-
Notifications
You must be signed in to change notification settings - Fork 41
/
Copy patharray_dict_vectorizer.html
497 lines (344 loc) · 42.7 KB
/
array_dict_vectorizer.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
<!DOCTYPE html>
<html class="writer-html5" lang="en" >
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Array & Dictionary — vectorai 0.1.0 documentation</title>
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
<!--[if lt IE 9]>
<script src="_static/js/html5shiv.min.js"></script>
<![endif]-->
<script type="text/javascript" id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
<script src="_static/jquery.js"></script>
<script src="_static/underscore.js"></script>
<script src="_static/doctools.js"></script>
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
<script type="text/javascript" src="_static/js/theme.js"></script>
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="next" title="Dimensionality Reduction" href="dimensionality_reduction.html" />
<link rel="prev" title="Cluster" href="cluster.html" />
</head>
<body class="wy-body-for-nav">
<div class="wy-grid-for-nav">
<nav data-toggle="wy-nav-shift" class="wy-nav-side">
<div class="wy-side-scroll">
<div class="wy-side-nav-search" >
<a href="index.html" class="icon icon-home"> vectorai
</a>
<div role="search">
<form id="rtd-search-form" class="wy-form" action="search.html" method="get">
<input type="text" name="q" placeholder="Search docs" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
</div>
</div>
<div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
<p class="caption"><span class="caption-text">Contents</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="intro.html">Vector AI - Essentials</a></li>
<li class="toctree-l1"><a class="reference internal" href="quickstart.html">QuickStart</a></li>
</ul>
<p class="caption"><span class="caption-text">Guides</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="industry_ecommerce.html">Vector Search / Nearest Neighbors</a></li>
<li class="toctree-l1"><a class="reference internal" href="industry_ecommerce.html#Search">Search</a></li>
<li class="toctree-l1"><a class="reference internal" href="industry_ecommerce.html#Collection-Metadata">Collection Metadata</a></li>
<li class="toctree-l1"><a class="reference internal" href="industry_ecommerce.html#Advanced-Search">Advanced Search</a></li>
<li class="toctree-l1"><a class="reference internal" href="industry_ecommerce.html#Advanced-Vector-Search">Advanced Vector Search</a></li>
<li class="toctree-l1"><a class="reference internal" href="industry_ecommerce.html#Vector-based-Recommendations-(Search-by-Id)">Vector based Recommendations (Search by Id)</a></li>
<li class="toctree-l1"><a class="reference internal" href="industry_ecommerce.html#Vector-Analytics/Aggregation">Vector Analytics/Aggregation</a></li>
<li class="toctree-l1"><a class="reference internal" href="vector_analytics_example.html">Clustering</a></li>
<li class="toctree-l1"><a class="reference internal" href="vector_analytics_example.html#Dimensionality-Reduction">Dimensionality Reduction</a></li>
<li class="toctree-l1"><a class="reference internal" href="vector_analytics_example.html#Visualisations-(Advanced)">Visualisations (Advanced)</a></li>
<li class="toctree-l1"><a class="reference internal" href="custom_encodings_example.html">Custom Encodings</a></li>
</ul>
<p class="caption"><span class="caption-text">Case Studies</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="industry_nba_players.html">Example - Vector Recommendations With NBA Players</a></li>
</ul>
<p class="caption"><span class="caption-text">Frequently Asked Questions</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="FAQ.html">Frequently Asked Questions</a></li>
</ul>
<p class="caption"><span class="caption-text">Documentation</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="client.html">Client</a></li>
<li class="toctree-l1"><a class="reference internal" href="read.html">Read</a></li>
<li class="toctree-l1"><a class="reference internal" href="write.html">Write</a></li>
<li class="toctree-l1"><a class="reference internal" href="cluster.html">Cluster</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Array & Dictionary</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#id1">Array & Dictionary</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="dimensionality_reduction.html">Dimensionality Reduction</a></li>
<li class="toctree-l1"><a class="reference internal" href="vector_search.html">Search</a></li>
<li class="toctree-l1"><a class="reference internal" href="image.html">Images</a></li>
<li class="toctree-l1"><a class="reference internal" href="text.html">Texts</a></li>
<li class="toctree-l1"><a class="reference internal" href="audio.html">Audios</a></li>
<li class="toctree-l1"><a class="reference internal" href="analytics.html">Visualisations</a></li>
</ul>
</div>
</div>
</nav>
<section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
<nav class="wy-nav-top" aria-label="top navigation">
<i data-toggle="wy-nav-top" class="fa fa-bars"></i>
<a href="index.html">vectorai</a>
</nav>
<div class="wy-nav-content">
<div class="rst-content">
<div role="navigation" aria-label="breadcrumbs navigation">
<ul class="wy-breadcrumbs">
<li><a href="index.html" class="icon icon-home"></a> »</li>
<li>Array & Dictionary</li>
<li class="wy-breadcrumbs-aside">
<a href="_sources/array_dict_vectorizer.rst.txt" rel="nofollow"> View page source</a>
</li>
</ul>
<hr/>
</div>
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
<div itemprop="articleBody">
<style>
/* CSS overrides for sphinx_rtd_theme */
/* 24px margin */
.nbinput.nblast.container,
.nboutput.nblast.container {
margin-bottom: 19px; /* padding has already 5px */
}
/* ... except between code cells! */
.nblast.container + .nbinput.container {
margin-top: -19px;
}
.admonition > p:before {
margin-right: 4px; /* make room for the exclamation icon */
}
/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
.math {
text-align: unset;
}
</style>
<div class="section" id="array-dictionary">
<h1>Array & Dictionary<a class="headerlink" href="#array-dictionary" title="Permalink to this headline">¶</a></h1>
<div class="section" id="id1">
<h2>Array & Dictionary<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
<p>Array & Dictionary</p>
<span class="target" id="module-vectorai.api.array_dict_vectorizer"></span><dl class="py class">
<dt class="sig sig-object py" id="vectorai.api.array_dict_vectorizer.ViArrayDictClient">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">vectorai.api.array_dict_vectorizer.</span></span><span class="sig-name descname"><span class="pre">ViArrayDictClient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">username</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">api_key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">url</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#vectorai.api.array_dict_vectorizer.ViArrayDictClient" title="Permalink to this definition">¶</a></dt>
<dd><p>Search and Encoding for Array & Dictionary</p>
<dl class="py method">
<dt class="sig sig-object py" id="vectorai.api.array_dict_vectorizer.ViArrayDictClient.encode_dictionary_field">
<span class="sig-name descname"><span class="pre">encode_dictionary_field</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">collection_name</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dictionary_fields</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_curl</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#vectorai.api.array_dict_vectorizer.ViArrayDictClient.encode_dictionary_field" title="Permalink to this definition">¶</a></dt>
<dd><p>Encode all dictionaries in a field for collection into vectors</p>
<p>Within a collection encode the specified dictionary field in every document into vectors.</p>
<p>For example: a dictionary that represents a <strong>person’s characteristics visiting a store, field “person_characteristics”</strong>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">document</span> <span class="mi">1</span> <span class="n">field</span><span class="p">:</span> <span class="p">{</span><span class="s2">"person_characteristics"</span> <span class="p">:</span> <span class="p">{</span><span class="s2">"height"</span><span class="p">:</span><span class="mi">180</span><span class="p">,</span> <span class="s2">"age"</span><span class="p">:</span><span class="mi">40</span><span class="p">,</span> <span class="s2">"weight"</span><span class="p">:</span><span class="mi">70</span><span class="p">}}</span>
<span class="n">document</span> <span class="mi">2</span> <span class="n">field</span><span class="p">:</span> <span class="p">{</span><span class="s2">"person_characteristics"</span> <span class="p">:</span> <span class="p">{</span><span class="s2">"age"</span><span class="p">:</span><span class="mi">32</span><span class="p">,</span> <span class="s2">"purchases"</span><span class="p">:</span><span class="mi">10</span><span class="p">,</span> <span class="s2">"visits"</span><span class="p">:</span> <span class="mi">24</span><span class="p">}}</span>
<span class="o">-></span> <span class="o"><</span><span class="n">Encode</span> <span class="n">the</span> <span class="n">dictionaries</span> <span class="n">to</span> <span class="n">vectors</span><span class="o">></span> <span class="o">-></span>
<span class="o">|</span> <span class="n">height</span> <span class="o">|</span> <span class="n">age</span> <span class="o">|</span> <span class="n">weight</span> <span class="o">|</span> <span class="n">purchases</span> <span class="o">|</span> <span class="n">visits</span> <span class="o">|</span>
<span class="o">|--------|-----|--------|-----------|--------|</span>
<span class="o">|</span> <span class="mi">180</span> <span class="o">|</span> <span class="mi">40</span> <span class="o">|</span> <span class="mi">70</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span>
<span class="o">|</span> <span class="mi">0</span> <span class="o">|</span> <span class="mi">32</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span> <span class="mi">10</span> <span class="o">|</span> <span class="mi">24</span> <span class="o">|</span>
<span class="n">document</span> <span class="mi">1</span> <span class="n">dictionary</span> <span class="n">vector</span><span class="p">:</span> <span class="p">{</span><span class="s2">"person_characteristics_vector_"</span><span class="p">:</span> <span class="p">[</span><span class="mi">180</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">70</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]}</span>
<span class="n">document</span> <span class="mi">2</span> <span class="n">dictionary</span> <span class="n">vector</span><span class="p">:</span> <span class="p">{</span><span class="s2">"person_characteristics_vector_"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">24</span><span class="p">]}</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dictionary_fields</strong> – The dictionary field to train on to encode into vectors</p></li>
<li><p><strong>collection_name</strong> – Name of Collection</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="vectorai.api.array_dict_vectorizer.ViArrayDictClient.encode_dictionary">
<span class="sig-name descname"><span class="pre">encode_dictionary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">collection_name</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dictionary</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dictionary_field</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_curl</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#vectorai.api.array_dict_vectorizer.ViArrayDictClient.encode_dictionary" title="Permalink to this definition">¶</a></dt>
<dd><p>Encode an dictionary into a vector</p>
<p>For example: a dictionary that represents a <strong>person’s characteristics visiting a store, field “person_characteristics”</strong>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="s2">"height"</span><span class="p">:</span><span class="mi">180</span><span class="p">,</span> <span class="s2">"age"</span><span class="p">:</span><span class="mi">40</span><span class="p">,</span> <span class="s2">"weight"</span><span class="p">:</span><span class="mi">70</span><span class="p">}</span>
<span class="o">-></span> <span class="o"><</span><span class="n">Encode</span> <span class="n">the</span> <span class="n">dictionary</span> <span class="n">to</span> <span class="n">vector</span><span class="o">></span> <span class="o">-></span>
<span class="o">|</span> <span class="n">height</span> <span class="o">|</span> <span class="n">age</span> <span class="o">|</span> <span class="n">weight</span> <span class="o">|</span> <span class="n">purchases</span> <span class="o">|</span> <span class="n">visits</span> <span class="o">|</span>
<span class="o">|--------|-----|--------|-----------|--------|</span>
<span class="o">|</span> <span class="mi">180</span> <span class="o">|</span> <span class="mi">40</span> <span class="o">|</span> <span class="mi">70</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span>
<span class="n">dictionary</span> <span class="n">vector</span><span class="p">:</span> <span class="p">[</span><span class="mi">180</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">70</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>collection_name</strong> – Name of Collection</p></li>
<li><p><strong>dictionary</strong> – A dictionary to encode into vectors</p></li>
<li><p><strong>dictionary_field</strong> – The dictionary field that encoding of the dictionary is trained on</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="vectorai.api.array_dict_vectorizer.ViArrayDictClient.search_with_dictionary">
<span class="sig-name descname"><span class="pre">search_with_dictionary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">collection_name</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dictionary</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dictionary_field</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fields</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sum_fields</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'cosine'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">page</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">page_size</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_vector</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_count</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">asc</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_curl</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#vectorai.api.array_dict_vectorizer.ViArrayDictClient.search_with_dictionary" title="Permalink to this definition">¶</a></dt>
<dd><p>Search a dictionary field with a dictionary using Vector Search with a dictionary directly.</p>
<p>For example: a dictionary that represents a <strong>person’s characteristics visiting a store, field “person_characteristics”</strong>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="s2">"height"</span><span class="p">:</span><span class="mi">180</span><span class="p">,</span> <span class="s2">"age"</span><span class="p">:</span><span class="mi">40</span><span class="p">,</span> <span class="s2">"weight"</span><span class="p">:</span><span class="mi">70</span><span class="p">}</span>
<span class="o">-></span> <span class="o"><</span><span class="n">Encode</span> <span class="n">the</span> <span class="n">dictionary</span> <span class="n">to</span> <span class="n">vector</span><span class="o">></span> <span class="o">-></span>
<span class="o">|</span> <span class="n">height</span> <span class="o">|</span> <span class="n">age</span> <span class="o">|</span> <span class="n">weight</span> <span class="o">|</span> <span class="n">purchases</span> <span class="o">|</span> <span class="n">visits</span> <span class="o">|</span>
<span class="o">|--------|-----|--------|-----------|--------|</span>
<span class="o">|</span> <span class="mi">180</span> <span class="o">|</span> <span class="mi">40</span> <span class="o">|</span> <span class="mi">70</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span>
<span class="n">dictionary</span> <span class="n">vector</span><span class="p">:</span> <span class="p">[</span><span class="mi">180</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">70</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="o">-></span> <span class="o"><</span><span class="n">Vector</span> <span class="n">Search</span><span class="o">></span> <span class="o">-></span>
<span class="n">Search</span> <span class="n">Results</span><span class="p">:</span> <span class="p">{</span><span class="o">...</span><span class="p">}</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>collection_name</strong> – Name of Collection</p></li>
<li><p><strong>search_fields</strong> – Vector fields to search against</p></li>
<li><p><strong>page_size</strong> – Size of each page of results</p></li>
<li><p><strong>page</strong> – Page of the results</p></li>
<li><p><strong>approx</strong> – Used for approximate search</p></li>
<li><p><strong>sum_fields</strong> – Whether to sum the multiple vectors similarity search score as 1 or seperate</p></li>
<li><p><strong>metric</strong> – Similarity Metric, choose from [‘cosine’, ‘l1’, ‘l2’, ‘dp’]</p></li>
<li><p><strong>min_score</strong> – Minimum score for similarity metric</p></li>
<li><p><strong>include_vector</strong> – Include vectors in the search results</p></li>
<li><p><strong>include_count</strong> – Include count in the search results</p></li>
<li><p><strong>hundred_scale</strong> – Whether to scale up the metric by 100</p></li>
<li><p><strong>dictionary</strong> – A dictionary to encode into vectors</p></li>
<li><p><strong>dictionary_field</strong> – <p>The dictionary field that encoding of the dictionary is trained on</p>
<dl class="simple">
<dt>asc:</dt><dd><p>Whether to sort the score by ascending order (default is false, for getting most similar results)</p>
</dd>
</dl>
</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="vectorai.api.array_dict_vectorizer.ViArrayDictClient.encode_array_field">
<span class="sig-name descname"><span class="pre">encode_array_field</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">collection_name</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array_fields</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_curl</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#vectorai.api.array_dict_vectorizer.ViArrayDictClient.encode_array_field" title="Permalink to this definition">¶</a></dt>
<dd><p>Encode all arrays in a field for a collection into vectors</p>
<p>Within a collection encode the specified array field in every document into vectors.</p>
<p>For example, array that represents a <strong>**movie’s categories, field “movie_categories”</strong>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">document</span> <span class="mi">1</span> <span class="n">array</span> <span class="n">field</span><span class="p">:</span> <span class="p">{</span><span class="s2">"category"</span> <span class="p">:</span> <span class="p">[</span><span class="s2">"sci-fi"</span><span class="p">,</span> <span class="s2">"thriller"</span><span class="p">,</span> <span class="s2">"comedy"</span><span class="p">]}</span>
<span class="n">document</span> <span class="mi">2</span> <span class="n">array</span> <span class="n">field</span><span class="p">:</span> <span class="p">{</span><span class="s2">"category"</span> <span class="p">:</span> <span class="p">[</span><span class="s2">"sci-fi"</span><span class="p">,</span> <span class="s2">"romance"</span><span class="p">,</span> <span class="s2">"drama"</span><span class="p">]}</span>
<span class="o">-></span> <span class="o"><</span><span class="n">Encode</span> <span class="n">the</span> <span class="n">arrays</span> <span class="n">to</span> <span class="n">vectors</span><span class="o">></span> <span class="o">-></span>
<span class="o">|</span> <span class="n">sci</span><span class="o">-</span><span class="n">fi</span> <span class="o">|</span> <span class="n">thriller</span> <span class="o">|</span> <span class="n">comedy</span> <span class="o">|</span> <span class="n">romance</span> <span class="o">|</span> <span class="n">drama</span> <span class="o">|</span>
<span class="o">|--------|----------|--------|---------|-------|</span>
<span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span>
<span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mi">1</span> <span class="o">|</span>
<span class="n">document</span> <span class="mi">1</span> <span class="n">array</span> <span class="n">vector</span><span class="p">:</span> <span class="p">{</span><span class="s2">"movie_categories_vector_"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]}</span>
<span class="n">document</span> <span class="mi">2</span> <span class="n">array</span> <span class="n">vector</span><span class="p">:</span> <span class="p">{</span><span class="s2">"movie_categories_vector_"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>array_fields</strong> – The array field to train on to encode into vectors</p></li>
<li><p><strong>collection_name</strong> – Name of Collection</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="vectorai.api.array_dict_vectorizer.ViArrayDictClient.encode_array">
<span class="sig-name descname"><span class="pre">encode_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">collection_name</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array_field</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_curl</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#vectorai.api.array_dict_vectorizer.ViArrayDictClient.encode_array" title="Permalink to this definition">¶</a></dt>
<dd><p>Encode an array into a vector</p>
<p>For example: an array that represents a <strong>movie’s categories, field “movie_categories”</strong>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="s2">"sci-fi"</span><span class="p">,</span> <span class="s2">"thriller"</span><span class="p">,</span> <span class="s2">"comedy"</span><span class="p">]</span>
<span class="o">-></span> <span class="o"><</span><span class="n">Encode</span> <span class="n">the</span> <span class="n">arrays</span> <span class="n">to</span> <span class="n">vectors</span><span class="o">></span> <span class="o">-></span>
<span class="o">|</span> <span class="n">sci</span><span class="o">-</span><span class="n">fi</span> <span class="o">|</span> <span class="n">thriller</span> <span class="o">|</span> <span class="n">comedy</span> <span class="o">|</span> <span class="n">romance</span> <span class="o">|</span> <span class="n">drama</span> <span class="o">|</span>
<span class="o">|--------|----------|--------|---------|-------|</span>
<span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span>
<span class="n">array</span> <span class="n">vector</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>array_field</strong> – The array field that encoding of the dictionary is trained on</p></li>
<li><p><strong>array</strong> – The array to encode into vectors</p></li>
<li><p><strong>collection_name</strong> – Name of Collection</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="vectorai.api.array_dict_vectorizer.ViArrayDictClient.search_with_array">
<span class="sig-name descname"><span class="pre">search_with_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">collection_name</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array_field</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fields</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sum_fields</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'cosine'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">page</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">page_size</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_vector</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_count</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">asc</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_curl</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#vectorai.api.array_dict_vectorizer.ViArrayDictClient.search_with_array" title="Permalink to this definition">¶</a></dt>
<dd><p>Search an array field with an array using Vector Search with an array directly.</p>
<p>For example: an array that represents a <strong>movie’s categories, field “movie_categories”</strong>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="s2">"sci-fi"</span><span class="p">,</span> <span class="s2">"thriller"</span><span class="p">,</span> <span class="s2">"comedy"</span><span class="p">]</span>
<span class="o">-></span> <span class="o"><</span><span class="n">Encode</span> <span class="n">the</span> <span class="n">arrays</span> <span class="n">to</span> <span class="n">vectors</span><span class="o">></span> <span class="o">-></span>
<span class="o">|</span> <span class="n">sci</span><span class="o">-</span><span class="n">fi</span> <span class="o">|</span> <span class="n">thriller</span> <span class="o">|</span> <span class="n">comedy</span> <span class="o">|</span> <span class="n">romance</span> <span class="o">|</span> <span class="n">drama</span> <span class="o">|</span>
<span class="o">|--------|----------|--------|---------|-------|</span>
<span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span> <span class="mi">0</span> <span class="o">|</span>
<span class="n">array</span> <span class="n">vector</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="o">-></span> <span class="o"><</span><span class="n">Vector</span> <span class="n">Search</span><span class="o">></span> <span class="o">-></span>
<span class="n">Search</span> <span class="n">Results</span><span class="p">:</span> <span class="p">{</span><span class="o">...</span><span class="p">}</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>array_field</strong> – The array field that encoding of the dictionary is trained on</p></li>
<li><p><strong>array</strong> – The array to encode into vectors</p></li>
<li><p><strong>collection_name</strong> – Name of Collection</p></li>
<li><p><strong>search_fields</strong> – Vector fields to search through</p></li>
<li><p><strong>approx</strong> – Used for approximate search</p></li>
<li><p><strong>sum_fields</strong> – Whether to sum the multiple vectors similarity search score as 1 or seperate</p></li>
<li><p><strong>page_size</strong> – Size of each page of results</p></li>
<li><p><strong>page</strong> – Page of the results</p></li>
<li><p><strong>metric</strong> – Similarity Metric, choose from [‘cosine’, ‘l1’, ‘l2’, ‘dp’]</p></li>
<li><p><strong>min_score</strong> – Minimum score for similarity metric</p></li>
<li><p><strong>include_vector</strong> – Include vectors in the search results</p></li>
<li><p><strong>include_count</strong> – Include count in the search results</p></li>
<li><p><strong>hundred_scale</strong> – <p>Whether to scale up the metric by 100</p>
<dl class="simple">
<dt>asc:</dt><dd><p>Whether to sort the score by ascending order (default is false, for getting most similar results)</p>
</dd>
</dl>
</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
</div>
</div>
</div>
<footer>
<div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
<a href="dimensionality_reduction.html" class="btn btn-neutral float-right" title="Dimensionality Reduction" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
<a href="cluster.html" class="btn btn-neutral float-left" title="Cluster" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
</div>
<hr/>
<div role="contentinfo">
<p>
© Copyright 2020, OnSearch Pty Ltd.
</p>
</div>
Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
<a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
provided by <a href="https://readthedocs.org">Read the Docs</a>.
</footer>
</div>
</div>
</section>
</div>
<script type="text/javascript">
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
});
</script>
</body>
</html>