diff --git a/examples/langchain_multimodal_rag.ipynb b/examples/langchain_multimodal_rag.ipynb
index 62bbe7e..395834a 100644
--- a/examples/langchain_multimodal_rag.ipynb
+++ b/examples/langchain_multimodal_rag.ipynb
@@ -13,7 +13,7 @@
    "id": "91ece9e3-155a-44f4-81e5-2f9492c62a2f",
    "metadata": {},
    "source": [
-    "This notebook shows how to perform RAG on the table, chart, and text extraction results of nv-ingest's pdf extraction tools using LangChain"
+    "This notebook shows how to perform RAG on the table, chart, and text extraction results of NV-Ingest's pdf extraction tools using LangChain"
    ]
   },
   {
@@ -21,7 +21,7 @@
    "id": "c6905d11-0ec3-43c8-961b-24cb52e36bfe",
    "metadata": {},
    "source": [
-    "**Note:** In order to run this notebook, you'll need to have the NV-Ingest microservice running along with all of the other included microservices. To do this, make sure all of the services are uncommented in the file: [docker-compose.yaml](https://github.com/NVIDIA/nv-ingest/blob/main/docker-compose.yaml) and follow the [quickstart guide](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#quickstart) to start everything up. You'll also need to have the NV-Ingest client installed as demonstrated [here](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#step-2-installing-python-dependencies)."
+    "**Note:** In order to run this notebook, you'll need to have the NV-Ingest microservice running along with all of the other included microservices. To do this, make sure all of the services are uncommented in the file: [docker-compose.yaml](https://github.com/NVIDIA/nv-ingest/blob/main/docker-compose.yaml) and follow the [quickstart guide](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#quickstart) to start everything up. You'll also need to have the NV-Ingest python client installed as demonstrated [here](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#step-2-installing-python-dependencies)."
    ]
   },
   {
@@ -29,7 +29,7 @@
    "id": "81014734-f765-48fc-8fc2-4c19f5f28eae",
    "metadata": {},
    "source": [
-    "To start, make sure Langchain and pymilvus are installed and up to date"
+    "To start, make sure LangChain and pymilvus are installed and up to date"
    ]
   },
   {
@@ -47,7 +47,7 @@
    "id": "d888ba26-04cf-4577-81a3-5bcd537fc2f6",
    "metadata": {},
    "source": [
-    "Then, we'll use NV-Ingest's Ingestor interface extract the tables and charts from a test pdf, embed them, and upload them to our Milvus vector database (VDB)"
+    "Then, we'll use NV-Ingest's Ingestor interface to extract the tables and charts from a test pdf, embed them, and upload them to our Milvus vector database (VDB)"
    ]
   },
   {
@@ -80,7 +80,7 @@
    "id": "02131711-31bf-4536-81b7-8c464c7473e3",
    "metadata": {},
    "source": [
-    "Now, the text, table, and chart content is extracted and stored in the Milvus VDB along with the embeddings. Next we'll connect LlamaIndex to Milvus and create a vector store so that we can query our extraction results"
+    "Now, the text, table, and chart content is extracted and stored in the Milvus VDB along with the embeddings. Next we'll connect LangChain to Milvus and create a vector store so that we can query our extraction results"
    ]
   },
   {
@@ -111,7 +111,7 @@
    "id": "b87111b5-e5a8-45a0-9663-2ae6d9ea2ab6",
    "metadata": {},
    "source": [
-    "Finally, we'll create an RAG chain using [llama-3.1-405b-instruct](https://build.nvidia.com/meta/llama-3_1-405b-instruct) that we can use to query our pdf in natural language"
+    "Then, we'll create an RAG chain using [llama-3.1-405b-instruct](https://build.nvidia.com/meta/llama-3_1-405b-instruct) that we can use to query our pdf in natural language"
    ]
   },
   {
@@ -125,28 +125,17 @@
     "from langchain_nvidia_ai_endpoints import ChatNVIDIA\n",
     "\n",
     "# TODO: Add your NVIDIA API key\n",
-    "os.environ[\"NVIDIA_API_KEY\"] = \"<YOUR_NVIDIA_API_KEY>\"\n",
+    "os.environ[\"NVIDIA_API_KEY\"] = \"[YOUR NVIDIA API KEY HERE]\"\n",
     "\n",
     "llm = ChatNVIDIA(model=\"meta/llama-3.1-405b-instruct\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "b547a19a-9ada-4a40-a246-6d7bc4d24482",
+   "execution_count": 17,
+   "id": "77fd17f8-eac0-4457-b6fb-6e5c8ce90c84",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The dog is chasing a squirrel in the front yard.'"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain_core.prompts import PromptTemplate\n",
     "from langchain_core.runnables import RunnablePassthrough\n",
@@ -169,7 +158,35 @@
     "    | prompt\n",
     "    | llm\n",
     "    | StrOutputParser()\n",
-    ")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc2ee8fb-a154-46c9-9181-29a035fdcfbb",
+   "metadata": {},
+   "source": [
+    "And now we can ask our pdf questions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b547a19a-9ada-4a40-a246-6d7bc4d24482",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The dog is chasing a squirrel in the front yard.'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
     "rag_chain.invoke(\"What is the dog doing and where?\")"
    ]
   },
diff --git a/examples/llama_index_multimodal_rag.ipynb b/examples/llama_index_multimodal_rag.ipynb
index 96482e1..a60e7d1 100644
--- a/examples/llama_index_multimodal_rag.ipynb
+++ b/examples/llama_index_multimodal_rag.ipynb
@@ -21,7 +21,7 @@
    "id": "c65edc4b-2084-47c9-a837-733264201802",
    "metadata": {},
    "source": [
-    "**Note:** In order to run this notebook, you'll need to have the NV-Ingest microservice running along with all of the other included microservices. To do this, make sure all of the services are uncommented in the file: [docker-compose.yaml](https://github.com/NVIDIA/nv-ingest/blob/main/docker-compose.yaml) and follow the [quickstart guide](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#quickstart) to start everything up. You'll also need to have the NV-Ingest client installed as demonstrated [here](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#step-2-installing-python-dependencies)."
+    "**Note:** In order to run this notebook, you'll need to have the NV-Ingest microservice running along with all of the other included microservices. To do this, make sure all of the services are uncommented in the file: [docker-compose.yaml](https://github.com/NVIDIA/nv-ingest/blob/main/docker-compose.yaml) and follow the [quickstart guide](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#quickstart) to start everything up. You'll also need to have the NV-Ingest python client installed as demonstrated [here](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#step-2-installing-python-dependencies)."
    ]
   },
   {
@@ -47,7 +47,7 @@
    "id": "45412661-9516-47f9-8bea-6f857e0e173f",
    "metadata": {},
    "source": [
-    "Then, we'll use NV-Ingest's Ingestor interface extract the tables and charts from a test pdf, embed them, and upload them to our Milvus vector database (VDB)"
+    "Then, we'll use NV-Ingest's Ingestor interface to extract the tables and charts from a test pdf, embed them, and upload them to our Milvus vector database (VDB)"
    ]
   },
   {
@@ -127,7 +127,7 @@
     "from llama_index.llms.nvidia import NVIDIA\n",
     "\n",
     "# TODO: Add your NVIDIA API key\n",
-    "os.environ[\"NVIDIA_API_KEY\"] = \"<YOUR_NVIDIA_API_KEY>\"\n",
+    "os.environ[\"NVIDIA_API_KEY\"] = \"[YOUR NVIDIA API KEY HERE]\"\n",
     "\n",
     "llm = NVIDIA(model=\"meta/llama-3.1-405b-instruct\")\n",
     "query_engine = index.as_query_engine(llm=llm)"
diff --git a/examples/store_and_display_images.ipynb b/examples/store_and_display_images.ipynb
index f74f50a..e5a31b4 100644
--- a/examples/store_and_display_images.ipynb
+++ b/examples/store_and_display_images.ipynb
@@ -21,7 +21,7 @@
    "id": "2a598d15-adf0-406a-95c6-6d49c0939508",
    "metadata": {},
    "source": [
-    "**Note:** In order to run this notebook, you'll need to have the NV-Ingest microservice running along with all of the other included microservices. To do this, make sure all of the services are uncommented in the file: [docker-compose.yaml](https://github.com/NVIDIA/nv-ingest/blob/main/docker-compose.yaml) and follow the [quickstart guide](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#quickstart) to start everything up. You'll also need to have the NV-Ingest client installed as demonstrated [here](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#step-2-installing-python-dependencies)."
+    "**Note:** In order to run this notebook, you'll need to have the NV-Ingest microservice running along with all of the other included microservices. To do this, make sure all of the services are uncommented in the file: [docker-compose.yaml](https://github.com/NVIDIA/nv-ingest/blob/main/docker-compose.yaml) and follow the [quickstart guide](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#quickstart) to start everything up. You'll also need to have the NV-Ingest python client installed as demonstrated [here](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#step-2-installing-python-dependencies)."
    ]
   },
   {
@@ -29,7 +29,7 @@
    "id": "380f6180-e90a-4019-8cc9-418884a62444",
    "metadata": {},
    "source": [
-    "To start make sure the minio python client is installed and up to date"
+    "To start, make sure the minio python client is installed and up to date"
    ]
   },
   {