| import itertools | |
| import os | |
| import numpy as np | |
| import faiss | |
| from app.database import ItemDatabase | |
| class RecommenderSystem: | |
| def __init__(self, faiss_index_path, db_path): | |
| self._index = faiss.read_index(faiss_index_path) | |
| self._db = ItemDatabase(db_path) | |
| def recommend_items(self, query, n_items=10): | |
| query_embedding = self._db.get_item(query)["embedding"] | |
| _, results = self._index.search(query_embedding, k=n_items+1) | |
| results = filter(lambda item: item != query, results[0]) | |
| return itertools.islice(results, n_items) | |