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FairytaleDJ 🎵🏰🔮: Recommending Disney songs with Langchain and DeepLake

TL;DR We used LangChain, OpenAI ChatGPT, DeepLake and Streamlit to create a web app that recommends Disney songs based on a user input.

A demo is on Hugging Face 🤗

Hey there! Today we will see how to leverage DeepLake to create a document retrieval system. This won't be your usual Q&A demo app were we just directly a user's query to embedded documents using LangChain. Nope, we will showcase how we can leverage LLMs to encode our data in such a way that will make our matching easier, better and faster.

Step by step, we'll unpack the behind-the-scenes of FairytaleDJ a web app to recommend Disney songs based on user input. The goal is simple: We ask how the user is feeling and we want to somehow retrieve Disney songs that go "well" with that input. For example, if the user is sad, probably a song like Reflection from Mulan would be appropriate.

This is a perfect example where vanilla Q&A fails. If you try to find similarities between users' feelings (like, "Today I am great") and song lyrics, you won't really get too good results. That's because song embeddings capture everything in the lyrics, making them "more open". Instead, what we want to do is to encode both inputs, users and lyrics, into a similar representation and then run the search. We won't spoil too much here, so shopping list time. We need mainly three things: data, a way to encode it and a way to match it with user input.

Getting the data

To get our songs, we decided to scrape https://www.disneyclips.com/lyrics/, a website containing all the lyrics for all Disney songs ever made. The code is here and it relies on asyncio to speed up things, we won't focus too much on it.

Then, we used Spotify Python APIs to get all the embedding URL for each song into the "Disney Hits" Playlist. We proceed to remove all the songs that we had scraped but are not in this playlist. By doing so, we end up with 85 songs.

We end up with a json looking like this.

{
  "Aladdin": [
    {
      "name": "Arabian Nights",
      "text": "Oh, I come from a land, from a faraway place. Where the caravan camels roam. Where it's flat and immense. And the heat is intense. It's barbaric, but, hey, it's home. . When the wind's from the East. And the sun's from the West. And the sand in the glass is right. Come on down. Stop on by. Hop a carpet and fly. To another Arabian night. . Arabian nights. Like Arabian days. More often than not. Are hotter than hot. In a lot of good ways. . Arabian nights. 'Neath Arabian moons. A fool off his guard. Could fall and fall hard. Out there on the dunes. . ",
      "embed_url": "https://open.spotify.com/embed/track/0CKmN3Wwk8W4zjU0pqq2cv?utm_source=generator"
    },
    ...
  ],

Data encoding

We were looking for a good way to retrieve the songs. We evaluated different approaches. We used ActiveLoop DeepLake vector db and more specifically its implementation in LangChain.

Creating the dataset was very easy. Given the previous json file, we proceed to embed the text field using langchain.embeddings.openai.OpenaAIEmbeddings and add all the rest of keys/values as metadata

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.vectorstores import DeepLake

def create_db(dataset_path: str, json_filepath: str) -> DeepLake:
    with open(json_filepath, "r") as f:
        data = json.load(f)

    texts = []
    metadatas = []

    for movie, lyrics in data.items():
        for lyric in lyrics:
            texts.append(lyric["text"])
            metadatas.append(
                {
                    "movie": movie,
                    "name": lyric["name"],
                    "embed_url": lyric["embed_url"],
                }
            )

    embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")

    db = DeepLake.from_texts(
        texts, embeddings, metadatas=metadatas, dataset_path=dataset_path
    )

    return db

To load it, we can simply:

def load_db(dataset_path: str, *args, **kwargs) -> DeepLake:
    db = DeepLake(dataset_path, *args, **kwargs)
    return db

My dataset_path is hub://<ACTIVELOOP_ORGANIZATION_ID>/<DATASET_NAME>, but you can also store it locally. Their doc is here

Matching

Next step was to find a way to match our songs with a given user inputs, we tried several things till we found out a cheap way that works qualitavely well. So let's start with the failures 😅

What didn't work

Similarity search of direct embeddings.

This approach was straightforward. We create embeddings for the lyrics and the user input with gpt3 and do a similarity search. Unfortunatly, we noticed very bad suggestions, this is due to the fact that we want to match user's emotions to the songs not exactly what it is saying.

For example, if we search for similar songs using "I am sad", we will see very similar scores across all documents

 db.similarity_search_with_score("I am happy", distance_metric="cos", k=100)

If we plot the scores using a box plot, we will see they mostly are around 0.74

While the first 10 songs do not really match so well

The World Es Mi Familia 0.7777353525161743
Go the Distance 0.7724394202232361
Waiting on a Miracle 0.7692896127700806
Happy Working Song 0.7679054141044617
In Summer 0.7620900273323059
So Close 0.7601353526115417
When I Am Older 0.7582702040672302
How Far I'll Go 0.7560539245605469
You're Welcome 0.7539903521537781
What Else Can I Do? 0.7535801529884338

Using ChatGPT as a retrieval system

We also tried to nuke the whole lyrics into chatGPT and asked it to return matching songs with the user input. We had to first create a one-sentence summary of each lyric to fit into 4096 tokens. Resulting in around 3k tokens per request (0.006$). It follows the prompt template, very simple but very long. The {songs} variable holds the JSON with all the songs

You act like a song retrivial system. We want to propose three songs based on the user input. We provide you a list of song with their themes in the format <MOVIE_NAME>;<SONG_TITLE>:<SONG_THEMES>. To match the user input to the song try to find themes/emotions from it, try imagine what emotions the user may have and what song may be just nice to listen to. Add a bit of randomness in your decision.
If you don't find a match provide your best guess. Try to look at each song's themes to provide more variations in the match. Please only output songs contained in following list.

{songs}

Given a input, output three songs as a list that goes well with the input. The list of songs will be used to retrieve them from our database. The type of the reply is List[str, str, str]. Please follow the following example formats

Examples:
Input: "Today I am not feeling great"
["<MOVIE_NAME>;<SONG_TITLE>", "<MOVIE_NAME>;<SONG_TITLE>", "<MOVIE_NAME>;<SONG_TITLE>"]
Input: "I am great today"
["<MOVIE_NAME>;<SONG_TITLE>", "<MOVIE_NAME>;<SONG_TITLE>", "<MOVIE_NAME>;<SONG_TITLE>"]

The user input is {user_input}

That did work okayish but was overkill. Later on, we also tried the emotional encoding that we will talk about in the next section. It had a comparable performance.

What did work: Similarity search of emotions embeddings.

Finally, we arrived at an approach that is unexpensive to run and gives good results. We convert each lyric to a list of 8 emotions using ChatGPT. The prompt is the following

I am building a retrieval system. Given the following song lyric

{song}

You are tasked to produce a list of 8 emotions that I will later use to retrieve the song. 

Please provide only a list of comma separated  emotions

For example, using the "Arabian Nights" from Aladdin (shown in the previous section), we obtained "nostalgic, adventurous, exotic, intense, romantic, mysterious, whimsical, passionate".

We then embed each emotion for each song with gpt3 and store it into.

The full script is here

Now, we need to convert the user input to a list of emotions, we used again ChatGPT with a custom prompt.

We have a simple song retrieval system. It accepts 8 emotions. You are tasked to suggest between 1 and 4 emotions to match the users' feelings. Suggest more emotions for longer sentences and just one or two for small ones, trying to condense the main theme of the input

Examples:

Input: "I had a great day!" 
"Joy"
Input: "I am very tired today and I am not feeling well"
"Exhaustion, Discomfort, and Fatigue"
Input: "I am in Love"
"Love"

Please, suggest emotions for input = "{user_input}", reply ONLY with a list of emotions/feelings/vibes

Here we tasked the model to provide between one and four emotions, this worked best empirically given the fact that most inputs are short.

Let's see some examples:

"I'm happy and sad today" -> "Happiness, Sadness"
"hey rock you" -> "Energy, excitement, enthusiasm"
"I need to cry" -> "Sadness, Grief, Sorrow, Despair" 

Then we used these emotions to actually perform the similarity search on the db.

user_input = "I am happy"
# we use chatGPT to get emotions from a user input
emotions = chain.run(user_input=user_input)
# we find the k more similar song
matches = db.similarity_search_with_score(emotions, distance_metric="cos", k=k)

These are the scores obtained from that search (k=100), they are more spreaded apart.

And the songs makes more sense.

Down in New Orleans (Finale) 0.9068354368209839
Happy Working Song 0.9066014885902405
Love is an Open Door 0.8957026600837708
Circle of Life 0.8907418251037598
Where You Are 0.8890194892883301
In Summer 0.8889626264572144
Dig a Little Deeper 0.8887585401535034
When We're Human 0.8860496282577515
Hakuna Matata 0.8856213688850403
The World Es Mi Familia 0.884093165397644

We also implement some postprocessing. We first filter out the low-scoring one

def filter_scores(matches: Matches, th: float = 0.8) -> Matches:
    return [(doc, score) for (doc, score) in matches if score > th]

matches = filter_scores(matches, 0.8)

To add more variations, aka not always recommend the first one, we need to sample from the list of candidate matches. To do so, we first ensure the scores sum to one by diving by their sum.

def normalize_scores_by_sum(matches: Matches) -> Matches:
    scores = [score for _, score in matches]
    tot = sum(scores)
    return [(doc, (score / tot)) for doc, score in matches]

Then we sample n songs using a modified version of np.random.choice(..., p=scores), basically everything we sample we remove the element we have sampled. This ensures we don't sample two times the same element.

docs, scores = zip(*matches)
docs = weighted_random_sample(
    np.array(docs), np.array(scores), n=number_of_displayed_songs
).tolist()
for doc in docs:
    print(doc.metadata["name"])

And finally we have our songs. Then, we created a webapp using Streamlit and we hosted the app on an Hugging Face space

Conclusion

While we explained how to mix these technologies together to create a song recommendation system you can apply the same principles to more use cases. The main takeaway here is to understand how to leverage LLMs to make the data work for you by transforming it to fit your task better. This was crucial for us since only after the converted both users' inputs and songs' lyrics to a list of emotions, we were able to have good matches.

That's all folks 🎉

Thanks for reading and see you in the next one 💜

Francesco