LLM Recommendation Engine

An open source recommendation service that converts any text image or video description into clean tags with a local LLM then serves personalized results using light vector math. Copy paste into your stack and chill. It uses the same modern approach that companies like X (using Grok for recommendations) and Meta (using Llama for recommendations) are using.

Why you want it

• Universal Works for products posts clips bios anything described in words
• Bring your own tags Define enums in YAML and you are set
• Local friendly Runs with Ollama meaning your data never leaves your box
• Pluggable storage SQLite for hack nights Firestore or DynamoDB for prod
• Fully typed FastAPI endpoints plus a CLI for quick experiments

Fast track

poetry install
poetry run uvicorn api.server:app --reload

Swagger UI pops up at http://127.0.0.1:8000/docs

• POST /tag get tags for a description
• POST /item store an item with tags and vector
• POST /interact update a user profile after a click view or like
• POST /recommend grab the top matches for that user

Requirements

• Python 3.10 or newer
• Ollama running with your fave model

ollama pull llama3.2
ollama serve

Config

Edit config.yaml

• model_url points at Ollama
• storage_backend selects your adapter
• enum_list lists valid tags

Switch backend by setting storage_backend to one of

• storage.sqlite_adapter.SQLiteStorage
• storage.firestore_adapter.FirestoreStorage
• storage.dynamodb_adapter.DynamoDBStorage

Cloud notes

Firestore

export GOOGLE_APPLICATION_CREDENTIALS=your-key.json

DynamoDB

aws configure

CLI vibe

poetry run python -m cli "A 4K sixty Hz monitor with ultra thin bezels"

Tests

poetry run pytest -q

Ship it and have fun.