diff --git a/README.md b/README.md
index 262a74d..ef0a4f0 100644
--- a/README.md
+++ b/README.md
@@ -51,29 +51,18 @@ Refer to [input schema](.actor/input_schema.json) for details.
 - `datasetFields` - Array of datasetFields you want to save, e.g., `["url", "text", "metadata.title"]`.
 - `filePrefix` - Delete and create files using a filePrefix, streamlining vector store updates.
 - `fileIdsToDelete` - Delete specified file IDs from vector store as needed.
-- `datasetId`: _[Debug]_ Dataset ID (when running Actor as standalone without integration).
-- `keyValueStoreId`: _[Debug]_ Key Value Store ID (when running Actor as standalone without integration).
+- `datasetId`: _[Debug]_ Apify's Dataset ID (when running Actor as standalone without integration).
+- `keyValueStoreId`: _[Debug]_ Apify's Key Value Store ID (when running Actor as standalone without integration).
 - `saveInApifyKeyValueStore`: _[Debug]_ Save all created files in the Apify Key-Value Store to easily check and retrieve all files (this is typically used when debugging)
 
 ## Outputs
 
 This integration saves selected `datasetFields` from your Actor to the OpenAI Assistant and optionally to Actor Key Value Storage (useful for debugging).
 
-## Want to talk to other devs or get help?
-
-Join our [developer community on Discord](https://discord.com/invite/jyEM2PRvMU) to connect with others and discuss this and other integrations.
-
-## Need data for your LLMs?
-
-Utilize the Apify platform to [gather data for your large language models](https://apify.com/data-for-generative-ai).
-Our Actors can automatically ingest entire websites, such as customer documentation, knowledge bases, help centers,
-forums, blog posts, and other information sources to train or prompt your LLMs.
-Integrate Apify into your product and allow your customers to upload their content in minutes.
-
 ## Save data from Website Content Crawler to OpenAI Vector Store
 
 To use this integration, you need an OpenAI account and an `OpenAI API KEY`.
-Additionally, you need to create an OpenAI Vector Store (vectorStoreId).
+Additionally, you need to create an OpenAI Vector Store (`vectorStoreId`).
 
 The Website Content Crawler can deeply crawl websites and save web page content to Apify's dataset.
 It also stores files such as PDFs, PPTXs, and DOCXs.
@@ -121,3 +110,42 @@ The settings for the integration are as follows:
   "vectorStoreId": "YOUR-VECTOR-STORE-ID"
 }
 ```
+
+### Save Amazon Products to OpenAI Vector Store
+
+You can also save Amazon products to the OpenAI Vector Store.
+Again, you need to have an OpenAI account and an `OpenAI API KEY` with a created OpenAI Vector Store (`vectorStoreId`).
+
+To scrape Amazon products, you can use the [Amazon Product Scraper](https://apify.com/junglee/amazon-crawler) Actor.
+
+Let's say that you want to scrape "Apple Watch" and store all the scraped data in the OpenAI Assistant.
+For the product URL `https://www.amazon.com/s?k=apple+watch`, the scraper can yield the following results (truncated for brevity):
+
+```json
+[
+  {
+    "title": "Apple Watch Ultra 2 [GPS + Cellular 49mm] Smartwatch with Rugged Titanium Case ....",
+    "asin": "B0CSVGK51Y",
+    "brand": "Apple",
+    "stars": 4.7,
+    "reviewsCount": 357,
+    "thumbnailImage": "https://m.media-amazon.com/images/I/81pjcQFaDJL.__AC_SY445_SX342_QL70_FMwebp_.jpg",
+    "price": {
+      "value": 794,
+      "currency": "$"
+    },
+    "url": "https://www.amazon.com/dp/B0CSVGK51Y"
+  }
+]
+```
+
+You can easily save the data to the OpenAI Vector Store by creating an integration (in the Amazon Product Scraper integration section) and specifying the fields you want to save:
+
+```json
+{
+  "assistantId": "YOUR-ASSISTANT-ID",
+  "datasetFields": ["title", "brand", "stars", "reviewsCount", "thumbnailImage", "price.value", "price.currency", "url"],
+  "openaiApiKey": "YOUR-OPENAI-API-KEY",
+  "vectorStoreId": "YOUR-VECTOR-STORE-ID"
+}
+```
diff --git a/src/main.py b/src/main.py
index 14443f7..cbd35b3 100644
--- a/src/main.py
+++ b/src/main.py
@@ -4,6 +4,7 @@
 from io import BytesIO
 from typing import TYPE_CHECKING
 
+import openai
 import tiktoken
 from apify import Actor
 from apify_client import ApifyClientAsync
@@ -58,13 +59,19 @@ async def main() -> None:
 async def check_inputs(client: AsyncOpenAI, actor_input: ActorInput, payload: dict) -> Assistant | None:
     """Check that provided input exists at OpenAI or at Apify."""
 
-    if not (await client.beta.vector_stores.retrieve(actor_input.vectorStoreId)):
+    try:
+        await client.beta.vector_stores.retrieve(actor_input.vectorStoreId)
+    except openai.NotFoundError:
         msg = (
-            f"Unable to find the Vector Store with the ID: {actor_input.vectorStoreId} on OpenAI. Please verify that the Vector Store has "
-            f"been correctly created and that the `vectorStoreId` provided is accurate."
+            f"Unable to find the OpenAI Vector Store with the ID: {actor_input.vectorStoreId}. Please verify that the Vector Store has "
+            "been correctly created and that the `vectorStoreId` provided is accurate."
         )
         Actor.log.error(msg)
         await Actor.fail(status_message=msg)
+    except openai.AuthenticationError:
+        msg = "The OpenAI API Key provided is invalid. Please verify that the `OPENAI_API_KEY` is correctly set."
+        Actor.log.error(msg)
+        await Actor.fail(status_message=msg)
 
     assistant = None
     if actor_input.assistantId and not (assistant := await client.beta.assistants.retrieve(actor_input.assistantId)):
@@ -79,7 +86,8 @@ async def check_inputs(client: AsyncOpenAI, actor_input: ActorInput, payload: di
 
     if not (dataset_id or key_value_store_id):
         msg = (
-            "The `datasetId` or `keyValueStoreId` are not provided. There are two ways to specify the `datasetId` or `keyValueStoreId`:"
+            "The Apify's `datasetId` or Apify's `keyValueStoreId` are not provided. "
+            "There are two ways to specify the `datasetId` or `keyValueStoreId`: "
             "1. Automatic Input: If this integration is used with other Actors, such as the Website Content Crawler, the variables should be "
             "automatically passed in the 'payload'. Please check the `Input` payload to ensure that they are included."
             "2. Manual Input: If you are running this Actor independently, you can to manually specify the 'datasetId' or `keyValueStoreId. "
@@ -167,7 +175,7 @@ async def create_file(client: AsyncOpenAI, filename: str, data: bytes | BytesIO)
         await Actor.push_data({"filename": filename, "file_id": file.id, "status": "created"})
         return file  # noqa: TRY300
     except Exception as e:
-        Actor.log.exception(e)
+        Actor.log.error("Failed to create OpenAI file: %s, error: %s", filename, e)
 
     return None