update notebooks for search in community

Author: davidhou17

README.md

@@ -1,7 +1,7 @@
 # MongoDB Documentation Notebooks
 
 This repository contains Jupyter Notebooks that follow
-tutorials and code examples in MongoDB's official [Atlas Vector Search documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/). You can run, download, and modify these notebooks as you learn how to use MongoDB Atlas Vector Search for your use case.
+tutorials and code examples in MongoDB's official [MongoDB Vector Search documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/). You can run, download, and modify these notebooks as you learn how to use MongoDB Vector Search for your use case.
 
 ## Overview
 
@@ -22,7 +22,7 @@ The following table summarizes the contents of the notebooks in each directory:
 
 ## Other Resources
 
-- [MongoDB Official Documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/)
+- [MongoDB Vector Search Documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/)
 - [Generative AI Use Cases Repository](https://github.com/mongodb-developer/GenAI-Showcase/tree/main)
 
 ## License

ai-integrations/README.md

@@ -1,10 +1,10 @@
 # AI Integrations
 
-This folder contains Jupyter Notebooks that demonstrate how to integrate various AI frameworks with MongoDB. These notebooks show you how to implement RAG and other features for your AI-powered and agentic applications by leveraging MongoDB Atlas as both a vector database and document database.
+This folder contains Jupyter Notebooks that demonstrate how to integrate various AI frameworks with MongoDB. These notebooks show you how to implement RAG and other features for your AI-powered and agentic applications by leveraging MongoDB as both a vector database and document database.
 
 | Notebook | Description |
 |----------|-------------|
-| [langchain](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/langchain.ipynb) | Implement basic RAG with LangChain and MongoDB Atlas Vector Search |
+| [langchain](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/langchain.ipynb) | Implement basic RAG with LangChain and MongoDB Vector Search |
 | [langchain-memory-semantic-cache](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/langchain-memory-semantic-cache.ipynb) | Implement RAG with memory with LangChain and MongoDB |
 | [langchain-hybrid-search](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/langchain-hybrid-search.ipynb) | Combine vector search with full-text search using LangChain and MongoDB |
 | [langchain-parent-document-retrieval](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/langchain-parent-document-retrieval.ipynb) | Perform parent-document retrieval with LangChain and MongoDB |
@@ -13,6 +13,6 @@ This folder contains Jupyter Notebooks that demonstrate how to integrate various
 | [langchain-graphrag](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/langchain-graphrag.ipynb) | Implement graph-based RAG with LangChain and MongoDB |
 | [langchain-natural-language](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/langchain-natural-language.ipynb) | Perform natural language querying with LangChain and MongoDB |
 | [langgraph](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/langgraph.ipynb) | Build an AI agent with LangGraph and MongoDB |
-| [llamaindex](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/llamaindex.ipynb) | Implement basic RAG with LlamaIndex and MongoDB Atlas |
-| [haystack](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/haystack.ipynb) | Implement basic RAG with Haystack and MongoDB Atlas |
-| [semantic-kernel](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/semantic-kernel.ipynb) | Implement basic RAG with Microsoft Semantic Kernel and MongoDB Atlas |
+| [llamaindex](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/llamaindex.ipynb) | Implement basic RAG with LlamaIndex and MongoDB |
+| [haystack](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/haystack.ipynb) | Implement basic RAG with Haystack and MongoDB |
+| [semantic-kernel](https://github.com/mongodb/docs-notebooks/blob/main/ai-integrations/semantic-kernel.ipynb) | Implement basic RAG with Microsoft Semantic Kernel and MongoDB |

ai-integrations/haystack.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Atlas Vector Search - Haystack Integration"
+    "# MongoDB Vector Search - Haystack Integration"
    ]
   },
   {

ai-integrations/langchain-graphrag.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "This notebook is a companion to the [GraphRAG with MongoDB and LangChain](https://www.mongodb.com/docs/atlas/ai-integrations/langchain/graph-rag/) tutorial. Refer to the page for set-up instructions and detailed explanations.\n",
     "\n",
-    "This notebook demonstrates a GraphRAG implementation using MongoDB Atlas and LangChain. Compared to vector-based RAG, which structures your data as vector embeddings, GraphRAG structures data as a knowledge graph with entities and their relationships. This enables relationship-aware retrieval and multi-hop reasoning.\n",
+    "This notebook demonstrates a GraphRAG implementation using MongoDB and LangChain. Compared to vector-based RAG, which structures your data as vector embeddings, GraphRAG structures data as a knowledge graph with entities and their relationships. This enables relationship-aware retrieval and multi-hop reasoning.\n",
     "\n",
     "<a target=\"_blank\" href=\"https://colab.research.google.com/github/mongodb/docs-notebooks/blob/main/ai-integrations/langchain-graphrag.ipynb\">\n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
@@ -37,7 +37,7 @@
     "\n",
     "Before you begin, make sure you have the following:\n",
     "\n",
-    "- An Atlas cluster up and running (you'll need the [connection string](https://www.mongodb.com/docs/guides/atlas/connection-string/))\n",
+    "- A MongoDB cluster up and running (you'll need the [connection string](https://www.mongodb.com/docs/manual/reference/connection-string/))\n",
     "- An API key to access an LLM (This tutorial uses a model from OpenAI, but you can use any model [supported by LangChain](https://python.langchain.com/docs/integrations/chat/))"
    ]
   },
@@ -51,17 +51,17 @@
     "import os\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = \"<api-key>\"\n",
-    "ATLAS_CONNECTION_STRING = \"<connection-string>\"\n",
-    "ATLAS_DB_NAME = \"langchain_db\"    # MongoDB database to store the knowledge graph\n",
-    "ATLAS_COLLECTION = \"wikipedia\"    # MongoDB collection to store the knowledge graph"
+    "MONGODB_URI = \"<connection-string>\"\n",
+    "DB_NAME = \"langchain_db\"    # MongoDB database to store the knowledge graph\n",
+    "COLLECTION = \"wikipedia\"    # MongoDB collection to store the knowledge graph"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "0adf66a8",
    "metadata": {},
    "source": [
-    "## Use MongoDB Atlas as a knowledge graph\n",
+    "## Use MongoDB as a knowledge graph\n",
     "\n",
     "Use the `MongoDBGraphStore` component to store your data as a knowledge graph. This component allows you to implement GraphRAG by storing entities (nodes) and their relationships (edges) in a MongoDB collection. It stores each entity as a document with relationship fields that reference other documents in your collection."
    ]
@@ -111,9 +111,9 @@
     "from langchain_mongodb.graphrag.graph import MongoDBGraphStore\n",
     "\n",
     "graph_store = MongoDBGraphStore(\n",
-    "    connection_string = ATLAS_CONNECTION_STRING,\n",
-    "    database_name = ATLAS_DB_NAME,\n",
-    "    collection_name = ATLAS_COLLECTION,\n",
+    "    connection_string = MONGODB_URI,\n",
+    "    database_name = DB_NAME,\n",
+    "    collection_name = COLLECTION,\n",
     "    entity_extraction_model = chat_model\n",
     ")"
    ]
@@ -125,7 +125,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Extract entities and create knowledge graph in Atlas\n",
+    "# Extract entities and create knowledge graph in MongoDB\n",
     "# This might take a few minutes; you can ignore any warnings\n",
     "graph_store.add_documents(wikipedia_docs)"
    ]
@@ -222,9 +222,9 @@
     "from IPython.display import HTML, display\n",
     "from pymongo import MongoClient\n",
     "\n",
-    "client = MongoClient(ATLAS_CONNECTION_STRING)\n",
+    "client = MongoClient(MONGODB_URI)\n",
     "\n",
-    "collection = client[ATLAS_DB_NAME][ATLAS_COLLECTION]\n",
+    "collection = client[DB_NAME][COLLECTION]\n",
     "html = visualize_graph(collection)\n",
     "\n",
     "display(HTML(html))"

ai-integrations/langchain-hybrid-search.ipynb

@@ -41,7 +41,7 @@
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = \"<api-key>\"\n",
     "os.environ[\"VOYAGE_API_KEY\"] = \"<voyage-api-key>\"\n",
-    "ATLAS_CONNECTION_STRING = \"<connection-string>\""
+    "MONGODB_URI = \"<connection-string>\""
    ]
   },
   {
@@ -55,7 +55,7 @@
     "\n",
     "# Create the vector store\n",
     "vector_store = MongoDBAtlasVectorSearch.from_connection_string(\n",
-    "   connection_string = ATLAS_CONNECTION_STRING,\n",
+    "   connection_string = MONGODB_URI,\n",
     "   embedding = VoyageAIEmbeddings(model = \"voyage-3-large\", output_dimension = 2048),\n",
     "   namespace = \"sample_mflix.embedded_movies\",\n",
     "   text_key = \"plot\",\n",
@@ -87,7 +87,7 @@
     "from pymongo import MongoClient\n",
     "\n",
     "# Connect to your cluster\n",
-    "client = MongoClient(ATLAS_CONNECTION_STRING)\n",
+    "client = MongoClient(MONGODB_URI)\n",
     "\n",
     "# Use helper method to create the search index\n",
     "create_fulltext_search_index(\n",

ai-integrations/langchain-local-rag.ipynb

@@ -167,7 +167,7 @@
     "from langchain_core.runnables import RunnablePassthrough\n",
     "import pprint\n",
     "\n",
-    "# Instantiate Atlas Vector Search as a retriever\n",
+    "# Instantiate MongoDB Vector Search as a retriever\n",
     "retriever = vector_store.as_retriever()\n",
     "\n",
     "# Define prompt template\n",

ai-integrations/langchain-natural-language.ipynb

@@ -5,17 +5,17 @@
    "id": "f9696293",
    "metadata": {},
    "source": [
-    "# Query Atlas with Natural Language Using LangChain and LangGraph"
+    "# Query MongoDB with Natural Language Using LangChain and LangGraph"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "e696dea0",
    "metadata": {},
    "source": [
-    "This notebook is a companion to the [Query Atlas with Natural Language Using LangChain and LangGraph](https://www.mongodb.com/docs/atlas/ai-integrations/langchain/natural-language-to-mql/) tutorial. Refer to the page for set-up instructions and detailed explanations.\n",
+    "This notebook is a companion to the [Query MongoDB with Natural Language Using LangChain and LangGraph](https://www.mongodb.com/docs//ai-integrations/langchain/natural-language-to-mql/) tutorial. Refer to the page for set-up instructions and detailed explanations.\n",
     "\n",
-    "This notebook demonstrates how to query an Atlas cluster with a natural language prompt using an AI agent built with the [LangChain MongoDB Toolkit](https://langchain-mongodb.readthedocs.io/en/latest/langchain_mongodb/agent_toolkit/langchain_mongodb.agent_toolkit.toolkit.MongoDBDatabaseToolkit.html#langchain_mongodb.agent_toolkit.toolkit.MongoDBDatabaseToolkit) and the [LangGraph ReAct Agent Framework](https://langchain-ai.github.io/langgraph/agents/agents/).\n",
+    "This notebook demonstrates how to query a MongoDB cluster with a natural language prompt using an AI agent built with the [LangChain MongoDB Toolkit](https://langchain-mongodb.readthedocs.io/en/latest/langchain_mongodb/agent_toolkit/langchain_mongodb.agent_toolkit.toolkit.MongoDBDatabaseToolkit.html#langchain_mongodb.agent_toolkit.toolkit.MongoDBDatabaseToolkit) and the [LangGraph ReAct Agent Framework](https://langchain-ai.github.io/langgraph/agents/agents/).\n",
     "\n",
     "<a target=\"_blank\" href=\"https://colab.research.google.com/github/mongodb/docs-notebooks/blob/main/ai-integrations/langchain-natural-language.ipynb\">\n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
@@ -45,7 +45,7 @@
     "\n",
     "Before you begin, make sure you have the following:\n",
     "\n",
-    "- An Atlas cluster up and running (you'll need the [connection string](https://www.mongodb.com/docs/guides/atlas/connection-string/))\n",
+    "- A MongoDB cluster up and running (you'll need the [connection string](https://www.mongodb.com/docs/manual/reference/connection-string/))\n",
     "- An API key to access an LLM (This tutorial uses a model from OpenAI, but you can use any model [supported by LangChain](https://python.langchain.com/docs/integrations/chat/))"
    ]
   },
@@ -57,8 +57,8 @@
    "outputs": [],
    "source": [
     "os.environ[\"OPENAI_API_KEY\"] = '<api-key>'\n",
-    "ATLAS_CONNECTION_STRING = '<atlas-connection-string>'\n",
-    "ATLAS_DB_NAME = 'sample_restaurants'\n",
+    "MONGODB_URI = '<connection-string>'\n",
+    "DB_NAME = 'sample_restaurants'\n",
     "NATURAL_LANGUAGE_QUERY = 'Find all restaurants that serve hamburgers.'"
    ]
   },
@@ -107,8 +107,8 @@
     "        self.llm = ChatOpenAI(model=\"gpt-4o-mini\", timeout=60)\n",
     "        self.system_message = MONGODB_AGENT_SYSTEM_PROMPT.format(top_k=5)\n",
     "        self.db_wrapper = MongoDBDatabase.from_connection_string(\n",
-    "                            ATLAS_CONNECTION_STRING, \n",
-    "                            database=ATLAS_DB_NAME)\n",
+    "                            MONGODB_URI, \n",
+    "                            database=DB_NAME)\n",
     "        self.toolkit = MongoDBDatabaseToolkit(db=self.db_wrapper, llm=self.llm)\n",
     "        self.agent = create_react_agent(\n",
     "                        self.llm, \n",
@@ -158,25 +158,25 @@
    ]
   }
  ],
-"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"
-   }
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
- }
+  "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
+}

ai-integrations/langchain-parent-document-retrieval.ipynb

@@ -41,7 +41,7 @@
     "\n",
     "os.environ[\"VOYAGE_API_KEY\"] = \"<voyage-api-key>\"\n",
     "os.environ[\"OPENAI_API_KEY\"] = \"<openai-api-key>\"\n",
-    "ATLAS_CONNECTION_STRING = \"<connection-string>\""
+    "MONGODB_URI = \"<connection-string>\""
    ]
   },
   {
@@ -86,7 +86,7 @@
     "\n",
     "# Create the parent document retriever\n",
     "parent_doc_retriever = MongoDBAtlasParentDocumentRetriever.from_connection_string(\n",
-    "    connection_string = ATLAS_CONNECTION_STRING,\n",
+    "    connection_string = MONGODB_URI,\n",
     "    child_splitter = child_splitter,\n",
     "    embedding_model = embedding_model,\n",
     "    database_name = database_name,\n",

ai-integrations/langchain-self-query-retrieval.ipynb

@@ -154,7 +154,7 @@
    "id": "a5762ee0",
    "metadata": {},
    "source": [
-    "## Create the Atlas Vector Search index with filters"
+    "## Create the Vector Search index with filters"
    ]
   },
   {

ai-integrations/langchain.ipynb

@@ -41,7 +41,7 @@
     "\n",
     "os.environ[\"VOYAGE_API_KEY\"] = \"<voyage-api-key>\"\n",
     "os.environ[\"OPENAI_API_KEY\"] = \"<openai-api-key>\"\n",
-    "ATLAS_CONNECTION_STRING = \"<connection-string>\""
+    "MONGODB_URI = \"<connection-string>\""
    ]
   },
   {
@@ -76,7 +76,7 @@
     "\n",
     "# Instantiate the vector store using your MongoDB connection string\n",
     "vector_store = MongoDBAtlasVectorSearch.from_connection_string(\n",
-    "  connection_string = ATLAS_CONNECTION_STRING,\n",
+    "  connection_string = MONGODB_URI,\n",
     "  namespace = \"langchain_db.test\",\n",
     "  embedding =  VoyageAIEmbeddings(model=\"voyage-3-large\"),\n",
     "  index_name = \"vector_index\"\n",
@@ -184,7 +184,7 @@
     "from langchain_openai import ChatOpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
     "\n",
-    "# Instantiate Atlas Vector Search as a retriever\n",
+    "# Instantiate MongoDB Vector Search as a retriever\n",
     "retriever = vector_store.as_retriever(\n",
     "   search_type = \"similarity\",\n",
     "   search_kwargs = { \"k\": 10 }\n",
@@ -241,7 +241,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Instantiate Atlas Vector Search as a retriever\n",
+    "# Instantiate MongoDB Vector Search as a retriever\n",
     "retriever = vector_store.as_retriever(\n",
     "   search_type = \"similarity\",\n",
     "   search_kwargs = {\n",