diff --git a/README.md b/README.md
index 1b00e9b..0f9b5ba 100644
--- a/README.md
+++ b/README.md
@@ -22,7 +22,7 @@ Check out the Fondant [website](https://fondant.ai/) if you want to learn more a
 
 ### A simple RAG indexing pipeline
 
-A [**notebook**](./src/pipeline.ipynb) with a simple Fondant pipeline to index your data into a 
+A [**notebook**](./src/indexing.ipynb) with a simple Fondant pipeline to index your data into a 
 RAG system.
 
 ### Iterative tuning of a RAG indexing pipeline
@@ -84,4 +84,4 @@ fondant --help
 There are two options to run the pipeline:
 
 - [**Via python files and the Fondant CLI:**](https://fondant.ai/en/latest/pipeline/#running-a-pipeline) how you should run Fondant in production
-- [**Via a Jupyter notebook**](./src/pipeline.ipynb): ideal to learn about Fondant
+- [**Via a Jupyter notebook**](./src/indexing.ipynb): ideal to learn about Fondant
diff --git a/requirements.txt b/requirements.txt
index 609e239..374d700 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,3 @@
-fondant==0.9.0
+fondant==0.10.0
 notebook==7.0.6
 weaviate-client==3.25.3
diff --git a/src/components/aggregate_eval_results/requirements.txt b/src/components/aggregate_eval_results/requirements.txt
index 53e5d83..894a8b5 100644
--- a/src/components/aggregate_eval_results/requirements.txt
+++ b/src/components/aggregate_eval_results/requirements.txt
@@ -1 +1 @@
-fondant[component]==0.9.0
\ No newline at end of file
+fondant[component]==0.10.0
\ No newline at end of file
diff --git a/src/components/text_cleaning/Dockerfile b/src/components/text_cleaning/Dockerfile
deleted file mode 100644
index d32a608..0000000
--- a/src/components/text_cleaning/Dockerfile
+++ /dev/null
@@ -1,13 +0,0 @@
-FROM --platform=linux/amd64 python:3.8-slim
-
-# Install requirements
-COPY requirements.txt /
-RUN pip3 install --no-cache-dir -r requirements.txt
-
-# Set the working directory to the component folder
-WORKDIR /component/src
-
-# Copy over src-files
-COPY src/ .
-
-ENTRYPOINT ["fondant", "execute", "main"]
diff --git a/src/components/text_cleaning/fondant_component.yaml b/src/components/text_cleaning/fondant_component.yaml
deleted file mode 100644
index ee2d561..0000000
--- a/src/components/text_cleaning/fondant_component.yaml
+++ /dev/null
@@ -1,11 +0,0 @@
-name: Text cleaning component
-description: Clean text passages
-image: ghcr.io/ml6team/text_cleaning:dev
-
-consumes:
-  text:
-    type: string
-
-produces:
-  text:
-    type: string
diff --git a/src/components/text_cleaning/requirements.txt b/src/components/text_cleaning/requirements.txt
deleted file mode 100644
index f2c5454..0000000
--- a/src/components/text_cleaning/requirements.txt
+++ /dev/null
@@ -1 +0,0 @@
-fondant[component]==0.9.0
diff --git a/src/components/text_cleaning/src/main.py b/src/components/text_cleaning/src/main.py
deleted file mode 100644
index 687abdb..0000000
--- a/src/components/text_cleaning/src/main.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import pandas as pd
-from fondant.component import PandasTransformComponent
-
-
-class TextCleaningComponent(PandasTransformComponent):
-    def __init__(self, **kwargs):
-        """Initialize your component."""
-
-    def remove_empty_lines(self, text):
-        lines = text.split("\n")
-        non_empty_lines = [line.strip() for line in lines if line.strip()]
-        return "\n".join(non_empty_lines)
-
-    def transform(self, dataframe: pd.DataFrame) -> pd.DataFrame:
-        dataframe["text"] = dataframe["text"].apply(
-            self.remove_empty_lines,
-        )
-        return dataframe
diff --git a/src/pipeline.ipynb b/src/indexing.ipynb
similarity index 76%
rename from src/pipeline.ipynb
rename to src/indexing.ipynb
index 5735e42..dc2db5a 100644
--- a/src/pipeline.ipynb
+++ b/src/indexing.ipynb
@@ -109,7 +109,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -r ../requirements.txt"
+    "!pip install -r ../requirements.txt\n",
+    "# TODO: remove after component inspection PR is merged \n",
+    "!pip3 install \"fondant[component,aws,azure,gcp]@git+https://github.com/ml6team/fondant@f0326c09c1c681a5d275605fe57eeb65918ce6c7\""
    ]
   },
   {
@@ -156,7 +158,7 @@
     "import pyarrow as pa\n",
     "\n",
     "text = pipeline.read(\n",
-    "    \"load_from_hf_hub\",\n",
+    "    \"components/load_from_hf_hub\",\n",
     "    arguments={\n",
     "        # Add arguments\n",
     "        \"dataset_name\": \"wikitext@~parquet\",\n",
@@ -168,6 +170,90 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import typing as t \n",
+    "\n",
+    "from fondant.component import PandasTransformComponent\n",
+    "from fondant.pipeline import lightweight_component\n",
+    "import logging\n",
+    "import typing as t \n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "\n",
+    "\n",
+    "#TODO: Move all imports defined within functions under the class definition after https://github.com/ml6team/fondant/pull/835 is merged \n",
+    "@lightweight_component(\n",
+    "    consumes={\"text\":pa.string()},\n",
+    "    produces={\"text\":pa.string(), \"original_document_id\":pa.string()},\n",
+    "    extra_requires=[\"langchain==0.0.329\"]\n",
+    ")\n",
+    "class ChunkTextComponent(PandasTransformComponent):\n",
+    "    \"\"\"Component that chunks text into smaller segments.\n",
+    "    More information about the different chunking strategies can be here:\n",
+    "      - https://python.langchain.com/docs/modules/data_connection/document_transformers/\n",
+    "      - https://www.pinecone.io/learn/chunking-strategies/.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        *,\n",
+    "        chunk_size: int,\n",
+    "        chunk_overlap: int,\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Args:\n",
+    "            chunk_size: the chunk size \n",
+    "            chunk_overlap: the overlap between chunks\n",
+    "        \"\"\"\n",
+    "        import logging\n",
+    "        import typing as t \n",
+    "        from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "\n",
+    "        self.logger = logging.getLogger(__name__)\n",
+    "        self.chunker = RecursiveCharacterTextSplitter(\n",
+    "            chunk_size=chunk_size,\n",
+    "            chunk_overlap=chunk_overlap\n",
+    "        )\n",
+    "\n",
+    "    def chunk_text(self, row) -> t.List[t.Tuple]:\n",
+    "        # Multi-index df has id under the name attribute\n",
+    "        doc_id = row.name\n",
+    "        text_data = row[\"text\"]\n",
+    "        docs = self.chunker.create_documents([text_data])\n",
+    "\n",
+    "        return [\n",
+    "            (doc_id, f\"{doc_id}_{chunk_id}\", chunk.page_content)\n",
+    "            for chunk_id, chunk in enumerate(docs)\n",
+    "        ]\n",
+    "\n",
+    "    def transform(self, dataframe: pd.DataFrame) -> pd.DataFrame:\n",
+    "        import itertools\n",
+    "        \n",
+    "        self.logger.info(f\"Chunking {len(dataframe)} documents...\")\n",
+    "\n",
+    "        results = dataframe.apply(\n",
+    "            self.chunk_text,\n",
+    "            axis=1,\n",
+    "        ).to_list()\n",
+    "\n",
+    "        # Flatten results\n",
+    "        results = list(itertools.chain.from_iterable(results))\n",
+    "\n",
+    "        # Turn into dataframes\n",
+    "        results_df = pd.DataFrame(\n",
+    "            results,\n",
+    "            columns=[\"original_document_id\", \"id\", \"text\"],\n",
+    "        )\n",
+    "        results_df = results_df.set_index(\"id\")\n",
+    "\n",
+    "        return results_df\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -183,16 +269,18 @@
    "source": [
     "import utils\n",
     "\n",
+    "# TODO: remove /components after using a stable release \n",
     "\n",
     "chunks = text.apply(\n",
-    "    \"chunk_text\",\n",
+    "    ChunkTextComponent,\n",
     "    arguments={\n",
-    "        \"chunk_args\": {\"chunk_size\": 512, \"chunk_overlap\": 32}\n",
+    "        \"chunk_size\": 512, \"chunk_overlap\": 32\n",
     "    }\n",
     ")\n",
     "\n",
+    "\n",
     "embeddings = chunks.apply(\n",
-    "    \"embed_text\",\n",
+    "    \"components/embed_text\",\n",
     "    arguments={\n",
     "        \"model_provider\": \"huggingface\",\n",
     "        \"model\": \"all-MiniLM-L6-v2\"\n",
@@ -205,7 +293,7 @@
     ")\n",
     "\n",
     "embeddings.write(\n",
-    "    \"index_weaviate\",\n",
+    "    \"components/index_weaviate\",\n",
     "    arguments={\n",
     "        \"weaviate_url\": f\"http://{utils.get_host_ip()}:8081\",\n",
     "        \"class_name\": \"index\",\n",
@@ -328,19 +416,7 @@
     "\n",
     "Certainly, you can create your own custom components and use them in the pipeline. Let's consider building a component that cleans our text articles. For demo purpose we will implement a component thats removes all empty lines.\n",
     "\n",
-    "To implement a custom component, a couple of files need to be defined:\n",
-    "\n",
-    "- Fondant component specification\n",
-    "- main.py script in a src folder\n",
-    "- Dockerfile\n",
-    "- requirements.txt\n",
-    "\n",
-    "If you want to learn more about the creating custom components checkout [our documentation](https://fondant.ai/en/latest/components/custom_component/).\n",
-    "\n",
-    "\n",
-    "### Component specification\n",
-    "\n",
-    "The component specification is represented by a single `fondant_component.yaml` file. There you can define which fields your component consumes and produces. "
+    "We will implement this component as a lightweight component, checkout our [guide](https://fondant.ai/en/latest/components/lightweight_components/) on lightweight components for more info. "
    ]
   },
   {
@@ -349,48 +425,28 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%writefile components/text_cleaning/fondant_component.yaml\n",
-    "name: Text cleaning component\n",
-    "description: Clean text passages\n",
-    "image: ghcr.io/ml6team/text_cleaning:dev\n",
-    "\n",
-    "consumes:\n",
-    "  text:\n",
-    "    type: string\n",
-    "\n",
-    "produces:\n",
-    "  text:\n",
-    "    type: string"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Main.py script\n",
-    "\n",
-    "The core logic of the component should be implemented in a `main.py` script in a folder called `src`. We can implement the text cleaning logic as a class. We will inherit from the base class `PandasTransformComponent`. The `PandasTransformComponent` operates on pandas dataframes. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile components/text_cleaning/src/main.py\n",
-    "import pandas as pd\n",
-    "from fondant.component import PandasTransformComponent\n",
-    "\n",
-    "\n",
+    "@lightweight_component(\n",
+    "    consumes={\"text\":pa.string()},\n",
+    "    produces={\"text\":pa.string()},\n",
+    ")\n",
     "class TextCleaningComponent(PandasTransformComponent):\n",
-    "    def __init__(self, **kwargs):\n",
-    "        \"\"\"Initialize your component\"\"\"\n",
+    "    def __init__(\n",
+    "        self\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Args:\n",
+    "            chunk_size: the chunk size \n",
+    "            chunk_overlap: the overlap between chunks\n",
+    "        \"\"\"\n",
+    "        import logging\n",
+    "        import typing as t \n",
+    "\n",
+    "        self.logger = logging.getLogger(__name__)\n",
     "\n",
     "    def remove_empty_lines(self, text):\n",
     "        lines = text.split(\"\\n\")\n",
     "        non_empty_lines = [line.strip() for line in lines if line.strip()]\n",
-    "        return \"\\n\".join(non_empty_lines)\n",
+    "        return 1\n",
     "\n",
     "    def transform(self, dataframe: pd.DataFrame) -> pd.DataFrame:\n",
     "        dataframe[\"text\"] = dataframe[\"text\"].apply(\n",
@@ -399,62 +455,13 @@
     "        return dataframe"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dockerfile \n",
-    "The Dockerfile defines how to build the component into a Docker image. You can use the following:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile components/text_cleaning/Dockerfile\n",
-    "FROM --platform=linux/amd64 python:3.8-slim\n",
-    "\n",
-    "# Install requirements\n",
-    "COPY requirements.txt /\n",
-    "RUN pip3 install --no-cache-dir -r requirements.txt\n",
-    "\n",
-    "# Set the working directory to the component folder\n",
-    "WORKDIR /component/src\n",
-    "\n",
-    "# Copy over src-files\n",
-    "COPY src/ .\n",
-    "\n",
-    "ENTRYPOINT [\"fondant\", \"execute\", \"main\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Requirements.txt\n",
-    "\n",
-    "In the requirements.txt we define all dependencies of the component."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile components/text_cleaning/requirements.txt\n",
-    "fondant[component]==0.9.0"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Add the new component to the pipeline\n",
     "\n",
-    "Now we can add the new component to the pipeline with the `Dataset.apply` function. We just specify the path to the directory containing the custom component instead of the name of the reusable component."
+    "Now we can add the new component to the pipeline with the `Dataset.apply` function. We just specify the reference to the component class containing the custom component instead of the name of the reusable component."
    ]
   },
   {
@@ -467,6 +474,7 @@
     "from fondant.pipeline import Pipeline\n",
     "\n",
     "\n",
+    "# TODO: remove /components after using a stable release \n",
     "pipeline = Pipeline(\n",
     "    name=\"ingestion-pipeline\",\n",
     "    description=\"Pipeline to prepare and process data for building a RAG solution\",\n",
@@ -474,7 +482,7 @@
     ")\n",
     "\n",
     "text = pipeline.read(\n",
-    "    \"load_from_hf_hub\",\n",
+    "    \"components/load_from_hf_hub\",\n",
     "    arguments={\n",
     "        \"dataset_name\": \"wikitext@~parquet\",\n",
     "        \"n_rows_to_load\": 1000,\n",
@@ -485,11 +493,11 @@
     ")\n",
     "\n",
     "cleaned_text = text.apply(\n",
-    "    \"components/text_cleaning\",  # Path to custom component\n",
+    "    TextCleaningComponent\n",
     ")\n",
     "\n",
     "chunks = cleaned_text.apply(\n",
-    "    \"chunk_text\",\n",
+    "    \"components/chunk_text\",\n",
     "    arguments={\n",
     "        \"chunk_size\": 512,\n",
     "        \"chunk_overlap\": 32,\n",
@@ -497,7 +505,7 @@
     ")\n",
     "\n",
     "embeddings = chunks.apply(\n",
-    "    \"embed_text\",\n",
+    "    \"components/embed_text\",\n",
     "    arguments={\n",
     "        \"model_provider\": \"huggingface\",\n",
     "        \"model\": \"all-MiniLM-L6-v2\",\n",
@@ -505,7 +513,7 @@
     ")\n",
     "\n",
     "embeddings.write(\n",
-    "    \"index_weaviate\",\n",
+    "    \"components/index_weaviate\",\n",
     "    arguments={\n",
     "        \"weaviate_url\": f\"http://{utils.get_host_ip()}:8081\",\n",
     "        \"class_name\": \"index\",\n",
diff --git a/src/parameter_search.ipynb b/src/parameter_search.ipynb
index 6cdb36b..8615693 100644
--- a/src/parameter_search.ipynb
+++ b/src/parameter_search.ipynb
@@ -400,9 +400,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from utils import ParameterSearch\n",