Add Dropbox-to-Pinecone connector API quickstart to notebook examples page etc. (#739)

Author: Paul-Cornell

api-reference/workflow/overview.mdx

@@ -60,7 +60,7 @@ allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; pic
 allowfullscreen
 ></iframe>
 
-Open a related [notebook](https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/Unstructured_Platform_Workflow_Endpoint_Quickstart.ipynb) that covers many of 
+Open a related [notebook](https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/Dropbox_To_Pinecone_Connector_Quickstart.ipynb) that covers many of 
 the concepts that are shown in the preceding videos.
 
 The [Unstructured Python SDK](https://github.com/Unstructured-IO/unstructured-python-client), beginning with version 0.30.6, 

examplecode/notebooks.mdx

@@ -6,6 +6,13 @@ description: "Notebooks contain complete working sample code for end-to-end solu
 ---
 
 <CardGroup cols={2}>
+    <Card title="Dropbox-to-Pinecone Connector API Quickstart" href="https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/Dropbox_To_Pinecone_Connector_Quickstart.ipynb">
+    <br/>
+       Learn how to set up and run a custom workflow that uses a free Dropbox storage location as a source and a free Pinecone serverless index as a destination, suitable for powering RAG applications.
+    <br/>
+       ``Unstructured API`` ``Workflows`` ``Dropbox`` ``Pinecone`` 
+    <br/>
+    </Card>
     <Card title="Getting Started with Unstructured API and PostgreSQL" href="https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/Getting_Started_with_Unstructured_API_and_PostgreSQL.ipynb">
     <br/>
        Learn how to build data processing workflows using the Unstructured API and Python SDK to preprocess unstructured files from S3 and store the structured outputs in PostgreSQL for retrieval.

snippets/quickstarts/platform-api.mdx

@@ -1,40 +1,4 @@
 This quickstart uses the Unstructured Python SDK to call the Unstructured Workflow Endpoint to get your data RAG-ready. The Python code for this 
 quickstart is in a remote hosted Google Colab notebook. Data is processed on Unstructured-hosted compute resources.
 
-The requirements are as follows:
-
-- A compatible source (input) location that contains your data for Unstructured to process. [See the list of supported source types](/ui/connectors#sources). 
-  This quickstart uses an Amazon S3 bucket as the source location. If you use a different source type, you will need to modify the quickstart notebook accordingly.
-- For document-based source locations, compatible files in that location. [See the list of supported file types](/ui/supported-file-types). If you do not have any files available, you can download some from the [example-docs](https://github.com/Unstructured-IO/unstructured-ingest/tree/main/example-docs) folder in the `Unstructured-IO/unstructured-ingest` repository in GitHub.
-- A compatible destination (output) location for Unstructured to put the processed data. [See the list of supported destination types](/ui/connectors#destinations). 
-  For this quickstart's destination location, a different folder in the same Amazon S3 bucket as the source location is used. If you use a different destination S3 bucket or a different destination type, you will need to modify the quickstart notebook accordingly.
-
-import GetStartedSimpleAPIOnly from '/snippets/general-shared-text/get-started-simple-api-only.mdx';
-
-<Steps>
-    <Step title="Sign up, sign in, and get your API key">
-        <GetStartedSimpleAPIOnly />
-    </Step>
-    <Step title="Create and set up the S3 bucket">
-        This quickstart uses an Amazon S3 bucket as both the source location and the destination location. 
-        (You can use other source and destination types that are supported by Unstructured. 
-        If you use a different source or destination type, or if you use a different S3 bucket for the destination location, 
-        you will need to modify the quickstart notebook accordingly.)
-        
-        Inside of the S3 bucket, a folder named `input` represents the 
-        source location. This is where your files to be processed will be stored. 
-        The S3 URI to the source location will be `s3://<your-bucket-name>/input`.
-        
-        Inside of the same S3 bucket, a folder inside named `output` represents the destination location. This 
-        is where Unstructured will put the processed data. 
-        The S3 URI to the destination location will be `s3://<your-bucket-name>/output`.
-
-        Learn how to [create an S3 bucket and set it up for Unstructured](/api-reference/workflow/sources/s3). (Do not run the Python SDK code or REST commands at the end of those setup instructions.)
-    </Step>
-    <Step title="Run the quickstart notebook">
-       After your S3 bucket is created and set up, follow the instructions in this [quickstart notebook](https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/Unstructured_Platform_Workflow_Endpoint_Quickstart.ipynb).
-    </Step>
-    <Step title="View the processed data">
-        After you run the quickstart notebook, go to your destination location to view the processed data.
-    </Step>
-</Steps>
+To run this quickstart, open the [notebook](https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/Dropbox_To_Pinecone_Connector_Quickstart.ipynb) and begin following the notebook's on-screen instructions.