diff --git a/ai-integrations/langchain-graphrag.ipynb b/ai-integrations/langchain-graphrag.ipynb
index e8d1c02..fa49cbb 100644
--- a/ai-integrations/langchain-graphrag.ipynb
+++ b/ai-integrations/langchain-graphrag.ipynb
@@ -5,7 +5,7 @@
    "id": "b5dcbf95-9a30-416d-afed-d5b2bf0e8651",
    "metadata": {},
    "source": [
-    "# GraphRAG with MongoDB and LangChain\n",
+    "# LangChain MongoDB Integration - GraphRAG\n",
     "\n",
     "This notebook is a companion to the [GraphRAG with MongoDB and LangChain](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/graph-rag/) tutorial. Refer to the page for set-up instructions and detailed explanations.\n",
     "\n",
diff --git a/ai-integrations/langchain-hybrid-search.ipynb b/ai-integrations/langchain-hybrid-search.ipynb
index aeed8a5..f6b458b 100644
--- a/ai-integrations/langchain-hybrid-search.ipynb
+++ b/ai-integrations/langchain-hybrid-search.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Atlas Vector Search - LangChain Integration - Hybrid Search"
+    "# LangChain MongoDB Integration - Hybrid Search"
    ]
   },
   {
diff --git a/ai-integrations/langchain-local-rag.ipynb b/ai-integrations/langchain-local-rag.ipynb
new file mode 100644
index 0000000..6252a22
--- /dev/null
+++ b/ai-integrations/langchain-local-rag.ipynb
@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LangChain MongoDB Integration - Implement RAG Locally"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook is a companion to the [LangChain Local RAG](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/get-started/) tutorial. Refer to the page for set-up instructions and detailed explanations.\n",
+    "\n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/mongodb/docs-notebooks/blob/main/ai-integrations/langchain-local-rag.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "source": [
+    "## Create a local Atlas deployment\n",
+    "\n",
+    "Run the following commands in your terminal to set up your local Atlas deployment. \n",
+    "\n",
+    "```\n",
+    "atlas deployments setup\n",
+    "curl  https://atlas-education.s3.amazonaws.com/sampledata.archive -o sampledata.archive\n",
+    "mongorestore --archive=sampledata.archive --port=<port-number>\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "source": [
+    "## Set up the environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pip install --quiet --upgrade pymongo langchain gpt4all langchain-huggingface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MONGODB_URI = (\"mongodb://localhost:<port-number>/?directConnection=true\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create embeddings with local model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pymongo import MongoClient\n",
+    "from langchain_huggingface import HuggingFaceEmbeddings\n",
+    "\n",
+    "# Connect to your local Atlas deployment or Atlas Cluster\n",
+    "client = MongoClient(MONGODB_URI)\n",
+    "collection = client[\"sample_airbnb\"][\"listingsAndReviews\"]\n",
+    "\n",
+    "# Load the embedding model (https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1)\n",
+    "embedding_model = HuggingFaceEmbeddings(model_name=\"mixedbread-ai/mxbai-embed-large-v1\")\n",
+    "\n",
+    "# Filters for only documents with a summary field and without an embeddings field\n",
+    "filter = { '$and': [ { 'summary': { '$exists': True, \"$nin\": [ None, \"\" ] } }, { 'embeddings': { '$exists': False } } ] }\n",
+    "\n",
+    "# Creates embeddings for subset of the collection\n",
+    "updated_doc_count = 0\n",
+    "for document in collection.find(filter).limit(50):\n",
+    "    text = document['summary']\n",
+    "    embedding = embedding_model.embed_documents([text])[0]\n",
+    "    collection.update_one({ '_id': document['_id'] }, { \"$set\": { 'embeddings': embedding } }, upsert=True)\n",
+    "    updated_doc_count += 1\n",
+    "\n",
+    "print(\"Documents updated: {}\".format(updated_doc_count))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure the vector store"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_mongodb import MongoDBAtlasVectorSearch\n",
+    "# Instantiate vector store\n",
+    "vector_store = MongoDBAtlasVectorSearch.from_connection_string(\n",
+    "   connection_string = MONGODB_URI,\n",
+    "   namespace = \"sample_airbnb.listingsAndReviews\",\n",
+    "   embedding=embedding_model,\n",
+    "   index_name=\"vector_index\",\n",
+    "   embedding_key=\"embeddings\",\n",
+    "   text_key=\"summary\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vector_store.create_vector_search_index(\n",
+    "  dimensions = 1024,       # The dimensions of the vector embeddings to be indexed\n",
+    "  wait_until_complete = 60 # Number of seconds to wait for the index to build (can take around a minute)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implement RAG with a local LLM\n",
+    "Before running the following code, [download the local model](https://gpt4all.io/models/gguf/mistral-7b-openorca.gguf2.Q4_0.gguf)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain_community.llms import GPT4All\n",
+    "\n",
+    "# Configure the LLM\n",
+    "local_path = \"<path-to-model>\"\n",
+    "\n",
+    "# Callbacks support token-wise streaming\n",
+    "callbacks = [StreamingStdOutCallbackHandler()]\n",
+    "\n",
+    "# Verbose is required to pass to the callback manager\n",
+    "llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "import pprint\n",
+    "\n",
+    "# Instantiate Atlas Vector Search as a retriever\n",
+    "retriever = vector_store.as_retriever()\n",
+    "\n",
+    "# Define prompt template\n",
+    "template = \"\"\"\n",
+    "Use the following pieces of context to answer the question at the end.\n",
+    "{context}\n",
+    "Question: {question}\n",
+    "\"\"\"\n",
+    "custom_rag_prompt = PromptTemplate.from_template(template)\n",
+    "\n",
+    "def format_docs(docs):\n",
+    "   return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "# Create chain   \n",
+    "rag_chain = (\n",
+    "   {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
+    "   | custom_rag_prompt\n",
+    "   | llm\n",
+    "   | StrOutputParser()\n",
+    ")\n",
+    "\n",
+    "# Prompt the chain\n",
+    "question = \"Can you recommend me a few AirBnBs that are beach houses?\"\n",
+    "answer = rag_chain.invoke(question)\n",
+    "\n",
+    "# Return source documents\n",
+    "documents = retriever.invoke(question)\n",
+    "print(\"\\nSource documents:\")\n",
+    "pprint.pprint(documents)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ai-integrations/langchain-memory-semantic-cache.ipynb b/ai-integrations/langchain-memory-semantic-cache.ipynb
new file mode 100644
index 0000000..2caa03e
--- /dev/null
+++ b/ai-integrations/langchain-memory-semantic-cache.ipynb
@@ -0,0 +1,351 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "922b6c28",
+   "metadata": {},
+   "source": [
+    "# LangChain MongoDB Integration - Memory and Semantic Caching for RAG"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fc29d11",
+   "metadata": {},
+   "source": [
+    "This notebook is a companion to the [Memory and Semantic Caching](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/memory-semantic-cache/) tutorial. Refer to the page for set-up instructions and detailed explanations.\n",
+    "\n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/mongodb/docs-notebooks/blob/main/ai-integrations/langchain-memory-and-semantic-caching.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a289ba35",
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pip install --quiet --upgrade langchain langchain-community langchain-core langchain-mongodb langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c672ba1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"<api-key>\"\n",
+    "MONGODB_URI = \"<connection-string>\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8384c99d",
+   "metadata": {},
+   "source": [
+    "## Configure the Vector Store"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f90ce770",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_mongodb import MongoDBAtlasVectorSearch\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "\n",
+    "# Use text-embedding-ada-002 since that's what was used to create embeddings in the movies dataset\n",
+    "embedding_model = OpenAIEmbeddings(model=\"text-embedding-ada-002\")\n",
+    "\n",
+    "# Create the vector store\n",
+    "vector_store = MongoDBAtlasVectorSearch.from_connection_string(\n",
+    "   connection_string = MONGODB_URI,\n",
+    "   embedding = embedding_model,\n",
+    "   namespace = \"sample_mflix.embedded_movies\",\n",
+    "   text_key = \"plot\",\n",
+    "   embedding_key = \"plot_embedding\",\n",
+    "   relevance_score_fn = \"dotProduct\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bf1bff8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use helper method to create the vector search index\n",
+    "vector_store.create_vector_search_index(\n",
+    "   dimensions = 1536,       # The dimensions of the vector embeddings to be indexed\n",
+    "   wait_until_complete = 60 # Number of seconds to wait for the index to build (can take around a minute)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c3b6654",
+   "metadata": {},
+   "source": [
+    "## Implement RAG with Memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55583167",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "# Define the model to use for chat completion\n",
+    "llm = ChatOpenAI(model = \"gpt-4o\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b3b0361",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory\n",
+    "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
+    "from langchain_core.prompts import MessagesPlaceholder\n",
+    "         \n",
+    "# Define a function that gets the chat message history \n",
+    "def get_session_history(session_id: str) -> MongoDBChatMessageHistory:\n",
+    "    return MongoDBChatMessageHistory(\n",
+    "        connection_string=MONGODB_URI,\n",
+    "        session_id=session_id,\n",
+    "        database_name=\"sample_mflix\",\n",
+    "        collection_name=\"embedded_movies\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74dfa896",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "# Create a prompt to generate standalone questions from follow-up questions\n",
+    "standalone_system_prompt = \"\"\"\n",
+    "  Given a chat history and a follow-up question, rephrase the follow-up question to be a standalone question.\n",
+    "  Do NOT answer the question, just reformulate it if needed, otherwise return it as is.\n",
+    "  Only return the final standalone question.\n",
+    "\"\"\"\n",
+    "\n",
+    "standalone_question_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", standalone_system_prompt),\n",
+    "        MessagesPlaceholder(variable_name=\"history\"),\n",
+    "        (\"human\", \"{question}\"),\n",
+    "    ]\n",
+    ")\n",
+    "# Parse output as a string\n",
+    "parse_output = StrOutputParser()\n",
+    "\n",
+    "question_chain = standalone_question_prompt | llm | parse_output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7ad7c83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "\n",
+    "# Create a retriever\n",
+    "retriever = vector_store.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": 3})\n",
+    "\n",
+    "# Specify that the retrieved context includes both title and plot fields\n",
+    "get_documents = lambda docs: \"\\n\\n\".join([f\"Title: {d.metadata.get('title')}\\n{d.page_content}\" for d in docs])\n",
+    "\n",
+    "# Create a retriever chain that processes the question with history and retrieves documents\n",
+    "retriever_chain = RunnablePassthrough.assign(\n",
+    "    context=question_chain | retriever | get_documents\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c15d460d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a prompt template that includes the retrieved context and chat history\n",
+    "rag_system_prompt = \"\"\"Answer the question based only on the following context:\n",
+    "{context}\n",
+    "\"\"\"\n",
+    "\n",
+    "rag_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", rag_system_prompt),\n",
+    "        MessagesPlaceholder(variable_name=\"history\"),\n",
+    "        (\"human\", \"{question}\"),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4401715b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Build the RAG chain\n",
+    "rag_chain = (\n",
+    "    retriever_chain\n",
+    "    | rag_prompt\n",
+    "    | llm\n",
+    "    | parse_output\n",
+    ")\n",
+    "\n",
+    "# Wrap the chain with message history\n",
+    "rag_with_memory = RunnableWithMessageHistory(\n",
+    "    rag_chain,\n",
+    "    get_session_history,\n",
+    "    input_messages_key=\"question\",\n",
+    "    history_messages_key=\"history\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2093d8c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First question\n",
+    "response_1 = rag_with_memory.invoke(\n",
+    "    {\"question\": \"What are some good science fiction movies?\"},\n",
+    "    {\"configurable\": {\"session_id\": \"user_1\"}}\n",
+    ")\n",
+    "print(response_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14513bb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Follow-up question that references the previous question\n",
+    "response_2 = rag_with_memory.invoke(\n",
+    "    {\"question\": \"Which one has the best special effects?\"},\n",
+    "    {\"configurable\": {\"session_id\": \"user_1\"}}\n",
+    ")\n",
+    "print(response_2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9b2c3c5",
+   "metadata": {},
+   "source": [
+    "## Add Semantic Caching\n",
+    "\n",
+    "The semantic cache caches only the input to the LLM. When using it in retrieval chains, \n",
+    "note that documents retrieved can change between runs, resulting in cache misses for \n",
+    "semantically similar queries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "594315fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_mongodb.cache import MongoDBAtlasSemanticCache\n",
+    "from langchain_core.globals import set_llm_cache\n",
+    "\n",
+    "# Configure the semantic cache\n",
+    "set_llm_cache(MongoDBAtlasSemanticCache(\n",
+    "    connection_string = MONGODB_URI,\n",
+    "    database_name = \"sample_mflix\",\n",
+    "    collection_name = \"semantic_cache\",\n",
+    "    embedding = embedding_model,\n",
+    "    index_name = \"vector_index\",\n",
+    "    similarity_threshold = 0.5  # Adjust based on your requirements\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8063217",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "\n",
+    "# First query (not cached)\n",
+    "rag_with_memory.invoke(\n",
+    "  {\"question\": \"What is the plot of Titanic?\"},\n",
+    "  {\"configurable\": {\"session_id\": \"user_2\"}}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df4b0318",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "\n",
+    "# Second query (cached)\n",
+    "rag_with_memory.invoke(\n",
+    "  {\"question\": \"Tell me about the movie, Titanic\"},\n",
+    "  {\"configurable\": {\"session_id\": \"user_2\"}}\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ai-integrations/langchain-parent-document-retrieval.ipynb b/ai-integrations/langchain-parent-document-retrieval.ipynb
index 3bfbc50..0ba6392 100644
--- a/ai-integrations/langchain-parent-document-retrieval.ipynb
+++ b/ai-integrations/langchain-parent-document-retrieval.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Atlas Vector Search - LangChain Integration - Parent Document Retrieval"
+    "# LangChain MongoDB Integration - Parent Document Retrieval"
    ]
   },
   {
@@ -112,18 +112,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import time\n",
-    "\n",
     "# Get the vector store instance from the retriever\n",
     "vector_store = parent_doc_retriever.vectorstore\n",
     "\n",
     "# Use helper method to create the vector search index\n",
     "vector_store.create_vector_search_index(\n",
-    "   dimensions = 1536 # The dimensions of the vector embeddings to be indexed\n",
-    ")\n",
-    "\n",
-    "# Wait for the index to build (this can take around a minute)\n",
-    "time.sleep(60)"
+    "   dimensions = 1536,       # The dimensions of the vector embeddings to be indexed\n",
+    "   wait_until_complete = 60 # Number of seconds to wait for the index to build (can take around a minute)\n",
+    ")\n"
    ]
   },
   {
diff --git a/ai-integrations/langchain.ipynb b/ai-integrations/langchain.ipynb
index ba353b8..f275284 100644
--- a/ai-integrations/langchain.ipynb
+++ b/ai-integrations/langchain.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Atlas Vector Search - LangChain Integration - Get Started"
+    "# LangChain MongoDB Integration - Implement RAG"
    ]
   },
   {
@@ -91,16 +91,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import time\n",
-    "\n",
     "# Use helper method to create the vector search index\n",
     "vector_store.create_vector_search_index(\n",
     "   dimensions = 3072, # The dimensions of the vector embeddings to be indexed\n",
-    "   filters = [ \"page_label\" ]\n",
-    ")\n",
-    "\n",
-    "# Wait for the index to build (this can take around a minute)\n",
-    "time.sleep(60)"
+    "   filters = [ \"page_label\" ],\n",
+    "   wait_until_complete = 60 # Number of seconds to wait for the index to build (can take around a minute)\n",
+    ")"
    ]
   },
   {
diff --git a/ai-integrations/langgraph.ipynb b/ai-integrations/langgraph.ipynb
index bb47d84..7a5071f 100644
--- a/ai-integrations/langgraph.ipynb
+++ b/ai-integrations/langgraph.ipynb
@@ -6,7 +6,7 @@
     "id": "3kMALXaMv-MS"
    },
    "source": [
-    "# Atlas Vector Search - LangGraph Integration - RAG Agent"
+    "# LangGraph MongoDB Integration - Agentic RAG"
    ]
   },
   {
@@ -112,7 +112,6 @@
    "source": [
     "from langchain_mongodb.index import create_fulltext_search_index\n",
     "from pymongo import MongoClient\n",
-    "import time\n",
     "\n",
     "# Connect to your cluster\n",
     "client = MongoClient(MONGODB_URI)\n",
@@ -121,11 +120,9 @@
     "create_fulltext_search_index( \n",
     "   collection = client[\"sample_mflix\"][\"embedded_movies\"],\n",
     "   field = \"title\",\n",
-    "   index_name = \"search_index\"\n",
-    ")\n",
-    "\n",
-    "# Wait for the index to build (this can take around a minute)\n",
-    "time.sleep(60)"
+    "   index_name = \"search_index\",\n",
+    "   wait_until_complete = 60 # Number of seconds to wait for the index to build (can take around a minute)\n",
+    ")"
    ]
   },
   {
diff --git a/use-cases/local-rag.ipynb b/use-cases/local-rag.ipynb
index 5fd511d..14b7c94 100644
--- a/use-cases/local-rag.ipynb
+++ b/use-cases/local-rag.ipynb
@@ -20,6 +20,21 @@
     "</a>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a local Atlas deployment\n",
+    "\n",
+    "Run the following commands in your terminal to set up your local Atlas deployment. \n",
+    "\n",
+    "```\n",
+    "atlas deployments setup\n",
+    "curl  https://atlas-education.s3.amazonaws.com/sampledata.archive -o sampledata.archive\n",
+    "mongorestore --archive=sampledata.archive --port=<port-number>\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -39,7 +54,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ATLAS_CONNECTION_STRING = (\"<connection-string>\")\n",
+    "MONGODB_URI = (\"<connection-string>\")\n",
     "# Use \"mongodb://localhost:<port-number>/?directConnection=true\" for local Atlas deployments"
    ]
   },
@@ -53,13 +68,13 @@
     "from sentence_transformers import SentenceTransformer\n",
     "\n",
     "# Connect to your local Atlas deployment or Atlas Cluster\n",
-    "client = MongoClient(ATLAS_CONNECTION_STRING)\n",
+    "client = MongoClient(MONGODB_URI)\n",
     "\n",
     "# Select the sample_airbnb.listingsAndReviews collection\n",
     "collection = client[\"sample_airbnb\"][\"listingsAndReviews\"]\n",
     "\n",
-    "# Load the embedding model (https://huggingface.co/sentence-transformers/mixedbread-ai/mxbai-embed-large-v1)\n",
-    "model_path = \"<model-path>\"\n",
+    "# Load the embedding model (https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1)\n",
+    "model_path = \"<path-to-save-model>\"\n",
     "model = SentenceTransformer('mixedbread-ai/mxbai-embed-large-v1')\n",
     "model.save(model_path)\n",
     "model = SentenceTransformer(model_path)\n",