| """Build the vector store from data/dataset/chunks.jsonl.
|
|
|
| python scripts/build_vectorstore.py # tfidf (offline default)
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| python scripts/build_vectorstore.py --backend sbert # dense embeddings
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| python scripts/build_vectorstore.py --backend openai # OpenAI-compatible API
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| """
|
|
|
| import argparse
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| import sys
|
| from pathlib import Path
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|
|
| sys.path.insert(0, str(Path(__file__).resolve().parent))
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| from vectorstore import build_index
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|
|
| ROOT = Path(__file__).resolve().parent.parent
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|
|
| if __name__ == "__main__":
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| ap = argparse.ArgumentParser()
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| ap.add_argument("--chunks", default=str(ROOT / "data" / "dataset" / "chunks.jsonl"))
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| ap.add_argument("--out", default=str(ROOT / "vectorstore"))
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| ap.add_argument("--backend", default="tfidf", choices=["tfidf", "sbert", "openai"])
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| ap.add_argument("--provider", default=None, help="cloud embedding provider preset, e.g. siliconflow / zhipu / qwen")
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| ap.add_argument("--model", default=None)
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| args = ap.parse_args()
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| build_index(args.chunks, args.out, backend=args.backend, model=args.model, provider=args.provider)
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|
|
