See the react interface live here (for as long as I keep the API running).

Search for Steam games based off a text description.

Describe the kind of game you're looking for, and get a list of games that closest match the vibe you're looking for based off the store description and reviews.

• Python
• Linux or Mac
  • Ability to execute .sh scripts.
  • Windows untested, might work with WSL. (GPU support unknown/unlikely)
• ~20 GB disk space to hold libraries and models.

• A GPU with > 6 GB VRAM
• Lots of time 😊

• Clone this repository.
• Install Python, if you don't already have it.
• Download prepopulated database here (X.XX GB).
  • Will let you skip to step 04, bypassing GPU requirement and many hours of calling Steam API.
  • Not yet available.

• There are multiple numbered folders in this repository that should be run in order.
• In each directory, there is an init.sh script that will create a virtual environment directory (e.g. .venv_01 for step 01) and install the required dependencies for that step.
• Once the virtual environment is created, activate it (e.g. source ./.venv_01/bin/activate), then run python run.py.
• The first step will initialize and populate a database.
• You should use the same database file for all steps.
  • Scripts will only add/process new data for that step, so you can run them multiple times as you add more data to the database.

• Requirements: A web connection, time (~6 hours)
• Overview: Queries the Steam API to populate the database with steam app ids, game names, store descriptions, and top 100 reviews (decided by Steam).
• Use --new to create a new database.
• Rate limited to 1 request per 1.5 seconds, to meet Valve's rate limiting policy.
• Currently only populates randomly selected app ids with store descriptions and reviews per run.
  • Controllable with --limit <number> (default 5,000)
  • Populating the database with all 180k app ids and their reviews will take about a week.
• Takes about 2 hours for 1,000 app ids, 6-10 hours for 5,000 app ids.
• Re-running the script with the same database will add more app ids to the database.
• Example invocation: python run.py --db ./steam.db --limit 1000

• Requirements: A CUDA GPU (highly recommended), or without that, time (a couple hours)
• Overview: Creates embeddings for the store descriptions and reviews.
• Uses Instructor to create embeddings.
• Can specify which model is used with the --model-name argument.
  • --model-name hkunlp/instructor-large (default, requires 2.5 GB VRAM)
  • --model-name hkunlp/instructor-xl (highly recommended, requires ~6 GB VRAM)
• With RTX 3090, takes about 30 minutes per 5,000 new items added by step 01.
• Example invocation: python run.py --db ./steam.db --model-name hkunlp/instructor-xl

• Requirements: A good CPU is recommended
• Overview: Uses hnswlib to create a nearest neighbor index for the embeddings.
• Work in progress.

• Requirements: Fairly low. A CPU with 3-6 GB of RAM.
• Overview: Queries the database for games that match a given query.
• Takes about 10 seconds to complete and return results.
  • Most of the time spent in this script is loading Instructor to generate an embedding for your query.
• Important: Use the same --model-name you used to generate the database in step 02.
• You can use --query-for-type <type> to limit search to all, description or review embeddings.
• Example invocation: python run.py --db ./steam.db --query "I want a game that is like a mix of Minecraft and Skyrim"

• Requirements: Same as 04
• Overview: A Flask API that can be used to query the database.
• Important: Not very configurable at the current moment. Has hardcoded assumptions about the database and model.
• Includes Dockerfile and docker-compose.yml for easy deployment.
• Can also be executed with gunicorn or flask run.
• Example invocation: python run.py

• Requirements: Static file hosting.
• Overview: A simple React interface for querying the API.
• Important: Not very configurable at the current moment. API is hardcoded to my home computer.
• Use npm run build to build the static files (available in ./build).
• Use npm start to start a development server.

Note: These results are from a small testing dataset of 5,000 games. The results should be better with a larger dataset.

Output is formatted as:

  <app_id>: <game_name> (<source>)
    Match: <cosine_similarity>

• app_id: The Steam app id for the game.
• game_name: The name of the game.
• source: The source of the embedding. Either description or review.
• cosine_similarity: The cosine similarity between the query and the embedding.

  1748370: The Hunt (review)
    Match: 78.49%
  1466060: Tainted Grail: The Fall of Avalon (review)
    Match: 78.28%
  611670: The Elder Scrolls V: Skyrim VR (review)
    Match: 78.03%
  2157290: Azarine Heart (review)
    Match: 77.88%
  641980: Block Survival: Legend of the Lost Islands (review)
    Match: 77.04%
  367030: Lootfest (review)
    Match: 76.45%
  2175570: Eternamine (review)
    Match: 76.21%
  1604030: V Rising (review)
    Match: 76.20%
  1179410: Lootcraft (review)
    Match: 76.15%
  1507910: Survival Engine (review)
    Match: 76.06%

  848260: Super GTR Racing (description)
    Match: 80.96%
  1586930: Extreme racing (description)
    Match: 80.66%
  808080: Ultimate Racing 2D (description)
    Match: 80.59%
  1318280: OverShoot Battle Race (review)
    Match: 80.46%
  661290: Arizona Derby (review)
    Match: 80.11%
  397640: Nulldrifters (review)
    Match: 79.83%
  1814150: Atomic Cyclecar Racing (review)
    Match: 79.77%
  2178650: Fastraq (description)
    Match: 79.64%
  1634090: Mini Racing World (review)
    Match: 79.59%
  1927710: Supreme Race on Highway (review)
    Match: 79.41%

  1758320: Lofty Quest (review)
    Match: 85.18%
  360840: Lumini (review)
    Match: 83.94%
  1783370: Aery - Dreamscape (review)
    Match: 83.53%
  1536520: Equinoxe (review)
    Match: 81.90%
  612740: Bokida - Heartfelt Reunion (review)
    Match: 81.85%
  406220: Gnomes Vs. Fairies (review)
    Match: 81.68%
  2487460: Lost Forest (review)
    Match: 81.53%
  1121140: Ionia (review)
    Match: 81.53%
  1941890: Santo Island Incident (review)
    Match: 81.43%
  1481000: Triversal (review)
    Match: 81.43%

Related Repository: https://github.com/Netruk44/repo-search

• Previous version of this idea.
• Searches through text documents/code for content that matches a description.