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Viberary

🚧 🚧

Viberary is a search engine that recommends you books based not on genre or title, but vibe by performing semantic search across a set of learned embeddings on a dataset of books from Goodreads and their metadata.

It returns a list of book recommendations based on the vibe of the book that you put in. So you don't put in "I want science fiction", you'd but in "atmospheric, female lead, worldbuilding, funny" as a prompt, and get back a list of books. This project came out of experiences I had where recommendations for movies, TV, and music have fairly been good, but book recommendations are always a problem.

Viberary is now in maintenance mode!

For much, much more detail see the how page on the project website.

App Architecture

drawing

It's a two-tower semantic retrieval model that encodes both the query and the corpus using the Sentence Transformers pretrained asymmetric MSMarco Model.

drawing

The training data is generated locally in DuckDB and the model is converted to ONNX for low-latency inference, with corpus embeddings learned on AWS P3 instances against the same model and stored in Redis and retrieved using the Redis Search module using the HNSW algorithm included as part of the Redis search module. Results are served through a Flask API running Gunicorn and served to a Bootstrap front-end.

drawing

It's served from one Digital Ocean droplets behind a Digital Ocean load balancer and Nginx, as a Dockerized application with networking spun up through Docker compose between a web server and Redis image, with data persisted to external volumes in DigitalOcean, with AWS Route53 serving as the domain registrar and load balancer router.

The artifact is generated through GitHub actions on the main branch of the repo. I manually pull the new docker image on the droplet.

Running the project

If you don't have the model (start from training data)

  1. Fork/clone the repo
  2. Go to the project root (viberary)
  3. Download the corpus embeddings file to /viberary/src/training_data.
  4. Go to the root of the repo and run make onnx to generate the runnable model artifact.
  5. make build - Builds the docker image
  6. make up-arm (or make up-intel) - Docker compose running in background depending on your system architecture
  7. make embed - indexes the embeddings once the web server is running
  8. localhost:8000 - Loads the web server

If you have the model: (Get from DigitalOcean Spaces)

  1. Get model artifacts from here: https://github.com/veekaybee/viberary_model and add them to sentence_transformers
  2. make build - Builds the docker image
  3. make up-arm( or make up-intel) - Docker compose running in background depending on your system architecture
  4. make embed - indexes the embeddings once the web server is running
  5. localhost:8000 - Loads the web server

Running Evaluation

  1. cd eval

Monitoring the project

make logs for logs

Repo Structure

  • src - where all the code is
    • api - Flask sever that calls the model, includes a search endpoint. Eventually will be rewritten in Go (for performance reasons)
    • training_data - generated training data
    • model - The actual BERT model. Includes data generated for generating embeddings and also the code used to generate the embeddings, on an EC2 instance.
    • index includes an indexer which indexes embeddings generated in model into a Redis instance. Redis and the Flask app talk to each other through an app running via docker-compose and the Dockerfile for the main app instance.
    • search - performs the search calls from api
    • store - Small model store to interface with DO Spaces
    • conf - There are some utilities such as data directory access, io operations and a separate indexer that indexes titles into Redis for easy retrieval by the application
    • notebooks - Exploration and development of the input data, various concepts, algorithms, etc. The best resource there is this notebook, which covers the end-to-end workflow of starting with raw data, processing in DuckDB, learning a Word2Vec embeddings model, (Annotated output is here.) and storing and querying those embeddings in Redis Search. This is the solution I eventually turned into the application directory structure.
  • docs - This serves and rebuilds viberary.pizza

CONTRIBUTING

Please follow instructions above for building and testing locally, make sure all unit tests pass and your branch passes locally before issuing a PR.

Relevant Literature and Bibliography

Input Data

UCSD Book Graph, with the critical part being the user-generated shelf labels.. Sample row: Note these are all encoded as strings!

{
  "isbn": "0413675106",
  "text_reviews_count": "2",
  "series": [
    "1070125"
  ],
  "country_code": "US",
  "language_code": "",
  "popular_shelves": [
    {
      "count": "2979",
      "name": "to-read"
    },
    {
      "count": "291",
      "name": "philosophy"
    },
    {
      "count": "187",
      "name": "non-fiction"
    },
    {
      "count": "80",
      "name": "religion"
    },
    {
      "count": "76",
      "name": "spirituality"
    },
    {
      "count": "76",
      "name": "nonfiction"
    }
  ],
  "asin": "",
  "is_ebook": "false",
  "average_rating": "3.81",
  "kindle_asin": "",
  "similar_books": [
    "888460"
  ],
  "description": "Taoist philosophy explained using examples from A A Milne's Winnie-the-Pooh.",
  "format": "",
  "link": "https://www.goodreads.com/book/show/89371.The_Te_Of_Piglet",
  "authors": [
    {
      "author_id": "27397",
      "role": ""
    }
  ],
  "publisher": "",
  "num_pages": "",
  "publication_day": "",
  "isbn13": "9780413675101",
  "publication_month": "",
  "edition_information": "",
  "publication_year": "",
  "url": "https://www.goodreads.com/book/show/89371.The_Te_Of_Piglet",
  "image_url": "https://s.gr-assets.com/assets/nophoto/book/111x148-bcc042a9c91a29c1d680899eff700a03.png",
  "book_id": "89371",
  "ratings_count": "11",
  "work_id": "41333541",
  "title": "The Te Of Piglet",
  "title_without_series": "The Te Of Piglet"
}

Roadmap

See issues.