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