| 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) |
|
|
