| import faiss |
| import json |
| import numpy as np |
| import zipfile |
|
|
| from sentence_transformers import SentenceTransformer |
|
|
|
|
| class Retriever: |
| def __init__(self): |
| self.archive = zipfile.ZipFile('data/en/paragraphs.zip', 'r') |
| self.index = faiss.read_index("data/en/embs_IVF16384_HNSW32_2lvl_full.idx") |
| self.index.nprobe = 128 |
| self.model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2', device='cuda') |
| self.model.max_seq_length = 512 |
|
|
|
|
| def get_paragraph_by_vec_idx(self, vec_idx): |
| chunk_id = vec_idx // 100000 |
| line_id = vec_idx % 100000 |
| with self.archive.open('enwiki_paragraphs_clean/enwiki_paragraphs_%03d.jsonl' % chunk_id) as f: |
| for i,l in enumerate(f): |
| if i == line_id: |
| data = json.loads(l) |
| break |
| return data |
|
|
|
|
| def search(self, query, k=5): |
| emb = self.model.encode(query) |
| _, neighbors = self.index.search(emb[np.newaxis, ...], k) |
| results = [] |
| for n in neighbors[0]: |
| data = get_paragraph_by_vec_idx(n) |
| results.append(data) |
| return results |
|
|