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from datasets import concatenate_datasets, load_dataset
from sentence_transformers import SentenceTransformer
from tqdm import tqdm
from vicinity import Backend, Metric, Vicinity
print("Collecting Scandinavian wikipedia")
subsets = {
"danish": "20231101.da",
"norwegian_bokmål": "20231101.no",
"norwegian_nynorsk": "20231101.nn",
"swedish": "20231101.sv",
}
datasets = []
for lang in tqdm(subsets, desc="Going through languages"):
ds = load_dataset("wikimedia/wikipedia", subsets[lang], split="train")
ds = ds.map(lambda example: {**example, "language": lang})
datasets.append(ds)
dataset = concatenate_datasets(datasets)
print("Encoding texts")
encoder = SentenceTransformer("static-similarity-mrl-multilingual-v1")
text = [
f"{title}\n\n {content}"
for title, content in zip(dataset["title"], dataset["text"])
]
embeddings = encoder.encode(text, show_progress_bar=True, batch_size=256)
print("Building vector store")
store = Vicinity.from_vectors_and_items(
vectors=embeddings,
items=list(dataset),
metric=Metric.COSINE,
index_type="hnsw",
backend_type=Backend.FAISS,
)
print("Saving vector store")
store.save("scandi_wiki_vector_store")
print("DONE")
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