Rename Atlas Search → MongoDB Search across codebase

apps/RT-voice-ts-store-agent/scripts/seed-db.js

@@ -5,7 +5,7 @@
  *
  * This script loads product data from a JSON file, computes embeddings for each document
  * using the Vercel AI SDK, seeds (upserts) each document into the "products" collection in the
- * "ai_shop" database, and then creates both an Atlas Search index and a vector search index.
+ * "ai_shop" database, and then creates both an MongoDB Search index and a vector search index.
  *
  * It uses a dedicated MongoDB client instance created with custom appName settings.
  *
@@ -90,7 +90,7 @@ async function createSearchIndex(client) {
         }
       }]
     });
-    console.log("Successfully created Atlas Search index on 'products' collection");
+    console.log("Successfully created MongoDB Search index on 'products' collection");
   } catch (e) {
     console.error("Failed to create search index:", e);
   }

apps/graph_rag_demo/README.md

@@ -43,7 +43,7 @@ This guide explores how to leverage MongoDB's capabilities to create and manipul
         } ]
     }
     ```
-    After this create an Atlas Search Index on the <code>knowledge_graph</code> collection. [Please refer this document](https://www.mongodb.com/docs/compass/current/indexes/create-search-index/). Please name the Atlas Search Index as <code>default</code>
+    After this create an MongoDB Search Index on the <code>knowledge_graph</code> collection. [Please refer this document](https://www.mongodb.com/docs/compass/current/indexes/create-search-index/). Please name the MongoDB Search Index as <code>default</code>
 
 
 

apps/graph_rag_demo/addEmbeddings.js

@@ -17,7 +17,7 @@ async function run() {
         const collection = database.collection("knowledge_graph");
         const dbConfig = {
             collection: collection,
-            indexName: "vector_index", // The name of the Atlas search index to use.
+            indexName: "vector_index", // The name of the MongoDB search index to use.
             textKey: "chunks", // Field name for the raw text content. Defaults to "text".
             embeddingKey: "embedding", // Field name for the vector embeddings. Defaults to "embedding".
         };

apps/local-bot/README.md

@@ -1,6 +1,6 @@
 # Step-by-Step Guide: Building a Local Chatbot with Streamlit, LangChain, Ollama, and MongoDB Atlas
 
-In this tutorial, we'll set up a local chatbot using **Streamlit**, **LangChain**, **Ollama**, and **MongoDB Atlas Search**. This bot will leverage MongoDB's powerful Atlas Search capabilities alongside local LLMs (Large Language Models) via Ollama, allowing you to enhance user queries with context from chat history.
+In this tutorial, we'll set up a local chatbot using **Streamlit**, **LangChain**, **Ollama**, and **MongoDB Search**. This bot will leverage MongoDB's powerful MongoDB Search capabilities alongside local LLMs (Large Language Models) via Ollama, allowing you to enhance user queries with context from chat history.
 
 ## Prerequisites
 Before starting, make sure you have the following installed:
@@ -40,7 +40,7 @@ Here’s a quick rundown of the tools we’re using in this project:
 *	*[Streamlit](https://streamlit.io)*: A Python library for easily creating data-based web applications. We'll use it to create a local chatbot interface.
 *	*[LangChain](https://langchain.com)*: A framework that simplifies working with LLMs and document processing. It will assist processing user queries and generate responses.
 *	*[Ollama](https://ollama.com)*: A solution for deploying LLMs locally without external API dependency. It to host our models.
-*	*[MongoDB Atlas Search](https://www.mongodb.com/products/platform/atlas-search)*: Adds a powerful, flexible vector search functionality to our app. It will store user queries and responses in MongoDB.
+*	*[MongoDB Search](https://www.mongodb.com/products/platform/atlas-search)*: Adds a powerful, flexible vector search functionality to our app. It will store user queries and responses in MongoDB.
 
 ### Setting Up `requirements.txt`
 
@@ -268,7 +268,7 @@ At this point, you can start prompting with inputs like “Who started AT&T?”
 
 ## Conclusion and Next Steps
 
-In this tutorial, we built a local chatbot setup using MongoDB Atlas Search and local LLMs via Ollama, integrated through Streamlit. This project forms a robust foundation for further development and deployment.
+In this tutorial, we built a local chatbot setup using MongoDB Search and local LLMs via Ollama, integrated through Streamlit. This project forms a robust foundation for further development and deployment.
 
 Possible Extensions:
 

apps/lyric-semantic-search/README.md

@@ -3,7 +3,7 @@ This repo is support the [Building a Semantic Search Service With Spring AI and
 
 Spring AI is an application framework from [Spring](https://spring.io/) that allows you to combine various AI services and plugins with your applications. With support for many chat, text-to-image, and embedding models, you can get your AI powered Java application set up for a variety of AI use cases.
 
-With Spring AI, MongoDB Atlas is supported as a vector database, all with [Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search) to power your semantic search and implement your RAG applications. To learn more about RAG and other key concepts in AI, check out the [MongoDB AI integration docs](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/#std-label-ai-key-concepts).
+With Spring AI, MongoDB Atlas is supported as a vector database, all with [MongoDB Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search) to power your semantic search and implement your RAG applications. To learn more about RAG and other key concepts in AI, check out the [MongoDB AI integration docs](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/#std-label-ai-key-concepts).
 
 In this tutorial, we’ll go through what you need to get started with Spring AI and MongoDB. Adding documents to your database with the vectorised content (embeddings), and searching this content with semantic search. The full code for this tutorial is available in this [Github repository](https://github.com/timotheekelly/lyric-semantic-search).
 

apps/minimal-ts-agent/README.md

@@ -1,6 +1,6 @@
 # RAG Agent Demo — How to Create an AI Agent with Minimal Coding
 
-Companion project for the **"How to Create an AI Agent with Minimal Coding"** video. This demo builds a fully functional RAG (Retrieval-Augmented Generation) agent that answers questions about the MongoDB Brand Book using MongoDB Atlas Vector Search, Voyage AI embeddings, and the Vercel AI SDK's `ToolLoopAgent`.
+Companion project for the **"How to Create an AI Agent with Minimal Coding"** video. This demo builds a fully functional RAG (Retrieval-Augmented Generation) agent that answers questions about the MongoDB Brand Book using MongoDB Vector Search, Voyage AI embeddings, and the Vercel AI SDK's `ToolLoopAgent`.
 
 ## What It Does
 
@@ -23,7 +23,7 @@ flowchart TD
     D -->|Reasoning step| E{Need more context?}
     E -->|Yes| F[searchDocumentation tool]
     F -->|Embed query| G[Voyage AI]
-    G -->|Query vector| H[MongoDB Atlas Vector Search]
+    G -->|Query vector| H[MongoDB Vector Search]
     H -->|Top 5 results| D
     E -->|No| I[Generate final response]
     I -->|Stream| B
@@ -92,7 +92,7 @@ VOYAGE_AI_API_KEY=your-voyage-ai-api-key
 npx ts-node scripts/ingest.ts
 ```
 
-This embeds 14 brand book sections with Voyage AI, inserts them into the `brand_demo.brand_book` collection, and automatically creates the Atlas Vector Search index. The index may take a minute to become ready after creation.
+This embeds 14 brand book sections with Voyage AI, inserts them into the `brand_demo.brand_book` collection, and automatically creates the MongoDB Vector Search index. The index may take a minute to become ready after creation.
 
 ### 4. Run the app
 

apps/minimal-ts-agent/src/app/layout.tsx

@@ -15,7 +15,7 @@ const geistMono = Geist_Mono({
 export const metadata: Metadata = {
   title: "MongoDB RAG Agent — Brand Book Expert",
   description:
-    "A fully autonomous RAG agent powered by Vercel AI SDK ToolLoopAgent and MongoDB Atlas Vector Search",
+    "A fully autonomous RAG agent powered by Vercel AI SDK ToolLoopAgent and MongoDB Vector Search",
 };
 
 export default function RootLayout({

apps/minimal-ts-agent/src/app/page.tsx

@@ -64,7 +64,7 @@ export default function ChatPage() {
         <div>
           <h1 className="text-lg font-semibold">MongoDB Brand Expert</h1>
           <p className="text-sm text-muted">
-            RAG Agent powered by ToolLoopAgent + Atlas Vector Search
+            RAG Agent powered by ToolLoopAgent + MongoDB Vector Search
           </p>
         </div>
         <div className="ml-auto flex items-center gap-2 text-xs text-muted">
@@ -84,7 +84,7 @@ export default function ChatPage() {
                 Ask me about the MongoDB Brand Book
               </h2>
               <p className="text-muted max-w-md">
-                I use an agentic reasoning loop to search MongoDB Atlas Vector
+                I use an agentic reasoning loop to search MongoDB Vector
                 Search and deliver precise answers from the official brand
                 guidelines.
               </p>

apps/mongo-feed/components/past-analysis.tsx

@@ -52,7 +52,7 @@ export function PastAnalysis() {
               <InfoIcon className="h-4 w-4 text-muted-foreground" />
             </TooltipTrigger>
             <TooltipContent>
-              <p>Past analysis results are stored in MongoDB and can be quickly retrieved using Atlas vector search.</p>
+              <p>Past analysis results are stored in MongoDB and can be quickly retrieved using MongoDB Vector Search.</p>
               <p className="mt-2 text-sm text-muted-foreground">
                 Potential query: "Find similar analyses to the most recent customer feedback report"
               </p>

apps/mongo-mp/scripts/seed-db.js

@@ -78,7 +78,7 @@ async function createSearchIndex(client) {
       }]
     });
 
-    console.log("Successfully created Atlas Search index");
+    console.log("Successfully created MongoDB Search index");
   } catch (e) {
     console.error('Failed to create search index:', e);
   }

apps/mongostory/README.md

@@ -40,7 +40,7 @@ MongoStory is a cloud-native platform designed to empower content creators, edit
 ### Backend
 - **API Routes**: Next.js API routes for server-side functionality
 - **Database**: MongoDB for flexible document storage
-- **Vector Search**: MongoDB Atlas Vector Search for semantic content operations
+- **Vector Search**: MongoDB Vector Search for semantic content operations
 - **AI Integration**: Integration with AI models via AI SDK - xAI (Grok)
 
 ### AI Integration
@@ -60,7 +60,7 @@ MongoStory leverages MongoDB's document model for flexible content storage and i
   - `clusters`: AI-generated content clusters
   - `socialMediaPosts`: Generated social media content
 
-- **Vector Search**: Uses MongoDB Atlas Vector Search for semantic operations:
+- **Vector Search**: Uses MongoDB Vector Search for semantic operations:
   - Content similarity detection
   - Semantic search functionality
   - Automatic content clustering

apps/mongostory/components/semantic-content-explorer.tsx

@@ -103,7 +103,7 @@ export function SemanticContentExplorer() {
     }
   }
 
-  // Update the handleSearch function to better reflect MongoDB Atlas vector search
+  // Update the handleSearch function to better reflect MongoDB Vector Search
   const handleSearch = async () => {
     if (!searchQuery.trim()) return
 

apps/springai-terraform-rag/README.md

@@ -1,6 +1,6 @@
 # MongoDB Atlas + Terraform Spring Boot RAG Application
 
-This repository demonstrates how to build a **Retrieval-Augmented Generation (RAG)** application using **Spring Boot**, **OpenAI embeddings**, and **MongoDB Atlas Vector Search**. The infrastructure is automated using **Terraform** to provision and manage MongoDB Atlas resources.
+This repository demonstrates how to build a **Retrieval-Augmented Generation (RAG)** application using **Spring Boot**, **OpenAI embeddings**, and **MongoDB Vector Search**. The infrastructure is automated using **Terraform** to provision and manage MongoDB Atlas resources.
 
 ## Overview
 
@@ -11,7 +11,7 @@ In this project, we:
 
 ## Features
 - **Automated Infrastructure**: Terraform is used to provision MongoDB Atlas resources including clusters, vector search indices, and access controls.
-- **RAG Implementation**: A Spring Boot application that uses OpenAI to generate embeddings, with MongoDB Atlas vector search to perform semantic searches.
+- **RAG Implementation**: A Spring Boot application that uses OpenAI to generate embeddings, with MongoDB Vector Search to perform semantic searches.
 - **Document Loading**: Upload documents and store their embeddings for use in vector search.
 - **Querying with Vector Search**: Search documents by semantic similarity using a custom `/question` endpoint.
 
@@ -85,6 +85,6 @@ To run this project, you'll need:
 
 ## Technologies Used
 - **Spring Boot**: Java-based framework for building REST APIs.
-- **MongoDB Atlas**: An integrated suite of data services (including Atlas Vector Search) centered around a cloud database designed to accelerate and simplify how you build with data. Build faster and build smarter with a developer data platform that helps solve your data challenges. Click [here](https://www.mongodb.com/products/platform/atlas-database) to learn more.
+- **MongoDB Atlas**: An integrated suite of data services (including MongoDB Vector Search) centered around a cloud database designed to accelerate and simplify how you build with data. Build faster and build smarter with a developer data platform that helps solve your data challenges. Click [here](https://www.mongodb.com/products/platform/atlas-database) to learn more.
 - **OpenAI**: Generates embeddings for semantic searches.
 - **Terraform**: Automates infrastructure management for MongoDB Atlas.

apps/video-intelligence/backend/setup_indexes.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-MongoDB Atlas Search Index Setup Script
+MongoDB Search Index Setup Script
 Creates the required vector search and text search indexes for the Video Intelligence app.
 """
 
@@ -13,12 +13,12 @@
 
 
 def setup_indexes():
-    """Setup MongoDB Atlas search indexes"""
+    """Setup MongoDB Search indexes"""
 
     # Get embedding dimensions from environment variable
     EMBEDDING_DIM_SIZE = int(os.getenv("EMBEDDING_DIM_SIZE", "1024"))
     print(f"Using embedding dimensions: {EMBEDDING_DIM_SIZE}")
-    print("🔧 Setting up MongoDB Atlas Search Indexes")
+    print("🔧 Setting up MongoDB Search Indexes")
     print("=" * 50)
 
     # Load environment variables

apps/voice-memory-demo/README.md

@@ -64,13 +64,13 @@ Open [http://localhost:3000](http://localhost:3000) in your browser.
 
 ### 4. Create MongoDB Indexes
 
-For hybrid search (vector + text) with `$rankFusion` (require latest Atlas version cluster), the code creates on startup two Atlas Search indexes on the `memories` collection:
+For hybrid search (vector + text) with `$rankFusion` (require latest Atlas version cluster), the code creates on startup two MongoDB Search indexes on the `memories` collection:
 
 #### Vector Search Index
 
 **Index Name:** `memory_vector_index`
 
-Optional: In Atlas UI: **Search Indexes** → **Create Search Index** → **Atlas Vector Search**
+Optional: In Atlas UI: **Search Indexes** → **Create Search Index** → **MongoDB Vector Search**
 
 ```json
 {
@@ -97,11 +97,11 @@ Optional: In Atlas UI: **Search Indexes** → **Create Search Index** → **Atla
 }
 ```
 
-#### Atlas Search Index (Text)
+#### MongoDB Search Index (Text)
 
 **Index Name:** `memory_text_index`
 
-Optional: In Atlas UI: **Search Indexes** → **Create Search Index** → **Atlas Search**
+Optional: In Atlas UI: **Search Indexes** → **Create Search Index** → **MongoDB Search**
 
 ```json
 {

apps/voice-memory-demo/src/lib/mongodb.ts

@@ -39,7 +39,7 @@ const VECTOR_INDEX_DEFINITION = {
   },
 };
 
-// Atlas Search (text) index definition
+// MongoDB Search (text) index definition
 const TEXT_INDEX_DEFINITION = {
   name: 'memory_text_index',
   type: 'search' as const,
@@ -70,7 +70,7 @@ const TEXT_INDEX_DEFINITION = {
 };
 
 /**
- * Ensures Atlas Search indexes exist on the memories collection.
+ * Ensures MongoDB Search indexes exist on the memories collection.
  * Creates them if they don't exist.
  * Note: Indexes take 1-5 minutes to build after creation.
  */

apps/voice-openai-mongo-rentals-agent/README.md

@@ -1,4 +1,4 @@
-#  Voice Agent with MongoDB Atlas Vector Search
+#  Voice Agent with MongoDB Vector Search
 
 <img src="./youtube-thumbnail-updated.svg" width="400" alt="Thumbnail">
 
@@ -112,7 +112,7 @@ print("All records ingested successfully!")
 
 bookings = db['bookings']
 
-## Create Atlas Search index
+## Create MongoDB Search index
 
 db.create_collection("bookings")
 search_index_model = SearchIndexModel(

apps/voice-openai-mongo-rentals-agent/src/server/tools.py

@@ -37,7 +37,7 @@ class Booking(BaseModel):
 @tool
 def rentlas_search_tool(query: str, k: int = 5):
     """
-    Perform a vector similarity search using MongoDB Atlas Vector Search to find rentals.
+    Perform a vector similarity search using MongoDB Vector Search to find rentals.
 
     Args:
         query (str): The search query string.
@@ -48,7 +48,7 @@ def rentlas_search_tool(query: str, k: int = 5):
               and score is the similarity score (lower is more similar).
 
     Note:
-        Uses MongoDB Atlas Vector Search for semantic search capabilities.
+        Uses MongoDB Vector Search for semantic search capabilities.
     """
     vector_store = MongoDBAtlasVectorSearch.from_connection_string(
         connection_string=os.environ["MONGODB_ATLAS_URI"],

misc/low-code/BuildShip.md

@@ -63,7 +63,7 @@ This workflow serves as a versatile template for creating various types of AI ag
 
 ### Extend MongoDB Integration
 - Implement complex aggregation pipelines for advanced queries
-- Add MongoDB Atlas Search for full-text search capabilities
+- Add MongoDB Search for full-text search capabilities
 - Utilize MongoDB Change Streams for real-time updates
 - Incorporate MongoDB Charts for data visualization
 

notebooks/advanced_techniques/automatic_quantization_of_nomic_emebddings_with_mongodb.ipynb

@@ -1186,7 +1186,7 @@
         "Cost Reduction and Resource Efficiency: Efficient data storage and retrieval reduce the need for excessive computational resources, leading to cost savings.\n",
         "By examining the trade-offs between retrieval accuracy and performance across different embedding formats (float32, int8, and binary), we showcased how MongoDB's capabilities, such as vector indexing and automatic quantization, can streamline data storage, retrieval, and analysis. \n",
         "\n",
-        "From this tutorial, we’ve explored Atlas Vector Search native capabilities for scalar quantization as well as binary quantization with rescoring. Our implementation showed that automatic quantization increases scalability and cost savings by reducing the storage and computational resources for efficient processing of vectors. In most cases, automatic quantization reduces the RAM for mongot by 3.75x for scalar and by 24x for binary; the vector values shrink by 4x and 32x, respectively, but the Hierarchical Navigable Small Worlds graph itself does not shrink.\n",
+        "From this tutorial, we’ve explored MongoDB Vector Search native capabilities for scalar quantization as well as binary quantization with rescoring. Our implementation showed that automatic quantization increases scalability and cost savings by reducing the storage and computational resources for efficient processing of vectors. In most cases, automatic quantization reduces the RAM for mongot by 3.75x for scalar and by 24x for binary; the vector values shrink by 4x and 32x, respectively, but the Hierarchical Navigable Small Worlds graph itself does not shrink.\n",
         "\n",
         "We recommend automatic quantization if you have a large number of full-fidelity vectors, typically over 10M vectors. After quantization, you index reduced representation vectors without compromising the accuracy of your retrieval.\n",
         "To further explore quantization techniques and their applications, refer to resources like [Ingesting Quantized Vectors with Cohere](https://www.mongodb.com/developer/products/atlas/ingesting_quantized_vectors_with_cohere/). An [additional notebook](https://github.com/mongodb-developer/GenAI-Showcase/blob/main/notebooks/advanced_techniques/advanced_evaluation_of_quantized_vectors_using_cohere_mongodb_beir.ipynb) for comparing retrieval accuracy between quantized and non-quantized vectors is also available to deepen your understanding of these methods."