import pandas as pd

import numpy as np

from datetime import datetime, timedelta

now = datetime.now()

embeddings = []

for i in range(10):

embeddings.append(

{

"document_id": f"doc_{i}",

"embedding": np.random.rand(768).astype(np.float32),

"event_timestamp": now - timedelta(days=i),

"created_timestamp": now - timedelta(days=i, hours=1),

}

)

df_embeddings = pd.DataFrame(embeddings)

df_embeddings.to_parquet("data/document_embeddings.parquet", index=False)

metadata = []

for i in range(10):

metadata.append(

{

"document_id": f"doc_{i}",

"title": f"Document {i}",

"content": f"This is the content of document {i}",

"source": "web",

"author": f"author_{i}",

"publish_date": (now - timedelta(days=i * 30)).strftime("%Y-%m-%d"),

"event_timestamp": now - timedelta(days=i),

"created_timestamp": now - timedelta(days=i, hours=1),

}

)

df_metadata = pd.DataFrame(metadata)

df_metadata.to_parquet("data/document_metadata.parquet", index=False)

print("Created RAG data files successfully!")