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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}")
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