🎹🎻🎸 Searching for similar music tracks 🎼🎶
A presentation at Elastic Sofia Meetup in in Sofia, Bulgaria by David Pilato
🎹 Searching for similar musics 🎻🎸 David Pilato | @dadoonet
Elasticsearch You Know, for Search
These are not the droids you are looking for.
GET /_analyze { “char_filter”: [ “html_strip” ], “tokenizer”: “standard”, “filter”: [ “lowercase”, “stop”, “snowball” ], “text”: “These are <em>not</em> the droids you are looking for.” }
These are <em>not</em> the droids you are looking for. { “tokens”: [{ “token”: “droid”, “start_offset”: 27, “end_offset”: 33, “type”: “<ALPHANUM>”, “position”: 4 },{ “token”: “you”, “start_offset”: 34, “end_offset”: 37, “type”: “<ALPHANUM>”, “position”: 5 }, { “token”: “look”, “start_offset”: 42, “end_offset”: 49, “type”: “<ALPHANUM>”, “position”: 7 }]}
Elasticsearch You Know, for Search
Elasticsearch You Know, for Vector Search
Example: 1-dimensional vector Character Vector [ 1 ] ] Realistic
[ Embeddings represent your data Cartoon 1
represent different data aspects Human Character Vector [ 1, 1 Realistic Cartoon ] ] Machine
[ Multiple dimensions 1, 0
is grouped together Human Character Vector [ 1.0, 1.0 Realistic Cartoon 1.0, 0.0 [ 1.0, 0.8 ] ] ]
Machine
[ Similar data
Vector search ranks objects by similarity (~relevance) to the query Human Rank Query 1 Realistic Cartoon 2 3 4 5 Machine Result
Data Ingestion and Embedding Generation POST /_doc { “_id”:”product-1234”, “product_name”:”Summer Dress”, “description”:”Our best-selling…”, “Price”: 118, “color”:”blue”, “fabric”:”cotton”, “fabric”:”cotton” } “desc_embedding”:[0.452,0.3242,…], “desc_embedding”:[0.452,0.3242,…] } “img_embedding”:[0.012,0.0,…] } Source data POST /_doc
Vector Query GET product-catalog/_search { “query” : { “bool”: { “must”: [{ “knn”: { “field”: “desc_embbeding”, “num_candidates”: 50, “query_vector”: [0.123, 0.244,…] } }], “filter”: { “term”: { “department”: “women” } } } } }, “size”: 10
But how does it really work?
Similarity: cosine (cosine) Human q cos(θ) = d1 d2 Realistic θ q⃗ × d ⃗ | q⃗ | × | d |⃗ _score = 1 + cos(θ) 2
Similarity: cosine (cosine) 1+1 _score = =1 2 1+0 _score = = 0.5 2 1−1 _score = =0 2
https://djdadoo.pilato.fr/
https://github.com/dadoonet/music-search/
🎹 Searching for similar musics 🎻🎸 David Pilato | @dadoonet
In this session, we will use the principles of vector search to find pieces of music that are (maybe) similar to others. To do this, we will review the principles of generating embeddings to represent any type of data, whether textual or binary.
Resources
The following resources were mentioned during the presentation or are useful additional information.
Buzz and feedback
Here’s what was said about this presentation on social media.
