evaluation_pipeline/retrieval.py [123:135]:
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    EMBEDDING_SIZE = embeddings_sizes[model_name]
    items = []
    for idx, vec in enumerate(embeddings_dict[model_name]):
        items.append((idx, list(vec)))
    model_name_normalized = model_name.replace("/","_").replace("-","_").replace(".","_")
    db.execute(f"CREATE VIRTUAL TABLE vec_items_{model_name_normalized} USING vec0(embedding float[{EMBEDDING_SIZE}])")

    with db:
        for item in items:
            db.execute(
                f"INSERT INTO vec_items_{model_name_normalized}(rowid, embedding) VALUES (?, ?)",
                [item[0], serialize_f32(item[1])],
            )
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src/history_search_app.py [65:79]:
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    EMBEDDING_SIZE = embeddings_sizes[model_name]

    items = []
    for idx, vec in enumerate(embeddings_dict[model_name]):
        items.append((idx, list(vec)))
    model_name_normalized = model_name.replace("/","_").replace("-","_").replace(".","_")

    db.execute(f"CREATE VIRTUAL TABLE vec_items_{model_name_normalized} USING vec0(embedding float[{EMBEDDING_SIZE}])")

    with db:
        for item in items:
            db.execute(
                f"INSERT INTO vec_items_{model_name_normalized}(rowid, embedding) VALUES (?, ?)",
                [item[0], serialize_f32(item[1])],
            )
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