diff --git a/common/requirements.txt b/common/requirements.txt index 562c2f6..d5a2d5b 100644 --- a/common/requirements.txt +++ b/common/requirements.txt @@ -110,7 +110,8 @@ packaging==24.2 pandas==2.2.3 #pathtools==0.1.2 pillow==11.2.1 -PyMuPDF==1.26.4 +PyMuPDF==1.26.6 +pymupdf4llm==0.2.0 platformdirs==4.3.8 pluggy==1.6.0 prometheus_client==0.22.1 diff --git a/common/utils/image_data_extractor.py b/common/utils/image_data_extractor.py index bde9c97..1959e8c 100644 --- a/common/utils/image_data_extractor.py +++ b/common/utils/image_data_extractor.py @@ -1,165 +1,63 @@ import base64 import io import logging -import os -import uuid -import hashlib -from pathlib import Path from langchain_core.messages import HumanMessage, SystemMessage from common.config import get_multimodal_service logger = logging.getLogger(__name__) - - -def describe_image_with_llm(image_input): +def describe_image_with_llm(file_path): """ - Send image (pixmap or PIL image) to LLM vision model and return description. - Uses multimodal_service from config if available, otherwise falls back to completion_service. - Currently supports: OpenAI, Azure OpenAI, Google GenAI, and Google VertexAI + Read image file and convert to base64 to send to LLM. """ try: + from PIL import Image as PILImage + client = get_multimodal_service() if not client: return "[Image: Failed to create multimodal LLM client]" - + + # Read image and convert to base64 + pil_image = PILImage.open(file_path) buffer = io.BytesIO() - # Convert to RGB if needed for better compatibility - if image_input.mode != 'RGB': - image_input = image_input.convert('RGB') - image_input.save(buffer, format="JPEG", quality=95) - b64_img = base64.b64encode(buffer.getvalue()).decode("utf-8") + if pil_image.mode != 'RGB': + pil_image = pil_image.convert('RGB') + pil_image.save(buffer, format="JPEG", quality=95) + image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8') - # Build messages (system + human) messages = [ - SystemMessage( - content="You are a helpful assistant that describes images concisely for document analysis." - ), - HumanMessage( - content=[ - { - "type": "text", - "text": ( - "Please describe what you see in this image and " - "if the image has scanned text then extract all the text. " - "if the image has any logo, icon, or branding element, try to describe it with text. " - "Focus on any text, diagrams, charts, or other visual elements." - "If the image is purely a logo, icon, or branding element, start your response with 'LOGO:' or 'ICON:'." - ), - }, - { - "type": "image_url", - "image_url": {"url": f"data:image/jpeg;base64,{b64_img}"}, - }, - ] - ), + SystemMessage( + content="You are a helpful assistant that describes images concisely for document analysis." + ), + HumanMessage( + content=[ + { + "type": "text", + "text": ( + "Please describe what you see in this image and " + "if the image has scanned text then extract all the text. " + "If the image has any graph, chart, table, or other diagram, describe it. " + "If the image has any logo, identify and describe the logo." + ), + }, + { + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}, + }, + ], + ), ] - # Get response from LangChain LLM client - # Access the underlying LangChain client langchain_client = client.llm response = langchain_client.invoke(messages) - return response.content if hasattr(response, 'content') else str(response) + return response.content if hasattr(response, "content") else str(response) except Exception as e: logger.error(f"Failed to describe image with LLM: {str(e)}") return "[Image: Error processing image description]" -def save_image_and_get_markdown(image_input, context_info="", graphname=None): - """ - Save image locally to static/images/ folder and return markdown reference with description. - - LEGACY/OLD APPROACH: Used for backward compatibility with JSONL-based loading. - Images are saved as files and served via /ui/images/ endpoint with img:// protocol. - - For NEW direct loading approach, images are stored in Image vertex as base64 - and served via /ui/image_vertex/ endpoint with image:// protocol. - - Args: - image_input: PIL Image object - context_info: Optional context (e.g., "page 3 of invoice.pdf") - graphname: Graph name to organize images by graph (optional) - - Returns: - dict with: - - 'markdown': Markdown string with img:// reference - - 'image_id': Unique identifier for the saved image - - 'image_path': Path where image was saved to static/images/ - """ - try: - # FIRST: Get description from LLM to check if it's a logo - description = describe_image_with_llm(image_input) - - # Check if the image is a logo, icon, or decorative element BEFORE saving - # These should be filtered out as they're not content-relevant - description_lower = description.lower() - logo_indicators = ['logo', 'icon', 'branding', 'watermark', 'trademark', 'company logo', 'brand logo'] - - if any(indicator in description_lower for indicator in logo_indicators): - logger.info(f"Detected logo/icon in image, skipping: {description[:100]}") - return None - - # If not a logo, proceed with saving the image - # Generate unique image ID using hash of image content - buffer = io.BytesIO() - if image_input.mode != 'RGB': - image_input = image_input.convert('RGB') - image_input.save(buffer, format="JPEG", quality=95) - image_bytes = buffer.getvalue() - - # Create hash-based ID (deterministic for same image) - image_hash = hashlib.sha256(image_bytes).hexdigest()[:16] - image_id = f"{image_hash}.jpg" - - # Save image to local storage directory organized by graphname - project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - - # If graphname is provided, organize images by graph - if graphname: - images_dir = os.path.join(project_root, "static", "images", graphname) - # Include graphname in the image reference for URL construction - image_reference = f"{graphname}/{image_id}" - else: - images_dir = os.path.join(project_root, "static", "images") - image_reference = image_id - - os.makedirs(images_dir, exist_ok=True) - - image_path = os.path.join(images_dir, image_id) - - # Save image file (skip if already exists with same hash) - if not os.path.exists(image_path): - with open(image_path, 'wb') as f: - f.write(image_bytes) - logger.info(f"Saved content image to: {image_path}") - else: - logger.debug(f"Image already exists: {image_path}") - - # Generate markdown with custom img:// protocol (will be replaced later) - # Format: ![description](img://graphname/image_id) or ![description](img://image_id) - markdown = f"![{description}](img://{image_reference})" - - logger.info(f"Created image reference: {image_reference} with description") - - return { - 'markdown': markdown, - 'image_id': image_reference, - 'image_path': image_path, - 'description': description - } - - except Exception as e: - logger.error(f"Failed to save image and generate markdown: {str(e)}") - # Fallback to text description only - fallback_desc = f"[Image: {context_info} - processing failed]" - return { - 'markdown': fallback_desc, - 'image_id': None, - 'image_path': None, - 'description': fallback_desc - } diff --git a/common/utils/text_extractors.py b/common/utils/text_extractors.py index da3e22d..127f6ce 100644 --- a/common/utils/text_extractors.py +++ b/common/utils/text_extractors.py @@ -8,6 +8,8 @@ import uuid import base64 import io +import re +import threading from pathlib import Path import shutil import asyncio @@ -15,6 +17,59 @@ logger = logging.getLogger(__name__) +# Global lock for pymupdf4llm calls (not thread-safe) +_pymupdf4llm_lock = threading.Lock() + + +# regex for markdown images: ![alt](path) +_md_pattern = re.compile(r'!\[([^\]]*)\]\(([^)\s]+)\)') + +def extract_images(md_text): + """ + Returns list of {"path": path, "image_id": image_id} + image_id = basename without extension + """ + images = [] + for m in _md_pattern.finditer(md_text): + path = m.group(2) + basename = os.path.basename(path) + image_id = os.path.splitext(basename)[0] + images.append({"path": path, "image_id": image_id}) + return images + + +def insert_description_by_id(md_text, image_id, description): + """ + Replace the description for an image whose basename == image_id. + """ + def repl(m): + old_path = m.group(2) + candidate_id = os.path.splitext(os.path.basename(old_path))[0] + + if candidate_id == image_id: + return f'![{description}]({old_path})' + + return m.group(0) + + return _md_pattern.sub(repl, md_text) + + +def replace_path_with_tg_protocol(md_text, image_id, tg_reference): + """ + Replace the file path for an image whose basename == image_id with tg:// protocol reference. + tg_reference should be like 'Graphs_image_1' + """ + def repl(m): + old_path = m.group(2) + candidate_id = os.path.splitext(os.path.basename(old_path))[0] + + if candidate_id == image_id: + alt_text = m.group(1) + return f'![{alt_text}](tg://{tg_reference})' + + return m.group(0) + + return _md_pattern.sub(repl, md_text) class TextExtractor: """Class for handling text extraction from various file formats and cleanup.""" @@ -38,10 +93,16 @@ def __init__(self): '.jpg': 'image/jpeg' } - async def _process_file_async(self, file_path, folder_path_obj, graphname): + async def _process_file_async(self, file_path, graphname, temp_folder): """ Async helper to process a single file. Runs in thread pool to avoid blocking on I/O operations. + Creates one JSONL file per input file. + + Args: + file_path: Absolute path to the input file to be processed (e.g., "C:/data/docs/report.pdf"). + graphname: Name of the knowledge graph this file belongs to, used for metadata tagging. + temp_folder: Absolute path to the temporary directory where output JSONL files are written. """ try: loop = asyncio.get_event_loop() @@ -53,11 +114,25 @@ async def _process_file_async(self, file_path, folder_path_obj, graphname): graphname ) + # Create one JSONL file per input file + if doc_entries: + # Use the original filename (stem) for the JSONL file + file_stem = Path(file_path).stem + jsonl_file = os.path.join(temp_folder, f"{file_stem}.jsonl") + + await loop.run_in_executor( + None, + self._write_to_jsonl, + jsonl_file, + doc_entries + ) + + # Return metadata only, documents already saved to JSONL return { 'success': True, 'file_path': str(file_path), - 'documents': doc_entries, - 'num_documents': len(doc_entries) + 'num_documents': len(doc_entries), + 'jsonl_file': f"{Path(file_path).stem}.jsonl" } except FileNotFoundError: @@ -67,11 +142,21 @@ async def _process_file_async(self, file_path, folder_path_obj, graphname): except Exception as e: logger.warning(f"Failed to process file {file_path}: {e}") return {'success': False, 'file_path': str(file_path), 'error': str(e)} + + def _write_to_jsonl(self, jsonl_file, doc_entries): + """ + Write document entries to a JSONL file (one file per input file). + Each document is written as a separate line. + """ + with open(jsonl_file, 'w', encoding='utf-8') as f: + for doc_data in doc_entries: + json_line = json.dumps(doc_data, ensure_ascii=False) + f.write(json_line + '\n') - async def _process_folder_async(self, folder_path, graphname=None, max_concurrent=10): + async def _process_folder_async(self, folder_path, graphname, temp_folder, max_concurrent=10): """ Async version of process_folder for parallel file processing. - This prevents conflicts when multiple users process folders simultaneously. + Creates one JSONL file per input file. """ logger.info(f"Processing local folder ASYNC: {folder_path} for graph: {graphname} (max_concurrent={max_concurrent})") @@ -83,6 +168,10 @@ async def _process_folder_async(self, folder_path, graphname=None, max_concurren if not folder_path_obj.is_dir(): raise Exception(f"Path is not a directory: {folder_path}") + # Create temp folder for JSONL files + os.makedirs(temp_folder, exist_ok=True) + logger.info(f"Saving processed documents to: {temp_folder}") + def safe_walk(path): try: for item in path.iterdir(): @@ -110,13 +199,13 @@ def safe_walk(path): async def process_with_semaphore(file_path): async with semaphore: - return await self._process_file_async(file_path, folder_path_obj, graphname) + return await self._process_file_async(file_path, graphname, temp_folder) tasks = [process_with_semaphore(fp) for fp in files_to_process] results = await asyncio.gather(*tasks, return_exceptions=True) - all_documents = [] processed_files_info = [] + total_docs = 0 for result in results: if isinstance(result, Exception): @@ -124,10 +213,13 @@ async def process_with_semaphore(file_path): continue if result.get('success'): - all_documents.extend(result.get('documents', [])) + num_docs = result.get('num_documents', 0) + total_docs += num_docs + processed_files_info.append({ 'file_path': result['file_path'], - 'num_documents': result.get('num_documents', len(result.get('documents', []))), + 'num_documents': num_docs, + 'jsonl_file': result.get('jsonl_file'), 'status': 'success' }) else: @@ -137,23 +229,30 @@ async def process_with_semaphore(file_path): 'error': result.get('error', 'Unknown error') }) - logger.info(f"Processed {len(processed_files_info)} files, extracted {len(all_documents)} total documents") + logger.info(f"Processed {len(processed_files_info)} files, extracted {total_docs} total documents") + logger.info(f"Created {len([f for f in processed_files_info if f.get('status') == 'success'])} JSONL files in {temp_folder}") return { 'statusCode': 200, - 'message': f'Processed {len(processed_files_info)} files, {len(all_documents)} documents', - 'documents': all_documents, + 'message': f'Processed {len(processed_files_info)} files, {total_docs} documents', 'files': processed_files_info, - 'num_documents': len(all_documents) + 'num_documents': total_docs, + 'temp_folder': temp_folder } - def process_folder(self, folder_path, graphname=None): + def process_folder(self, folder_path, graphname, temp_folder): """ Process local folder with multiple file formats and extract text content. Uses async processing internally for parallel file handling. + Creates one JSONL file per input file. + + Args: + folder_path: Path to the folder containing files to process + graphname: Name of the graph (for context) + temp_folder: Path to save processed documents as JSONL files (one per input file) """ logger.info(f"Processing local folder: {folder_path} for graph: {graphname}") - return asyncio.run(self._process_folder_async(folder_path, graphname)) + return asyncio.run(self._process_folder_async(folder_path, graphname, temp_folder)) def extract_text_from_file_with_images_as_docs(file_path, graphname=None): @@ -183,137 +282,166 @@ def extract_text_from_file_with_images_as_docs(file_path, graphname=None): def _extract_pdf_with_images_as_docs(file_path, base_doc_id, graphname=None): """ - Extract PDF as ONE markdown document with inline image references. + Extract PDF as ONE markdown document with inline image references using pymupdf4llm. + Uses unique temporary folder per PDF to allow parallel processing. + After processing, delete the extracted image folder. """ + # Use unique folder per PDF to allow parallel processing without conflicts + unique_folder_id = uuid.uuid4().hex[:12] + image_output_folder = Path(f"tg_temp_{unique_folder_id}") + try: - import fitz # PyMuPDF + import pymupdf4llm from PIL import Image as PILImage + from common.utils.image_data_extractor import describe_image_with_llm + + # Ensure clean slate - remove folder if it exists from failed previous run + if image_output_folder.exists(): + shutil.rmtree(image_output_folder, ignore_errors=True) + + # Convert PDF to markdown with extracted image files + # Use lock because pymupdf4llm's table extraction is not thread-safe + # See: https://github.com/pymupdf/PyMuPDF/issues/3241 + with _pymupdf4llm_lock: + try: + markdown_content = pymupdf4llm.to_markdown( + file_path, + write_images=True, + image_path=str(image_output_folder), # unique folder per PDF + margins=0, + image_size_limit=0.08, + ) + except Exception: + # Retry with table_strategy="lines" if first attempt fails + try: + markdown_content = pymupdf4llm.to_markdown( + file_path, + write_images=True, + image_path=str(image_output_folder), # unique folder per PDF + margins=0, + image_size_limit=0.08, + table_strategy="lines", + ) + except Exception as e: + logger.error(f"pymupdf4llm failed for {file_path}: {e}") + # Cleanup folder if it was created + if image_output_folder.exists(): + shutil.rmtree(image_output_folder, ignore_errors=True) + return [{ + "doc_id": base_doc_id, + "doc_type": "markdown", + "content": f"[PDF extraction failed: {e}]", + "position": 0 + }] + + if not markdown_content or not markdown_content.strip(): + logger.warning(f"No content extracted from PDF: {file_path}") + + # Extract image references from markdown + image_refs = extract_images(markdown_content) + + if not image_refs: + # cleanup folder anyway + if image_output_folder.exists(): + shutil.rmtree(image_output_folder, ignore_errors=True) + + return [{ + "doc_id": base_doc_id, + "doc_type": "markdown", + "content": markdown_content, + "position": 0 + }] - doc = fitz.open(file_path) - markdown_parts = [] image_entries = [] image_counter = 0 - for page_num, page in enumerate(doc, start=1): - if page_num > 1: - markdown_parts.append("\n\n") - markdown_parts.append(f"--- Page {page_num} ---\n") #Avoid to be splitted as a single chunk - - blocks = page.get_text("blocks", sort=True) - text_blocks_with_pos = [] - - for block in blocks: - block_type = block[6] if len(block) > 6 else 0 - if block_type == 0: - text = block[4].strip() - if text: - y_pos = block[1] - text_blocks_with_pos.append({'type': 'text', 'content': text, 'y_pos': y_pos}) - - image_list = page.get_images(full=True) - images_with_pos = [] - - if image_list: - for img_index, img_info in enumerate(image_list): - try: - xref = img_info[0] - base_image = doc.extract_image(xref) - image_bytes = base_image["image"] - image_ext = base_image["ext"] - - img_rects = page.get_image_rects(xref) - y_pos = img_rects[0].y0 if img_rects else 999999 - - pil_image = PILImage.open(io.BytesIO(image_bytes)) - if pil_image.width < 100 or pil_image.height < 100: - continue - - from common.utils.image_data_extractor import describe_image_with_llm - description = describe_image_with_llm(pil_image) - description_lower = description.lower() - logo_indicators = [ - 'logo:', 'icon:', 'logo', 'icon', 'branding', - 'watermark', 'trademark', 'stylized letter', - 'stylized text', 'word "', "word '" - ] - if any(indicator in description_lower for indicator in logo_indicators): - continue - - buffer = io.BytesIO() - if pil_image.mode != 'RGB': - pil_image = pil_image.convert('RGB') - pil_image.save(buffer, format="JPEG", quality=95) - image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8') - - image_counter += 1 - image_doc_id = f"{base_doc_id}_image_{image_counter}" - - images_with_pos.append({ - 'type': 'image', - 'image_doc_id': image_doc_id, - 'description': description, - 'y_pos': y_pos, - 'image_data': image_base64, - 'image_format': image_ext, - 'width': pil_image.width, - 'height': pil_image.height - }) - except Exception as img_error: - logger.warning(f"Failed to extract image on page {page_num}: {img_error}") - - all_elements = text_blocks_with_pos + images_with_pos - all_elements.sort(key=lambda x: x['y_pos']) - - for element in all_elements: - if element['type'] == 'text': - markdown_parts.append(element['content']) - markdown_parts.append("\n\n") - else: - # Add image description as text, then markdown image reference - # Use short alt text in markdown, full description as regular text - markdown_parts.append(f"![{element['description']}](tg://{element['image_doc_id']})\n\n") - - image_entries.append({ - "doc_id": element['image_doc_id'], - "doc_type": "image", - "image_description": element['description'], - "image_data": element['image_data'], - "image_format": element['image_format'], - "parent_doc": base_doc_id, - "page_number": page_num, - "width": element['width'], - "height": element['height'], - "position": int(element['image_doc_id'].split('_')[-1]) - }) - - doc.close() - - markdown_content = "".join(markdown_parts) if markdown_parts else "" #No content extracted from PDF - if not markdown_content: - return [] + for img_ref in image_refs: + try: + img_path = Path(img_ref["path"]) # convert to Path + image_id = img_ref["image_id"] + + # Image description + description = describe_image_with_llm(str(img_path)) + + markdown_content = insert_description_by_id( + markdown_content, + image_id, + description + ) + + # Convert image to base64 + pil_image = PILImage.open(img_path) + buffer = io.BytesIO() + + if pil_image.mode != "RGB": + pil_image = pil_image.convert("RGB") + + pil_image.save(buffer, format="JPEG", quality=95) + image_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8") + + image_counter += 1 + image_doc_id = f"{base_doc_id}_image_{image_counter}" + + # Replace file path with tg:// protocol reference in markdown + markdown_content = replace_path_with_tg_protocol( + markdown_content, + image_id, + image_doc_id + ) + + image_entries.append({ + "doc_id": image_doc_id, + "doc_type": "image", + "image_description": description, + "image_data": image_base64, + "image_format": "jpg", + "parent_doc": base_doc_id, + "page_number": 0, + "width": pil_image.width, + "height": pil_image.height, + "position": image_counter + }) + + except Exception as img_error: + logger.warning(f"Failed to process image {img_ref.get('path')}: {img_error}") + # FINAL CLEANUP — delete folder after processing everything + if image_output_folder.exists() and image_output_folder.is_dir(): + try: + shutil.rmtree(image_output_folder) + logger.debug(f"Deleted image folder: {image_output_folder}") + except Exception as delete_err: + logger.warning(f"Failed to delete folder {image_output_folder}: {delete_err}") + + # Build final result result = [{ "doc_id": base_doc_id, - "doc_type": "", + "doc_type": "markdown", "content": markdown_content, "position": 0 }] result.extend(image_entries) + return result - except ImportError: - logger.error("PyMuPDF not available") + except ImportError as import_err: + logger.error(f"Required library missing: {import_err}") + # Cleanup on import error + if image_output_folder.exists(): + shutil.rmtree(image_output_folder, ignore_errors=True) return [{ "doc_id": base_doc_id, - "doc_type": "", - "content": "[PDF extraction requires PyMuPDF]", + "doc_type": "markdown", + "content": "[PDF extraction requires pymupdf4llm and PyMuPDF]", "position": 0 }] except Exception as e: logger.error(f"Error extracting PDF: {e}") + # Cleanup on any other error + if image_output_folder.exists(): + shutil.rmtree(image_output_folder, ignore_errors=True) raise - def _extract_standalone_image_as_doc(file_path, base_doc_id, graphname=None): """ Extract standalone image file as ONE markdown document with inline image reference. @@ -324,25 +452,9 @@ def _extract_standalone_image_as_doc(file_path, base_doc_id, graphname=None): pil_image = PILImage.open(file_path) if pil_image.width < 100 or pil_image.height < 100: - return [{ - "doc_id": base_doc_id, - "doc_type": "", - "content": f"[Skipped small image: {file_path.name}]", - "position": 0 - }] + pass - description = describe_image_with_llm(pil_image) - description_lower = description.lower() - logo_indicators = ['logo:', 'icon:', 'logo', 'icon', 'branding', - 'watermark', 'trademark', 'stylized letter', - 'stylized text', 'word "', "word '"] - if any(indicator in description_lower for indicator in logo_indicators): - return [{ - "doc_id": base_doc_id, - "doc_type": "", - "content": f"[Skipped logo/icon: {file_path.name}]", - "position": 0 - }] + description = describe_image_with_llm(str(Path(file_path).absolute())) buffer = io.BytesIO() if pil_image.mode != 'RGB': @@ -353,7 +465,6 @@ def _extract_standalone_image_as_doc(file_path, base_doc_id, graphname=None): image_id = f"{base_doc_id}_image_1" # Put description as text, then markdown image reference with short alt text content = f"![{description}](tg://{image_id})" - return [ { "doc_id": base_doc_id, @@ -379,7 +490,7 @@ def _extract_standalone_image_as_doc(file_path, base_doc_id, graphname=None): logger.error(f"Error extracting image: {e}") return [{ "doc_id": base_doc_id, - "doc_type": "", + "doc_type": "markdown", "content": f"[Image extraction failed: {str(e)}]", "position": 0 }] @@ -441,12 +552,10 @@ def get_doc_type_from_extension(extension): if extension in ['.html', '.htm']: return 'html' - elif extension in ['.md']: - return 'markdown' elif extension in ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp']: return 'image' else: - return '' + return 'markdown' def get_supported_extensions(): @@ -457,4 +566,4 @@ def get_supported_extensions(): def is_supported_file(file_path): """Check if a file is supported for text extraction.""" extension = Path(file_path).suffix.lower() - return extension in get_supported_extensions() + return extension in get_supported_extensions() \ No newline at end of file diff --git a/graphrag-ui/src/pages/Setup.tsx b/graphrag-ui/src/pages/Setup.tsx index b7d357d..03df78a 100644 --- a/graphrag-ui/src/pages/Setup.tsx +++ b/graphrag-ui/src/pages/Setup.tsx @@ -56,7 +56,9 @@ const Setup = () => { const [uploadMessage, setUploadMessage] = useState(""); const [isIngesting, setIsIngesting] = useState(false); const [ingestMessage, setIngestMessage] = useState(""); - const [activeTab, setActiveTab] = useState("upload"); + // Ingestion job data state + const [ingestJobData, setIngestJobData] = useState(null); + const [directIngestion, setDirectIngestion] = useState(false); // Refresh state const [refreshOpen, setRefreshOpen] = useState(false); @@ -67,6 +69,7 @@ const Setup = () => { const [isCheckingStatus, setIsCheckingStatus] = useState(false); // S3 state + const [awsAccessKey, setAwsAccessKey] = useState(""); const [awsSecretKey, setAwsSecretKey] = useState(""); const [inputBucket, setInputBucket] = useState(""); @@ -92,7 +95,7 @@ const Setup = () => { const [downloadedFiles, setDownloadedFiles] = useState([]); const [isDownloading, setIsDownloading] = useState(false); const [downloadMessage, setDownloadMessage] = useState(""); - +const [activeTab, setActiveTab] = useState("upload"); // Fetch uploaded files const fetchUploadedFiles = async () => { if (!ingestGraphName) return; @@ -159,15 +162,25 @@ const Setup = () => { const data = await response.json(); if (data.status === "success") { - setUploadMessage(`✅ ${data.message}`); + const uploadedCount = selectedFiles?.length || 0; + setUploadMessage("✅ Successfully uploaded the files. Processing..."); setSelectedFiles(null); await fetchUploadedFiles(); + setIsUploading(false); + + // Step 2: Call create_ingest to process uploaded files in background + console.log("Calling handleCreateIngestAfterUpload from main upload..."); + handleCreateIngestAfterUpload("uploaded", uploadedCount).catch((err) => { + console.error("Error in background processing:", err); + setUploadMessage(`❌ Processing error: ${err.message}`); + }); } else { setUploadMessage(`⚠️ ${data.message}`); + setIsUploading(false); } } catch (error: any) { + console.error("Upload error:", error); setUploadMessage(`❌ Error: ${error.message}`); - } finally { setIsUploading(false); } }; @@ -219,14 +232,20 @@ const Setup = () => { // Show final result if (failedCount === 0) { - setUploadMessage(`✅ Successfully uploaded all ${uploadedCount} files (uploaded individually).`); + setUploadMessage(`✅ Successfully uploaded all ${uploadedCount} files. Processing...`); } else { - setUploadMessage(`⚠️ Uploaded ${uploadedCount} files successfully, ${failedCount} failed.`); + setUploadMessage(`⚠️ Uploaded ${uploadedCount} files successfully, ${failedCount} failed. Processing...`); } setSelectedFiles(null); await fetchUploadedFiles(); + + // Step 2: Call create_ingest to process uploaded files + console.log("Calling handleCreateIngestAfterUpload..."); + await handleCreateIngestAfterUpload("uploaded", uploadedCount); + console.log("handleCreateIngestAfterUpload completed"); } catch (error: any) { + console.error("Upload error:", error); setUploadMessage(`❌ Batch upload error: ${error.message}`); } finally { setIsUploading(false); @@ -236,20 +255,27 @@ const Setup = () => { // Delete a specific file const handleDeleteFile = async (filename: string) => { if (!ingestGraphName) return; + + console.log("Deleting file:", filename); try { const creds = localStorage.getItem("creds"); - const response = await fetch( - `/ui/${ingestGraphName}/uploads?filename=${encodeURIComponent(filename)}`, - { + + // Delete original file + const url = `/ui/${ingestGraphName}/uploads?filename=${encodeURIComponent(filename)}`; + + const response = await fetch(url, { method: "DELETE", headers: { Authorization: `Basic ${creds}` }, - } - ); + }); const data = await response.json(); setUploadMessage(`✅ ${data.message}`); await fetchUploadedFiles(); + + // Clear ingest message when deleting files + setIngestMessage(""); } catch (error: any) { + console.error("Delete error:", error); setUploadMessage(`❌ Error: ${error.message}`); } }; @@ -268,6 +294,11 @@ const Setup = () => { headers: { Authorization: `Basic ${creds}` }, }); const data = await response.json(); + + // Clear all temp state + setIngestJobData(null); + setIngestMessage(""); // Clear any previous ingestion messages + setUploadMessage(`✅ ${data.message}`); await fetchUploadedFiles(); } catch (error: any) { @@ -360,12 +391,21 @@ const Setup = () => { const data = await response.json(); if (data.status === "success") { - setDownloadMessage(`✅ ${data.message}`); + const downloadCount = data.downloaded_files?.length || downloadedFiles.length; + setDownloadMessage("✅ Successfully downloaded the files. Processing..."); await fetchDownloadedFiles(); + setIsDownloading(false); + // Step 2: Call create_ingest to process downloaded files in background + handleCreateIngestAfterUpload("downloaded", downloadCount).catch((err) => { + console.error("Error in background processing:", err); + setDownloadMessage(`❌ Processing error: ${err.message}`); + }); } else if (data.status === "warning") { setDownloadMessage(`⚠️ ${data.message}`); + setIsDownloading(false); } else { setDownloadMessage(`❌ ${data.message || "Download failed"}`); + setIsDownloading(false); } } catch (error: any) { setDownloadMessage(`❌ Error: ${error.message}`); @@ -380,13 +420,14 @@ const Setup = () => { try { const creds = localStorage.getItem("creds"); - const response = await fetch( - `/ui/${ingestGraphName}/cloud/delete?filename=${encodeURIComponent(filename)}`, - { + + // Delete original file + const url = `/ui/${ingestGraphName}/cloud/delete?filename=${encodeURIComponent(filename)}`; + + const response = await fetch(url, { method: "DELETE", headers: { Authorization: `Basic ${creds}` }, - } - ); + }); const data = await response.json(); setDownloadMessage(`✅ ${data.message}`); await fetchDownloadedFiles(); @@ -416,6 +457,57 @@ const Setup = () => { } }; + + // Ingest flows (create ingest, run ingest) + // ------------------------- + const handleRunIngest = async () => { + if (!ingestJobData) { + setIngestMessage("❌ No ingest job data available"); + return; + } + + setIsIngesting(true); + setIngestMessage("Running final document ingest..."); + + try { + const creds = localStorage.getItem("creds"); + + const loadingInfo = { + load_job_id: ingestJobData.load_job_id, + data_source_id: ingestJobData.data_source_id, + file_path: ingestJobData.data_path, + }; + + const ingestResponse = await fetch(`/ui/${ingestGraphName}/ingest`, { + method: "POST", + headers: { + "Content-Type": "application/json", + Authorization: `Basic ${creds}`, + }, + body: JSON.stringify(loadingInfo), + }); + + if (!ingestResponse.ok) { + const errorData = await ingestResponse.json(); + throw new Error(errorData.detail || `Failed to run ingest: ${ingestResponse.statusText}`); + } + + const ingestData = await ingestResponse.json(); + console.log("Ingest response:", ingestData); + + setIngestMessage(`✅ Ingestion completed successfully!`); + setUploadMessage(""); // Clear the "Ready for ingestion" message + + // Clear ingest job data + setIngestJobData(null); + } catch (error: any) { + console.error("Error running ingest:", error); + setIngestMessage(`❌ Error: ${error.message}`); + } finally { + setIsIngesting(false); + } + }; + // Ingest files into knowledge graph (uploaded or downloaded) const handleIngestDocuments = async (sourceType: "uploaded" | "downloaded" = "uploaded") => { if (!ingestGraphName) { @@ -423,9 +515,8 @@ const Setup = () => { return; } - const folderPath = sourceType === "uploaded" - ? `uploads/${ingestGraphName}` - : `downloaded_files_cloud/${ingestGraphName}`; + const folderPath = sourceType === "uploaded" ? `uploads/${ingestGraphName}` : `downloaded_files_cloud/${ingestGraphName}`; + const fileCount = sourceType === "uploaded" ? uploadedFiles.length : downloadedFiles.length; setIsIngesting(true); setIngestMessage("Step 1/2: Creating ingest job..."); @@ -437,7 +528,7 @@ const Setup = () => { const createIngestConfig = { data_source: "server", data_source_config: { - folder_path: folderPath + data_path: folderPath, }, loader_config: {}, file_format: "multi" @@ -458,15 +549,27 @@ const Setup = () => { } const createData = await createResponse.json(); - //console.log("Create ingest response:", createData); + console.log("Create ingest response:", createData); + + // Store ingest job data for later use (store folderPath as source_data_path for temp folder deletion) + setIngestJobData({ + load_job_id: createData.load_job_id, + data_source_id: createData.data_source_id, + data_path: folderPath, // Use the source folderPath, not the backend's "in_temp_storage" + }); - // Step 2: Run ingest + if (!directIngestion) { + // Files are saved to temp storage - show message for review (only if not direct ingestion) + setIngestMessage(`✅ ${fileCount} file(s) ready for ingestion.`); + setIsIngesting(false); + } else { + // Direct ingestion enabled - proceed directly to ingest setIngestMessage("Step 2/2: Running document ingest..."); const loadingInfo = { load_job_id: createData.load_job_id, data_source_id: createData.data_source_id, - file_path: createData.data_path || createData.file_path, // Handle both field names + file_path: createData.data_path || createData.file_path, }; const ingestResponse = await fetch(`/ui/${ingestGraphName}/ingest`, { @@ -484,16 +587,92 @@ const Setup = () => { } const ingestData = await ingestResponse.json(); - //console.log("Ingest response:", ingestData); + console.log("Ingest response:", ingestData); setIngestMessage(`✅ Data ingested successfully! Processed documents from ${folderPath}/`); + setIsIngesting(false); + } } catch (error: any) { console.error("Error ingesting data:", error); setIngestMessage(`❌ Error: ${error.message}`); - } finally { setIsIngesting(false); } }; + // Called automatically after upload or cloud download finishes. + // Creates an ingest job that processes files into JSONL format in temp folder. + const handleCreateIngestAfterUpload = async (sourceType: "uploaded" | "downloaded" = "uploaded", fileCountParam?: number) => { + console.log("handleCreateIngestAfterUpload called with sourceType:", sourceType); + console.log("ingestGraphName:", ingestGraphName); + + if (!ingestGraphName) { + console.log("No graph name, returning early"); + return; + } + + const folderPath = sourceType === "uploaded" ? `uploads/${ingestGraphName}` : `downloaded_files_cloud/${ingestGraphName}`; + // Use passed file count or fallback to state arrays + const fileCount = fileCountParam || (sourceType === "uploaded" ? uploadedFiles.length : downloadedFiles.length); + + console.log("folderPath:", folderPath); + console.log("fileCount:", fileCount); + + try { + const creds = localStorage.getItem("creds"); + + // Call create_ingest to process files + const createIngestConfig = { + data_source: "server", + data_source_config: { + data_path: folderPath, + }, + loader_config: {}, + file_format: "multi", + }; + + console.log("Calling create_ingest with config:", createIngestConfig); + + const createResponse = await fetch(`/ui/${ingestGraphName}/create_ingest`, { + method: "POST", + headers: { + "Content-Type": "application/json", + Authorization: `Basic ${creds}`, + }, + body: JSON.stringify(createIngestConfig), + }); + + console.log("create_ingest response status:", createResponse.status); + + if (!createResponse.ok) { + const errorData = await createResponse.json(); + console.error("create_ingest error:", errorData); + throw new Error(errorData.detail || `Failed to create ingest job: ${createResponse.statusText}`); + } + + const createData = await createResponse.json(); + console.log("create_ingest response data:", createData); + + // Save ingest job data for later (store folderPath as data_path for temp folder deletion) + setIngestJobData({ + load_job_id: createData.load_job_id, + data_source_id: createData.data_source_id, + data_path: folderPath, // Use the source folderPath, not the backend's "in_temp_storage" + }); + + console.log("Direct ingestion enabled:", directIngestion); + + if (directIngestion) { + // Direct ingestion - proceed to ingest immediately + setUploadMessage("Running direct ingestion..."); + await handleRunIngest(); + } else { + // Save for later - files ready for ingestion + setUploadMessage(`✅ ${fileCount} file(s) ready for ingestion.`); + } + } catch (error: any) { + console.error("Error in create_ingest:", error); + setUploadMessage(`❌ Processing error: ${error.message}`); + } + }; // Ingest files from S3 with Amazon BDA const handleAmazonBDAIngest = async () => { @@ -1221,43 +1400,16 @@ const Setup = () => { )} - {/* Ingest Data Section */} - {uploadedFiles.length > 0 && ( -
-

- Ingest Documents into Knowledge Graph -

-

- Process uploaded files and add them to the knowledge graph -

- - {ingestMessage && ( -
- {ingestMessage} -
- )} + {/* Ingestion Status Message */} + {ingestMessage && ( +
+ {ingestMessage}
)} @@ -1289,6 +1441,35 @@ const Setup = () => {
)} + + {/* Ingest Data Section */} + {uploadedFiles.length > 0 && ingestJobData && ( +
+

+ Ingest Documents into Knowledge Graph +

+

+ Process uploaded files and add them to the knowledge graph +

+ +
+ )} @@ -1566,7 +1747,7 @@ const Setup = () => { )} {/* Ingest Downloaded Data Section */} - {downloadedFiles.length > 0 && ( + {downloadedFiles.length > 0 && ingestJobData && (

Ingest Documents into Knowledge Graph @@ -1575,8 +1756,8 @@ const Setup = () => { Process downloaded files and add them to the knowledge graph

- {ingestMessage && ( -
- {ingestMessage} -
- )}

)} diff --git a/graphrag/app/routers/ui.py b/graphrag/app/routers/ui.py index 9637347..7f11934 100644 --- a/graphrag/app/routers/ui.py +++ b/graphrag/app/routers/ui.py @@ -61,7 +61,7 @@ ResponseType, Role, ) - +from common.utils.text_extractors import TextExtractor logger = logging.getLogger(__name__) use_cypher = os.getenv("USE_CYPHER", "false").lower() == "true" @@ -1010,7 +1010,23 @@ async def clear_uploaded_files( deleted_files = [] if filename: - # Delete specific file + # Delete corresponding JSONL file from temp folder FIRST + temp_folder = os.path.join("uploads", "ingestion_temp", graphname) + if os.path.exists(temp_folder): + from pathlib import Path + file_stem = Path(filename).stem + jsonl_file = os.path.join(temp_folder, f"{file_stem}.jsonl") + + if os.path.exists(jsonl_file): + os.remove(jsonl_file) + logger.info(f"Deleted corresponding JSONL file: {file_stem}.jsonl") + + # If temp folder is now empty, remove it + if not os.listdir(temp_folder): + os.rmdir(temp_folder) + logger.info(f"Removed empty temp folder for graph {graphname}") + + # Then delete the raw file file_path = os.path.join(upload_dir, filename) if os.path.exists(file_path) and os.path.isfile(file_path): os.remove(file_path) @@ -1030,6 +1046,13 @@ async def clear_uploaded_files( if not os.listdir(upload_dir): os.rmdir(upload_dir) + # Also delete the entire temp folder for this graph + temp_folder = os.path.join("uploads", "ingestion_temp", graphname) + if os.path.exists(temp_folder): + import shutil + shutil.rmtree(temp_folder) + logger.info(f"Deleted temp folder for graph {graphname}") + logger.info(f"Deleted {len(deleted_files)} file(s) for graph {graphname}") return { @@ -1342,7 +1365,23 @@ async def delete_cloud_downloads( deleted_files = [] if filename: - # Delete specific file + # Delete corresponding JSONL file from temp folder FIRST + temp_folder = os.path.join("downloaded_files_cloud", "ingestion_temp", graphname) + if os.path.exists(temp_folder): + from pathlib import Path + file_stem = Path(filename).stem + jsonl_file = os.path.join(temp_folder, f"{file_stem}.jsonl") + + if os.path.exists(jsonl_file): + os.remove(jsonl_file) + logger.info(f"Deleted corresponding JSONL file: {file_stem}.jsonl") + + # If temp folder is now empty, remove it + if not os.listdir(temp_folder): + os.rmdir(temp_folder) + logger.info(f"Removed empty temp folder for graph {graphname}") + + # Then delete the raw file file_path = os.path.join(download_dir, filename) if os.path.exists(file_path) and os.path.isfile(file_path): os.remove(file_path) @@ -1362,6 +1401,13 @@ async def delete_cloud_downloads( if not os.listdir(download_dir): os.rmdir(download_dir) + # Also delete the entire temp folder for this graph + temp_folder = os.path.join("downloaded_files_cloud", "ingestion_temp", graphname) + if os.path.exists(temp_folder): + import shutil + shutil.rmtree(temp_folder) + logger.info(f"Deleted temp folder for graph {graphname}") + logger.info(f"Deleted {len(deleted_files)} cloud download(s) for graph {graphname}") return { @@ -1377,5 +1423,4 @@ async def delete_cloud_downloads( exc = traceback.format_exc() logger.error(f"Error deleting cloud downloads for graph {graphname}: {e}") logger.debug_pii(f"Delete error trace:\n{exc}") - raise HTTPException(status_code=500, detail=f"Error deleting files: {str(e)}") - + raise HTTPException(status_code=500, detail=f"Error deleting files: {str(e)}") \ No newline at end of file diff --git a/graphrag/app/supportai/supportai.py b/graphrag/app/supportai/supportai.py index d2efe8a..69727f4 100644 --- a/graphrag/app/supportai/supportai.py +++ b/graphrag/app/supportai/supportai.py @@ -337,9 +337,9 @@ def create_ingest( conn: TigerGraphConnection, ): # Check for invalid combination of multi format and non-s3 data source - if ingest_config.data_source.lower() in ["bda", "server"] and ingest_config.get("file_format", "").lower() != "multi": - logger.warning(f"File format {ingest_config.get('file_format', '').lower()} is not supported for data source {ingest_config.data_source.lower()}") - ingest_config["file_format"] = "multi" + if ingest_config.data_source.lower() in ["bda", "server"] and getattr(ingest_config, "file_format", "").lower() != "multi": + logger.warning(f"File format {getattr(ingest_config, 'file_format', '')} is not supported for data source {ingest_config.data_source.lower()}") + ingest_config.file_format = "multi" res_ingest_config = {"data_source": ingest_config.data_source.lower()} res_ingest_config["file_format"] = ingest_config.file_format.lower() @@ -485,18 +485,28 @@ def create_ingest( if data_path is None: raise Exception("Data path not provided for server processing") try: + #create temp folder + base_dir = os.path.dirname(data_path) + temp_folder = os.path.join(base_dir, "ingestion_temp", graphname) + + # Process files and save immediately to temp folder (memory efficient) extractor = TextExtractor() - server_processing_result = extractor.process_folder(data_path, graphname=graphname) + server_processing_result = extractor.process_folder( + data_path, + graphname=graphname, + temp_folder=temp_folder # Extractor saves files as it processes + ) if server_processing_result.get("statusCode") != 200: raise Exception(f"Server folder processing failed: {server_processing_result}") - else: - logger.info(f"Server folder processing completed successfully: {server_processing_result}") + + doc_count = server_processing_result.get("num_documents", 0) + logger.info(f"Server folder processing completed: {server_processing_result.get('message')}") - res_ingest_config["server_jobs"] = server_processing_result.get("documents", []) + res_ingest_config["data_path"] = temp_folder + res_ingest_config["file_count"] = doc_count res_ingest_config["data_source_id"] = "DocumentContent" - # Use a placeholder path that doesn't start with "/" to avoid pyTigerGraph treating it as a file - # The actual folder path is stored in server_jobs, this is just for the API call - res["data_path"] = "in_response" + # Use a placeholder path to indicate temp storage + res["data_path"] = "in_temp_storage" res["data_source_id"] = res_ingest_config except Exception as e: raise Exception(f"Error during server folder processing: {e}") @@ -648,42 +658,67 @@ def ingest( } elif ingest_config.get("data_source") == "server": try: - processed_files = [] data_source_id = ingest_config.get("data_source_id", "DocumentContent") - if ingest_config.get("server_jobs"): - for doc_data in ingest_config.get("server_jobs"): - if not doc_data.get("doc_id") or not doc_data.get("content"): - continue - if doc_data.get("image_data"): - payload = { - "doc_id": doc_data.get("doc_id", ""), - "doc_type": "image", - "image_data": doc_data.get("image_data", ""), - "image_format": doc_data.get("image_format", "jpg"), - "parent_doc": doc_data.get("parent_doc", ""), - "page_number": doc_data.get("page_number", 0), - "position": doc_data.get("position", 0), - "content": "" - } - else: - payload = { - "doc_id": doc_data.get("doc_id", ""), - "doc_type": doc_data.get("doc_type", "markdown"), - "content": doc_data.get("content", "") - } - payload_json = json.dumps(payload) - conn.runLoadingJobWithData(payload_json, data_source_id, loader_info.load_job_id) - processed_files.append({ - 'file_path': doc_data.get("doc_id", ""), - 'parent_doc': doc_data.get("parent_doc", ""), + + # Read from temporary folder containing JSONL files (one per input file) + data_path = ingest_config.get("data_path") + if not data_path or not os.path.exists(data_path): + raise Exception(f"Data path not found: {data_path}") + + # Get all JSONL files from temp folder + jsonl_files = [f for f in os.listdir(data_path) if f.endswith('.jsonl')] + + if not jsonl_files: + raise Exception(f"No JSONL files found in: {data_path}") + + logger.info(f"Found {len(jsonl_files)} JSONL files to ingest from: {data_path}") + + total_doc_count = 0 + ingested_files = [] + + # Process each JSONL file separately + for jsonl_filename in jsonl_files: + jsonl_file = os.path.join(data_path, jsonl_filename) + logger.info(f"Processing JSONL file: {jsonl_filename}") + + try: + # Load documents directly from file - more memory efficient + conn.runLoadingJobWithFile(jsonl_file, data_source_id, loader_info.load_job_id) + + # Count documents for reporting + with open(jsonl_file, 'r', encoding='utf-8') as f: + doc_count = sum(1 for line in f if line.strip()) + total_doc_count += doc_count + + ingested_files.append({ + 'jsonl_file': jsonl_filename, + 'document_count': doc_count, + 'status': 'success' }) - logger.info(f"Data uploading done for doc_id: {doc_data.get('doc_id', 'unknown')}") + + logger.info(f"Successfully ingested {doc_count} documents from {jsonl_filename}") + + except Exception as file_error: + logger.error(f"Failed to ingest {jsonl_filename}: {file_error}") + ingested_files.append({ + 'jsonl_file': jsonl_filename, + 'status': 'failed', + 'error': str(file_error) + }) + + # Keep temp files for potential re-ingestion (faster, no need to re-process PDFs/images) + # Files will be cleaned up when user deletes source files via delete endpoints + logger.info(f"Ingestion complete. Temp files preserved at: {data_path}") + except Exception as e: raise Exception(f"Error during server markdown extraction and TigerGraph loading: {e}") return { "job_name": loader_info.load_job_id, - "summary": processed_files + "summary": f"Successfully ingested {total_doc_count} documents from {len(jsonl_files)} JSONL files", + "document_count": total_doc_count, + "ingested_files": ingested_files } + else: raise Exception("Data source and file format combination not implemented") else: diff --git a/licenses/pymupdf4llm-AGPL-3.0.txt b/licenses/pymupdf4llm-AGPL-3.0.txt new file mode 100644 index 0000000..0ad25db --- /dev/null +++ b/licenses/pymupdf4llm-AGPL-3.0.txt @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +our General Public Licenses are intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. + + When we speak of free software, we are referring to freedom, not +price. Our General Public Licenses are designed to make sure that you +have the freedom to distribute copies of free software (and charge for +them if you wish), that you receive source code or can get it if you +want it, that you can change the software or use pieces of it in new +free programs, and that you know you can do these things. + + Developers that use our General Public Licenses protect your rights +with two steps: (1) assert copyright on the software, and (2) offer +you this License which gives you legal permission to copy, distribute +and/or modify the software. + + A secondary benefit of defending all users' freedom is that +improvements made in alternate versions of the program, if they +receive widespread use, become available for other developers to +incorporate. Many developers of free software are heartened and +encouraged by the resulting cooperation. 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