions in the LAION dataset. This component doesn’t return the actual images, only URLs.
+This component retrieves image URLs from the [LAION-5B dataset](https://laion.ai/blog/laion-5b/) 
+based on text prompts. The retrieval itself is done based on CLIP embeddings similarity between 
+the prompt sentences and the captions in the LAION dataset. 
 
-### **Inputs/Outputs**
+This component doesn’t return the actual images, only URLs.
 
-See the [`component specification`](fondant_component.yaml) for a more detailed description of all the input/output parameters. 
+
+### Inputs / outputs
+
+**This component consumes:**
+- prompts
+  - text: string
+
+**This component produces:**
+- images
+  - url: string
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| num_images | int | Number of images to retrieve for each prompt | / |
+| aesthetic_score | int | Aesthetic embedding to add to the query embedding, between 0 and 9 (higher is prettier). | 9 |
+| aesthetic_weight | float | Weight of the aesthetic embedding when added to the query, between 0 and 1 | 0.5 |
+| url | str | The url of the backend clip retrieval service, defaults to the public service | https://knn.laion.ai/knn-service |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+prompt_based_laion_retrieval_op = ComponentOp.from_registry(
+    name="prompt_based_laion_retrieval",
+    arguments={
+        # Add arguments
+        # "num_images": 0,
+        # "aesthetic_score": 9,
+        # "aesthetic_weight": 0.5,
+        # "url": "https://knn.laion.ai/knn-service",
+    }
+)
+pipeline.add_op(prompt_based_laion_retrieval_op, dependencies=[...])  #Add previous component as dependency
+```
 
diff --git a/components/prompt_based_laion_retrieval/fondant_component.yaml b/components/prompt_based_laion_retrieval/fondant_component.yaml
index 544f7afc8..88f8d20dd 100644
--- a/components/prompt_based_laion_retrieval/fondant_component.yaml
+++ b/components/prompt_based_laion_retrieval/fondant_component.yaml
@@ -1,5 +1,10 @@
 name: LAION retrieval
-description: A component that retrieves image URLs from LAION-5B based on a set of seed prompts
+description: |
+  This component retrieves image URLs from the [LAION-5B dataset](https://laion.ai/blog/laion-5b/) 
+  based on text prompts. The retrieval itself is done based on CLIP embeddings similarity between 
+  the prompt sentences and the captions in the LAION dataset. 
+  
+  This component doesn’t return the actual images, only URLs.
 image: ghcr.io/ml6team/prompt_based_laion_retrieval:dev
 
 consumes:
diff --git a/components/segment_images/README.md b/components/segment_images/README.md
new file mode 100644
index 000000000..fdb057414
--- /dev/null
+++ b/components/segment_images/README.md
@@ -0,0 +1,43 @@
+# Segment images
+
+### Description
+Component that creates segmentation masks for images using a model from the Hugging Face hub
+
+### Inputs / outputs
+
+**This component consumes:**
+- images
+  - data: binary
+
+**This component produces:**
+- segmentations
+  - data: binary
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| model_id | str | id of the model on the Hugging Face hub | openmmlab/upernet-convnext-small |
+| batch_size | int | batch size to use | / |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+segment_images_op = ComponentOp.from_registry(
+    name="segment_images",
+    arguments={
+        # Add arguments
+        # "model_id": "openmmlab/upernet-convnext-small",
+        # "batch_size": 0,
+    }
+)
+pipeline.add_op(segment_images_op, dependencies=[...])  #Add previous component as dependency
+```
+
diff --git a/components/text_length_filter/README.md b/components/text_length_filter/README.md
new file mode 100644
index 000000000..5eec6a9a7
--- /dev/null
+++ b/components/text_length_filter/README.md
@@ -0,0 +1,47 @@
+# Filter text length
+
+### Description
+A component that filters out text based on their length
+
+### Inputs / outputs
+
+**This component consumes:**
+- text
+  - data: string
+
+**This component produces no data.**
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| min_characters_length | int | Minimum number of characters | / |
+| min_words_length | int | Mininum number of words | / |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+text_length_filter_op = ComponentOp.from_registry(
+    name="text_length_filter",
+    arguments={
+        # Add arguments
+        # "min_characters_length": 0,
+        # "min_words_length": 0,
+    }
+)
+pipeline.add_op(text_length_filter_op, dependencies=[...])  #Add previous component as dependency
+```
+
+### Testing
+
+You can run the tests using docker with BuildKit. From this directory, run:
+```
+docker build . --target test
+```
diff --git a/components/text_normalization/README.md b/components/text_normalization/README.md
index 7b01ecbd7..79fad72a2 100644
--- a/components/text_normalization/README.md
+++ b/components/text_normalization/README.md
@@ -1,12 +1,65 @@
-# Text normalization component
+# Normalize text
 
-This component implements several text normalization techniques to clean and preprocess textual data:
+### Description
+This component implements several text normalization techniques to clean and preprocess textual 
+data:
 
 - Apply lowercasing: Converts all text to lowercase
 - Remove unnecessary whitespaces: Eliminates extra spaces between words, e.g. tabs
 - Apply NFC normalization: Converts characters to their canonical representation
-- Remove common seen patterns in webpages following the implementation of [Penedo et al.](https://arxiv.org/pdf/2306.01116.pdf)
+- Remove common seen patterns in webpages following the implementation of 
+  [Penedo et al.](https://arxiv.org/pdf/2306.01116.pdf)
 - Remove punctuation: Strips punctuation marks from the text
 
-These text normalization techniques are valuable for preparing text data before using it for 
-the training of large language models.
\ No newline at end of file
+These text normalization techniques are valuable for preparing text data before using it for
+the training of large language models.
+
+
+### Inputs / outputs
+
+**This component consumes:**
+- text
+  - data: string
+
+**This component produces no data.**
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| remove_additional_whitespaces | bool | If true remove all additional whitespace, tabs. | / |
+| apply_nfc | bool | If true apply nfc normalization | / |
+| normalize_lines | bool | If true analyze documents line-by-line and apply various rules to discard or edit lines. Used to removed common patterns in webpages, e.g. counter | / |
+| do_lowercase | bool | If true apply lowercasing | / |
+| remove_punctuation | str | If true punctuation will be removed | / |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+text_normalization_op = ComponentOp.from_registry(
+    name="text_normalization",
+    arguments={
+        # Add arguments
+        # "remove_additional_whitespaces": False,
+        # "apply_nfc": False,
+        # "normalize_lines": False,
+        # "do_lowercase": False,
+        # "remove_punctuation": ,
+    }
+)
+pipeline.add_op(text_normalization_op, dependencies=[...])  #Add previous component as dependency
+```
+
+### Testing
+
+You can run the tests using docker with BuildKit. From this directory, run:
+```
+docker build . --target test
+```
diff --git a/components/text_normalization/fondant_component.yaml b/components/text_normalization/fondant_component.yaml
index f9d2bfabb..b99625bcc 100644
--- a/components/text_normalization/fondant_component.yaml
+++ b/components/text_normalization/fondant_component.yaml
@@ -1,6 +1,18 @@
-name: Normalize text.
-description: A component that normalizes text.
+name: Normalize text
 image: ghcr.io/ml6team/text_normalization:latest
+description: |
+  This component implements several text normalization techniques to clean and preprocess textual 
+  data:
+
+  - Apply lowercasing: Converts all text to lowercase
+  - Remove unnecessary whitespaces: Eliminates extra spaces between words, e.g. tabs
+  - Apply NFC normalization: Converts characters to their canonical representation
+  - Remove common seen patterns in webpages following the implementation of 
+    [Penedo et al.](https://arxiv.org/pdf/2306.01116.pdf)
+  - Remove punctuation: Strips punctuation marks from the text
+  
+  These text normalization techniques are valuable for preparing text data before using it for
+  the training of large language models.
 
 consumes:
   text:
diff --git a/components/write_to_hf_hub/README.md b/components/write_to_hf_hub/README.md
new file mode 100644
index 000000000..318b8ee5b
--- /dev/null
+++ b/components/write_to_hf_hub/README.md
@@ -0,0 +1,47 @@
+# Write to hub
+
+### Description
+Component that writes a dataset to the hub
+
+### Inputs / outputs
+
+**This component consumes:**
+- dummy_variable
+  - data: binary
+
+**This component produces no data.**
+
+### Arguments
+
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+| hf_token | str | The hugging face token used to write to the hub | / |
+| username | str | The username under which to upload the dataset | / |
+| dataset_name | str | The name of the dataset to upload | / |
+| image_column_names | list | A list containing the image column names. Used to format to image to HF hub format | None |
+| column_name_mapping | dict | Mapping of the consumed fondant column names to the written hub column names | None |
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+write_to_hf_hub_op = ComponentOp.from_registry(
+    name="write_to_hf_hub",
+    arguments={
+        # Add arguments
+        # "hf_token": ,
+        # "username": ,
+        # "dataset_name": ,
+        # "image_column_names": "None",
+        # "column_name_mapping": "None",
+    }
+)
+pipeline.add_op(write_to_hf_hub_op, dependencies=[...])  #Add previous component as dependency
+```
+
diff --git a/scripts/component_readme/generate_readme.py b/scripts/component_readme/generate_readme.py
new file mode 100644
index 000000000..7edd5902c
--- /dev/null
+++ b/scripts/component_readme/generate_readme.py
@@ -0,0 +1,53 @@
+import argparse
+from pathlib import Path
+
+import jinja2
+from fondant.component_spec import ComponentSpec
+
+
+def read_component_spec(component_spec_path: Path) -> ComponentSpec:
+    return ComponentSpec.from_file(component_spec_path)
+
+
+def generate_readme(component_spec: ComponentSpec, *, component_dir: Path) -> str:
+    env = jinja2.Environment(
+        loader=jinja2.loaders.FileSystemLoader(Path(__file__).parent),
+        trim_blocks=True
+    )
+    env.filters["eval"] = eval
+
+    template = env.get_template("readme_template.md")
+
+    return template.render(
+        id=component_dir.name,
+        name=component_spec.name,
+        description=component_spec.description,
+        consumes=component_spec.consumes,
+        produces=component_spec.produces,
+        arguments=component_spec.args.values(),
+        tests=(component_dir / "tests").exists()
+    )
+
+
+def write_readme(readme: str, component_dir: Path) -> None:
+    with open(component_dir / "README.md", "w") as f:
+        f.write(readme)
+
+
+def main(component_spec_path: Path):
+    component_spec = read_component_spec(component_spec_path)
+    component_dir = component_spec_path.parent
+    readme = generate_readme(component_spec, component_dir=component_dir)
+    write_readme(readme, component_dir=component_dir)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("component_specs",
+                        nargs="+",
+                        type=Path,
+                        help="Path to the component spec to generate a readme from")
+    args = parser.parse_args()
+
+    for spec in args.component_specs:
+        main(spec)
diff --git a/scripts/component_readme/readme_template.md b/scripts/component_readme/readme_template.md
new file mode 100644
index 000000000..672ef6faa
--- /dev/null
+++ b/scripts/component_readme/readme_template.md
@@ -0,0 +1,77 @@
+# {{ name }}
+
+### Description
+{{ description }}
+
+### Inputs / outputs
+
+{% if consumes %}
+**This component consumes:**
+{% for subset_name, subset in consumes.items() %}
+- {{ subset_name }}
+{% for field in subset.fields.values() %}
+  - {{ field.name }}: {{ field.type.value }}
+{% endfor %}
+{% endfor %}
+{% else %}
+**This component consumes no data.**
+{% endif %}
+
+{% if produces %}
+**This component produces:**
+{% for subset_name, subset in produces.items() %}
+- {{ subset_name }}
+{% for field in subset.fields.values() %}
+  - {{ field.name }}: {{ field.type.value }}
+{% endfor %}
+{% endfor %}
+{% else %}
+**This component produces no data.**
+{% endif %}
+
+### Arguments
+
+{% if arguments %}
+The component takes the following arguments to alter its behavior:
+
+| argument | type | description | default |
+| -------- | ---- | ----------- | ------- |
+{% for argument in arguments %}
+| {{ argument.name }} | {{ argument.type }} | {{ argument.description.replace("\n", "") }} | {{ argument.default or "/" }} |
+{% endfor %}
+{% else %}
+This component takes no arguments.
+{% endif %}
+
+### Usage
+
+You can add this component to your pipeline using the following code:
+
+```python
+from fondant.pipeline import ComponentOp
+
+
+{{ id }}_op = ComponentOp.from_registry(
+    name="{{ id }}",
+    arguments={
+        # Add arguments
+{% for argument in arguments %}
+{% if argument.default %}
+        # "{{ argument.name }}": {{ '\"' + argument.default + '\"' if argument.default is string else argument.default }},
+{% else %}
+        # "{{ argument.name }}": {{ (argument.type|eval)() }},
+{% endif %}
+{% endfor %}
+    }
+)
+pipeline.add_op({{ id }}_op, dependencies=[...])  #Add previous component as dependency
+```
+
+{% if tests %}
+### Testing
+
+You can run the tests using docker with BuildKit. From this directory, run:
+```
+docker build . --target test
+```
+{% endif %}
diff --git a/tests/example_pipelines/compiled_pipeline/example_2/docker-compose.yml b/tests/example_pipelines/compiled_pipeline/example_2/docker-compose.yml
index a662b8311..1452bde94 100644
--- a/tests/example_pipelines/compiled_pipeline/example_2/docker-compose.yml
+++ b/tests/example_pipelines/compiled_pipeline/example_2/docker-compose.yml
@@ -41,8 +41,20 @@ services:
     - --cluster_type
     - default
     - --component_spec
-    - '{"name": "Image cropping", "description": "Component that removes single-colored
-      borders around images and crops them appropriately", "image": "ghcr.io/ml6team/image_cropping:dev",
+    - '{"name": "Image cropping", "image": "ghcr.io/ml6team/image_cropping:dev", "description":
+      "This component crops out image borders. This is typically useful when working
+      with graphical \nimages that have single-color borders (e.g. logos, icons, etc.).\n\nThe
+      component takes an image and calculates which color is most present in the border.
+      It then \ncrops the image in order to minimize this single-color border. The
+      `padding` argument will add \nextra border to the image before cropping it,
+      in order to avoid cutting off parts of the image.\nThe resulting crop will always
+      be square. If a crop is not possible, the component will return \nthe original
+      image.\n\n#### Examples\nExamples of image cropping by removing the single-color
+      border. Left side is original image, \nright side is border-cropped image.\n\n![Example
+      of image cropping by removing the single-color border. Left side is original,
+      right side is cropped image](../../docs/art/components/image_cropping/component_border_crop_1.png)\n![Example
+      of image cropping by removing the single-color border. Left side is original,
+      right side is cropped image](../../docs/art/components/image_cropping/component_border_crop_0.png)\n",
       "consumes": {"images": {"fields": {"data": {"type": "binary"}}}}, "produces":
       {"images": {"fields": {"data": {"type": "binary"}, "width": {"type": "int32"},
       "height": {"type": "int32"}}}}, "args": {"cropping_threshold": {"description":
diff --git a/tests/example_pipelines/compiled_pipeline/example_2/kubeflow_pipeline.yml b/tests/example_pipelines/compiled_pipeline/example_2/kubeflow_pipeline.yml
index c583ce539..02cade54a 100644
--- a/tests/example_pipelines/compiled_pipeline/example_2/kubeflow_pipeline.yml
+++ b/tests/example_pipelines/compiled_pipeline/example_2/kubeflow_pipeline.yml
@@ -1,21 +1,16 @@
 apiVersion: argoproj.io/v1alpha1
 kind: Workflow
 metadata:
-  annotations:
-    pipelines.kubeflow.org/kfp_sdk_version: 1.8.22
-    pipelines.kubeflow.org/pipeline_compilation_time: '2023-01-01T00:00:00'
-    pipelines.kubeflow.org/pipeline_spec: '{"description": "description of the test
-      pipeline", "name": "test_pipeline"}'
   generateName: test-pipeline-
-  labels:
-    pipelines.kubeflow.org/kfp_sdk_version: 1.8.22
+  annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.22, pipelines.kubeflow.org/pipeline_compilation_time: '2023-01-01T00:00:00',
+    pipelines.kubeflow.org/pipeline_spec: '{"description": "description of the test
+      pipeline", "name": "test_pipeline"}'}
+  labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.22}
 spec:
-  arguments:
-    parameters: []
   entrypoint: test-pipeline
-  serviceAccountName: pipeline-runner
   templates:
-  - container:
+  - name: first-component
+    container:
       args: []
       command:
       - fondant
@@ -34,7 +29,7 @@ spec:
       - --input_partition_rows
       - None
       - --cache
-      - 'False'
+      - "False"
       - --storage_args
       - a dummy string arg
       - --output_manifest_path
@@ -49,27 +44,21 @@ spec:
       artifacts:
       - name: input_manifest_path
         path: /tmp/inputs/input_manifest_path/data
-        raw:
-          data: ''
+        raw: {data: ''}
+    outputs:
+      artifacts:
+      - {name: first-component-output_manifest_path, path: /tmp/outputs/output_manifest_path/data}
     metadata:
-      annotations:
-        pipelines.kubeflow.org/arguments.parameters: '{"cache": "False", "client_kwargs":
-          "{}", "cluster_type": "default", "component_spec": "{\"args\": {\"storage_args\":
-          {\"description\": \"Storage arguments\", \"type\": \"str\"}}, \"description\":
-          \"This is an example component\", \"image\": \"example_component:latest\",
-          \"name\": \"First component\", \"produces\": {\"captions\": {\"fields\":
-          {\"data\": {\"type\": \"string\"}}}, \"images\": {\"fields\": {\"data\":
-          {\"type\": \"binary\"}}}}}", "input_partition_rows": "None", "metadata":
-          "{\"base_path\": \"/foo/bar\", \"pipeline_name\": \"test_pipeline\", \"run_id\":
-          \"test_pipeline-20230101000000\", \"component_id\": \"first_component\",
-          \"cache_key\": \"1\"}", "storage_args": "a dummy string arg"}'
-        pipelines.kubeflow.org/component_ref: '{"digest": "ba182d1dd6a5f8fdffb3c9e487c84d1d1b9ebbfe4b5a137a4af02be832c0c820"}'
-        pipelines.kubeflow.org/component_spec: '{"description": "This is an example
-          component", "implementation": {"container": {"command": ["fondant", "execute",
-          "main", "--input_manifest_path", {"inputPath": "input_manifest_path"}, "--metadata",
-          {"inputValue": "metadata"}, "--component_spec", {"inputValue": "component_spec"},
-          "--input_partition_rows", {"inputValue": "input_partition_rows"}, "--cache",
-          {"inputValue": "cache"}, "--storage_args", {"inputValue": "storage_args"},
+      labels:
+        pipelines.kubeflow.org/kfp_sdk_version: 1.8.22
+        pipelines.kubeflow.org/pipeline-sdk-type: kfp
+        pipelines.kubeflow.org/enable_caching: "true"
+      annotations: {pipelines.kubeflow.org/component_spec: '{"description": "This
+          is an example component", "implementation": {"container": {"command": ["fondant",
+          "execute", "main", "--input_manifest_path", {"inputPath": "input_manifest_path"},
+          "--metadata", {"inputValue": "metadata"}, "--component_spec", {"inputValue":
+          "component_spec"}, "--input_partition_rows", {"inputValue": "input_partition_rows"},
+          "--cache", {"inputValue": "cache"}, "--storage_args", {"inputValue": "storage_args"},
           "--output_manifest_path", {"outputPath": "output_manifest_path"}, "--cluster_type",
           {"inputValue": "cluster_type"}, "--client_kwargs", {"inputValue": "client_kwargs"}],
           "image": "example_component:latest"}}, "inputs": [{"description": "Path
@@ -87,17 +76,19 @@ spec:
           dask client", "name": "client_kwargs", "type": "JsonObject"}, {"description":
           "Storage arguments", "name": "storage_args", "type": "String"}], "name":
           "First component", "outputs": [{"description": "Path to the output manifest",
-          "name": "output_manifest_path", "type": "String"}]}'
-      labels:
-        pipelines.kubeflow.org/enable_caching: 'true'
-        pipelines.kubeflow.org/kfp_sdk_version: 1.8.22
-        pipelines.kubeflow.org/pipeline-sdk-type: kfp
-    name: first-component
-    outputs:
-      artifacts:
-      - name: first-component-output_manifest_path
-        path: /tmp/outputs/output_manifest_path/data
-  - container:
+          "name": "output_manifest_path", "type": "String"}]}', pipelines.kubeflow.org/component_ref: '{"digest":
+          "ba182d1dd6a5f8fdffb3c9e487c84d1d1b9ebbfe4b5a137a4af02be832c0c820"}', pipelines.kubeflow.org/arguments.parameters: '{"cache":
+          "False", "client_kwargs": "{}", "cluster_type": "default", "component_spec":
+          "{\"args\": {\"storage_args\": {\"description\": \"Storage arguments\",
+          \"type\": \"str\"}}, \"description\": \"This is an example component\",
+          \"image\": \"example_component:latest\", \"name\": \"First component\",
+          \"produces\": {\"captions\": {\"fields\": {\"data\": {\"type\": \"string\"}}},
+          \"images\": {\"fields\": {\"data\": {\"type\": \"binary\"}}}}}", "input_partition_rows":
+          "None", "metadata": "{\"base_path\": \"/foo/bar\", \"pipeline_name\": \"test_pipeline\",
+          \"run_id\": \"test_pipeline-20230101000000\", \"component_id\": \"first_component\",
+          \"cache_key\": \"1\"}", "storage_args": "a dummy string arg"}'}
+  - name: image-cropping
+    container:
       args: []
       command:
       - fondant
@@ -115,14 +106,27 @@ spec:
         is -30", "type": "int"}, "padding": {"default": 10, "description": "Padding
         for the image cropping. The padding is added to all borders of the image.",
         "type": "int"}}, "consumes": {"images": {"fields": {"data": {"type": "binary"}}}},
-        "description": "Component that removes single-colored borders around images
-        and crops them appropriately", "image": "ghcr.io/ml6team/image_cropping:dev",
-        "name": "Image cropping", "produces": {"images": {"fields": {"data": {"type":
-        "binary"}, "height": {"type": "int32"}, "width": {"type": "int32"}}}}}'
+        "description": "This component crops out image borders. This is typically
+        useful when working with graphical \nimages that have single-color borders
+        (e.g. logos, icons, etc.).\n\nThe component takes an image and calculates
+        which color is most present in the border. It then \ncrops the image in order
+        to minimize this single-color border. The `padding` argument will add \nextra
+        border to the image before cropping it, in order to avoid cutting off parts
+        of the image.\nThe resulting crop will always be square. If a crop is not
+        possible, the component will return \nthe original image.\n\n#### Examples\nExamples
+        of image cropping by removing the single-color border. Left side is original
+        image, \nright side is border-cropped image.\n\n![Example of image cropping
+        by removing the single-color border. Left side is original, right side is
+        cropped image](../../docs/art/components/image_cropping/component_border_crop_1.png)\n![Example
+        of image cropping by removing the single-color border. Left side is original,
+        right side is cropped image](../../docs/art/components/image_cropping/component_border_crop_0.png)\n",
+        "image": "ghcr.io/ml6team/image_cropping:dev", "name": "Image cropping", "produces":
+        {"images": {"fields": {"data": {"type": "binary"}, "height": {"type": "int32"},
+        "width": {"type": "int32"}}}}}'
       - --input_partition_rows
       - None
       - --cache
-      - 'True'
+      - "True"
       - --cropping_threshold
       - '0'
       - --padding
@@ -137,38 +141,38 @@ spec:
       imagePullPolicy: Always
     inputs:
       artifacts:
-      - name: first-component-output_manifest_path
-        path: /tmp/inputs/input_manifest_path/data
+      - {name: first-component-output_manifest_path, path: /tmp/inputs/input_manifest_path/data}
+    outputs:
+      artifacts:
+      - {name: image-cropping-output_manifest_path, path: /tmp/outputs/output_manifest_path/data}
     metadata:
-      annotations:
-        pipelines.kubeflow.org/arguments.parameters: '{"cache": "True", "client_kwargs":
-          "{}", "cluster_type": "default", "component_spec": "{\"args\": {\"cropping_threshold\":
-          {\"default\": -30, \"description\": \"Threshold parameter used for detecting
-          borders. A lower (negative) parameter results in a more performant border
-          detection, but can cause overcropping. Default is -30\", \"type\": \"int\"},
-          \"padding\": {\"default\": 10, \"description\": \"Padding for the image
-          cropping. The padding is added to all borders of the image.\", \"type\":
-          \"int\"}}, \"consumes\": {\"images\": {\"fields\": {\"data\": {\"type\":
-          \"binary\"}}}}, \"description\": \"Component that removes single-colored
-          borders around images and crops them appropriately\", \"image\": \"ghcr.io/ml6team/image_cropping:dev\",
-          \"name\": \"Image cropping\", \"produces\": {\"images\": {\"fields\": {\"data\":
-          {\"type\": \"binary\"}, \"height\": {\"type\": \"int32\"}, \"width\": {\"type\":
-          \"int32\"}}}}}", "cropping_threshold": "0", "input_partition_rows": "None",
-          "metadata": "{\"base_path\": \"/foo/bar\", \"pipeline_name\": \"test_pipeline\",
-          \"run_id\": \"test_pipeline-20230101000000\", \"component_id\": \"image_cropping\",
-          \"cache_key\": \"2\"}", "padding": "0"}'
-        pipelines.kubeflow.org/component_ref: '{"digest": "d31e5d546956a42a470f033e2be84f229d3e926dfa7a7a1703c94ff47a1cb992"}'
-        pipelines.kubeflow.org/component_spec: '{"description": "Component that removes
-          single-colored borders around images and crops them appropriately", "implementation":
-          {"container": {"command": ["fondant", "execute", "main", "--input_manifest_path",
-          {"inputPath": "input_manifest_path"}, "--metadata", {"inputValue": "metadata"},
-          "--component_spec", {"inputValue": "component_spec"}, "--input_partition_rows",
-          {"inputValue": "input_partition_rows"}, "--cache", {"inputValue": "cache"},
-          "--cropping_threshold", {"inputValue": "cropping_threshold"}, "--padding",
-          {"inputValue": "padding"}, "--output_manifest_path", {"outputPath": "output_manifest_path"},
-          "--cluster_type", {"inputValue": "cluster_type"}, "--client_kwargs", {"inputValue":
-          "client_kwargs"}], "image": "ghcr.io/ml6team/image_cropping:dev"}}, "inputs":
-          [{"description": "Path to the input manifest", "name": "input_manifest_path",
+      labels:
+        pipelines.kubeflow.org/kfp_sdk_version: 1.8.22
+        pipelines.kubeflow.org/pipeline-sdk-type: kfp
+        pipelines.kubeflow.org/enable_caching: "true"
+      annotations: {pipelines.kubeflow.org/component_spec: '{"description": "This
+          component crops out image borders. This is typically useful when working
+          with graphical \nimages that have single-color borders (e.g. logos, icons,
+          etc.).\n\nThe component takes an image and calculates which color is most
+          present in the border. It then \ncrops the image in order to minimize this
+          single-color border. The `padding` argument will add \nextra border to the
+          image before cropping it, in order to avoid cutting off parts of the image.\nThe
+          resulting crop will always be square. If a crop is not possible, the component
+          will return \nthe original image.\n\n#### Examples\nExamples of image cropping
+          by removing the single-color border. Left side is original image, \nright
+          side is border-cropped image.\n\n![Example of image cropping by removing
+          the single-color border. Left side is original, right side is cropped image](../../docs/art/components/image_cropping/component_border_crop_1.png)\n![Example
+          of image cropping by removing the single-color border. Left side is original,
+          right side is cropped image](../../docs/art/components/image_cropping/component_border_crop_0.png)\n",
+          "implementation": {"container": {"command": ["fondant", "execute", "main",
+          "--input_manifest_path", {"inputPath": "input_manifest_path"}, "--metadata",
+          {"inputValue": "metadata"}, "--component_spec", {"inputValue": "component_spec"},
+          "--input_partition_rows", {"inputValue": "input_partition_rows"}, "--cache",
+          {"inputValue": "cache"}, "--cropping_threshold", {"inputValue": "cropping_threshold"},
+          "--padding", {"inputValue": "padding"}, "--output_manifest_path", {"outputPath":
+          "output_manifest_path"}, "--cluster_type", {"inputValue": "cluster_type"},
+          "--client_kwargs", {"inputValue": "client_kwargs"}], "image": "ghcr.io/ml6team/image_cropping:dev"}},
+          "inputs": [{"description": "Path to the input manifest", "name": "input_manifest_path",
           "type": "String"}, {"description": "Metadata arguments containing the run
           id and base path", "name": "metadata", "type": "String"}, {"default": "None",
           "description": "The component specification as a dictionary", "name": "component_spec",
@@ -186,26 +190,47 @@ spec:
           "Integer"}, {"default": 10, "description": "Padding for the image cropping.
           The padding is added to all borders of the image.", "name": "padding", "type":
           "Integer"}], "name": "Image cropping", "outputs": [{"description": "Path
-          to the output manifest", "name": "output_manifest_path", "type": "String"}]}'
-      labels:
-        pipelines.kubeflow.org/enable_caching: 'true'
-        pipelines.kubeflow.org/kfp_sdk_version: 1.8.22
-        pipelines.kubeflow.org/pipeline-sdk-type: kfp
-    name: image-cropping
-    outputs:
-      artifacts:
-      - name: image-cropping-output_manifest_path
-        path: /tmp/outputs/output_manifest_path/data
-  - dag:
+          to the output manifest", "name": "output_manifest_path", "type": "String"}]}',
+        pipelines.kubeflow.org/component_ref: '{"digest": "bd073ded3bbd5c9bc5fd3abd3b8e8d19d65c17d6914f117596c78a5eddbd99d0"}',
+        pipelines.kubeflow.org/arguments.parameters: '{"cache": "True", "client_kwargs":
+          "{}", "cluster_type": "default", "component_spec": "{\"args\": {\"cropping_threshold\":
+          {\"default\": -30, \"description\": \"Threshold parameter used for detecting
+          borders. A lower (negative) parameter results in a more performant border
+          detection, but can cause overcropping. Default is -30\", \"type\": \"int\"},
+          \"padding\": {\"default\": 10, \"description\": \"Padding for the image
+          cropping. The padding is added to all borders of the image.\", \"type\":
+          \"int\"}}, \"consumes\": {\"images\": {\"fields\": {\"data\": {\"type\":
+          \"binary\"}}}}, \"description\": \"This component crops out image borders.
+          This is typically useful when working with graphical \\nimages that have
+          single-color borders (e.g. logos, icons, etc.).\\n\\nThe component takes
+          an image and calculates which color is most present in the border. It then
+          \\ncrops the image in order to minimize this single-color border. The `padding`
+          argument will add \\nextra border to the image before cropping it, in order
+          to avoid cutting off parts of the image.\\nThe resulting crop will always
+          be square. If a crop is not possible, the component will return \\nthe original
+          image.\\n\\n#### Examples\\nExamples of image cropping by removing the single-color
+          border. Left side is original image, \\nright side is border-cropped image.\\n\\n![Example
+          of image cropping by removing the single-color border. Left side is original,
+          right side is cropped image](../../docs/art/components/image_cropping/component_border_crop_1.png)\\n![Example
+          of image cropping by removing the single-color border. Left side is original,
+          right side is cropped image](../../docs/art/components/image_cropping/component_border_crop_0.png)\\n\",
+          \"image\": \"ghcr.io/ml6team/image_cropping:dev\", \"name\": \"Image cropping\",
+          \"produces\": {\"images\": {\"fields\": {\"data\": {\"type\": \"binary\"},
+          \"height\": {\"type\": \"int32\"}, \"width\": {\"type\": \"int32\"}}}}}",
+          "cropping_threshold": "0", "input_partition_rows": "None", "metadata": "{\"base_path\":
+          \"/foo/bar\", \"pipeline_name\": \"test_pipeline\", \"run_id\": \"test_pipeline-20230101000000\",
+          \"component_id\": \"image_cropping\", \"cache_key\": \"2\"}", "padding":
+          "0"}'}
+  - name: test-pipeline
+    dag:
       tasks:
-      - name: first-component
-        template: first-component
-      - arguments:
-          artifacts:
-          - from: '{{tasks.first-component.outputs.artifacts.first-component-output_manifest_path}}'
-            name: first-component-output_manifest_path
-        dependencies:
-        - first-component
-        name: image-cropping
+      - {name: first-component, template: first-component}
+      - name: image-cropping
         template: image-cropping
-    name: test-pipeline
+        dependencies: [first-component]
+        arguments:
+          artifacts:
+          - {name: first-component-output_manifest_path, from: '{{tasks.first-component.outputs.artifacts.first-component-output_manifest_path}}'}
+  arguments:
+    parameters: []
+  serviceAccountName: pipeline-runner