tools/ingest.py

# Import package

import os
from pathlib import Path
import re
import requests
import pandas as pd
import dateutil.parser
from typing import Type, List, Tuple
import shutil
import numpy as np
import gradio as gr
import zipfile
import tempfile
from pathlib import Path

from tools.embeddings import HuggingFaceEmbeddings
from tools.faiss_store import FAISS, InMemoryDocstore
from tools.text_splitter import RecursiveCharacterTextSplitter
from tools.document import Document
from typing import Protocol # For type hinting
from tqdm import tqdm
from uuid import uuid4 # To generate unique IDs for documents in the docstore

# Type hint for embeddings
class Embeddings(Protocol):
    def embed_documents(self, texts: List[str]) -> List[List[float]]: ...
    def embed_query(self, text: str) -> List[float]: ...
from bs4 import BeautifulSoup
from docx import Document as Doc
from pypdf import PdfReader
import faiss # For directly creating the FAISS index

from tools.config import EMBEDDINGS_MODEL_NAME

PandasDataFrame = Type[pd.DataFrame]

split_strat = ["\n\n", "\n", ". ", "! ", "? "]
chunk_size = 300
chunk_overlap = 0
start_index = True

## Parse files
def determine_file_type(file_path):
        """
        Determine the file type based on its extension.
    
        Parameters:
            file_path (str): Path to the file.
    
        Returns:
            str: File extension (e.g., '.pdf', '.docx', '.txt', '.html', '.md').
        """
        return os.path.splitext(file_path)[1].lower()

def parse_file(file_paths, text_column='text'):
    """
    Accepts a list of file paths, determines each file's type based on its extension,
    and passes it to the relevant parsing function.
    
    Parameters:
        file_paths (list): List of file paths.
        text_column (str): Name of the column in CSV/Excel files that contains the text content.
    
    Returns:
        dict: A dictionary with file paths as keys and their parsed content (or error message) as values.
    """
    
    

    if not isinstance(file_paths, list):
        raise ValueError("Expected a list of file paths.")
    
    extension_to_parser = {
        '.pdf': parse_pdf,
        '.docx': parse_docx,
        '.txt': parse_txt,
        '.md': parse_markdown,
        '.html': parse_html,
        '.htm': parse_html,  # Considering both .html and .htm for HTML files
        '.csv': lambda file_path: parse_csv_or_excel(file_path, text_column),
        '.xlsx': lambda file_path: parse_csv_or_excel(file_path, text_column)
    }
    
    parsed_contents = {}
    file_names = []

    for file_path in file_paths:
        print(file_path.name)
        #file = open(file_path.name, 'r')
        #print(file)
        file_extension = determine_file_type(file_path.name)
        if file_extension in extension_to_parser:
            parsed_contents[file_path.name] = extension_to_parser[file_extension](file_path.name)
        else:
            parsed_contents[file_path.name] = f"Unsupported file type: {file_extension}"

        filename_end = get_file_path_end(file_path.name)

        file_names.append(filename_end)
    
    return parsed_contents, file_names

def text_regex_clean(text):
    # Merge hyphenated words
        text = re.sub(r"(\w+)-\n(\w+)", r"\1\2", text)
        # If a double newline ends in a letter, add a full stop.
        text = re.sub(r'(?<=[a-zA-Z])\n\n', '.\n\n', text)
        # Fix newlines in the middle of sentences
        text = re.sub(r"(?<!\n\s)\n(?!\s\n)", " ", text.strip())
        # Remove multiple newlines
        text = re.sub(r"\n\s*\n", "\n\n", text)
        text = re.sub(r"  ", " ", text)
        # Add full stops and new lines between words with no space between where the second one has a capital letter
        text = re.sub(r'(?<=[a-z])(?=[A-Z])', '. \n\n', text)

        return text

def parse_csv_or_excel(file_paths, text_column = "text"):
        """
        Read in a CSV or Excel file.
        
        Parameters:
            file_path (str): Path to the CSV file.
            text_column (str): Name of the column in the CSV file that contains the text content.
        
        Returns:
            Pandas DataFrame: Dataframe output from file read
        """

        file_names = []
        out_df = pd.DataFrame()

        for file_path in file_paths:
            file_extension = determine_file_type(file_path.name)
            file_name = get_file_path_end(file_path.name)

            if file_extension == ".csv":
                df = pd.read_csv(file_path.name)
                if text_column not in df.columns: return pd.DataFrame(), ['Please choose a valid column name']
                df['source'] = file_name
                df['page_section'] = ""
            elif file_extension == ".xlsx":
                df = pd.read_excel(file_path.name, engine='openpyxl')
                if text_column not in df.columns: return pd.DataFrame(), ['Please choose a valid column name']
                df['source'] = file_name
                df['page_section'] = ""
            else:
                print(f"Unsupported file type: {file_extension}")
                return pd.DataFrame(), ['Please choose a valid file type']
            
            file_names.append(file_name)
            out_df = pd.concat([out_df, df])
        
        #if text_column not in df.columns:
        #    return f"Column '{text_column}' not found in {file_path}"
        #text_out = " ".join(df[text_column].dropna().astype(str))
        return out_df, file_names

def parse_excel(file_path, text_column):
        """
        Read text from an Excel file.
        
        Parameters:
            file_path (str): Path to the Excel file.
            text_column (str): Name of the column in the Excel file that contains the text content.
        
        Returns:
            Pandas DataFrame: Dataframe output from file read
        """
        df = pd.read_excel(file_path, engine='openpyxl')
        #if text_column not in df.columns:
        #    return f"Column '{text_column}' not found in {file_path}"
        #text_out = " ".join(df[text_column].dropna().astype(str))
        return df

def parse_pdf(file) -> List[str]:

    """
    Extract text from a PDF file.
    
    Parameters:
        file_path (str): Path to the PDF file.
    
    Returns:
        List[str]: Extracted text from the PDF.
    """
    
    output = []
    #for file in files:
    print(file) # .name
    pdf = PdfReader(file) #[i] .name[i]
    
    for page in pdf.pages:
            text = page.extract_text()
            
            text = text_regex_clean(text)

            output.append(text)
    return output

def parse_docx(file_path):
    """
    Reads the content of a .docx file and returns it as a string.

    Parameters:
    - file_path (str): Path to the .docx file.

    Returns:
    - str: Content of the .docx file.
    """
    doc = Doc(file_path)
    full_text = []
    for para in doc.paragraphs:
        para = text_regex_clean(para)

        full_text.append(para.text.replace("  ", " ").strip())
    return '\n'.join(full_text)

def parse_txt(file_path):
    """
    Read text from a TXT, HTML, or MD file.
    
    Parameters:
        file_path (str): Path to the TXT, HTML, or MD file.
    
    Returns:
        str: Text content of the file.
    """
    with open(file_path, 'r', encoding="utf-8") as file:
        file_contents = file.read().replace("  ", " ").strip()

        file_contents = text_regex_clean(file_contents)

        return file_contents

def parse_markdown(file_path):
    """
    Read text from a MD file.
    """
    with open(file_path, 'r', encoding="utf-8") as file:
        file_contents = file.read().replace("  ", " ").strip()

        file_contents = text_regex_clean(file_contents)

        return file_contents

def parse_html(page_url, div_filter="p"):
    """
    Determine if the source is a web URL or a local HTML file, extract the content based on the div of choice. Also tries to extract dates (WIP)

    Parameters:
        page_url (str): The web URL or local file path.

    Returns:
        str: Extracted content.
    """

    def is_web_url(s):
        """
        Check if the input string is a web URL.
        """
        return s.startswith("http://") or s.startswith("https://")

    def is_local_html_file(s):
        """
        Check if the input string is a path to a local HTML file.
        """
        return (s.endswith(".html") or s.endswith(".htm")) and os.path.isfile(s)

    def extract_text_from_source(source):
        """
        Determine if the source is a web URL or a local HTML file, 
        and then extract its content accordingly.

        Parameters:
        source (str): The web URL or local file path.

        Returns:
        str: Extracted content.
        """
        if is_web_url(source):
            response = requests.get(source)
            response.raise_for_status()  # Raise an HTTPError for bad responses
            return response.text.replace("  ", " ").strip()
        elif is_local_html_file(source):
            with open(source, 'r', encoding='utf-8') as file:
                file_out = file.read().replace
                return file_out
        else:
            raise ValueError("Input is neither a valid web URL nor a local HTML file path.")
               

    def clean_html_data(data, date_filter="", div_filt="p"):
        """
        Extracts and cleans data from HTML content.

        Parameters:
            data (str): HTML content to be parsed.
            date_filter (str, optional): Date string to filter results. If set, only content with a date greater than this will be returned.
            div_filt (str, optional): HTML tag to search for text content. Defaults to "p".

        Returns:
            tuple: Contains extracted text and date as strings. Returns empty strings if not found.
        """
    
        soup = BeautifulSoup(data, 'html.parser')

        # Function to exclude div with id "bar"
        def exclude_div_with_id_bar(tag):
            return tag.has_attr('id') and tag['id'] == 'related-links'

        text_elements = soup.find_all(div_filt)
        date_elements = soup.find_all(div_filt, {"class": "page-neutral-intro__meta"})
    
        # Extract date
        date_out = ""
        if date_elements:
            date_out = re.search(">(.*?)<", str(date_elements[0])).group(1)
            date_dt = dateutil.parser.parse(date_out)

            if date_filter:
                date_filter_dt = dateutil.parser.parse(date_filter)
                if date_dt < date_filter_dt:
                    return '', date_out

        # Extract text
        text_out_final = ""
        if text_elements:
            text_out_final = '\n'.join(paragraph.text for paragraph in text_elements)
            text_out_final = text_regex_clean(text_out_final)
        else:
            print(f"No elements found with tag '{div_filt}'. No text returned.")
    
        return text_out_final, date_out
    

    #page_url = "https://pypi.org/project/InstructorEmbedding/" #'https://www.ons.gov.uk/visualisations/censusareachanges/E09000022/index.html'

    html_text = extract_text_from_source(page_url)
    #print(page.text)

    texts = []
    metadatas = []

    clean_text, date = clean_html_data(html_text, date_filter="", div_filt=div_filter)
    texts.append(clean_text)
    metadatas.append({"source": page_url, "date":str(date)})

    #print(metadatas)

    return texts, metadatas, page_url

def get_file_path_end(file_path):
    match = re.search(r'(.*[\/\\])?(.+)$', file_path)
        
    filename_end = match.group(2) if match else ''

    return filename_end

# +
# Convert parsed text to docs
# -

def text_to_docs(text_dict: dict, chunk_size: int = chunk_size) -> List[Document]:
    """
    Converts the output of parse_file (a dictionary of file paths to content)
    to a list of Documents with metadata.
    """
    
    doc_sections = []
    parent_doc_sections = []

    for file_path, content in text_dict.items():
        ext = os.path.splitext(file_path)[1].lower()

        # Depending on the file extension, handle the content
        if ext == '.pdf':
            docs, page_docs = pdf_text_to_docs(content, chunk_size=chunk_size)
        elif ext in ['.html', '.htm', '.txt', '.docx', '.md']:
            docs = html_text_to_docs(content, chunk_size=chunk_size)
        elif ext in ['.csv', '.xlsx']:
            docs, page_docs = csv_excel_text_to_docs(content, chunk_size=chunk_size)
        else:
            print(f"Unsupported file type {ext} for {file_path}. Skipping.")
            continue

        
        filename_end = get_file_path_end(file_path)

        #match = re.search(r'(.*[\/\\])?(.+)$', file_path)
        #filename_end = match.group(2) if match else ''

        # Add filename as metadata
        for doc in docs: doc.metadata["source"] = filename_end
        #for parent_doc in parent_docs: parent_doc.metadata["source"] = filename_end
        
        doc_sections.extend(docs)
        #parent_doc_sections.extend(parent_docs)

    return doc_sections#, page_docs

def pdf_text_to_docs(text, chunk_size: int = chunk_size) -> List[Document]:
    """Converts a string or list of strings to a list of Documents
    with metadata."""

    #print(text)

    if isinstance(text, str):
        # Take a single string as one page
        text = [text]
        
    page_docs = [Document(page_content=page, metadata={"page": page}) for page in text]
  

    # Add page numbers as metadata
    for i, doc in enumerate(page_docs):
        doc.metadata["page"] = i + 1

    print("page docs are: ")
    print(page_docs)

    # Split pages into sections
    doc_sections = []

    for doc in page_docs:

        #print("page content: ")
        #print(doc.page_content)

        if doc.page_content == '':
            sections = ['']

        else:
            text_splitter = RecursiveCharacterTextSplitter(
                chunk_size=chunk_size,
                separators=split_strat,#["\n\n", "\n", ".", "!", "?", ",", " ", ""],
                chunk_overlap=chunk_overlap,
                add_start_index=True
            )
            sections = text_splitter.split_text(doc.page_content)
        
        for i, section in enumerate(sections):
            doc = Document(
                   page_content=section, metadata={"page": doc.metadata["page"], "section": i, "page_section": f"{doc.metadata['page']}-{i}"})

            
            doc_sections.append(doc)

    return doc_sections, page_docs#, parent_doc

def html_text_to_docs(texts:list[str], metadatas:dict={}, chunk_size:int = chunk_size):

    text_splitter = RecursiveCharacterTextSplitter(
        separators=split_strat,#["\n\n", "\n", ".", "!", "?", ",", " ", ""],
        chunk_size=chunk_size,
        chunk_overlap=chunk_overlap,
        length_function=len,
        add_start_index=True
    )

    #print(texts)
    #print(metadatas)

    documents = text_splitter.create_documents(texts, metadatas=metadatas)

    for i, section in enumerate(documents):
        section.metadata["page_section"] = i + 1

    

    return documents

def write_out_metadata_as_string(metadata_in):
    # If metadata_in is a single dictionary, wrap it in a list
    if isinstance(metadata_in, dict):
        metadata_in = [metadata_in]

    metadata_string = [f"{'  '.join(f'{k}: {v}' for k, v in d.items() if k != 'page_section')}" for d in metadata_in] # ['metadata']
    return metadata_string

def csv_excel_text_to_docs(df, text_column='text', chunk_size=None) -> List[Document]:
    """Converts a DataFrame's content to a list of Documents with metadata."""
    
    doc_sections = []
    df[text_column] = df[text_column].astype(str) # Ensure column is a string column

    # For each row in the dataframe
    for idx, row in df.iterrows():
        # Extract the text content for the document
        doc_content = row[text_column]
        
        # Generate metadata containing other columns' data
        metadata = {"row": idx + 1}
        for col, value in row.items():
            if col != text_column:
                metadata[col] = value

        metadata_string = write_out_metadata_as_string(metadata)[0]

        

        # If chunk_size is provided, split the text into chunks
        if chunk_size:
            # Assuming you have a text splitter function similar to the PDF handling
            text_splitter = RecursiveCharacterTextSplitter(
                chunk_size=chunk_size,
                # Other arguments as required by the splitter
            )
            sections = text_splitter.split_text(doc_content)

            
            # For each section, create a Document object
            for i, section in enumerate(sections):
                section = '. '.join([metadata_string, section])
                doc = Document(page_content=section, 
                               metadata={**metadata, "section": i, "row_section": f"{metadata['row']}-{i}"})
                doc_sections.append(doc)
        else:
            # If no chunk_size is provided, create a single Document object for the row
            doc_content = '. '.join([metadata_string, doc_content])
            doc = Document(page_content=doc_content, metadata=metadata)
            doc_sections.append(doc)
    
    return doc_sections

# # Functions for working with documents after loading them back in

def pull_out_data(series):

    # define a lambda function to convert each string into a tuple
    to_tuple = lambda x: eval(x)

    # apply the lambda function to each element of the series
    series_tup = series.apply(to_tuple)

    series_tup_content = list(zip(*series_tup))[1]

    series = pd.Series(list(series_tup_content))#.str.replace("^Main post content", "", regex=True).str.strip()

    return series

def docs_from_csv(df):

    import ast
    
    documents = []
    
    page_content = pull_out_data(df["0"])
    metadatas = pull_out_data(df["1"])

    for x in range(0,len(df)):       
        new_doc = Document(page_content=page_content[x], metadata=metadatas[x])
        documents.append(new_doc)
        
    return documents

def docs_from_lists(docs, metadatas):

    documents = []

    for x, doc in enumerate(docs):
        new_doc = Document(page_content=doc, metadata=metadatas[x])
        documents.append(new_doc)
        
    return documents

def docs_elements_from_csv_save(docs_path="documents.csv"):

    documents = pd.read_csv(docs_path)

    docs_out = docs_from_csv(documents)

    out_df = pd.DataFrame(docs_out)

    docs_content = pull_out_data(out_df[0].astype(str))

    docs_meta = pull_out_data(out_df[1].astype(str))

    doc_sources = [d['source'] for d in docs_meta]

    return out_df, docs_content, docs_meta, doc_sources

# ## Create embeddings and save faiss vector store to the path specified in `save_to`

def load_embeddings_model(embeddings_model = EMBEDDINGS_MODEL_NAME):

    embeddings_func = HuggingFaceEmbeddings(model_name=embeddings_model)

    #global embeddings

    #embeddings = embeddings_func

    return embeddings_func

# def embed_faiss_save_to_zip(docs_out, save_folder, embeddings_model_object, save_to="faiss_embeddings", model_name="mixedbread-ai/mxbai-embed-xsmall-v1"):

#     print(f"> Total split documents: {len(docs_out)}")

#     vectorstore = FAISS.from_documents(documents=docs_out, embedding=embeddings_model_object)

#     save_to_path = Path(save_folder, save_to)
#     save_to_path.mkdir(parents=True, exist_ok=True)

#     vectorstore.save_local(folder_path=str(save_to_path))

#     print("> FAISS index saved")
#     print(f"> Saved to: {save_to}")

#     # Ensure files are written before archiving
#     index_faiss = save_to_path / "index.faiss"
#     index_pkl = save_to_path / "index.pkl"

#     if not index_faiss.exists() or not index_pkl.exists():
#         raise FileNotFoundError("Expected FAISS index files not found before zipping.")

#     # Flush file system writes by forcing a sync (works best on Unix)
#     try:
#         os.sync()
#     except AttributeError:
#         pass  # os.sync() not available on Windows

#     # Create ZIP archive
#     final_zip_path = shutil.make_archive(str(save_to_path), 'zip', root_dir=str(save_to_path))

#     # Remove individual index files to avoid leaking large raw files
#     index_faiss.unlink(missing_ok=True)
#     index_pkl.unlink(missing_ok=True)

#     # Move ZIP inside the folder for easier reference
#     #final_zip_path = save_to_path.with_suffix('.zip')

#     print("> Archive complete")
#     print(f"> Final ZIP path: {final_zip_path}")

#     return "Document processing complete", vectorstore, final_zip_path



def embed_faiss_save_to_zip(
    docs_out: List[Document],
    save_folder: str,
    embeddings_model_object: Embeddings, # Type hint for clarity
    save_to: str = "faiss_embeddings",
    model_name: str = "mixedbread-ai/mxbai-embed-xsmall-v1", # This is a descriptive name, not directly used in FAISS build
    progress: gr.Progress = gr.Progress(track_tqdm=True)
) -> Tuple[str, FAISS, Path]:

    print(f"> Total split documents: {len(docs_out)}")

    # --- Progress Bar Integration Starts Here ---
    print("Starting embedding generation and FAISS index construction...")

    texts = []
    metadatas = []
    vectors = []
    docstore = InMemoryDocstore()
    index_to_docstore_id = {} # Maps FAISS index position to docstore ID

    if not docs_out:
        print("No documents provided. Skipping FAISS index creation.")
        return "No documents to process", None, None # Or handle as an error

    # 1. Generate Embeddings and Populate Data Structures with tqdm
    # Wrap the iteration over docs_out with tqdm for a progress bar
    for i, doc in tqdm(enumerate(docs_out), desc="Generating Embeddings", total=len(docs_out)):
        # Store text and metadata
        texts.append(doc.page_content)
        metadatas.append(doc.metadata)

        # Generate embedding for the current document
        # embeddings_model_object.embed_documents expects a list of strings
        # and returns a list of lists (embeddings). We take the first element.
        vector = embeddings_model_object.embed_documents([doc.page_content])[0]
        vectors.append(vector)

        # Populate the internal docstore that FAISS uses
        doc_id = str(uuid4()) # Generate a unique ID for each document
        docstore.add({doc_id: doc}) # Add the full Document object to the docstore
        index_to_docstore_id[i] = doc_id # Map FAISS index position (i) to its doc_id

    print("\nEmbedding generation complete. Building FAISS index...")

    # 2. Build the Raw FAISS Index
    # Ensure all embeddings are numpy float32, which FAISS expects.
    # BGE models (like bge-base-en-v1.5) typically produce L2-normalized embeddings,
    # which are ideal for Inner Product (IP) similarity, equivalent to cosine similarity.
    # If your model *does not* output normalized vectors and you want cosine similarity,
    # you must normalize them here: `np.array([v / np.linalg.norm(v) for v in vectors]).astype("float32")`
    # Otherwise, you might use IndexFlatL2 for Euclidean distance.
    # For common embedding models and cosine similarity, `IndexFlatIP` with pre-normalized vectors is standard.
    embeddings_np = np.array(vectors).astype("float32")
    embedding_dimension = embeddings_np.shape[1]

    # Create a raw FAISS index (e.g., IndexFlatIP for cosine similarity)
    raw_faiss_index = faiss.IndexFlatIP(embedding_dimension)
    raw_faiss_index.add(embeddings_np) # Add all vectors to the raw FAISS index

    # 3. Create the FAISS Vectorstore from the components
    # The `embedding_function` is used for subsequent queries to the vectorstore,
    # not for building the initial index here (as we've already done that).
    vectorstore = FAISS(
        embedding_function=embeddings_model_object.embed_query,
        index=raw_faiss_index,
        docstore=docstore,
        index_to_docstore_id=index_to_docstore_id
    )
    # --- Progress Bar Integration Ends Here ---

    save_to_path = Path(save_folder, save_to)
    save_to_path.mkdir(parents=True, exist_ok=True)

    vectorstore.save_local(folder_path=str(save_to_path))

    print("> FAISS index saved")
    print(f"> Saved to: {save_to}")

    # Ensure files are written before archiving
    index_faiss = save_to_path / "index.faiss"
    index_pkl = save_to_path / "index.pkl"

    if not index_faiss.exists() or not index_pkl.exists():
        raise FileNotFoundError("Expected FAISS index files not found before zipping.")

    # Flush file system writes by forcing a sync (works best on Unix)
    try:
        os.sync()
    except AttributeError:
        pass  # os.sync() not available on Windows

    # Create ZIP archive
    final_zip_path = shutil.make_archive(str(save_to_path), 'zip', root_dir=str(save_to_path))

    # Remove individual index files to avoid leaking large raw files
    index_faiss.unlink(missing_ok=True)
    index_pkl.unlink(missing_ok=True)

    print("> Archive complete")
    print(f"> Final ZIP path: {final_zip_path}")

    return "Document processing complete", vectorstore, final_zip_path # Return Path object for consistency

def get_faiss_store(zip_file_path: str, embeddings_model: Embeddings) -> FAISS:
    """
    Loads a FAISS vector store from a ZIP archive.

    Args:
        zip_file_path: The string path pointing to the .zip archive containing
                       index.faiss and index.pkl. This should be the
                       final_zip_path returned by embed_faiss_save_to_zip.
        embeddings_model: The embeddings model object (e.g., OpenAIEmbeddings, HuggingFaceEmbeddings)
                          used to create the index. This is crucial for proper deserialization.

    Returns:
        A FAISS vector store object.
    """

    zip_file_path = Path(zip_file_path)

    if not zip_file_path.exists():
        raise FileNotFoundError(f"ZIP archive not found at: {zip_file_path}")
    if not zip_file_path.suffix == '.zip':
        raise ValueError(f"Expected a .zip file, but got: {zip_file_path}")

    # Create a temporary directory to extract the FAISS index files
    # tempfile.TemporaryDirectory() handles cleanup automatically when the 'with' block exits.
    with tempfile.TemporaryDirectory() as temp_dir_str:
        temp_extract_path = Path(temp_dir_str)

        print(f"> Extracting {zip_file_path} to temporary directory: {temp_extract_path}")
        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
            # The zip file contains 'index.faiss' and 'index.pkl' directly at its root.
            # So, extracting to temp_extract_path will place them as temp_extract_path/index.faiss
            zip_ref.extractall(temp_extract_path)

        # Verify that the files were extracted successfully
        extracted_faiss_file = temp_extract_path / "index.faiss"
        extracted_pkl_file = temp_extract_path / "index.pkl"

        if not extracted_faiss_file.exists() or not extracted_pkl_file.exists():
            raise FileNotFoundError(
                f"Required FAISS index files (index.faiss, index.pkl) not found "
                f"in extracted location: {temp_extract_path}. "
                f"ZIP content might be structured unexpectedly."
            )

        print("> Loading FAISS index from extracted files...")
        faiss_vstore = FAISS.load_local(
            folder_path=str(temp_extract_path), # FAISS.load_local expects a string path
            embeddings=embeddings_model,
            allow_dangerous_deserialization=True
        )
        print("> FAISS index loaded successfully.")

    # The temporary directory and its contents are automatically removed here
    # when the `with tempfile.TemporaryDirectory()` block exits.
    # No need for manual os.remove() calls for index.faiss and index.pkl.

    return faiss_vstore


# def sim_search_local_saved_vec(query, k_val, save_to="faiss_lambeth_census_embedding"):

#     load_embeddings()

#     docsearch = FAISS.load_local(folder_path=save_to, embeddings=embeddings)


#     display(Markdown(question))

#     search = docsearch.similarity_search_with_score(query, k=k_val)

#     for item in search:
#         print(item[0].page_content)
#         print(f"Page: {item[0].metadata['source']}")
#         print(f"Date: {item[0].metadata['date']}")
#         print(f"Score: {item[1]}")
#         print("---")