diff --git a/huggingface_pipelines/builder.py b/huggingface_pipelines/builder.py
index 2e91e1a..c5e1911 100644
--- a/huggingface_pipelines/builder.py
+++ b/huggingface_pipelines/builder.py
@@ -6,8 +6,8 @@
 
 from .audio import AudioToEmbeddingPipelineFactory  # type: ignore
 from .metric_analyzer import MetricAnalyzerPipelineFactory  # type: ignore
-from .pipeline import Pipeline  # type: ignore
-from .text import (  # type: ignore
+from .pipeline import Pipeline, PipelineFactory  # type: ignore
+from .text import (  # type: ignore[import]
     EmbeddingToTextPipelineFactory,
     TextSegmentationPipelineFactory,
     TextToEmbeddingPipelineFactory,
@@ -26,11 +26,35 @@
 
 
 class PipelineBuilder:
+    """
+    A class for building and managing different types of processing pipelines.
+
+    This class provides methods to create pipelines for various operations such as
+    text-to-embedding, embedding-to-text, text segmentation, metric analysis, and
+    audio-to-embedding. It uses the Factory pattern to create specific pipeline
+    instances based on the operation type and configuration.
+
+
+    Attributes:
+        config_dir (Path): The directory containing configuration files for pipelines.
+        pipeline_factories (Dict[SupportedOperation, PipelineFactory]): A dictionary mapping
+            operations to their respective factory classes.
+
+    Args:
+        config_dir (Union[str, Path], optional): The directory containing configuration
+            files. Defaults to "huggingface_pipelines/datacards".
+
+    Example:
+        >>> builder = PipelineBuilder()
+        >>> text_to_embedding_pipeline = builder.create_pipeline("sonar", "text_to_embedding")
+        >>> processed_dataset = text_to_embedding_pipeline(input_dataset)
+    """
+
     def __init__(
         self, config_dir: Union[str, Path] = "huggingface_pipelines/datacards"
     ):
         self.config_dir = Path(config_dir)
-        self.pipeline_factories: Dict[SupportedOperation, Any] = {
+        self.pipeline_factories: Dict[SupportedOperation, PipelineFactory] = {
             "text_to_embedding": TextToEmbeddingPipelineFactory(),
             "embedding_to_text": EmbeddingToTextPipelineFactory(),
             "text_segmentation": TextSegmentationPipelineFactory(),
@@ -41,6 +65,23 @@ def __init__(
     def load_config(
         self, dataset_name: str, operation: SupportedOperation
     ) -> Dict[str, Any]:
+        """
+        Load the configuration for a specific dataset and operation.
+
+        This method reads the YAML configuration file for the specified dataset and operation.
+        The configuration is used to set up the PipelineConfig for the requested pipeline.
+
+        Args:
+            dataset_name (str): The name of the dataset.
+            operation (SupportedOperation): The type of operation to perform.
+
+        Returns:
+            Dict[str, Any]: The configuration dictionary.
+
+        Raises:
+            FileNotFoundError: If the configuration file is not found.
+
+        """
         config_file = self.config_dir / f"{dataset_name}/{operation}.yaml"
         try:
             with open(config_file, "r") as f:
@@ -54,10 +95,35 @@ def load_config(
     def create_pipeline(
         self, dataset_name: str, operation: SupportedOperation
     ) -> Pipeline:
+        """
+        Create a pipeline for a specific dataset and operation.
+
+        This method uses the appropriate PipelineFactory to create a Pipeline instance
+        based on the specified operation and configuration. The created Pipeline
+        adheres to the abstract Pipeline class structure and uses a PipelineConfig
+        for its configuration.
+
+        Args:
+            dataset_name (str): The name of the dataset.
+            operation (SupportedOperation): The type of operation to perform.
+
+        Returns:
+            Pipeline: The created pipeline instance, which can be called with a dataset.
+
+        Raises:
+            ValueError: If the operation is not supported.
+
+        Example:
+            >>> builder = PipelineBuilder()
+            >>> text_to_embedding_pipeline = builder.create_pipeline("dataset_name", "text_to_embedding")
+            >>> processed_dataset = text_to_embedding_pipeline(input_dataset)
+
+            >>> audio_to_embedding_pipeline = builder.create_pipeline("dataset_name", "audio_to_embedding")
+            >>> processed_audio_dataset = audio_to_embedding_pipeline(input_audio_dataset)
+        """
         if operation not in self.pipeline_factories:
             raise ValueError(
                 f"Unsupported operation: {operation}. Supported operations are: {', '.join(self.pipeline_factories.keys())}"
             )
-
         config = self.load_config(dataset_name, operation)
         return self.pipeline_factories[operation].create_pipeline(config)