diff --git a/docs/api/query.rst b/docs/api/query.rst
index 22616007..70a29bf3 100644
--- a/docs/api/query.rst
+++ b/docs/api/query.rst
@@ -231,3 +231,23 @@ MultiVectorQuery
    :inherited-members:
    :show-inheritance:
    :exclude-members: add_filter,get_args,highlight,return_field,summarize
+
+
+SQLQuery
+========
+
+.. currentmodule:: redisvl.query
+
+
+.. autoclass:: SQLQuery
+   :members:
+   :show-inheritance:
+
+.. note::
+   SQLQuery requires the optional ``sql-redis`` package. Install with:
+   ``pip install redisvl[sql-redis]``
+
+.. note::
+   SQLQuery translates SQL SELECT statements into Redis FT.SEARCH or FT.AGGREGATE commands.
+   The SQL syntax supports WHERE clauses, field selection, ordering, and parameterized queries
+   for vector similarity searches.
diff --git a/docs/user_guide/02_hybrid_queries.ipynb b/docs/user_guide/02_hybrid_queries.ipynb
index e7f8d225..b76f0c51 100644
--- a/docs/user_guide/02_hybrid_queries.ipynb
+++ b/docs/user_guide/02_hybrid_queries.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,18 +92,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 52,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13:00:56 [RedisVL] INFO   Indices:\n",
-      "13:00:56 [RedisVL] INFO   1. user_queries\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# use the CLI to see the created index\n",
     "!rvl index listall"
@@ -111,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
@@ -130,7 +121,7 @@
        "7"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -160,13 +151,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808946609</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -174,6 +165,16 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'@credit_score:{high}=>[KNN 10 @user_embedding $vector AS vector_distance] RETURN 7 user credit_score age job office_location last_updated vector_distance SORTBY vector_distance ASC DIALECT 2 LIMIT 0 10'"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -190,18 +191,39 @@
     ")\n",
     "\n",
     "results = index.query(v)\n",
-    "result_print(results)"
+    "result_print(results)\n",
+    "str(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'@credit_score:{high}=>[KNN 10 @user_embedding $vector AS vector_distance]'"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v.query_string()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.217882037163</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -242,13 +264,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808946609</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -268,13 +290,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808946609</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -305,13 +327,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808946609</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.217882037163</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -340,13 +362,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.217882037163</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -367,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -393,13 +415,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808946609</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.217882037163</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -428,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -441,7 +463,7 @@
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.217882037163</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -466,7 +488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -479,7 +501,7 @@
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.158808946609</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -505,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -518,7 +540,7 @@
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.158808946609</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -554,7 +576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -582,13 +604,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808946609</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.217882037163</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -608,7 +630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -634,7 +656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -660,7 +682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -686,13 +708,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808946609</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.217882037163</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -719,14 +741,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[{'id': 'user_queries_docs:01JY4J5VC91SV4C91BM4D0FCV2',\n",
-       "  'score': 0.9090908893868948,\n",
+       "[{'id': 'user_queries_docs:01KG0AV1K9NY8H2BBKRSRZ90PY',\n",
+       "  'score': 1.8181817787737895,\n",
        "  'vector_distance': '0',\n",
        "  'user': 'john',\n",
        "  'credit_score': 'high',\n",
@@ -734,7 +756,7 @@
        "  'job': 'engineer',\n",
        "  'office_location': '-122.4194,37.7749',\n",
        "  'last_updated': '1741627789'},\n",
-       " {'id': 'user_queries_docs:01JY4J5VC90DRSFJ0WKXXN49JT',\n",
+       " {'id': 'user_queries_docs:01KG0AV1K9NY8H2BBKRSRZ90PZ',\n",
        "  'score': 0.0,\n",
        "  'vector_distance': '0',\n",
        "  'user': 'derrick',\n",
@@ -743,8 +765,8 @@
        "  'job': 'doctor',\n",
        "  'office_location': '-122.4194,37.7749',\n",
        "  'last_updated': '1741627789'},\n",
-       " {'id': 'user_queries_docs:01JY4J5VC9QTPMCD60YP40Q6PW',\n",
-       "  'score': 0.9090908893868948,\n",
+       " {'id': 'user_queries_docs:01KG0AV1K9NY8H2BBKRSRZ90Q1',\n",
+       "  'score': 1.8181817787737895,\n",
        "  'vector_distance': '0.109129190445',\n",
        "  'user': 'tyler',\n",
        "  'credit_score': 'high',\n",
@@ -752,25 +774,25 @@
        "  'job': 'engineer',\n",
        "  'office_location': '-122.0839,37.3861',\n",
        "  'last_updated': '1742232589'},\n",
-       " {'id': 'user_queries_docs:01JY4J5VC9FW7QQNJKDJ4Z7PRG',\n",
+       " {'id': 'user_queries_docs:01KG0AV1K9NY8H2BBKRSRZ90Q2',\n",
        "  'score': 0.0,\n",
-       "  'vector_distance': '0.158808946609',\n",
+       "  'vector_distance': '0.158808887005',\n",
        "  'user': 'tim',\n",
        "  'credit_score': 'high',\n",
        "  'age': '12',\n",
        "  'job': 'dermatologist',\n",
        "  'office_location': '-122.0839,37.3861',\n",
        "  'last_updated': '1739644189'},\n",
-       " {'id': 'user_queries_docs:01JY4J5VC940DJ9F47EJ6KN2MH',\n",
+       " {'id': 'user_queries_docs:01KG0AV1K9NY8H2BBKRSRZ90Q3',\n",
        "  'score': 0.0,\n",
-       "  'vector_distance': '0.217882037163',\n",
+       "  'vector_distance': '0.217881977558',\n",
        "  'user': 'taimur',\n",
        "  'credit_score': 'low',\n",
        "  'age': '15',\n",
        "  'job': 'CEO',\n",
        "  'office_location': '-122.0839,37.3861',\n",
        "  'last_updated': '1742232589'},\n",
-       " {'id': 'user_queries_docs:01JY4J5VC9D53KQD7ZTRP14KCE',\n",
+       " {'id': 'user_queries_docs:01KG0AV1K9NY8H2BBKRSRZ90Q0',\n",
        "  'score': 0.0,\n",
        "  'vector_distance': '0.266666650772',\n",
        "  'user': 'nancy',\n",
@@ -779,7 +801,7 @@
        "  'job': 'doctor',\n",
        "  'office_location': '-122.4194,37.7749',\n",
        "  'last_updated': '1710696589'},\n",
-       " {'id': 'user_queries_docs:01JY4J5VC9806MD90GBZNP0MNY',\n",
+       " {'id': 'user_queries_docs:01KG0AV1K9NY8H2BBKRSRZ90Q4',\n",
        "  'score': 0.0,\n",
        "  'vector_distance': '0.653301358223',\n",
        "  'user': 'joe',\n",
@@ -790,7 +812,7 @@
        "  'last_updated': '1742232589'}]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -813,7 +835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -841,13 +863,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table><tr><th>score</th><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0.4545454446934474</td><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.4545454446934474</td><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.4545454446934474</td><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.4545454446934474</td><td>0.158808946609</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.4545454446934474</td><td>0.217882037163</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.4545454446934474</td><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.4545454446934474</td><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
+       "<table><tr><th>score</th><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0.4545454446934474</td><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.4545454446934474</td><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.4545454446934474</td><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.4545454446934474</td><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.4545454446934474</td><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.4545454446934474</td><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.4545454446934474</td><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -867,13 +889,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table><tr><th>score</th><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0.0</td><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.0</td><td>0.158808946609</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.0</td><td>0.217882037163</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.0</td><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
+       "<table><tr><th>score</th><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0.0</td><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.0</td><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.0</td><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.0</td><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -904,7 +926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
@@ -948,13 +970,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td></tr><tr><td>0.158808946609</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td></tr><tr><td>0.217882037163</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -992,7 +1014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1007,7 +1029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -1032,7 +1054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -1057,7 +1079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -1082,13 +1104,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td></tr><tr><td>0.158808946609</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td></tr><tr><td>0.217882037163</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1116,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
@@ -1158,7 +1180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -1192,13 +1214,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td></tr><tr><td>0.158808946609</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td></tr></table>"
+       "<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1233,7 +1255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -1264,7 +1286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
@@ -1304,7 +1326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
@@ -1345,7 +1367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
@@ -1354,7 +1376,7 @@
        "'@job:(\"engineer\")=>[KNN 5 @user_embedding $vector AS vector_distance] RETURN 6 user credit_score age job office_location vector_distance SORTBY age DESC DIALECT 3 LIMIT 0 5'"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1366,7 +1388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
@@ -1375,7 +1397,7 @@
        "'@credit_score:{high}'"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1388,7 +1410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
@@ -1397,7 +1419,7 @@
        "'((@credit_score:{high} @age:[18 +inf]) @age:[-inf 100])'"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1422,17 +1444,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'id': 'user_queries_docs:01JY4J5VC91SV4C91BM4D0FCV2', 'payload': None, 'user': 'john', 'age': '18', 'job': 'engineer', 'credit_score': 'high', 'office_location': '-122.4194,37.7749', 'user_embedding': '==\\x00\\x00\\x00?', 'last_updated': '1741627789'}\n",
-      "{'id': 'user_queries_docs:01JY4J5VC9D53KQD7ZTRP14KCE', 'payload': None, 'user': 'nancy', 'age': '94', 'job': 'doctor', 'credit_score': 'high', 'office_location': '-122.4194,37.7749', 'user_embedding': '333?=\\x00\\x00\\x00?', 'last_updated': '1710696589'}\n",
-      "{'id': 'user_queries_docs:01JY4J5VC9QTPMCD60YP40Q6PW', 'payload': None, 'user': 'tyler', 'age': '100', 'job': 'engineer', 'credit_score': 'high', 'office_location': '-122.0839,37.3861', 'user_embedding': '=>\\x00\\x00\\x00?', 'last_updated': '1742232589'}\n",
-      "{'id': 'user_queries_docs:01JY4J5VC9FW7QQNJKDJ4Z7PRG', 'payload': None, 'user': 'tim', 'age': '12', 'job': 'dermatologist', 'credit_score': 'high', 'office_location': '-122.0839,37.3861', 'user_embedding': '>>\\x00\\x00\\x00?', 'last_updated': '1739644189'}\n"
+      "{'id': 'user_queries_docs:01KG0AV1K9NY8H2BBKRSRZ90PY', 'payload': None, 'user': 'john', 'age': '18', 'job': 'engineer', 'credit_score': 'high', 'office_location': '-122.4194,37.7749', 'user_embedding': '==\\x00\\x00\\x00?', 'last_updated': '1741627789'}\n",
+      "{'id': 'user_queries_docs:01KG0AV1K9NY8H2BBKRSRZ90Q0', 'payload': None, 'user': 'nancy', 'age': '94', 'job': 'doctor', 'credit_score': 'high', 'office_location': '-122.4194,37.7749', 'user_embedding': '333?=\\x00\\x00\\x00?', 'last_updated': '1710696589'}\n",
+      "{'id': 'user_queries_docs:01KG0AV1K9NY8H2BBKRSRZ90Q1', 'payload': None, 'user': 'tyler', 'age': '100', 'job': 'engineer', 'credit_score': 'high', 'office_location': '-122.0839,37.3861', 'user_embedding': '=>\\x00\\x00\\x00?', 'last_updated': '1742232589'}\n",
+      "{'id': 'user_queries_docs:01KG0AV1K9NY8H2BBKRSRZ90Q2', 'payload': None, 'user': 'tim', 'age': '12', 'job': 'dermatologist', 'credit_score': 'high', 'office_location': '-122.0839,37.3861', 'user_embedding': '>>\\x00\\x00\\x00?', 'last_updated': '1739644189'}\n"
      ]
     }
    ],
@@ -1444,7 +1466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1455,7 +1477,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "redisvl",
    "language": "python",
    "name": "python3"
   },
@@ -1469,10 +1491,10 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.8"
+   "version": "3.11.9"
   },
   "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/docs/user_guide/12_sql_to_redis_queries.ipynb b/docs/user_guide/12_sql_to_redis_queries.ipynb
new file mode 100644
index 00000000..7f26d08d
--- /dev/null
+++ b/docs/user_guide/12_sql_to_redis_queries.ipynb
@@ -0,0 +1,1008 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SQLQuery class\n",
+    "\n",
+    "It may arise that you want to use SQL-like queries to interact with your Redis vector database. While Redis does not natively support SQL, the `redisvl` library provides a `SQLQuery` class that allows you to write SQL-like queries that are automatically translated into Redis queries.\n",
+    "\n",
+    "The `SQLQuery` class is a wrapper around the [`sql-redis`](https://pypi.org/project/sql-redis/) package, which provides a SQL-to-Redis query translator. The `sql-redis` package is not installed by default with `redisvl`, so you will need to install with the optional syntax:\n",
+    "\n",
+    "`pip install redisvl[sql-redis]` or, if running locally, you can `uv sync --all-extras --all-groups`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an index to search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from redisvl.utils.vectorize import HFTextVectorizer\n",
+    "\n",
+    "hf = HFTextVectorizer()\n",
+    "\n",
+    "schema = {\n",
+    "    \"index\": {\n",
+    "        \"name\": \"user_simple\",\n",
+    "        \"prefix\": \"user_simple_docs\",\n",
+    "        \"storage_type\": \"json\",\n",
+    "    },\n",
+    "    \"fields\": [\n",
+    "        {\"name\": \"user\", \"type\": \"tag\"},\n",
+    "        {\"name\": \"region\", \"type\": \"tag\"},\n",
+    "        {\"name\": \"job\", \"type\": \"tag\"},\n",
+    "        {\"name\": \"job_description\", \"type\": \"text\"},\n",
+    "        {\"name\": \"age\", \"type\": \"numeric\"},\n",
+    "        {\n",
+    "            \"name\": \"job_embedding\",\n",
+    "            \"type\": \"vector\",\n",
+    "            \"attrs\": {\n",
+    "                \"dims\": len(hf.embed(\"get embed length\")),\n",
+    "                \"distance_metric\": \"cosine\",\n",
+    "                \"algorithm\": \"flat\",\n",
+    "                \"datatype\": \"float32\"\n",
+    "            }\n",
+    "        }\n",
+    "    ]\n",
+    "}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create sample dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [\n",
+    "    {\n",
+    "        'user': 'john',\n",
+    "        'age': 34,\n",
+    "        'job': 'software engineer',\n",
+    "        'region': 'us-west',\n",
+    "        'job_description': 'Designs, develops, and maintains software applications and systems.'\n",
+    "    },\n",
+    "    {\n",
+    "        'user': 'bill',\n",
+    "        'age': 54,\n",
+    "        'job': 'engineer',\n",
+    "        'region': 'us-central',\n",
+    "        'job_description': 'Applies scientific and mathematical principles to solve technical problems.'\n",
+    "    },\n",
+    "    {\n",
+    "        'user': 'mary',\n",
+    "        'age': 24,\n",
+    "        'job': 'doctor',\n",
+    "        'region': 'us-central',\n",
+    "        'job_description': 'Diagnoses and treats illnesses, injuries, and other medical conditions in the healthcare field.'\n",
+    "    },\n",
+    "    {\n",
+    "        'user': 'joe',\n",
+    "        'age': 27,\n",
+    "        'job': 'dentist',\n",
+    "        'region': 'us-east',\n",
+    "        'job_description': 'Provides oral healthcare including diagnosing and treating teeth and gum issues.'\n",
+    "    },\n",
+    "    {\n",
+    "        'user': 'stacy',\n",
+    "        'age': 61,\n",
+    "        'job': 'project manager',\n",
+    "        'region': 'us-west',\n",
+    "        'job_description': 'Plans, organizes, and oversees projects from inception to completion.'\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "data = [\n",
+    "    {  \n",
+    "        **d,\n",
+    "        \"job_embedding\": hf.embed(f\"{d['job_description']=} {d['job']=}\"),\n",
+    "    } \n",
+    "    for d in data\n",
+    "]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a `SearchIndex`\n",
+    "\n",
+    "With the schema and sample dataset ready, create a `SearchIndex`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bring your own Redis connection instance\n",
+    "\n",
+    "This is ideal in scenarios where you have custom settings on the connection instance or if your application will share a connection pool:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from redisvl.index import SearchIndex\n",
+    "from redis import Redis\n",
+    "\n",
+    "client = Redis.from_url(\"redis://localhost:6379\")\n",
+    "index = SearchIndex.from_dict(schema, redis_client=client, validate_on_load=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let the index manage the connection instance\n",
+    "\n",
+    "This is ideal for simple cases:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index = SearchIndex.from_dict(schema, redis_url=\"redis://localhost:6379\", validate_on_load=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create the index\n",
+    "\n",
+    "Now that we are connected to Redis, we need to run the create command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index.create(overwrite=True, drop=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Data to `SearchIndex`\n",
+    "\n",
+    "Load the sample dataset to Redis.\n",
+    "\n",
+    "### Validate data entries on load\n",
+    "RedisVL uses pydantic validation under the hood to ensure loaded data is valid and confirms to your schema. This setting is optional and can be configured in the `SearchIndex` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['user_simple_docs:01KGJDNVDAZ9A6XY69Q6BCRMX5', 'user_simple_docs:01KGJDNVE024SKZB3804R8B6C3', 'user_simple_docs:01KGJDNVEKW1BV836X4926K7S3', 'user_simple_docs:01KGJDNVF62TBDR9Y9V4WZ59ZG', 'user_simple_docs:01KGJDNVFS0BXM7GF6DA66JHSN']\n"
+     ]
+    }
+   ],
+   "source": [
+    "keys = index.load(data)\n",
+    "\n",
+    "print(keys)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a `SQLQuery` Object\n",
+    "\n",
+    "First, let's test a simple select statement such as the one below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from redisvl.query import SQLQuery\n",
+    "\n",
+    "sql_str = \"\"\"\n",
+    "    SELECT user, region, job, age\n",
+    "    FROM user_simple\n",
+    "    WHERE age > 17\n",
+    "    \"\"\"\n",
+    "\n",
+    "sql_query = SQLQuery(sql_str) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check the created query string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'FT.SEARCH user_simple \"@age:[(17 +inf]\" RETURN 4 user region job age'"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sql_query.redis_query_string(redis_url=\"redis://localhost:6379\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Executing the query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'user': 'john',\n",
+       "  'region': 'us-west',\n",
+       "  'job': 'software engineer',\n",
+       "  'age': '34'},\n",
+       " {'user': 'bill', 'region': 'us-central', 'job': 'engineer', 'age': '54'},\n",
+       " {'user': 'mary', 'region': 'us-central', 'job': 'doctor', 'age': '24'},\n",
+       " {'user': 'joe', 'region': 'us-east', 'job': 'dentist', 'age': '27'},\n",
+       " {'user': 'stacy', 'region': 'us-west', 'job': 'project manager', 'age': '61'}]"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = index.query(sql_query)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional query support\n",
+    "\n",
+    "### Conditional operators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resulting redis query:  FT.SEARCH user_simple \"@age:[(17 +inf] @region:{us\\-west}\" RETURN 4 user region job age\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'user': 'john',\n",
+       "  'region': 'us-west',\n",
+       "  'job': 'software engineer',\n",
+       "  'age': '34'},\n",
+       " {'user': 'stacy', 'region': 'us-west', 'job': 'project manager', 'age': '61'}]"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sql_str = \"\"\"\n",
+    "    SELECT user, region, job, age\n",
+    "    FROM user_simple\n",
+    "    WHERE age > 17 and region = 'us-west'\n",
+    "\"\"\"\n",
+    "\n",
+    "# could maybe be nice to set a connection string at the class level\n",
+    "# this would deviate from our other query like classes though so thinking on it\n",
+    "sql_query = SQLQuery(sql_str)\n",
+    "redis_query = sql_query.redis_query_string(redis_url=\"redis://localhost:6379\")\n",
+    "print(\"Resulting redis query: \", redis_query)\n",
+    "results = index.query(sql_query)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resulting redis query:  FT.SEARCH user_simple \"((@region:{us\\-west})|(@region:{us\\-central}))\" RETURN 4 user region job age\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'user': 'john',\n",
+       "  'region': 'us-west',\n",
+       "  'job': 'software engineer',\n",
+       "  'age': '34'},\n",
+       " {'user': 'bill', 'region': 'us-central', 'job': 'engineer', 'age': '54'},\n",
+       " {'user': 'stacy', 'region': 'us-west', 'job': 'project manager', 'age': '61'},\n",
+       " {'user': 'mary', 'region': 'us-central', 'job': 'doctor', 'age': '24'}]"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sql_str = \"\"\"\n",
+    "    SELECT user, region, job, age\n",
+    "    FROM user_simple\n",
+    "    WHERE region = 'us-west' or region = 'us-central'\n",
+    "    \"\"\"\n",
+    "\n",
+    "sql_query = SQLQuery(sql_str)\n",
+    "redis_query = sql_query.redis_query_string(redis_url=\"redis://localhost:6379\")\n",
+    "print(\"Resulting redis query: \", redis_query)\n",
+    "results = index.query(sql_query)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resulting redis query:  FT.SEARCH user_simple \"@job:{software engineer|engineer|pancake tester}\" RETURN 4 user region job age\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'user': 'john',\n",
+       "  'region': 'us-west',\n",
+       "  'job': 'software engineer',\n",
+       "  'age': '34'},\n",
+       " {'user': 'bill', 'region': 'us-central', 'job': 'engineer', 'age': '54'}]"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# job is a tag field therefore this syntax works\n",
+    "sql_str = \"\"\"\n",
+    "    SELECT user, region, job, age\n",
+    "    FROM user_simple\n",
+    "    WHERE job IN ('software engineer', 'engineer', 'pancake tester')\n",
+    "    \"\"\"\n",
+    "\n",
+    "# could maybe be nice to set a connection string at the class level\n",
+    "# this would deviate from our other query like classes though so thinking on it\n",
+    "sql_query = SQLQuery(sql_str)\n",
+    "redis_query = sql_query.redis_query_string(redis_url=\"redis://localhost:6379\")\n",
+    "print(\"Resulting redis query: \", redis_query)\n",
+    "results = index.query(sql_query)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Text based searches\n",
+    "\n",
+    "See [the docs](https://redis.io/docs/latest/develop/ai/search-and-query/query/full-text/) for available text queries in Redis.\n",
+    "\n",
+    "For more on exact matching see [here](https://redis.io/docs/latest/develop/ai/search-and-query/query/exact-match/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resulting redis query:  FT.SEARCH user_simple \"@job_description:sci*\" RETURN 5 user region job job_description age\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'user': 'bill',\n",
+       "  'region': 'us-central',\n",
+       "  'job': 'engineer',\n",
+       "  'job_description': 'Applies scientific and mathematical principles to solve technical problems.',\n",
+       "  'age': '54'}]"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Prefix\n",
+    "sql_str = \"\"\"\n",
+    "    SELECT user, region, job, job_description, age\n",
+    "    FROM user_simple\n",
+    "    WHERE job_description = 'sci*'\n",
+    "\"\"\"\n",
+    "\n",
+    "# could maybe be nice to set a connection string at the class level\n",
+    "# this would deviate from our other query like classes though so thinking on it\n",
+    "sql_query = SQLQuery(sql_str)\n",
+    "redis_query = sql_query.redis_query_string(redis_url=\"redis://localhost:6379\")\n",
+    "print(\"Resulting redis query: \", redis_query)\n",
+    "results = index.query(sql_query)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resulting redis query:  FT.SEARCH user_simple \"@job_description:*care\" RETURN 5 user region job job_description age\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'user': 'mary',\n",
+       "  'region': 'us-central',\n",
+       "  'job': 'doctor',\n",
+       "  'job_description': 'Diagnoses and treats illnesses, injuries, and other medical conditions in the healthcare field.',\n",
+       "  'age': '24'},\n",
+       " {'user': 'joe',\n",
+       "  'region': 'us-east',\n",
+       "  'job': 'dentist',\n",
+       "  'job_description': 'Provides oral healthcare including diagnosing and treating teeth and gum issues.',\n",
+       "  'age': '27'}]"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Suffix\n",
+    "sql_str = \"\"\"\n",
+    "    SELECT user, region, job, job_description, age\n",
+    "    FROM user_simple\n",
+    "    WHERE job_description = '*care'\n",
+    "\"\"\"\n",
+    "\n",
+    "# could maybe be nice to set a connection string at the class level\n",
+    "# this would deviate from our other query like classes though so thinking on it\n",
+    "sql_query = SQLQuery(sql_str)\n",
+    "redis_query = sql_query.redis_query_string(redis_url=\"redis://localhost:6379\")\n",
+    "print(\"Resulting redis query: \", redis_query)\n",
+    "results = index.query(sql_query)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resulting redis query:  FT.SEARCH user_simple \"@job_description:%diagnose%\" RETURN 5 user region job job_description age\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'user': 'mary',\n",
+       "  'region': 'us-central',\n",
+       "  'job': 'doctor',\n",
+       "  'job_description': 'Diagnoses and treats illnesses, injuries, and other medical conditions in the healthcare field.',\n",
+       "  'age': '24'}]"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fuzzy\n",
+    "sql_str = \"\"\"\n",
+    "    SELECT user, region, job, job_description, age\n",
+    "    FROM user_simple\n",
+    "    WHERE job_description = '%diagnose%'\n",
+    "\"\"\"\n",
+    "\n",
+    "# could maybe be nice to set a connection string at the class level\n",
+    "# this would deviate from our other query like classes though so thinking on it\n",
+    "sql_query = SQLQuery(sql_str)\n",
+    "redis_query = sql_query.redis_query_string(redis_url=\"redis://localhost:6379\")\n",
+    "print(\"Resulting redis query: \", redis_query)\n",
+    "results = index.query(sql_query)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resulting redis query:  FT.SEARCH user_simple \"@job_description:\"healthcare including\"\" RETURN 5 user region job job_description age\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'user': 'joe',\n",
+       "  'region': 'us-east',\n",
+       "  'job': 'dentist',\n",
+       "  'job_description': 'Provides oral healthcare including diagnosing and treating teeth and gum issues.',\n",
+       "  'age': '27'}]"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Phrase no stop words\n",
+    "sql_str = \"\"\"\n",
+    "    SELECT user, region, job, job_description, age\n",
+    "    FROM user_simple\n",
+    "    WHERE job_description = 'healthcare including'\n",
+    "\"\"\"\n",
+    "\n",
+    "# could maybe be nice to set a connection string at the class level\n",
+    "# this would deviate from our other query like classes though so thinking on it\n",
+    "sql_query = SQLQuery(sql_str)\n",
+    "redis_query = sql_query.redis_query_string(redis_url=\"redis://localhost:6379\")\n",
+    "print(\"Resulting redis query: \", redis_query)\n",
+    "results = index.query(sql_query)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resulting redis query:  FT.SEARCH user_simple \"@job_description:\"diagnosing treating\"\" RETURN 5 user region job job_description age\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'user': 'joe',\n",
+       "  'region': 'us-east',\n",
+       "  'job': 'dentist',\n",
+       "  'job_description': 'Provides oral healthcare including diagnosing and treating teeth and gum issues.',\n",
+       "  'age': '27'}]"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Phrase with stop words currently limitation of core Redis\n",
+    "sql_str = \"\"\"\n",
+    "    SELECT user, region, job, job_description, age\n",
+    "    FROM user_simple\n",
+    "    WHERE job_description = 'diagnosing and treating'\n",
+    "\"\"\"\n",
+    "\n",
+    "# could maybe be nice to set a connection string at the class level\n",
+    "# this would deviate from our other query like classes though so thinking on it\n",
+    "sql_query = SQLQuery(sql_str)\n",
+    "redis_query = sql_query.redis_query_string(redis_url=\"redis://localhost:6379\")\n",
+    "print(\"Resulting redis query: \", redis_query)\n",
+    "results = index.query(sql_query)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resulting redis query:  FT.SEARCH user_simple \"@age:[40 60]\" RETURN 4 user region job age\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'user': 'bill', 'region': 'us-central', 'job': 'engineer', 'age': '54'}]"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sql_str = \"\"\"\n",
+    "    SELECT user, region, job, age\n",
+    "    FROM user_simple\n",
+    "    WHERE age BETWEEN 40 and 60\n",
+    "    \"\"\"\n",
+    "\n",
+    "# could maybe be nice to set a connection string at the class level\n",
+    "# this would deviate from our other query like classes though so thinking on it\n",
+    "sql_query = SQLQuery(sql_str)\n",
+    "redis_query = sql_query.redis_query_string(redis_url=\"redis://localhost:6379\")\n",
+    "print(\"Resulting redis query: \", redis_query)\n",
+    "results = index.query(sql_query)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregations\n",
+    "\n",
+    "See docs for redis supported reducer functions: [https://redis.io/docs/latest/develop/ai/search-and-query/advanced-concepts/aggregations/#supported-groupby-reducers](docs)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resulting redis query:  FT.AGGREGATE user_simple \"*\" LOAD 2 age region GROUPBY 1 @region REDUCE COUNT 0 AS count_age REDUCE COUNT_DISTINCT 1 @age AS count_distinct_age REDUCE MIN 1 @age AS min_age REDUCE MAX 1 @age AS max_age REDUCE AVG 1 @age AS avg_age REDUCE STDDEV 1 @age AS std_age REDUCE FIRST_VALUE 1 @age AS fist_value_age REDUCE TOLIST 1 @age AS to_list_age REDUCE QUANTILE 2 @age 0.99 AS quantile_age\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'region': 'us-west',\n",
+       "  'count_age': '2',\n",
+       "  'count_distinct_age': '2',\n",
+       "  'min_age': '34',\n",
+       "  'max_age': '61',\n",
+       "  'avg_age': '47.5',\n",
+       "  'std_age': '19.091883092',\n",
+       "  'fist_value_age': '34',\n",
+       "  'to_list_age': [b'34', b'61'],\n",
+       "  'quantile_age': '61'},\n",
+       " {'region': 'us-central',\n",
+       "  'count_age': '2',\n",
+       "  'count_distinct_age': '2',\n",
+       "  'min_age': '24',\n",
+       "  'max_age': '54',\n",
+       "  'avg_age': '39',\n",
+       "  'std_age': '21.2132034356',\n",
+       "  'fist_value_age': '54',\n",
+       "  'to_list_age': [b'24', b'54'],\n",
+       "  'quantile_age': '54'},\n",
+       " {'region': 'us-east',\n",
+       "  'count_age': '1',\n",
+       "  'count_distinct_age': '1',\n",
+       "  'min_age': '27',\n",
+       "  'max_age': '27',\n",
+       "  'avg_age': '27',\n",
+       "  'std_age': '0',\n",
+       "  'fist_value_age': '27',\n",
+       "  'to_list_age': [b'27'],\n",
+       "  'quantile_age': '27'}]"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sql_str = \"\"\"\n",
+    "    SELECT\n",
+    "        user,\n",
+    "        COUNT(age) as count_age,\n",
+    "        COUNT_DISTINCT(age) as count_distinct_age,\n",
+    "        MIN(age) as min_age,\n",
+    "        MAX(age) as max_age,\n",
+    "        AVG(age) as avg_age,\n",
+    "        STDEV(age) as std_age,\n",
+    "        FIRST_VALUE(age) as fist_value_age,\n",
+    "        ARRAY_AGG(age) as to_list_age,\n",
+    "        QUANTILE(age, 0.99) as quantile_age\n",
+    "    FROM user_simple\n",
+    "    GROUP BY region\n",
+    "    \"\"\"\n",
+    "\n",
+    "sql_query = SQLQuery(sql_str)\n",
+    "redis_query = sql_query.redis_query_string(redis_url=\"redis://localhost:6379\")\n",
+    "print(\"Resulting redis query: \", redis_query)\n",
+    "results = index.query(sql_query)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Vector search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resulting redis query:  FT.SEARCH user_simple \"*=>[KNN 10 @job_embedding $vector AS vector_distance]\" PARAMS 2 vector $vector DIALECT 2 RETURN 4 user job job_description vector_distance SORTBY vector_distance ASC\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'vector_distance': '0.823510587215',\n",
+       "  'user': 'bill',\n",
+       "  'job': 'engineer',\n",
+       "  'job_description': 'Applies scientific and mathematical principles to solve technical problems.'},\n",
+       " {'vector_distance': '0.965160429478',\n",
+       "  'user': 'john',\n",
+       "  'job': 'software engineer',\n",
+       "  'job_description': 'Designs, develops, and maintains software applications and systems.'},\n",
+       " {'vector_distance': '1.00401365757',\n",
+       "  'user': 'mary',\n",
+       "  'job': 'doctor',\n",
+       "  'job_description': 'Diagnoses and treats illnesses, injuries, and other medical conditions in the healthcare field.'},\n",
+       " {'vector_distance': '1.0062687397',\n",
+       "  'user': 'stacy',\n",
+       "  'job': 'project manager',\n",
+       "  'job_description': 'Plans, organizes, and oversees projects from inception to completion.'},\n",
+       " {'vector_distance': '1.01110625267',\n",
+       "  'user': 'joe',\n",
+       "  'job': 'dentist',\n",
+       "  'job_description': 'Provides oral healthcare including diagnosing and treating teeth and gum issues.'}]"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sql_str = \"\"\"\n",
+    "    SELECT user, job, job_description, cosine_distance(job_embedding, :vec) AS vector_distance\n",
+    "    FROM user_simple\n",
+    "    ORDER BY vector_distance ASC\n",
+    "    \"\"\"\n",
+    "\n",
+    "vec = hf.embed(\"looking for someone to use base principles to solve problems\", as_buffer=True)\n",
+    "sql_query = SQLQuery(sql_str, params={\"vec\": vec})\n",
+    "\n",
+    "redis_query = sql_query.redis_query_string(redis_url=\"redis://localhost:6379\")\n",
+    "print(\"Resulting redis query: \", redis_query)\n",
+    "results = index.query(sql_query)\n",
+    "\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resulting redis query:  FT.SEARCH user_simple \"(@region:{us\\-central})=>[KNN 10 @job_embedding $vector AS vector_distance]\" PARAMS 2 vector $vector DIALECT 2 RETURN 3 user region vector_distance SORTBY vector_distance ASC\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'vector_distance': '0.823510587215', 'user': 'bill', 'region': 'us-central'},\n",
+       " {'vector_distance': '1.00401365757', 'user': 'mary', 'region': 'us-central'}]"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sql_str = \"\"\"\n",
+    "    SELECT user, region, cosine_distance(job_embedding, :vec) AS vector_distance\n",
+    "    FROM user_simple\n",
+    "    WHERE region = 'us-central'\n",
+    "    ORDER BY vector_distance ASC\n",
+    "    \"\"\"\n",
+    "\n",
+    "vec = hf.embed(\"looking for someone to use base principles to solve problems\", as_buffer=True)\n",
+    "sql_query = SQLQuery(sql_str, params={\"vec\": vec})\n",
+    "\n",
+    "redis_query = sql_query.redis_query_string(redis_url=\"redis://localhost:6379\")\n",
+    "print(\"Resulting redis query: \", redis_query)\n",
+    "results = index.query(sql_query)\n",
+    "\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleanup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below we will clean up after our work. First, you can flush all data from Redis associated with the index by\n",
+    "using the `.clear()` method. This will leave the secondary index in place for future insertions or updates.\n",
+    "\n",
+    "But if you want to clean up everything, including the index, just use `.delete()`\n",
+    "which will by default remove the index AND the underlying data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Clear all data from Redis associated with the index\n",
+    "index.clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# But the index is still in place\n",
+    "index.exists()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove / delete the index in its entirety\n",
+    "index.delete()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "redisvl",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/user_guide/index.md b/docs/user_guide/index.md
index 602983b5..f89fe51e 100644
--- a/docs/user_guide/index.md
+++ b/docs/user_guide/index.md
@@ -23,4 +23,5 @@ User guides provide helpful resources for using RedisVL and its different compon
 09_svs_vamana
 10_embeddings_cache
 11_advanced_queries
+12_sql_to_redis_queries
 ```
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 9286b066..4469c9f6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "redisvl"
-version = "0.13.2"
+version = "0.14.0"
 description = "Python client library and CLI for using Redis as a vector database"
 authors = [{ name = "Redis Inc.", email = "applied.ai@redis.com" }]
 requires-python = ">=3.9.2,<3.14"
@@ -51,6 +51,9 @@ bedrock = [
 pillow = [
     "pillow>=11.3.0",
 ]
+sql-redis = [
+    "sql-redis>=0.1.1",
+]
 
 [project.urls]
 Homepage = "https://github.com/redis/redis-vl-python"
@@ -64,6 +67,9 @@ rvl = "redisvl.cli.runner:main"
 requires = ["hatchling"]
 build-backend = "hatchling.build"
 
+[tool.hatch.metadata]
+allow-direct-references = true
+
 [dependency-groups]
 dev = [
     "black>=25.1.0,<26",
diff --git a/redisvl/index/index.py b/redisvl/index/index.py
index 4bc66f67..40ce8ae0 100644
--- a/redisvl/index/index.py
+++ b/redisvl/index/index.py
@@ -31,6 +31,7 @@
 
 from redisvl.query.hybrid import HybridQuery
 from redisvl.query.query import VectorQuery
+from redisvl.query.sql import SQLQuery
 from redisvl.redis.utils import (
     _keys_share_hash_tag,
     async_cluster_create_index,
@@ -917,6 +918,49 @@ def _aggregate(self, aggregation_query: AggregationQuery) -> List[Dict[str, Any]
             storage_type=self.schema.index.storage_type,
         )
 
+    def _sql_query(self, sql_query: SQLQuery) -> List[Dict[str, Any]]:
+        """Execute a SQL query and return results.
+
+        Args:
+            sql_query: The SQLQuery object containing the SQL statement.
+
+        Returns:
+            List of dictionaries containing the query results.
+
+        Raises:
+            ImportError: If sql-redis package is not installed.
+        """
+        try:
+            from sql_redis.executor import Executor
+            from sql_redis.schema import SchemaRegistry
+        except ImportError:
+            raise ImportError(
+                "sql-redis is required for SQL query support. "
+                "Install it with: pip install redisvl[sql-redis]"
+            )
+
+        registry = SchemaRegistry(self._redis_client)
+        registry.load_all()  # Loads index schemas from Redis
+
+        executor = Executor(self._redis_client, registry)
+
+        # Execute the query with any params
+        result = executor.execute(sql_query.sql, params=sql_query.params)
+
+        # Decode bytes to strings in the results (Redis may return bytes)
+        decoded_rows = []
+        for row in result.rows:
+            decoded_row = {}
+            for key, value in row.items():
+                # Decode key if bytes
+                str_key = key.decode("utf-8") if isinstance(key, bytes) else key
+                # Decode value if bytes
+                str_value = value.decode("utf-8") if isinstance(value, bytes) else value
+                decoded_row[str_key] = str_value
+            decoded_rows.append(decoded_row)
+
+        return decoded_rows
+
     def aggregate(self, *args, **kwargs) -> "AggregateResult":
         """Perform an aggregation operation against the index.
 
@@ -1118,7 +1162,7 @@ def _query(self, query: BaseQuery) -> List[Dict[str, Any]]:
         return process_results(results, query=query, schema=self.schema)
 
     def query(
-        self, query: Union[BaseQuery, AggregationQuery, HybridQuery]
+        self, query: Union[BaseQuery, AggregationQuery, HybridQuery, SQLQuery]
     ) -> List[Dict[str, Any]]:
         """Execute a query on the index.
 
@@ -1146,6 +1190,8 @@ def query(
         """
         if isinstance(query, AggregationQuery):
             return self._aggregate(query)
+        elif isinstance(query, SQLQuery):
+            return self._sql_query(query)
         elif isinstance(query, HybridQuery):
             return self._hybrid_search(query)
         else:
diff --git a/redisvl/query/__init__.py b/redisvl/query/__init__.py
index aa84633e..3f78c755 100644
--- a/redisvl/query/__init__.py
+++ b/redisvl/query/__init__.py
@@ -15,6 +15,7 @@
     VectorQuery,
     VectorRangeQuery,
 )
+from redisvl.query.sql import SQLQuery
 
 __all__ = [
     "BaseQuery",
@@ -29,4 +30,5 @@
     "AggregateHybridQuery",
     "MultiVectorQuery",
     "Vector",
+    "SQLQuery",
 ]
diff --git a/redisvl/query/sql.py b/redisvl/query/sql.py
new file mode 100644
index 00000000..06dd2369
--- /dev/null
+++ b/redisvl/query/sql.py
@@ -0,0 +1,159 @@
+"""SQL Query class for executing SQL-like queries against Redis."""
+
+import re
+from typing import Any, Dict, Optional
+
+
+class SQLQuery:
+    """A query class that translates SQL-like syntax into Redis queries.
+
+    This class allows users to write SQL SELECT statements that are
+    automatically translated into Redis FT.SEARCH or FT.AGGREGATE commands.
+
+    .. code-block:: python
+
+        from redisvl.query import SQLQuery
+        from redisvl.index import SearchIndex
+
+        index = SearchIndex.from_existing("products", redis_url="redis://localhost:6379")
+
+        sql_query = SQLQuery('''
+            SELECT title, price, category
+            FROM products
+            WHERE category = 'electronics' AND price < 100
+        ''')
+
+        results = index.query(sql_query)
+
+    Note:
+        Requires the optional `sql-redis` package. Install with:
+        ``pip install redisvl[sql]``
+    """
+
+    def __init__(self, sql: str, params: Optional[Dict[str, Any]] = None):
+        """Initialize a SQLQuery.
+
+        Args:
+            sql: The SQL SELECT statement to execute.
+            params: Optional dictionary of parameters for parameterized queries.
+                   Useful for passing vector data for similarity searches.
+        """
+        self.sql = sql
+        self.params = params or {}
+
+    def _substitute_params(self, sql: str, params: Dict[str, Any]) -> str:
+        """Substitute parameter placeholders in SQL with actual values.
+
+        Uses token-based approach: splits SQL on :param patterns, then rebuilds
+        with substituted values. This prevents partial matching (e.g., :id
+        won't match inside :product_id) and is faster than regex at scale.
+
+        Args:
+            sql: The SQL string with :param placeholders.
+            params: Dictionary mapping parameter names to values.
+
+        Returns:
+            SQL string with parameters substituted.
+
+        Note:
+            - String values are wrapped in single quotes with proper escaping
+            - Numeric values are converted to strings
+            - Bytes values (e.g., vectors) are NOT substituted here
+        """
+        if not params:
+            return sql
+
+        # Split SQL on :param patterns, keeping the delimiters
+        # Pattern matches : followed by valid identifier (letter/underscore, then alphanumeric/underscore)
+        tokens = re.split(r"(:[a-zA-Z_][a-zA-Z0-9_]*)", sql)
+
+        result = []
+        for token in tokens:
+            if token.startswith(":"):
+                key = token[1:]  # Remove leading :
+                if key in params:
+                    value = params[key]
+                    if isinstance(value, (int, float)):
+                        result.append(str(value))
+                    elif isinstance(value, str):
+                        # Escape single quotes using SQL standard: ' -> ''
+                        escaped = value.replace("'", "''")
+                        result.append(f"'{escaped}'")
+                    else:
+                        # Keep placeholder for bytes (vectors handled by Executor)
+                        result.append(token)
+                else:
+                    # Keep unmatched placeholders as-is
+                    result.append(token)
+            else:
+                result.append(token)
+
+        return "".join(result)
+
+    def redis_query_string(
+        self,
+        redis_client: Optional[Any] = None,
+        redis_url: str = "redis://localhost:6379",
+    ) -> str:
+        """Translate the SQL query to a Redis command string.
+
+        This method uses the sql-redis translator to convert the SQL statement
+        into the equivalent Redis FT.SEARCH or FT.AGGREGATE command.
+
+        Args:
+            redis_client: A Redis client connection used to load index schemas.
+                If not provided, a connection will be created using redis_url.
+            redis_url: The Redis URL to connect to if redis_client is not provided.
+                Defaults to "redis://localhost:6379".
+
+        Returns:
+            The Redis command string (e.g., 'FT.SEARCH products "@category:{electronics}"').
+
+        Raises:
+            ImportError: If sql-redis package is not installed.
+
+        Example:
+            .. code-block:: python
+
+                from redisvl.query import SQLQuery
+
+                sql_query = SQLQuery("SELECT * FROM products WHERE category = 'electronics'")
+
+                # Using redis_url
+                redis_cmd = sql_query.redis_query_string(redis_url="redis://localhost:6379")
+
+                # Or using an existing client
+                from redis import Redis
+                client = Redis()
+                redis_cmd = sql_query.redis_query_string(redis_client=client)
+
+                print(redis_cmd)
+                # Output: FT.SEARCH products "@category:{electronics}"
+        """
+        try:
+            from sql_redis.schema import SchemaRegistry
+            from sql_redis.translator import Translator
+        except ImportError:
+            raise ImportError(
+                "sql-redis is required for SQL query support. "
+                "Install it with: pip install redisvl[sql]"
+            )
+
+        # Get or create Redis client
+        if redis_client is None:
+            from redis import Redis
+
+            redis_client = Redis.from_url(redis_url)
+
+        # Load schemas from Redis
+        registry = SchemaRegistry(redis_client)
+        registry.load_all()
+
+        # Translate SQL to Redis command
+        translator = Translator(registry)
+
+        # Substitute non-bytes params in SQL before translation
+        sql = self._substitute_params(self.sql, self.params)
+
+        translated = translator.translate(sql)
+        return translated.to_command_string()
diff --git a/tests/integration/test_redis_cluster_support.py b/tests/integration/test_redis_cluster_support.py
index 80b82420..0d18dea3 100644
--- a/tests/integration/test_redis_cluster_support.py
+++ b/tests/integration/test_redis_cluster_support.py
@@ -89,6 +89,7 @@ def test_search_index_cluster_info(redis_cluster_url):
     finally:
         index.delete(drop=True)
 
+
 @pytest.mark.requires_cluster
 @pytest.mark.asyncio
 async def test_async_search_index_cluster_info(redis_cluster_url):
@@ -110,6 +111,7 @@ async def test_async_search_index_cluster_info(redis_cluster_url):
         await index.delete(drop=True)
         await client.aclose()
 
+
 @pytest.mark.requires_cluster
 @pytest.mark.asyncio
 async def test_async_search_index_client(redis_cluster_url):
diff --git a/tests/integration/test_search_index.py b/tests/integration/test_search_index.py
index ae64a229..ebfedbe7 100644
--- a/tests/integration/test_search_index.py
+++ b/tests/integration/test_search_index.py
@@ -304,6 +304,7 @@ def test_search_index_delete(index):
     assert not index.exists()
     assert index.name not in convert_bytes(index.client.execute_command("FT._LIST"))
 
+
 @pytest.mark.parametrize("num_docs", [0, 1, 5, 10, 2042])
 def test_search_index_clear(index, num_docs):
     index.create(overwrite=True, drop=True)
diff --git a/tests/integration/test_sql_redis_hash.py b/tests/integration/test_sql_redis_hash.py
new file mode 100644
index 00000000..33560c35
--- /dev/null
+++ b/tests/integration/test_sql_redis_hash.py
@@ -0,0 +1,1134 @@
+"""Integration tests for SQLQuery class.
+
+These tests verify that SQLQuery can translate SQL-like syntax
+into proper Redis queries and return expected results.
+"""
+
+import uuid
+
+import pytest
+
+from redisvl.index import SearchIndex
+from redisvl.query import SQLQuery
+
+
+@pytest.fixture
+def sql_index(redis_url, worker_id):
+    """Create a products index for SQL query testing."""
+    unique_id = str(uuid.uuid4())[:8]
+    index_name = f"sql_products_{worker_id}_{unique_id}"
+
+    index = SearchIndex.from_dict(
+        {
+            "index": {
+                "name": index_name,
+                "prefix": f"product_{worker_id}_{unique_id}",
+                "storage_type": "hash",
+            },
+            "fields": [
+                {"name": "title", "type": "text", "attrs": {"sortable": True}},
+                {"name": "name", "type": "text", "attrs": {"sortable": True}},
+                {"name": "price", "type": "numeric", "attrs": {"sortable": True}},
+                {"name": "stock", "type": "numeric", "attrs": {"sortable": True}},
+                {"name": "rating", "type": "numeric", "attrs": {"sortable": True}},
+                {"name": "category", "type": "tag", "attrs": {"sortable": True}},
+                {"name": "tags", "type": "tag"},
+            ],
+        },
+        redis_url=redis_url,
+    )
+
+    index.create(overwrite=True)
+
+    # Load test data
+    products = [
+        {
+            "title": "Gaming laptop Pro",
+            "name": "Gaming Laptop",
+            "price": 899,
+            "stock": 10,
+            "rating": 4.5,
+            "category": "electronics",
+            "tags": "sale,featured",
+        },
+        {
+            "title": "Budget laptop Basic",
+            "name": "Budget Laptop",
+            "price": 499,
+            "stock": 25,
+            "rating": 3.8,
+            "category": "electronics",
+            "tags": "sale",
+        },
+        {
+            "title": "Premium laptop Ultra",
+            "name": "Premium Laptop",
+            "price": 1299,
+            "stock": 5,
+            "rating": 4.9,
+            "category": "electronics",
+            "tags": "featured",
+        },
+        {
+            "title": "Python Programming",
+            "name": "Python Book",
+            "price": 45,
+            "stock": 100,
+            "rating": 4.7,
+            "category": "books",
+            "tags": "bestseller",
+        },
+        {
+            "title": "Redis in Action",
+            "name": "Redis Book",
+            "price": 55,
+            "stock": 50,
+            "rating": 4.6,
+            "category": "books",
+            "tags": "featured",
+        },
+        {
+            "title": "Data Science Guide",
+            "name": "DS Book",
+            "price": 65,
+            "stock": 30,
+            "rating": 4.4,
+            "category": "books",
+            "tags": "sale",
+        },
+        {
+            "title": "Wireless Mouse",
+            "name": "Mouse",
+            "price": 29,
+            "stock": 200,
+            "rating": 4.2,
+            "category": "electronics",
+            "tags": "sale",
+        },
+        {
+            "title": "Mechanical Keyboard",
+            "name": "Keyboard",
+            "price": 149,
+            "stock": 75,
+            "rating": 4.6,
+            "category": "electronics",
+            "tags": "featured",
+        },
+        {
+            "title": "USB Hub",
+            "name": "Hub",
+            "price": 25,
+            "stock": 150,
+            "rating": 3.9,
+            "category": "electronics",
+            "tags": "sale",
+        },
+        {
+            "title": "Monitor Stand",
+            "name": "Stand",
+            "price": 89,
+            "stock": 40,
+            "rating": 4.1,
+            "category": "accessories",
+            "tags": "sale,featured",
+        },
+        {
+            "title": "Desk Lamp",
+            "name": "Lamp",
+            "price": 35,
+            "stock": 80,
+            "rating": 4.0,
+            "category": "accessories",
+            "tags": "sale",
+        },
+        {
+            "title": "Notebook Set",
+            "name": "Notebooks",
+            "price": 15,
+            "stock": 300,
+            "rating": 4.3,
+            "category": "stationery",
+            "tags": "bestseller",
+        },
+        {
+            "title": "Laptop and Keyboard Bundle",
+            "name": "Bundle Pack",
+            "price": 999,
+            "stock": 15,
+            "rating": 4.7,
+            "category": "electronics",
+            "tags": "featured,sale",
+        },
+    ]
+
+    index.load(products)
+
+    yield index
+
+    # Cleanup
+    index.delete(drop=True)
+
+
+class TestSQLQueryBasic:
+    """Tests for basic SQL SELECT queries."""
+
+    def test_import_sql_query(self):
+        """Verify SQLQuery can be imported from redisvl.query."""
+        from redisvl.query import SQLQuery
+
+        assert SQLQuery is not None
+
+    def test_select_all_fields(self, sql_index):
+        """Test SELECT * returns all fields."""
+        sql_query = SQLQuery(f"SELECT * FROM {sql_index.name}")
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        # Verify results contain expected fields
+        assert "title" in results[0]
+        assert "price" in results[0]
+
+    def test_select_specific_fields(self, sql_index):
+        """Test SELECT with specific field list."""
+        sql_query = SQLQuery(f"SELECT title, price FROM {sql_index.name}")
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        # Results should contain requested fields
+        assert "title" in results[0]
+        assert "price" in results[0]
+
+    def test_redis_query_string_with_client(self, sql_index):
+        """Test redis_query_string() with redis_client returns the Redis command string."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE category = 'electronics'
+        """
+        )
+
+        # Get the Redis command string using redis_client
+        redis_cmd = sql_query.redis_query_string(redis_client=sql_index._redis_client)
+
+        # Verify it's a valid FT.SEARCH command
+        assert redis_cmd.startswith("FT.SEARCH")
+        assert sql_index.name in redis_cmd
+        assert "electronics" in redis_cmd
+
+    def test_redis_query_string_with_url(self, sql_index, redis_url):
+        """Test redis_query_string() with redis_url returns the Redis command string."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE category = 'electronics'
+        """
+        )
+
+        # Get the Redis command string using redis_url
+        redis_cmd = sql_query.redis_query_string(redis_url=redis_url)
+
+        # Verify it's a valid FT.SEARCH command
+        assert redis_cmd.startswith("FT.SEARCH")
+        assert sql_index.name in redis_cmd
+        assert "electronics" in redis_cmd
+
+    def test_redis_query_string_aggregate(self, sql_index):
+        """Test redis_query_string() returns FT.AGGREGATE for aggregation queries."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, COUNT(*) as count
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+
+        redis_cmd = sql_query.redis_query_string(redis_client=sql_index._redis_client)
+
+        # Verify it's a valid FT.AGGREGATE command
+        assert redis_cmd.startswith("FT.AGGREGATE")
+        assert sql_index.name in redis_cmd
+        assert "GROUPBY" in redis_cmd
+
+
+class TestSQLQueryWhere:
+    """Tests for SQL WHERE clause filtering."""
+
+    def test_where_tag_equals(self, sql_index):
+        """Test WHERE with tag field equality."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price, category
+            FROM {sql_index.name}
+            WHERE category = 'electronics'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert result["category"] == "electronics"
+
+    def test_where_numeric_comparison(self, sql_index):
+        """Test WHERE with numeric field comparison."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE price < 50
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert float(result["price"]) < 50
+
+    def test_where_combined_and(self, sql_index):
+        """Test WHERE with AND combining multiple conditions."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price, category
+            FROM {sql_index.name}
+            WHERE category = 'electronics' AND price < 100
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        for result in results:
+            assert result["category"] == "electronics"
+            assert float(result["price"]) < 100
+
+    def test_where_numeric_range(self, sql_index):
+        """Test WHERE with numeric range (BETWEEN equivalent)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE price >= 25 AND price <= 50
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        for result in results:
+            price = float(result["price"])
+            assert 25 <= price <= 50
+
+
+class TestSQLQueryTagOperators:
+    """Tests for SQL tag field operators."""
+
+    def test_tag_not_equals(self, sql_index):
+        """Test tag != operator."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, category
+            FROM {sql_index.name}
+            WHERE category != 'electronics'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert result["category"] != "electronics"
+
+    def test_tag_in(self, sql_index):
+        """Test tag IN operator."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, category
+            FROM {sql_index.name}
+            WHERE category IN ('books', 'accessories')
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert result["category"] in ("books", "accessories")
+
+
+class TestSQLQueryNumericOperators:
+    """Tests for SQL numeric field operators."""
+
+    def test_numeric_greater_than(self, sql_index):
+        """Test numeric > operator."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE price > 100
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert float(result["price"]) > 100
+
+    def test_numeric_equals(self, sql_index):
+        """Test numeric = operator."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE price = 45
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            assert float(result["price"]) == 45
+
+    def test_numeric_not_equals(self, sql_index):
+        """Test numeric != operator."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE price != 45
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert float(result["price"]) != 45
+
+    @pytest.mark.xfail(reason="Numeric IN operator not yet supported in sql-redis")
+    def test_numeric_in(self, sql_index):
+        """Test numeric IN operator."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE price IN (45, 55, 65)
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            assert float(result["price"]) in (45, 55, 65)
+
+    def test_numeric_between(self, sql_index):
+        """Test numeric BETWEEN operator."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE price BETWEEN 40 AND 60
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            price = float(result["price"])
+            assert 40 <= price <= 60
+
+
+class TestSQLQueryTextOperators:
+    """Tests for SQL text field operators."""
+
+    def test_text_equals(self, sql_index):
+        """Test text = operator (full-text search)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, name
+            FROM {sql_index.name}
+            WHERE title = 'laptop'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            assert "laptop" in result["title"].lower()
+
+    def test_text_not_equals(self, sql_index):
+        """Test text != operator (negated full-text search)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, name
+            FROM {sql_index.name}
+            WHERE title != 'laptop'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            # Results should not contain 'laptop' as a primary match
+            assert "laptop" not in result["title"].lower()
+
+    def test_text_prefix(self, sql_index):
+        """Test text prefix search with wildcard (term*)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, name
+            FROM {sql_index.name}
+            WHERE title = 'lap*'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            # Should match titles starting with "lap" (e.g., "laptop")
+            assert "lap" in result["title"].lower()
+
+    def test_text_suffix(self, sql_index):
+        """Test text suffix search with wildcard (*term)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, name
+            FROM {sql_index.name}
+            WHERE name = '*book'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            # Should match names ending with "book" (e.g., "Python Book")
+            assert "book" in result["name"].lower()
+
+    def test_text_fuzzy(self, sql_index):
+        """Test text fuzzy search with Levenshtein distance (%term%)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, name
+            FROM {sql_index.name}
+            WHERE title = '%laptap%'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            # Should fuzzy match "laptop" even with typo "laptap"
+            assert "laptop" in result["title"].lower()
+
+    def test_text_phrase(self, sql_index):
+        """Test text phrase search (multi-word exact phrase)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, name
+            FROM {sql_index.name}
+            WHERE title = 'gaming laptop'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            # Should match exact phrase "gaming laptop"
+            title_lower = result["title"].lower()
+            assert "gaming" in title_lower and "laptop" in title_lower
+
+    def test_text_phrase_with_stopword(self, sql_index):
+        """Test text phrase search containing stop words.
+
+        Redis does not index stop words (like 'and', 'the', 'is') by default.
+        The sql-redis library works around this by automatically stripping
+        stop words from phrase searches and emitting a warning.
+        See: https://redis.io/docs/latest/develop/ai/search-and-query/advanced-concepts/stopwords/
+        """
+        import warnings
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+
+            sql_query = SQLQuery(
+                f"""
+                SELECT title, name
+                FROM {sql_index.name}
+                WHERE title = 'laptop and keyboard'
+            """
+            )
+            results = sql_index.query(sql_query)
+
+            # Should find the "Laptop and Keyboard Bundle" product
+            assert len(results) >= 1
+            # Verify at least one result contains both "laptop" and "keyboard"
+            found_match = False
+            for result in results:
+                title_lower = result["title"].lower()
+                if "laptop" in title_lower and "keyboard" in title_lower:
+                    found_match = True
+                    break
+            assert found_match, "Expected to find a result with 'laptop' and 'keyboard'"
+
+            # Verify a warning was emitted about stopword removal
+            stopword_warnings = [
+                warning
+                for warning in w
+                if "Stopwords" in str(warning.message)
+                and "and" in str(warning.message).lower()
+            ]
+            assert (
+                len(stopword_warnings) >= 1
+            ), "Expected a warning about stopword removal"
+
+    @pytest.mark.xfail(reason="Text IN operator not yet supported in sql-redis")
+    def test_text_in(self, sql_index):
+        """Test text IN operator (multiple term search)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, name
+            FROM {sql_index.name}
+            WHERE title IN ('Python', 'Redis')
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            title_lower = result["title"].lower()
+            assert "python" in title_lower or "redis" in title_lower
+
+
+class TestSQLQueryOrderBy:
+    """Tests for SQL ORDER BY clause."""
+
+    def test_order_by_asc(self, sql_index):
+        """Test ORDER BY ascending."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            ORDER BY price ASC
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        prices = [float(r["price"]) for r in results]
+        assert prices == sorted(prices)
+
+    def test_order_by_desc(self, sql_index):
+        """Test ORDER BY descending."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            ORDER BY price DESC
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        prices = [float(r["price"]) for r in results]
+        assert prices == sorted(prices, reverse=True)
+
+
+class TestSQLQueryLimit:
+    """Tests for SQL LIMIT and OFFSET clauses."""
+
+    def test_limit(self, sql_index):
+        """Test LIMIT clause."""
+        sql_query = SQLQuery(f"SELECT title FROM {sql_index.name} LIMIT 3")
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 3
+
+    def test_limit_with_offset(self, sql_index):
+        """Test LIMIT with OFFSET for pagination."""
+        # First page
+        sql_query1 = SQLQuery(
+            f"SELECT title FROM {sql_index.name} ORDER BY price ASC LIMIT 3 OFFSET 0"
+        )
+        results1 = sql_index.query(sql_query1)
+
+        # Second page
+        sql_query2 = SQLQuery(
+            f"SELECT title FROM {sql_index.name} ORDER BY price ASC LIMIT 3 OFFSET 3"
+        )
+        results2 = sql_index.query(sql_query2)
+
+        assert len(results1) == 3
+        assert len(results2) == 3
+        # Pages should have different results
+        titles1 = {r["title"] for r in results1}
+        titles2 = {r["title"] for r in results2}
+        assert titles1.isdisjoint(titles2)
+
+
+class TestSQLQueryAggregation:
+    """Tests for SQL aggregation (GROUP BY, COUNT, AVG, etc.)."""
+
+    def test_count_all(self, sql_index):
+        """Test COUNT(*) aggregation."""
+        sql_query = SQLQuery(f"SELECT COUNT(*) as total FROM {sql_index.name}")
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 1
+        assert int(results[0]["total"]) == 13  # 13 products in test data
+
+    def test_group_by_with_count(self, sql_index):
+        """Test GROUP BY with COUNT."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, COUNT(*) as count
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        # Should have groups for electronics, books, accessories, stationery
+        categories = {r["category"] for r in results}
+        assert "electronics" in categories
+        assert "books" in categories
+
+    def test_group_by_with_avg(self, sql_index):
+        """Test GROUP BY with AVG."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, AVG(price) as avg_price
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        # All results should have category and avg_price
+        for result in results:
+            assert "category" in result
+            assert "avg_price" in result
+            assert float(result["avg_price"]) > 0
+
+    def test_group_by_with_filter(self, sql_index):
+        """Test GROUP BY with WHERE filter."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, AVG(price) as avg_price
+            FROM {sql_index.name}
+            WHERE stock > 50
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert "category" in result
+            assert "avg_price" in result
+
+    def test_group_by_with_sum(self, sql_index):
+        """Test GROUP BY with SUM reducer."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, SUM(price) as total_price
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert "category" in result
+            assert "total_price" in result
+            assert float(result["total_price"]) > 0
+
+    def test_group_by_with_min(self, sql_index):
+        """Test GROUP BY with MIN reducer."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, MIN(price) as min_price
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert "category" in result
+            assert "min_price" in result
+            assert float(result["min_price"]) > 0
+
+    def test_group_by_with_max(self, sql_index):
+        """Test GROUP BY with MAX reducer."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, MAX(price) as max_price
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert "category" in result
+            assert "max_price" in result
+            assert float(result["max_price"]) > 0
+
+    def test_global_sum(self, sql_index):
+        """Test global SUM aggregation (no GROUP BY)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT SUM(price) as total
+            FROM {sql_index.name}
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 1
+        assert "total" in results[0]
+        assert float(results[0]["total"]) > 0
+
+    def test_global_min(self, sql_index):
+        """Test global MIN aggregation (no GROUP BY)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT MIN(price) as min_price
+            FROM {sql_index.name}
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 1
+        assert "min_price" in results[0]
+        assert float(results[0]["min_price"]) > 0
+
+    def test_global_max(self, sql_index):
+        """Test global MAX aggregation (no GROUP BY)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT MAX(price) as max_price
+            FROM {sql_index.name}
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 1
+        assert "max_price" in results[0]
+        assert float(results[0]["max_price"]) > 0
+
+    def test_multiple_reducers(self, sql_index):
+        """Test multiple reducers in a single query."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, COUNT(*) as count, SUM(price) as total, AVG(price) as avg_price, MIN(price) as min_price, MAX(price) as max_price
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert "category" in result
+            assert "count" in result
+            assert "total" in result
+            assert "avg_price" in result
+            assert "min_price" in result
+            assert "max_price" in result
+
+    def test_count_distinct(self, sql_index):
+        """Test COUNT_DISTINCT reducer using Redis-specific syntax."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT COUNT_DISTINCT(category) as unique_categories
+            FROM {sql_index.name}
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 1
+        assert "unique_categories" in results[0]
+        # Should have 4 unique categories: electronics, books, accessories, stationery
+        assert int(results[0]["unique_categories"]) == 4
+
+    def test_stddev(self, sql_index):
+        """Test STDDEV reducer."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT STDDEV(price) as price_stddev
+            FROM {sql_index.name}
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 1
+        assert "price_stddev" in results[0]
+        # Verify it's a valid numeric value
+        stddev_value = float(results[0]["price_stddev"])
+        assert stddev_value >= 0  # Standard deviation is always non-negative
+
+    def test_quantile(self, sql_index):
+        """Test QUANTILE reducer."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT QUANTILE(price, 0.5) as median_price
+            FROM {sql_index.name}
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 1
+        assert "median_price" in results[0]
+        # Verify it's a valid numeric value
+        median_value = float(results[0]["median_price"])
+        assert median_value >= 0
+
+    def test_tolist(self, sql_index):
+        """Test TOLIST reducer via ARRAY_AGG SQL function."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, ARRAY_AGG(title) as titles
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert "titles" in result
+            # TOLIST returns a comma-separated string or list of values
+            assert result["titles"] is not None
+
+    def test_first_value(self, sql_index):
+        """Test FIRST_VALUE reducer."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, FIRST_VALUE(title) as first_title
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert "first_title" in result
+            # Verify it's a non-empty string
+            assert isinstance(result["first_title"], str)
+            assert len(result["first_title"]) > 0
+
+
+class TestSQLQueryIntegration:
+    """End-to-end integration tests matching proposal examples."""
+
+    def test_proposal_example_basic(self, sql_index):
+        """Test the basic example from the MLP proposal."""
+        # Example from proposal doc (adapted for our test data)
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price, category
+            FROM {sql_index.name}
+            WHERE category = 'books'
+        """
+        )
+
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert result["category"] == "books"
+            assert "title" in result
+            assert "price" in result
+
+
+@pytest.fixture
+def vector_index(redis_url, worker_id):
+    """Create a books index with vector embeddings for SQL query testing."""
+    import numpy as np
+
+    unique_id = str(uuid.uuid4())[:8]
+    index_name = f"sql_books_{worker_id}_{unique_id}"
+
+    index = SearchIndex.from_dict(
+        {
+            "index": {
+                "name": index_name,
+                "prefix": f"book_{worker_id}_{unique_id}",
+                "storage_type": "hash",
+            },
+            "fields": [
+                {"name": "title", "type": "text", "attrs": {"sortable": True}},
+                {"name": "genre", "type": "tag", "attrs": {"sortable": True}},
+                {"name": "price", "type": "numeric", "attrs": {"sortable": True}},
+                {
+                    "name": "embedding",
+                    "type": "vector",
+                    "attrs": {
+                        "dims": 4,
+                        "distance_metric": "cosine",
+                        "algorithm": "flat",
+                        "datatype": "float32",
+                    },
+                },
+            ],
+        },
+        redis_url=redis_url,
+    )
+
+    index.create(overwrite=True)
+
+    # Create test books with embeddings
+    books = [
+        {
+            "title": "Dune",
+            "genre": "Science Fiction",
+            "price": 15,
+            "embedding": np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32).tobytes(),
+        },
+        {
+            "title": "Foundation",
+            "genre": "Science Fiction",
+            "price": 18,
+            "embedding": np.array([0.15, 0.25, 0.35, 0.45], dtype=np.float32).tobytes(),
+        },
+        {
+            "title": "Neuromancer",
+            "genre": "Science Fiction",
+            "price": 12,
+            "embedding": np.array([0.2, 0.3, 0.4, 0.5], dtype=np.float32).tobytes(),
+        },
+        {
+            "title": "The Hobbit",
+            "genre": "Fantasy",
+            "price": 14,
+            "embedding": np.array([0.9, 0.8, 0.7, 0.6], dtype=np.float32).tobytes(),
+        },
+        {
+            "title": "1984",
+            "genre": "Dystopian",
+            "price": 25,
+            "embedding": np.array([0.5, 0.5, 0.5, 0.5], dtype=np.float32).tobytes(),
+        },
+    ]
+
+    index.load(books)
+
+    yield index
+
+    # Cleanup
+    index.delete(drop=True)
+
+
+class TestSQLQueryVectorSearch:
+    """Tests for SQL vector similarity search using cosine_distance() and vector_distance()."""
+
+    def test_vector_distance_function(self, vector_index):
+        """Test vector search with vector_distance() function."""
+        import numpy as np
+
+        query_vector = np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32).tobytes()
+
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, vector_distance(embedding, :vec) AS score
+            FROM {vector_index.name}
+            LIMIT 3
+            """,
+            params={"vec": query_vector},
+        )
+
+        results = vector_index.query(sql_query)
+
+        assert len(results) > 0
+        assert len(results) <= 3
+        for result in results:
+            assert "title" in result
+            assert "score" in result
+            # Score should be a valid non-negative distance value
+            score = float(result["score"])
+            assert score >= 0
+
+    def test_vector_cosine_similarity(self, vector_index):
+        """Test vector search with cosine_distance() function - pgvector style."""
+        import numpy as np
+
+        # Query vector similar to Science Fiction books
+        query_vector = np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32).tobytes()
+
+        sql_query = SQLQuery(
+            f"""
+            SELECT
+                title,
+                genre,
+                price,
+                cosine_distance(embedding, :query_vector) AS vector_distance
+            FROM {vector_index.name}
+            WHERE genre = 'Science Fiction'
+                AND price <= 20
+            ORDER BY cosine_distance(embedding, :query_vector)
+            LIMIT 3
+            """,
+            params={"query_vector": query_vector},
+        )
+
+        results = vector_index.query(sql_query)
+
+        # Should return Science Fiction books under $20
+        assert len(results) > 0
+        assert len(results) <= 3
+        for result in results:
+            assert result["genre"] == "Science Fiction"
+            assert float(result["price"]) <= 20
+            # Verify vector_distance is returned (like VectorQuery with return_score=True)
+            assert "vector_distance" in result
+            # Distance should be a valid non-negative value
+            distance = float(result["vector_distance"])
+            assert distance >= 0
+
+    def test_vector_redis_query_string(self, vector_index, redis_url):
+        """Test redis_query_string() returns correct KNN query for vector search."""
+        import numpy as np
+
+        # Query vector
+        query_vector = np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32).tobytes()
+
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, cosine_distance(embedding, :vec) AS vector_distance
+            FROM {vector_index.name}
+            LIMIT 3
+            """,
+            params={"vec": query_vector},
+        )
+
+        # Get the Redis command string
+        redis_cmd = sql_query.redis_query_string(redis_url=redis_url)
+
+        # Verify it's a valid FT.SEARCH with KNN syntax
+        assert redis_cmd.startswith("FT.SEARCH")
+        assert vector_index.name in redis_cmd
+        assert "KNN 3" in redis_cmd
+        assert "@embedding" in redis_cmd
+        assert "$vector" in redis_cmd
+        assert "vector_distance" in redis_cmd
+
+    def test_vector_search_with_prefilter_redis_query_string(
+        self, vector_index, redis_url
+    ):
+        """Test redis_query_string() returns correct prefiltered KNN query."""
+        import numpy as np
+
+        query_vector = np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32).tobytes()
+
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, genre, cosine_distance(embedding, :vec) AS vector_distance
+            FROM {vector_index.name}
+            WHERE genre = 'Science Fiction'
+            LIMIT 3
+            """,
+            params={"vec": query_vector},
+        )
+
+        redis_cmd = sql_query.redis_query_string(redis_url=redis_url)
+
+        # Verify prefilter syntax: (filter)=>[KNN ...]
+        assert redis_cmd.startswith("FT.SEARCH")
+        assert "Science Fiction" in redis_cmd or "Science\\ Fiction" in redis_cmd
+        assert "=>[KNN" in redis_cmd
+        assert "KNN 3" in redis_cmd
diff --git a/tests/integration/test_sql_redis_json.py b/tests/integration/test_sql_redis_json.py
new file mode 100644
index 00000000..76191af0
--- /dev/null
+++ b/tests/integration/test_sql_redis_json.py
@@ -0,0 +1,1143 @@
+"""Integration tests for SQLQuery class.
+
+These tests verify that SQLQuery can translate SQL-like syntax
+into proper Redis queries and return expected results.
+"""
+
+import uuid
+
+import pytest
+
+from redisvl.index import SearchIndex
+from redisvl.query import SQLQuery
+
+
+@pytest.fixture
+def sql_index(redis_url, worker_id):
+    """Create a products index for SQL query testing."""
+    unique_id = str(uuid.uuid4())[:8]
+    index_name = f"sql_products_{worker_id}_{unique_id}"
+
+    index = SearchIndex.from_dict(
+        {
+            "index": {
+                "name": index_name,
+                "prefix": f"product_{worker_id}_{unique_id}",
+                "storage_type": "json",
+            },
+            "fields": [
+                {"name": "title", "type": "text", "attrs": {"sortable": True}},
+                {"name": "name", "type": "text", "attrs": {"sortable": True}},
+                {"name": "price", "type": "numeric", "attrs": {"sortable": True}},
+                {"name": "stock", "type": "numeric", "attrs": {"sortable": True}},
+                {"name": "rating", "type": "numeric", "attrs": {"sortable": True}},
+                {"name": "category", "type": "tag", "attrs": {"sortable": True}},
+                {"name": "tags", "type": "tag"},
+            ],
+        },
+        redis_url=redis_url,
+    )
+
+    index.create(overwrite=True)
+
+    # Load test data
+    products = [
+        {
+            "title": "Gaming laptop Pro",
+            "name": "Gaming Laptop",
+            "price": 899,
+            "stock": 10,
+            "rating": 4.5,
+            "category": "electronics",
+            "tags": "sale,featured",
+        },
+        {
+            "title": "Budget laptop Basic",
+            "name": "Budget Laptop",
+            "price": 499,
+            "stock": 25,
+            "rating": 3.8,
+            "category": "electronics",
+            "tags": "sale",
+        },
+        {
+            "title": "Premium laptop Ultra",
+            "name": "Premium Laptop",
+            "price": 1299,
+            "stock": 5,
+            "rating": 4.9,
+            "category": "electronics",
+            "tags": "featured",
+        },
+        {
+            "title": "Python Programming",
+            "name": "Python Book",
+            "price": 45,
+            "stock": 100,
+            "rating": 4.7,
+            "category": "books",
+            "tags": "bestseller",
+        },
+        {
+            "title": "Redis in Action",
+            "name": "Redis Book",
+            "price": 55,
+            "stock": 50,
+            "rating": 4.6,
+            "category": "books",
+            "tags": "featured",
+        },
+        {
+            "title": "Data Science Guide",
+            "name": "DS Book",
+            "price": 65,
+            "stock": 30,
+            "rating": 4.4,
+            "category": "books",
+            "tags": "sale",
+        },
+        {
+            "title": "Wireless Mouse",
+            "name": "Mouse",
+            "price": 29,
+            "stock": 200,
+            "rating": 4.2,
+            "category": "electronics",
+            "tags": "sale",
+        },
+        {
+            "title": "Mechanical Keyboard",
+            "name": "Keyboard",
+            "price": 149,
+            "stock": 75,
+            "rating": 4.6,
+            "category": "electronics",
+            "tags": "featured",
+        },
+        {
+            "title": "USB Hub",
+            "name": "Hub",
+            "price": 25,
+            "stock": 150,
+            "rating": 3.9,
+            "category": "electronics",
+            "tags": "sale",
+        },
+        {
+            "title": "Monitor Stand",
+            "name": "Stand",
+            "price": 89,
+            "stock": 40,
+            "rating": 4.1,
+            "category": "accessories",
+            "tags": "sale,featured",
+        },
+        {
+            "title": "Desk Lamp",
+            "name": "Lamp",
+            "price": 35,
+            "stock": 80,
+            "rating": 4.0,
+            "category": "accessories",
+            "tags": "sale",
+        },
+        {
+            "title": "Notebook Set",
+            "name": "Notebooks",
+            "price": 15,
+            "stock": 300,
+            "rating": 4.3,
+            "category": "stationery",
+            "tags": "bestseller",
+        },
+        {
+            "title": "Laptop and Keyboard Bundle",
+            "name": "Bundle Pack",
+            "price": 999,
+            "stock": 15,
+            "rating": 4.7,
+            "category": "electronics",
+            "tags": "featured,sale",
+        },
+    ]
+
+    index.load(products)
+
+    yield index
+
+    # Cleanup
+    index.delete(drop=True)
+
+
+class TestSQLQueryBasic:
+    """Tests for basic SQL SELECT queries."""
+
+    def test_import_sql_query(self):
+        """Verify SQLQuery can be imported from redisvl.query."""
+        from redisvl.query import SQLQuery
+
+        assert SQLQuery is not None
+
+    def test_select_all_fields(self, sql_index):
+        """Test SELECT * returns all fields."""
+        sql_query = SQLQuery(f"SELECT * FROM {sql_index.name}")
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        # For JSON storage, results may contain '$' key with JSON string or parsed fields
+        first_result = results[0]
+        if "$" in first_result:
+            # JSON storage returns data under '$' key
+            import json
+
+            data = json.loads(first_result["$"])
+            assert "title" in data
+            assert "price" in data
+        else:
+            assert "title" in first_result
+            assert "price" in first_result
+
+    def test_select_specific_fields(self, sql_index):
+        """Test SELECT with specific field list."""
+        sql_query = SQLQuery(f"SELECT title, price FROM {sql_index.name}")
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        # Results should contain requested fields
+        assert "title" in results[0]
+        assert "price" in results[0]
+
+    def test_redis_query_string_with_client(self, sql_index):
+        """Test redis_query_string() with redis_client returns the Redis command string."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE category = 'electronics'
+        """
+        )
+
+        # Get the Redis command string using redis_client
+        redis_cmd = sql_query.redis_query_string(redis_client=sql_index._redis_client)
+
+        # Verify it's a valid FT.SEARCH command
+        assert redis_cmd.startswith("FT.SEARCH")
+        assert sql_index.name in redis_cmd
+        assert "electronics" in redis_cmd
+
+    def test_redis_query_string_with_url(self, sql_index, redis_url):
+        """Test redis_query_string() with redis_url returns the Redis command string."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE category = 'electronics'
+        """
+        )
+
+        # Get the Redis command string using redis_url
+        redis_cmd = sql_query.redis_query_string(redis_url=redis_url)
+
+        # Verify it's a valid FT.SEARCH command
+        assert redis_cmd.startswith("FT.SEARCH")
+        assert sql_index.name in redis_cmd
+        assert "electronics" in redis_cmd
+
+    def test_redis_query_string_aggregate(self, sql_index):
+        """Test redis_query_string() returns FT.AGGREGATE for aggregation queries."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, COUNT(*) as count
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+
+        redis_cmd = sql_query.redis_query_string(redis_client=sql_index._redis_client)
+
+        # Verify it's a valid FT.AGGREGATE command
+        assert redis_cmd.startswith("FT.AGGREGATE")
+        assert sql_index.name in redis_cmd
+        assert "GROUPBY" in redis_cmd
+
+
+class TestSQLQueryWhere:
+    """Tests for SQL WHERE clause filtering."""
+
+    def test_where_tag_equals(self, sql_index):
+        """Test WHERE with tag field equality."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price, category
+            FROM {sql_index.name}
+            WHERE category = 'electronics'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert result["category"] == "electronics"
+
+    def test_where_numeric_comparison(self, sql_index):
+        """Test WHERE with numeric field comparison."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE price < 50
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert float(result["price"]) < 50
+
+    def test_where_combined_and(self, sql_index):
+        """Test WHERE with AND combining multiple conditions."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price, category
+            FROM {sql_index.name}
+            WHERE category = 'electronics' AND price < 100
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        for result in results:
+            assert result["category"] == "electronics"
+            assert float(result["price"]) < 100
+
+    def test_where_numeric_range(self, sql_index):
+        """Test WHERE with numeric range (BETWEEN equivalent)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE price >= 25 AND price <= 50
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        for result in results:
+            price = float(result["price"])
+            assert 25 <= price <= 50
+
+
+class TestSQLQueryTagOperators:
+    """Tests for SQL tag field operators."""
+
+    def test_tag_not_equals(self, sql_index):
+        """Test tag != operator."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, category
+            FROM {sql_index.name}
+            WHERE category != 'electronics'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert result["category"] != "electronics"
+
+    def test_tag_in(self, sql_index):
+        """Test tag IN operator."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, category
+            FROM {sql_index.name}
+            WHERE category IN ('books', 'accessories')
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert result["category"] in ("books", "accessories")
+
+
+class TestSQLQueryNumericOperators:
+    """Tests for SQL numeric field operators."""
+
+    def test_numeric_greater_than(self, sql_index):
+        """Test numeric > operator."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE price > 100
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert float(result["price"]) > 100
+
+    def test_numeric_equals(self, sql_index):
+        """Test numeric = operator."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE price = 45
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            assert float(result["price"]) == 45
+
+    def test_numeric_not_equals(self, sql_index):
+        """Test numeric != operator."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE price != 45
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert float(result["price"]) != 45
+
+    @pytest.mark.xfail(reason="Numeric IN operator not yet supported in sql-redis")
+    def test_numeric_in(self, sql_index):
+        """Test numeric IN operator."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE price IN (45, 55, 65)
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            assert float(result["price"]) in (45, 55, 65)
+
+    def test_numeric_between(self, sql_index):
+        """Test numeric BETWEEN operator."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            WHERE price BETWEEN 40 AND 60
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            price = float(result["price"])
+            assert 40 <= price <= 60
+
+
+class TestSQLQueryTextOperators:
+    """Tests for SQL text field operators."""
+
+    def test_text_equals(self, sql_index):
+        """Test text = operator (full-text search)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, name
+            FROM {sql_index.name}
+            WHERE title = 'laptop'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            assert "laptop" in result["title"].lower()
+
+    def test_text_not_equals(self, sql_index):
+        """Test text != operator (negated full-text search)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, name
+            FROM {sql_index.name}
+            WHERE title != 'laptop'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            # Results should not contain 'laptop' as a primary match
+            assert "laptop" not in result["title"].lower()
+
+    def test_text_prefix(self, sql_index):
+        """Test text prefix search with wildcard (term*)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, name
+            FROM {sql_index.name}
+            WHERE title = 'lap*'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            # Should match titles starting with "lap" (e.g., "laptop")
+            assert "lap" in result["title"].lower()
+
+    def test_text_suffix(self, sql_index):
+        """Test text suffix search with wildcard (*term)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, name
+            FROM {sql_index.name}
+            WHERE name = '*book'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            # Should match names ending with "book" (e.g., "Python Book")
+            assert "book" in result["name"].lower()
+
+    def test_text_fuzzy(self, sql_index):
+        """Test text fuzzy search with Levenshtein distance (%term%)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, name
+            FROM {sql_index.name}
+            WHERE title = '%laptap%'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            # Should fuzzy match "laptop" even with typo "laptap"
+            assert "laptop" in result["title"].lower()
+
+    def test_text_phrase(self, sql_index):
+        """Test text phrase search (multi-word exact phrase)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, name
+            FROM {sql_index.name}
+            WHERE title = 'gaming laptop'
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            # Should match exact phrase "gaming laptop"
+            title_lower = result["title"].lower()
+            assert "gaming" in title_lower and "laptop" in title_lower
+
+    def test_text_phrase_with_stopword(self, sql_index):
+        """Test text phrase search containing stop words.
+
+        Redis does not index stop words (like 'and', 'the', 'is') by default.
+        The sql-redis library works around this by automatically stripping
+        stop words from phrase searches and emitting a warning.
+        See: https://redis.io/docs/latest/develop/ai/search-and-query/advanced-concepts/stopwords/
+        """
+        import warnings
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+
+            sql_query = SQLQuery(
+                f"""
+                SELECT title, name
+                FROM {sql_index.name}
+                WHERE title = 'laptop and keyboard'
+            """
+            )
+            results = sql_index.query(sql_query)
+
+            # Should find the "Laptop and Keyboard Bundle" product
+            assert len(results) >= 1
+            # Verify at least one result contains both "laptop" and "keyboard"
+            found_match = False
+            for result in results:
+                title_lower = result["title"].lower()
+                if "laptop" in title_lower and "keyboard" in title_lower:
+                    found_match = True
+                    break
+            assert found_match, "Expected to find a result with 'laptop' and 'keyboard'"
+
+            # Verify a warning was emitted about stopword removal
+            stopword_warnings = [
+                warning
+                for warning in w
+                if "Stopwords" in str(warning.message)
+                and "and" in str(warning.message).lower()
+            ]
+            assert (
+                len(stopword_warnings) >= 1
+            ), "Expected a warning about stopword removal"
+
+    @pytest.mark.xfail(reason="Text IN operator not yet supported in sql-redis")
+    def test_text_in(self, sql_index):
+        """Test text IN operator (multiple term search)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, name
+            FROM {sql_index.name}
+            WHERE title IN ('Python', 'Redis')
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) >= 1
+        for result in results:
+            title_lower = result["title"].lower()
+            assert "python" in title_lower or "redis" in title_lower
+
+
+class TestSQLQueryOrderBy:
+    """Tests for SQL ORDER BY clause."""
+
+    def test_order_by_asc(self, sql_index):
+        """Test ORDER BY ascending."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            ORDER BY price ASC
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        prices = [float(r["price"]) for r in results]
+        assert prices == sorted(prices)
+
+    def test_order_by_desc(self, sql_index):
+        """Test ORDER BY descending."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price
+            FROM {sql_index.name}
+            ORDER BY price DESC
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        prices = [float(r["price"]) for r in results]
+        assert prices == sorted(prices, reverse=True)
+
+
+class TestSQLQueryLimit:
+    """Tests for SQL LIMIT and OFFSET clauses."""
+
+    def test_limit(self, sql_index):
+        """Test LIMIT clause."""
+        sql_query = SQLQuery(f"SELECT title FROM {sql_index.name} LIMIT 3")
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 3
+
+    def test_limit_with_offset(self, sql_index):
+        """Test LIMIT with OFFSET for pagination."""
+        # First page
+        sql_query1 = SQLQuery(
+            f"SELECT title FROM {sql_index.name} ORDER BY price ASC LIMIT 3 OFFSET 0"
+        )
+        results1 = sql_index.query(sql_query1)
+
+        # Second page
+        sql_query2 = SQLQuery(
+            f"SELECT title FROM {sql_index.name} ORDER BY price ASC LIMIT 3 OFFSET 3"
+        )
+        results2 = sql_index.query(sql_query2)
+
+        assert len(results1) == 3
+        assert len(results2) == 3
+        # Pages should have different results
+        titles1 = {r["title"] for r in results1}
+        titles2 = {r["title"] for r in results2}
+        assert titles1.isdisjoint(titles2)
+
+
+class TestSQLQueryAggregation:
+    """Tests for SQL aggregation (GROUP BY, COUNT, AVG, etc.)."""
+
+    def test_count_all(self, sql_index):
+        """Test COUNT(*) aggregation."""
+        sql_query = SQLQuery(f"SELECT COUNT(*) as total FROM {sql_index.name}")
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 1
+        assert int(results[0]["total"]) == 13  # 13 products in test data
+
+    def test_group_by_with_count(self, sql_index):
+        """Test GROUP BY with COUNT."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, COUNT(*) as count
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        # Should have groups for electronics, books, accessories, stationery
+        categories = {r["category"] for r in results}
+        assert "electronics" in categories
+        assert "books" in categories
+
+    def test_group_by_with_avg(self, sql_index):
+        """Test GROUP BY with AVG."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, AVG(price) as avg_price
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        # All results should have category and avg_price
+        for result in results:
+            assert "category" in result
+            assert "avg_price" in result
+            assert float(result["avg_price"]) > 0
+
+    def test_group_by_with_filter(self, sql_index):
+        """Test GROUP BY with WHERE filter."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, AVG(price) as avg_price
+            FROM {sql_index.name}
+            WHERE stock > 50
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert "category" in result
+            assert "avg_price" in result
+
+    def test_group_by_with_sum(self, sql_index):
+        """Test GROUP BY with SUM reducer."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, SUM(price) as total_price
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert "category" in result
+            assert "total_price" in result
+            assert float(result["total_price"]) > 0
+
+    def test_group_by_with_min(self, sql_index):
+        """Test GROUP BY with MIN reducer."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, MIN(price) as min_price
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert "category" in result
+            assert "min_price" in result
+            assert float(result["min_price"]) > 0
+
+    def test_group_by_with_max(self, sql_index):
+        """Test GROUP BY with MAX reducer."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, MAX(price) as max_price
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert "category" in result
+            assert "max_price" in result
+            assert float(result["max_price"]) > 0
+
+    def test_global_sum(self, sql_index):
+        """Test global SUM aggregation (no GROUP BY)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT SUM(price) as total
+            FROM {sql_index.name}
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 1
+        assert "total" in results[0]
+        assert float(results[0]["total"]) > 0
+
+    def test_global_min(self, sql_index):
+        """Test global MIN aggregation (no GROUP BY)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT MIN(price) as min_price
+            FROM {sql_index.name}
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 1
+        assert "min_price" in results[0]
+        assert float(results[0]["min_price"]) > 0
+
+    def test_global_max(self, sql_index):
+        """Test global MAX aggregation (no GROUP BY)."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT MAX(price) as max_price
+            FROM {sql_index.name}
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 1
+        assert "max_price" in results[0]
+        assert float(results[0]["max_price"]) > 0
+
+    def test_multiple_reducers(self, sql_index):
+        """Test multiple reducers in a single query."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, COUNT(*) as count, SUM(price) as total, AVG(price) as avg_price, MIN(price) as min_price, MAX(price) as max_price
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert "category" in result
+            assert "count" in result
+            assert "total" in result
+            assert "avg_price" in result
+            assert "min_price" in result
+            assert "max_price" in result
+
+    def test_count_distinct(self, sql_index):
+        """Test COUNT_DISTINCT reducer using Redis-specific syntax."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT COUNT_DISTINCT(category) as unique_categories
+            FROM {sql_index.name}
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 1
+        assert "unique_categories" in results[0]
+        # Should have 4 unique categories: electronics, books, accessories, stationery
+        assert int(results[0]["unique_categories"]) == 4
+
+    def test_stddev(self, sql_index):
+        """Test STDDEV reducer."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT STDDEV(price) as price_stddev
+            FROM {sql_index.name}
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 1
+        assert "price_stddev" in results[0]
+        # Verify it's a valid numeric value
+        stddev_value = float(results[0]["price_stddev"])
+        assert stddev_value >= 0  # Standard deviation is always non-negative
+
+    def test_quantile(self, sql_index):
+        """Test QUANTILE reducer."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT QUANTILE(price, 0.5) as median_price
+            FROM {sql_index.name}
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) == 1
+        assert "median_price" in results[0]
+        # Verify it's a valid numeric value
+        median_value = float(results[0]["median_price"])
+        assert median_value >= 0
+
+    def test_tolist(self, sql_index):
+        """Test TOLIST reducer via ARRAY_AGG SQL function."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, ARRAY_AGG(title) as titles
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert "titles" in result
+            # TOLIST returns a comma-separated string or list of values
+            assert result["titles"] is not None
+
+    def test_first_value(self, sql_index):
+        """Test FIRST_VALUE reducer."""
+        sql_query = SQLQuery(
+            f"""
+            SELECT category, FIRST_VALUE(title) as first_title
+            FROM {sql_index.name}
+            GROUP BY category
+        """
+        )
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert "first_title" in result
+            # Verify it's a non-empty string
+            assert isinstance(result["first_title"], str)
+            assert len(result["first_title"]) > 0
+
+
+class TestSQLQueryIntegration:
+    """End-to-end integration tests matching proposal examples."""
+
+    def test_proposal_example_basic(self, sql_index):
+        """Test the basic example from the MLP proposal."""
+        # Example from proposal doc (adapted for our test data)
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, price, category
+            FROM {sql_index.name}
+            WHERE category = 'books'
+        """
+        )
+
+        results = sql_index.query(sql_query)
+
+        assert len(results) > 0
+        for result in results:
+            assert result["category"] == "books"
+            assert "title" in result
+            assert "price" in result
+
+
+@pytest.fixture
+def vector_index(redis_url, worker_id):
+    """Create a books index with vector embeddings for SQL query testing."""
+    import numpy as np
+
+    unique_id = str(uuid.uuid4())[:8]
+    index_name = f"sql_books_{worker_id}_{unique_id}"
+
+    index = SearchIndex.from_dict(
+        {
+            "index": {
+                "name": index_name,
+                "prefix": f"book_{worker_id}_{unique_id}",
+                "storage_type": "hash",
+            },
+            "fields": [
+                {"name": "title", "type": "text", "attrs": {"sortable": True}},
+                {"name": "genre", "type": "tag", "attrs": {"sortable": True}},
+                {"name": "price", "type": "numeric", "attrs": {"sortable": True}},
+                {
+                    "name": "embedding",
+                    "type": "vector",
+                    "attrs": {
+                        "dims": 4,
+                        "distance_metric": "cosine",
+                        "algorithm": "flat",
+                        "datatype": "float32",
+                    },
+                },
+            ],
+        },
+        redis_url=redis_url,
+    )
+
+    index.create(overwrite=True)
+
+    # Create test books with embeddings
+    books = [
+        {
+            "title": "Dune",
+            "genre": "Science Fiction",
+            "price": 15,
+            "embedding": np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32).tobytes(),
+        },
+        {
+            "title": "Foundation",
+            "genre": "Science Fiction",
+            "price": 18,
+            "embedding": np.array([0.15, 0.25, 0.35, 0.45], dtype=np.float32).tobytes(),
+        },
+        {
+            "title": "Neuromancer",
+            "genre": "Science Fiction",
+            "price": 12,
+            "embedding": np.array([0.2, 0.3, 0.4, 0.5], dtype=np.float32).tobytes(),
+        },
+        {
+            "title": "The Hobbit",
+            "genre": "Fantasy",
+            "price": 14,
+            "embedding": np.array([0.9, 0.8, 0.7, 0.6], dtype=np.float32).tobytes(),
+        },
+        {
+            "title": "1984",
+            "genre": "Dystopian",
+            "price": 25,
+            "embedding": np.array([0.5, 0.5, 0.5, 0.5], dtype=np.float32).tobytes(),
+        },
+    ]
+
+    index.load(books)
+
+    yield index
+
+    # Cleanup
+    index.delete(drop=True)
+
+
+class TestSQLQueryVectorSearch:
+    """Tests for SQL vector similarity search using cosine_distance() and vector_distance()."""
+
+    def test_vector_distance_function(self, vector_index):
+        """Test vector search with vector_distance() function."""
+        import numpy as np
+
+        query_vector = np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32).tobytes()
+
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, vector_distance(embedding, :vec) AS score
+            FROM {vector_index.name}
+            LIMIT 3
+            """,
+            params={"vec": query_vector},
+        )
+
+        results = vector_index.query(sql_query)
+
+        assert len(results) > 0
+        assert len(results) <= 3
+        for result in results:
+            assert "title" in result
+            assert "score" in result
+            # Score should be a valid non-negative distance value
+            score = float(result["score"])
+            assert score >= 0
+
+    def test_vector_cosine_similarity(self, vector_index):
+        """Test vector search with cosine_distance() function - pgvector style."""
+        import numpy as np
+
+        # Query vector similar to Science Fiction books
+        query_vector = np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32).tobytes()
+
+        sql_query = SQLQuery(
+            f"""
+            SELECT
+                title,
+                genre,
+                price,
+                cosine_distance(embedding, :query_vector) AS vector_distance
+            FROM {vector_index.name}
+            WHERE genre = 'Science Fiction'
+                AND price <= 20
+            ORDER BY cosine_distance(embedding, :query_vector)
+            LIMIT 3
+            """,
+            params={"query_vector": query_vector},
+        )
+
+        results = vector_index.query(sql_query)
+
+        # Should return Science Fiction books under $20
+        assert len(results) > 0
+        assert len(results) <= 3
+        for result in results:
+            assert result["genre"] == "Science Fiction"
+            assert float(result["price"]) <= 20
+            # Verify vector_distance is returned (like VectorQuery with return_score=True)
+            assert "vector_distance" in result
+            # Distance should be a valid non-negative value
+            distance = float(result["vector_distance"])
+            assert distance >= 0
+
+    def test_vector_redis_query_string(self, vector_index, redis_url):
+        """Test redis_query_string() returns correct KNN query for vector search."""
+        import numpy as np
+
+        # Query vector
+        query_vector = np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32).tobytes()
+
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, cosine_distance(embedding, :vec) AS vector_distance
+            FROM {vector_index.name}
+            LIMIT 3
+            """,
+            params={"vec": query_vector},
+        )
+
+        # Get the Redis command string
+        redis_cmd = sql_query.redis_query_string(redis_url=redis_url)
+
+        # Verify it's a valid FT.SEARCH with KNN syntax
+        assert redis_cmd.startswith("FT.SEARCH")
+        assert vector_index.name in redis_cmd
+        assert "KNN 3" in redis_cmd
+        assert "@embedding" in redis_cmd
+        assert "$vector" in redis_cmd
+        assert "vector_distance" in redis_cmd
+
+    def test_vector_search_with_prefilter_redis_query_string(
+        self, vector_index, redis_url
+    ):
+        """Test redis_query_string() returns correct prefiltered KNN query."""
+        import numpy as np
+
+        query_vector = np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32).tobytes()
+
+        sql_query = SQLQuery(
+            f"""
+            SELECT title, genre, cosine_distance(embedding, :vec) AS vector_distance
+            FROM {vector_index.name}
+            WHERE genre = 'Science Fiction'
+            LIMIT 3
+            """,
+            params={"vec": query_vector},
+        )
+
+        redis_cmd = sql_query.redis_query_string(redis_url=redis_url)
+
+        # Verify prefilter syntax: (filter)=>[KNN ...]
+        assert redis_cmd.startswith("FT.SEARCH")
+        assert "Science Fiction" in redis_cmd or "Science\\ Fiction" in redis_cmd
+        assert "=>[KNN" in redis_cmd
+        assert "KNN 3" in redis_cmd
diff --git a/tests/unit/test_sql_parameter_substitution.py b/tests/unit/test_sql_parameter_substitution.py
new file mode 100644
index 00000000..3ec51f28
--- /dev/null
+++ b/tests/unit/test_sql_parameter_substitution.py
@@ -0,0 +1,224 @@
+"""Unit tests for SQL parameter substitution in SQLQuery.
+
+These tests verify that parameter substitution correctly handles:
+1. Partial matching bug: :id should not replace inside :product_id
+2. Quote escaping bug: Single quotes in values should be SQL-escaped
+3. Edge cases: Multiple occurrences, similar names, special characters
+"""
+
+import pytest
+
+from redisvl.query.sql import SQLQuery
+
+
+def buggy_substitute_params(sql: str, params: dict) -> str:
+    """Simulate the CURRENT buggy implementation for comparison.
+
+    This is the exact code from redisvl/query/sql.py lines 105-113.
+    """
+    for key, value in params.items():
+        placeholder = f":{key}"
+        if isinstance(value, (int, float)):
+            sql = sql.replace(placeholder, str(value))
+        elif isinstance(value, str):
+            sql = sql.replace(placeholder, f"'{value}'")
+    return sql
+
+
+class TestBuggyBehaviorDemonstration:
+    """Tests that DEMONSTRATE the bugs in the current implementation.
+
+    These tests show what goes wrong with the naive str.replace() approach.
+    They should PASS (demonstrating the bug exists) before the fix,
+    and some assertions will need to change after the fix.
+    """
+
+    def test_partial_match_bug_exists(self):
+        """Demonstrate that :id incorrectly replaces inside :product_id."""
+        sql = "SELECT * FROM idx WHERE id = :id AND product_id = :product_id"
+        params = {"id": 123, "product_id": 456}
+
+        result = buggy_substitute_params(sql, params)
+
+        # BUG: :id gets replaced inside :product_id first (dict ordering dependent)
+        # This demonstrates the bug - the result is corrupted
+        # Depending on dict ordering, we might get "product_123" corruption
+        assert ":id" not in result or "product_" in result  # Some substitution happened
+
+    def test_quote_escaping_bug_exists(self):
+        """Demonstrate that quotes are NOT escaped in current implementation."""
+        sql = "SELECT * FROM idx WHERE name = :name"
+        params = {"name": "O'Brien"}
+
+        result = buggy_substitute_params(sql, params)
+
+        # BUG: The quote is NOT escaped - this produces invalid SQL
+        assert "O'Brien" in result  # Raw quote, not escaped
+        assert "O''Brien" not in result  # Proper escaping is missing
+
+
+class TestParameterSubstitutionPartialMatching:
+    """Tests for the partial string matching bug.
+
+    The bug: Using str.replace(':id', '123') would also replace
+    ':id' inside ':product_id', resulting in 'product_123'.
+    """
+
+    def test_similar_param_names_no_partial_match(self):
+        """Test that :id doesn't replace inside :product_id."""
+        sql_query = SQLQuery(
+            "SELECT * FROM idx WHERE id = :id AND product_id = :product_id",
+            params={"id": 123, "product_id": 456},
+        )
+
+        substituted = sql_query._substitute_params(sql_query.sql, sql_query.params)
+
+        assert "id = 123" in substituted
+        assert "product_id = 456" in substituted
+        # Should NOT have "product_123"
+        assert "product_123" not in substituted
+
+    def test_prefix_param_names(self):
+        """Test params where one is a prefix of another: :user, :user_id, :user_name."""
+        sql_query = SQLQuery(
+            "SELECT * FROM idx WHERE user = :user AND user_id = :user_id AND user_name = :user_name",
+            params={"user": "alice", "user_id": 42, "user_name": "Alice Smith"},
+        )
+
+        substituted = sql_query._substitute_params(sql_query.sql, sql_query.params)
+
+        assert "user = 'alice'" in substituted
+        assert "user_id = 42" in substituted
+        assert "user_name = 'Alice Smith'" in substituted
+        # Should NOT have corrupted values
+        assert "'alice'_id" not in substituted
+        assert "'alice'_name" not in substituted
+
+    def test_suffix_param_names(self):
+        """Test params where one is a suffix pattern: :vec, :query_vec."""
+        sql_query = SQLQuery(
+            "SELECT * FROM idx WHERE vec = :vec AND query_vec = :query_vec",
+            params={"vec": 1.0, "query_vec": 2.0},
+        )
+
+        substituted = sql_query._substitute_params(sql_query.sql, sql_query.params)
+
+        assert "vec = 1.0" in substituted or "vec = 1" in substituted
+        assert "query_vec = 2.0" in substituted or "query_vec = 2" in substituted
+
+
+class TestParameterSubstitutionQuoteEscaping:
+    """Tests for the quote escaping bug.
+
+    The bug: String values with single quotes like "O'Brien" would
+    produce invalid SQL: 'O'Brien' instead of 'O''Brien'.
+    """
+
+    def test_single_quote_in_value(self):
+        """Test that single quotes are properly escaped."""
+        sql_query = SQLQuery(
+            "SELECT * FROM idx WHERE name = :name",
+            params={"name": "O'Brien"},
+        )
+
+        substituted = sql_query._substitute_params(sql_query.sql, sql_query.params)
+
+        # SQL standard escaping: ' becomes ''
+        assert "name = 'O''Brien'" in substituted
+
+    def test_multiple_quotes_in_value(self):
+        """Test multiple single quotes in a value."""
+        sql_query = SQLQuery(
+            "SELECT * FROM idx WHERE phrase = :phrase",
+            params={"phrase": "It's a 'test' string"},
+        )
+
+        substituted = sql_query._substitute_params(sql_query.sql, sql_query.params)
+
+        assert "phrase = 'It''s a ''test'' string'" in substituted
+
+    def test_apostrophe_names(self):
+        """Test common names with apostrophes."""
+        test_cases = [
+            ("McDonald's", "'McDonald''s'"),
+            ("O'Reilly", "'O''Reilly'"),
+            ("D'Angelo", "'D''Angelo'"),
+        ]
+
+        for name, expected in test_cases:
+            sql_query = SQLQuery(
+                "SELECT * FROM idx WHERE name = :name",
+                params={"name": name},
+            )
+            substituted = sql_query._substitute_params(sql_query.sql, sql_query.params)
+            assert f"name = {expected}" in substituted, f"Failed for {name}"
+
+
+class TestParameterSubstitutionEdgeCases:
+    """Tests for edge cases in parameter substitution."""
+
+    def test_multiple_occurrences_same_param(self):
+        """Test that a parameter used multiple times is substituted everywhere."""
+        sql_query = SQLQuery(
+            "SELECT * FROM idx WHERE category = :cat OR subcategory = :cat",
+            params={"cat": "electronics"},
+        )
+
+        substituted = sql_query._substitute_params(sql_query.sql, sql_query.params)
+
+        assert substituted.count("'electronics'") == 2
+
+    def test_empty_string_value(self):
+        """Test empty string parameter value."""
+        sql_query = SQLQuery(
+            "SELECT * FROM idx WHERE name = :name",
+            params={"name": ""},
+        )
+
+        substituted = sql_query._substitute_params(sql_query.sql, sql_query.params)
+
+        assert "name = ''" in substituted
+
+    def test_numeric_types(self):
+        """Test integer and float parameter values."""
+        sql_query = SQLQuery(
+            "SELECT * FROM idx WHERE count = :count AND price = :price",
+            params={"count": 42, "price": 99.99},
+        )
+
+        substituted = sql_query._substitute_params(sql_query.sql, sql_query.params)
+
+        assert "count = 42" in substituted
+        assert "price = 99.99" in substituted
+
+    def test_bytes_param_not_substituted(self):
+        """Test that bytes parameters are not substituted (handled separately)."""
+        sql_query = SQLQuery(
+            "SELECT * FROM idx WHERE embedding = :vec",
+            params={"vec": b"\x00\x01\x02\x03"},
+        )
+
+        substituted = sql_query._substitute_params(sql_query.sql, sql_query.params)
+
+        # Bytes should remain as placeholder
+        assert ":vec" in substituted
+
+    def test_special_characters_in_value(self):
+        """Test special characters that might interfere with regex."""
+        special_values = [
+            "hello@world.com",
+            "path/to/file",
+            "price: $100",
+            "regex.*pattern",
+            "back\\slash",
+        ]
+
+        for value in special_values:
+            sql_query = SQLQuery(
+                "SELECT * FROM idx WHERE field = :field",
+                params={"field": value},
+            )
+            substituted = sql_query._substitute_params(sql_query.sql, sql_query.params)
+            # Should contain the value wrapped in quotes (with any necessary escaping)
+            assert ":field" not in substituted, f"Failed to substitute for value: {value}"
+
diff --git a/uv.lock b/uv.lock
index ad61bd62..44e652aa 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.9.2, <3.14"
 resolution-markers = [
     "python_full_version >= '3.13'",
@@ -4255,7 +4255,7 @@ wheels = [
 
 [[package]]
 name = "redisvl"
-version = "0.13.2"
+version = "0.14.0"
 source = { editable = "." }
 dependencies = [
     { name = "jsonpath-ng" },
@@ -4299,6 +4299,9 @@ pillow = [
 sentence-transformers = [
     { name = "sentence-transformers" },
 ]
+sql-redis = [
+    { name = "sql-redis" },
+]
 vertexai = [
     { name = "google-cloud-aiplatform" },
     { name = "protobuf" },
@@ -4355,11 +4358,12 @@ requires-dist = [
     { name = "pyyaml", specifier = ">=5.4,<7.0" },
     { name = "redis", specifier = ">=5.0,<7.2" },
     { name = "sentence-transformers", marker = "extra == 'sentence-transformers'", specifier = ">=3.4.0,<4" },
+    { name = "sql-redis", marker = "extra == 'sql-redis'", specifier = ">=0.1.1" },
     { name = "tenacity", specifier = ">=8.2.2" },
     { name = "urllib3", marker = "extra == 'bedrock'", specifier = "<2.2.0" },
     { name = "voyageai", marker = "extra == 'voyageai'", specifier = ">=0.2.2" },
 ]
-provides-extras = ["mistralai", "openai", "nltk", "cohere", "voyageai", "sentence-transformers", "langcache", "vertexai", "bedrock", "pillow"]
+provides-extras = ["mistralai", "openai", "nltk", "cohere", "voyageai", "sentence-transformers", "langcache", "vertexai", "bedrock", "pillow", "sql-redis"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -5263,6 +5267,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/52/a7/d2782e4e3f77c8450f727ba74a8f12756d5ba823d81b941f1b04da9d033a/sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl", hash = "sha256:6e2cb0eef194e10c27ec0023bfeb25badbbb5868244cf5bc5bdc04e4464bf331", size = 92072, upload-time = "2024-07-29T01:10:08.203Z" },
 ]
 
+[[package]]
+name = "sql-redis"
+version = "0.1.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "redis", version = "7.0.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "redis", version = "7.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+    { name = "sqlglot" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ed/ef/9ef69125be3b8a9906010f4bfd84d3b12fce86d9ecc9ed18443ff5fa9af6/sql_redis-0.1.1.tar.gz", hash = "sha256:1b763bd33e8963811a8c3d191506d5572f6584bfa5bbfa9c8af09a51f07baf02", size = 103713, upload-time = "2026-02-03T19:29:47.878Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/04/cf/c9e13d253acb3c08dc9113dc3e75962ebb69584d6286b931f364dfb9225d/sql_redis-0.1.1-py3-none-any.whl", hash = "sha256:8369e8c61990b0f9aa5ad1a9d4b03060f770af5a7b856b84e88e819efcacb1ed", size = 18716, upload-time = "2026-02-03T19:29:46.899Z" },
+]
+
 [[package]]
 name = "sqlalchemy"
 version = "2.0.44"
@@ -5316,6 +5334,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/9c/5e/6a29fa884d9fb7ddadf6b69490a9d45fded3b38541713010dad16b77d015/sqlalchemy-2.0.44-py3-none-any.whl", hash = "sha256:19de7ca1246fbef9f9d1bff8f1ab25641569df226364a0e40457dc5457c54b05", size = 1928718, upload-time = "2025-10-10T15:29:45.32Z" },
 ]
 
+[[package]]
+name = "sqlglot"
+version = "28.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/46/b6/f188b9616bef49943353f3622d726af30fdb08acbd081deef28ba43ceb48/sqlglot-28.6.0.tar.gz", hash = "sha256:8c0a432a6745c6c7965bbe99a17667c5a3ca1d524a54b31997cf5422b1727f6a", size = 5676522, upload-time = "2026-01-13T17:39:24.389Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8f/a6/21b1e19994296ba4a34bc7abaf4fcb40d7e7787477bdfde58cd843594459/sqlglot-28.6.0-py3-none-any.whl", hash = "sha256:8af76e825dc8456a49f8ce049d69bbfcd116655dda3e53051754789e2edf8eba", size = 575186, upload-time = "2026-01-13T17:39:22.327Z" },
+]
+
 [[package]]
 name = "stack-data"
 version = "0.6.3"