""" API endpoints for PARSER Service """ import logging import uuid from pathlib import Path from typing import Optional from fastapi import APIRouter, UploadFile, File, HTTPException, Form from fastapi.responses import JSONResponse from app.schemas import ( ParseRequest, ParseResponse, ParsedDocument, ParsedChunk, QAPair, ChunksResponse ) from app.core.config import settings from app.runtime.inference import parse_document_from_images from app.runtime.preprocessing import ( convert_pdf_to_images, load_image, detect_file_type, validate_file_size ) from app.runtime.postprocessing import ( build_chunks, build_qa_pairs, build_markdown ) logger = logging.getLogger(__name__) router = APIRouter() @router.post("/parse", response_model=ParseResponse) async def parse_document_endpoint( file: Optional[UploadFile] = File(None), doc_url: Optional[str] = Form(None), output_mode: str = Form("raw_json"), dao_id: Optional[str] = Form(None), doc_id: Optional[str] = Form(None) ): """ Parse document (PDF or image) using dots.ocr Supports: - PDF files (multi-page) - Image files (PNG, JPEG, TIFF) Output modes: - raw_json: Full structured JSON - markdown: Markdown representation - qa_pairs: Q&A pairs extracted from document - chunks: Semantic chunks for RAG """ try: # Validate input if not file and not doc_url: raise HTTPException( status_code=400, detail="Either 'file' or 'doc_url' must be provided" ) # Process file if file: # Read file content content = await file.read() # Validate file size try: validate_file_size(content) except ValueError as e: raise HTTPException(status_code=413, detail=str(e)) # Detect file type try: doc_type = detect_file_type(content, file.filename) except ValueError as e: raise HTTPException(status_code=400, detail=str(e)) # Convert to images if doc_type == "pdf": images = convert_pdf_to_images(content) else: image = load_image(content) images = [image] else: # TODO: Download from doc_url raise HTTPException( status_code=501, detail="doc_url download not yet implemented" ) # Parse document from images logger.info(f"Parsing document: {len(images)} page(s), mode: {output_mode}") parsed_doc = parse_document_from_images( images=images, output_mode=output_mode, doc_id=doc_id or str(uuid.uuid4()), doc_type=doc_type ) # Build response based on output_mode response_data = {"metadata": { "doc_id": parsed_doc.doc_id, "doc_type": parsed_doc.doc_type, "page_count": len(parsed_doc.pages) }} if output_mode == "raw_json": response_data["document"] = parsed_doc elif output_mode == "markdown": response_data["markdown"] = build_markdown(parsed_doc) elif output_mode == "qa_pairs": response_data["qa_pairs"] = build_qa_pairs(parsed_doc) elif output_mode == "chunks": response_data["chunks"] = build_chunks(parsed_doc, dao_id=dao_id) return ParseResponse(**response_data) except HTTPException: raise except Exception as e: logger.error(f"Error parsing document: {e}", exc_info=True) raise HTTPException(status_code=500, detail=f"Parsing failed: {str(e)}") @router.post("/parse_qa", response_model=ParseResponse) async def parse_qa_endpoint( file: Optional[UploadFile] = File(None), doc_url: Optional[str] = Form(None) ): """Parse document and return Q&A pairs""" return await parse_document_endpoint( file=file, doc_url=doc_url, output_mode="qa_pairs" ) @router.post("/parse_markdown", response_model=ParseResponse) async def parse_markdown_endpoint( file: Optional[UploadFile] = File(None), doc_url: Optional[str] = Form(None) ): """Parse document and return Markdown""" return await parse_document_endpoint( file=file, doc_url=doc_url, output_mode="markdown" ) @router.post("/parse_chunks", response_model=ChunksResponse) async def parse_chunks_endpoint( file: Optional[UploadFile] = File(None), doc_url: Optional[str] = Form(None), dao_id: str = Form(...), doc_id: Optional[str] = Form(None) ): """Parse document and return chunks for RAG""" response = await parse_document_endpoint( file=file, doc_url=doc_url, output_mode="chunks", dao_id=dao_id, doc_id=doc_id ) if not response.chunks: raise HTTPException(status_code=500, detail="Failed to generate chunks") return ChunksResponse( chunks=response.chunks, total_chunks=len(response.chunks), doc_id=response.chunks[0].metadata.get("doc_id", doc_id or "unknown"), dao_id=dao_id )