1#@!#!123s

: / usr / share / doc / python3-redis / examples /
Filename : search_vector_similarity_examples.ipynb
back
{
 "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\"})"
   ]
  }
 ],
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