[Refactor] Converge Pipeline and App classes (#1021)

Co-authored-by: Deven Patel <deven298@yahoo.com>

docs/components/embedding-models.mdx

@@ -25,7 +25,7 @@ Once you have obtained the key, you can use it like this:
 
 ```python main.py
 import os
-from embedchain import Pipeline as App
+from embedchain import App
 
 os.environ['OPENAI_API_KEY'] = 'xxx'
 
@@ -52,7 +52,7 @@ To use Google AI embedding function, you have to set the `GOOGLE_API_KEY` enviro
 <CodeGroup>
 ```python main.py
 import os
-from embedchain import Pipeline as App
+from embedchain import App
 
 os.environ["GOOGLE_API_KEY"] = "xxx"
 
@@ -81,7 +81,7 @@ To use Azure OpenAI embedding model, you have to set some of the azure openai re
 
 ```python main.py
 import os
-from embedchain import Pipeline as App
+from embedchain import App
 
 os.environ["OPENAI_API_TYPE"] = "azure"
 os.environ["AZURE_OPENAI_ENDPOINT"] = "https://xxx.openai.azure.com/"
@@ -119,7 +119,7 @@ GPT4All supports generating high quality embeddings of arbitrary length document
 <CodeGroup>
 
 ```python main.py
-from embedchain import Pipeline as App
+from embedchain import App
 
 # load embedding model configuration from config.yaml file
 app = App.from_config(config_path="config.yaml")
@@ -148,7 +148,7 @@ Hugging Face supports generating embeddings of arbitrary length documents of tex
 <CodeGroup>
 
 ```python main.py
-from embedchain import Pipeline as App
+from embedchain import App
 
 # load embedding model configuration from config.yaml file
 app = App.from_config(config_path="config.yaml")
@@ -179,7 +179,7 @@ Embedchain supports Google's VertexAI embeddings model through a simple interfac
 <CodeGroup>
 
 ```python main.py
-from embedchain import Pipeline as App
+from embedchain import App
 
 # load embedding model configuration from config.yaml file
 app = App.from_config(config_path="config.yaml")