"""Service for processing documents and ingesting to vector store."""

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.documents import Document as LangChainDocument
from src.db.postgres.vector_store import get_vector_store
from src.storage.az_blob.az_blob import blob_storage
from src.db.postgres.models import Document as DBDocument
from src.config.settings import settings
from sqlalchemy.ext.asyncio import AsyncSession
from src.middlewares.logging import get_logger
from azure.ai.documentintelligence.aio import DocumentIntelligenceClient
from azure.core.credentials import AzureKeyCredential
from typing import List
import pypdf
import docx
from io import BytesIO

logger = get_logger("knowledge_processing")


class KnowledgeProcessingService:
    """Service for processing documents and ingesting to vector store."""

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

    async def process_document(self, db_doc: DBDocument, db: AsyncSession) -> int:
        """Process document and ingest to vector store.

        Returns:
            Number of chunks ingested
        """
        try:
            logger.info(f"Processing document {db_doc.id}")
            content = await blob_storage.download_file(db_doc.blob_name)

            if db_doc.file_type == "pdf":
                documents = await self._build_pdf_documents(content, db_doc)
            else:
                text = self._extract_text(content, db_doc.file_type)
                if not text.strip():
                    raise ValueError("No text extracted from document")
                chunks = self.text_splitter.split_text(text)
                documents = [
                    LangChainDocument(
                        page_content=chunk,
                        metadata={
                            "document_id": db_doc.id,
                            "user_id": db_doc.user_id,
                            "filename": db_doc.filename,
                            "chunk_index": i,
                        }
                    )
                    for i, chunk in enumerate(chunks)
                ]

            if not documents:
                raise ValueError("No text extracted from document")

            vector_store = get_vector_store()
            await vector_store.aadd_documents(documents)

            logger.info(f"Processed {db_doc.id}: {len(documents)} chunks ingested")
            return len(documents)

        except Exception as e:
            logger.error(f"Failed to process document {db_doc.id}", error=str(e))
            raise

    async def _build_pdf_documents(
        self, content: bytes, db_doc: DBDocument
    ) -> List[LangChainDocument]:
        """Build LangChain documents from PDF with page_label metadata.

        Uses Azure Document Intelligence (per-page) when credentials are present,
        falls back to pypdf (also per-page) otherwise.
        """
        documents: List[LangChainDocument] = []

        if settings.azureai_docintel_endpoint and settings.azureai_docintel_key:
            async with DocumentIntelligenceClient(
                endpoint=settings.azureai_docintel_endpoint,
                credential=AzureKeyCredential(settings.azureai_docintel_key),
            ) as client:
                poller = await client.begin_analyze_document(
                    model_id="prebuilt-read",
                    body=BytesIO(content),
                    content_type="application/pdf",
                )
                result = await poller.result()
                logger.info(f"Azure DI extracted {len(result.pages or [])} pages")

                for page in result.pages or []:
                    page_text = "\n".join(
                        line.content for line in (page.lines or [])
                    )
                    if not page_text.strip():
                        continue
                    for chunk in self.text_splitter.split_text(page_text):
                        documents.append(LangChainDocument(
                            page_content=chunk,
                            metadata={
                                "document_id": db_doc.id,
                                "user_id": db_doc.user_id,
                                "filename": db_doc.filename,
                                "chunk_index": len(documents),
                                "page_label": page.page_number,
                            }
                        ))
        else:
            logger.warning("Azure DI not configured, using pypdf")
            pdf_reader = pypdf.PdfReader(BytesIO(content))
            for page_num, page in enumerate(pdf_reader.pages, start=1):
                page_text = page.extract_text() or ""
                if not page_text.strip():
                    continue
                for chunk in self.text_splitter.split_text(page_text):
                    documents.append(LangChainDocument(
                        page_content=chunk,
                        metadata={
                            "document_id": db_doc.id,
                            "user_id": db_doc.user_id,
                            "filename": db_doc.filename,
                            "chunk_index": len(documents),
                            "page_label": page_num,
                        }
                    ))

        return documents

    def _extract_text(self, content: bytes, file_type: str) -> str:
        """Extract text from DOCX or TXT content."""
        if file_type == "docx":
            doc = docx.Document(BytesIO(content))
            return "\n".join(p.text for p in doc.paragraphs)
        elif file_type == "txt":
            return content.decode("utf-8")
        else:
            raise ValueError(f"Unsupported file type: {file_type}")


knowledge_processor = KnowledgeProcessingService()