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<!DOCTYPE html>
<html class="writer-html5" lang="en" >
<head>
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<title>QuickStart — vectorai 0.1.0 documentation</title>
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<p class="caption"><span class="caption-text">Contents</span></p>
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<li class="toctree-l1"><a class="reference internal" href="intro.html">Vector AI - Essentials</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">QuickStart</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#Build-Image-Search-In-5-Minutes">Build Image Search In 5 Minutes</a></li>
<li class="toctree-l2"><a class="reference internal" href="#Build-Audio-Search-in-5-Minutes">Build Audio Search in 5 Minutes</a></li>
<li class="toctree-l2"><a class="reference internal" href="#Build-Text-QA-Search-in-5-minutes">Build Text QA Search in 5 minutes</a></li>
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<p class="caption"><span class="caption-text">Guides</span></p>
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<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>
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<li class="toctree-l1"><a class="reference internal" href="cluster.html">Cluster</a></li>
<li class="toctree-l1"><a class="reference internal" href="array_dict_vectorizer.html">Array & Dictionary</a></li>
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</style>
<div class="section" id="QuickStart">
<h1>QuickStart<a class="headerlink" href="#QuickStart" title="Permalink to this headline">¶</a></h1>
<div class="section" id="Build-Image-Search-In-5-Minutes">
<h2>Build Image Search In 5 Minutes<a class="headerlink" href="#Build-Image-Search-In-5-Minutes" title="Permalink to this headline">¶</a></h2>
<div class="nbinput nblast docutils container">
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</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
<span></span>collection_name = 'pokemon_images'
documents = []
for i in range(1, 20):
documents.append({
'image': 'https://assets.pokemon.com/assets/cms2/img/pokedex/full/{}.png'.format(f'{i:03}'),
'pokemon_id' : str(i),
'_id': i
})
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</div>
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</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
<span></span>#1. specify the vdb client
from vectorai.client import ViClient
vi_client = ViClient(username, api_key, url)
vi_client.delete_collection(collection_name)
#2. specify an image encoder
from vectorai.models.deployed import ViImage2Vec
image_encoder = ViImage2Vec(username, api_key, url)
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Logged in. Welcome public-demo. To view list of available collections, call list_collections() method.
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{'status': 'complete', 'message': 'pokemon_images deleted'}
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<span></span>#3. insert the documents and encode images simultaneously
# using jobs means that the encoding process takes place on our servers as opposed to your computer
use_jobs = False
if use_jobs:
vi_client.insert_documents(collection_name, documents)
job = vi_client.encode_image_job(collection_name, 'image')
vi_client.wait_till_jobs_complete(collection_name, job['job_id'], job['job_name'])
else:
vi_client.insert_documents(collection_name, documents, models={'image':image_encoder.encode})
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<script type="application/vnd.jupyter.widget-view+json">{"model_id": "82cb6f5ed0d5415eac27727b6ee1bba8", "version_major": 2, "version_minor": 0}</script></div>
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{'inserted_successfully': 19, 'failed': 0, 'failed_document_ids': []}
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<span></span>#4. search
search_results = vi_client.search(collection_name,
image_encoder.encode('https://assets.pokemon.com/assets/cms2/img/pokedex/full/003.png'),
'image_vector_', page_size=5)
#4.2 first result is the query audio itself
vi_client.show_json(search_results, image_fields=['image'], image_width=150)
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</div>
</div>
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
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</div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>_id</th>
<th>image</th>
<th>pokemon_id</th>
<th>insert_date_</th>
<th>_search_score</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>3</td>
<td><img src="https://assets.pokemon.com/assets/cms2/img/pokedex/full/003.png" width="150" ></td>
<td>3</td>
<td>2020-10-02T07:07:18.799559</td>
<td>1.000000</td>
</tr>
<tr>
<th>1</th>
<td>2</td>
<td><img src="https://assets.pokemon.com/assets/cms2/img/pokedex/full/002.png" width="150" ></td>
<td>2</td>
<td>2020-10-02T07:07:18.797693</td>
<td>0.920337</td>
</tr>
<tr>
<th>2</th>
<td>1</td>
<td><img src="https://assets.pokemon.com/assets/cms2/img/pokedex/full/001.png" width="150" ></td>
<td>1</td>
<td>2020-10-02T07:07:18.795655</td>
<td>0.838996</td>
</tr>
<tr>
<th>3</th>
<td>17</td>
<td><img src="https://assets.pokemon.com/assets/cms2/img/pokedex/full/017.png" width="150" ></td>
<td>17</td>
<td>2020-10-02T07:07:30.587841</td>
<td>0.835111</td>
</tr>
<tr>
<th>4</th>
<td>16</td>
<td><img src="https://assets.pokemon.com/assets/cms2/img/pokedex/full/016.png" width="150" ></td>
<td>16</td>
<td>2020-10-02T07:07:30.585399</td>
<td>0.813012</td>
</tr>
</tbody>
</table></div>
</div>
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<span></span>#5 recommendation by id
search_by_id_results = vi_client.search_by_id(collection_name, '2', 'image_vector_', page_size=5)
#5.2 first result is the id's audio itself
vi_client.show_json(search_by_id_results, image_fields=['image'], image_width=150)
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</div>
</div>
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</div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>_id</th>
<th>image</th>
<th>pokemon_id</th>
<th>insert_date_</th>
<th>_search_score</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>2</td>
<td><img src="https://assets.pokemon.com/assets/cms2/img/pokedex/full/002.png" width="150" ></td>
<td>2</td>
<td>2020-10-02T07:07:18.797693</td>
<td>1.000000</td>
</tr>
<tr>
<th>1</th>
<td>3</td>
<td><img src="https://assets.pokemon.com/assets/cms2/img/pokedex/full/003.png" width="150" ></td>
<td>3</td>
<td>2020-10-02T07:07:18.799559</td>
<td>0.920337</td>
</tr>
<tr>
<th>2</th>
<td>1</td>
<td><img src="https://assets.pokemon.com/assets/cms2/img/pokedex/full/001.png" width="150" ></td>
<td>1</td>
<td>2020-10-02T07:07:18.795655</td>
<td>0.895991</td>
</tr>
<tr>
<th>3</th>
<td>7</td>
<td><img src="https://assets.pokemon.com/assets/cms2/img/pokedex/full/007.png" width="150" ></td>
<td>7</td>
<td>2020-10-02T07:07:18.807199</td>
<td>0.839945</td>
</tr>
<tr>
<th>4</th>
<td>17</td>
<td><img src="https://assets.pokemon.com/assets/cms2/img/pokedex/full/017.png" width="150" ></td>
<td>17</td>
<td>2020-10-02T07:07:30.587841</td>
<td>0.833277</td>
</tr>
</tbody>
</table></div>
</div>
</div>
<div class="section" id="Build-Audio-Search-in-5-Minutes">
<h2>Build Audio Search in 5 Minutes<a class="headerlink" href="#Build-Audio-Search-in-5-Minutes" title="Permalink to this headline">¶</a></h2>
<p>Building Audio search is easy with Vi!</p>
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<span></span>collection_name = 'audio_quickstart'
#create the documents
documents = []
for i in range(1, 1001):
documents.append({
'audio': 'https://vecsearch-bucket.s3.us-east-2.amazonaws.com/voices/common_voice_en_{}.wav'.format(i),
'name' : 'common_voice_en_{}.wav'.format(i),
'_id': i
})
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</div>
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<span></span>#1. specify the vdb client
from vectorai.client import ViClient
vi_client = ViClient(username, api_key, url)
vi_client.delete_collection(collection_name)
#2. specify an audio encoder
from vectorai.models.deployed import ViAudio2Vec
audio_encoder = ViAudio2Vec(username, api_key, url)
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Logged in. Welcome public-demo. To view list of available collections, call list_collections() method.
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{'status': 'complete', 'message': 'audio_quickstart deleted'}
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<span></span>#3. insert the documents and encode audio simultaneously
use_jobs = True
if use_jobs:
vi_client.insert_documents(collection_name, documents)
job = vi_client.encode_audio_job(collection_name, 'audio')
vi_client.wait_till_jobs_complete(collection_name, job['job_id'], job['job_name'])
else:
vi_client.insert_documents(collection_name, documents, models={'audio':audio_encoder.encode})
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d:\kda\vectorai\vectorai\read.py:351: UserWarning: Potential issue. Cannot find a vector field. Check that the vector field is _vector_.
"Potential issue. Cannot find a vector field. Check that the vector field is _vector_."
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{'inserted_successfully': 1000, 'failed': 0, 'failed_document_ids': []}
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{'status': 'Finished'}
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'Done'
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<span></span>import IPython.display as ipd
#4. search
search_results = vi_client.search(collection_name, audio_encoder.encode(documents[0]['audio']),
'audio_vector_', page_size=5)
vi_client.show_json(search_results, audio_fields=['audio'])
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>_id</th>
<th>name</th>
<th>insert_date_</th>
<th>audio</th>
<th>_search_score</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1</td>
<td>common_voice_en_1.wav</td>
<td>2020-10-02T07:07:34.725378</td>
<td><audio controls><source src='https://vecsearch-bucket.s3.us-east-2.amazonaws.com/voices/common_voice_en_1.wav' type='audio/wav'></audio></td>
<td>1.000000</td>
</tr>
<tr>
<th>1</th>
<td>12</td>
<td>common_voice_en_12.wav</td>
<td>2020-10-02T07:07:34.726100</td>
<td><audio controls><source src='https://vecsearch-bucket.s3.us-east-2.amazonaws.com/voices/common_voice_en_12.wav' type='audio/wav'></audio></td>
<td>0.893219</td>
</tr>
<tr>
<th>2</th>
<td>32</td>
<td>common_voice_en_32.wav</td>
<td>2020-10-02T07:07:35.271638</td>
<td><audio controls><source src='https://vecsearch-bucket.s3.us-east-2.amazonaws.com/voices/common_voice_en_32.wav' type='audio/wav'></audio></td>
<td>0.891373</td>
</tr>
<tr>
<th>3</th>
<td>20</td>
<td>common_voice_en_20.wav</td>
<td>2020-10-02T07:07:35.046128</td>
<td><audio controls><source src='https://vecsearch-bucket.s3.us-east-2.amazonaws.com/voices/common_voice_en_20.wav' type='audio/wav'></audio></td>
<td>0.882336</td>
</tr>
<tr>
<th>4</th>
<td>15</td>
<td>common_voice_en_15.wav</td>
<td>2020-10-02T07:07:34.726251</td>
<td><audio controls><source src='https://vecsearch-bucket.s3.us-east-2.amazonaws.com/voices/common_voice_en_15.wav' type='audio/wav'></audio></td>
<td>0.877323</td>
</tr>
</tbody>
</table></div>
</div>
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<span></span>#5 recommendation by id
search_by_id_results = vi_client.search_by_id(collection_name, '2', 'audio_vector_', page_size=5)
vi_client.show_json(search_by_id_results, audio_fields=['audio'])
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</div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>_id</th>
<th>name</th>
<th>insert_date_</th>
<th>audio</th>
<th>_search_score</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>2</td>
<td>common_voice_en_2.wav</td>
<td>2020-10-02T07:07:34.725536</td>
<td><audio controls><source src='https://vecsearch-bucket.s3.us-east-2.amazonaws.com/voices/common_voice_en_2.wav' type='audio/wav'></audio></td>
<td>1.000000</td>
</tr>
<tr>
<th>1</th>
<td>40</td>
<td>common_voice_en_40.wav</td>
<td>2020-10-02T07:07:35.272603</td>
<td><audio controls><source src='https://vecsearch-bucket.s3.us-east-2.amazonaws.com/voices/common_voice_en_40.wav' type='audio/wav'></audio></td>
<td>0.884632</td>
</tr>
<tr>
<th>2</th>
<td>3</td>
<td>common_voice_en_3.wav</td>
<td>2020-10-02T07:07:34.725629</td>
<td><audio controls><source src='https://vecsearch-bucket.s3.us-east-2.amazonaws.com/voices/common_voice_en_3.wav' type='audio/wav'></audio></td>
<td>0.879187</td>
</tr>
<tr>
<th>3</th>
<td>14</td>
<td>common_voice_en_14.wav</td>
<td>2020-10-02T07:07:34.726200</td>
<td><audio controls><source src='https://vecsearch-bucket.s3.us-east-2.amazonaws.com/voices/common_voice_en_14.wav' type='audio/wav'></audio></td>
<td>0.874556</td>
</tr>
<tr>
<th>4</th>
<td>21</td>
<td>common_voice_en_21.wav</td>
<td>2020-10-02T07:07:35.046224</td>
<td><audio controls><source src='https://vecsearch-bucket.s3.us-east-2.amazonaws.com/voices/common_voice_en_21.wav' type='audio/wav'></audio></td>
<td>0.865409</td>
</tr>
</tbody>
</table></div>
</div>
</div>
<div class="section" id="Build-Text-QA-Search-in-5-minutes">
<h2>Build Text QA Search in 5 minutes<a class="headerlink" href="#Build-Text-QA-Search-in-5-minutes" title="Permalink to this headline">¶</a></h2>
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<span></span>%pip install datasets
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