Replace semantic with vector

mongodb-developer · Feb 4, 2025 · 8a1fd6f · 8a1fd6f
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diff --git a/docs/10-key-concepts/3-components-of-rag.mdx b/docs/10-key-concepts/3-components-of-rag.mdx
@@ -10,7 +10,7 @@ Retrieval mainly involves processing your data and constructing a knowledge base
 
 * **Embedding**: Convert a piece of information such as text, images, audio, video, etc. into an array of numbers a.k.a. vectors.
 
-* **Semantic Search**: Retrieve the most relevant documents from the knowledge base based on embedding similarity with the query vector.
+* **Vector Search**: Retrieve the most relevant documents from the knowledge base based on embedding similarity with the query vector.
 
 ## Generation
 

diff --git a/docs/40-perform-semantic-search/1-lecture-notes.mdx b/docs/40-perform-semantic-search/1-lecture-notes.mdx
diff --git a/docs/40-perform-semantic-search/_category_.json b/docs/40-perform-semantic-search/_category_.json
diff --git a/docs/40-perform-vector-search/1-lecture-notes.mdx b/docs/40-perform-vector-search/1-lecture-notes.mdx
@@ -0,0 +1,5 @@
+# 📘 Lecture notes
+
+## Vector Search in MongoDB
+
+You can learn more about vector search in MongoDB [here](https://mongodb-developer.github.io/vector-search-lab/docs/key-concepts/vector-search#vector-search-in-mongodb).
diff --git a/...semantic-search/2-create-vector-index.mdx → ...m-vector-search/2-create-vector-index.mdx b/...semantic-search/2-create-vector-index.mdx → ...m-vector-search/2-create-vector-index.mdx
@@ -19,12 +19,12 @@ collection.create_search_index(model=model)
 
 To verify that the index was created, navigate to the **Overview** page in the Atlas UI. In the **Clusters section**, select your cluster and click **Browse collections**.
 
-<Screenshot url="https://cloud.mongodb.com" src="img/screenshots/40-perform-semantic-search/2-create-vector-index/1-browse-collections.png" alt="Browse collections" />
+<Screenshot url="https://cloud.mongodb.com" src="img/screenshots/40-perform-vector-search/2-create-vector-index/1-browse-collections.png" alt="Browse collections" />
 
 Navigate to **Search Indexes** for the _knowledge_base_ collection in the _mongodb_genai_devday_ database.
 
-<Screenshot url="https://cloud.mongodb.com" src="img/screenshots/40-perform-semantic-search/2-create-vector-index/2-nav-search-indexes.png" alt="Navigate to search indexes" />
+<Screenshot url="https://cloud.mongodb.com" src="img/screenshots/40-perform-vector-search/2-create-vector-index/2-nav-search-indexes.png" alt="Navigate to search indexes" />
 
 The index is ready to use once the status changes from **PENDING** to **READY**.
 
-<Screenshot url="https://cloud.mongodb.com" src="img/screenshots/40-perform-semantic-search/2-create-vector-index/3-index-ready.png" alt="Index ready to use" />
+<Screenshot url="https://cloud.mongodb.com" src="img/screenshots/40-perform-vector-search/2-create-vector-index/3-index-ready.png" alt="Index ready to use" />
diff --git a/...rform-semantic-search/3-vector-search.mdx → ...perform-vector-search/3-vector-search.mdx b/...rform-semantic-search/3-vector-search.mdx → ...perform-vector-search/3-vector-search.mdx
@@ -1,8 +1,8 @@
-# 👐 Perform semantic search
+# 👐 Perform vector search queries
 
 Now let's run some vector search queries against the data present in MongoDB. 
 
-Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 7: Perform semantic search on your data** section in the notebook to run vector search queries against your data.
+Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 7: Perform vector search on your data** section in the notebook to run vector search queries against your data.
 
 The answers for code blocks in this section are as follows:
 

diff --git a/...rform-semantic-search/4-pre-filtering.mdx → ...perform-vector-search/4-pre-filtering.mdx b/...rform-semantic-search/4-pre-filtering.mdx → ...perform-vector-search/4-pre-filtering.mdx
diff --git a/docs/40-perform-vector-search/_category_.json b/docs/40-perform-vector-search/_category_.json
@@ -0,0 +1,8 @@
+{
+  "label": "Perform Vector Search on Your Data",
+  "position": 6,
+  "link": {
+    "type": "generated-index",
+    "description": "Perform vector search queries using MongoDB Atlas Vector Search"
+  }
+}
diff --git a/docs/intro.mdx b/docs/intro.mdx
@@ -11,7 +11,7 @@ import TabItem from '@theme/TabItem';
 |:-|:-|
 |What you'll learn|What is RAG |
 ||Components of a RAG application|
-||Perform semantic search queries using Mongo Atlas Vector Search|
+||Perform vector search queries using Mongo Atlas Vector Search|
 ||Build a RAG-based documentation chatbot using MongoDB Atlas|
 ||Add memory to your chatbot|
 |Time to complete|90 mins|

diff --git a/docs/summary.mdx b/docs/summary.mdx
@@ -7,7 +7,7 @@ sidebar_position: 100
 Congratulations! Following this lab, you have successfully:
 * learned what is Retrieval Augmented Generation a.k.a. RAG
 * learned when to use RAG
-* learned how to perform semantic search against data in MongoDB
+* learned how to perform vector search against data in MongoDB
 * built a RAG application
 * added memory to your RAG application
 

diff --git a/...ate-vector-index/1-browse-collections.png → ...ate-vector-index/1-browse-collections.png b/...ate-vector-index/1-browse-collections.png → ...ate-vector-index/1-browse-collections.png
diff --git a/...ate-vector-index/2-nav-search-indexes.png → ...ate-vector-index/2-nav-search-indexes.png b/...ate-vector-index/2-nav-search-indexes.png → ...ate-vector-index/2-nav-search-indexes.png
diff --git a/...h/2-create-vector-index/3-index-ready.png → ...h/2-create-vector-index/3-index-ready.png b/...h/2-create-vector-index/3-index-ready.png → ...h/2-create-vector-index/3-index-ready.png