| from argparse import ArgumentParser |
| import json |
| from tqdm import tqdm |
| from dataclasses import dataclass |
| from typing import List, Optional, Dict, Set |
| from sentence_transformers import SentenceTransformer |
| import numpy as np |
| import hnswlib |
|
|
| @dataclass |
| class Doc: |
| input: str |
| output: str |
| |
| @staticmethod |
| def from_json(doc: Dict): |
| return Doc(input=doc['input'], output=doc['output']) |
|
|
|
|
| if __name__ == "__main__": |
| parser = ArgumentParser(prog="convert.py", description="dadjokes reddit CSV parser") |
| parser.add_argument("--data", action="store", help="path to input JSON file", required=True) |
| parser.add_argument("--out", action="store", help="path to output file", required=True) |
| parser.add_argument("--inst", action="store", help="alpaca instruction", required=True) |
|
|
| args = parser.parse_args() |
| print(args) |
|
|
| model = SentenceTransformer("intfloat/e5-base-v2",device="cuda") |
|
|
| with open(args.data, 'r') as input: |
| docs: List[Doc] = [] |
| for line in tqdm(input.readlines()): |
| item = Doc.from_json(json.loads(line)) |
| docs.append(item) |
| embeddings = model.encode([f"passage: {doc.input} {doc.output}" for doc in docs], batch_size=512, show_progress_bar=True) |
| p = hnswlib.Index(space = 'cosine', dim = 768) |
| print("building index") |
| p.init_index(max_elements = len(docs), ef_construction = 200, M = 16) |
| p.add_items(embeddings, [id for id, doc in enumerate(docs)]) |
| print("computing similarity") |
| labels, distances = p.knn_query(embeddings, k = 10) |
| skips: Set[int] = set() |
| print("search done, exporting") |
| dupe_count = 0 |
| broken_count = 0 |
| with open(args.out,'w') as output: |
| for (index, doc), label_list, dist_list in zip(enumerate(docs), labels.tolist(), distances.tolist()): |
| if index not in skips: |
| if "http" not in doc.output: |
| jdoc = {"input": doc.input, "output": doc.output, "instruction": args.inst} |
| output.write(json.dumps(jdoc) + '\n') |
| else: |
| broken_count += 1 |
| else: |
| dupe_count += 1 |
| skips.add(index) |
| for label, dist in zip(label_list, dist_list): |
| if (dist < 0.07): |
| skips.add(label) |
|
|
| print(f"done: dupes={dupe_count} broken={broken_count}") |
|
|
