custom_io.py

"""Unstructured file reader.

A parser for unstructured text files using Unstructured.io.
Supports .txt, .docx, .pptx, .jpg, .png, .eml, .html, and .pdf documents.

"""
from datetime import datetime
import mimetypes
import os
from pathlib import Path
import re
from typing import Any, Dict, List, Optional

from llama_index.core.readers.base import BaseReader
from llama_index.core import Document


class UnstructuredReader(BaseReader):
    """General unstructured text reader for a variety of files."""

    def __init__(self, *args: Any, **kwargs: Any) -> None:
        """Init params."""
        super().__init__(*args, **kwargs)

        # Prerequisite for Unstructured.io to work
        import nltk

        nltk.download("punkt")
        nltk.download("averaged_perceptron_tagger")

    def load_data(
        self,
        file: Path,
        extra_info: Optional[Dict] = None,
        split_documents: Optional[bool] = True,
    ) -> List[Document]:
        """Parse file."""
        from unstructured.partition.auto import partition

        elements = partition(str(file))
        text_chunks = [" ".join(str(el).split()) for el in elements]

        if split_documents:
            return [
                Document(text=chunk, extra_info=extra_info or {})
                for chunk in text_chunks
            ]
        else:
            return [
                Document(text="\n\n".join(text_chunks), extra_info=extra_info or {})
            ]


class MarkdownReader(BaseReader):
    """General unstructured text reader for a variety of files."""

    def __init__(self, *args: Any, **kwargs: Any) -> None:
        """Init params."""
        super().__init__(*args, **kwargs)

    def load_data(
        self,
        file: Path,
        extra_info: Optional[Dict] = None,
        split_documents: Optional[bool] = True,
    ) -> List[Document]:
        """Parse file."""
        from unstructured.partition.auto import partition

        elements = parse_knowledge_units(str(file))

        if split_documents:
            return [
                Document(text=ele, extra_info=extra_info or {})
                for ele in elements 
            ]

def parse_knowledge_units(file_path):
    with open(file_path, 'r', encoding='utf-8') as file:
        lines = file.readlines()

    knowledge_units = []
    current_unit = ""
    unit_start_pattern = re.compile(r'^\d+\.\s')
    for line in lines:
        stripped_line = line.strip()
        if unit_start_pattern.match(stripped_line):
            if current_unit:
                knowledge_units.append(current_unit.strip())
                current_unit = ""
            current_unit += line
        else:
            current_unit += line

    if current_unit:
        knowledge_units.append(current_unit.strip())
    # for line in lines:
    #     if line.strip() and line[0].isdigit() and '.' in line:
    #         if current_unit:
    #             knowledge_units.append(current_unit.strip())
    #             current_unit = ""
    #         current_unit += line
    #     else:
    #         current_unit += line

    # if current_unit:
    #     knowledge_units.append(current_unit.strip())

    return knowledge_units

def default_file_metadata_func(file_path: str) -> Dict:
    """Get some handy metadate from filesystem.

    Args:
        file_path: str: file path in str
    """
    return {
        "file_path": file_path,
        "file_name": os.path.basename(file_path),
        "file_type": mimetypes.guess_type(file_path)[0],
        "file_size": os.path.getsize(file_path),
        "creation_date": datetime.fromtimestamp(
            Path(file_path).stat().st_ctime
        ).strftime("%Y-%m-%d"),
        "last_modified_date": datetime.fromtimestamp(
            Path(file_path).stat().st_mtime
        ).strftime("%Y-%m-%d"),
        "last_accessed_date": datetime.fromtimestamp(
            Path(file_path).stat().st_atime
        ).strftime("%Y-%m-%d"),
    }