Update user-provided embedder search examples with hybrid

learn/ai_powered_search/search_with_user_provided_embeddings.mdx

@@ -54,7 +54,12 @@ Once you have the query's vector, pass it to the `vector` search parameter to pe
 ```sh
 curl -X POST -H 'content-type: application/json' \
   'localhost:7700/indexes/products/search' \
-  --data-binary '{ "vector": [0, 1, 2] }'
+  --data-binary '{ 
+    "vector": [0, 1, 2],
+    "hybrid": {
+      "embedder": "image2text"
+    }
+  }'
 ```
 
 `vector` must be an array of numbers indicating the search vector. You must generate these yourself when using vector search with user-provided embeddings.
@@ -67,6 +72,9 @@ curl -X POST -H 'content-type: application/json' \
   --data-binary '{
     "vector": [0, 1, 2],
     "filter": "price < 10",
-    "sort": ["price:asc"]
+    "sort": ["price:asc"],
+    "hybrid": {
+      "embedder": "image2text"
+    }
   }'
 ```

snippets/samples/code_samples_analytics_event_conversion_1.mdx

@@ -4,7 +4,7 @@
 curl \
   -X POST 'https://PROJECT_URL/events' \
   -H 'Content-Type: application/json' \
-  -H 'Authorization: Bearer DEFAULT_SEARCH_API_KEY'
+  -H 'Authorization: Bearer DEFAULT_SEARCH_API_KEY' \
   --data-binary '{
     "eventType": "conversion",
     "eventName": "Product Added To Cart",