Merge pull request #218 from mongodb-developer/rename/atlas-search-to-mongodb-search

Rename Atlas Search → MongoDB Search across codebase

Author: ajosh0504

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);
   }