Christoph Lühr @chluehr / bePHPug Berlin 2023 "Introduction & basic concepts" Search, Embeddings & Vector-DBs 

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We Create Digital Strength for Your Business 

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(#) Internet 

Unicode Character 'HAPPY PERSON RAISING ONE HAND' (U+1F64B) 

"vintage style" Search 

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Full-Text-Index Token, Token, Token 

Synonyms Usergroup = Meetup 

Synonyms Horse = Pony 

Filters year < 2023 

Filters location = "Berlin" 

Ranking Scoring: Token "PHP" = Boost +2 

"Where to meet experts in Web & script languages?" NO CONTEXT 

Vector Databases 

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[ 0 ... 0.5 ... 1 ] is_cute? 

[ 0 ... 0.5 ... 1 ] is_cute? 

[ 0 ... 0.5 ... 1 ] is_cute? ? 

Vector Lat / Lon 

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Vector [ 52.520, 13.404 ] 

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Distance "Similarity" 

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● cuteness: 0.999 ● domesticated: 0.865 ● pet: 0.950 ● likes_water: 0.021 ● foo: ... ● bar: ... ● ... 

● cuteness: 0.999 ● domesticated: 0.865 ● pet: 0.950 ● likes_water: 0.021 ● foo: ... ● bar: ... ● ... The cat (Felis catus) is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae and is commonly referred to as the domestic cat or house cat to distinguish it from the wild members of the family. Cats are commonly kept as house pets but can also be farm cats or feral cats; the feral cat ranges freely and avoids human contact. Domestic cats are valued by humans for companionship and their ability to kill small rodents. About 60 cat breeds are recognized by various cat registries. 

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Dimensions 

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[ 1, 2, ... n=2000 ] Distance? 

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Vectors? 

Manual Classification (data) 

Machine Learning (traditional) 

Embeddings! Large Language Models 

Embeddings! Multi-Modal Models 

OpenAI GPT-3 Embeddings API [ 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790 ] 

CLIP Embeddings API [ 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790 ] 

CLIP Embeddings API [ 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790 ] 

ANY MODEL Embeddings API [ 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790 ] 

[ 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790 ] 

Search? 

➊ Encode all content to vectors 

ANY MODEL Embeddings API [ 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790 ] 

➋ Store in Vector DB 

[ 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790 ] 

➌ Encode query as Vector, too 

ANY MODEL Embeddings API [ 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790, 0.874, 0.332, 0.687, 0.042, 0.955, 0.725, 0.117, 0.388, 0.574, 0.837, 0.238, 0.976, 0.409, 0.693, 0.853, 0.183, 0.509, 0.789, 0.612, 0.949, 0.125, 0.546, 0.368, 0.707, 0.831, 0.271, 0.930, 0.654, 0.432, 0.769, 0.214, 0.613, 0.790, 0.891, 0.428, 0.935, 0.752, 0.648, 0.373, 0.989, 0.513, 0.227, 0.651, 0.899, 0.310, 0.971, 0.672, 0.447, 0.108, 0.790 ] 

➍ Search by distance 

No content

Embeddings, SaaS, API 

Vector DB, SaaS, API 

➊,➋ Encode all content to vectors, Store in Vector DB 

No content

➌,➍ Encode query as Vector, Search (by distance) 

No content

Easy! 

"Explainability" PROBLEM 

(like ElasticSearch) + Filters 

Filters location = "Berlin" 

Token limit, context, vector space Content Partitioning 

In distributional semantics, a quantitative methodological approach to understanding meaning in observed language, word embeddings or semantic vector space models have been used as a knowledge representation for some time.[11] Such models aim to quantify and categorize semantic similarities between linguistic items based on their distributional properties in large samples of language data. The underlying idea that "a word is characterized by the company it keeps" was proposed in a 1957 article by John Rupert Firth,[12] but also has roots in the contemporaneous work on search systems[13] and in cognitive psychology.[14] The notion of a semantic space with lexical items (words or multi-word terms) represented as vectors or embeddings is based on the computational challenges of capturing distributional characteristics and using them for practical application to measure similarity between words, phrases, or entire documents. The first generation of semantic space models is the vector space model for information retrieval.[15][16][17] Such vector space models for words and their distributional data implemented in their simplest form results in a very sparse vector space of high dimensionality (cf. curse of dimensionality). Reducing the number of dimensions using linear algebraic methods such as singular value decomposition then led to the introduction of latent semantic analysis in the late 1980s and the random indexing approach for collecting word cooccurrence contexts.[18][19][20][21] In 2000, Bengio et al. provided in a series of papers titled "Neural probabilistic language models" to reduce the high dimensionality of word representations in contexts by "learning a distributed representation for words".[22][23] A study published in NeurIPS (NIPS) 2002 introduced the use of both word and document embeddings applying the method of kernel CCA to bilingual (and multi-lingual) corpora, also providing an early example of self-supervised learning of word embeddings [24] Word embeddings come in two different styles, one in which words are expressed as vectors of co-occurring words, and another in which words are expressed as vectors of linguistic contexts in which the words occur; these different styles are studied in Lavelli et al., 2004.[25] Roweis and Saul published in Science how to use "locally linear embedding" (LLE) to discover representations of high dimensional data structures.[26] Most new word embedding techniques after about 2005 rely on a neural network architecture instead of more probabilistic and algebraic models, after foundational work done by Yoshua Bengio and colleagues.[27][28] 

... via prompting Question Answering 

In distributional semantics, a quantitative methodological approach to understanding meaning in observed language, word embeddings or semantic vector space models have been used as a knowledge representation for some time.[11] Such models aim to quantify and categorize semantic similarities between linguistic items based on their distributional properties in large samples of language data. The underlying idea that "a word is characterized by the company it keeps" was proposed in a 1957 article by John Rupert Firth,[12] but also has roots in the contemporaneous work on search systems[13] and in cognitive psychology.[14] The notion of a semantic space with lexical items (words or multi-word terms) represented as vectors or embeddings is based on the computational challenges of capturing distributional characteristics and using them for practical application to measure similarity between words, phrases, or entire documents. The first generation of semantic space models is the vector space model for information retrieval.[15][16][17] Such vector space models for words and their distributional data implemented in their simplest form results in a very sparse vector space of high dimensionality (cf. curse of dimensionality). Reducing the number of dimensions using linear algebraic methods such as singular value decomposition then led to the introduction of latent semantic analysis in the late 1980s and the random indexing approach for collecting word cooccurrence contexts.[18][19][20][21] In 2000, Bengio et al. provided in a series of papers titled "Neural probabilistic language models" to reduce the high dimensionality of word representations in contexts by "learning a distributed representation for words".[22][23] A study published in NeurIPS (NIPS) 2002 introduced the use of both word and document embeddings applying the method of kernel CCA to bilingual (and multi-lingual) corpora, also providing an early example of self-supervised learning of word embeddings [24] Word embeddings come in two different styles, one in which words are expressed as vectors of co-occurring words, and another in which words are expressed as vectors of linguistic contexts in which the words occur; these different styles are studied in Lavelli et al., 2004.[25] Roweis and Saul published in Science how to use "locally linear embedding" (LLE) to discover representations of high dimensional data structures.[26] Most new word embedding techniques after about 2005 rely on a neural network architecture instead of more probabilistic and algebraic models, after foundational work done by Yoshua Bengio and colleagues.[27][28] Why are Vector DBs great for searching? 

Answer the question as truthfully as possible using the provided text, and if the answer is not contained within the text below, say "I don't know". ------------------------- $context ------------------------- Question: $question 

No content

No content

No content

& slow $ $ $ 

Demo 

Image source: http://www.flickr.com/photos/rietje/76566707/ CC BY 2.0 

Thanks! Questions? Christoph Lühr [email protected] [email protected] @chluehr Slides license Attribution-NonCommercial-ShareAlike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/