diff --git a/common/requirements.txt b/common/requirements.txt index 562c2f6..3bbd096 100644 --- a/common/requirements.txt +++ b/common/requirements.txt @@ -108,9 +108,10 @@ ordered-set==4.1.0 orjson==3.10.18 packaging==24.2 pandas==2.2.3 -#pathtools==0.1.2 +pathtools==0.1.2 pillow==11.2.1 -PyMuPDF==1.26.4 +#PyMuPDF==1.26.4 +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..74e8d2f 100644 --- a/common/utils/image_data_extractor.py +++ b/common/utils/image_data_extractor.py @@ -11,155 +11,54 @@ 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. " + ), + }, + { + "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..72e3a0c 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 +17,63 @@ logger = logging.getLogger(__name__) +# Global lock for pymupdf4llm calls (not thread-safe) +_pymupdf4llm_lock = threading.Lock() + +# Regex for markdown images: ![alt](path) +_markdown_image_pattern = re.compile(r'!\[([^\]]*)\]\(([^)\s]+)\)') + + +def extract_markdown_images(md_text): + """ + Extract markdown image entries from text. + Returns list of {"path": path, "image_id": image_id} + image_id = basename without extension + """ + images = [] + for m in _markdown_image_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_image_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: + # Insert new description + return f'![{description}]({old_path})' + + return m.group(0) + + return _markdown_image_pattern.sub(repl, md_text) + + +def replace_image_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: + # Replace path with tg:// protocol reference + alt_text = m.group(1) + return f'![{alt_text}](tg://{tg_reference})' + + return m.group(0) + + return _markdown_image_pattern.sub(repl, md_text) + class TextExtractor: """Class for handling text extraction from various file formats and cleanup.""" @@ -183,137 +242,166 @@ 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_markdown_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_image_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_image_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 +412,15 @@ 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 = describe_image_with_llm(str(Path(file_path).absolute())) 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 - }] + return [] buffer = io.BytesIO() if pil_image.mode != 'RGB': @@ -353,7 +431,6 @@ 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 +456,7 @@ 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 +518,10 @@ 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 +532,4 @@ 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..2aaee99 100644 --- a/graphrag-ui/src/pages/Setup.tsx +++ b/graphrag-ui/src/pages/Setup.tsx @@ -2,7 +2,7 @@ import React, { useState, useEffect } from "react"; import { useNavigate } from "react-router-dom"; import { Button } from "@/components/ui/button"; import { Input } from "@/components/ui/input"; -import { Database, Upload, RefreshCw, Loader2, Trash2, FolderUp, Cloud, ArrowLeft, CloudDownload, CloudLightning } from "lucide-react"; +import { Database, Upload, RefreshCw, Loader2, Trash2, FolderUp, Cloud, ArrowLeft, CloudDownload, CloudCog } from "lucide-react"; import { Dialog, DialogContent, @@ -56,7 +56,6 @@ const Setup = () => { const [uploadMessage, setUploadMessage] = useState(""); const [isIngesting, setIsIngesting] = useState(false); const [ingestMessage, setIngestMessage] = useState(""); - const [activeTab, setActiveTab] = useState("upload"); // Refresh state const [refreshOpen, setRefreshOpen] = useState(false); @@ -67,12 +66,13 @@ const Setup = () => { const [isCheckingStatus, setIsCheckingStatus] = useState(false); // S3 state + const [fileFormat, setFileFormat] = useState<"json" | "multi">("json"); const [awsAccessKey, setAwsAccessKey] = useState(""); const [awsSecretKey, setAwsSecretKey] = useState(""); + const [dataPath, setDataPath] = useState(""); const [inputBucket, setInputBucket] = useState(""); const [outputBucket, setOutputBucket] = useState(""); const [regionName, setRegionName] = useState(""); - const [skipBDAProcessing, setSkipBDAProcessing] = useState(false); // Cloud Download state const [cloudProvider, setCloudProvider] = useState<"s3" | "gcs" | "azure">("s3"); @@ -458,7 +458,7 @@ const Setup = () => { } const createData = await createResponse.json(); - //console.log("Create ingest response:", createData); + console.log("Create ingest response:", createData); // Step 2: Run ingest setIngestMessage("Step 2/2: Running document ingest..."); @@ -484,7 +484,7 @@ const Setup = () => { } const ingestData = await ingestResponse.json(); - //console.log("Ingest response:", ingestData); + console.log("Ingest response:", ingestData); setIngestMessage(`✅ Data ingested successfully! Processed documents from ${folderPath}/`); } catch (error: any) { @@ -495,8 +495,8 @@ const Setup = () => { } }; - // Ingest files from S3 with Amazon BDA - const handleAmazonBDAIngest = async () => { + // Ingest files from S3 with Bedrock BDA + const handleS3BedrockIngest = async () => { if (!ingestGraphName) { setIngestMessage("Please select a graph"); return; @@ -508,112 +508,92 @@ const Setup = () => { return; } - if (skipBDAProcessing) { - // When skipping BDA, only output bucket and region are required - if (!outputBucket || !regionName) { - setIngestMessage("❌ Please provide Output Bucket and Region Name"); - return; - } - } else { - // When using BDA, all fields are required + if (fileFormat === "multi") { if (!inputBucket || !outputBucket || !regionName) { setIngestMessage("❌ Please provide Input Bucket, Output Bucket, and Region Name"); return; } - } - // Ask for confirmation - const confirmMessage = skipBDAProcessing - ? `You're skipping Amazon BDA processing and will ingest directly from the output bucket (${outputBucket}). Please confirm to proceed.` - : `You're using Amazon BDA for multimodal document processing. This will trigger Amazon BDA to process your documents from the input bucket (${inputBucket}) and store the results in the output bucket (${outputBucket}) and then ingest them into your knowledge graph. Please confirm to proceed.`; - - const shouldProceed = await confirm(confirmMessage); - if (!shouldProceed) { - setIngestMessage("Operation cancelled by user."); - return; + // Ask for confirmation if using Bedrock (multi format) + const shouldProceed = await confirm( + `Are you using AWS Bedrock for multimodal document processing? This will trigger AWS Bedrock BDA to process your documents from the input bucket (${inputBucket}) and store the results in the output bucket (${outputBucket}).` + ); + if (!shouldProceed) { + setIngestMessage("Operation cancelled by user."); + return; + } + } else if (fileFormat === "json") { + if (!dataPath) { + setIngestMessage("❌ Please provide Data Path (e.g., s3://bucket-name/path/to/data)"); + return; + } } setIsIngesting(true); + setIngestMessage("Step 1/2: Creating ingest job..."); try { const creds = localStorage.getItem("creds"); - let loadingInfo: any = {}; - if (skipBDAProcessing) { - // Skip BDA processing - create ingest job that reads directly from output bucket - const runIngestConfig: any = { - data_source: "bda", + // Step 1: Create ingest job + const createIngestConfig: any = { + data_source: "s3", + data_source_config: { aws_access_key: awsAccessKey, aws_secret_key: awsSecretKey, - output_bucket: outputBucket, - region_name: regionName, - bda_jobs:[], - loader_config: { - doc_id_field: "doc_id", - content_field: "content", - doc_type: "markdown", - }, - file_format: "multi" - }; - - setIngestMessage("Step 1/2: Creating ingest job from output bucket..."); - - // Run ingest directly - loadingInfo = { - load_job_id: "load_documents_content_json", - data_source_id: runIngestConfig, - file_path: outputBucket, - }; - setIngestMessage(`Step 2/2: Running document ingestion for all files in ${outputBucket}...`); - } else { - // Step 1: Create ingest job with BDA processing - const createIngestConfig: any = { - data_source: "bda", - data_source_config: { - aws_access_key: awsAccessKey, - aws_secret_key: awsSecretKey, - input_bucket: inputBucket, - output_bucket: outputBucket, - region_name: regionName, - }, - loader_config: { - doc_id_field: "doc_id", - content_field: "content", - doc_type: "markdown", - }, - file_format: "multi" - }; + }, + loader_config: { + doc_id_field: "doc_id", + content_field: "content", + doc_type: fileFormat === "multi" ? "markdown" : "", + }, + file_format: fileFormat + }; - setIngestMessage("Step 1/2: Triggering Amazon BDA processing and creating ingest job..."); + // Add format-specific configuration + if (fileFormat === "multi") { + createIngestConfig.data_source_config.input_bucket = inputBucket; + createIngestConfig.data_source_config.output_bucket = outputBucket; + createIngestConfig.data_source_config.region_name = regionName; + setIngestMessage("Step 1/2: Creating ingest job and triggering AWS Bedrock BDA processing..."); + } else if (fileFormat === "json") { + createIngestConfig.loader_config.doc_id_field = "url"; + } - const createResponse = await fetch(`/ui/${ingestGraphName}/create_ingest`, { - method: "POST", - headers: { - "Content-Type": "application/json", - Authorization: `Basic ${creds}`, - }, - body: JSON.stringify(createIngestConfig), - }); + const createResponse = await fetch(`/ui/${ingestGraphName}/create_ingest`, { + method: "POST", + headers: { + "Content-Type": "application/json", + Authorization: `Basic ${creds}`, + }, + body: JSON.stringify(createIngestConfig), + }); - if (!createResponse.ok) { - const errorData = await createResponse.json(); - throw new Error(errorData.detail || `Failed to create ingest job: ${createResponse.statusText}`); - } + if (!createResponse.ok) { + const errorData = await createResponse.json(); + throw new Error(errorData.detail || `Failed to create ingest job: ${createResponse.statusText}`); + } - const createData = await createResponse.json(); - //console.log("Create ingest response:", createData); + const createData = await createResponse.json(); + console.log("Create ingest response:", createData); - // Step 2: Run ingest - loadingInfo = { - load_job_id: createData.load_job_id, - data_source_id: createData.data_source_id, - file_path: outputBucket, - }; + // Step 2: Run ingest + setIngestMessage("Step 2/2: Running document ingest..."); - const filesToIngest = createData.data_source_id.bda_jobs.map((job: any) => job.jobId.split("/")[-1]); - setIngestMessage(`Step 2/2: Running document ingest for ${filesToIngest.length} files in ${outputBucket}...`); + // Determine file path based on format + let filePath = ""; + if (fileFormat === "multi") { + filePath = outputBucket; // For multi format, use output bucket + } else if (fileFormat === "json") { + filePath = dataPath; // For json format, use the provided data path } + const loadingInfo = { + load_job_id: createData.load_job_id, + data_source_id: createData.data_source_id, + file_path: filePath, + }; + const ingestResponse = await fetch(`/ui/${ingestGraphName}/ingest`, { method: "POST", headers: { @@ -629,13 +609,15 @@ const Setup = () => { } const ingestData = await ingestResponse.json(); - //console.log("Ingest response:", ingestData); - const filesIngested = ingestData.summary.map((file: any) => file.file_path); - - setIngestMessage(`✅ Document ingestion completed successfully! Ingested ${filesIngested.length} into your knowledge graph.`); + console.log("Ingest response:", ingestData); + if (fileFormat === "multi") { + setIngestMessage(`✅ Data ingested successfully! AWS Bedrock BDA processed documents from ${inputBucket} and loaded results from ${outputBucket}.`); + } else { + setIngestMessage(`✅ Data ingested successfully! Processed documents from ${dataPath}.`); + } } catch (error: any) { - console.error("Error ingesting files:", error); + console.error("Error ingesting S3 data:", error); setIngestMessage(`❌ Error: ${error.message}`); } finally { setIsIngesting(false); @@ -1121,8 +1103,8 @@ const Setup = () => { - + @@ -1139,35 +1121,32 @@ const Setup = () => { )} + {ingestGraphName && ( +

+ Files will be uploaded to: uploads/{ingestGraphName}/ +

+ )} - { - // Block tab switching when ingesting - if (!isIngesting) { - setActiveTab(value); - } - }} className="w-full"> + - + Upload Files - + Download from Cloud - - - Use Amazon BDA + + + Amazon BDA Configuration {/* Upload Data Tab */}
-

- Upload local files to the server and ingest them into your knowledge graph. -

@@ -1295,9 +1274,6 @@ const Setup = () => { {/* Download from Cloud Storage Tab */}
-

- Download files from cloud storage and ingest them into your knowledge graph. -

)} - {ingestGraphName && ( -

- Download destination: downloaded_files_cloud/{ingestGraphName}/ -

- )}
+

+ Files will be downloaded to: downloaded_files_cloud/{ingestGraphName}/ +

- {/* Amazon BDA Configuration Tab */} - -
-

- Process multimodal documents stored in S3 with Amazon Bedrock Data Automation and ingest them into your knowledge graph. -

+ {/* S3 Bedrock Configuration Tab */} + +
+
+ + +
{/* Common fields */}
@@ -1625,7 +1610,6 @@ const Setup = () => { onChange={(e) => setAwsAccessKey(e.target.value)} placeholder="Enter AWS access key" className="dark:border-[#3D3D3D] dark:bg-shadeA" - disabled={isIngesting} />
@@ -1639,74 +1623,76 @@ const Setup = () => { onChange={(e) => setAwsSecretKey(e.target.value)} placeholder="Enter AWS secret key" className="dark:border-[#3D3D3D] dark:bg-shadeA" - disabled={isIngesting} />
-
-
- -
- -
- - setOutputBucket(e.target.value)} - placeholder="Enter output bucket name" - className="dark:border-[#3D3D3D] dark:bg-shadeA" - disabled={isIngesting} - /> -
+ ) : ( + <> +
+ + setInputBucket(e.target.value)} + placeholder="Enter input bucket name" + className="dark:border-[#3D3D3D] dark:bg-shadeA" + /> +
-
- - setRegionName(e.target.value)} - placeholder="e.g., us-east-1" - className="dark:border-[#3D3D3D] dark:bg-shadeA" - disabled={isIngesting} - /> -
+
+ + setOutputBucket(e.target.value)} + placeholder="Enter output bucket name" + className="dark:border-[#3D3D3D] dark:bg-shadeA" + /> +
- {ingestGraphName && ( -

- Processing destination: Input bucket ({inputBucket || "not specified"}) → Output bucket ({outputBucket || "not specified"}) → Knowledge graph ({ingestGraphName}) -

+
+ + setRegionName(e.target.value)} + placeholder="e.g., us-east-1" + className="dark:border-[#3D3D3D] dark:bg-shadeA" + /> +
+ )} - {/* Ingest S3 Files with Amazon BDA Section */} + {/* Ingest S3 Bedrock Data Section */}
+

+ Ingest S3 Data into Knowledge Graph +

+

+ Process S3 data and add it to the knowledge graph using AWS Bedrock BDA for multimodal documents +

@@ -1771,7 +1757,7 @@ const Setup = () => { Refresh Knowledge Graph - Rebuild the graph content and rerun community detection for your knowledge graph + Rebuild the graph content of your knowledge graph @@ -1780,8 +1766,8 @@ const Setup = () => { - + @@ -1805,7 +1791,7 @@ const Setup = () => { ⚠️ Warning

- This operation will process new documents and rerun community detection that will interrupt related queries. + This operation will rebuild the graph content that will interrupt related queries. Please confirm to proceed.

diff --git a/graphrag/app/routers/ui.py b/graphrag/app/routers/ui.py index 9637347..114b489 100644 --- a/graphrag/app/routers/ui.py +++ b/graphrag/app/routers/ui.py @@ -395,6 +395,7 @@ async def serve_image_from_vertex( LogWriter.info(f"Serving image {image_id} from graph {graphname}") # Fetch the Image vertex by ID + # TigerGraph loading job uses gsql_lower() so all IDs are stored in lowercase image_vertices = conn.getVerticesById('Image', [image_id.lower()]) if not image_vertices: diff --git a/graphrag/app/supportai/supportai.py b/graphrag/app/supportai/supportai.py index d2efe8a..ea15c5a 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 ingest_config.file_format.lower() != "multi": + logger.warning(f"File format {ingest_config.file_format.lower()} 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() @@ -483,7 +483,7 @@ def create_ingest( elif ingest_config.data_source.lower() == "server": data_path = ingest_config.data_source_config.get("data_path", None) if data_path is None: - raise Exception("Data path not provided for server processing") + raise Exception("Folder path not provided for server processing") try: extractor = TextExtractor() server_processing_result = extractor.process_folder(data_path, graphname=graphname) @@ -652,7 +652,10 @@ def ingest( 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"): + if not doc_data.get("doc_id"): + continue + # Skip documents with neither content nor image_data + if not doc_data.get("content") and not doc_data.get("image_data"): continue if doc_data.get("image_data"): payload = { @@ -660,8 +663,11 @@ def ingest( "doc_type": "image", "image_data": doc_data.get("image_data", ""), "image_format": doc_data.get("image_format", "jpg"), + "image_description": doc_data.get("image_description", ""), "parent_doc": doc_data.get("parent_doc", ""), "page_number": doc_data.get("page_number", 0), + "width": doc_data.get("width", 0), + "height": doc_data.get("height", 0), "position": doc_data.get("position", 0), "content": "" } 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. 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No Surrender of Others' Freedom. + + If conditions are imposed on you (whether by court order, agreement or +otherwise) that contradict the conditions of this License, they do not +excuse you from the conditions of this License. If you cannot convey a +covered work so as to satisfy simultaneously your obligations under this +License and any other pertinent obligations, then as a consequence you may +not convey it at all. For example, if you agree to terms that obligate you +to collect a royalty for further conveying from those to whom you convey +the Program, the only way you could satisfy both those terms and this +License would be to refrain entirely from conveying the Program. + + 13. Remote Network Interaction; Use with the GNU General Public License. + + Notwithstanding any other provision of this License, if you modify the +Program, your modified version must prominently offer all users +interacting with it remotely through a computer network (if your version +supports such interaction) an opportunity to receive the Corresponding +Source of your version by providing access to the Corresponding Source +from a network server at no charge, through some standard or customary +means of facilitating copying of software. This Corresponding Source +shall include the Corresponding Source for any work covered by version 3 +of the GNU General Public License that is incorporated pursuant to the +following paragraph. + + Notwithstanding any other provision of this License, you have +permission to link or combine any covered work with a work licensed +under version 3 of the GNU General Public License into a single +combined work, and to convey the resulting work. The terms of this +License will continue to apply to the part which is the covered work, +but the work with which it is combined will remain governed by version +3 of the GNU General Public License. + + 14. Revised Versions of this License. + + The Free Software Foundation may publish revised and/or new versions of +the GNU Affero General Public License from time to time. Such new versions +will be similar in spirit to the present version, but may differ in detail to +address new problems or concerns. + + Each version is given a distinguishing version number. If the +Program specifies that a certain numbered version of the GNU Affero General +Public License "or any later version" applies to it, you have the +option of following the terms and conditions either of that numbered +version or of any later version published by the Free Software +Foundation. If the Program does not specify a version number of the +GNU Affero General Public License, you may choose any version ever published +by the Free Software Foundation. + + If the Program specifies that a proxy can decide which future +versions of the GNU Affero General Public License can be used, that proxy's +public statement of acceptance of a version permanently authorizes you +to choose that version for the Program. + + Later license versions may give you additional or different +permissions. However, no additional obligations are imposed on any +author or copyright holder as a result of your choosing to follow a +later version. + + 15. Disclaimer of Warranty. + + THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY +APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT +HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY +OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, +THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM +IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF +ALL NECESSARY SERVICING, REPAIR OR CORRECTION. + + 16. Limitation of Liability. + + IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS +THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY +GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE +USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF +DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD +PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), +EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF +SUCH DAMAGES. + + 17. Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU Affero General Public License as published + by the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Affero General Public License for more details. + + You should have received a copy of the GNU Affero General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +.