Video and Audio Content Analysis notebook

Author: elizabethfuentes12

notebooks/01_build_pdf_vector_db.ipynb

@@ -36,9 +36,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'langchain_aws'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mlangchain\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtext_splitter\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m RecursiveCharacterTextSplitter \u001b[38;5;66;03m# to split documents into smaller chunks.\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mlangchain_community\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvectorstores\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FAISS \u001b[38;5;66;03m# to store the documents in a vector database.\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mlangchain_aws\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BedrockEmbeddings \u001b[38;5;66;03m# to create embeddings for the documents.\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mlangchain_experimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtext_splitter\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SemanticChunker \u001b[38;5;66;03m# to split documents into smaller chunks.\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m#https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.text_splitter\u001b[39;00m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'langchain_aws'"
+     ]
+    }
+   ],
    "source": [
     "import boto3 # to interact with AWS services.\n",
     "from langchain_community.document_loaders import PyPDFLoader, PyPDFDirectoryLoader # to load documents from PDF files.\n",

notebooks/04_video_understanding.ipynb

@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,8 +84,7 @@
    "source": [
     "bedrock_client              = boto3.client(\"bedrock-runtime\", region_name='us-east-1') \n",
     "boto3_bedrock               = boto3.client('bedrock', region_name='us-east-1')\n",
-    "s3_client                   = boto3.client('s3')\n",
-    "s3_uri = \"s3://reinventagentstack-bucketagendabucket1c1c4a36-iiafx6tdvrak/video_demo/\""
+    "s3_client                   = boto3.client('s3')"
    ]
   },
   {