Create demo.py

demo.py

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+import duckdb
+import polars as pl
+from datasets import load_dataset
+from model2vec import StaticModel
+
+# Load a model from the HuggingFace hub (in this case the potion-base-8M model)
+model_name = "minishlab/potion-base-8M"
+model = StaticModel.from_pretrained(model_name)
+
+# Make embeddings
+ds = load_dataset("fka/awesome-chatgpt-prompts")
+df = ds["train"].to_polars()
+embeddings = model.encode(df["act"])
+df = df.with_columns(pl.Series(embeddings).alias("embeddings"))
+vector = model.encode("An Ethereum Developer", show_progress_bar=True)
+duckdb.sql(
+    query=f"""
+    SELECT *
+    FROM df
+    ORDER BY array_cosine_distance(embeddings, {vector.tolist()}::FLOAT[256])
+    LIMIT 10
+    """
+).show()