rag-solution/services/rag/langchain/enrichment.py

"""Document enrichment module for loading documents into vector storage."""

import hashlib
import os
import queue
import threading
from pathlib import Path
from typing import List, Optional, Tuple

from dotenv import load_dotenv
from langchain_community.document_loaders import PyPDFLoader
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from loguru import logger
from sqlalchemy import Column, Integer, String, create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

# Dynamically import other loaders to handle optional dependencies
try:
    from langchain_community.document_loaders import UnstructuredWordDocumentLoader
except ImportError:
    UnstructuredWordDocumentLoader = None

try:
    from langchain_community.document_loaders import UnstructuredPowerPointLoader
except ImportError:
    UnstructuredPowerPointLoader = None

try:
    from langchain_community.document_loaders import UnstructuredExcelLoader
except ImportError:
    UnstructuredExcelLoader = None

try:
    from langchain_community.document_loaders import UnstructuredImageLoader
except ImportError:
    UnstructuredImageLoader = None

try:
    from langchain_community.document_loaders import UnstructuredODTLoader
except ImportError:
    UnstructuredODTLoader = None

from helpers import (
    LocalFilesystemAdaptiveCollection,
    YandexDiskAdaptiveCollection,
    YandexDiskAdaptiveFile,
    _AdaptiveCollection,
    _AdaptiveFile,
    extract_russian_event_names,
    extract_years_from_text,
)

# Load environment variables
load_dotenv()

# Define the path to the data directory
DATA_DIR = Path("../../../data").resolve()
DB_PATH = Path("document_tracking.db").resolve()
ENRICHMENT_SOURCE = os.getenv("ENRICHMENT_SOURCE", "local").lower()
ENRICHMENT_LOCAL_PATH = os.getenv("ENRICHMENT_LOCAL_PATH")
ENRICHMENT_YADISK_PATH = os.getenv("ENRICHMENT_YADISK_PATH")
YADISK_TOKEN = os.getenv("YADISK_TOKEN")

ENRICHMENT_PROCESSING_MODE = os.getenv("ENRICHMENT_PROCESSING_MODE", "async").lower()
ENRICHMENT_ADAPTIVE_FILES_QUEUE_LIMIT = int(
    os.getenv("ENRICHMENT_ADAPTIVE_FILES_QUEUE_LIMIT", "5")
)
ENRICHMENT_ADAPTIVE_FILE_PROCESS_THREADS = int(
    os.getenv("ENRICHMENT_ADAPTIVE_FILE_PROCESS_THREADS", "4")
)
ENRICHMENT_ADAPTIVE_DOCUMENT_UPLOADS_THREADS = int(
    os.getenv("ENRICHMENT_ADAPTIVE_DOCUMENT_UPLOADS_THREADS", "4")
)

SUPPORTED_EXTENSIONS = {
    ".pdf",
    ".docx",
    ".doc",
    ".pptx",
    ".xlsx",
    ".xls",
    ".jpg",
    ".jpeg",
    ".png",
    ".gif",
    ".bmp",
    ".tiff",
    ".webp",
    ".odt",
    ".txt",  # this one is obvious but was unexpected to see in data lol
}

Base = declarative_base()


class ProcessedDocument(Base):
    """Database model for tracking processed documents."""

    __tablename__ = "processed_documents"

    id = Column(Integer, primary_key=True)
    file_path = Column(String, unique=True, nullable=False)
    file_hash = Column(String, nullable=False)


# to guess the filetype in russian language, for searching it
def try_guess_file_type(extension: str) -> str:
    if extension in [".xlsx", "xls"]:
        return "таблица"
    elif extension in [".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".webp"]:
        return "изображение"
    elif extension in [".pptx"]:
        return "презентация"
    else:
        return "документ"


def identify_adaptive_file_source(adaptive_file: _AdaptiveFile) -> str:
    if isinstance(adaptive_file, YandexDiskAdaptiveFile):
        return "Яндекс Диск"
    else:
        return "Локальный Файл"


class DocumentEnricher:
    """Class responsible for enriching documents and loading them to vector storage."""

    def __init__(self, vector_store):
        self.vector_store = vector_store
        self.text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000,
            chunk_overlap=200,
            length_function=len,
        )

        # In sync mode we force minimal concurrency values.
        if ENRICHMENT_PROCESSING_MODE == "sync":
            self.adaptive_files_queue_limit = 1
            self.file_process_threads_count = 1
            self.document_upload_threads_count = 1
        else:
            self.adaptive_files_queue_limit = max(
                1, ENRICHMENT_ADAPTIVE_FILES_QUEUE_LIMIT
            )
            self.file_process_threads_count = max(
                1, ENRICHMENT_ADAPTIVE_FILE_PROCESS_THREADS
            )
            self.document_upload_threads_count = max(
                1, ENRICHMENT_ADAPTIVE_DOCUMENT_UPLOADS_THREADS
            )

        # Phase 13 queues
        self.ADAPTIVE_FILES_QUEUE: queue.Queue = queue.Queue(
            maxsize=self.adaptive_files_queue_limit
        )
        self.PROCESSED_DOCUMENTS_QUEUE: queue.Queue = queue.Queue(
            maxsize=max(1, self.adaptive_files_queue_limit * 2)
        )

        # Shared state for thread lifecycle
        self.collection_finished = threading.Event()
        self.processing_finished = threading.Event()

        # Initialize database for tracking processed documents
        self._init_db()

    def _init_db(self):
        """Initialize the SQLite database for tracking processed documents."""
        self.engine = create_engine(f"sqlite:///{DB_PATH}")
        Base.metadata.create_all(self.engine)
        self.SessionLocal = sessionmaker(bind=self.engine)

    def _get_file_hash(self, file_path: str) -> str:
        """Calculate SHA256 hash of a file."""
        hash_sha256 = hashlib.sha256()
        with open(file_path, "rb") as file_handle:
            for chunk in iter(lambda: file_handle.read(4096), b""):
                hash_sha256.update(chunk)
        return hash_sha256.hexdigest()

    def _is_document_hash_processed(self, file_hash: str) -> bool:
        """Check if a document hash has already been processed."""
        session = self.SessionLocal()
        try:
            existing = (
                session.query(ProcessedDocument).filter_by(file_hash=file_hash).first()
            )
            return existing is not None
        finally:
            session.close()

    def _mark_document_processed(self, file_identifier: str, file_hash: str):
        """Mark a document as processed in the database."""
        session = self.SessionLocal()
        try:
            existing = (
                session.query(ProcessedDocument)
                .filter_by(file_path=file_identifier)
                .first()
            )
            if existing is not None:
                existing.file_hash = file_hash
            else:
                session.add(
                    ProcessedDocument(file_path=file_identifier, file_hash=file_hash)
                )
            session.commit()
        finally:
            session.close()

    def _get_loader_for_extension(self, file_path: str):
        """Get the appropriate loader for a given file extension."""
        ext = Path(file_path).suffix.lower()

        if ext == ".pdf":
            return PyPDFLoader(file_path)
        if ext in [".docx", ".doc"]:
            if UnstructuredWordDocumentLoader is None:
                logger.warning(
                    f"UnstructuredWordDocumentLoader not available for {file_path}. Skipping."
                )
                return None
            return UnstructuredWordDocumentLoader(
                file_path, **{"strategy": "hi_res", "languages": ["rus"]}
            )
        if ext == ".pptx":
            if UnstructuredPowerPointLoader is None:
                logger.warning(
                    f"UnstructuredPowerPointLoader not available for {file_path}. Skipping."
                )
                return None
            return UnstructuredPowerPointLoader(
                file_path, **{"strategy": "hi_res", "languages": ["rus"]}
            )
        if ext in [".xlsx", ".xls"]:
            if UnstructuredExcelLoader is None:
                logger.warning(
                    f"UnstructuredExcelLoader not available for {file_path}. Skipping."
                )
                return None
            return UnstructuredExcelLoader(
                file_path, **{"strategy": "hi_res", "languages": ["rus"]}
            )
        if ext in [".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".webp"]:
            if UnstructuredImageLoader is None:
                logger.warning(
                    f"UnstructuredImageLoader not available for {file_path}. Skipping."
                )
                return None
            return UnstructuredImageLoader(
                file_path, **{"strategy": "ocr_only", "languages": ["rus"]}
            )
        if ext == ".odt":
            if UnstructuredODTLoader is None:
                logger.warning(
                    f"UnstructuredODTLoader not available for {file_path}. Skipping."
                )
                return None
            return UnstructuredODTLoader(
                file_path, **{"strategy": "hi_res", "languages": ["rus"]}
            )
        return None

    def _load_one_adaptive_file(
        self, adaptive_file: _AdaptiveFile
    ) -> Tuple[List[Document], Optional[Tuple[str, str]]]:
        """Load and split one adaptive file by using its local working callback."""
        loaded_docs: List[Document] = []
        processed_record: Optional[Tuple[str, str]] = None
        source_identifier = identify_adaptive_file_source(adaptive_file)
        extension = adaptive_file.extension.lower()
        file_type = try_guess_file_type(extension)

        def process_local_file(local_file_path: str):
            nonlocal loaded_docs, processed_record

            file_hash = self._get_file_hash(local_file_path)
            if self._is_document_hash_processed(file_hash):
                logger.info(
                    f"SKIPPING already processed document hash for: {source_identifier}"
                )
                return
            else:
                logger.info("Document is not processed! Doing it")

            loader = self._get_loader_for_extension(local_file_path)
            if loader is None:
                logger.warning(f"No loader available for file: {source_identifier}")
                return

            docs = loader.load()
            for doc in docs:
                doc.metadata["file_type"] = file_type
                doc.metadata["source"] = source_identifier
                doc.metadata["filename"] = adaptive_file.filename
                doc.metadata["file_path"] = source_identifier
                doc.metadata["file_size"] = os.path.getsize(local_file_path)
                doc.metadata["file_extension"] = extension

                if "page" in doc.metadata:
                    doc.metadata["page_number"] = doc.metadata["page"]

            split_docs = self.text_splitter.split_documents(docs)
            for chunk in split_docs:
                chunk.metadata["years"] = extract_years_from_text(chunk.page_content)
                chunk.metadata["events"] = extract_russian_event_names(
                    chunk.page_content
                )

            loaded_docs = split_docs
            processed_record = (source_identifier, file_hash)

        adaptive_file.work_with_file_locally(process_local_file)
        return loaded_docs, processed_record

    # Phase 13 API: inserts adaptive files into ADAPTIVE_FILES_QUEUE
    def insert_adaptive_files_queue(
        self, adaptive_collection: _AdaptiveCollection, recursive: bool = True
    ):
        for adaptive_file in adaptive_collection.iterate(recursive=recursive):
            if adaptive_file.extension.lower() not in SUPPORTED_EXTENSIONS:
                logger.debug(
                    f"Skipping unsupported file extension for {adaptive_file.filename}: {adaptive_file.extension}"
                )
                continue

            self.ADAPTIVE_FILES_QUEUE.put(adaptive_file)

        logger.debug("ADAPTIVE COLLECTION DEPLETED!")
        self.collection_finished.set()

    # Phase 13 API: reads adaptive files and writes processed docs into PROCESSED_DOCUMENTS_QUEUE
    def process_adaptive_files_queue(self):
        while True:
            try:
                adaptive_file = self.ADAPTIVE_FILES_QUEUE.get(timeout=0.2)
            except queue.Empty:
                if self.collection_finished.is_set():
                    return
                continue

            try:
                split_docs, processed_record = self._load_one_adaptive_file(
                    adaptive_file
                )
                if split_docs:
                    self.PROCESSED_DOCUMENTS_QUEUE.put((split_docs, processed_record))
            except Exception as error:
                logger.error(f"Error processing {adaptive_file.filename}: {error}")
            finally:
                self.ADAPTIVE_FILES_QUEUE.task_done()

    # Phase 13 API: uploads chunked docs and marks file processed
    def upload_processed_documents_from_queue(self):
        while True:
            try:
                payload = self.PROCESSED_DOCUMENTS_QUEUE.get(timeout=0.2)
            except queue.Empty:
                if self.processing_finished.is_set():
                    return
                continue

            try:
                documents, processed_record = payload
                self.vector_store.add_documents(documents)

                if processed_record is not None:
                    self._mark_document_processed(
                        processed_record[0], processed_record[1]
                    )
            except Exception as error:
                logger.error(
                    f"Error uploading processed documents: {error}. But swallowing error. NOT raising."
                )
            finally:
                self.PROCESSED_DOCUMENTS_QUEUE.task_done()

    def _run_threaded_pipeline(self, adaptive_collection: _AdaptiveCollection):
        """Run Phase 13 queue/thread pipeline."""
        process_threads = [
            threading.Thread(
                target=self.process_adaptive_files_queue,
                name=f"adaptive-file-processor-{index}",
                daemon=True,
            )
            for index in range(self.file_process_threads_count)
        ]
        upload_threads = [
            threading.Thread(
                target=self.upload_processed_documents_from_queue,
                name=f"document-uploader-{index}",
                daemon=True,
            )
            for index in range(self.document_upload_threads_count)
        ]

        for thread in process_threads:
            thread.start()
        for thread in upload_threads:
            thread.start()

        # This one intentionally runs on main thread per Phase 13 requirement.
        self.insert_adaptive_files_queue(adaptive_collection, recursive=True)

        # Wait file queue completion and processing threads end.
        self.ADAPTIVE_FILES_QUEUE.join()
        for thread in process_threads:
            thread.join()

        # Signal upload workers no more payload is expected.
        self.processing_finished.set()

        # Wait upload completion and upload threads end.
        self.PROCESSED_DOCUMENTS_QUEUE.join()
        for thread in upload_threads:
            thread.join()

    def _run_sync_pipeline(self, adaptive_collection: _AdaptiveCollection):
        """Sequential pipeline for sync mode."""
        logger.info("Running enrichment in sync mode")
        self.insert_adaptive_files_queue(adaptive_collection, recursive=True)
        self.process_adaptive_files_queue()
        self.processing_finished.set()
        self.upload_processed_documents_from_queue()

    def enrich_and_store(self, adaptive_collection: _AdaptiveCollection):
        """Load, enrich, and store documents in the vector store."""
        logger.info("Starting enrichment process...")

        if ENRICHMENT_PROCESSING_MODE == "sync":
            logger.info("Document enrichment process starting in SYNC mode")
            self._run_sync_pipeline(adaptive_collection)
            return

        logger.info("Document enrichment process starting in ASYNC/THREAD mode")
        self._run_threaded_pipeline(adaptive_collection)


def get_enrichment_adaptive_collection(
    data_dir: str = str(DATA_DIR),
) -> _AdaptiveCollection:
    """Create adaptive collection based on environment source configuration."""
    source = ENRICHMENT_SOURCE
    if source == "local":
        local_path = ENRICHMENT_LOCAL_PATH or data_dir
        logger.info(f"Using local adaptive collection from path: {local_path}")
        return LocalFilesystemAdaptiveCollection(local_path)

    if source == "yadisk":
        if not YADISK_TOKEN:
            raise ValueError("YADISK_TOKEN must be set when ENRICHMENT_SOURCE=yadisk")
        if not ENRICHMENT_YADISK_PATH:
            raise ValueError(
                "ENRICHMENT_YADISK_PATH must be set when ENRICHMENT_SOURCE=yadisk"
            )
        logger.info(
            f"Using Yandex Disk adaptive collection from path: {ENRICHMENT_YADISK_PATH}"
        )
        return YandexDiskAdaptiveCollection(
            token=YADISK_TOKEN,
            base_dir=ENRICHMENT_YADISK_PATH,
        )

    raise ValueError(
        f"Unsupported ENRICHMENT_SOURCE='{source}'. Allowed values: local, yadisk"
    )


def run_enrichment_process(vector_store, data_dir: str = str(DATA_DIR)):
    """Run the full enrichment process."""
    logger.info("Starting document enrichment process")

    adaptive_collection = get_enrichment_adaptive_collection(data_dir=data_dir)

    # Initialize the document enricher
    enricher = DocumentEnricher(vector_store)

    # Run the enrichment process
    enricher.enrich_and_store(adaptive_collection)

    logger.info("Document enrichment process completed!")


if __name__ == "__main__":
    from vector_storage import initialize_vector_store

    vector_store = initialize_vector_store()
    run_enrichment_process(vector_store)