Update mongodb_atlas_vector_search_setup.md

mongodb_atlas_vector_search_setup.md

@@ -1,73 +1,79 @@
-# Author     : Georgios Ioannou
-#
-# Copyright © 2024 by Georgios Ioannou
-
-# Setting Up MongoDB with Atlas Vector Search
-
-## Initial Setup
-1. Create and log in to your MongoDB account at [mongodb.com](https://mongodb.com)
-
-2. Create Organization and Project
-   - Create a new MongoDB organization
-   - Name your organization as desired
-   - Create a new MongoDB project
-   - Name your project as desired
-
-3. Create Cluster
-   - Select M0 (FREE) tier
-   - Wait 1-3 minutes for cluster creation
-
-## Security Setup
-4. Create Database User
-   - Create username and password
-   - **Important**: Save these credentials securely
-
-5. Configure Network Access
-   - Navigate on the left menu to Security -> Network Access
-   - Add IP Address: `0.0.0.0/0` (Allows access from anywhere)
-
-## Database Creation
-6. Set Up Database and Collection
-   - Click "Browse Collections"
-   - Click "Add My Own Data"
-   - Create database (e.g., "txts")
-   - Create collection (e.g., "txts_collection")
-   - Click "Create"
-
-## Vector Search Configuration
-7. Create Vector Search Index
-   - Click "Search Indexes" (This will bring you to "Atlas Search" tab)
-   - Click "Create Search Index"
-   - Select "JSON Editor" under "Atlas Vector Search"
-   - Click "Next"
-   - Configure index settings:
-     ```json
-     {
-       "name": "vector_index", // Adjust it to your desired name.
-       "type": "vector",
-       "path": "embedding", // Adjust it to your index/embedding column.
-       "numDimensions": 384,  // Adjust based on your model. (e.g., 384 for all-MiniLM-l6-v2)
-       "similarity": "euclidean" // Adjust it to your desired similarity function.
-     }
-     ```
-   - Optional: Add filter fields if needed:
-     ```json
-     {
-       "type": "filter",
-       "path": "source" // Adjust it to the column you would like to filter with.
-     }
-     ```
-   - Assign your index to the collection name created in step 6 using the drop down menu on the left.
-   - Click "Next"
-   - Click "Create Search Index"
-   - Wait for status to change from "Pending" to "Active"
-
-**Note**: While vector search index creation can be done programmatically, the GUI method described above is recommended for simplicity.
-
-## Connection String
-8. Get Your MongoDB URI
-   - Click "Clusters" under DATABASE on the left menu
-   - Click "Connect" next to name of your cluster
-   - Select "Drivers"
-   - Copy the connection/MongoURI string at the very end of the modal that just opened
+# Author     : Georgios Ioannou
+#
+# Copyright © 2024 by Georgios Ioannou
+
+# Setting Up MongoDB with Atlas Vector Search
+
+## Initial Setup
+1. Create and log in to your MongoDB account at [mongodb.com](https://mongodb.com)
+
+2. Create Organization and Project
+   - Note that 
+   - Create a new MongoDB organization
+   - Name your organization as desired
+   - Create a new MongoDB project
+   - Name your project as desired
+
+3. Create Cluster
+   - Select M0 (FREE) tier
+   - Make sure to uncheck "Preload sample dataset"
+   - Keep everything else default.
+   - Wait 1-3 minutes for cluster creation
+
+## Security Setup
+4. Create Database User
+   - Create username and password
+   - Then, click on continue.
+   - Then, just close no need to choose a connection method.
+   - **Important**: Save these credentials securely
+
+5. Configure Network Access
+   - Navigate on the left menu to Security -> Network Access
+   - Add IP Address: `0.0.0.0/0` (Allows access from anywhere)
+
+## Database Creation
+6. Set Up Database and Collection
+   - Click "Clusters" on the left under "Database".
+   - Click "Browse Collections"
+   - Click "Add My Own Data"
+   - Create database (e.g., "txts")
+   - Create collection (e.g., "txts_collection")
+   - Click "Create"
+
+## Vector Search Configuration
+7. Create Vector Search Index
+   - Click "Search Indexes" (This will bring you to "Atlas Search" tab)
+   - Click "Create Search Index"
+   - Select "JSON Editor" under "Atlas Vector Search"
+   - Click "Next"
+   - Configure index settings:
+     ```json
+     {
+       "name": "vector_index", // Adjust it to your desired name.
+       "type": "vector",
+       "path": "embedding", // Adjust it to your index/embedding column.
+       "numDimensions": 384,  // Adjust based on your model. (e.g., 384 for all-MiniLM-l6-v2)
+       "similarity": "euclidean" // Adjust it to your desired similarity function.
+     }
+     ```
+   - Optional: Add filter fields if needed:
+     ```json
+     {
+       "type": "filter",
+       "path": "source" // Adjust it to the column you would like to filter with.
+     }
+     ```
+   - Assign your index to the collection name created in step 6 using the drop down menu on the left.
+   - Click "Next"
+   - Click "Create Search Index"
+   - Wait for status to change from "Pending" to "Active"
+
+**Note**: While vector search index creation can be done programmatically, the GUI method described above is recommended for simplicity.
+
+## Connection String
+8. Get Your MongoDB URI
+   - Click "Clusters" under DATABASE on the left menu
+   - Click "Connect" next to name of your cluster
+   - Select "Drivers"
+   - Copy the connection/MongoURI string at the very end of the modal that just opened
    - Replace `<password>` with your database user password