Vector similarity search example (#2083)

* copy example

* fix

2 files changed, 108 insertions, 0 deletions

diff --git a/docs/examples.rst b/docs/examples.rst
index 6d659a0..be577c0 100644
--- a/docs/examples.rst
+++ b/docs/examples.rst
@@ -10,3 +10,4 @@ Examples
    examples/asyncio_examples
    examples/search_json_examples
    examples/set_and_get_examples
+   examples/search_vector_similarity_examples
\ No newline at end of file
diff --git a/docs/examples/search_vector_similarity_examples.ipynb b/docs/examples/search_vector_similarity_examples.ipynb
new file mode 100644
index 0000000..42c03f0
--- /dev/null
+++ b/docs/examples/search_vector_similarity_examples.ipynb
@@ -0,0 +1,107 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Vector Similarity\n",
+    "## Adding Vector Fields"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b'OK'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import redis\n",
+    "from redis.commands.search.field import VectorField\n",
+    "from redis.commands.search.query import Query\n",
+    "\n",
+    "r = redis.Redis(host='localhost', port=36379)\n",
+    "\n",
+    "schema = (VectorField(\"v\", \"HNSW\", {\"TYPE\": \"FLOAT32\", \"DIM\": 2, \"DISTANCE_METRIC\": \"L2\"}),)\n",
+    "r.ft().create_index(schema)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Searching"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Querying vector fields"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Result{2 total, docs: [Document {'id': 'a', 'payload': None, '__v_score': '0'}, Document {'id': 'b', 'payload': None, '__v_score': '3.09485009821e+26'}]}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r.hset(\"a\", \"v\", \"aaaaaaaa\")\n",
+    "r.hset(\"b\", \"v\", \"aaaabaaa\")\n",
+    "r.hset(\"c\", \"v\", \"aaaaabaa\")\n",
+    "\n",
+    "q = Query(\"*=>[KNN 2 @v $vec]\").return_field(\"__v_score\")\n",
+    "r.ft().search(q, query_params={\"vec\": \"aaaaaaaa\"})"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "d45c99ba0feda92868abafa8257cbb4709c97f1a0b5dc62bbeebdf89d4fad7fe"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.12 64-bit ('venv': venv)",
+   "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": "3.9.2"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}