 b/modules/chatbot/dataloader.py
+import faiss
+import numpy as np
+import pandas as pd
+from datasets import load_dataset
+def convert(item: str) -> np.ndarray:
+    """
+    Convert a string representation of an array to a numpy array.
+    Args:
+        item (str): String representation of an array.
+    Returns:
+        np.ndarray: Numpy array converted from the string representation.
+    """
+    item = item.strip()
+    item = item[1:-1]
+    item = np.fromstring(item, sep=" ")
+    return item
+def get_dataset(huggingface_repo: str) -> pd.DataFrame:
+    """
+    Load dataset from Hugging Face repository and convert to pandas DataFrame.
+    Args:
+        huggingface_repo (str): Name of the Hugging Face repository.
+    Returns:
+        pd.DataFrame: Pandas DataFrame containing the loaded dataset.
+    """
+    df = load_dataset(huggingface_repo, "csv")
+    df = pd.DataFrame(df["train"])
+    df["Q_FFNN_embeds"] = df["Q_FFNN_embeds"].apply(convert)
+    df["A_FFNN_embeds"] = df["A_FFNN_embeds"].apply(convert)
+    return df
+def get_bert_index(
+    df: pd.DataFrame, target_columns: Union[str, List[str]]
+) -> faiss.IndexFlatIP:
+    """
+    Build and return the FAISS index for BERT embeddings.
+    Args:
+        df (pd.DataFrame): DataFrame containing the BERT embeddings.
+        target_columns (Union[str, List[str]]): Name or list of names of the columns containing BERT embeddings.
+    Returns:
+        faiss.IndexFlatIP: FAISS index for BERT embeddings.
+    """
+    embedded_bert = df[target_columns].tolist()
+    embedded_bert = np.array(embedded_bert, dtype="float32")
+    index = faiss.IndexFlatIP(embedded_bert.shape[-1])
+    index.add(embedded_bert)
+    return index