code/modules/dataloader/data_loader.py

import os
import re
import requests
import pysrt
from langchain_community.document_loaders import (
    PyMuPDFLoader,
    Docx2txtLoader,
    YoutubeLoader,
    WebBaseLoader,
    TextLoader,
)
from langchain_community.document_loaders import UnstructuredMarkdownLoader
from llama_parse import LlamaParse
from langchain.schema import Document
import logging
from langchain.text_splitter import RecursiveCharacterTextSplitter
from ragatouille import RAGPretrainedModel
from langchain.chains import LLMChain
from langchain_community.llms import OpenAI
from langchain import PromptTemplate
import json
from concurrent.futures import ThreadPoolExecutor

from modules.dataloader.helpers import get_metadata


class PDFReader:
    def __init__(self):
        pass

    def get_loader(self, pdf_path):
        loader = PyMuPDFLoader(pdf_path)
        return loader

    def get_documents(self, loader):
        return loader.load()


class FileReader:
    def __init__(self, logger):
        self.pdf_reader = PDFReader()
        self.logger = logger

    def extract_text_from_pdf(self, pdf_path):
        text = ""
        with open(pdf_path, "rb") as file:
            reader = PyPDF2.PdfReader(file)
            num_pages = len(reader.pages)
            for page_num in range(num_pages):
                page = reader.pages[page_num]
                text += page.extract_text()
        return text

    def download_pdf_from_url(self, pdf_url):
        response = requests.get(pdf_url)
        if response.status_code == 200:
            with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
                temp_file.write(response.content)
                temp_file_path = temp_file.name
            return temp_file_path
        else:
            self.logger.error(f"Failed to download PDF from URL: {pdf_url}")
            return None

    def read_pdf(self, temp_file_path: str):
        loader = self.pdf_reader.get_loader(temp_file_path)
        documents = self.pdf_reader.get_documents(loader)
        return documents

    def read_txt(self, temp_file_path: str):
        loader = TextLoader(temp_file_path, autodetect_encoding=True)
        return loader.load()

    def read_docx(self, temp_file_path: str):
        loader = Docx2txtLoader(temp_file_path)
        return loader.load()

    def read_srt(self, temp_file_path: str):
        subs = pysrt.open(temp_file_path)
        text = ""
        for sub in subs:
            text += sub.text
        return [Document(page_content=text)]

    def read_youtube_transcript(self, url: str):
        loader = YoutubeLoader.from_youtube_url(
            url, add_video_info=True, language=["en"], translation="en"
        )
        return loader.load()

    def read_html(self, url: str):
        loader = WebBaseLoader(url)
        return loader.load()

    def read_tex_from_url(self, tex_url):
        response = requests.get(tex_url)
        if response.status_code == 200:
            return [Document(page_content=response.text)]
        else:
            self.logger.error(f"Failed to fetch .tex file from URL: {tex_url}")
            return None


class ChunkProcessor:
    def __init__(self, config, logger):
        self.config = config
        self.logger = logger

        self.document_data = {}
        self.document_metadata = {}
        self.document_chunks_full = []

        if config["splitter_options"]["use_splitter"]:
            if config["splitter_options"]["split_by_token"]:
                self.splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
                    chunk_size=config["splitter_options"]["chunk_size"],
                    chunk_overlap=config["splitter_options"]["chunk_overlap"],
                    separators=config["splitter_options"]["chunk_separators"],
                    disallowed_special=(),
                )
            else:
                self.splitter = RecursiveCharacterTextSplitter(
                    chunk_size=config["splitter_options"]["chunk_size"],
                    chunk_overlap=config["splitter_options"]["chunk_overlap"],
                    separators=config["splitter_options"]["chunk_separators"],
                    disallowed_special=(),
                )
        else:
            self.splitter = None
        self.logger.info("ChunkProcessor instance created")

    def remove_delimiters(self, document_chunks: list):
        for chunk in document_chunks:
            for delimiter in self.config["splitter_options"]["delimiters_to_remove"]:
                chunk.page_content = re.sub(delimiter, " ", chunk.page_content)
        return document_chunks

    def remove_chunks(self, document_chunks: list):
        front = self.config["splitter_options"]["front_chunk_to_remove"]
        end = self.config["splitter_options"]["last_chunks_to_remove"]
        for _ in range(front):
            del document_chunks[0]
        for _ in range(end):
            document_chunks.pop()
        return document_chunks

    def process_chunks(
        self, documents, file_type="txt", source="", page=0, metadata={}
    ):
        documents = [Document(page_content=documents, source=source, page=page)]
        if (
            file_type == "txt"
            or file_type == "docx"
            or file_type == "srt"
            or file_type == "tex"
        ):
            document_chunks = self.splitter.split_documents(documents)
        elif file_type == "pdf":
            document_chunks = documents  # Full page for now

        # add the source and page number back to the metadata
        for chunk in document_chunks:
            chunk.metadata["source"] = source
            chunk.metadata["page"] = page

            # add the metadata extracted from the document
            for key, value in metadata.items():
                chunk.metadata[key] = value

        if self.config["splitter_options"]["remove_leftover_delimiters"]:
            document_chunks = self.remove_delimiters(document_chunks)
        if self.config["splitter_options"]["remove_chunks"]:
            document_chunks = self.remove_chunks(document_chunks)

        return document_chunks

    def chunk_docs(self, file_reader, uploaded_files, weblinks):
        addl_metadata = get_metadata(
            "https://dl4ds.github.io/sp2024/lectures/",
            "https://dl4ds.github.io/sp2024/schedule/",
        )  # For any additional metadata

        with ThreadPoolExecutor() as executor:
            executor.map(
                self.process_file,
                uploaded_files,
                range(len(uploaded_files)),
                [file_reader] * len(uploaded_files),
                [addl_metadata] * len(uploaded_files),
            )
            executor.map(
                self.process_weblink,
                weblinks,
                range(len(weblinks)),
                [file_reader] * len(weblinks),
                [addl_metadata] * len(weblinks),
            )

        document_names = [
            f"{file_name}_{page_num}"
            for file_name, pages in self.document_data.items()
            for page_num in pages.keys()
        ]
        documents = [
            page for doc in self.document_data.values() for page in doc.values()
        ]
        document_metadata = [
            page for doc in self.document_metadata.values() for page in doc.values()
        ]

        self.save_document_data()

        self.logger.info(
            f"Total document chunks extracted: {len(self.document_chunks_full)}"
        )

        return self.document_chunks_full, document_names, documents, document_metadata

    def process_documents(
        self, documents, file_path, file_type, metadata_source, addl_metadata
    ):
        file_data = {}
        file_metadata = {}

        for doc in documents:
            # if len(doc.page_content) <= 400: # better approach to filter out non-informative documents
            #     continue

            page_num = doc.metadata.get("page", 0)
            file_data[page_num] = doc.page_content

            # Create a new dictionary for metadata in each iteration
            metadata = addl_metadata.get(file_path, {}).copy()
            metadata["page"] = page_num
            metadata["source"] = file_path
            file_metadata[page_num] = metadata

            if self.config["vectorstore"]["db_option"] not in ["RAGatouille"]:
                document_chunks = self.process_chunks(
                    doc.page_content,
                    file_type,
                    source=file_path,
                    page=page_num,
                    metadata=metadata,
                )
                self.document_chunks_full.extend(document_chunks)

        self.document_data[file_path] = file_data
        self.document_metadata[file_path] = file_metadata

    def process_file(self, file_path, file_index, file_reader, addl_metadata):
        file_name = os.path.basename(file_path)
        if file_name in self.document_data:
            return

        file_type = file_name.split(".")[-1].lower()
        self.logger.info(f"Reading file {file_index + 1}: {file_path}")

        read_methods = {
            "pdf": file_reader.read_pdf,
            "txt": file_reader.read_txt,
            "docx": file_reader.read_docx,
            "srt": file_reader.read_srt,
            "tex": file_reader.read_tex_from_url,
        }
        if file_type not in read_methods:
            self.logger.warning(f"Unsupported file type: {file_type}")
            return

        try:
            documents = read_methods[file_type](file_path)
            self.process_documents(
                documents, file_path, file_type, "file", addl_metadata
            )
        except Exception as e:
            self.logger.error(f"Error processing file {file_name}: {str(e)}")

    def process_weblink(self, link, link_index, file_reader, addl_metadata):
        if link in self.document_data:
            return

        self.logger.info(f"Reading link {link_index + 1} : {link}")

        try:
            if "youtube" in link:
                documents = file_reader.read_youtube_transcript(link)
            else:
                documents = file_reader.read_html(link)

            self.process_documents(documents, link, "txt", "link", addl_metadata)
        except Exception as e:
            self.logger.error(f"Error Reading link {link_index + 1} : {link}: {str(e)}")

    def save_document_data(self):
        if not os.path.exists(f"{self.config['log_chunk_dir']}/docs"):
            os.makedirs(f"{self.config['log_chunk_dir']}/docs")
            self.logger.info(
                f"Creating directory {self.config['log_chunk_dir']}/docs for document data"
            )
        self.logger.info(
            f"Saving document content to {self.config['log_chunk_dir']}/docs/doc_content.json"
        )
        if not os.path.exists(f"{self.config['log_chunk_dir']}/metadata"):
            os.makedirs(f"{self.config['log_chunk_dir']}/metadata")
            self.logger.info(
                f"Creating directory {self.config['log_chunk_dir']}/metadata for document metadata"
            )
        self.logger.info(
            f"Saving document metadata to {self.config['log_chunk_dir']}/metadata/doc_metadata.json"
        )
        with open(
            f"{self.config['log_chunk_dir']}/docs/doc_content.json", "w"
        ) as json_file:
            json.dump(self.document_data, json_file, indent=4)
        with open(
            f"{self.config['log_chunk_dir']}/metadata/doc_metadata.json", "w"
        ) as json_file:
            json.dump(self.document_metadata, json_file, indent=4)

    def load_document_data(self):
        with open(
            f"{self.config['log_chunk_dir']}/docs/doc_content.json", "r"
        ) as json_file:
            self.document_data = json.load(json_file)
        with open(
            f"{self.config['log_chunk_dir']}/metadata/doc_metadata.json", "r"
        ) as json_file:
            self.document_metadata = json.load(json_file)


class DataLoader:
    def __init__(self, config, logger=None):
        self.file_reader = FileReader(logger=logger)
        self.chunk_processor = ChunkProcessor(config, logger=logger)

    def get_chunks(self, uploaded_files, weblinks):
        return self.chunk_processor.chunk_docs(
            self.file_reader, uploaded_files, weblinks
        )


if __name__ == "__main__":
    import yaml

    logger = logging.getLogger(__name__)
    logger.setLevel(logging.INFO)

    with open("../code/modules/config/config.yml", "r") as f:
        config = yaml.safe_load(f)

    data_loader = DataLoader(config, logger=logger)
    document_chunks, document_names, documents, document_metadata = (
        data_loader.get_chunks(
            [],
            ["https://dl4ds.github.io/sp2024/"],
        )
    )

    print(document_names)
    print(len(document_chunks))