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rohit20001221
rohit20001221

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Building Simple Recommendation Engine With KNN Algorithm using Redis

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Hi guys today i tought of building a recommendation engine which recommends us music on giving a audio file as input

let us see how we can build this engine using redis with the help of inbuilt vector similarity search

start the redis stack server

docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
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preparing the dataset

for the dataset i had downloaded the music from this channel https://www.youtube.com/@TheFatRat i had downloaded the music in .wav format

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and i had create a data.json

[ { "file": "1.wav", "link": "https://www.youtube.com/watch?v=2QdPxdcMhFQ" }, { "file": "2.wav", "link": "https://www.youtube.com/watch?v=2Ax_EIb1zks" }, { "file": "3.wav", "link": "https://www.youtube.com/watch?v=wgip631nFdY" }, { "file": "4.wav", "link": "https://www.youtube.com/watch?v=bgyO9bNbfb8" }, { "file": "5.wav", "link": "https://www.youtube.com/watch?v=gHgv19ip-0c" }, { "file": "6.wav", "link": "https://www.youtube.com/watch?v=3VTkBuxU4yk" }, { "file": "7.wav", "link": "https://www.youtube.com/watch?v=cJglBxApcDM" }, { "file": "8.wav", "link": "https://www.youtube.com/watch?v=j-2DGYNXRx0" }, { "file": "9.wav", "link": "https://www.youtube.com/watch?v=M-P4QBt-FWw" }, { "file": "10.wav", "link": "https://www.youtube.com/watch?v=cMg8KaMdDYo" } ]
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now to insert the data into redis we can run the below script

import librosa import threading import numpy as np import json import os import uuid dataset = json.load(open("./dataset/dataset.json", "r")) music_data = [] def load_music_file(data): vector, sr = librosa.load("./dataset/" + data['file']) music_data.append([vector, sr, data['link']]) threads = [] for data in dataset: t = threading.Thread(target=load_music_file, args=[data,]) t.start() threads.append(t) for t in threads: t.join() features = [] for y, sr, url in music_data: feature = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=200) features.append([np.mean(feature.T, axis=0), url]) import redis redis_client = redis.Redis() pipeline = redis_client.pipeline() for feature, url in features: pipeline.hmset( "music:" + str(uuid.uuid4()), {"url": url, "vec": feature.astype(np.float32).tobytes()} ) pipeline.execute()
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we are using librosa python package to preprocess the audio files and to convert the music file into a vector representation we are using mfcc https://medium.com/prathena/the-dummys-guide-to-mfcc-aceab2450fd

now if you navigate to http://localhost:8001 you can find that the music files have be added into our redis database

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using redis knn command to get the recommendations

first step you need to do inorder to use knn feature of redis you must create a vector index which can be done via following command

FT.CREATE "idx:music" ON HASH PREFIX 1 "music:" SCHEMA "url" TEXT "vec" VECTOR HNSW 6 "TYPE" "FLOAT32" "DIM" 128 // <-- 128 because the mfcc vector has a dimension of 1 x 128 "DISTANCE_METRIC" "COSINE"
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now if you want to query the items using redis command you can use the following command

FT.SEARCH idx:music "*=>[KNN $K @vec $query_vector as vector_score]" "PARAMS" "4" K 2 "query_vector" <binary_form_of_vector> RETURN 2 vector_score url SORTBY vector_score DIALECT 2
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but i recommend you to use python instead of the above query because it is easy you can query recommendations using the below code

y, sr = librosa.load("./example.wav") feature = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=200) vector = np.mean(feature.T, axis=0) vector = vector.astype(np.float32).tobytes() q = Query( f"(*)=>[KNN 3 @vec $vec_param as vector_score]" ).sort_by("vector_score").return_fields("url","vector_score").dialect(2) params = { "vec_param": vector } results = [] query_results = redis_client.ft("idx:music").search(query=q, query_params=params) for result in query_results.docs: results.append(result.url) print(results)
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i have created one flask api so that we can run this model in the browser

# app.py import tempfile import librosa import numpy as np import redis from flask import Flask, jsonify, render_template, request from redis.commands.search.query import Query redis_client = redis.Redis() app = Flask(__name__) @app.route('/') def index(): return render_template("index.html") @app.route("/get_recommendations", methods=['POST']) def get_recommendations(): tmp = tempfile.NamedTemporaryFile(delete=True) request.files.get("music_file").save(tmp) y, sr = librosa.load(tmp.name) feature = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=200) vector = np.mean(feature.T, axis=0) vector = vector.astype(np.float32).tobytes() q = Query( f"(*)=>[KNN 3 @vec $vec_param as vector_score]" ).sort_by("vector_score").return_fields("url", "vector_score").dialect(2) params = { "vec_param": vector } results = [] query_results = redis_client.ft("idx:music").search(query=q, query_params=params) for result in query_results.docs: results.append(result.url) tmp.close() return jsonify({ "results": results }) if __name__ == '__main__': app.run(debug=True)
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<!--- templates/index.html ---> <script> async function submitForm(event) { event.preventDefault() const fd = new FormData(event.currentTarget) const data = await (await fetch("{{url_for('get_recommendations')}}", { method: 'POST', body: fd, })).json() document.getElementById("results").innerHTML = "" data["results"].forEach((url) => { document.getElementById("results").innerHTML += `<a href="${url}">${url}</a><br/>` }) return false } </script> <form action="#" onsubmit="submitForm(event)" enctype="multipart/form-data"> <input type="file" name="music_file" id=""> <button type="submit">submit</button> </form> <div id="results"></div>
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you can find the complete code in the github
https://github.com/rohit20001221/music_recommendation_engine

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