fix link

recipes/3p_integrations/octoai/video_summary.ipynb

@@ -8,7 +8,7 @@
     "## This demo app shows:\n",
     "* How to use LangChain's YoutubeLoader to retrieve the caption in a YouTube video\n",
     "* How to ask Llama 3 to summarize the content (per the Llama's input size limit) of the video in a naive way using LangChain's stuff method\n",
-    "* How to bypass the limit of Llama 3's max input token size by using a more sophisticated way using LangChain's map_reduce and refine methods - see [here](https://python.langchain.com/docs/use_cases/summarization) for more info"
+    "* How to bypass the limit of Llama 3's max input token size by using a more sophisticated way using LangChain's map_reduce and refine methods - see [here](https://python.langchain.com/docs/tutorials/summarization/) for more info"
    ]
   },
   {
@@ -22,7 +22,7 @@
     "- [tiktoken](https://github.com/openai/tiktoken) BytePair Encoding tokenizer\n",
     "- [pytube](https://pytube.io/en/latest/) Utility for downloading YouTube videos\n",
     "\n",
-    "**Note** This example uses OctoAI to host the Llama 3 model. If you have not set up/or used OctoAI before, we suggest you take a look at the [HelloLlamaCloud](HelloLlamaCloud.ipynb) example for information on how to set up OctoAI before continuing with this example.\n",
+    "**Note** This example uses OctoAI to host the Llama 3 model. If you have not set up/or used OctoAI before, we suggest you take a look at the [hello_llama_cloud](hello_llama_cloud.ipynb) example for information on how to set up OctoAI before continuing with this example.\n",
     "If you do not want to use OctoAI, you will need to make some changes to this notebook as you go along."
    ]
   },
@@ -205,7 +205,7 @@
    "id": "e112845f-de16-4c2f-8afe-6cca31f6fa38",
    "metadata": {},
    "source": [
-    "To fix this, you can use LangChain's load_summarize_chain method (detail [here](https://python.langchain.com/docs/use_cases/summarization)).\n",
+    "To fix this, you can use LangChain's load_summarize_chain method (detail [here](https://python.langchain.com/docs/tutorials/summarization/)).\n",
     "\n",
     "First you'll create splits or sub-documents of the original content, then use the LangChain's `load_summarize_chain` with the `refine` or `map_reduce type`.\n",
     "\n",
@@ -221,7 +221,6 @@
    "source": [
     "import os\n",
     "os.environ[\"LANGCHAIN_API_KEY\"] = \"your_langchain_api_key\"\n",
-    "os.environ[\"LANGCHAIN_API_KEY\"] = \"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\"\n",
     "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "os.environ[\"LANGCHAIN_PROJECT\"] = \"Video Summary with Llama 3\""
    ]