Fix non-functional sample code and remove orphaned stub pages

getting-started/mongo-shell-quickstart.md

@@ -163,21 +163,26 @@ Each index above is on a distinct set of keys, which matters: when you don't pas
 
 Note also that a unique index is not sparse unless you say so. Documents that lack the indexed field are all treated as sharing a single "missing" value, so the second such document violates uniqueness. Add `sparse: true` when the field is optional.
 
-To create a vector index on an embedding field, use the `cosmosSearchOptions` index spec accepted by the DocumentDB gateway:
+To create a vector index on an embedding field, use `createIndexes` via `runCommand`. The
+`cosmosSearchOptions` spec is not accepted through the `createIndex` helper, which fails with
+`Index type 'CosmosSearch' was requested, but the 'cosmosSearch' options were not provided.`
 
 ```javascript
-db.products.createIndex(
-  { embedding: "cosmosSearch" },
-  {
-    name: "vectorIndex",
-    cosmosSearchOptions: {
-      kind: "vector-ivf",
-      numLists: 100,
-      similarity: "COS",
-      dimensions: 3
+db.runCommand({
+  createIndexes: "products",
+  indexes: [
+    {
+      name: "vectorIndex",
+      key: { embedding: "cosmosSearch" },
+      cosmosSearchOptions: {
+        kind: "vector-ivf",
+        numLists: 100,
+        similarity: "COS",
+        dimensions: 3
+      }
     }
-  }
-)
+  ]
+})
 ```
 
 `dimensions` must match the length of the vectors you store and query — a query vector of a different length is rejected. Three is used here only to keep the example short; a real embedding field is typically 384, 768, or 1536 wide, depending on the model.

getting-started/nodejs-setup.md

@@ -9,7 +9,7 @@ Learn how to set up and use DocumentDB with Node.js using the official MongoDB N
 
 ## Prerequisites
 
-- Node.js 14.x or later
+- Node.js 20.19 or later (required by the current `mongodb` driver)
 - npm or yarn package manager
 - DocumentDB installed and running
 - Docker installed (if set up is not completed yet)
@@ -82,62 +82,36 @@ async function connect() {
    ```
 
 2. Document operations
-   - Insert operations
-   - Find operations
-   - Update operations
-   - Delete operations
 
-## Working with Promises and Async/Await
-
-1. Promise-based operations
-2. Async/await patterns
-3. Error handling
-4. Connection management
-
-## Advanced Features
-
-1. Bulk operations
-2. Aggregation framework
-3. Vector search
-4. Geospatial queries
-5. Change streams
-6. Transactions
-
-## Error Handling
-
-1. Connection errors
-2. Operation errors
-3. Timeout handling
-4. Retry strategies
-
-## Best Practices
+   ```javascript
+   const users = db.collection('users');
 
-1. Connection pooling
-2. Query optimization
-3. Bulk operations
-4. Error handling
-5. Security considerations
+   await users.insertOne({ name: 'John Doe', email: 'john@example.com', createdAt: new Date() });
+   await users.insertMany([
+     { name: 'Jane Smith', email: 'jane@example.com' },
+     { name: 'Bob Johnson', email: 'bob@example.com' },
+   ]);
 
-## Sample Applications
+   await users.updateOne({ name: 'John Doe' }, { $set: { status: 'active' } });
+   console.log(await users.findOne({ name: 'John Doe' }));
+   console.log(await users.countDocuments());
 
-1. Basic CRUD application
-2. REST API with Express
-3. Vector search example
-4. Real-time applications with change streams
+   await users.deleteOne({ name: 'Bob Johnson' });
+   ```
 
-## Testing
+   Always close the client when the process is done:
 
-1. Setting up test environment
-2. Unit testing with Jest/Mocha
-3. Integration testing
-4. Mock testing
+   ```javascript
+   await client.close();
+   ```
 
-## Deployment
+## Beyond CRUD
 
-1. Development setup
-2. Production considerations
-3. Monitoring and logging
-4. Performance optimization
+Aggregation pipelines, vector search, geospatial queries and change streams use the
+same syntax as the MongoDB shell. See the
+[Mongo Shell Quick Start](https://documentdb.io/docs/getting-started/mongo-shell-quickstart/)
+for worked examples, and the [API reference](https://documentdb.io/docs/api-reference/)
+for the supported operator set.
 
 ## Next Steps
 

getting-started/python-setup.md

@@ -9,7 +9,7 @@ Learn how to set up and use DocumentDB with Python using the official MongoDB Py
 
 ## Prerequisites
 
-- Python 3.7+
+- Python 3.9 or later (required by PyMongo 4.x)
 - pip package manager
 - DocumentDB installed and running (see [Pre-built Packages](https://documentdb.io/docs/getting-started/packages/))
 - Docker (if DocumentDB is not set up yet)
@@ -101,11 +101,13 @@ DocumentDB Local accepts TLS connections on the gateway port and requires authen
 
 2. Document operations
    ```python
+   from datetime import datetime, timezone
+
    # Insert a single document
    collection.insert_one({
        'name': 'John Doe',
        'email': 'john@example.com',
-       'created_at': datetime.utcnow()
+       'created_at': datetime.now(timezone.utc)
    })
 
    # Insert multiple documents
@@ -145,28 +147,26 @@ DocumentDB Local accepts TLS connections on the gateway port and requires authen
 
 2. DateTime
    ```python
-   from datetime import datetime
+   from datetime import datetime, timezone
 
    # Insert with timestamp
    collection.insert_one({
        'name': 'Event',
-       'timestamp': datetime.utcnow()
+       'timestamp': datetime.now(timezone.utc)
    })
    ```
 
 ## Advanced Features
 
 1. Bulk operations
    ```python
-   # Initialize bulk operations
-   bulk = collection.initialize_ordered_bulk_op()
-
-   # Add operations
-   bulk.find({'status': 'pending'}).update({'$set': {'status': 'processed'}})
-   bulk.find({'age': {'$lt': 18}}).delete()
-
-   # Execute
-   result = bulk.execute()
+   from pymongo import UpdateMany, DeleteMany
+
+   result = collection.bulk_write([
+       UpdateMany({'status': 'pending'}, {'$set': {'status': 'processed'}}),
+       DeleteMany({'age': {'$lt': 18}}),
+   ])
+   print(result.modified_count, result.deleted_count)
    ```
 
 2. Aggregation framework
@@ -183,40 +183,33 @@ DocumentDB Local accepts TLS connections on the gateway port and requires authen
    ```
 
 3. Vector search
-   ```python
-   # Vector similarity search
-   results = collection.find({
-       '$vectorSearch': {
-           'queryVector': [0.1, 0.2, 0.3],
-           'path': 'embeddings',
-           'numCandidates': 100,
-           'limit': 10
-       }
-   })
-   ```
 
-4. PostgreSQL Integration
+   `$vectorSearch` is an aggregation stage and must be the first stage in the
+   pipeline — it does not work inside `find()`.
+
    ```python
-   # Access PostgreSQL features directly
-   from documentdb_api import DocumentDB
-
-   # Initialize DocumentDB with PostgreSQL support
-   db = DocumentDB(client)
-
-   # Execute SQL queries on BSON documents
-   result = db.sql_query(
-       "SELECT jsonb_path_query(data, '$.name') FROM collection WHERE data @? '$.age > 21'"
-   )
+   results = collection.aggregate([
+       {
+           '$vectorSearch': {
+               'queryVector': [0.1, 0.2, 0.3],
+               'path': 'embeddings',
+               'numCandidates': 100,
+               'limit': 10
+           }
+       }
+   ])
    ```
 
 ## Error Handling
 
 1. Connection errors
    ```python
+   from pymongo.errors import ConnectionFailure
+
    try:
        client = pymongo.MongoClient(connection_string)
        client.admin.command('ping')
-   except pymongo.errors.ConnectionError as e:
+   except ConnectionFailure as e:
        print(f"Connection error: {e}")
    ```
 
@@ -250,9 +243,8 @@ DocumentDB Local accepts TLS connections on the gateway port and requires authen
 
 3. Proper cleanup
    ```python
-   # Always close connections when done
    try:
-       # Your code here
+       collection.insert_one({'name': 'Example'})
    finally:
        client.close()
    ```