diff --git a/docs/overview/cli.ipynb b/docs/overview/cli.ipynb
index 2db0dab3..5764ee24 100644
--- a/docs/overview/cli.ipynb
+++ b/docs/overview/cli.ipynb
@@ -17,14 +17,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m09:58:03\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   RedisVL version 0.5.2\n"
+      "19:16:18 [RedisVL] INFO   RedisVL version 0.5.2\n"
      ]
     }
    ],
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -99,15 +99,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m18:12:32\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Using Redis address from environment variable, REDIS_URL\n",
-      "\u001b[32m18:12:32\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Index created successfully\n"
+      "19:16:21 [RedisVL] INFO   Index created successfully\n"
      ]
     }
    ],
@@ -118,16 +117,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m18:12:35\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Using Redis address from environment variable, REDIS_URL\n",
-      "\u001b[32m18:12:35\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Indices:\n",
-      "\u001b[32m18:12:35\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   1. vectorizers\n"
+      "19:16:24 [RedisVL] INFO   Indices:\n",
+      "19:16:24 [RedisVL] INFO   1. vectorizers\n"
      ]
     }
    ],
@@ -138,29 +136,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m18:12:37\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Using Redis address from environment variable, REDIS_URL\n",
       "\n",
       "\n",
       "Index Information:\n",
-      "╭──────────────┬────────────────┬────────────┬─────────────────┬────────────╮\n",
-      "│ Index Name   │ Storage Type   │ Prefixes   │ Index Options   │   Indexing │\n",
-      "├──────────────┼────────────────┼────────────┼─────────────────┼────────────┤\n",
-      "│ vectorizers  │ HASH           │ ['doc']    │ []              │          0 │\n",
-      "╰──────────────┴────────────────┴────────────┴─────────────────┴────────────╯\n",
+      "╭───────────────┬───────────────┬───────────────┬───────────────┬───────────────╮\n",
+      "│ Index Name    │ Storage Type  │ Prefixes      │ Index Options │ Indexing      │\n",
+      "├───────────────┼───────────────┼───────────────┼───────────────┼───────────────┤\n",
+      "| vectorizers   | HASH          | ['doc']       | []            | 0             |\n",
+      "╰───────────────┴───────────────┴───────────────┴───────────────┴───────────────╯\n",
       "Index Fields:\n",
-      "╭───────────┬─────────────┬────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────╮\n",
-      "│ Name      │ Attribute   │ Type   │ Field Option   │ Option Value   │ Field Option   │ Option Value   │ Field Option   │   Option Value │ Field Option    │ Option Value   │\n",
-      "├───────────┼─────────────┼────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┤\n",
-      "│ sentence  │ sentence    │ TEXT   │ WEIGHT         │ 1              │                │                │                │                │                 │                │\n",
-      "│ embedding │ embedding   │ VECTOR │ algorithm      │ FLAT           │ data_type      │ FLOAT32        │ dim            │            768 │ distance_metric │ COSINE         │\n",
-      "╰───────────┴─────────────┴────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────╯\n"
+      "╭─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────╮\n",
+      "│ Name            │ Attribute       │ Type            │ Field Option    │ Option Value    │ Field Option    │ Option Value    │ Field Option    │ Option Value    │ Field Option    │ Option Value    │\n",
+      "├─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┤\n",
+      "│ sentence        │ sentence        │ TEXT            │ WEIGHT          │ 1               │                 │                 │                 │                 │                 │                 │\n",
+      "│ embedding       │ embedding       │ VECTOR          │ algorithm       │ FLAT            │ data_type       │ FLOAT32         │ dim             │ 768             │ distance_metric │ COSINE          │\n",
+      "╰─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────╯\n"
      ]
     }
    ],
@@ -171,15 +168,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m18:12:40\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Using Redis address from environment variable, REDIS_URL\n",
-      "\u001b[32m18:12:40\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Index deleted successfully\n"
+      "19:16:29 [RedisVL] INFO   Index deleted successfully\n"
      ]
     }
    ],
@@ -190,15 +186,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m18:12:43\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Using Redis address from environment variable, REDIS_URL\n",
-      "\u001b[32m18:12:43\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Indices:\n"
+      "19:16:32 [RedisVL] INFO   Indices:\n"
      ]
     }
    ],
@@ -218,16 +213,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m18:13:21\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Using Redis address from environment variable, REDIS_URL\n",
-      "\u001b[32m18:13:21\u001b[0m \u001b[34mredisvl.index.index\u001b[0m \u001b[1;30mINFO\u001b[0m   Index already exists, not overwriting.\n",
-      "\u001b[32m18:13:21\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Index created successfully\n"
+      "19:16:35 [RedisVL] INFO   Index created successfully\n"
      ]
     }
    ],
@@ -239,16 +232,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m18:13:25\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Using Redis address from environment variable, REDIS_URL\n",
-      "\u001b[32m18:13:25\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Indices:\n",
-      "\u001b[32m18:13:25\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   1. vectorizers\n"
+      "19:16:38 [RedisVL] INFO   Indices:\n",
+      "19:16:38 [RedisVL] INFO   1. vectorizers\n"
      ]
     }
    ],
@@ -259,39 +251,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m18:13:31\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Using Redis address from environment variable, REDIS_URL\n",
       "\n",
       "Statistics:\n",
-      "╭─────────────────────────────┬─────────╮\n",
-      "│ Stat Key                    │ Value   │\n",
-      "├─────────────────────────────┼─────────┤\n",
-      "│ num_docs                    │ 0       │\n",
-      "│ num_terms                   │ 0       │\n",
-      "│ max_doc_id                  │ 0       │\n",
-      "│ num_records                 │ 0       │\n",
-      "│ percent_indexed             │ 1       │\n",
-      "│ hash_indexing_failures      │ 3       │\n",
-      "│ number_of_uses              │ 2       │\n",
-      "│ bytes_per_record_avg        │ nan     │\n",
-      "│ doc_table_size_mb           │ 0       │\n",
-      "│ inverted_sz_mb              │ 0       │\n",
-      "│ key_table_size_mb           │ 0       │\n",
-      "│ offset_bits_per_record_avg  │ nan     │\n",
-      "│ offset_vectors_sz_mb        │ 0       │\n",
-      "│ offsets_per_term_avg        │ nan     │\n",
-      "│ records_per_doc_avg         │ nan     │\n",
-      "│ sortable_values_size_mb     │ 0       │\n",
-      "│ total_indexing_time         │ 0.02    │\n",
-      "│ total_inverted_index_blocks │ 0       │\n",
-      "│ vector_index_sz_mb          │ 0       │\n",
-      "╰─────────────────────────────┴─────────╯\n"
+      "╭─────────────────────────────┬────────────╮\n",
+      "│ Stat Key                    │ Value      │\n",
+      "├─────────────────────────────┼────────────┤\n",
+      "│ num_docs                    │ 0          │\n",
+      "│ num_terms                   │ 0          │\n",
+      "│ max_doc_id                  │ 0          │\n",
+      "│ num_records                 │ 0          │\n",
+      "│ percent_indexed             │ 1          │\n",
+      "│ hash_indexing_failures      │ 0          │\n",
+      "│ number_of_uses              │ 1          │\n",
+      "│ bytes_per_record_avg        │ nan        │\n",
+      "│ doc_table_size_mb           │ 0          │\n",
+      "│ inverted_sz_mb              │ 0          │\n",
+      "│ key_table_size_mb           │ 0          │\n",
+      "│ offset_bits_per_record_avg  │ nan        │\n",
+      "│ offset_vectors_sz_mb        │ 0          │\n",
+      "│ offsets_per_term_avg        │ nan        │\n",
+      "│ records_per_doc_avg         │ nan        │\n",
+      "│ sortable_values_size_mb     │ 0          │\n",
+      "│ total_indexing_time         │ 0          │\n",
+      "│ total_inverted_index_blocks │ 0          │\n",
+      "│ vector_index_sz_mb          │ 0.00818634 │\n",
+      "╰─────────────────────────────┴────────────╯\n"
      ]
     }
    ],
@@ -327,16 +318,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m18:13:36\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Using Redis address from environment variable, REDIS_URL\n",
-      "\u001b[32m18:13:36\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Indices:\n",
-      "\u001b[32m18:13:36\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   1. vectorizers\n"
+      "19:16:43 [RedisVL] INFO   Indices:\n",
+      "19:16:43 [RedisVL] INFO   1. vectorizers\n"
      ]
     }
    ],
@@ -356,9 +346,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19:16:46 [RedisVL] ERROR   Error 8 connecting to rediss:6379. nodename nor servname provided, or not known.\n"
+     ]
+    }
+   ],
    "source": [
     "# connect to rediss://jane_doe:password123@localhost:6379\n",
     "!rvl index listall --user jane_doe -a password123 --ssl"
@@ -366,9 +364,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19:16:49 [RedisVL] INFO   Index deleted successfully\n"
+     ]
+    }
+   ],
    "source": [
     "!rvl index destroy -i vectorizers"
    ]
@@ -390,7 +396,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.2"
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/user_guide/01_getting_started.ipynb b/docs/user_guide/01_getting_started.ipynb
index bf097415..2c64b3d5 100644
--- a/docs/user_guide/01_getting_started.ipynb
+++ b/docs/user_guide/01_getting_started.ipynb
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -209,20 +209,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<redisvl.index.index.SearchIndex at 0x10faca900>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "index = SearchIndex.from_dict(schema, redis_url=\"redis://localhost:6379\", validate_on_load=True)\n",
     "\n",
@@ -241,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,15 +254,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m10:59:25\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Indices:\n",
-      "\u001b[32m10:59:25\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   1. user_simple\n"
+      "19:17:09 [RedisVL] INFO   Indices:\n",
+      "19:17:09 [RedisVL] INFO   1. user_simple\n"
      ]
     }
    ],
@@ -283,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -293,21 +282,21 @@
       "\n",
       "\n",
       "Index Information:\n",
-      "╭──────────────┬────────────────┬──────────────────────┬─────────────────┬────────────╮\n",
-      "│ Index Name   │ Storage Type   │ Prefixes             │ Index Options   │   Indexing │\n",
-      "├──────────────┼────────────────┼──────────────────────┼─────────────────┼────────────┤\n",
-      "│ user_simple  │ HASH           │ ['user_simple_docs'] │ []              │          0 │\n",
-      "╰──────────────┴────────────────┴──────────────────────┴─────────────────┴────────────╯\n",
+      "╭──────────────────────┬──────────────────────┬──────────────────────┬──────────────────────┬──────────────────────╮\n",
+      "│ Index Name           │ Storage Type         │ Prefixes             │ Index Options        │ Indexing             │\n",
+      "├──────────────────────┼──────────────────────┼──────────────────────┼──────────────────────┼──────────────────────┤\n",
+      "| user_simple          | HASH                 | ['user_simple_docs'] | []                   | 0                    |\n",
+      "╰──────────────────────┴──────────────────────┴──────────────────────┴──────────────────────┴──────────────────────╯\n",
       "Index Fields:\n",
-      "╭────────────────┬────────────────┬─────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────╮\n",
-      "│ Name           │ Attribute      │ Type    │ Field Option   │ Option Value   │ Field Option   │ Option Value   │ Field Option   │   Option Value │ Field Option    │ Option Value   │\n",
-      "├────────────────┼────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┤\n",
-      "│ user           │ user           │ TAG     │ SEPARATOR      │ ,              │                │                │                │                │                 │                │\n",
-      "│ credit_score   │ credit_score   │ TAG     │ SEPARATOR      │ ,              │                │                │                │                │                 │                │\n",
-      "│ job            │ job            │ TEXT    │ WEIGHT         │ 1              │                │                │                │                │                 │                │\n",
-      "│ age            │ age            │ NUMERIC │                │                │                │                │                │                │                 │                │\n",
-      "│ user_embedding │ user_embedding │ VECTOR  │ algorithm      │ FLAT           │ data_type      │ FLOAT32        │ dim            │              3 │ distance_metric │ COSINE         │\n",
-      "╰────────────────┴────────────────┴─────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────╯\n"
+      "╭─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────╮\n",
+      "│ Name            │ Attribute       │ Type            │ Field Option    │ Option Value    │ Field Option    │ Option Value    │ Field Option    │ Option Value    │ Field Option    │ Option Value    │\n",
+      "├─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┤\n",
+      "│ user            │ user            │ TAG             │ SEPARATOR       │ ,               │                 │                 │                 │                 │                 │                 │\n",
+      "│ credit_score    │ credit_score    │ TAG             │ SEPARATOR       │ ,               │                 │                 │                 │                 │                 │                 │\n",
+      "│ job             │ job             │ TEXT            │ WEIGHT          │ 1               │                 │                 │                 │                 │                 │                 │\n",
+      "│ age             │ age             │ NUMERIC         │                 │                 │                 │                 │                 │                 │                 │                 │\n",
+      "│ user_embedding  │ user_embedding  │ VECTOR          │ algorithm       │ FLAT            │ data_type       │ FLOAT32         │ dim             │ 3               │ distance_metric │ COSINE          │\n",
+      "╰─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────╯\n"
      ]
     }
    ],
@@ -329,14 +318,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['user_simple_docs:01JQ9FEZ4GAAYT9W7BWAF7CV18', 'user_simple_docs:01JQ9FEZ4JCE5FD1D5QY6BAJ0J', 'user_simple_docs:01JQ9FEZ4KF9AZYBKMYNMYBZ5A']\n"
+      "['user_simple_docs:01JT4PPPNJZMSK2395RKD208T9', 'user_simple_docs:01JT4PPPNM63J55ZESZ4TV1VR8', 'user_simple_docs:01JT4PPPNM59RCKS2YQ58B1HQW']\n"
      ]
     }
    ],
@@ -363,22 +352,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "11:00:03 redisvl.index.index ERROR   Schema validation error while loading data\n",
+      "19:17:21 redisvl.index.index ERROR   Schema validation error while loading data\n",
       "Traceback (most recent call last):\n",
-      "  File \"/Users/tyler.hutcherson/Documents/AppliedAI/redis-vl-python/redisvl/index/storage.py\", line 204, in _preprocess_and_validate_objects\n",
+      "  File \"/Users/justin.cechmanek/Documents/redisvl/redisvl/index/storage.py\", line 204, in _preprocess_and_validate_objects\n",
       "    processed_obj = self._validate(processed_obj)\n",
-      "  File \"/Users/tyler.hutcherson/Documents/AppliedAI/redis-vl-python/redisvl/index/storage.py\", line 160, in _validate\n",
+      "  File \"/Users/justin.cechmanek/Documents/redisvl/redisvl/index/storage.py\", line 160, in _validate\n",
       "    return validate_object(self.index_schema, obj)\n",
-      "  File \"/Users/tyler.hutcherson/Documents/AppliedAI/redis-vl-python/redisvl/schema/validation.py\", line 274, in validate_object\n",
+      "  File \"/Users/justin.cechmanek/Documents/redisvl/redisvl/schema/validation.py\", line 276, in validate_object\n",
       "    validated = model_class.model_validate(flat_obj)\n",
-      "  File \"/Users/tyler.hutcherson/Library/Caches/pypoetry/virtualenvs/redisvl-VnTEShF2-py3.13/lib/python3.13/site-packages/pydantic/main.py\", line 627, in model_validate\n",
+      "  File \"/Users/justin.cechmanek/.pyenv/versions/3.13/envs/redisvl-dev/lib/python3.13/site-packages/pydantic/main.py\", line 627, in model_validate\n",
       "    return cls.__pydantic_validator__.validate_python(\n",
       "           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n",
       "        obj, strict=strict, from_attributes=from_attributes, context=context\n",
@@ -393,7 +382,7 @@
       "The above exception was the direct cause of the following exception:\n",
       "\n",
       "Traceback (most recent call last):\n",
-      "  File \"/Users/tyler.hutcherson/Documents/AppliedAI/redis-vl-python/redisvl/index/index.py\", line 586, in load\n",
+      "  File \"/Users/justin.cechmanek/Documents/redisvl/redisvl/index/index.py\", line 686, in load\n",
       "    return self._storage.write(\n",
       "           ~~~~~~~~~~~~~~~~~~~^\n",
       "        self._redis_client,  # type: ignore\n",
@@ -403,13 +392,13 @@
       "        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
       "    )\n",
       "    ^\n",
-      "  File \"/Users/tyler.hutcherson/Documents/AppliedAI/redis-vl-python/redisvl/index/storage.py\", line 265, in write\n",
+      "  File \"/Users/justin.cechmanek/Documents/redisvl/redisvl/index/storage.py\", line 265, in write\n",
       "    prepared_objects = self._preprocess_and_validate_objects(\n",
       "        list(objects),  # Convert Iterable to List\n",
       "    ...<3 lines>...\n",
       "        validate=validate,\n",
       "    )\n",
-      "  File \"/Users/tyler.hutcherson/Documents/AppliedAI/redis-vl-python/redisvl/index/storage.py\", line 211, in _preprocess_and_validate_objects\n",
+      "  File \"/Users/justin.cechmanek/Documents/redisvl/redisvl/index/storage.py\", line 211, in _preprocess_and_validate_objects\n",
       "    raise SchemaValidationError(str(e), index=i) from e\n",
       "redisvl.exceptions.SchemaValidationError: Validation failed for object at index 0: 1 validation error for user_simple__PydanticModel\n",
       "user_embedding\n",
@@ -424,17 +413,17 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
-      "File \u001b[0;32m~/Documents/AppliedAI/redis-vl-python/redisvl/index/storage.py:204\u001b[0m, in \u001b[0;36mBaseStorage._preprocess_and_validate_objects\u001b[0;34m(self, objects, id_field, keys, preprocess, validate)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m validate:\n\u001b[0;32m--> 204\u001b[0m     processed_obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocessed_obj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;66;03m# Store valid object with its key for writing\u001b[39;00m\n",
-      "File \u001b[0;32m~/Documents/AppliedAI/redis-vl-python/redisvl/index/storage.py:160\u001b[0m, in \u001b[0;36mBaseStorage._validate\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[38;5;66;03m# Pass directly to validation function and let any errors propagate\u001b[39;00m\n\u001b[0;32m--> 160\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvalidate_object\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex_schema\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Documents/AppliedAI/redis-vl-python/redisvl/schema/validation.py:274\u001b[0m, in \u001b[0;36mvalidate_object\u001b[0;34m(schema, obj)\u001b[0m\n\u001b[1;32m    273\u001b[0m \u001b[38;5;66;03m# Validate against model\u001b[39;00m\n\u001b[0;32m--> 274\u001b[0m validated \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_validate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mflat_obj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    275\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m validated\u001b[38;5;241m.\u001b[39mmodel_dump(exclude_none\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
-      "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/redisvl-VnTEShF2-py3.13/lib/python3.13/site-packages/pydantic/main.py:627\u001b[0m, in \u001b[0;36mBaseModel.model_validate\u001b[0;34m(cls, obj, strict, from_attributes, context)\u001b[0m\n\u001b[1;32m    626\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 627\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pydantic_validator__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_python\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    628\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_attributes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_attributes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontext\u001b[49m\n\u001b[1;32m    629\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/redisvl/redisvl/index/storage.py:204\u001b[0m, in \u001b[0;36mBaseStorage._preprocess_and_validate_objects\u001b[0;34m(self, objects, id_field, keys, preprocess, validate)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m validate:\n\u001b[0;32m--> 204\u001b[0m     processed_obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocessed_obj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;66;03m# Store valid object with its key for writing\u001b[39;00m\n",
+      "File \u001b[0;32m~/Documents/redisvl/redisvl/index/storage.py:160\u001b[0m, in \u001b[0;36mBaseStorage._validate\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[38;5;66;03m# Pass directly to validation function and let any errors propagate\u001b[39;00m\n\u001b[0;32m--> 160\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvalidate_object\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex_schema\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/redisvl/redisvl/schema/validation.py:276\u001b[0m, in \u001b[0;36mvalidate_object\u001b[0;34m(schema, obj)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[38;5;66;03m# Validate against model\u001b[39;00m\n\u001b[0;32m--> 276\u001b[0m validated \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_validate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mflat_obj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    277\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m validated\u001b[38;5;241m.\u001b[39mmodel_dump(exclude_none\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "File \u001b[0;32m~/.pyenv/versions/3.13/envs/redisvl-dev/lib/python3.13/site-packages/pydantic/main.py:627\u001b[0m, in \u001b[0;36mBaseModel.model_validate\u001b[0;34m(cls, obj, strict, from_attributes, context)\u001b[0m\n\u001b[1;32m    626\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 627\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pydantic_validator__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_python\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    628\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_attributes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_attributes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontext\u001b[49m\n\u001b[1;32m    629\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mValidationError\u001b[0m: 1 validation error for user_simple__PydanticModel\nuser_embedding\n  Input should be a valid bytes [type=bytes_type, input_value=True, input_type=bool]\n    For further information visit https://errors.pydantic.dev/2.10/v/bytes_type",
       "\nThe above exception was the direct cause of the following exception:\n",
       "\u001b[0;31mSchemaValidationError\u001b[0m                     Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m keys \u001b[38;5;241m=\u001b[39m \u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muser_embedding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m}\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Documents/AppliedAI/redis-vl-python/redisvl/index/index.py:586\u001b[0m, in \u001b[0;36mSearchIndex.load\u001b[0;34m(self, data, id_field, keys, ttl, preprocess, batch_size)\u001b[0m\n\u001b[1;32m    556\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Load objects to the Redis database. Returns the list of keys loaded\u001b[39;00m\n\u001b[1;32m    557\u001b[0m \u001b[38;5;124;03mto Redis.\u001b[39;00m\n\u001b[1;32m    558\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    583\u001b[0m \u001b[38;5;124;03m    RedisVLError: If there's an error loading data to Redis.\u001b[39;00m\n\u001b[1;32m    584\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    585\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 586\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_storage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    587\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_redis_client\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[1;32m    588\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobjects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    589\u001b[0m \u001b[43m        \u001b[49m\u001b[43mid_field\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mid_field\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    590\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[43m        \u001b[49m\u001b[43mttl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mttl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    592\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreprocess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreprocess\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    593\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    594\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_on_load\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    595\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    596\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SchemaValidationError:\n\u001b[1;32m    597\u001b[0m     \u001b[38;5;66;03m# Pass through validation errors directly\u001b[39;00m\n\u001b[1;32m    598\u001b[0m     logger\u001b[38;5;241m.\u001b[39mexception(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSchema validation error while loading data\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m~/Documents/AppliedAI/redis-vl-python/redisvl/index/storage.py:265\u001b[0m, in \u001b[0;36mBaseStorage.write\u001b[0;34m(self, redis_client, objects, id_field, keys, ttl, preprocess, batch_size, validate)\u001b[0m\n\u001b[1;32m    262\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m []\n\u001b[1;32m    264\u001b[0m \u001b[38;5;66;03m# Pass 1: Preprocess and validate all objects\u001b[39;00m\n\u001b[0;32m--> 265\u001b[0m prepared_objects \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_preprocess_and_validate_objects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    266\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobjects\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Convert Iterable to List\u001b[39;49;00m\n\u001b[1;32m    267\u001b[0m \u001b[43m    \u001b[49m\u001b[43mid_field\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mid_field\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    268\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    269\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpreprocess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreprocess\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    270\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    271\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    273\u001b[0m \u001b[38;5;66;03m# Pass 2: Write all valid objects in batches\u001b[39;00m\n\u001b[1;32m    274\u001b[0m added_keys \u001b[38;5;241m=\u001b[39m []\n",
-      "File \u001b[0;32m~/Documents/AppliedAI/redis-vl-python/redisvl/index/storage.py:211\u001b[0m, in \u001b[0;36mBaseStorage._preprocess_and_validate_objects\u001b[0;34m(self, objects, id_field, keys, preprocess, validate)\u001b[0m\n\u001b[1;32m    207\u001b[0m     prepared_objects\u001b[38;5;241m.\u001b[39mappend((key, processed_obj))\n\u001b[1;32m    209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ValidationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    210\u001b[0m     \u001b[38;5;66;03m# Convert Pydantic ValidationError to SchemaValidationError with index context\u001b[39;00m\n\u001b[0;32m--> 211\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m SchemaValidationError(\u001b[38;5;28mstr\u001b[39m(e), index\u001b[38;5;241m=\u001b[39mi) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    213\u001b[0m     \u001b[38;5;66;03m# Capture other exceptions with context\u001b[39;00m\n\u001b[1;32m    214\u001b[0m     object_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mat index \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n",
+      "Cell \u001b[0;32mIn[31], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# NBVAL_SKIP\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m keys \u001b[38;5;241m=\u001b[39m \u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muser_embedding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m}\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/redisvl/redisvl/index/index.py:686\u001b[0m, in \u001b[0;36mSearchIndex.load\u001b[0;34m(self, data, id_field, keys, ttl, preprocess, batch_size)\u001b[0m\n\u001b[1;32m    656\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Load objects to the Redis database. Returns the list of keys loaded\u001b[39;00m\n\u001b[1;32m    657\u001b[0m \u001b[38;5;124;03mto Redis.\u001b[39;00m\n\u001b[1;32m    658\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    683\u001b[0m \u001b[38;5;124;03m    RedisVLError: If there's an error loading data to Redis.\u001b[39;00m\n\u001b[1;32m    684\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    685\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 686\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_storage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    687\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_redis_client\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[1;32m    688\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobjects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    689\u001b[0m \u001b[43m        \u001b[49m\u001b[43mid_field\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mid_field\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    690\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    691\u001b[0m \u001b[43m        \u001b[49m\u001b[43mttl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mttl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    692\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreprocess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreprocess\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    693\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    694\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_on_load\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    695\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    696\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SchemaValidationError:\n\u001b[1;32m    697\u001b[0m     \u001b[38;5;66;03m# Pass through validation errors directly\u001b[39;00m\n\u001b[1;32m    698\u001b[0m     logger\u001b[38;5;241m.\u001b[39mexception(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSchema validation error while loading data\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/Documents/redisvl/redisvl/index/storage.py:265\u001b[0m, in \u001b[0;36mBaseStorage.write\u001b[0;34m(self, redis_client, objects, id_field, keys, ttl, preprocess, batch_size, validate)\u001b[0m\n\u001b[1;32m    262\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m []\n\u001b[1;32m    264\u001b[0m \u001b[38;5;66;03m# Pass 1: Preprocess and validate all objects\u001b[39;00m\n\u001b[0;32m--> 265\u001b[0m prepared_objects \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_preprocess_and_validate_objects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    266\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobjects\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Convert Iterable to List\u001b[39;49;00m\n\u001b[1;32m    267\u001b[0m \u001b[43m    \u001b[49m\u001b[43mid_field\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mid_field\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    268\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    269\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpreprocess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreprocess\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    270\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    271\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    273\u001b[0m \u001b[38;5;66;03m# Pass 2: Write all valid objects in batches\u001b[39;00m\n\u001b[1;32m    274\u001b[0m added_keys \u001b[38;5;241m=\u001b[39m []\n",
+      "File \u001b[0;32m~/Documents/redisvl/redisvl/index/storage.py:211\u001b[0m, in \u001b[0;36mBaseStorage._preprocess_and_validate_objects\u001b[0;34m(self, objects, id_field, keys, preprocess, validate)\u001b[0m\n\u001b[1;32m    207\u001b[0m     prepared_objects\u001b[38;5;241m.\u001b[39mappend((key, processed_obj))\n\u001b[1;32m    209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ValidationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    210\u001b[0m     \u001b[38;5;66;03m# Convert Pydantic ValidationError to SchemaValidationError with index context\u001b[39;00m\n\u001b[0;32m--> 211\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m SchemaValidationError(\u001b[38;5;28mstr\u001b[39m(e), index\u001b[38;5;241m=\u001b[39mi) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    213\u001b[0m     \u001b[38;5;66;03m# Capture other exceptions with context\u001b[39;00m\n\u001b[1;32m    214\u001b[0m     object_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mat index \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n",
       "\u001b[0;31mSchemaValidationError\u001b[0m: Validation failed for object at index 0: 1 validation error for user_simple__PydanticModel\nuser_embedding\n  Input should be a valid bytes [type=bytes_type, input_value=True, input_type=bool]\n    For further information visit https://errors.pydantic.dev/2.10/v/bytes_type"
      ]
     }
@@ -455,14 +444,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['user_simple_docs:01JQ9FHCB1B64GXF6WPK127VZ6']\n"
+      "['user_simple_docs:01JT4PPX63CH5YRN2BGEYB5TS2']\n"
      ]
     }
    ],
@@ -492,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -517,7 +506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -550,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -569,7 +558,7 @@
        "    'datatype': 'float32'}}]}"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -580,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -593,7 +582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -634,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -659,14 +648,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "11:01:30 redisvl.index.index INFO   Index already exists, overwriting.\n"
+      "19:17:29 redisvl.index.index INFO   Index already exists, overwriting.\n"
      ]
     }
    ],
@@ -677,7 +666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -709,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -718,29 +707,29 @@
      "text": [
       "\n",
       "Statistics:\n",
-      "╭─────────────────────────────┬─────────────╮\n",
-      "│ Stat Key                    │ Value       │\n",
-      "├─────────────────────────────┼─────────────┤\n",
-      "│ num_docs                    │ 4           │\n",
-      "│ num_terms                   │ 0           │\n",
-      "│ max_doc_id                  │ 4           │\n",
-      "│ num_records                 │ 20          │\n",
-      "│ percent_indexed             │ 1           │\n",
-      "│ hash_indexing_failures      │ 0           │\n",
-      "│ number_of_uses              │ 2           │\n",
-      "│ bytes_per_record_avg        │ 47.8        │\n",
-      "│ doc_table_size_mb           │ 0.000423431 │\n",
-      "│ inverted_sz_mb              │ 0.000911713 │\n",
-      "│ key_table_size_mb           │ 0.000165939 │\n",
-      "│ offset_bits_per_record_avg  │ nan         │\n",
-      "│ offset_vectors_sz_mb        │ 0           │\n",
-      "│ offsets_per_term_avg        │ 0           │\n",
-      "│ records_per_doc_avg         │ 5           │\n",
-      "│ sortable_values_size_mb     │ 0           │\n",
-      "│ total_indexing_time         │ 6.529       │\n",
-      "│ total_inverted_index_blocks │ 11          │\n",
-      "│ vector_index_sz_mb          │ 0.235947    │\n",
-      "╰─────────────────────────────┴─────────────╯\n"
+      "╭─────────────────────────────┬────────────╮\n",
+      "│ Stat Key                    │ Value      │\n",
+      "├─────────────────────────────┼────────────┤\n",
+      "│ num_docs                    │ 4          │\n",
+      "│ num_terms                   │ 0          │\n",
+      "│ max_doc_id                  │ 4          │\n",
+      "│ num_records                 │ 20         │\n",
+      "│ percent_indexed             │ 1          │\n",
+      "│ hash_indexing_failures      │ 0          │\n",
+      "│ number_of_uses              │ 2          │\n",
+      "│ bytes_per_record_avg        │ 48.2000007 │\n",
+      "│ doc_table_size_mb           │ 4.23431396 │\n",
+      "│ inverted_sz_mb              │ 9.19342041 │\n",
+      "│ key_table_size_mb           │ 1.93595886 │\n",
+      "│ offset_bits_per_record_avg  │ nan        │\n",
+      "│ offset_vectors_sz_mb        │ 0          │\n",
+      "│ offsets_per_term_avg        │ 0          │\n",
+      "│ records_per_doc_avg         │ 5          │\n",
+      "│ sortable_values_size_mb     │ 0          │\n",
+      "│ total_indexing_time         │ 0.74400001 │\n",
+      "│ total_inverted_index_blocks │ 11         │\n",
+      "│ vector_index_sz_mb          │ 0.23560333 │\n",
+      "╰─────────────────────────────┴────────────╯\n"
      ]
     }
    ],
@@ -768,7 +757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -777,7 +766,7 @@
        "4"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -789,7 +778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -798,7 +787,7 @@
        "True"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -810,7 +799,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -821,7 +810,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "env",
+   "display_name": "redisvl-dev",
    "language": "python",
    "name": "python3"
   },
@@ -835,7 +824,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.2"
+   "version": "3.13.0"
   },
   "orig_nbformat": 4
  },
diff --git a/docs/user_guide/03_llmcache.ipynb b/docs/user_guide/03_llmcache.ipynb
index 0ddcc30c..4234a4e3 100644
--- a/docs/user_guide/03_llmcache.ipynb
+++ b/docs/user_guide/03_llmcache.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,13 +49,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "19:17:51 httpx INFO   HTTP Request: POST https://api.openai.com/v1/completions \"HTTP/1.1 200 OK\"\n",
       "The capital of France is Paris.\n"
      ]
     }
@@ -76,30 +77,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "22:11:38 redisvl.index.index INFO   Index already exists, not overwriting.\n"
+      "19:17:51 sentence_transformers.SentenceTransformer INFO   Use pytorch device_name: mps\n",
+      "19:17:51 sentence_transformers.SentenceTransformer INFO   Load pretrained SentenceTransformer: redis/langcache-embed-v1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 17.57it/s]\n"
      ]
     }
    ],
    "source": [
     "from redisvl.extensions.cache.llm import SemanticCache\n",
+    "from redisvl.utils .vectorize import HFTextVectorizer\n",
     "\n",
     "llmcache = SemanticCache(\n",
-    "    name=\"llmcache\",                     # underlying search index name\n",
-    "    redis_url=\"redis://localhost:6379\",  # redis connection url string\n",
-    "    distance_threshold=0.1               # semantic cache distance threshold\n",
+    "    name=\"llmcache\",                                          # underlying search index name\n",
+    "    redis_url=\"redis://localhost:6379\",                       # redis connection url string\n",
+    "    distance_threshold=0.1,                                   # semantic cache distance threshold\n",
+    "    vectorizer=HFTextVectorizer(\"redis/langcache-embed-v1\"),  # embdding model\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
@@ -109,21 +120,21 @@
       "\n",
       "\n",
       "Index Information:\n",
-      "╭──────────────┬────────────────┬──────────────┬─────────────────┬────────────╮\n",
-      "│ Index Name   │ Storage Type   │ Prefixes     │ Index Options   │   Indexing │\n",
-      "├──────────────┼────────────────┼──────────────┼─────────────────┼────────────┤\n",
-      "│ llmcache     │ HASH           │ ['llmcache'] │ []              │          0 │\n",
-      "╰──────────────┴────────────────┴──────────────┴─────────────────┴────────────╯\n",
+      "╭───────────────┬───────────────┬───────────────┬───────────────┬───────────────╮\n",
+      "│ Index Name    │ Storage Type  │ Prefixes      │ Index Options │ Indexing      │\n",
+      "├───────────────┼───────────────┼───────────────┼───────────────┼───────────────┤\n",
+      "| llmcache      | HASH          | ['llmcache']  | []            | 0             |\n",
+      "╰───────────────┴───────────────┴───────────────┴───────────────┴───────────────╯\n",
       "Index Fields:\n",
-      "╭───────────────┬───────────────┬─────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────╮\n",
-      "│ Name          │ Attribute     │ Type    │ Field Option   │ Option Value   │ Field Option   │ Option Value   │ Field Option   │   Option Value │ Field Option    │ Option Value   │\n",
-      "├───────────────┼───────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┤\n",
-      "│ prompt        │ prompt        │ TEXT    │ WEIGHT         │ 1              │                │                │                │                │                 │                │\n",
-      "│ response      │ response      │ TEXT    │ WEIGHT         │ 1              │                │                │                │                │                 │                │\n",
-      "│ inserted_at   │ inserted_at   │ NUMERIC │                │                │                │                │                │                │                 │                │\n",
-      "│ updated_at    │ updated_at    │ NUMERIC │                │                │                │                │                │                │                 │                │\n",
-      "│ prompt_vector │ prompt_vector │ VECTOR  │ algorithm      │ FLAT           │ data_type      │ FLOAT32        │ dim            │            768 │ distance_metric │ COSINE         │\n",
-      "╰───────────────┴───────────────┴─────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────╯\n"
+      "╭─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────╮\n",
+      "│ Name            │ Attribute       │ Type            │ Field Option    │ Option Value    │ Field Option    │ Option Value    │ Field Option    │ Option Value    │ Field Option    │ Option Value    │\n",
+      "├─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┤\n",
+      "│ prompt          │ prompt          │ TEXT            │ WEIGHT          │ 1               │                 │                 │                 │                 │                 │                 │\n",
+      "│ response        │ response        │ TEXT            │ WEIGHT          │ 1               │                 │                 │                 │                 │                 │                 │\n",
+      "│ inserted_at     │ inserted_at     │ NUMERIC         │                 │                 │                 │                 │                 │                 │                 │                 │\n",
+      "│ updated_at      │ updated_at      │ NUMERIC         │                 │                 │                 │                 │                 │                 │                 │                 │\n",
+      "│ prompt_vector   │ prompt_vector   │ VECTOR          │ algorithm       │ FLAT            │ data_type       │ FLOAT32         │ dim             │ 768             │ distance_metric │ COSINE          │\n",
+      "╰─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────╯\n"
      ]
     }
    ],
@@ -141,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,15 +161,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 18.30it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Empty cache\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
     }
    ],
    "source": [
@@ -179,16 +204,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 26.10it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "'llmcache:115049a298532be2f181edb03f766770c0db84c22aff39003fec340deaec7545'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -211,9 +243,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 12.36it/s]\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -232,16 +271,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 12.22it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "'Paris'"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -266,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,16 +322,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 19.20it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "'Paris'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -299,16 +352,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 26.71it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "[]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -335,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -344,9 +404,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 123,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 20.45it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "llmcache.store(\"This is a TTL test\", \"This is a TTL test response\")\n",
     "\n",
@@ -355,15 +423,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 17.02it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "[]\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
     }
    ],
    "source": [
@@ -375,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -394,7 +476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -418,14 +500,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 14.88it/s]\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Without caching, a call to openAI to answer this simple question took 0.9034533500671387 seconds.\n"
+      "19:18:04 httpx INFO   HTTP Request: POST https://api.openai.com/v1/completions \"HTTP/1.1 200 OK\"\n",
+      "Without caching, a call to openAI to answer this simple question took 0.8826751708984375 seconds.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 18.38it/s]\n"
      ]
     },
     {
@@ -434,7 +531,7 @@
        "'llmcache:67e0f6e28fe2a61c0022fd42bf734bb8ffe49d3e375fd69d692574295a20fc1a'"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -454,15 +551,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 13.65it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 27.94it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 27.19it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 27.53it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 28.12it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 27.38it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 25.39it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 26.34it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 28.07it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 27.35it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Avg time taken with LLM cache enabled: 0.09753389358520508\n",
-      "Percentage of time saved: 89.2%\n"
+      "Avg time taken with LLM cache enabled: 0.0463670015335083\n",
+      "Percentage of time saved: 94.75%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
      ]
     }
    ],
@@ -483,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
@@ -492,29 +612,29 @@
      "text": [
       "\n",
       "Statistics:\n",
-      "╭─────────────────────────────┬─────────────╮\n",
-      "│ Stat Key                    │ Value       │\n",
-      "├─────────────────────────────┼─────────────┤\n",
-      "│ num_docs                    │ 1           │\n",
-      "│ num_terms                   │ 19          │\n",
-      "│ max_doc_id                  │ 6           │\n",
-      "│ num_records                 │ 53          │\n",
-      "│ percent_indexed             │ 1           │\n",
-      "│ hash_indexing_failures      │ 0           │\n",
-      "│ number_of_uses              │ 45          │\n",
-      "│ bytes_per_record_avg        │ 45.0566     │\n",
-      "│ doc_table_size_mb           │ 0.000134468 │\n",
-      "│ inverted_sz_mb              │ 0.00227737  │\n",
-      "│ key_table_size_mb           │ 2.76566e-05 │\n",
-      "│ offset_bits_per_record_avg  │ 8           │\n",
-      "│ offset_vectors_sz_mb        │ 3.91006e-05 │\n",
-      "│ offsets_per_term_avg        │ 0.773585    │\n",
-      "│ records_per_doc_avg         │ 53          │\n",
-      "│ sortable_values_size_mb     │ 0           │\n",
-      "│ total_indexing_time         │ 19.454      │\n",
-      "│ total_inverted_index_blocks │ 21          │\n",
-      "│ vector_index_sz_mb          │ 3.0161      │\n",
-      "╰─────────────────────────────┴─────────────╯\n"
+      "╭─────────────────────────────┬────────────╮\n",
+      "│ Stat Key                    │ Value      │\n",
+      "├─────────────────────────────┼────────────┤\n",
+      "│ num_docs                    │ 1          │\n",
+      "│ num_terms                   │ 19         │\n",
+      "│ max_doc_id                  │ 3          │\n",
+      "│ num_records                 │ 29         │\n",
+      "│ percent_indexed             │ 1          │\n",
+      "│ hash_indexing_failures      │ 0          │\n",
+      "│ number_of_uses              │ 19         │\n",
+      "│ bytes_per_record_avg        │ 75.9655151 │\n",
+      "│ doc_table_size_mb           │ 1.34468078 │\n",
+      "│ inverted_sz_mb              │ 0.00210094 │\n",
+      "│ key_table_size_mb           │ 2.76565551 │\n",
+      "│ offset_bits_per_record_avg  │ 8          │\n",
+      "│ offset_vectors_sz_mb        │ 2.09808349 │\n",
+      "│ offsets_per_term_avg        │ 0.75862067 │\n",
+      "│ records_per_doc_avg         │ 29         │\n",
+      "│ sortable_values_size_mb     │ 0          │\n",
+      "│ total_indexing_time         │ 3.875      │\n",
+      "│ total_inverted_index_blocks │ 21         │\n",
+      "│ vector_index_sz_mb          │ 3.01609802 │\n",
+      "╰─────────────────────────────┴────────────╯\n"
      ]
     }
    ],
@@ -525,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -545,16 +665,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19:18:07 [RedisVL] WARNING   The default vectorizer has changed from `sentence-transformers/all-mpnet-base-v2` to `redis/langcache-embed-v1` in version 0.6.0 of RedisVL. For more information about this model, please refer to https://arxiv.org/abs/2504.02268 or visit https://huggingface.co/redis/langcache-embed-v1. To continue using the old vectorizer, please specify it explicitly in the constructor as: vectorizer=HFTextVectorizer(model='sentence-transformers/all-mpnet-base-v2')\n",
+      "19:18:07 sentence_transformers.SentenceTransformer INFO   Use pytorch device_name: mps\n",
+      "19:18:07 sentence_transformers.SentenceTransformer INFO   Load pretrained SentenceTransformer: redis/langcache-embed-v1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00,  8.98it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 24.89it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 26.95it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "'private_cache:5de9d651f802d9cc3f62b034ced3466bf886a542ce43fe1c2b4181726665bf9c'"
+       "'private_cache:2831a0659fb888e203cd9fedb9f65681bfa55e4977c092ed1bf87d42d2655081'"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -580,9 +718,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 27.98it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -590,6 +735,13 @@
       "found 1 entry \n",
       "The number on file is 123-555-0000\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
     }
    ],
    "source": [
@@ -609,7 +761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -626,16 +778,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19:18:09 [RedisVL] WARNING   The default vectorizer has changed from `sentence-transformers/all-mpnet-base-v2` to `redis/langcache-embed-v1` in version 0.6.0 of RedisVL. For more information about this model, please refer to https://arxiv.org/abs/2504.02268 or visit https://huggingface.co/redis/langcache-embed-v1. To continue using the old vectorizer, please specify it explicitly in the constructor as: vectorizer=HFTextVectorizer(model='sentence-transformers/all-mpnet-base-v2')\n",
+      "19:18:09 sentence_transformers.SentenceTransformer INFO   Use pytorch device_name: mps\n",
+      "19:18:09 sentence_transformers.SentenceTransformer INFO   Load pretrained SentenceTransformer: redis/langcache-embed-v1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 13.54it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 16.76it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 21.82it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 28.80it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 21.04it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "'account_data:d48ebb3a2efbdbc17930a8c7559c548a58b562b2572ef0be28f0bb4ece2382e1'"
+       "'account_data:944f89729b09ca46b99923d223db45e0bccf584cfd53fcaf87d2a58f072582d3'"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -675,9 +847,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 28.15it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -685,6 +864,13 @@
       "found 1 entry\n",
       "Your most recent transaction was for $350\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
     }
    ],
    "source": [
@@ -707,7 +893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -718,7 +904,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "rvl",
+   "display_name": "redisvl-dev",
    "language": "python",
    "name": "python3"
   },
@@ -732,7 +918,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.13.0"
   },
   "orig_nbformat": 4
  },
diff --git a/docs/user_guide/08_semantic_router.ipynb b/docs/user_guide/08_semantic_router.ipynb
index 514d7bfa..376e62a9 100644
--- a/docs/user_guide/08_semantic_router.ipynb
+++ b/docs/user_guide/08_semantic_router.ipynb
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,15 +88,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19:18:32 sentence_transformers.SentenceTransformer INFO   Use pytorch device_name: mps\n",
+      "19:18:32 sentence_transformers.SentenceTransformer INFO   Load pretrained SentenceTransformer: sentence-transformers/all-mpnet-base-v2\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/robert.shelton/Library/Caches/pypoetry/virtualenvs/redisvl-56gG2io_-py3.11/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 17.78it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 37.43it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 27.28it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 48.76it/s]\n"
      ]
     }
    ],
@@ -119,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -129,20 +139,20 @@
       "\n",
       "\n",
       "Index Information:\n",
-      "╭──────────────┬────────────────┬──────────────────┬─────────────────┬────────────╮\n",
-      "│ Index Name   │ Storage Type   │ Prefixes         │ Index Options   │   Indexing │\n",
-      "├──────────────┼────────────────┼──────────────────┼─────────────────┼────────────┤\n",
-      "│ topic-router │ HASH           │ ['topic-router'] │ []              │          0 │\n",
-      "╰──────────────┴────────────────┴──────────────────┴─────────────────┴────────────╯\n",
+      "╭──────────────────┬──────────────────┬──────────────────┬──────────────────┬──────────────────╮\n",
+      "│ Index Name       │ Storage Type     │ Prefixes         │ Index Options    │ Indexing         │\n",
+      "├──────────────────┼──────────────────┼──────────────────┼──────────────────┼──────────────────┤\n",
+      "| topic-router     | HASH             | ['topic-router'] | []               | 0                |\n",
+      "╰──────────────────┴──────────────────┴──────────────────┴──────────────────┴──────────────────╯\n",
       "Index Fields:\n",
-      "╭──────────────┬──────────────┬────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────╮\n",
-      "│ Name         │ Attribute    │ Type   │ Field Option   │ Option Value   │ Field Option   │ Option Value   │ Field Option   │   Option Value │ Field Option    │ Option Value   │\n",
-      "├──────────────┼──────────────┼────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┤\n",
-      "│ reference_id │ reference_id │ TAG    │ SEPARATOR      │ ,              │                │                │                │                │                 │                │\n",
-      "│ route_name   │ route_name   │ TAG    │ SEPARATOR      │ ,              │                │                │                │                │                 │                │\n",
-      "│ reference    │ reference    │ TEXT   │ WEIGHT         │ 1              │                │                │                │                │                 │                │\n",
-      "│ vector       │ vector       │ VECTOR │ algorithm      │ FLAT           │ data_type      │ FLOAT32        │ dim            │            768 │ distance_metric │ COSINE         │\n",
-      "╰──────────────┴──────────────┴────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────╯\n"
+      "╭─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────╮\n",
+      "│ Name            │ Attribute       │ Type            │ Field Option    │ Option Value    │ Field Option    │ Option Value    │ Field Option    │ Option Value    │ Field Option    │ Option Value    │\n",
+      "├─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┤\n",
+      "│ reference_id    │ reference_id    │ TAG             │ SEPARATOR       │ ,               │                 │                 │                 │                 │                 │                 │\n",
+      "│ route_name      │ route_name      │ TAG             │ SEPARATOR       │ ,               │                 │                 │                 │                 │                 │                 │\n",
+      "│ reference       │ reference       │ TEXT            │ WEIGHT          │ 1               │                 │                 │                 │                 │                 │                 │\n",
+      "│ vector          │ vector          │ VECTOR          │ algorithm       │ FLAT            │ data_type       │ FLOAT32         │ dim             │ 768             │ distance_metric │ COSINE          │\n",
+      "╰─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────╯\n"
      ]
     }
    ],
@@ -153,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -162,7 +172,7 @@
        "11"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -180,16 +190,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00,  6.40it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "RouteMatch(name='technology', distance=0.419145862261)"
+       "RouteMatch(name='technology', distance=0.419145842393)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -202,16 +219,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 39.83it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "RouteMatch(name=None, distance=None)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -231,17 +255,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 40.50it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "[RouteMatch(name='technology', distance=0.556493759155),\n",
-       " RouteMatch(name='sports', distance=0.67106004556)]"
+       "[RouteMatch(name='technology', distance=0.556493878365),\n",
+       " RouteMatch(name='sports', distance=0.671060125033)]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -254,17 +285,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 66.18it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "[RouteMatch(name='technology', distance=0.556493759155),\n",
-       " RouteMatch(name='sports', distance=0.62926441431)]"
+       "[RouteMatch(name='technology', distance=0.556493878365),\n",
+       " RouteMatch(name='sports', distance=0.629264354706)]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -293,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -306,16 +344,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 41.89it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "[RouteMatch(name='sports', distance=0.663253903389)]"
+       "[RouteMatch(name='sports', distance=0.663254022598)]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -334,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -366,7 +411,7 @@
        " 'routing_config': {'max_k': 3, 'aggregation_method': 'min'}}"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -377,14 +422,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m11:57:04\u001b[0m \u001b[34mredisvl.index.index\u001b[0m \u001b[1;30mINFO\u001b[0m   Index already exists, not overwriting.\n"
+      "19:18:38 sentence_transformers.SentenceTransformer INFO   Use pytorch device_name: mps\n",
+      "19:18:38 sentence_transformers.SentenceTransformer INFO   Load pretrained SentenceTransformer: sentence-transformers/all-mpnet-base-v2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 54.94it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19:18:40 redisvl.index.index INFO   Index already exists, not overwriting.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
      ]
     }
    ],
@@ -396,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -405,14 +472,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m11:57:06\u001b[0m \u001b[34mredisvl.index.index\u001b[0m \u001b[1;30mINFO\u001b[0m   Index already exists, not overwriting.\n"
+      "19:18:40 sentence_transformers.SentenceTransformer INFO   Use pytorch device_name: mps\n",
+      "19:18:40 sentence_transformers.SentenceTransformer INFO   Load pretrained SentenceTransformer: sentence-transformers/all-mpnet-base-v2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 18.77it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19:18:41 redisvl.index.index INFO   Index already exists, not overwriting.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
      ]
     }
    ],
@@ -431,9 +520,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 13.22it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -441,7 +537,7 @@
        " 'topic-router:technology:7e4bca5853c1c3298b4d001de13c3c7a79a6e0f134f81acc2e7cddbd6845961f']"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -459,13 +555,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[{'id': 'topic-router:technology:f243fb2d073774e81c7815247cb3013794e6225df3cbe3769cee8c6cefaca777',\n",
+       "[{'id': 'topic-router:technology:7e4bca5853c1c3298b4d001de13c3c7a79a6e0f134f81acc2e7cddbd6845961f',\n",
+       "  'reference_id': '7e4bca5853c1c3298b4d001de13c3c7a79a6e0f134f81acc2e7cddbd6845961f',\n",
+       "  'route_name': 'technology',\n",
+       "  'reference': 'new tech gadgets'},\n",
+       " {'id': 'topic-router:technology:f243fb2d073774e81c7815247cb3013794e6225df3cbe3769cee8c6cefaca777',\n",
        "  'reference_id': 'f243fb2d073774e81c7815247cb3013794e6225df3cbe3769cee8c6cefaca777',\n",
        "  'route_name': 'technology',\n",
        "  'reference': 'latest AI trends'},\n",
@@ -480,14 +580,10 @@
        " {'id': 'topic-router:technology:85cc73a1437df27caa2f075a29c497e5a2e532023fbb75378aedbae80779ab37',\n",
        "  'reference_id': '85cc73a1437df27caa2f075a29c497e5a2e532023fbb75378aedbae80779ab37',\n",
        "  'route_name': 'technology',\n",
-       "  'reference': 'tell me about the newest gadgets'},\n",
-       " {'id': 'topic-router:technology:7e4bca5853c1c3298b4d001de13c3c7a79a6e0f134f81acc2e7cddbd6845961f',\n",
-       "  'reference_id': '7e4bca5853c1c3298b4d001de13c3c7a79a6e0f134f81acc2e7cddbd6845961f',\n",
-       "  'route_name': 'technology',\n",
-       "  'reference': 'new tech gadgets'}]"
+       "  'reference': 'tell me about the newest gadgets'}]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -500,19 +596,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[{'id': 'topic-router:technology:f243fb2d073774e81c7815247cb3013794e6225df3cbe3769cee8c6cefaca777',\n",
-       "  'reference_id': 'f243fb2d073774e81c7815247cb3013794e6225df3cbe3769cee8c6cefaca777',\n",
+       "[{'id': 'topic-router:technology:7e4bca5853c1c3298b4d001de13c3c7a79a6e0f134f81acc2e7cddbd6845961f',\n",
+       "  'reference_id': '7e4bca5853c1c3298b4d001de13c3c7a79a6e0f134f81acc2e7cddbd6845961f',\n",
        "  'route_name': 'technology',\n",
-       "  'reference': 'latest AI trends'}]"
+       "  'reference': 'new tech gadgets'}]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -532,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -541,7 +637,7 @@
        "5"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -554,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -563,7 +659,7 @@
        "1"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -583,7 +679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -593,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -604,7 +700,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "redisvl-56gG2io_-py3.11",
+   "display_name": "redisvl-dev",
    "language": "python",
    "name": "python3"
   },
@@ -618,7 +714,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.13.0"
   },
   "orig_nbformat": 4
  },
diff --git a/docs/user_guide/09_threshold_optimization.ipynb b/docs/user_guide/09_threshold_optimization.ipynb
index 602892df..071f7fbc 100644
--- a/docs/user_guide/09_threshold_optimization.ipynb
+++ b/docs/user_guide/09_threshold_optimization.ipynb
@@ -22,14 +22,43 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/justin.cechmanek/.pyenv/versions/3.13/envs/redisvl-dev/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16:16:11 sentence_transformers.SentenceTransformer INFO   Use pytorch device_name: mps\n",
+      "16:16:11 sentence_transformers.SentenceTransformer INFO   Load pretrained SentenceTransformer: redis/langcache-embed-v1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches:   0%|          | 0/1 [00:00<?, ?it/s]Compiling the model with `torch.compile` and using a `torch.mps` device is not supported. Falling back to non-compiled mode.\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00,  3.38it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00,  1.02it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 25.04it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "from redisvl.extensions.cache.llm import SemanticCache\n",
+    "from redisvl.utils.vectorize import HFTextVectorizer\n",
     "\n",
     "sem_cache = SemanticCache(\n",
-    "    name=\"sem_cache\",                    # underlying search index name\n",
-    "    redis_url=\"redis://localhost:6379\",  # redis connection url string\n",
-    "    distance_threshold=0.5               # semantic cache distance threshold\n",
+    "    name=\"sem_cache\",                                       # underlying search index name\n",
+    "    redis_url=\"redis://localhost:6379\",                     # redis connection url string\n",
+    "    distance_threshold=0.5,                                 # semantic cache distance threshold\n",
+    "    vectorizer=HFTextVectorizer(\"redis/langcache-embed-v1\") # embedding model\n",
     ")\n",
     "\n",
     "paris_key = sem_cache.store(prompt=\"what is the capital of france?\", response=\"paris\")\n",
@@ -48,15 +77,22 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00,  1.24it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "[{'entry_id': 'c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3',\n",
        "  'prompt': 'what is the capital of france?',\n",
        "  'response': 'paris',\n",
-       "  'vector_distance': 0.421104669571,\n",
-       "  'inserted_at': 1741039231.99,\n",
-       "  'updated_at': 1741039231.99,\n",
+       "  'vector_distance': 0.335606634617,\n",
+       "  'inserted_at': 1746051375.81,\n",
+       "  'updated_at': 1746051375.81,\n",
        "  'key': 'sem_cache:c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3'}]"
       ]
      },
@@ -107,8 +143,25 @@
      "output_type": "stream",
      "text": [
       "Distance threshold before: 0.5 \n",
-      "\n",
-      "Distance threshold after: 0.13050847457627118 \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00,  1.09it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 23.17it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 24.43it/s]\n",
+      "/Users/justin.cechmanek/.pyenv/versions/3.13/envs/redisvl-dev/lib/python3.13/site-packages/ranx/metrics/f1.py:36: NumbaTypeSafetyWarning: unsafe cast from uint64 to int64. Precision may be lost.\n",
+      "  scores[i] = _f1(qrels[i], run[i], k, rel_lvl)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Distance threshold after: 0.10372881355932204 \n",
       "\n"
      ]
     }
@@ -149,6 +202,13 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 12.39it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -176,15 +236,22 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 25.92it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "[{'entry_id': 'c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3',\n",
        "  'prompt': 'what is the capital of france?',\n",
        "  'response': 'paris',\n",
-       "  'vector_distance': 0.0835866332054,\n",
-       "  'inserted_at': 1741039231.99,\n",
-       "  'updated_at': 1741039231.99,\n",
+       "  'vector_distance': 0.043138384819,\n",
+       "  'inserted_at': 1746051375.81,\n",
+       "  'updated_at': 1746051375.81,\n",
        "  'key': 'sem_cache:c990cc06e5e77570e5f03360426d2b7f947cbb5a67daa8af8164bfe0b3e24fe3'}]"
       ]
      },
@@ -243,7 +310,25 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16:16:41 sentence_transformers.SentenceTransformer INFO   Use pytorch device_name: mps\n",
+      "16:16:41 sentence_transformers.SentenceTransformer INFO   Load pretrained SentenceTransformer: sentence-transformers/all-mpnet-base-v2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00,  5.90it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00,  4.97it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 61.29it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "from redisvl.extensions.router import SemanticRouter\n",
@@ -332,9 +417,693 @@
      "output_type": "stream",
      "text": [
       "Route thresholds before: {'greeting': 0.5, 'farewell': 0.5} \n",
-      "\n",
-      "Eval metric F1: start 0.438, end 0.719 \n",
-      "Ending thresholds: {'greeting': 1.0858585858585856, 'farewell': 0.5545454545454545}\n"
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00,  7.52it/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 66.81it/s]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eval metric F1: start 0.438, end 0.812 \n",
+      "Ending thresholds: {'greeting': 0.5828282828282831, 'farewell': 0.7545454545454545}\n"
      ]
     }
    ],
@@ -358,10 +1127,17 @@
    "execution_count": 10,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 55.72it/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "RouteMatch(name='greeting', distance=0.295984119177)"
+       "RouteMatch(name='greeting', distance=0.295984089375)"
       ]
      },
      "execution_count": 10,
@@ -395,7 +1171,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "redisvl-Q9FZQJWe-py3.11",
+   "display_name": "redisvl-dev",
    "language": "python",
    "name": "python3"
   },
@@ -409,7 +1185,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.13.0"
   },
   "orig_nbformat": 4
  },
diff --git a/redisvl/cli/index.py b/redisvl/cli/index.py
index 29b5ea23..f53d0a67 100644
--- a/redisvl/cli/index.py
+++ b/redisvl/cli/index.py
@@ -69,7 +69,7 @@ def info(self, args: Namespace):
             rvl index info -i <index_name> | -s <schema_path>
         """
         index = self._connect_to_index(args)
-        _display_in_table(index.info(), output_format=args.format)
+        _display_in_table(index.info())
 
     def listall(self, args: Namespace):
         """List all indices.
@@ -125,7 +125,7 @@ def _connect_to_index(self, args: Namespace) -> SearchIndex:
         return index
 
 
-def _display_in_table(index_info, output_format="rounded_outline"):
+def _display_in_table(index_info):
     print("\n")
     attributes = index_info.get("attributes", [])
     definition = make_dict(index_info.get("index_definition"))
@@ -138,9 +138,10 @@ def _display_in_table(index_info, output_format="rounded_outline"):
     ]
 
     # Display the index information in tabular format
-    col_width = 17
+    headers = ["Index Name", "Storage Type", "Prefixes", "Index Options", "Indexing"]
+    col_width = max(len(str(info)) for info in index_info + headers) + 2
 
-    def print_table_edge(length, start, mid, stop):
+    def print_table_edge(length, col_width, start, mid, stop):
         print(f"{start}", end="")
         for _ in range(length):
             print("─" * col_width, mid, sep="", end="")
@@ -148,22 +149,21 @@ def print_table_edge(length, start, mid, stop):
 
     print("Index Information:")
 
-    print_table_edge(len(index_info), "╭", "┬", "╮")
+    print_table_edge(len(index_info), col_width, "╭", "┬", "╮")
 
     # print header row
-    headers = ["Index Name", "Storage Type", "Prefixes", "Index Options", "Indexing"]
     for header in headers:
         print(f"│ {header.ljust(col_width-2)} ", end="")
     print("│")
 
-    print_table_edge(len(index_info), "├", "┼", "┤")
+    print_table_edge(len(index_info), col_width, "├", "┼", "┤")
 
     # print data row
     for info in index_info:
-        print(f"| {str(info):<15} ", end="")
+        print(f"| {str(info).ljust(col_width-2)} ", end="")
     print("|")
 
-    print_table_edge(len(index_info), "╰", "┴", "╯")
+    print_table_edge(len(index_info), col_width, "╰", "┴", "╯")
 
     attr_values = []
     headers = [
@@ -188,20 +188,25 @@ def print_table_edge(length, start, mid, stop):
 
     # Display the attributes in tabular format
     print("Index Fields:")
-    print_table_edge(len(headers[:-2]), "╭", "┬", "╮")
+    headers = headers[
+        : max(len(row) for row in attr_values)
+    ]  # remove extra headers with no attr values
+    col_widths = [max([len(str(attr)) + 2 for attr in row]) for row in attr_values]
+    print_table_edge(len(headers), max(col_widths), "╭", "┬", "╮")
 
     # print header row
-    print(f"│", end="")
-    for header in headers[:-2]:
-        print(f" {str(header):<15} │", end="")
-    print()
+    for header in headers:
+        print(f"│ {str(header).ljust(max(col_widths)-2)} ", end="")
+    print("│")
 
-    print_table_edge(len(headers[:-2]), "├", "┼", "┤")
+    print_table_edge(len(headers), max(col_widths), "├", "┼", "┤")
 
     # print data rows
+    num_cols = max(len(row) for row in attr_values)
     for row in attr_values:
+        row.extend([""] * (num_cols - len(row)))
         for attr in row:
-            print(f"│ {attr:<15} ", end="")
-        print()
+            print(f"│ {str(attr).ljust(max(col_widths)-2)} ", end="")
+        print("│")
 
-    print_table_edge(len(headers[:-2]), "╰", "┴", "╯")
+    print_table_edge(len(headers), max(col_widths), "╰", "┴", "╯")