From 06e5ec0f16dff7502fa7681f36fd6889fe4e783c Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Mon, 17 Nov 2025 17:07:34 +0530 Subject: [PATCH 01/20] Add S3 Bedrock BDA ingestion support with user confirmation and pymupdf4llm integration --- common/requirements.txt | 3 +- common/utils/image_data_extractor.py | 163 +++--------- common/utils/markdown_parsing.py | 63 +++++ common/utils/text_extractors.py | 254 +++++++++--------- graphrag-ui/src/pages/Setup.tsx | 370 +++++++++++++-------------- 5 files changed, 403 insertions(+), 450 deletions(-) create mode 100644 common/utils/markdown_parsing.py diff --git a/common/requirements.txt b/common/requirements.txt index 562c2f6..f0022f3 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==1.26.4 +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/markdown_parsing.py b/common/utils/markdown_parsing.py new file mode 100644 index 0000000..7c8c476 --- /dev/null +++ b/common/utils/markdown_parsing.py @@ -0,0 +1,63 @@ +import re +import os +import pymupdf4llm + +class MarkdownProcessor: + """ + A helper class to extract markdown image entries and + update descriptions based on image_id. + """ + + # regex for markdown images: ![alt](path) + _pattern = re.compile(r'!\[([^\]]*)\]\(([^)\s]+)\)') + + @classmethod + def extract_images(cls, md_text): + """ + Returns list of {"path": path, "image_id": image_id} + image_id = basename without extension + """ + images = [] + for m in cls._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 + + @classmethod + def insert_description_by_id(cls, 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 cls._pattern.sub(repl, md_text) + + @classmethod + def replace_path_with_tg_protocol(cls, 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 cls._pattern.sub(repl, md_text) \ No newline at end of file diff --git a/common/utils/text_extractors.py b/common/utils/text_extractors.py index da3e22d..b900cae 100644 --- a/common/utils/text_extractors.py +++ b/common/utils/text_extractors.py @@ -183,137 +183,154 @@ 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 + from common.utils.markdown_parsing import MarkdownProcessor + + # 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 + try: + markdown_content = pymupdf4llm.to_markdown( + file_path, + write_images=True, + image_path=str(image_output_folder), # unique folder per PDF + force_text=False, + margins=0, + image_size_limit=0.08, + ) + 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 = MarkdownProcessor.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 = MarkdownProcessor.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 = MarkdownProcessor.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 +341,15 @@ 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 +360,6 @@ 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 +385,7 @@ 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 +447,10 @@ 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(): 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.

From 788fe2ac6b39b17c99c7fab82d717be89347697e Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Tue, 18 Nov 2025 18:27:24 +0530 Subject: [PATCH 02/20] Update README for OpenAI and Bedrock config, add pymupdf4llm license --- README.md | 78 ++-- licenses/pymupdf4llm-AGPL-3.0.txt | 661 ++++++++++++++++++++++++++++++ 2 files changed, 704 insertions(+), 35 deletions(-) create mode 100644 licenses/pymupdf4llm-AGPL-3.0.txt diff --git a/README.md b/README.md index 8c38f6c..13c88b3 100644 --- a/README.md +++ b/README.md @@ -103,24 +103,23 @@ Organizing the data as a knowledge graph allows a chatbot to access accurate, fa ### Quick Start #### Use TigerGraph Docker-Based Instance -Set your LLM Provider (supported `openai` or `gemini`) api key as environment varabiel LLM_API_KEY and use the following command for a one-step quick deployment with TigerGraph Community Edition and default configurations: +Set your OpenAI api key as environment varabiel OPENAI_API_KEY and use the following command for a one-step quick deployment with TigerGraph Community Edition and default configurations: ``` -curl -k https://raw.githubusercontent.com/tigergraph/graphrag/refs/heads/main/docs/tutorials/setup_graphrag.sh | bash +curl -k https://raw.githubusercontent.com/tigergraph/graphrag/refs/heads/main/docs/tutorials/setup_graphrag.sh | sh ``` The GraphRAG instances will be deployed at `./graphrag` folder and TigerGraph instance will be available at `http://localhost:14240`. -To change installation folder, use `bash -s -- ` instead of `bash` at the end of the above command. - -> Note: for other LLM providers, manually update `configs/server_config.json` accordingly and re-run `docker compose up -d` +To change installation folder, use `sh -s -- ` instead of `sh` at the end of the above command. #### Use Pre-Installed TigerGraph Instance -Similar to the above setup, and use the following command for a one-step quick deployment connecting to a pre-installed TigerGraph with default configurations: + +Using the following command for a one-step quick deployment with TigerGraph Community Edition and default configurations: ``` -curl -k https://raw.githubusercontent.com/tigergraph/graphrag/refs/heads/main/docs/tutorials/setup_graphrag_tg.sh | bash +curl -k https://raw.githubusercontent.com/tigergraph/graphrag/refs/heads/main/docs/tutorials/setup_graphrag_tg.sh | sh ``` The GraphRAG instances will be deployed at `./graphrag` folder and connect to TigerGraph instance at `http://localhost:14240` by default. -To change installation folder, TigerGraph instance location or username/password, use `bash -s -- ` instead of `bash` at the end of the above command. +To change installation folder, TigerGraph instance location or username/password, use `sh -s -- ` instead of `sh` at the end of the above command. [Go back to top](#top) @@ -152,7 +151,7 @@ Here’s what the folder structure looks like: ##### Step 3: Adjust configurations -Edit `llm_config` section of `configs/server_config.json` and replace `` to your own LLM_API_KEY for the LLM provider. +Edit `llm_config` section of `configs/server_config.json` and replace `` to your own OPENAI_API_KEY. > If desired, you can also change the model to be used for the embedding service and completion service to your preferred models to adjust the output from the LLM service. @@ -470,23 +469,27 @@ In addition to the `OPENAI_API_KEY`, `llm_model` and `model_name` can be edited ```json { "llm_config": { + "authentication_configuration": { + "OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_HERE" + }, "embedding_service": { - "embedding_model_service": "openai", "model_name": "text-embedding-3-small", - "authentication_configuration": { - "OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_HERE" - } + "embedding_model_service": "openai" }, "completion_service": { "llm_service": "openai", "llm_model": "gpt-4.1-mini", - "authentication_configuration": { - "OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_HERE" - }, "model_kwargs": { "temperature": 0 }, "prompt_path": "./common/prompts/openai_gpt4/" + }, + "multimodal_service": { + "llm_service": "openai", + "llm_model": "gpt-4o-mini", + "model_kwargs": { + "temperature": 0 + } } } } @@ -546,7 +549,7 @@ And your JSON config should follow as: "model_kwargs": { "temperature": 0 }, - "prompt_path": "./common/prompts/gcp_vertexai_palm/" + "prompt_path": "./app/prompts/gcp_vertexai_palm/" } } } @@ -583,7 +586,7 @@ In addition to the `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `azure_d "model_kwargs": { "temperature": 0 }, - "prompt_path": "./common/prompts/azure_open_ai_gpt35_turbo_instruct/" + "prompt_path": "./app/prompts/azure_open_ai_gpt35_turbo_instruct/" } } } @@ -594,27 +597,32 @@ In addition to the `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `azure_d ```json { "llm_config": { + "authentication_configuration": { + "AWS_ACCESS_KEY_ID": "YOUR_AWS_ACCESS_KEY", + "AWS_SECRET_ACCESS_KEY": "YOUR_AWS_SECRET_KEY", + "AWS_REGION_NAME": "us-west-2" + }, "embedding_service": { + "model_name": "amazon.titan-embed-text-v1", "embedding_model_service": "bedrock", - "model_name":"amazon.titan-embed-text-v2", - "region_name":"us-west-2", - "authentication_configuration": { - "AWS_ACCESS_KEY_ID": "ACCESS_KEY", - "AWS_SECRET_ACCESS_KEY": "SECRET" - } + "dimensions": 1536 }, "completion_service": { "llm_service": "bedrock", - "llm_model": "us.anthropic.claude-3-7-sonnet-20250219-v1:0", - "region_name":"us-west-2", - "authentication_configuration": { - "AWS_ACCESS_KEY_ID": "ACCESS_KEY", - "AWS_SECRET_ACCESS_KEY": "SECRET" - }, + "llm_model": "anthropic.claude-3-5-sonnet-20240620-v1:0", "model_kwargs": { "temperature": 0, + "max_tokens": 4096 }, - "prompt_path": "./common/prompts/aws_bedrock_claude3haiku/" + "prompt_path": "./common/prompts/openai_gpt4/" + }, + "multimodal_service": { + "llm_service": "bedrock", + "llm_model": "anthropic.claude-3-5-sonnet-20240620-v1:0", + "model_kwargs": { + "temperature": 0, + "max_tokens": 4096 + } } } } @@ -640,7 +648,7 @@ In addition to the `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `azure_d "model_kwargs": { "temperature": 0.0000001 }, - "prompt_path": "./common/prompts/openai_gpt4/" + "prompt_path": "./app/prompts/openai_gpt4/" } } } @@ -670,7 +678,7 @@ Example configuration for a model on Hugging Face with a dedicated endpoint is s "model_kwargs": { "temperature": 0.1 }, - "prompt_path": "./common/prompts/openai_gpt4/" + "prompt_path": "./app/prompts/openai_gpt4/" } } } @@ -697,7 +705,7 @@ Example configuration for a model on Hugging Face with a serverless endpoint is "model_kwargs": { "temperature": 0.1 }, - "prompt_path": "./common/prompts/llama_70b/" + "prompt_path": "./app/prompts/llama_70b/" } } } @@ -724,7 +732,7 @@ Example configuration for a model on Hugging Face with a serverless endpoint is "model_kwargs": { "temperature": 0.1 }, - "prompt_path": "./common/prompts/openai_gpt4/" + "prompt_path": "./app/prompts/openai_gpt4/" } } } 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. 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From 58a86d11200bdd0f469fbcada816f83f18e04030 Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Tue, 18 Nov 2025 18:32:55 +0530 Subject: [PATCH 03/20] Update README for OpenAI and Bedrock config, add pymupdf4llm license --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 13c88b3..9469ad6 100644 --- a/README.md +++ b/README.md @@ -482,7 +482,7 @@ In addition to the `OPENAI_API_KEY`, `llm_model` and `model_name` can be edited "model_kwargs": { "temperature": 0 }, - "prompt_path": "./common/prompts/openai_gpt4/" + "prompt_path": "./app/prompts/openai_gpt4/" }, "multimodal_service": { "llm_service": "openai", @@ -614,7 +614,7 @@ In addition to the `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `azure_d "temperature": 0, "max_tokens": 4096 }, - "prompt_path": "./common/prompts/openai_gpt4/" + "prompt_path": "./app/prompts/aws_bedrock_claude3haiku/" }, "multimodal_service": { "llm_service": "bedrock", From c20aff8fc3f48a39778d17beb023c1cf0f9e0acb Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Tue, 18 Nov 2025 22:55:22 +0530 Subject: [PATCH 04/20] Fix prompt_path to use ./common/prompts/ for OpenAI and Bedrock --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9469ad6..13c88b3 100644 --- a/README.md +++ b/README.md @@ -482,7 +482,7 @@ In addition to the `OPENAI_API_KEY`, `llm_model` and `model_name` can be edited "model_kwargs": { "temperature": 0 }, - "prompt_path": "./app/prompts/openai_gpt4/" + "prompt_path": "./common/prompts/openai_gpt4/" }, "multimodal_service": { "llm_service": "openai", @@ -614,7 +614,7 @@ In addition to the `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `azure_d "temperature": 0, "max_tokens": 4096 }, - "prompt_path": "./app/prompts/aws_bedrock_claude3haiku/" + "prompt_path": "./common/prompts/openai_gpt4/" }, "multimodal_service": { "llm_service": "bedrock", From 5a0f87cd9638f78b8c52f9ae1e9c8c5e6fde60fe Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Fri, 21 Nov 2025 20:57:53 +0530 Subject: [PATCH 05/20] bug fixes --- graphrag/app/routers/ui.py | 1 + graphrag/app/supportai/supportai.py | 18 ++++++++++++------ 2 files changed, 13 insertions(+), 6 deletions(-) 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..6b93df0 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() @@ -481,9 +481,9 @@ def create_ingest( except Exception as e: raise Exception(f"Error during Amazon BDA preprocessing: {e}") elif ingest_config.data_source.lower() == "server": - data_path = ingest_config.data_source_config.get("data_path", None) + data_path = ingest_config.data_source_config.get("folder_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": "" } From 3bfe5c1ddf10eb650e6fd9729703023aba0a21d4 Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Mon, 24 Nov 2025 16:01:07 +0530 Subject: [PATCH 06/20] Fix PDF extractions --- common/utils/text_extractors.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/utils/text_extractors.py b/common/utils/text_extractors.py index b900cae..21dc2ff 100644 --- a/common/utils/text_extractors.py +++ b/common/utils/text_extractors.py @@ -207,9 +207,9 @@ def _extract_pdf_with_images_as_docs(file_path, base_doc_id, graphname=None): file_path, write_images=True, image_path=str(image_output_folder), # unique folder per PDF - force_text=False, margins=0, image_size_limit=0.08, + table_strategy="lines" ) except Exception as e: logger.error(f"pymupdf4llm failed for {file_path}: {e}") From a660bb72999a55f449c09f35278060bc14217571 Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Mon, 24 Nov 2025 20:03:54 +0530 Subject: [PATCH 07/20] Fix PDF extraction threading issue: add lock for pymupdf4llm (not thread-safe) --- common/utils/text_extractors.py | 59 +++++++++++++++++++++------------ 1 file changed, 38 insertions(+), 21 deletions(-) diff --git a/common/utils/text_extractors.py b/common/utils/text_extractors.py index 21dc2ff..ec5b140 100644 --- a/common/utils/text_extractors.py +++ b/common/utils/text_extractors.py @@ -8,6 +8,7 @@ import uuid import base64 import io +import threading from pathlib import Path import shutil import asyncio @@ -15,6 +16,9 @@ logger = logging.getLogger(__name__) +# Global lock for pymupdf4llm calls (not thread-safe) +_pymupdf4llm_lock = threading.Lock() + class TextExtractor: """Class for handling text extraction from various file formats and cleanup.""" @@ -202,26 +206,39 @@ def _extract_pdf_with_images_as_docs(file_path, base_doc_id, graphname=None): shutil.rmtree(image_output_folder, ignore_errors=True) # Convert PDF to markdown with extracted image files - 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 - }] + # 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}") @@ -461,4 +478,4 @@ 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 From 27438590a99c916b9275fc329d54519517ee56f3 Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Mon, 17 Nov 2025 17:07:34 +0530 Subject: [PATCH 08/20] Add S3 Bedrock BDA ingestion support with user confirmation and pymupdf4llm integration --- common/requirements.txt | 3 +- common/utils/image_data_extractor.py | 163 +++--------- common/utils/markdown_parsing.py | 63 +++++ common/utils/text_extractors.py | 254 +++++++++--------- graphrag-ui/src/pages/Setup.tsx | 370 +++++++++++++-------------- 5 files changed, 403 insertions(+), 450 deletions(-) create mode 100644 common/utils/markdown_parsing.py diff --git a/common/requirements.txt b/common/requirements.txt index 562c2f6..f0022f3 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==1.26.4 +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/markdown_parsing.py b/common/utils/markdown_parsing.py new file mode 100644 index 0000000..7c8c476 --- /dev/null +++ b/common/utils/markdown_parsing.py @@ -0,0 +1,63 @@ +import re +import os +import pymupdf4llm + +class MarkdownProcessor: + """ + A helper class to extract markdown image entries and + update descriptions based on image_id. + """ + + # regex for markdown images: ![alt](path) + _pattern = re.compile(r'!\[([^\]]*)\]\(([^)\s]+)\)') + + @classmethod + def extract_images(cls, md_text): + """ + Returns list of {"path": path, "image_id": image_id} + image_id = basename without extension + """ + images = [] + for m in cls._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 + + @classmethod + def insert_description_by_id(cls, 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 cls._pattern.sub(repl, md_text) + + @classmethod + def replace_path_with_tg_protocol(cls, 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 cls._pattern.sub(repl, md_text) \ No newline at end of file diff --git a/common/utils/text_extractors.py b/common/utils/text_extractors.py index da3e22d..b900cae 100644 --- a/common/utils/text_extractors.py +++ b/common/utils/text_extractors.py @@ -183,137 +183,154 @@ 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 + from common.utils.markdown_parsing import MarkdownProcessor + + # 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 + try: + markdown_content = pymupdf4llm.to_markdown( + file_path, + write_images=True, + image_path=str(image_output_folder), # unique folder per PDF + force_text=False, + margins=0, + image_size_limit=0.08, + ) + 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 = MarkdownProcessor.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 = MarkdownProcessor.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 = MarkdownProcessor.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 +341,15 @@ 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 +360,6 @@ 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 +385,7 @@ 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 +447,10 @@ 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(): 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.

From ddae372fcf764f40f1a051b5a3d6f67c83a2a874 Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Tue, 18 Nov 2025 18:27:24 +0530 Subject: [PATCH 09/20] Update README for OpenAI and Bedrock config, add pymupdf4llm license --- README.md | 78 ++-- licenses/pymupdf4llm-AGPL-3.0.txt | 661 ++++++++++++++++++++++++++++++ 2 files changed, 704 insertions(+), 35 deletions(-) create mode 100644 licenses/pymupdf4llm-AGPL-3.0.txt diff --git a/README.md b/README.md index 8c38f6c..13c88b3 100644 --- a/README.md +++ b/README.md @@ -103,24 +103,23 @@ Organizing the data as a knowledge graph allows a chatbot to access accurate, fa ### Quick Start #### Use TigerGraph Docker-Based Instance -Set your LLM Provider (supported `openai` or `gemini`) api key as environment varabiel LLM_API_KEY and use the following command for a one-step quick deployment with TigerGraph Community Edition and default configurations: +Set your OpenAI api key as environment varabiel OPENAI_API_KEY and use the following command for a one-step quick deployment with TigerGraph Community Edition and default configurations: ``` -curl -k https://raw.githubusercontent.com/tigergraph/graphrag/refs/heads/main/docs/tutorials/setup_graphrag.sh | bash +curl -k https://raw.githubusercontent.com/tigergraph/graphrag/refs/heads/main/docs/tutorials/setup_graphrag.sh | sh ``` The GraphRAG instances will be deployed at `./graphrag` folder and TigerGraph instance will be available at `http://localhost:14240`. -To change installation folder, use `bash -s -- ` instead of `bash` at the end of the above command. - -> Note: for other LLM providers, manually update `configs/server_config.json` accordingly and re-run `docker compose up -d` +To change installation folder, use `sh -s -- ` instead of `sh` at the end of the above command. #### Use Pre-Installed TigerGraph Instance -Similar to the above setup, and use the following command for a one-step quick deployment connecting to a pre-installed TigerGraph with default configurations: + +Using the following command for a one-step quick deployment with TigerGraph Community Edition and default configurations: ``` -curl -k https://raw.githubusercontent.com/tigergraph/graphrag/refs/heads/main/docs/tutorials/setup_graphrag_tg.sh | bash +curl -k https://raw.githubusercontent.com/tigergraph/graphrag/refs/heads/main/docs/tutorials/setup_graphrag_tg.sh | sh ``` The GraphRAG instances will be deployed at `./graphrag` folder and connect to TigerGraph instance at `http://localhost:14240` by default. -To change installation folder, TigerGraph instance location or username/password, use `bash -s -- ` instead of `bash` at the end of the above command. +To change installation folder, TigerGraph instance location or username/password, use `sh -s -- ` instead of `sh` at the end of the above command. [Go back to top](#top) @@ -152,7 +151,7 @@ Here’s what the folder structure looks like: ##### Step 3: Adjust configurations -Edit `llm_config` section of `configs/server_config.json` and replace `` to your own LLM_API_KEY for the LLM provider. +Edit `llm_config` section of `configs/server_config.json` and replace `` to your own OPENAI_API_KEY. > If desired, you can also change the model to be used for the embedding service and completion service to your preferred models to adjust the output from the LLM service. @@ -470,23 +469,27 @@ In addition to the `OPENAI_API_KEY`, `llm_model` and `model_name` can be edited ```json { "llm_config": { + "authentication_configuration": { + "OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_HERE" + }, "embedding_service": { - "embedding_model_service": "openai", "model_name": "text-embedding-3-small", - "authentication_configuration": { - "OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_HERE" - } + "embedding_model_service": "openai" }, "completion_service": { "llm_service": "openai", "llm_model": "gpt-4.1-mini", - "authentication_configuration": { - "OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_HERE" - }, "model_kwargs": { "temperature": 0 }, "prompt_path": "./common/prompts/openai_gpt4/" + }, + "multimodal_service": { + "llm_service": "openai", + "llm_model": "gpt-4o-mini", + "model_kwargs": { + "temperature": 0 + } } } } @@ -546,7 +549,7 @@ And your JSON config should follow as: "model_kwargs": { "temperature": 0 }, - "prompt_path": "./common/prompts/gcp_vertexai_palm/" + "prompt_path": "./app/prompts/gcp_vertexai_palm/" } } } @@ -583,7 +586,7 @@ In addition to the `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `azure_d "model_kwargs": { "temperature": 0 }, - "prompt_path": "./common/prompts/azure_open_ai_gpt35_turbo_instruct/" + "prompt_path": "./app/prompts/azure_open_ai_gpt35_turbo_instruct/" } } } @@ -594,27 +597,32 @@ In addition to the `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `azure_d ```json { "llm_config": { + "authentication_configuration": { + "AWS_ACCESS_KEY_ID": "YOUR_AWS_ACCESS_KEY", + "AWS_SECRET_ACCESS_KEY": "YOUR_AWS_SECRET_KEY", + "AWS_REGION_NAME": "us-west-2" + }, "embedding_service": { + "model_name": "amazon.titan-embed-text-v1", "embedding_model_service": "bedrock", - "model_name":"amazon.titan-embed-text-v2", - "region_name":"us-west-2", - "authentication_configuration": { - "AWS_ACCESS_KEY_ID": "ACCESS_KEY", - "AWS_SECRET_ACCESS_KEY": "SECRET" - } + "dimensions": 1536 }, "completion_service": { "llm_service": "bedrock", - "llm_model": "us.anthropic.claude-3-7-sonnet-20250219-v1:0", - "region_name":"us-west-2", - "authentication_configuration": { - "AWS_ACCESS_KEY_ID": "ACCESS_KEY", - "AWS_SECRET_ACCESS_KEY": "SECRET" - }, + "llm_model": "anthropic.claude-3-5-sonnet-20240620-v1:0", "model_kwargs": { "temperature": 0, + "max_tokens": 4096 }, - "prompt_path": "./common/prompts/aws_bedrock_claude3haiku/" + "prompt_path": "./common/prompts/openai_gpt4/" + }, + "multimodal_service": { + "llm_service": "bedrock", + "llm_model": "anthropic.claude-3-5-sonnet-20240620-v1:0", + "model_kwargs": { + "temperature": 0, + "max_tokens": 4096 + } } } } @@ -640,7 +648,7 @@ In addition to the `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `azure_d "model_kwargs": { "temperature": 0.0000001 }, - "prompt_path": "./common/prompts/openai_gpt4/" + "prompt_path": "./app/prompts/openai_gpt4/" } } } @@ -670,7 +678,7 @@ Example configuration for a model on Hugging Face with a dedicated endpoint is s "model_kwargs": { "temperature": 0.1 }, - "prompt_path": "./common/prompts/openai_gpt4/" + "prompt_path": "./app/prompts/openai_gpt4/" } } } @@ -697,7 +705,7 @@ Example configuration for a model on Hugging Face with a serverless endpoint is "model_kwargs": { "temperature": 0.1 }, - "prompt_path": "./common/prompts/llama_70b/" + "prompt_path": "./app/prompts/llama_70b/" } } } @@ -724,7 +732,7 @@ Example configuration for a model on Hugging Face with a serverless endpoint is "model_kwargs": { "temperature": 0.1 }, - "prompt_path": "./common/prompts/openai_gpt4/" + "prompt_path": "./app/prompts/openai_gpt4/" } } } 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. 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From feb734562998f256f6d7ebbbaa8d5a19233d2bb6 Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Tue, 18 Nov 2025 18:32:55 +0530 Subject: [PATCH 10/20] Update README for OpenAI and Bedrock config, add pymupdf4llm license --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 13c88b3..9469ad6 100644 --- a/README.md +++ b/README.md @@ -482,7 +482,7 @@ In addition to the `OPENAI_API_KEY`, `llm_model` and `model_name` can be edited "model_kwargs": { "temperature": 0 }, - "prompt_path": "./common/prompts/openai_gpt4/" + "prompt_path": "./app/prompts/openai_gpt4/" }, "multimodal_service": { "llm_service": "openai", @@ -614,7 +614,7 @@ In addition to the `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `azure_d "temperature": 0, "max_tokens": 4096 }, - "prompt_path": "./common/prompts/openai_gpt4/" + "prompt_path": "./app/prompts/aws_bedrock_claude3haiku/" }, "multimodal_service": { "llm_service": "bedrock", From 7a6789665e17bf5ebe285251b130661a5d96c10c Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Tue, 18 Nov 2025 22:55:22 +0530 Subject: [PATCH 11/20] Fix prompt_path to use ./common/prompts/ for OpenAI and Bedrock --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9469ad6..13c88b3 100644 --- a/README.md +++ b/README.md @@ -482,7 +482,7 @@ In addition to the `OPENAI_API_KEY`, `llm_model` and `model_name` can be edited "model_kwargs": { "temperature": 0 }, - "prompt_path": "./app/prompts/openai_gpt4/" + "prompt_path": "./common/prompts/openai_gpt4/" }, "multimodal_service": { "llm_service": "openai", @@ -614,7 +614,7 @@ In addition to the `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `azure_d "temperature": 0, "max_tokens": 4096 }, - "prompt_path": "./app/prompts/aws_bedrock_claude3haiku/" + "prompt_path": "./common/prompts/openai_gpt4/" }, "multimodal_service": { "llm_service": "bedrock", From 7f51feaf1a491736338da8fee4713e87e863816a Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Fri, 21 Nov 2025 20:57:53 +0530 Subject: [PATCH 12/20] bug fixes --- graphrag/app/routers/ui.py | 1 + graphrag/app/supportai/supportai.py | 18 ++++++++++++------ 2 files changed, 13 insertions(+), 6 deletions(-) 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..6b93df0 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() @@ -481,9 +481,9 @@ def create_ingest( except Exception as e: raise Exception(f"Error during Amazon BDA preprocessing: {e}") elif ingest_config.data_source.lower() == "server": - data_path = ingest_config.data_source_config.get("data_path", None) + data_path = ingest_config.data_source_config.get("folder_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": "" } From a2b8d90e63c2647678b53cef9b952af05d73c5ae Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Mon, 24 Nov 2025 20:00:28 +0530 Subject: [PATCH 13/20] Add local temp file storage for ingestion review --- common/utils/text_extractors.py | 59 +++-- graphrag-ui/src/pages/Setup.tsx | 319 +++++++++++++++++++++++++--- graphrag/app/routers/ui.py | 136 ++++++++++++ graphrag/app/supportai/supportai.py | 70 +++++- 4 files changed, 530 insertions(+), 54 deletions(-) diff --git a/common/utils/text_extractors.py b/common/utils/text_extractors.py index b900cae..ec5b140 100644 --- a/common/utils/text_extractors.py +++ b/common/utils/text_extractors.py @@ -8,6 +8,7 @@ import uuid import base64 import io +import threading from pathlib import Path import shutil import asyncio @@ -15,6 +16,9 @@ logger = logging.getLogger(__name__) +# Global lock for pymupdf4llm calls (not thread-safe) +_pymupdf4llm_lock = threading.Lock() + class TextExtractor: """Class for handling text extraction from various file formats and cleanup.""" @@ -202,26 +206,39 @@ def _extract_pdf_with_images_as_docs(file_path, base_doc_id, graphname=None): shutil.rmtree(image_output_folder, ignore_errors=True) # Convert PDF to markdown with extracted image files - try: - markdown_content = pymupdf4llm.to_markdown( - file_path, - write_images=True, - image_path=str(image_output_folder), # unique folder per PDF - force_text=False, - margins=0, - image_size_limit=0.08, - ) - 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 - }] + # 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}") @@ -461,4 +478,4 @@ 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 2aaee99..c844896 100644 --- a/graphrag-ui/src/pages/Setup.tsx +++ b/graphrag-ui/src/pages/Setup.tsx @@ -56,6 +56,12 @@ const Setup = () => { const [uploadMessage, setUploadMessage] = useState(""); const [isIngesting, setIsIngesting] = useState(false); const [ingestMessage, setIngestMessage] = useState(""); + + // Ingestion temp files state + const [tempSessionId, setTempSessionId] = useState(null); + const [tempFiles, setTempFiles] = useState([]); + const [showTempFiles, setShowTempFiles] = useState(false); + const [ingestJobData, setIngestJobData] = useState(null); // Refresh state const [refreshOpen, setRefreshOpen] = useState(false); @@ -416,6 +422,125 @@ const Setup = () => { } }; + // Fetch temp processed files + const fetchTempFiles = async (sessionId: string) => { + if (!ingestGraphName || !sessionId) return; + + try { + const creds = localStorage.getItem("creds"); + const response = await fetch(`/ui/${ingestGraphName}/ingestion_temp/list?session_id=${sessionId}`, { + headers: { Authorization: `Basic ${creds}` }, + }); + const data = await response.json(); + if (data.status === "success" && data.sessions.length > 0) { + setTempFiles(data.sessions[0].files || []); + setShowTempFiles(true); + } + } catch (error) { + console.error("Error fetching temp files:", error); + } + }; + + // Delete a specific temp file + const handleDeleteTempFile = async (filename: string) => { + if (!ingestGraphName || !tempSessionId) return; + + try { + const creds = localStorage.getItem("creds"); + const response = await fetch( + `/ui/${ingestGraphName}/ingestion_temp/delete?session_id=${tempSessionId}&filename=${encodeURIComponent(filename)}`, + { + method: "DELETE", + headers: { Authorization: `Basic ${creds}` }, + } + ); + const data = await response.json(); + if (data.status === "success") { + setIngestMessage(`✅ ${data.message}`); + // Refresh the temp files list + await fetchTempFiles(tempSessionId); + } + } catch (error: any) { + setIngestMessage(`❌ Error: ${error.message}`); + } + }; + + // Delete all temp files for session + const handleDeleteAllTempFiles = async () => { + if (!ingestGraphName || !tempSessionId) return; + + try { + const creds = localStorage.getItem("creds"); + const response = await fetch( + `/ui/${ingestGraphName}/ingestion_temp/delete?session_id=${tempSessionId}`, + { + method: "DELETE", + headers: { Authorization: `Basic ${creds}` }, + } + ); + const data = await response.json(); + if (data.status === "success") { + setIngestMessage(`✅ ${data.message}`); + setTempFiles([]); + setShowTempFiles(false); + setTempSessionId(null); + } + } catch (error: any) { + setIngestMessage(`❌ Error: ${error.message}`); + } + }; + + // Run final ingest after user reviews temp files + const handleRunIngest = async () => { + if (!ingestJobData) { + setIngestMessage("❌ No ingest job data available"); + return; + } + + setIsIngesting(true); + setIngestMessage("Running final document ingest..."); + + try { + const creds = localStorage.getItem("creds"); + + const loadingInfo = { + load_job_id: ingestJobData.load_job_id, + data_source_id: ingestJobData.data_source_id, + file_path: ingestJobData.data_path, + }; + + const ingestResponse = await fetch(`/ui/${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(`✅ Data ingested successfully! Processed ${tempFiles.length} documents.`); + + // Clear temp state + setTempFiles([]); + setShowTempFiles(false); + setTempSessionId(null); + 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) { @@ -460,37 +585,53 @@ const Setup = () => { const createData = await createResponse.json(); console.log("Create ingest response:", createData); - // Step 2: Run 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 - }; + // Check if temp files were created (for server data source) + const sessionId = createData.data_source_id?.temp_session_id; + + if (sessionId) { + // Files are saved to temp storage - show them for review + setTempSessionId(sessionId); + setIngestJobData({ + load_job_id: createData.load_job_id, + data_source_id: createData.data_source_id, + data_path: createData.data_path || createData.file_path, + }); + setIngestMessage(`✅ Processed ${createData.data_source_id.file_count} files. Review them below before ingesting.`); + await fetchTempFiles(sessionId); + setIsIngesting(false); + } else { + // No temp files (e.g., S3 Bedrock) - proceed directly to ingest + setIngestMessage("Step 2/2: Running document ingest..."); - const ingestResponse = await fetch(`/ui/${ingestGraphName}/ingest`, { - method: "POST", - headers: { - "Content-Type": "application/json", - Authorization: `Basic ${creds}`, - }, - body: JSON.stringify(loadingInfo), - }); + const loadingInfo = { + load_job_id: createData.load_job_id, + data_source_id: createData.data_source_id, + file_path: createData.data_path || createData.file_path, + }; - if (!ingestResponse.ok) { - const errorData = await ingestResponse.json(); - throw new Error(errorData.detail || `Failed to run ingest: ${ingestResponse.statusText}`); - } + const ingestResponse = await fetch(`/ui/${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); + const ingestData = await ingestResponse.json(); + console.log("Ingest response:", ingestData); - setIngestMessage(`✅ Data ingested successfully! Processed documents from ${folderPath}/`); + 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); } }; @@ -1237,6 +1378,71 @@ const Setup = () => { {ingestMessage}
)} + + {/* Processed Temp Files - Review before ingesting */} + {showTempFiles && tempFiles.length > 0 && ( +
+
+

+ Processed Files ({tempFiles.length}) +

+ +
+

+ Review the processed files below. You can delete any file before ingesting. +

+
+ {tempFiles.map((file, index) => ( +
+
+

+ {file.doc_id} +

+

+ {(file.size / 1024).toFixed(2)} KB +

+
+ +
+ ))} +
+ +
+ )}
)} @@ -1576,6 +1782,71 @@ const Setup = () => { {ingestMessage}
)} + + {/* Processed Temp Files - Review before ingesting */} + {showTempFiles && tempFiles.length > 0 && ( +
+
+

+ Processed Files ({tempFiles.length}) +

+ +
+

+ Review the processed files below. You can delete any file before ingesting. +

+
+ {tempFiles.map((file, index) => ( +
+
+

+ {file.doc_id} +

+

+ {(file.size / 1024).toFixed(2)} KB +

+
+ +
+ ))} +
+ +
+ )}
)}
diff --git a/graphrag/app/routers/ui.py b/graphrag/app/routers/ui.py index 114b489..9b012ec 100644 --- a/graphrag/app/routers/ui.py +++ b/graphrag/app/routers/ui.py @@ -1380,3 +1380,139 @@ async def delete_cloud_downloads( logger.debug_pii(f"Delete error trace:\n{exc}") raise HTTPException(status_code=500, detail=f"Error deleting files: {str(e)}") + +# Ingestion Temp Files Endpoints + +@router.get(route_prefix + "/{graphname}/ingestion_temp/list") +async def list_ingestion_temp_files( + graphname: str, + credentials: Annotated[HTTPBase, Depends(security)], + session_id: str = None, +): + """ + List processed files in the ingestion temp folder for a specific graph. + """ + try: + base_temp_dir = os.path.join("uploads", "ingestion_temp", graphname) + + if not os.path.exists(base_temp_dir): + return { + "status": "success", + "graphname": graphname, + "sessions": [], + "total_files": 0, + } + + sessions = [] + total_files = 0 + + # If session_id provided, list only that session + if session_id: + session_dir = os.path.join(base_temp_dir, session_id) + if os.path.exists(session_dir) and os.path.isdir(session_dir): + files = [] + for filename in os.listdir(session_dir): + filepath = os.path.join(session_dir, filename) + if os.path.isfile(filepath) and filename.endswith('.json'): + file_stat = os.stat(filepath) + # Read doc_id from file + try: + with open(filepath, 'r', encoding='utf-8') as f: + doc_data = json.load(f) + doc_id = doc_data.get('doc_id', 'unknown') + except: + doc_id = 'unknown' + + files.append({ + "filename": filename, + "doc_id": doc_id, + "size": file_stat.st_size, + "modified": file_stat.st_mtime, + }) + sessions.append({ + "session_id": session_id, + "files": files, + "file_count": len(files), + }) + total_files = len(files) + + return { + "status": "success", + "graphname": graphname, + "sessions": sessions, + "total_files": total_files, + } + + except Exception as e: + exc = traceback.format_exc() + logger.error(f"Error listing ingestion temp files for graph {graphname}: {e}") + logger.debug_pii(f"List error trace:\n{exc}") + raise HTTPException(status_code=500, detail=f"Error listing temp files: {str(e)}") + + +@router.delete(route_prefix + "/{graphname}/ingestion_temp/delete") +async def delete_ingestion_temp_files( + graphname: str, + credentials: Annotated[HTTPBase, Depends(security)], + session_id: str = None, + filename: str = None, +): + """ + Delete files from ingestion temp folder. + """ + try: + base_temp_dir = os.path.join("uploads", "ingestion_temp", graphname) + + if not session_id: + raise HTTPException(status_code=400, detail="session_id is required") + + session_dir = os.path.join(base_temp_dir, session_id) + + if not os.path.exists(session_dir): + return { + "status": "success", + "message": f"No temp files found for session {session_id}", + "deleted_files": [], + } + + deleted_files = [] + + if filename: + # Delete specific file + file_path = os.path.join(session_dir, filename) + if os.path.exists(file_path) and os.path.isfile(file_path): + os.remove(file_path) + deleted_files.append(filename) + logger.info(f"Deleted temp file {filename} from session {session_id}") + + # If session folder is now empty, remove it + if not os.listdir(session_dir): + os.rmdir(session_dir) + logger.info(f"Removed empty session folder {session_id}") + else: + raise HTTPException(status_code=404, detail=f"File {filename} not found") + else: + # Delete entire session folder + import shutil + for filename in os.listdir(session_dir): + if os.path.isfile(os.path.join(session_dir, filename)): + deleted_files.append(filename) + + shutil.rmtree(session_dir) + logger.info(f"Deleted session folder {session_id} for graph {graphname}") + + return { + "status": "success", + "message": f"Successfully deleted {len(deleted_files)} file(s)", + "deleted_files": deleted_files, + "session_id": session_id, + } + + except HTTPException: + raise + except Exception as e: + exc = traceback.format_exc() + logger.error(f"Error deleting ingestion temp files for graph {graphname}: {e}") + logger.debug_pii(f"Delete error trace:\n{exc}") + raise HTTPException(status_code=500, detail=f"Error deleting temp files: {str(e)}") + diff --git a/graphrag/app/supportai/supportai.py b/graphrag/app/supportai/supportai.py index 6b93df0..88542dc 100644 --- a/graphrag/app/supportai/supportai.py +++ b/graphrag/app/supportai/supportai.py @@ -489,14 +489,37 @@ def create_ingest( server_processing_result = extractor.process_folder(data_path, graphname=graphname) 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}") - - res_ingest_config["server_jobs"] = server_processing_result.get("documents", []) + + # Log only summary, NOT the full documents to avoid memory logging + logger.info(f"Server folder processing completed: {server_processing_result.get('message')}") + + # Save processed documents to temporary folder instead of keeping in memory + temp_session_id = str(uuid.uuid4()) + temp_folder = os.path.join("uploads", "ingestion_temp", graphname, temp_session_id) + os.makedirs(temp_folder, exist_ok=True) + + documents = server_processing_result.get("documents", []) + doc_count = len(documents) + + # Save each document as a separate JSON file + for idx, doc_data in enumerate(documents): + doc_filename = f"doc_{idx}_{doc_data.get('doc_id', 'unknown')}.json" + doc_filepath = os.path.join(temp_folder, doc_filename) + with open(doc_filepath, 'w', encoding='utf-8') as f: + json.dump(doc_data, f, ensure_ascii=False, indent=2) + + # Clear documents from memory immediately after saving + documents.clear() + server_processing_result.clear() + + logger.info(f"Saved {doc_count} processed documents to {temp_folder}") + + res_ingest_config["temp_session_id"] = temp_session_id + res_ingest_config["temp_folder"] = 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}") @@ -650,13 +673,30 @@ def ingest( 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"): + + # Read from temporary folder + temp_folder = ingest_config.get("temp_folder") + if not temp_folder or not os.path.exists(temp_folder): + raise Exception(f"Temporary folder not found: {temp_folder}") + + # Read all JSON files from temp folder + json_files = [f for f in os.listdir(temp_folder) if f.endswith('.json')] + logger.info(f"Reading {len(json_files)} documents from {temp_folder}") + + for json_filename in json_files: + json_filepath = os.path.join(temp_folder, json_filename) + try: + with open(json_filepath, 'r', encoding='utf-8') as f: + doc_data = json.load(f) + if not doc_data.get("doc_id"): + logger.warning(f"Skipping invalid document: {json_filename}") continue # Skip documents with neither content nor image_data if not doc_data.get("content") and not doc_data.get("image_data"): + logger.warning(f"Skipping document with no content: {json_filename}") continue + if doc_data.get("image_data"): payload = { "doc_id": doc_data.get("doc_id", ""), @@ -684,6 +724,18 @@ def ingest( 'parent_doc': doc_data.get("parent_doc", ""), }) logger.info(f"Data uploading done for doc_id: {doc_data.get('doc_id', 'unknown')}") + except Exception as file_error: + logger.error(f"Error processing file {json_filename}: {file_error}") + continue + + # Clean up temp folder after successful ingestion + try: + import shutil + shutil.rmtree(temp_folder) + logger.info(f"Cleaned up temporary folder: {temp_folder}") + except Exception as cleanup_error: + logger.warning(f"Failed to cleanup temp folder {temp_folder}: {cleanup_error}") + except Exception as e: raise Exception(f"Error during server markdown extraction and TigerGraph loading: {e}") return { From 38619e045a0411bd25132c2c325f7afaea9c0121 Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Mon, 24 Nov 2025 20:31:36 +0530 Subject: [PATCH 14/20] Add direct ingestion option with checkbox to skip file review --- graphrag-ui/src/pages/Setup.tsx | 37 ++++++++++++++++++++++++++++++--- 1 file changed, 34 insertions(+), 3 deletions(-) diff --git a/graphrag-ui/src/pages/Setup.tsx b/graphrag-ui/src/pages/Setup.tsx index c844896..e86eefb 100644 --- a/graphrag-ui/src/pages/Setup.tsx +++ b/graphrag-ui/src/pages/Setup.tsx @@ -62,6 +62,7 @@ const Setup = () => { const [tempFiles, setTempFiles] = useState([]); const [showTempFiles, setShowTempFiles] = useState(false); const [ingestJobData, setIngestJobData] = useState(null); + const [directIngestion, setDirectIngestion] = useState(false); // Refresh state const [refreshOpen, setRefreshOpen] = useState(false); @@ -588,8 +589,8 @@ const Setup = () => { // Check if temp files were created (for server data source) const sessionId = createData.data_source_id?.temp_session_id; - if (sessionId) { - // Files are saved to temp storage - show them for review + if (sessionId && !directIngestion) { + // Files are saved to temp storage - show them for review (only if not direct ingestion) setTempSessionId(sessionId); setIngestJobData({ load_job_id: createData.load_job_id, @@ -600,7 +601,7 @@ const Setup = () => { await fetchTempFiles(sessionId); setIsIngesting(false); } else { - // No temp files (e.g., S3 Bedrock) - proceed directly to ingest + // No temp files (e.g., S3 Bedrock) OR direct ingestion enabled - proceed directly to ingest setIngestMessage("Step 2/2: Running document ingest..."); const loadingInfo = { @@ -1350,6 +1351,21 @@ const Setup = () => {

Process uploaded files and add them to the knowledge graph

+ + {/* Direct Ingestion Checkbox */} +
+ setDirectIngestion(e.target.checked)} + className="mr-2 h-4 w-4 rounded border-gray-300 text-blue-600 focus:ring-blue-500" + /> + +
+
@@ -1352,23 +1542,9 @@ const Setup = () => { Process uploaded files and add them to the knowledge graph

- {/* Direct Ingestion Checkbox */} -
- setDirectIngestion(e.target.checked)} - className="mr-2 h-4 w-4 rounded border-gray-300 text-blue-600 focus:ring-blue-500" - /> - -
-
)} - - {/* Processed Temp Files - Review before ingesting */} - {showTempFiles && tempFiles.length > 0 && ( -
-
-

- Processed Files ({tempFiles.length}) -

- -
-

- Review the processed files below. You can delete any file before ingesting. -

-
- {tempFiles.map((file, index) => ( -
-
-

- {file.doc_id} -

-

- {(file.size / 1024).toFixed(2)} KB -

-
- -
- ))} -
- -
- )}
)} @@ -1771,23 +1882,9 @@ const Setup = () => { Process downloaded files and add them to the knowledge graph

- {/* Direct Ingestion Checkbox */} -
- setDirectIngestion(e.target.checked)} - className="mr-2 h-4 w-4 rounded border-gray-300 text-blue-600 focus:ring-blue-500" - /> - -
-
)} - - {/* Processed Temp Files - Review before ingesting */} - {showTempFiles && tempFiles.length > 0 && ( -
-
-

- Processed Files ({tempFiles.length}) -

- -
-

- Review the processed files below. You can delete any file before ingesting. -

-
- {tempFiles.map((file, index) => ( -
-
-

- {file.doc_id} -

-

- {(file.size / 1024).toFixed(2)} KB -

-
- -
- ))} -
- -
- )}
)} From dd5772453ee8e676b2e1af9bdc6ffa9eee495254 Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Mon, 1 Dec 2025 17:16:28 +0530 Subject: [PATCH 16/20] Merge latest main and consolidate markdown_parsing.py into text_extractors.py --- common/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/requirements.txt b/common/requirements.txt index f0022f3..3bbd096 100644 --- a/common/requirements.txt +++ b/common/requirements.txt @@ -108,7 +108,7 @@ 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 pymupdf4llm==0.2.0 From 5d474687274707e3004b2ca1669ea346898bdd99 Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Mon, 1 Dec 2025 20:44:56 +0530 Subject: [PATCH 17/20] Fix merge conflict resolution: add missing try block and remove incorrect temp_folder cleanup --- graphrag/app/supportai/supportai.py | 79 +++++++++++++---------------- 1 file changed, 36 insertions(+), 43 deletions(-) diff --git a/graphrag/app/supportai/supportai.py b/graphrag/app/supportai/supportai.py index 17e1d2a..c2030cd 100644 --- a/graphrag/app/supportai/supportai.py +++ b/graphrag/app/supportai/supportai.py @@ -675,50 +675,43 @@ 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"): + try: + 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 = { + "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"), + "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": "" + } + 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", ""), + }) + logger.info(f"Data uploading done for doc_id: {doc_data.get('doc_id', 'unknown')}") + except Exception as file_error: + logger.error(f"Error processing document {doc_data.get('doc_id', 'unknown')}: {file_error}") 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 = { - "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"), - "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": "" - } - 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", ""), - }) - logger.info(f"Data uploading done for doc_id: {doc_data.get('doc_id', 'unknown')}") - except Exception as file_error: - logger.error(f"Error processing file {json_filename}: {file_error}") - continue - - # Clean up temp folder after successful ingestion - try: - import shutil - shutil.rmtree(temp_folder) - logger.info(f"Cleaned up temporary folder: {temp_folder}") - except Exception as cleanup_error: - logger.warning(f"Failed to cleanup temp folder {temp_folder}: {cleanup_error}") except Exception as e: raise Exception(f"Error during server markdown extraction and TigerGraph loading: {e}") From 7fd1ab28e7ddc90d7105221eb2506ca5737dc6b4 Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Tue, 2 Dec 2025 22:09:08 +0530 Subject: [PATCH 18/20] Supportai merge issue fix for temp file ingestion --- graphrag/app/supportai/supportai.py | 99 ++++++++++++++++++----------- 1 file changed, 61 insertions(+), 38 deletions(-) diff --git a/graphrag/app/supportai/supportai.py b/graphrag/app/supportai/supportai.py index c2030cd..88542dc 100644 --- a/graphrag/app/supportai/supportai.py +++ b/graphrag/app/supportai/supportai.py @@ -673,45 +673,68 @@ def ingest( 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"): - try: - 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 = { - "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"), - "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": "" - } - 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", ""), - }) - logger.info(f"Data uploading done for doc_id: {doc_data.get('doc_id', 'unknown')}") - except Exception as file_error: - logger.error(f"Error processing document {doc_data.get('doc_id', 'unknown')}: {file_error}") + + # Read from temporary folder + temp_folder = ingest_config.get("temp_folder") + if not temp_folder or not os.path.exists(temp_folder): + raise Exception(f"Temporary folder not found: {temp_folder}") + + # Read all JSON files from temp folder + json_files = [f for f in os.listdir(temp_folder) if f.endswith('.json')] + logger.info(f"Reading {len(json_files)} documents from {temp_folder}") + + for json_filename in json_files: + json_filepath = os.path.join(temp_folder, json_filename) + try: + with open(json_filepath, 'r', encoding='utf-8') as f: + doc_data = json.load(f) + + if not doc_data.get("doc_id"): + logger.warning(f"Skipping invalid document: {json_filename}") continue + # Skip documents with neither content nor image_data + if not doc_data.get("content") and not doc_data.get("image_data"): + logger.warning(f"Skipping document with no content: {json_filename}") + 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"), + "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": "" + } + 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", ""), + }) + logger.info(f"Data uploading done for doc_id: {doc_data.get('doc_id', 'unknown')}") + except Exception as file_error: + logger.error(f"Error processing file {json_filename}: {file_error}") + continue + + # Clean up temp folder after successful ingestion + try: + import shutil + shutil.rmtree(temp_folder) + logger.info(f"Cleaned up temporary folder: {temp_folder}") + except Exception as cleanup_error: + logger.warning(f"Failed to cleanup temp folder {temp_folder}: {cleanup_error}") except Exception as e: raise Exception(f"Error during server markdown extraction and TigerGraph loading: {e}") From aa1ce342988f86d67c12b4bf4ebbb23d79a1fad6 Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Wed, 3 Dec 2025 15:23:46 +0530 Subject: [PATCH 19/20] Redesign temp file storage: save immediately during file processing instead of after --- common/utils/text_extractors.py | 77 ++++++++++++++++++++++++----- graphrag/app/supportai/supportai.py | 35 +++++-------- 2 files changed, 77 insertions(+), 35 deletions(-) diff --git a/common/utils/text_extractors.py b/common/utils/text_extractors.py index 72e3a0c..eefc451 100644 --- a/common/utils/text_extractors.py +++ b/common/utils/text_extractors.py @@ -97,10 +97,11 @@ 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, folder_path_obj, graphname, temp_folder=None, file_counter=None): """ Async helper to process a single file. Runs in thread pool to avoid blocking on I/O operations. + If temp_folder is provided, saves documents immediately and returns metadata only. """ try: loop = asyncio.get_event_loop() @@ -112,6 +113,27 @@ async def _process_file_async(self, file_path, folder_path_obj, graphname): graphname ) + # If temp_folder provided, save immediately and return metadata only + if temp_folder and doc_entries: + saved_files = [] + for idx, doc_data in enumerate(doc_entries): + # Use file_counter for unique naming across all files + counter_val = next(file_counter) if file_counter else idx + doc_filename = f"doc_{counter_val}_{doc_data.get('doc_id', 'unknown')}.json" + doc_filepath = os.path.join(temp_folder, doc_filename) + with open(doc_filepath, 'w', encoding='utf-8') as f: + json.dump(doc_data, f, ensure_ascii=False, indent=2) + saved_files.append(doc_filename) + + # Return metadata only, not full documents (memory efficient) + return { + 'success': True, + 'file_path': str(file_path), + 'saved_files': saved_files, + 'num_documents': len(doc_entries) + } + + # No temp_folder - return documents in memory (legacy behavior) return { 'success': True, 'file_path': str(file_path), @@ -127,10 +149,11 @@ async def _process_file_async(self, file_path, folder_path_obj, graphname): logger.warning(f"Failed to process file {file_path}: {e}") return {'success': False, 'file_path': str(file_path), 'error': str(e)} - async def _process_folder_async(self, folder_path, graphname=None, max_concurrent=10): + async def _process_folder_async(self, folder_path, graphname=None, max_concurrent=10, temp_folder=None): """ Async version of process_folder for parallel file processing. This prevents conflicts when multiple users process folders simultaneously. + If temp_folder is provided, saves documents immediately to disk instead of holding in memory. """ logger.info(f"Processing local folder ASYNC: {folder_path} for graph: {graphname} (max_concurrent={max_concurrent})") @@ -142,6 +165,11 @@ 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 if provided + if temp_folder: + 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(): @@ -166,16 +194,20 @@ def safe_walk(path): logger.info(f"Found {len(files_to_process)} files to process") semaphore = asyncio.Semaphore(max_concurrent) + + # Thread-safe counter for unique file naming + file_counter = iter(range(100000)) if temp_folder else None 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, folder_path_obj, graphname, temp_folder, file_counter) 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_saved_files = [] for result in results: if isinstance(result, Exception): @@ -183,10 +215,15 @@ async def process_with_semaphore(file_path): continue if result.get('success'): - all_documents.extend(result.get('documents', [])) + # If temp_folder was used, documents are saved to disk + if temp_folder: + total_saved_files.extend(result.get('saved_files', [])) + else: + all_documents.extend(result.get('documents', [])) + processed_files_info.append({ 'file_path': result['file_path'], - 'num_documents': result.get('num_documents', len(result.get('documents', []))), + 'num_documents': result.get('num_documents', 0), 'status': 'success' }) else: @@ -196,23 +233,39 @@ 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") + total_docs = len(total_saved_files) if temp_folder else len(all_documents) + logger.info(f"Processed {len(processed_files_info)} files, extracted {total_docs} total documents") - return { + response = { '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 } + + # Only include documents in response if NOT saving to temp_folder + if temp_folder: + response['saved_to_temp'] = True + response['temp_folder'] = temp_folder + response['saved_files'] = total_saved_files + else: + response['documents'] = all_documents + + return response - def process_folder(self, folder_path, graphname=None): + def process_folder(self, folder_path, graphname=None, temp_folder=None): """ Process local folder with multiple file formats and extract text content. Uses async processing internally for parallel file handling. + + Args: + folder_path: Path to the folder containing files to process + graphname: Name of the graph (for context) + temp_folder: Optional path to save processed documents immediately. + If provided, documents are saved to disk instead of returned in memory. """ 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=temp_folder)) def extract_text_from_file_with_images_as_docs(file_path, graphname=None): diff --git a/graphrag/app/supportai/supportai.py b/graphrag/app/supportai/supportai.py index 88542dc..2fb2e45 100644 --- a/graphrag/app/supportai/supportai.py +++ b/graphrag/app/supportai/supportai.py @@ -485,34 +485,23 @@ def create_ingest( if data_path is None: raise Exception("Folder path not provided for server processing") try: - extractor = TextExtractor() - server_processing_result = extractor.process_folder(data_path, graphname=graphname) - if server_processing_result.get("statusCode") != 200: - raise Exception(f"Server folder processing failed: {server_processing_result}") - - # Log only summary, NOT the full documents to avoid memory logging - logger.info(f"Server folder processing completed: {server_processing_result.get('message')}") - - # Save processed documents to temporary folder instead of keeping in memory + # Create temp folder BEFORE processing so extractor can save directly temp_session_id = str(uuid.uuid4()) temp_folder = os.path.join("uploads", "ingestion_temp", graphname, temp_session_id) - os.makedirs(temp_folder, exist_ok=True) - documents = server_processing_result.get("documents", []) - doc_count = len(documents) - - # Save each document as a separate JSON file - for idx, doc_data in enumerate(documents): - doc_filename = f"doc_{idx}_{doc_data.get('doc_id', 'unknown')}.json" - doc_filepath = os.path.join(temp_folder, doc_filename) - with open(doc_filepath, 'w', encoding='utf-8') as f: - json.dump(doc_data, f, ensure_ascii=False, indent=2) + # Process files and save immediately to temp folder (memory efficient) + extractor = TextExtractor() + server_processing_result = extractor.process_folder( + data_path, + graphname=graphname, + temp_folder=temp_folder # Extractor saves files as it processes + ) - # Clear documents from memory immediately after saving - documents.clear() - server_processing_result.clear() + if server_processing_result.get("statusCode") != 200: + raise Exception(f"Server folder processing failed: {server_processing_result}") - logger.info(f"Saved {doc_count} processed documents to {temp_folder}") + doc_count = server_processing_result.get("num_documents", 0) + logger.info(f"Server folder processing completed: {server_processing_result.get('message')}") res_ingest_config["temp_session_id"] = temp_session_id res_ingest_config["temp_folder"] = temp_folder From 845fd9133dfd7b3030536b00828ea2e2442b9090 Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Wed, 3 Dec 2025 17:29:22 +0530 Subject: [PATCH 20/20] Add Server Configuration UI for real-time LLM and GraphRAG config updates --- common/config.py | 56 ++++ graphrag-ui/src/pages/Setup.tsx | 441 +++++++++++++++++++++++++++++++- graphrag/app/routers/ui.py | 71 ++++- 3 files changed, 566 insertions(+), 2 deletions(-) diff --git a/common/config.py b/common/config.py index 703d3f8..be4e7fe 100644 --- a/common/config.py +++ b/common/config.py @@ -51,6 +51,62 @@ # Configs SERVER_CONFIG = os.getenv("SERVER_CONFIG", "configs/server_config.json") + + +def get_config_file_path(): + """Get the path to the server config file.""" + return SERVER_CONFIG + + +def get_current_config(): + """Get the current in-memory configuration (llm_config and graphrag_config).""" + return { + "llm_config": llm_config, + "graphrag_config": graphrag_config, + } + + +def update_config(new_llm_config: dict = None, new_graphrag_config: dict = None, persist: bool = True): + """ + Update the in-memory configuration and optionally persist to file. + This allows config changes to take effect immediately without container restart. + + Args: + new_llm_config: New LLM configuration to apply + new_graphrag_config: New GraphRAG configuration to apply + persist: If True, also save changes to server_config.json file + """ + global llm_config, graphrag_config + + # Update llm_config in memory + if new_llm_config is not None: + llm_config.clear() + llm_config.update(new_llm_config) + + # Update graphrag_config in memory + if new_graphrag_config is not None: + graphrag_config.clear() + graphrag_config.update(new_graphrag_config) + + # Persist to file if requested + if persist: + config_path = get_config_file_path() + if config_path[-5:] == ".json": + # Read current file config + with open(config_path, "r") as f: + file_config = json.load(f) + + # Update with new values + if new_llm_config is not None: + file_config["llm_config"] = new_llm_config + if new_graphrag_config is not None: + file_config["graphrag_config"] = new_graphrag_config + + # Write back to file + with open(config_path, "w") as f: + json.dump(file_config, f, indent=2) + + return True PATH_PREFIX = os.getenv("PATH_PREFIX", "") PRODUCTION = os.getenv("PRODUCTION", "false").lower() == "true" diff --git a/graphrag-ui/src/pages/Setup.tsx b/graphrag-ui/src/pages/Setup.tsx index 17f952c..8216e86 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, CloudCog } from "lucide-react"; +import { Database, Upload, RefreshCw, Loader2, Trash2, FolderUp, Cloud, ArrowLeft, CloudDownload, CloudCog, Settings } from "lucide-react"; import { Dialog, DialogContent, @@ -100,6 +100,26 @@ const Setup = () => { const [isDownloading, setIsDownloading] = useState(false); const [downloadMessage, setDownloadMessage] = useState(""); + // Server Configuration state + const [configOpen, setConfigOpen] = useState(false); + const [isLoadingConfig, setIsLoadingConfig] = useState(false); + const [isSavingConfig, setIsSavingConfig] = useState(false); + const [configMessage, setConfigMessage] = useState(""); + const [configMessageType, setConfigMessageType] = useState<"success" | "error" | "">(""); + + // LLM Config state + const [llmService, setLlmService] = useState("openai"); + const [llmModel, setLlmModel] = useState(""); + const [llmApiKey, setLlmApiKey] = useState(""); + const [llmTemperature, setLlmTemperature] = useState("0"); + const [embeddingService, setEmbeddingService] = useState("openai"); + const [embeddingModel, setEmbeddingModel] = useState(""); + + // GraphRAG Config state + const [chunkerType, setChunkerType] = useState("semantic"); + const [extractorType, setExtractorType] = useState("llm"); + const [reuseEmbedding, setReuseEmbedding] = useState(true); + // Fetch uploaded files const fetchUploadedFiles = async () => { if (!ingestGraphName) return; @@ -585,6 +605,153 @@ const Setup = () => { } }; + // Fetch server configuration + const fetchServerConfig = async () => { + setIsLoadingConfig(true); + setConfigMessage(""); + setConfigMessageType(""); + + try { + const creds = localStorage.getItem("creds"); + const response = await fetch("/ui/config", { + headers: { Authorization: `Basic ${creds}` }, + }); + + if (!response.ok) { + throw new Error("Failed to fetch configuration"); + } + + const data = await response.json(); + + if (data.status === "success" && data.config) { + const { llm_config, graphrag_config } = data.config; + + // Set LLM config values + if (llm_config) { + const completionService = llm_config.completion_service || {}; + const embeddingServiceConfig = llm_config.embedding_service || {}; + const authConfig = llm_config.authentication_configuration || {}; + + setLlmService(completionService.llm_service || "openai"); + setLlmModel(completionService.llm_model || ""); + setLlmTemperature(String(completionService.model_kwargs?.temperature ?? "0")); + setEmbeddingService(embeddingServiceConfig.embedding_model_service || "openai"); + setEmbeddingModel(embeddingServiceConfig.model_name || ""); + + // Get API key (masked for display) + const apiKey = authConfig.OPENAI_API_KEY || authConfig.AZURE_OPENAI_API_KEY || ""; + setLlmApiKey(apiKey ? "••••••••" : ""); + } + + // Set GraphRAG config values + if (graphrag_config) { + setChunkerType(graphrag_config.chunker || "semantic"); + setExtractorType(graphrag_config.extractor || "llm"); + setReuseEmbedding(graphrag_config.reuse_embedding !== false); + } + } + } catch (error: any) { + console.error("Error fetching config:", error); + setConfigMessage(`❌ Error loading configuration: ${error.message}`); + setConfigMessageType("error"); + } finally { + setIsLoadingConfig(false); + } + }; + + // Save server configuration + const handleSaveConfig = async () => { + setIsSavingConfig(true); + setConfigMessage(""); + setConfigMessageType(""); + + try { + const creds = localStorage.getItem("creds"); + + // First fetch the current config to preserve other settings + const fetchResponse = await fetch("/ui/config", { + headers: { Authorization: `Basic ${creds}` }, + }); + + if (!fetchResponse.ok) { + throw new Error("Failed to fetch current configuration"); + } + + const currentData = await fetchResponse.json(); + const currentConfig = currentData.config || {}; + + // Build updated config + const updatedLlmConfig = { + ...currentConfig.llm_config, + completion_service: { + ...currentConfig.llm_config?.completion_service, + llm_service: llmService, + llm_model: llmModel, + model_kwargs: { + ...currentConfig.llm_config?.completion_service?.model_kwargs, + temperature: parseFloat(llmTemperature) || 0, + }, + }, + embedding_service: { + ...currentConfig.llm_config?.embedding_service, + embedding_model_service: embeddingService, + model_name: embeddingModel, + }, + }; + + // Only update API key if user entered a new one (not masked) + if (llmApiKey && !llmApiKey.includes("•")) { + updatedLlmConfig.authentication_configuration = { + ...currentConfig.llm_config?.authentication_configuration, + OPENAI_API_KEY: llmApiKey, + }; + } + + const updatedGraphragConfig = { + ...currentConfig.graphrag_config, + chunker: chunkerType, + extractor: extractorType, + reuse_embedding: reuseEmbedding, + }; + + // Save updated config + const saveResponse = await fetch("/ui/config", { + method: "PUT", + headers: { + "Content-Type": "application/json", + Authorization: `Basic ${creds}`, + }, + body: JSON.stringify({ + llm_config: updatedLlmConfig, + graphrag_config: updatedGraphragConfig, + }), + }); + + if (!saveResponse.ok) { + const errorData = await saveResponse.json(); + throw new Error(errorData.detail || "Failed to save configuration"); + } + + const saveData = await saveResponse.json(); + setConfigMessage("✅ Configuration saved successfully! Changes are now active."); + setConfigMessageType("success"); + + } catch (error: any) { + console.error("Error saving config:", error); + setConfigMessage(`❌ Error saving configuration: ${error.message}`); + setConfigMessageType("error"); + } finally { + setIsSavingConfig(false); + } + }; + + // Load config when dialog opens + useEffect(() => { + if (configOpen) { + fetchServerConfig(); + } + }, [configOpen]); + // Run final ingest after user reviews temp files const handleRunIngest = async () => { if (!ingestJobData) { @@ -1286,6 +1453,33 @@ const Setup = () => { + {/* Server Configuration - Separate row */} +
+ {/* Section 4: Server Configuration */} +
+
+
+ +
+

+ Server Configuration +

+

+ Configure LLM settings and GraphRAG options for your server. +

+
+
+ +
+
+
+ {/* Initialize Graph Dialog */} { + {/* Server Configuration Dialog */} + { + if (!open && isConfirmDialogOpen) { + return; + } + setConfigOpen(open); + }} + > + e.preventDefault()} + > + + Server Configuration + + Configure LLM and GraphRAG settings. Changes take effect immediately without restart. + + + + {isLoadingConfig ? ( +
+ + Loading configuration... +
+ ) : ( + + + LLM Configuration + GraphRAG Configuration + + + {/* LLM Configuration Tab */} + +
+ + +
+ +
+ + setLlmModel(e.target.value)} + placeholder="e.g., gpt-4o-mini, gpt-4.1-mini" + className="dark:border-[#3D3D3D] dark:bg-shadeA" + /> +
+ +
+ + setLlmApiKey(e.target.value)} + placeholder="Enter API key (leave blank to keep current)" + className="dark:border-[#3D3D3D] dark:bg-shadeA" + /> +

+ Leave blank to keep the current API key +

+
+ +
+ + setLlmTemperature(e.target.value)} + placeholder="0" + className="dark:border-[#3D3D3D] dark:bg-shadeA" + /> +
+ +
+

Embedding Service

+ +
+
+ + +
+ +
+ + setEmbeddingModel(e.target.value)} + placeholder="e.g., text-embedding-3-small" + className="dark:border-[#3D3D3D] dark:bg-shadeA" + /> +
+
+
+ + + {/* GraphRAG Configuration Tab */} + +
+ + +

+ Semantic chunking uses AI to create meaningful chunks +

+
+ +
+ + +

+ LLM extractor uses AI to extract entities and relationships +

+
+ +
+ setReuseEmbedding(e.target.checked)} + className="h-4 w-4 rounded border-gray-300 text-tigerOrange focus:ring-tigerOrange" + /> + +
+

+ When enabled, existing embeddings will be reused instead of regenerating +

+ + + )} + + {configMessage && ( +
+ {configMessage} +
+ )} + + + + + + + + {/* User Confirmation Dialog */} {confirmDialog} diff --git a/graphrag/app/routers/ui.py b/graphrag/app/routers/ui.py index 9b012ec..4d99c15 100644 --- a/graphrag/app/routers/ui.py +++ b/graphrag/app/routers/ui.py @@ -46,7 +46,7 @@ from pyTigerGraph import TigerGraphConnection from tools.validation_utils import MapQuestionToSchemaException -from common.config import db_config, graphrag_config, embedding_service, llm_config, service_status +from common.config import db_config, graphrag_config, embedding_service, llm_config, service_status, get_current_config, update_config from common.db.connections import get_db_connection_pwd_manual from common.logs.log import req_id_cv from common.logs.logwriter import LogWriter @@ -1516,3 +1516,72 @@ async def delete_ingestion_temp_files( logger.debug_pii(f"Delete error trace:\n{exc}") raise HTTPException(status_code=500, detail=f"Error deleting temp files: {str(e)}") + +# ===================================================== +# Server Configuration Endpoints +# ===================================================== + +@router.get(f"{route_prefix}/config") +def get_server_config_endpoint( + creds: Annotated[tuple[list[str], HTTPBasicCredentials], Depends(ui_basic_auth)], +): + """ + Get the current server configuration (LLM config and GraphRAG config). + This returns the in-memory configuration that is actively being used. + """ + try: + config = get_current_config() + + return { + "status": "success", + "config": config + } + except Exception as e: + logger.error(f"Error reading server config: {e}") + raise HTTPException( + status_code=500, + detail=f"Error reading server configuration: {str(e)}" + ) + + +@router.put(f"{route_prefix}/config") +def update_server_config_endpoint( + creds: Annotated[tuple[list[str], HTTPBasicCredentials], Depends(ui_basic_auth)], + config_update: dict = Body(...), +): + """ + Update the server configuration (LLM config and/or GraphRAG config). + Changes take effect immediately in memory AND are persisted to server_config.json. + No container restart required! + + Parameters: + - config_update: JSON body containing llm_config and/or graphrag_config to update + """ + try: + new_llm_config = config_update.get("llm_config") + new_graphrag_config = config_update.get("graphrag_config") + + # Update in-memory config and persist to file + update_config( + new_llm_config=new_llm_config, + new_graphrag_config=new_graphrag_config, + persist=True + ) + + logger.info("Server configuration updated successfully (in-memory and persisted)") + + # Return the updated config + updated_config = get_current_config() + + return { + "status": "success", + "message": "Configuration updated successfully. Changes are now active.", + "config": updated_config + } + except Exception as e: + logger.error(f"Error updating server config: {e}") + raise HTTPException( + status_code=500, + detail=f"Error updating server configuration: {str(e)}" + ) +