Merge branch 'main' into pg-optimization

env.example

@@ -123,7 +123,7 @@ MAX_PARALLEL_INSERT=2
 
 ###########################################################
 ### LLM Configuration
-### LLM_BINDING type: openai, ollama, lollms, azure_openai
+### LLM_BINDING type: openai, ollama, lollms, azure_openai, aws_bedrock
 ###########################################################
 ### LLM temperature setting for all llm binding (openai, azure_openai, ollama)
 # TEMPERATURE=1.0

lightrag/api/README-zh.md

@@ -40,6 +40,7 @@ LightRAG 需要同时集成 LLM（大型语言模型）和嵌入模型以有效
 * lollms
 * openai 或 openai 兼容
 * azure_openai
+* aws_bedrock
 
 建议使用环境变量来配置 LightRAG 服务器。项目根目录中有一个名为 `env.example` 的示例环境变量文件。请将此文件复制到启动目录并重命名为 `.env`。之后，您可以在 `.env` 文件中修改与 LLM 和嵌入模型相关的参数。需要注意的是，LightRAG 服务器每次启动时都会将 `.env` 中的环境变量加载到系统环境变量中。**LightRAG 服务器会优先使用系统环境变量中的设置**。
 
@@ -359,6 +360,7 @@ LightRAG 支持绑定到各种 LLM/嵌入后端：
 * openai 和 openai 兼容
 * azure_openai
 * lollms
+* aws_bedrock
 
 使用环境变量 `LLM_BINDING` 或 CLI 参数 `--llm-binding` 选择 LLM 后端类型。使用环境变量 `EMBEDDING_BINDING` 或 CLI 参数 `--embedding-binding` 选择嵌入后端类型。
 

lightrag/api/README.md

@@ -40,6 +40,7 @@ LightRAG necessitates the integration of both an LLM (Large Language Model) and
 * lollms
 * openai or openai compatible
 * azure_openai
+* aws_bedrock
 
 It is recommended to use environment variables to configure the LightRAG Server. There is an example environment variable file named `env.example` in the root directory of the project. Please copy this file to the startup directory and rename it to `.env`. After that, you can modify the parameters related to the LLM and Embedding models in the `.env` file. It is important to note that the LightRAG Server will load the environment variables from `.env` into the system environment variables each time it starts. **LightRAG Server will prioritize the settings in the system environment variables to .env file**.
 
@@ -362,6 +363,7 @@ LightRAG supports binding to various LLM/Embedding backends:
 * openai & openai compatible
 * azure_openai
 * lollms
+* aws_bedrock
 
 Use environment variables `LLM_BINDING` or CLI argument `--llm-binding` to select the LLM backend type. Use environment variables `EMBEDDING_BINDING` or CLI argument `--embedding-binding` to select the Embedding backend type.
 
@@ -461,8 +463,8 @@ You cannot change storage implementation selection after adding documents to Lig
 | --ssl-keyfile         | None          | Path to SSL private key file (required if --ssl is enabled)                                                                     |
 | --top-k               | 50            | Number of top-k items to retrieve; corresponds to entities in "local" mode and relationships in "global" mode.                  |
 | --cosine-threshold    | 0.4           | The cosine threshold for nodes and relation retrieval, works with top-k to control the retrieval of nodes and relations.        |
-| --llm-binding         | ollama        | LLM binding type (lollms, ollama, openai, openai-ollama, azure_openai)                                                          |
-| --embedding-binding   | ollama        | Embedding binding type (lollms, ollama, openai, azure_openai)                                                                   |
+| --llm-binding         | ollama        | LLM binding type (lollms, ollama, openai, openai-ollama, azure_openai, aws_bedrock)                                                          |
+| --embedding-binding   | ollama        | Embedding binding type (lollms, ollama, openai, azure_openai, aws_bedrock)                                                                   |
 | --auto-scan-at-startup| -             | Scan input directory for new files and start indexing                                                                           |
 
 ### Additional Ollama Binding Options

lightrag/api/__init__.py

@@ -1 +1 @@
-__api_version__ = "0201"
+__api_version__ = "0202"

lightrag/api/config.py

@@ -209,14 +209,21 @@ def parse_args() -> argparse.Namespace:
         "--llm-binding",
         type=str,
         default=get_env_value("LLM_BINDING", "ollama"),
-        choices=["lollms", "ollama", "openai", "openai-ollama", "azure_openai"],
+        choices=[
+            "lollms",
+            "ollama",
+            "openai",
+            "openai-ollama",
+            "azure_openai",
+            "aws_bedrock",
+        ],
         help="LLM binding type (default: from env or ollama)",
     )
     parser.add_argument(
         "--embedding-binding",
         type=str,
         default=get_env_value("EMBEDDING_BINDING", "ollama"),
-        choices=["lollms", "ollama", "openai", "azure_openai"],
+        choices=["lollms", "ollama", "openai", "azure_openai", "aws_bedrock", "jina"],
         help="Embedding binding type (default: from env or ollama)",
     )
 

lightrag/api/lightrag_server.py

@@ -104,8 +104,8 @@ def create_app(args):
         "lollms",
         "ollama",
         "openai",
-        "openai-ollama",
         "azure_openai",
+        "aws_bedrock",
     ]:
         raise Exception("llm binding not supported")
 
@@ -114,6 +114,7 @@ def create_app(args):
         "ollama",
         "openai",
         "azure_openai",
+        "aws_bedrock",
         "jina",
     ]:
         raise Exception("embedding binding not supported")
@@ -188,10 +189,12 @@ def create_app(args):
     # Initialize FastAPI
     app_kwargs = {
         "title": "LightRAG Server API",
-        "description": "Providing API for LightRAG core, Web UI and Ollama Model Emulation"
-        + "(With authentication)"
-        if api_key
-        else "",
+        "description": (
+            "Providing API for LightRAG core, Web UI and Ollama Model Emulation"
+            + "(With authentication)"
+            if api_key
+            else ""
+        ),
         "version": __api_version__,
         "openapi_url": "/openapi.json",  # Explicitly set OpenAPI schema URL
         "docs_url": "/docs",  # Explicitly set docs URL
@@ -244,9 +247,9 @@ def create_app(args):
             azure_openai_complete_if_cache,
             azure_openai_embed,
         )
-    if args.llm_binding_host == "openai-ollama" or args.embedding_binding == "ollama":
-        from lightrag.llm.openai import openai_complete_if_cache
-        from lightrag.llm.ollama import ollama_embed
+    if args.llm_binding == "aws_bedrock" or args.embedding_binding == "aws_bedrock":
+        from lightrag.llm.bedrock import bedrock_complete_if_cache, bedrock_embed
+    if args.embedding_binding == "ollama":
         from lightrag.llm.binding_options import OllamaEmbeddingOptions
     if args.embedding_binding == "jina":
         from lightrag.llm.jina import jina_embed
@@ -312,41 +315,80 @@ def create_app(args):
             **kwargs,
         )
 
+    async def bedrock_model_complete(
+        prompt,
+        system_prompt=None,
+        history_messages=None,
+        keyword_extraction=False,
+        **kwargs,
+    ) -> str:
+        keyword_extraction = kwargs.pop("keyword_extraction", None)
+        if keyword_extraction:
+            kwargs["response_format"] = GPTKeywordExtractionFormat
+        if history_messages is None:
+            history_messages = []
+
+        # Use global temperature for Bedrock
+        kwargs["temperature"] = args.temperature
+
+        return await bedrock_complete_if_cache(
+            args.llm_model,
+            prompt,
+            system_prompt=system_prompt,
+            history_messages=history_messages,
+            **kwargs,
+        )
+
     embedding_func = EmbeddingFunc(
         embedding_dim=args.embedding_dim,
-        func=lambda texts: lollms_embed(
-            texts,
-            embed_model=args.embedding_model,
-            host=args.embedding_binding_host,
-            api_key=args.embedding_binding_api_key,
-        )
-        if args.embedding_binding == "lollms"
-        else ollama_embed(
-            texts,
-            embed_model=args.embedding_model,
-            host=args.embedding_binding_host,
-            api_key=args.embedding_binding_api_key,
-            options=OllamaEmbeddingOptions.options_dict(args),
-        )
-        if args.embedding_binding == "ollama"
-        else azure_openai_embed(
-            texts,
-            model=args.embedding_model,  # no host is used for openai,
-            api_key=args.embedding_binding_api_key,
-        )
-        if args.embedding_binding == "azure_openai"
-        else jina_embed(
-            texts,
-            dimensions=args.embedding_dim,
-            base_url=args.embedding_binding_host,
-            api_key=args.embedding_binding_api_key,
-        )
-        if args.embedding_binding == "jina"
-        else openai_embed(
-            texts,
-            model=args.embedding_model,
-            base_url=args.embedding_binding_host,
-            api_key=args.embedding_binding_api_key,
+        func=lambda texts: (
+            lollms_embed(
+                texts,
+                embed_model=args.embedding_model,
+                host=args.embedding_binding_host,
+                api_key=args.embedding_binding_api_key,
+            )
+            if args.embedding_binding == "lollms"
+            else (
+                ollama_embed(
+                    texts,
+                    embed_model=args.embedding_model,
+                    host=args.embedding_binding_host,
+                    api_key=args.embedding_binding_api_key,
+                    options=OllamaEmbeddingOptions.options_dict(args),
+                )
+                if args.embedding_binding == "ollama"
+                else (
+                    azure_openai_embed(
+                        texts,
+                        model=args.embedding_model,  # no host is used for openai,
+                        api_key=args.embedding_binding_api_key,
+                    )
+                    if args.embedding_binding == "azure_openai"
+                    else (
+                        bedrock_embed(
+                            texts,
+                            model=args.embedding_model,
+                        )
+                        if args.embedding_binding == "aws_bedrock"
+                        else (
+                            jina_embed(
+                                texts,
+                                dimensions=args.embedding_dim,
+                                base_url=args.embedding_binding_host,
+                                api_key=args.embedding_binding_api_key,
+                            )
+                            if args.embedding_binding == "jina"
+                            else openai_embed(
+                                texts,
+                                model=args.embedding_model,
+                                base_url=args.embedding_binding_host,
+                                api_key=args.embedding_binding_api_key,
+                            )
+                        )
+                    )
+                )
+            )
         ),
     )
 
@@ -386,28 +428,36 @@ def create_app(args):
     )
 
     # Initialize RAG
-    if args.llm_binding in ["lollms", "ollama", "openai"]:
+    if args.llm_binding in ["lollms", "ollama", "openai", "aws_bedrock"]:
         rag = LightRAG(
             working_dir=args.working_dir,
             workspace=args.workspace,
-            llm_model_func=lollms_model_complete
-            if args.llm_binding == "lollms"
-            else ollama_model_complete
-            if args.llm_binding == "ollama"
-            else openai_alike_model_complete,
+            llm_model_func=(
+                lollms_model_complete
+                if args.llm_binding == "lollms"
+                else (
+                    ollama_model_complete
+                    if args.llm_binding == "ollama"
+                    else bedrock_model_complete
+                    if args.llm_binding == "aws_bedrock"
+                    else openai_alike_model_complete
+                )
+            ),
             llm_model_name=args.llm_model,
             llm_model_max_async=args.max_async,
             summary_max_tokens=args.max_tokens,
             chunk_token_size=int(args.chunk_size),
             chunk_overlap_token_size=int(args.chunk_overlap_size),
-            llm_model_kwargs={
-                "host": args.llm_binding_host,
-                "timeout": args.timeout,
-                "options": OllamaLLMOptions.options_dict(args),
-                "api_key": args.llm_binding_api_key,
-            }
-            if args.llm_binding == "lollms" or args.llm_binding == "ollama"
-            else {},
+            llm_model_kwargs=(
+                {
+                    "host": args.llm_binding_host,
+                    "timeout": args.timeout,
+                    "options": OllamaLLMOptions.options_dict(args),
+                    "api_key": args.llm_binding_api_key,
+                }
+                if args.llm_binding == "lollms" or args.llm_binding == "ollama"
+                else {}
+            ),
             embedding_func=embedding_func,
             kv_storage=args.kv_storage,
             graph_storage=args.graph_storage,

lightrag/api/routers/document_routes.py

@@ -734,7 +734,7 @@ class DocumentManager:
         new_files = []
         for ext in self.supported_extensions:
             logger.debug(f"Scanning for {ext} files in {self.input_dir}")
-            for file_path in self.input_dir.rglob(f"*{ext}"):
+            for file_path in self.input_dir.glob(f"*{ext}"):
                 if file_path not in self.indexed_files:
                     new_files.append(file_path)
         return new_files
@@ -746,6 +746,39 @@ class DocumentManager:
         return any(filename.lower().endswith(ext) for ext in self.supported_extensions)
 
 
+def get_unique_filename_in_enqueued(target_dir: Path, original_name: str) -> str:
+    """Generate a unique filename in the target directory by adding numeric suffixes if needed
+
+    Args:
+        target_dir: Target directory path
+        original_name: Original filename
+
+    Returns:
+        str: Unique filename (may have numeric suffix added)
+    """
+    from pathlib import Path
+    import time
+
+    original_path = Path(original_name)
+    base_name = original_path.stem
+    extension = original_path.suffix
+
+    # Try original name first
+    if not (target_dir / original_name).exists():
+        return original_name
+
+    # Try with numeric suffixes 001-999
+    for i in range(1, 1000):
+        suffix = f"{i:03d}"
+        new_name = f"{base_name}_{suffix}{extension}"
+        if not (target_dir / new_name).exists():
+            return new_name
+
+    # Fallback with timestamp if all 999 slots are taken
+    timestamp = int(time.time())
+    return f"{base_name}_{timestamp}{extension}"
+
+
 async def pipeline_enqueue_file(
     rag: LightRAG, file_path: Path, track_id: str = None
 ) -> tuple[bool, str]:
@@ -759,201 +792,446 @@ async def pipeline_enqueue_file(
         tuple: (success: bool, track_id: str)
     """
 
+    # Generate track_id if not provided
+    if track_id is None:
+        track_id = generate_track_id("unknown")
+
     try:
         content = ""
         ext = file_path.suffix.lower()
+        file_size = 0
+
+        # Get file size for error reporting
+        try:
+            file_size = file_path.stat().st_size
+        except Exception:
+            file_size = 0
 
         file = None
-        async with aiofiles.open(file_path, "rb") as f:
-            file = await f.read()
+        try:
+            async with aiofiles.open(file_path, "rb") as f:
+                file = await f.read()
+        except PermissionError as e:
+            error_files = [
+                {
+                    "file_path": str(file_path.name),
+                    "error_description": "Permission denied - cannot read file",
+                    "original_error": str(e),
+                    "file_size": file_size,
+                }
+            ]
+            await rag.apipeline_enqueue_error_documents(error_files, track_id)
+            logger.error(f"Permission denied reading file: {file_path.name}")
+            return False, track_id
+        except FileNotFoundError as e:
+            error_files = [
+                {
+                    "file_path": str(file_path.name),
+                    "error_description": "File not found",
+                    "original_error": str(e),
+                    "file_size": file_size,
+                }
+            ]
+            await rag.apipeline_enqueue_error_documents(error_files, track_id)
+            logger.error(f"File not found: {file_path.name}")
+            return False, track_id
+        except Exception as e:
+            error_files = [
+                {
+                    "file_path": str(file_path.name),
+                    "error_description": "File reading error",
+                    "original_error": str(e),
+                    "file_size": file_size,
+                }
+            ]
+            await rag.apipeline_enqueue_error_documents(error_files, track_id)
+            logger.error(f"Error reading file {file_path.name}: {str(e)}")
+            return False, track_id
 
         # Process based on file type
-        match ext:
-            case (
-                ".txt"
-                | ".md"
-                | ".html"
-                | ".htm"
-                | ".tex"
-                | ".json"
-                | ".xml"
-                | ".yaml"
-                | ".yml"
-                | ".rtf"
-                | ".odt"
-                | ".epub"
-                | ".csv"
-                | ".log"
-                | ".conf"
-                | ".ini"
-                | ".properties"
-                | ".sql"
-                | ".bat"
-                | ".sh"
-                | ".c"
-                | ".cpp"
-                | ".py"
-                | ".java"
-                | ".js"
-                | ".ts"
-                | ".swift"
-                | ".go"
-                | ".rb"
-                | ".php"
-                | ".css"
-                | ".scss"
-                | ".less"
-            ):
-                try:
-                    # Try to decode as UTF-8
-                    content = file.decode("utf-8")
+        try:
+            match ext:
+                case (
+                    ".txt"
+                    | ".md"
+                    | ".html"
+                    | ".htm"
+                    | ".tex"
+                    | ".json"
+                    | ".xml"
+                    | ".yaml"
+                    | ".yml"
+                    | ".rtf"
+                    | ".odt"
+                    | ".epub"
+                    | ".csv"
+                    | ".log"
+                    | ".conf"
+                    | ".ini"
+                    | ".properties"
+                    | ".sql"
+                    | ".bat"
+                    | ".sh"
+                    | ".c"
+                    | ".cpp"
+                    | ".py"
+                    | ".java"
+                    | ".js"
+                    | ".ts"
+                    | ".swift"
+                    | ".go"
+                    | ".rb"
+                    | ".php"
+                    | ".css"
+                    | ".scss"
+                    | ".less"
+                ):
+                    try:
+                        # Try to decode as UTF-8
+                        content = file.decode("utf-8")
 
-                    # Validate content
-                    if not content or len(content.strip()) == 0:
-                        logger.error(f"Empty content in file: {file_path.name}")
-                        return False, ""
-
-                    # Check if content looks like binary data string representation
-                    if content.startswith("b'") or content.startswith('b"'):
-                        logger.error(
-                            f"File {file_path.name} appears to contain binary data representation instead of text"
-                        )
-                        return False, ""
-
-                except UnicodeDecodeError:
-                    logger.error(
-                        f"File {file_path.name} is not valid UTF-8 encoded text. Please convert it to UTF-8 before processing."
-                    )
-                    return False, ""
-            case ".pdf":
-                if global_args.document_loading_engine == "DOCLING":
-                    if not pm.is_installed("docling"):  # type: ignore
-                        pm.install("docling")
-                    from docling.document_converter import DocumentConverter  # type: ignore
-
-                    converter = DocumentConverter()
-                    result = converter.convert(file_path)
-                    content = result.document.export_to_markdown()
-                else:
-                    if not pm.is_installed("pypdf2"):  # type: ignore
-                        pm.install("pypdf2")
-                    from PyPDF2 import PdfReader  # type: ignore
-                    from io import BytesIO
-
-                    pdf_file = BytesIO(file)
-                    reader = PdfReader(pdf_file)
-                    for page in reader.pages:
-                        content += page.extract_text() + "\n"
-            case ".docx":
-                if global_args.document_loading_engine == "DOCLING":
-                    if not pm.is_installed("docling"):  # type: ignore
-                        pm.install("docling")
-                    from docling.document_converter import DocumentConverter  # type: ignore
-
-                    converter = DocumentConverter()
-                    result = converter.convert(file_path)
-                    content = result.document.export_to_markdown()
-                else:
-                    if not pm.is_installed("python-docx"):  # type: ignore
-                        try:
-                            pm.install("python-docx")
-                        except Exception:
-                            pm.install("docx")
-                    from docx import Document  # type: ignore
-                    from io import BytesIO
-
-                    docx_file = BytesIO(file)
-                    doc = Document(docx_file)
-                    content = "\n".join(
-                        [paragraph.text for paragraph in doc.paragraphs]
-                    )
-            case ".pptx":
-                if global_args.document_loading_engine == "DOCLING":
-                    if not pm.is_installed("docling"):  # type: ignore
-                        pm.install("docling")
-                    from docling.document_converter import DocumentConverter  # type: ignore
-
-                    converter = DocumentConverter()
-                    result = converter.convert(file_path)
-                    content = result.document.export_to_markdown()
-                else:
-                    if not pm.is_installed("python-pptx"):  # type: ignore
-                        pm.install("pptx")
-                    from pptx import Presentation  # type: ignore
-                    from io import BytesIO
-
-                    pptx_file = BytesIO(file)
-                    prs = Presentation(pptx_file)
-                    for slide in prs.slides:
-                        for shape in slide.shapes:
-                            if hasattr(shape, "text"):
-                                content += shape.text + "\n"
-            case ".xlsx":
-                if global_args.document_loading_engine == "DOCLING":
-                    if not pm.is_installed("docling"):  # type: ignore
-                        pm.install("docling")
-                    from docling.document_converter import DocumentConverter  # type: ignore
-
-                    converter = DocumentConverter()
-                    result = converter.convert(file_path)
-                    content = result.document.export_to_markdown()
-                else:
-                    if not pm.is_installed("openpyxl"):  # type: ignore
-                        pm.install("openpyxl")
-                    from openpyxl import load_workbook  # type: ignore
-                    from io import BytesIO
-
-                    xlsx_file = BytesIO(file)
-                    wb = load_workbook(xlsx_file)
-                    for sheet in wb:
-                        content += f"Sheet: {sheet.title}\n"
-                        for row in sheet.iter_rows(values_only=True):
-                            content += (
-                                "\t".join(
-                                    str(cell) if cell is not None else ""
-                                    for cell in row
-                                )
-                                + "\n"
+                        # Validate content
+                        if not content or len(content.strip()) == 0:
+                            error_files = [
+                                {
+                                    "file_path": str(file_path.name),
+                                    "error_description": "Empty file content",
+                                    "original_error": "File contains no content or only whitespace",
+                                    "file_size": file_size,
+                                }
+                            ]
+                            await rag.apipeline_enqueue_error_documents(
+                                error_files, track_id
                             )
-                        content += "\n"
-            case _:
-                logger.error(
-                    f"Unsupported file type: {file_path.name} (extension {ext})"
-                )
-                return False, ""
+                            logger.error(f"Empty content in file: {file_path.name}")
+                            return False, track_id
+
+                        # Check if content looks like binary data string representation
+                        if content.startswith("b'") or content.startswith('b"'):
+                            error_files = [
+                                {
+                                    "file_path": str(file_path.name),
+                                    "error_description": "Binary data in text file",
+                                    "original_error": "File appears to contain binary data representation instead of text",
+                                    "file_size": file_size,
+                                }
+                            ]
+                            await rag.apipeline_enqueue_error_documents(
+                                error_files, track_id
+                            )
+                            logger.error(
+                                f"File {file_path.name} appears to contain binary data representation instead of text"
+                            )
+                            return False, track_id
+
+                    except UnicodeDecodeError as e:
+                        error_files = [
+                            {
+                                "file_path": str(file_path.name),
+                                "error_description": "UTF-8 encoding error",
+                                "original_error": f"File is not valid UTF-8 encoded text: {str(e)}",
+                                "file_size": file_size,
+                            }
+                        ]
+                        await rag.apipeline_enqueue_error_documents(
+                            error_files, track_id
+                        )
+                        logger.error(
+                            f"File {file_path.name} is not valid UTF-8 encoded text. Please convert it to UTF-8 before processing."
+                        )
+                        return False, track_id
+
+                case ".pdf":
+                    try:
+                        if global_args.document_loading_engine == "DOCLING":
+                            if not pm.is_installed("docling"):  # type: ignore
+                                pm.install("docling")
+                            from docling.document_converter import DocumentConverter  # type: ignore
+
+                            converter = DocumentConverter()
+                            result = converter.convert(file_path)
+                            content = result.document.export_to_markdown()
+                        else:
+                            if not pm.is_installed("pypdf2"):  # type: ignore
+                                pm.install("pypdf2")
+                            from PyPDF2 import PdfReader  # type: ignore
+                            from io import BytesIO
+
+                            pdf_file = BytesIO(file)
+                            reader = PdfReader(pdf_file)
+                            for page in reader.pages:
+                                content += page.extract_text() + "\n"
+                    except Exception as e:
+                        error_files = [
+                            {
+                                "file_path": str(file_path.name),
+                                "error_description": "PDF processing error",
+                                "original_error": f"Failed to extract text from PDF: {str(e)}",
+                                "file_size": file_size,
+                            }
+                        ]
+                        await rag.apipeline_enqueue_error_documents(
+                            error_files, track_id
+                        )
+                        logger.error(f"Error processing PDF {file_path.name}: {str(e)}")
+                        return False, track_id
+
+                case ".docx":
+                    try:
+                        if global_args.document_loading_engine == "DOCLING":
+                            if not pm.is_installed("docling"):  # type: ignore
+                                pm.install("docling")
+                            from docling.document_converter import DocumentConverter  # type: ignore
+
+                            converter = DocumentConverter()
+                            result = converter.convert(file_path)
+                            content = result.document.export_to_markdown()
+                        else:
+                            if not pm.is_installed("python-docx"):  # type: ignore
+                                try:
+                                    pm.install("python-docx")
+                                except Exception:
+                                    pm.install("docx")
+                            from docx import Document  # type: ignore
+                            from io import BytesIO
+
+                            docx_file = BytesIO(file)
+                            doc = Document(docx_file)
+                            content = "\n".join(
+                                [paragraph.text for paragraph in doc.paragraphs]
+                            )
+                    except Exception as e:
+                        error_files = [
+                            {
+                                "file_path": str(file_path.name),
+                                "error_description": "DOCX processing error",
+                                "original_error": f"Failed to extract text from DOCX: {str(e)}",
+                                "file_size": file_size,
+                            }
+                        ]
+                        await rag.apipeline_enqueue_error_documents(
+                            error_files, track_id
+                        )
+                        logger.error(
+                            f"Error processing DOCX {file_path.name}: {str(e)}"
+                        )
+                        return False, track_id
+
+                case ".pptx":
+                    try:
+                        if global_args.document_loading_engine == "DOCLING":
+                            if not pm.is_installed("docling"):  # type: ignore
+                                pm.install("docling")
+                            from docling.document_converter import DocumentConverter  # type: ignore
+
+                            converter = DocumentConverter()
+                            result = converter.convert(file_path)
+                            content = result.document.export_to_markdown()
+                        else:
+                            if not pm.is_installed("python-pptx"):  # type: ignore
+                                pm.install("pptx")
+                            from pptx import Presentation  # type: ignore
+                            from io import BytesIO
+
+                            pptx_file = BytesIO(file)
+                            prs = Presentation(pptx_file)
+                            for slide in prs.slides:
+                                for shape in slide.shapes:
+                                    if hasattr(shape, "text"):
+                                        content += shape.text + "\n"
+                    except Exception as e:
+                        error_files = [
+                            {
+                                "file_path": str(file_path.name),
+                                "error_description": "PPTX processing error",
+                                "original_error": f"Failed to extract text from PPTX: {str(e)}",
+                                "file_size": file_size,
+                            }
+                        ]
+                        await rag.apipeline_enqueue_error_documents(
+                            error_files, track_id
+                        )
+                        logger.error(
+                            f"Error processing PPTX {file_path.name}: {str(e)}"
+                        )
+                        return False, track_id
+
+                case ".xlsx":
+                    try:
+                        if global_args.document_loading_engine == "DOCLING":
+                            if not pm.is_installed("docling"):  # type: ignore
+                                pm.install("docling")
+                            from docling.document_converter import DocumentConverter  # type: ignore
+
+                            converter = DocumentConverter()
+                            result = converter.convert(file_path)
+                            content = result.document.export_to_markdown()
+                        else:
+                            if not pm.is_installed("openpyxl"):  # type: ignore
+                                pm.install("openpyxl")
+                            from openpyxl import load_workbook  # type: ignore
+                            from io import BytesIO
+
+                            xlsx_file = BytesIO(file)
+                            wb = load_workbook(xlsx_file)
+                            for sheet in wb:
+                                content += f"Sheet: {sheet.title}\n"
+                                for row in sheet.iter_rows(values_only=True):
+                                    content += (
+                                        "\t".join(
+                                            str(cell) if cell is not None else ""
+                                            for cell in row
+                                        )
+                                        + "\n"
+                                    )
+                                content += "\n"
+                    except Exception as e:
+                        error_files = [
+                            {
+                                "file_path": str(file_path.name),
+                                "error_description": "XLSX processing error",
+                                "original_error": f"Failed to extract text from XLSX: {str(e)}",
+                                "file_size": file_size,
+                            }
+                        ]
+                        await rag.apipeline_enqueue_error_documents(
+                            error_files, track_id
+                        )
+                        logger.error(
+                            f"Error processing XLSX {file_path.name}: {str(e)}"
+                        )
+                        return False, track_id
+
+                case _:
+                    error_files = [
+                        {
+                            "file_path": str(file_path.name),
+                            "error_description": f"Unsupported file type: {ext}",
+                            "original_error": f"File extension {ext} is not supported",
+                            "file_size": file_size,
+                        }
+                    ]
+                    await rag.apipeline_enqueue_error_documents(error_files, track_id)
+                    logger.error(
+                        f"Unsupported file type: {file_path.name} (extension {ext})"
+                    )
+                    return False, track_id
+
+        except Exception as e:
+            error_files = [
+                {
+                    "file_path": str(file_path.name),
+                    "error_description": "File format processing error",
+                    "original_error": f"Unexpected error during file extracting: {str(e)}",
+                    "file_size": file_size,
+                }
+            ]
+            await rag.apipeline_enqueue_error_documents(error_files, track_id)
+            logger.error(
+                f"Unexpected error during {file_path.name} extracting: {str(e)}"
+            )
+            return False, track_id
 
         # Insert into the RAG queue
         if content:
             # Check if content contains only whitespace characters
             if not content.strip():
+                error_files = [
+                    {
+                        "file_path": str(file_path.name),
+                        "error_description": "File contains only whitespace",
+                        "original_error": "File content contains only whitespace characters",
+                        "file_size": file_size,
+                    }
+                ]
+                await rag.apipeline_enqueue_error_documents(error_files, track_id)
                 logger.warning(
-                    f"File contains only whitespace characters. file_paths={file_path.name}"
+                    f"File contains only whitespace characters: {file_path.name}"
+                )
+                return False, track_id
+
+            try:
+                await rag.apipeline_enqueue_documents(
+                    content, file_paths=file_path.name, track_id=track_id
                 )
 
-            # Generate track_id if not provided
-            if track_id is None:
-                track_id = generate_track_id("unkown")
+                logger.info(f"Successfully fetched and enqueued file: {file_path.name}")
 
-            await rag.apipeline_enqueue_documents(
-                content, file_paths=file_path.name, track_id=track_id
-            )
+                # Move file to __enqueued__ directory after enqueuing
+                try:
+                    enqueued_dir = file_path.parent / "__enqueued__"
+                    enqueued_dir.mkdir(exist_ok=True)
 
-            logger.info(f"Successfully fetched and enqueued file: {file_path.name}")
-            return True, track_id
+                    # Generate unique filename to avoid conflicts
+                    unique_filename = get_unique_filename_in_enqueued(
+                        enqueued_dir, file_path.name
+                    )
+                    target_path = enqueued_dir / unique_filename
+
+                    # Move the file
+                    file_path.rename(target_path)
+                    logger.debug(
+                        f"Moved file to enqueued directory: {file_path.name} -> {unique_filename}"
+                    )
+
+                except Exception as move_error:
+                    logger.error(
+                        f"Failed to move file {file_path.name} to __enqueued__ directory: {move_error}"
+                    )
+                    # Don't affect the main function's success status
+
+                return True, track_id
+
+            except Exception as e:
+                error_files = [
+                    {
+                        "file_path": str(file_path.name),
+                        "error_description": "Document enqueue error",
+                        "original_error": f"Failed to enqueue document: {str(e)}",
+                        "file_size": file_size,
+                    }
+                ]
+                await rag.apipeline_enqueue_error_documents(error_files, track_id)
+                logger.error(f"Error enqueueing document {file_path.name}: {str(e)}")
+                return False, track_id
         else:
-            logger.error(f"No content could be extracted from file: {file_path.name}")
-            return False, ""
+            error_files = [
+                {
+                    "file_path": str(file_path.name),
+                    "error_description": "No content extracted",
+                    "original_error": "No content could be extracted from file",
+                    "file_size": file_size,
+                }
+            ]
+            await rag.apipeline_enqueue_error_documents(error_files, track_id)
+            logger.error(f"No content extracted from file: {file_path.name}")
+            return False, track_id
 
     except Exception as e:
-        logger.error(f"Error processing or enqueueing file {file_path.name}: {str(e)}")
+        # Catch-all for any unexpected errors
+        try:
+            file_size = file_path.stat().st_size if file_path.exists() else 0
+        except Exception:
+            file_size = 0
+
+        error_files = [
+            {
+                "file_path": str(file_path.name),
+                "error_description": "Unexpected processing error",
+                "original_error": f"Unexpected error: {str(e)}",
+                "file_size": file_size,
+            }
+        ]
+        await rag.apipeline_enqueue_error_documents(error_files, track_id)
+        logger.error(f"Enqueuing file {file_path.name} error: {str(e)}")
         logger.error(traceback.format_exc())
+        return False, track_id
     finally:
         if file_path.name.startswith(temp_prefix):
             try:
                 file_path.unlink()
             except Exception as e:
                 logger.error(f"Error deleting file {file_path}: {str(e)}")
-    return False, ""
 
 
 async def pipeline_index_file(rag: LightRAG, file_path: Path, track_id: str = None):

lightrag/api/webui/assets/_basePickBy-C9JQvwyt.js

@@ -1 +1 @@
-import{e as o,c as l,g as b,k as O,h as P,j as p,l as w,m as c,n as v,t as A,o as N}from"./_baseUniq-Bd5eQEyI.js";import{a_ as g,aw as _,a$ as $,b0 as E,b1 as F,b2 as x,b3 as M,b4 as y,b5 as B,b6 as T}from"./mermaid-vendor-J2ju872p.js";var S=/\s/;function G(n){for(var r=n.length;r--&&S.test(n.charAt(r)););return r}var H=/^\s+/;function L(n){return n&&n.slice(0,G(n)+1).replace(H,"")}var m=NaN,R=/^[-+]0x[0-9a-f]+$/i,q=/^0b[01]+$/i,z=/^0o[0-7]+$/i,C=parseInt;function K(n){if(typeof n=="number")return n;if(o(n))return m;if(g(n)){var r=typeof n.valueOf=="function"?n.valueOf():n;n=g(r)?r+"":r}if(typeof n!="string")return n===0?n:+n;n=L(n);var t=q.test(n);return t||z.test(n)?C(n.slice(2),t?2:8):R.test(n)?m:+n}var W=1/0,X=17976931348623157e292;function Y(n){if(!n)return n===0?n:0;if(n=K(n),n===W||n===-1/0){var r=n<0?-1:1;return r*X}return n===n?n:0}function D(n){var r=Y(n),t=r%1;return r===r?t?r-t:r:0}function fn(n){var r=n==null?0:n.length;return r?l(n):[]}var I=Object.prototype,J=I.hasOwnProperty,dn=_(function(n,r){n=Object(n);var t=-1,e=r.length,i=e>2?r[2]:void 0;for(i&&$(r[0],r[1],i)&&(e=1);++t<e;)for(var f=r[t],a=E(f),s=-1,d=a.length;++s<d;){var u=a[s],h=n[u];(h===void 0||F(h,I[u])&&!J.call(n,u))&&(n[u]=f[u])}return n});function un(n){var r=n==null?0:n.length;return r?n[r-1]:void 0}function Q(n){return function(r,t,e){var i=Object(r);if(!x(r)){var f=b(t);r=O(r),t=function(s){return f(i[s],s,i)}}var a=n(r,t,e);return a>-1?i[f?r[a]:a]:void 0}}var U=Math.max;function Z(n,r,t){var e=n==null?0:n.length;if(!e)return-1;var i=t==null?0:D(t);return i<0&&(i=U(e+i,0)),P(n,b(r),i)}var hn=Q(Z);function V(n,r){var t=-1,e=x(n)?Array(n.length):[];return p(n,function(i,f,a){e[++t]=r(i,f,a)}),e}function gn(n,r){var t=M(n)?w:V;return t(n,b(r))}var j=Object.prototype,k=j.hasOwnProperty;function nn(n,r){return n!=null&&k.call(n,r)}function bn(n,r){return n!=null&&c(n,r,nn)}function rn(n,r){return n<r}function tn(n,r,t){for(var e=-1,i=n.length;++e<i;){var f=n[e],a=r(f);if(a!=null&&(s===void 0?a===a&&!o(a):t(a,s)))var s=a,d=f}return d}function mn(n){return n&&n.length?tn(n,y,rn):void 0}function an(n,r,t,e){if(!g(n))return n;r=v(r,n);for(var i=-1,f=r.length,a=f-1,s=n;s!=null&&++i<f;){var d=A(r[i]),u=t;if(d==="__proto__"||d==="constructor"||d==="prototype")return n;if(i!=a){var h=s[d];u=void 0,u===void 0&&(u=g(h)?h:B(r[i+1])?[]:{})}T(s,d,u),s=s[d]}return n}function on(n,r,t){for(var e=-1,i=r.length,f={};++e<i;){var a=r[e],s=N(n,a);t(s,a)&&an(f,v(a,n),s)}return f}export{rn as a,tn as b,V as c,on as d,mn as e,fn as f,hn as g,bn as h,dn as i,D as j,un as l,gn as m,Y as t};
+import{e as o,c as l,g as b,k as O,h as P,j as p,l as w,m as c,n as v,t as A,o as N}from"./_baseUniq-D1ajN3w6.js";import{a_ as g,aw as _,a$ as $,b0 as E,b1 as F,b2 as x,b3 as M,b4 as y,b5 as B,b6 as T}from"./mermaid-vendor-pCi8nQvB.js";var S=/\s/;function G(n){for(var r=n.length;r--&&S.test(n.charAt(r)););return r}var H=/^\s+/;function L(n){return n&&n.slice(0,G(n)+1).replace(H,"")}var m=NaN,R=/^[-+]0x[0-9a-f]+$/i,q=/^0b[01]+$/i,z=/^0o[0-7]+$/i,C=parseInt;function K(n){if(typeof n=="number")return n;if(o(n))return m;if(g(n)){var r=typeof n.valueOf=="function"?n.valueOf():n;n=g(r)?r+"":r}if(typeof n!="string")return n===0?n:+n;n=L(n);var t=q.test(n);return t||z.test(n)?C(n.slice(2),t?2:8):R.test(n)?m:+n}var W=1/0,X=17976931348623157e292;function Y(n){if(!n)return n===0?n:0;if(n=K(n),n===W||n===-1/0){var r=n<0?-1:1;return r*X}return n===n?n:0}function D(n){var r=Y(n),t=r%1;return r===r?t?r-t:r:0}function fn(n){var r=n==null?0:n.length;return r?l(n):[]}var I=Object.prototype,J=I.hasOwnProperty,dn=_(function(n,r){n=Object(n);var t=-1,e=r.length,i=e>2?r[2]:void 0;for(i&&$(r[0],r[1],i)&&(e=1);++t<e;)for(var f=r[t],a=E(f),s=-1,d=a.length;++s<d;){var u=a[s],h=n[u];(h===void 0||F(h,I[u])&&!J.call(n,u))&&(n[u]=f[u])}return n});function un(n){var r=n==null?0:n.length;return r?n[r-1]:void 0}function Q(n){return function(r,t,e){var i=Object(r);if(!x(r)){var f=b(t);r=O(r),t=function(s){return f(i[s],s,i)}}var a=n(r,t,e);return a>-1?i[f?r[a]:a]:void 0}}var U=Math.max;function Z(n,r,t){var e=n==null?0:n.length;if(!e)return-1;var i=t==null?0:D(t);return i<0&&(i=U(e+i,0)),P(n,b(r),i)}var hn=Q(Z);function V(n,r){var t=-1,e=x(n)?Array(n.length):[];return p(n,function(i,f,a){e[++t]=r(i,f,a)}),e}function gn(n,r){var t=M(n)?w:V;return t(n,b(r))}var j=Object.prototype,k=j.hasOwnProperty;function nn(n,r){return n!=null&&k.call(n,r)}function bn(n,r){return n!=null&&c(n,r,nn)}function rn(n,r){return n<r}function tn(n,r,t){for(var e=-1,i=n.length;++e<i;){var f=n[e],a=r(f);if(a!=null&&(s===void 0?a===a&&!o(a):t(a,s)))var s=a,d=f}return d}function mn(n){return n&&n.length?tn(n,y,rn):void 0}function an(n,r,t,e){if(!g(n))return n;r=v(r,n);for(var i=-1,f=r.length,a=f-1,s=n;s!=null&&++i<f;){var d=A(r[i]),u=t;if(d==="__proto__"||d==="constructor"||d==="prototype")return n;if(i!=a){var h=s[d];u=void 0,u===void 0&&(u=g(h)?h:B(r[i+1])?[]:{})}T(s,d,u),s=s[d]}return n}function on(n,r,t){for(var e=-1,i=r.length,f={};++e<i;){var a=r[e],s=N(n,a);t(s,a)&&an(f,v(a,n),s)}return f}export{rn as a,tn as b,V as c,on as d,mn as e,fn as f,hn as g,bn as h,dn as i,D as j,un as l,gn as m,Y as t};

lightrag/api/webui/assets/chunk-353BL4L5-DjMQec0j.js

@@ -1 +1 @@
-import{_ as l}from"./mermaid-vendor-J2ju872p.js";function m(e,c){var i,t,o;e.accDescr&&((i=c.setAccDescription)==null||i.call(c,e.accDescr)),e.accTitle&&((t=c.setAccTitle)==null||t.call(c,e.accTitle)),e.title&&((o=c.setDiagramTitle)==null||o.call(c,e.title))}l(m,"populateCommonDb");export{m as p};
+import{_ as l}from"./mermaid-vendor-pCi8nQvB.js";function m(e,c){var i,t,o;e.accDescr&&((i=c.setAccDescription)==null||i.call(c,e.accDescr)),e.accTitle&&((t=c.setAccTitle)==null||t.call(c,e.accTitle)),e.title&&((o=c.setDiagramTitle)==null||o.call(c,e.title))}l(m,"populateCommonDb");export{m as p};

lightrag/api/webui/assets/chunk-67H74DCK-BytGFags.js

@@ -1 +1 @@
-import{_ as n,a2 as x,j as l}from"./mermaid-vendor-J2ju872p.js";var c=n((a,t)=>{const e=a.append("rect");if(e.attr("x",t.x),e.attr("y",t.y),e.attr("fill",t.fill),e.attr("stroke",t.stroke),e.attr("width",t.width),e.attr("height",t.height),t.name&&e.attr("name",t.name),t.rx&&e.attr("rx",t.rx),t.ry&&e.attr("ry",t.ry),t.attrs!==void 0)for(const r in t.attrs)e.attr(r,t.attrs[r]);return t.class&&e.attr("class",t.class),e},"drawRect"),d=n((a,t)=>{const e={x:t.startx,y:t.starty,width:t.stopx-t.startx,height:t.stopy-t.starty,fill:t.fill,stroke:t.stroke,class:"rect"};c(a,e).lower()},"drawBackgroundRect"),g=n((a,t)=>{const e=t.text.replace(x," "),r=a.append("text");r.attr("x",t.x),r.attr("y",t.y),r.attr("class","legend"),r.style("text-anchor",t.anchor),t.class&&r.attr("class",t.class);const s=r.append("tspan");return s.attr("x",t.x+t.textMargin*2),s.text(e),r},"drawText"),h=n((a,t,e,r)=>{const s=a.append("image");s.attr("x",t),s.attr("y",e);const i=l.sanitizeUrl(r);s.attr("xlink:href",i)},"drawImage"),m=n((a,t,e,r)=>{const s=a.append("use");s.attr("x",t),s.attr("y",e);const i=l.sanitizeUrl(r);s.attr("xlink:href",`#${i}`)},"drawEmbeddedImage"),y=n(()=>({x:0,y:0,width:100,height:100,fill:"#EDF2AE",stroke:"#666",anchor:"start",rx:0,ry:0}),"getNoteRect"),p=n(()=>({x:0,y:0,width:100,height:100,"text-anchor":"start",style:"#666",textMargin:0,rx:0,ry:0,tspan:!0}),"getTextObj");export{d as a,p as b,m as c,c as d,h as e,g as f,y as g};
+import{_ as n,a2 as x,j as l}from"./mermaid-vendor-pCi8nQvB.js";var c=n((a,t)=>{const e=a.append("rect");if(e.attr("x",t.x),e.attr("y",t.y),e.attr("fill",t.fill),e.attr("stroke",t.stroke),e.attr("width",t.width),e.attr("height",t.height),t.name&&e.attr("name",t.name),t.rx&&e.attr("rx",t.rx),t.ry&&e.attr("ry",t.ry),t.attrs!==void 0)for(const r in t.attrs)e.attr(r,t.attrs[r]);return t.class&&e.attr("class",t.class),e},"drawRect"),d=n((a,t)=>{const e={x:t.startx,y:t.starty,width:t.stopx-t.startx,height:t.stopy-t.starty,fill:t.fill,stroke:t.stroke,class:"rect"};c(a,e).lower()},"drawBackgroundRect"),g=n((a,t)=>{const e=t.text.replace(x," "),r=a.append("text");r.attr("x",t.x),r.attr("y",t.y),r.attr("class","legend"),r.style("text-anchor",t.anchor),t.class&&r.attr("class",t.class);const s=r.append("tspan");return s.attr("x",t.x+t.textMargin*2),s.text(e),r},"drawText"),h=n((a,t,e,r)=>{const s=a.append("image");s.attr("x",t),s.attr("y",e);const i=l.sanitizeUrl(r);s.attr("xlink:href",i)},"drawImage"),m=n((a,t,e,r)=>{const s=a.append("use");s.attr("x",t),s.attr("y",e);const i=l.sanitizeUrl(r);s.attr("xlink:href",`#${i}`)},"drawEmbeddedImage"),y=n(()=>({x:0,y:0,width:100,height:100,fill:"#EDF2AE",stroke:"#666",anchor:"start",rx:0,ry:0}),"getNoteRect"),p=n(()=>({x:0,y:0,width:100,height:100,"text-anchor":"start",style:"#666",textMargin:0,rx:0,ry:0,tspan:!0}),"getTextObj");export{d as a,p as b,m as c,c as d,h as e,g as f,y as g};

lightrag/api/webui/assets/chunk-AACKK3MU-B5Ohn46S.js

@@ -1 +1 @@
-import{_ as s}from"./mermaid-vendor-J2ju872p.js";var t,e=(t=class{constructor(i){this.init=i,this.records=this.init()}reset(){this.records=this.init()}},s(t,"ImperativeState"),t);export{e as I};
+import{_ as s}from"./mermaid-vendor-pCi8nQvB.js";var t,e=(t=class{constructor(i){this.init=i,this.records=this.init()}reset(){this.records=this.init()}},s(t,"ImperativeState"),t);export{e as I};

lightrag/api/webui/assets/chunk-BFAMUDN2-ioRYrumN.js

@@ -1 +1 @@
-import{_ as a,d as o}from"./mermaid-vendor-J2ju872p.js";var d=a((t,e)=>{let n;return e==="sandbox"&&(n=o("#i"+t)),(e==="sandbox"?o(n.nodes()[0].contentDocument.body):o("body")).select(`[id="${t}"]`)},"getDiagramElement");export{d as g};
+import{_ as a,d as o}from"./mermaid-vendor-pCi8nQvB.js";var d=a((t,e)=>{let n;return e==="sandbox"&&(n=o("#i"+t)),(e==="sandbox"?o(n.nodes()[0].contentDocument.body):o("body")).select(`[id="${t}"]`)},"getDiagramElement");export{d as g};

lightrag/api/webui/assets/chunk-E2GYISFI-wNdHFpBQ.js

@@ -1,4 +1,4 @@
-import{_ as e}from"./mermaid-vendor-J2ju872p.js";var l=e(()=>`
+import{_ as e}from"./mermaid-vendor-pCi8nQvB.js";var l=e(()=>`
   /* Font Awesome icon styling - consolidated */
   .label-icon {
     display: inline-block;

lightrag/api/webui/assets/chunk-SKB7J2MH-BsoIgbf0.js

@@ -1 +1 @@
-import{_ as a,e as w,l as x}from"./mermaid-vendor-J2ju872p.js";var d=a((e,t,i,o)=>{e.attr("class",i);const{width:r,height:h,x:n,y:c}=u(e,t);w(e,h,r,o);const s=l(n,c,r,h,t);e.attr("viewBox",s),x.debug(`viewBox configured: ${s} with padding: ${t}`)},"setupViewPortForSVG"),u=a((e,t)=>{var o;const i=((o=e.node())==null?void 0:o.getBBox())||{width:0,height:0,x:0,y:0};return{width:i.width+t*2,height:i.height+t*2,x:i.x,y:i.y}},"calculateDimensionsWithPadding"),l=a((e,t,i,o,r)=>`${e-r} ${t-r} ${i} ${o}`,"createViewBox");export{d as s};
+import{_ as a,e as w,l as x}from"./mermaid-vendor-pCi8nQvB.js";var d=a((e,t,i,o)=>{e.attr("class",i);const{width:r,height:h,x:n,y:c}=u(e,t);w(e,h,r,o);const s=l(n,c,r,h,t);e.attr("viewBox",s),x.debug(`viewBox configured: ${s} with padding: ${t}`)},"setupViewPortForSVG"),u=a((e,t)=>{var o;const i=((o=e.node())==null?void 0:o.getBBox())||{width:0,height:0,x:0,y:0};return{width:i.width+t*2,height:i.height+t*2,x:i.x,y:i.y}},"calculateDimensionsWithPadding"),l=a((e,t,i,o,r)=>`${e-r} ${t-r} ${i} ${o}`,"createViewBox");export{d as s};

lightrag/api/webui/assets/classDiagram-M3E45YP4-C5IWamtR.js

@@ -1 +0,0 @@
-import{s as a,c as s,a as e,C as t}from"./chunk-SZ463SBG-DomaNiTF.js";import{_ as i}from"./mermaid-vendor-J2ju872p.js";import"./chunk-E2GYISFI-DtDXRKC8.js";import"./chunk-BFAMUDN2-nPRHB0S3.js";import"./chunk-SKB7J2MH-Cgz1KI3w.js";import"./feature-graph-BgVuQYut.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var c={parser:e,get db(){return new t},renderer:s,styles:a,init:i(r=>{r.class||(r.class={}),r.class.arrowMarkerAbsolute=r.arrowMarkerAbsolute},"init")};export{c as diagram};

lightrag/api/webui/assets/classDiagram-M3E45YP4-CIje27eO.js

@@ -0,0 +1 @@
+import{s as a,c as s,a as e,C as t}from"./chunk-SZ463SBG-BpuXKCOy.js";import{_ as i}from"./mermaid-vendor-pCi8nQvB.js";import"./chunk-E2GYISFI-wNdHFpBQ.js";import"./chunk-BFAMUDN2-ioRYrumN.js";import"./chunk-SKB7J2MH-BsoIgbf0.js";import"./feature-graph-O43AXICd.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var c={parser:e,get db(){return new t},renderer:s,styles:a,init:i(r=>{r.class||(r.class={}),r.class.arrowMarkerAbsolute=r.arrowMarkerAbsolute},"init")};export{c as diagram};

lightrag/api/webui/assets/classDiagram-v2-YAWTLIQI-C5IWamtR.js

@@ -1 +0,0 @@
-import{s as a,c as s,a as e,C as t}from"./chunk-SZ463SBG-DomaNiTF.js";import{_ as i}from"./mermaid-vendor-J2ju872p.js";import"./chunk-E2GYISFI-DtDXRKC8.js";import"./chunk-BFAMUDN2-nPRHB0S3.js";import"./chunk-SKB7J2MH-Cgz1KI3w.js";import"./feature-graph-BgVuQYut.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var c={parser:e,get db(){return new t},renderer:s,styles:a,init:i(r=>{r.class||(r.class={}),r.class.arrowMarkerAbsolute=r.arrowMarkerAbsolute},"init")};export{c as diagram};

lightrag/api/webui/assets/classDiagram-v2-YAWTLIQI-CIje27eO.js

@@ -0,0 +1 @@
+import{s as a,c as s,a as e,C as t}from"./chunk-SZ463SBG-BpuXKCOy.js";import{_ as i}from"./mermaid-vendor-pCi8nQvB.js";import"./chunk-E2GYISFI-wNdHFpBQ.js";import"./chunk-BFAMUDN2-ioRYrumN.js";import"./chunk-SKB7J2MH-BsoIgbf0.js";import"./feature-graph-O43AXICd.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var c={parser:e,get db(){return new t},renderer:s,styles:a,init:i(r=>{r.class||(r.class={}),r.class.arrowMarkerAbsolute=r.arrowMarkerAbsolute},"init")};export{c as diagram};

lightrag/api/webui/assets/clone-CzjUKlzi.js

@@ -1 +0,0 @@
-import{b as r}from"./_baseUniq-Bd5eQEyI.js";var e=4;function a(o){return r(o,e)}export{a as c};

lightrag/api/webui/assets/clone-DLVgXiWv.js

@@ -0,0 +1 @@
+import{b as r}from"./_baseUniq-D1ajN3w6.js";var e=4;function a(o){return r(o,e)}export{a as c};

lightrag/api/webui/assets/diagram-5UYTHUR4-lpdtuvSd.js

@@ -1,4 +1,4 @@
-import{p as y}from"./chunk-353BL4L5-leSB0xif.js";import{_ as l,s as B,g as S,t as z,q as F,a as P,b as E,F as v,K as W,e as T,z as D,G as _,H as A,l as w}from"./mermaid-vendor-J2ju872p.js";import{p as N}from"./treemap-75Q7IDZK-BiruUsnx.js";import"./feature-graph-BgVuQYut.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-Bd5eQEyI.js";import"./_basePickBy-C-Sjt21Q.js";import"./clone-CzjUKlzi.js";var x={packet:[]},m=structuredClone(x),L=A.packet,Y=l(()=>{const t=v({...L,..._().packet});return t.showBits&&(t.paddingY+=10),t},"getConfig"),G=l(()=>m.packet,"getPacket"),H=l(t=>{t.length>0&&m.packet.push(t)},"pushWord"),I=l(()=>{D(),m=structuredClone(x)},"clear"),u={pushWord:H,getPacket:G,getConfig:Y,clear:I,setAccTitle:E,getAccTitle:P,setDiagramTitle:F,getDiagramTitle:z,getAccDescription:S,setAccDescription:B},K=1e4,M=l(t=>{y(t,u);let e=-1,o=[],n=1;const{bitsPerRow:i}=u.getConfig();for(let{start:a,end:r,bits:c,label:f}of t.blocks){if(a!==void 0&&r!==void 0&&r<a)throw new Error(`Packet block ${a} - ${r} is invalid. End must be greater than start.`);if(a??(a=e+1),a!==e+1)throw new Error(`Packet block ${a} - ${r??a} is not contiguous. It should start from ${e+1}.`);if(c===0)throw new Error(`Packet block ${a} is invalid. Cannot have a zero bit field.`);for(r??(r=a+(c??1)-1),c??(c=r-a+1),e=r,w.debug(`Packet block ${a} - ${e} with label ${f}`);o.length<=i+1&&u.getPacket().length<K;){const[d,p]=O({start:a,end:r,bits:c,label:f},n,i);if(o.push(d),d.end+1===n*i&&(u.pushWord(o),o=[],n++),!p)break;({start:a,end:r,bits:c,label:f}=p)}}u.pushWord(o)},"populate"),O=l((t,e,o)=>{if(t.start===void 0)throw new Error("start should have been set during first phase");if(t.end===void 0)throw new Error("end should have been set during first phase");if(t.start>t.end)throw new Error(`Block start ${t.start} is greater than block end ${t.end}.`);if(t.end+1<=e*o)return[t,void 0];const n=e*o-1,i=e*o;return[{start:t.start,end:n,label:t.label,bits:n-t.start},{start:i,end:t.end,label:t.label,bits:t.end-i}]},"getNextFittingBlock"),q={parse:l(async t=>{const e=await N("packet",t);w.debug(e),M(e)},"parse")},R=l((t,e,o,n)=>{const i=n.db,a=i.getConfig(),{rowHeight:r,paddingY:c,bitWidth:f,bitsPerRow:d}=a,p=i.getPacket(),s=i.getDiagramTitle(),k=r+c,g=k*(p.length+1)-(s?0:r),b=f*d+2,h=W(e);h.attr("viewbox",`0 0 ${b} ${g}`),T(h,g,b,a.useMaxWidth);for(const[C,$]of p.entries())U(h,$,C,a);h.append("text").text(s).attr("x",b/2).attr("y",g-k/2).attr("dominant-baseline","middle").attr("text-anchor","middle").attr("class","packetTitle")},"draw"),U=l((t,e,o,{rowHeight:n,paddingX:i,paddingY:a,bitWidth:r,bitsPerRow:c,showBits:f})=>{const d=t.append("g"),p=o*(n+a)+a;for(const s of e){const k=s.start%c*r+1,g=(s.end-s.start+1)*r-i;if(d.append("rect").attr("x",k).attr("y",p).attr("width",g).attr("height",n).attr("class","packetBlock"),d.append("text").attr("x",k+g/2).attr("y",p+n/2).attr("class","packetLabel").attr("dominant-baseline","middle").attr("text-anchor","middle").text(s.label),!f)continue;const b=s.end===s.start,h=p-2;d.append("text").attr("x",k+(b?g/2:0)).attr("y",h).attr("class","packetByte start").attr("dominant-baseline","auto").attr("text-anchor",b?"middle":"start").text(s.start),b||d.append("text").attr("x",k+g).attr("y",h).attr("class","packetByte end").attr("dominant-baseline","auto").attr("text-anchor","end").text(s.end)}},"drawWord"),X={draw:R},j={byteFontSize:"10px",startByteColor:"black",endByteColor:"black",labelColor:"black",labelFontSize:"12px",titleColor:"black",titleFontSize:"14px",blockStrokeColor:"black",blockStrokeWidth:"1",blockFillColor:"#efefef"},J=l(({packet:t}={})=>{const e=v(j,t);return`
+import{p as y}from"./chunk-353BL4L5-DjMQec0j.js";import{_ as l,s as B,g as S,t as z,q as F,a as P,b as E,F as v,K as W,e as T,z as D,G as _,H as A,l as w}from"./mermaid-vendor-pCi8nQvB.js";import{p as N}from"./treemap-75Q7IDZK-DhCk0PFk.js";import"./feature-graph-O43AXICd.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-D1ajN3w6.js";import"./_basePickBy-C9JQvwyt.js";import"./clone-DLVgXiWv.js";var x={packet:[]},m=structuredClone(x),L=A.packet,Y=l(()=>{const t=v({...L,..._().packet});return t.showBits&&(t.paddingY+=10),t},"getConfig"),G=l(()=>m.packet,"getPacket"),H=l(t=>{t.length>0&&m.packet.push(t)},"pushWord"),I=l(()=>{D(),m=structuredClone(x)},"clear"),u={pushWord:H,getPacket:G,getConfig:Y,clear:I,setAccTitle:E,getAccTitle:P,setDiagramTitle:F,getDiagramTitle:z,getAccDescription:S,setAccDescription:B},K=1e4,M=l(t=>{y(t,u);let e=-1,o=[],n=1;const{bitsPerRow:i}=u.getConfig();for(let{start:a,end:r,bits:c,label:f}of t.blocks){if(a!==void 0&&r!==void 0&&r<a)throw new Error(`Packet block ${a} - ${r} is invalid. End must be greater than start.`);if(a??(a=e+1),a!==e+1)throw new Error(`Packet block ${a} - ${r??a} is not contiguous. It should start from ${e+1}.`);if(c===0)throw new Error(`Packet block ${a} is invalid. Cannot have a zero bit field.`);for(r??(r=a+(c??1)-1),c??(c=r-a+1),e=r,w.debug(`Packet block ${a} - ${e} with label ${f}`);o.length<=i+1&&u.getPacket().length<K;){const[d,p]=O({start:a,end:r,bits:c,label:f},n,i);if(o.push(d),d.end+1===n*i&&(u.pushWord(o),o=[],n++),!p)break;({start:a,end:r,bits:c,label:f}=p)}}u.pushWord(o)},"populate"),O=l((t,e,o)=>{if(t.start===void 0)throw new Error("start should have been set during first phase");if(t.end===void 0)throw new Error("end should have been set during first phase");if(t.start>t.end)throw new Error(`Block start ${t.start} is greater than block end ${t.end}.`);if(t.end+1<=e*o)return[t,void 0];const n=e*o-1,i=e*o;return[{start:t.start,end:n,label:t.label,bits:n-t.start},{start:i,end:t.end,label:t.label,bits:t.end-i}]},"getNextFittingBlock"),q={parse:l(async t=>{const e=await N("packet",t);w.debug(e),M(e)},"parse")},R=l((t,e,o,n)=>{const i=n.db,a=i.getConfig(),{rowHeight:r,paddingY:c,bitWidth:f,bitsPerRow:d}=a,p=i.getPacket(),s=i.getDiagramTitle(),k=r+c,g=k*(p.length+1)-(s?0:r),b=f*d+2,h=W(e);h.attr("viewbox",`0 0 ${b} ${g}`),T(h,g,b,a.useMaxWidth);for(const[C,$]of p.entries())U(h,$,C,a);h.append("text").text(s).attr("x",b/2).attr("y",g-k/2).attr("dominant-baseline","middle").attr("text-anchor","middle").attr("class","packetTitle")},"draw"),U=l((t,e,o,{rowHeight:n,paddingX:i,paddingY:a,bitWidth:r,bitsPerRow:c,showBits:f})=>{const d=t.append("g"),p=o*(n+a)+a;for(const s of e){const k=s.start%c*r+1,g=(s.end-s.start+1)*r-i;if(d.append("rect").attr("x",k).attr("y",p).attr("width",g).attr("height",n).attr("class","packetBlock"),d.append("text").attr("x",k+g/2).attr("y",p+n/2).attr("class","packetLabel").attr("dominant-baseline","middle").attr("text-anchor","middle").text(s.label),!f)continue;const b=s.end===s.start,h=p-2;d.append("text").attr("x",k+(b?g/2:0)).attr("y",h).attr("class","packetByte start").attr("dominant-baseline","auto").attr("text-anchor",b?"middle":"start").text(s.start),b||d.append("text").attr("x",k+g).attr("y",h).attr("class","packetByte end").attr("dominant-baseline","auto").attr("text-anchor","end").text(s.end)}},"drawWord"),X={draw:R},j={byteFontSize:"10px",startByteColor:"black",endByteColor:"black",labelColor:"black",labelFontSize:"12px",titleColor:"black",titleFontSize:"14px",blockStrokeColor:"black",blockStrokeWidth:"1",blockFillColor:"#efefef"},J=l(({packet:t}={})=>{const e=v(j,t);return`
 	.packetByte {
 		font-size: ${e.byteFontSize};
 	}

lightrag/api/webui/assets/flowDiagram-KYDEHFYC-C3redLoG.js

@@ -1,4 +1,4 @@
-import{g as q1}from"./chunk-E2GYISFI-DtDXRKC8.js";import{_ as m,o as O1,l as ee,c as be,d as Se,p as H1,r as X1,u as i1,b as Q1,s as J1,q as Z1,a as $1,g as et,t as tt,k as st,v as it,J as rt,x as nt,y as s1,z as at,A as ut,B as lt,C as ot}from"./mermaid-vendor-J2ju872p.js";import{g as ct}from"./chunk-BFAMUDN2-nPRHB0S3.js";import{s as ht}from"./chunk-SKB7J2MH-Cgz1KI3w.js";import"./feature-graph-BgVuQYut.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var dt="flowchart-",Pe,pt=(Pe=class{constructor(){this.vertexCounter=0,this.config=be(),this.vertices=new Map,this.edges=[],this.classes=new Map,this.subGraphs=[],this.subGraphLookup=new Map,this.tooltips=new Map,this.subCount=0,this.firstGraphFlag=!0,this.secCount=-1,this.posCrossRef=[],this.funs=[],this.setAccTitle=Q1,this.setAccDescription=J1,this.setDiagramTitle=Z1,this.getAccTitle=$1,this.getAccDescription=et,this.getDiagramTitle=tt,this.funs.push(this.setupToolTips.bind(this)),this.addVertex=this.addVertex.bind(this),this.firstGraph=this.firstGraph.bind(this),this.setDirection=this.setDirection.bind(this),this.addSubGraph=this.addSubGraph.bind(this),this.addLink=this.addLink.bind(this),this.setLink=this.setLink.bind(this),this.updateLink=this.updateLink.bind(this),this.addClass=this.addClass.bind(this),this.setClass=this.setClass.bind(this),this.destructLink=this.destructLink.bind(this),this.setClickEvent=this.setClickEvent.bind(this),this.setTooltip=this.setTooltip.bind(this),this.updateLinkInterpolate=this.updateLinkInterpolate.bind(this),this.setClickFun=this.setClickFun.bind(this),this.bindFunctions=this.bindFunctions.bind(this),this.lex={firstGraph:this.firstGraph.bind(this)},this.clear(),this.setGen("gen-2")}sanitizeText(i){return st.sanitizeText(i,this.config)}lookUpDomId(i){for(const n of this.vertices.values())if(n.id===i)return n.domId;return i}addVertex(i,n,a,u,l,f,c={},A){var V,C;if(!i||i.trim().length===0)return;let r;if(A!==void 0){let p;A.includes(`
+import{g as q1}from"./chunk-E2GYISFI-wNdHFpBQ.js";import{_ as m,o as O1,l as ee,c as be,d as Se,p as H1,r as X1,u as i1,b as Q1,s as J1,q as Z1,a as $1,g as et,t as tt,k as st,v as it,J as rt,x as nt,y as s1,z as at,A as ut,B as lt,C as ot}from"./mermaid-vendor-pCi8nQvB.js";import{g as ct}from"./chunk-BFAMUDN2-ioRYrumN.js";import{s as ht}from"./chunk-SKB7J2MH-BsoIgbf0.js";import"./feature-graph-O43AXICd.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var dt="flowchart-",Pe,pt=(Pe=class{constructor(){this.vertexCounter=0,this.config=be(),this.vertices=new Map,this.edges=[],this.classes=new Map,this.subGraphs=[],this.subGraphLookup=new Map,this.tooltips=new Map,this.subCount=0,this.firstGraphFlag=!0,this.secCount=-1,this.posCrossRef=[],this.funs=[],this.setAccTitle=Q1,this.setAccDescription=J1,this.setDiagramTitle=Z1,this.getAccTitle=$1,this.getAccDescription=et,this.getDiagramTitle=tt,this.funs.push(this.setupToolTips.bind(this)),this.addVertex=this.addVertex.bind(this),this.firstGraph=this.firstGraph.bind(this),this.setDirection=this.setDirection.bind(this),this.addSubGraph=this.addSubGraph.bind(this),this.addLink=this.addLink.bind(this),this.setLink=this.setLink.bind(this),this.updateLink=this.updateLink.bind(this),this.addClass=this.addClass.bind(this),this.setClass=this.setClass.bind(this),this.destructLink=this.destructLink.bind(this),this.setClickEvent=this.setClickEvent.bind(this),this.setTooltip=this.setTooltip.bind(this),this.updateLinkInterpolate=this.updateLinkInterpolate.bind(this),this.setClickFun=this.setClickFun.bind(this),this.bindFunctions=this.bindFunctions.bind(this),this.lex={firstGraph:this.firstGraph.bind(this)},this.clear(),this.setGen("gen-2")}sanitizeText(i){return st.sanitizeText(i,this.config)}lookUpDomId(i){for(const n of this.vertices.values())if(n.id===i)return n.domId;return i}addVertex(i,n,a,u,l,f,c={},A){var V,C;if(!i||i.trim().length===0)return;let r;if(A!==void 0){let p;A.includes(`
 `)?p=A+`
 `:p=`{
 `+A+`

lightrag/api/webui/assets/infoDiagram-LHK5PUON-MJbiXDA4.js

@@ -1,2 +1,2 @@
-import{_ as e,l as o,K as i,e as n,L as p}from"./mermaid-vendor-J2ju872p.js";import{p as m}from"./treemap-75Q7IDZK-BiruUsnx.js";import"./feature-graph-BgVuQYut.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-Bd5eQEyI.js";import"./_basePickBy-C-Sjt21Q.js";import"./clone-CzjUKlzi.js";var g={parse:e(async r=>{const a=await m("info",r);o.debug(a)},"parse")},v={version:p.version+""},d=e(()=>v.version,"getVersion"),c={getVersion:d},l=e((r,a,s)=>{o.debug(`rendering info diagram
+import{_ as e,l as o,K as i,e as n,L as p}from"./mermaid-vendor-pCi8nQvB.js";import{p as m}from"./treemap-75Q7IDZK-DhCk0PFk.js";import"./feature-graph-O43AXICd.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-D1ajN3w6.js";import"./_basePickBy-C9JQvwyt.js";import"./clone-DLVgXiWv.js";var g={parse:e(async r=>{const a=await m("info",r);o.debug(a)},"parse")},v={version:p.version+""},d=e(()=>v.version,"getVersion"),c={getVersion:d},l=e((r,a,s)=>{o.debug(`rendering info diagram
 `+r);const t=i(a);n(t,100,400,!0),t.append("g").append("text").attr("x",100).attr("y",40).attr("class","version").attr("font-size",32).style("text-anchor","middle").text(`v${s}`)},"draw"),f={draw:l},L={parser:g,db:c,renderer:f};export{L as diagram};

lightrag/api/webui/assets/journeyDiagram-EWQZEKCU-DQe_DHiv.js

@@ -1,4 +1,4 @@
-import{a as gt,g as lt,f as mt,d as xt}from"./chunk-67H74DCK-Dq7l-UYX.js";import{g as kt}from"./chunk-E2GYISFI-DtDXRKC8.js";import{_ as r,g as _t,s as bt,a as vt,b as wt,t as Tt,q as St,c as R,d as G,e as $t,z as Mt,N as et}from"./mermaid-vendor-J2ju872p.js";import"./feature-graph-BgVuQYut.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var U=function(){var t=r(function(h,n,a,l){for(a=a||{},l=h.length;l--;a[h[l]]=n);return a},"o"),e=[6,8,10,11,12,14,16,17,18],s=[1,9],c=[1,10],i=[1,11],f=[1,12],u=[1,13],y=[1,14],g={trace:r(function(){},"trace"),yy:{},symbols_:{error:2,start:3,journey:4,document:5,EOF:6,line:7,SPACE:8,statement:9,NEWLINE:10,title:11,acc_title:12,acc_title_value:13,acc_descr:14,acc_descr_value:15,acc_descr_multiline_value:16,section:17,taskName:18,taskData:19,$accept:0,$end:1},terminals_:{2:"error",4:"journey",6:"EOF",8:"SPACE",10:"NEWLINE",11:"title",12:"acc_title",13:"acc_title_value",14:"acc_descr",15:"acc_descr_value",16:"acc_descr_multiline_value",17:"section",18:"taskName",19:"taskData"},productions_:[0,[3,3],[5,0],[5,2],[7,2],[7,1],[7,1],[7,1],[9,1],[9,2],[9,2],[9,1],[9,1],[9,2]],performAction:r(function(n,a,l,d,p,o,v){var k=o.length-1;switch(p){case 1:return o[k-1];case 2:this.$=[];break;case 3:o[k-1].push(o[k]),this.$=o[k-1];break;case 4:case 5:this.$=o[k];break;case 6:case 7:this.$=[];break;case 8:d.setDiagramTitle(o[k].substr(6)),this.$=o[k].substr(6);break;case 9:this.$=o[k].trim(),d.setAccTitle(this.$);break;case 10:case 11:this.$=o[k].trim(),d.setAccDescription(this.$);break;case 12:d.addSection(o[k].substr(8)),this.$=o[k].substr(8);break;case 13:d.addTask(o[k-1],o[k]),this.$="task";break}},"anonymous"),table:[{3:1,4:[1,2]},{1:[3]},t(e,[2,2],{5:3}),{6:[1,4],7:5,8:[1,6],9:7,10:[1,8],11:s,12:c,14:i,16:f,17:u,18:y},t(e,[2,7],{1:[2,1]}),t(e,[2,3]),{9:15,11:s,12:c,14:i,16:f,17:u,18:y},t(e,[2,5]),t(e,[2,6]),t(e,[2,8]),{13:[1,16]},{15:[1,17]},t(e,[2,11]),t(e,[2,12]),{19:[1,18]},t(e,[2,4]),t(e,[2,9]),t(e,[2,10]),t(e,[2,13])],defaultActions:{},parseError:r(function(n,a){if(a.recoverable)this.trace(n);else{var l=new Error(n);throw l.hash=a,l}},"parseError"),parse:r(function(n){var a=this,l=[0],d=[],p=[null],o=[],v=this.table,k="",C=0,K=0,dt=2,Q=1,yt=o.slice.call(arguments,1),_=Object.create(this.lexer),I={yy:{}};for(var O in this.yy)Object.prototype.hasOwnProperty.call(this.yy,O)&&(I.yy[O]=this.yy[O]);_.setInput(n,I.yy),I.yy.lexer=_,I.yy.parser=this,typeof _.yylloc>"u"&&(_.yylloc={});var Y=_.yylloc;o.push(Y);var ft=_.options&&_.options.ranges;typeof I.yy.parseError=="function"?this.parseError=I.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function pt(w){l.length=l.length-2*w,p.length=p.length-w,o.length=o.length-w}r(pt,"popStack");function D(){var w;return w=d.pop()||_.lex()||Q,typeof w!="number"&&(w instanceof Array&&(d=w,w=d.pop()),w=a.symbols_[w]||w),w}r(D,"lex");for(var b,A,T,q,F={},N,M,tt,z;;){if(A=l[l.length-1],this.defaultActions[A]?T=this.defaultActions[A]:((b===null||typeof b>"u")&&(b=D()),T=v[A]&&v[A][b]),typeof T>"u"||!T.length||!T[0]){var X="";z=[];for(N in v[A])this.terminals_[N]&&N>dt&&z.push("'"+this.terminals_[N]+"'");_.showPosition?X="Parse error on line "+(C+1)+`:
+import{a as gt,g as lt,f as mt,d as xt}from"./chunk-67H74DCK-BytGFags.js";import{g as kt}from"./chunk-E2GYISFI-wNdHFpBQ.js";import{_ as r,g as _t,s as bt,a as vt,b as wt,t as Tt,q as St,c as R,d as G,e as $t,z as Mt,N as et}from"./mermaid-vendor-pCi8nQvB.js";import"./feature-graph-O43AXICd.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var U=function(){var t=r(function(h,n,a,l){for(a=a||{},l=h.length;l--;a[h[l]]=n);return a},"o"),e=[6,8,10,11,12,14,16,17,18],s=[1,9],c=[1,10],i=[1,11],f=[1,12],u=[1,13],y=[1,14],g={trace:r(function(){},"trace"),yy:{},symbols_:{error:2,start:3,journey:4,document:5,EOF:6,line:7,SPACE:8,statement:9,NEWLINE:10,title:11,acc_title:12,acc_title_value:13,acc_descr:14,acc_descr_value:15,acc_descr_multiline_value:16,section:17,taskName:18,taskData:19,$accept:0,$end:1},terminals_:{2:"error",4:"journey",6:"EOF",8:"SPACE",10:"NEWLINE",11:"title",12:"acc_title",13:"acc_title_value",14:"acc_descr",15:"acc_descr_value",16:"acc_descr_multiline_value",17:"section",18:"taskName",19:"taskData"},productions_:[0,[3,3],[5,0],[5,2],[7,2],[7,1],[7,1],[7,1],[9,1],[9,2],[9,2],[9,1],[9,1],[9,2]],performAction:r(function(n,a,l,d,p,o,v){var k=o.length-1;switch(p){case 1:return o[k-1];case 2:this.$=[];break;case 3:o[k-1].push(o[k]),this.$=o[k-1];break;case 4:case 5:this.$=o[k];break;case 6:case 7:this.$=[];break;case 8:d.setDiagramTitle(o[k].substr(6)),this.$=o[k].substr(6);break;case 9:this.$=o[k].trim(),d.setAccTitle(this.$);break;case 10:case 11:this.$=o[k].trim(),d.setAccDescription(this.$);break;case 12:d.addSection(o[k].substr(8)),this.$=o[k].substr(8);break;case 13:d.addTask(o[k-1],o[k]),this.$="task";break}},"anonymous"),table:[{3:1,4:[1,2]},{1:[3]},t(e,[2,2],{5:3}),{6:[1,4],7:5,8:[1,6],9:7,10:[1,8],11:s,12:c,14:i,16:f,17:u,18:y},t(e,[2,7],{1:[2,1]}),t(e,[2,3]),{9:15,11:s,12:c,14:i,16:f,17:u,18:y},t(e,[2,5]),t(e,[2,6]),t(e,[2,8]),{13:[1,16]},{15:[1,17]},t(e,[2,11]),t(e,[2,12]),{19:[1,18]},t(e,[2,4]),t(e,[2,9]),t(e,[2,10]),t(e,[2,13])],defaultActions:{},parseError:r(function(n,a){if(a.recoverable)this.trace(n);else{var l=new Error(n);throw l.hash=a,l}},"parseError"),parse:r(function(n){var a=this,l=[0],d=[],p=[null],o=[],v=this.table,k="",C=0,K=0,dt=2,Q=1,yt=o.slice.call(arguments,1),_=Object.create(this.lexer),I={yy:{}};for(var O in this.yy)Object.prototype.hasOwnProperty.call(this.yy,O)&&(I.yy[O]=this.yy[O]);_.setInput(n,I.yy),I.yy.lexer=_,I.yy.parser=this,typeof _.yylloc>"u"&&(_.yylloc={});var Y=_.yylloc;o.push(Y);var ft=_.options&&_.options.ranges;typeof I.yy.parseError=="function"?this.parseError=I.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function pt(w){l.length=l.length-2*w,p.length=p.length-w,o.length=o.length-w}r(pt,"popStack");function D(){var w;return w=d.pop()||_.lex()||Q,typeof w!="number"&&(w instanceof Array&&(d=w,w=d.pop()),w=a.symbols_[w]||w),w}r(D,"lex");for(var b,A,T,q,F={},N,M,tt,z;;){if(A=l[l.length-1],this.defaultActions[A]?T=this.defaultActions[A]:((b===null||typeof b>"u")&&(b=D()),T=v[A]&&v[A][b]),typeof T>"u"||!T.length||!T[0]){var X="";z=[];for(N in v[A])this.terminals_[N]&&N>dt&&z.push("'"+this.terminals_[N]+"'");_.showPosition?X="Parse error on line "+(C+1)+`:
 `+_.showPosition()+`
 Expecting `+z.join(", ")+", got '"+(this.terminals_[b]||b)+"'":X="Parse error on line "+(C+1)+": Unexpected "+(b==Q?"end of input":"'"+(this.terminals_[b]||b)+"'"),this.parseError(X,{text:_.match,token:this.terminals_[b]||b,line:_.yylineno,loc:Y,expected:z})}if(T[0]instanceof Array&&T.length>1)throw new Error("Parse Error: multiple actions possible at state: "+A+", token: "+b);switch(T[0]){case 1:l.push(b),p.push(_.yytext),o.push(_.yylloc),l.push(T[1]),b=null,K=_.yyleng,k=_.yytext,C=_.yylineno,Y=_.yylloc;break;case 2:if(M=this.productions_[T[1]][1],F.$=p[p.length-M],F._$={first_line:o[o.length-(M||1)].first_line,last_line:o[o.length-1].last_line,first_column:o[o.length-(M||1)].first_column,last_column:o[o.length-1].last_column},ft&&(F._$.range=[o[o.length-(M||1)].range[0],o[o.length-1].range[1]]),q=this.performAction.apply(F,[k,K,C,I.yy,T[1],p,o].concat(yt)),typeof q<"u")return q;M&&(l=l.slice(0,-1*M*2),p=p.slice(0,-1*M),o=o.slice(0,-1*M)),l.push(this.productions_[T[1]][0]),p.push(F.$),o.push(F._$),tt=v[l[l.length-2]][l[l.length-1]],l.push(tt);break;case 3:return!0}}return!0},"parse")},m=function(){var h={EOF:1,parseError:r(function(a,l){if(this.yy.parser)this.yy.parser.parseError(a,l);else throw new Error(a)},"parseError"),setInput:r(function(n,a){return this.yy=a||this.yy||{},this._input=n,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:r(function(){var n=this._input[0];this.yytext+=n,this.yyleng++,this.offset++,this.match+=n,this.matched+=n;var a=n.match(/(?:\r\n?|\n).*/g);return a?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),n},"input"),unput:r(function(n){var a=n.length,l=n.split(/(?:\r\n?|\n)/g);this._input=n+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-a),this.offset-=a;var d=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),l.length-1&&(this.yylineno-=l.length-1);var p=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:l?(l.length===d.length?this.yylloc.first_column:0)+d[d.length-l.length].length-l[0].length:this.yylloc.first_column-a},this.options.ranges&&(this.yylloc.range=[p[0],p[0]+this.yyleng-a]),this.yyleng=this.yytext.length,this},"unput"),more:r(function(){return this._more=!0,this},"more"),reject:r(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
 `+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:r(function(n){this.unput(this.match.slice(n))},"less"),pastInput:r(function(){var n=this.matched.substr(0,this.matched.length-this.match.length);return(n.length>20?"...":"")+n.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:r(function(){var n=this.match;return n.length<20&&(n+=this._input.substr(0,20-n.length)),(n.substr(0,20)+(n.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:r(function(){var n=this.pastInput(),a=new Array(n.length+1).join("-");return n+this.upcomingInput()+`

lightrag/api/webui/assets/kanban-definition-ZSS6B67P-CVYatLUm.js

@@ -1,4 +1,4 @@
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lightrag/api/webui/assets/pieDiagram-NIOCPIFQ-CPRo_wP0.js

@@ -1,4 +1,4 @@
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+import{p as N}from"./chunk-353BL4L5-DjMQec0j.js";import{_ as i,g as B,s as U,a as q,b as H,t as K,q as V,l as C,c as Z,F as j,K as J,M as Q,N as z,O as X,e as Y,z as tt,P as et,H as at}from"./mermaid-vendor-pCi8nQvB.js";import{p as rt}from"./treemap-75Q7IDZK-DhCk0PFk.js";import"./feature-graph-O43AXICd.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-D1ajN3w6.js";import"./_basePickBy-C9JQvwyt.js";import"./clone-DLVgXiWv.js";var it=at.pie,D={sections:new Map,showData:!1},f=D.sections,w=D.showData,st=structuredClone(it),ot=i(()=>structuredClone(st),"getConfig"),nt=i(()=>{f=new Map,w=D.showData,tt()},"clear"),lt=i(({label:t,value:a})=>{f.has(t)||(f.set(t,a),C.debug(`added new section: ${t}, with value: ${a}`))},"addSection"),ct=i(()=>f,"getSections"),pt=i(t=>{w=t},"setShowData"),dt=i(()=>w,"getShowData"),F={getConfig:ot,clear:nt,setDiagramTitle:V,getDiagramTitle:K,setAccTitle:H,getAccTitle:q,setAccDescription:U,getAccDescription:B,addSection:lt,getSections:ct,setShowData:pt,getShowData:dt},gt=i((t,a)=>{N(t,a),a.setShowData(t.showData),t.sections.map(a.addSection)},"populateDb"),ut={parse:i(async t=>{const a=await rt("pie",t);C.debug(a),gt(a,F)},"parse")},mt=i(t=>`
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lightrag/api/webui/assets/stateDiagram-v2-5AN5P6BG-Cn8a0a4g.js

@@ -1 +1 @@
-import{s as r,b as e,a,S as i}from"./chunk-OW32GOEJ-B9FViCRE.js";import{_ as s}from"./mermaid-vendor-J2ju872p.js";import"./chunk-BFAMUDN2-nPRHB0S3.js";import"./chunk-SKB7J2MH-Cgz1KI3w.js";import"./feature-graph-BgVuQYut.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var f={parser:a,get db(){return new i(2)},renderer:e,styles:r,init:s(t=>{t.state||(t.state={}),t.state.arrowMarkerAbsolute=t.arrowMarkerAbsolute},"init")};export{f as diagram};
+import{s as r,b as e,a,S as i}from"./chunk-OW32GOEJ-WZhy3vze.js";import{_ as s}from"./mermaid-vendor-pCi8nQvB.js";import"./chunk-BFAMUDN2-ioRYrumN.js";import"./chunk-SKB7J2MH-BsoIgbf0.js";import"./feature-graph-O43AXICd.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var f={parser:a,get db(){return new i(2)},renderer:e,styles:r,init:s(t=>{t.state||(t.state={}),t.state.arrowMarkerAbsolute=t.arrowMarkerAbsolute},"init")};export{f as diagram};

lightrag/api/webui/assets/timeline-definition-MYPXXCX6-DeoELNbk.js

@@ -1,4 +1,4 @@
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+import{_ as s,c as xt,l as E,d as q,a3 as kt,a4 as _t,a5 as bt,a6 as vt,N as nt,D as wt,a7 as St,z as Et}from"./mermaid-vendor-pCi8nQvB.js";import"./feature-graph-O43AXICd.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var X=function(){var n=s(function(f,r,a,h){for(a=a||{},h=f.length;h--;a[f[h]]=r);return a},"o"),t=[6,8,10,11,12,14,16,17,20,21],e=[1,9],l=[1,10],i=[1,11],d=[1,12],c=[1,13],g=[1,16],m=[1,17],p={trace:s(function(){},"trace"),yy:{},symbols_:{error:2,start:3,timeline:4,document:5,EOF:6,line:7,SPACE:8,statement:9,NEWLINE:10,title:11,acc_title:12,acc_title_value:13,acc_descr:14,acc_descr_value:15,acc_descr_multiline_value:16,section:17,period_statement:18,event_statement:19,period:20,event:21,$accept:0,$end:1},terminals_:{2:"error",4:"timeline",6:"EOF",8:"SPACE",10:"NEWLINE",11:"title",12:"acc_title",13:"acc_title_value",14:"acc_descr",15:"acc_descr_value",16:"acc_descr_multiline_value",17:"section",20:"period",21:"event"},productions_:[0,[3,3],[5,0],[5,2],[7,2],[7,1],[7,1],[7,1],[9,1],[9,2],[9,2],[9,1],[9,1],[9,1],[9,1],[18,1],[19,1]],performAction:s(function(r,a,h,u,y,o,S){var k=o.length-1;switch(y){case 1:return o[k-1];case 2:this.$=[];break;case 3:o[k-1].push(o[k]),this.$=o[k-1];break;case 4:case 5:this.$=o[k];break;case 6:case 7:this.$=[];break;case 8:u.getCommonDb().setDiagramTitle(o[k].substr(6)),this.$=o[k].substr(6);break;case 9:this.$=o[k].trim(),u.getCommonDb().setAccTitle(this.$);break;case 10:case 11:this.$=o[k].trim(),u.getCommonDb().setAccDescription(this.$);break;case 12:u.addSection(o[k].substr(8)),this.$=o[k].substr(8);break;case 15:u.addTask(o[k],0,""),this.$=o[k];break;case 16:u.addEvent(o[k].substr(2)),this.$=o[k];break}},"anonymous"),table:[{3:1,4:[1,2]},{1:[3]},n(t,[2,2],{5:3}),{6:[1,4],7:5,8:[1,6],9:7,10:[1,8],11:e,12:l,14:i,16:d,17:c,18:14,19:15,20:g,21:m},n(t,[2,7],{1:[2,1]}),n(t,[2,3]),{9:18,11:e,12:l,14:i,16:d,17:c,18:14,19:15,20:g,21:m},n(t,[2,5]),n(t,[2,6]),n(t,[2,8]),{13:[1,19]},{15:[1,20]},n(t,[2,11]),n(t,[2,12]),n(t,[2,13]),n(t,[2,14]),n(t,[2,15]),n(t,[2,16]),n(t,[2,4]),n(t,[2,9]),n(t,[2,10])],defaultActions:{},parseError:s(function(r,a){if(a.recoverable)this.trace(r);else{var h=new Error(r);throw h.hash=a,h}},"parseError"),parse:s(function(r){var a=this,h=[0],u=[],y=[null],o=[],S=this.table,k="",M=0,C=0,B=2,J=1,O=o.slice.call(arguments,1),_=Object.create(this.lexer),N={yy:{}};for(var L in this.yy)Object.prototype.hasOwnProperty.call(this.yy,L)&&(N.yy[L]=this.yy[L]);_.setInput(r,N.yy),N.yy.lexer=_,N.yy.parser=this,typeof _.yylloc>"u"&&(_.yylloc={});var v=_.yylloc;o.push(v);var $=_.options&&_.options.ranges;typeof N.yy.parseError=="function"?this.parseError=N.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function R(T){h.length=h.length-2*T,y.length=y.length-T,o.length=o.length-T}s(R,"popStack");function A(){var T;return T=u.pop()||_.lex()||J,typeof T!="number"&&(T instanceof Array&&(u=T,T=u.pop()),T=a.symbols_[T]||T),T}s(A,"lex");for(var w,H,I,K,F={},j,P,et,G;;){if(H=h[h.length-1],this.defaultActions[H]?I=this.defaultActions[H]:((w===null||typeof w>"u")&&(w=A()),I=S[H]&&S[H][w]),typeof I>"u"||!I.length||!I[0]){var Q="";G=[];for(j in S[H])this.terminals_[j]&&j>B&&G.push("'"+this.terminals_[j]+"'");_.showPosition?Q="Parse error on line "+(M+1)+`:
 `+_.showPosition()+`
 Expecting `+G.join(", ")+", got '"+(this.terminals_[w]||w)+"'":Q="Parse error on line "+(M+1)+": Unexpected "+(w==J?"end of input":"'"+(this.terminals_[w]||w)+"'"),this.parseError(Q,{text:_.match,token:this.terminals_[w]||w,line:_.yylineno,loc:v,expected:G})}if(I[0]instanceof Array&&I.length>1)throw new Error("Parse Error: multiple actions possible at state: "+H+", token: "+w);switch(I[0]){case 1:h.push(w),y.push(_.yytext),o.push(_.yylloc),h.push(I[1]),w=null,C=_.yyleng,k=_.yytext,M=_.yylineno,v=_.yylloc;break;case 2:if(P=this.productions_[I[1]][1],F.$=y[y.length-P],F._$={first_line:o[o.length-(P||1)].first_line,last_line:o[o.length-1].last_line,first_column:o[o.length-(P||1)].first_column,last_column:o[o.length-1].last_column},$&&(F._$.range=[o[o.length-(P||1)].range[0],o[o.length-1].range[1]]),K=this.performAction.apply(F,[k,C,M,N.yy,I[1],y,o].concat(O)),typeof K<"u")return K;P&&(h=h.slice(0,-1*P*2),y=y.slice(0,-1*P),o=o.slice(0,-1*P)),h.push(this.productions_[I[1]][0]),y.push(F.$),o.push(F._$),et=S[h[h.length-2]][h[h.length-1]],h.push(et);break;case 3:return!0}}return!0},"parse")},x=function(){var f={EOF:1,parseError:s(function(a,h){if(this.yy.parser)this.yy.parser.parseError(a,h);else throw new Error(a)},"parseError"),setInput:s(function(r,a){return this.yy=a||this.yy||{},this._input=r,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:s(function(){var r=this._input[0];this.yytext+=r,this.yyleng++,this.offset++,this.match+=r,this.matched+=r;var a=r.match(/(?:\r\n?|\n).*/g);return a?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),r},"input"),unput:s(function(r){var a=r.length,h=r.split(/(?:\r\n?|\n)/g);this._input=r+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-a),this.offset-=a;var u=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),h.length-1&&(this.yylineno-=h.length-1);var y=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:h?(h.length===u.length?this.yylloc.first_column:0)+u[u.length-h.length].length-h[0].length:this.yylloc.first_column-a},this.options.ranges&&(this.yylloc.range=[y[0],y[0]+this.yyleng-a]),this.yyleng=this.yytext.length,this},"unput"),more:s(function(){return this._more=!0,this},"more"),reject:s(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
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lightrag/api/webui/index.html

@@ -8,16 +8,16 @@
     <link rel="icon" type="image/png" href="favicon.png" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Lightrag</title>
-    <script type="module" crossorigin src="/webui/assets/index-MT1PAKNY.js"></script>
+    <script type="module" crossorigin src="/webui/assets/index-CfXratTv.js"></script>
     <link rel="modulepreload" crossorigin href="/webui/assets/react-vendor-DEwriMA6.js">
     <link rel="modulepreload" crossorigin href="/webui/assets/ui-vendor-CeCm8EER.js">
     <link rel="modulepreload" crossorigin href="/webui/assets/graph-vendor-B-X5JegA.js">
     <link rel="modulepreload" crossorigin href="/webui/assets/utils-vendor-BysuhMZA.js">
-    <link rel="modulepreload" crossorigin href="/webui/assets/feature-graph-BgVuQYut.js">
-    <link rel="modulepreload" crossorigin href="/webui/assets/feature-documents-DfpqEFJm.js">
-    <link rel="modulepreload" crossorigin href="/webui/assets/mermaid-vendor-J2ju872p.js">
+    <link rel="modulepreload" crossorigin href="/webui/assets/feature-graph-O43AXICd.js">
+    <link rel="modulepreload" crossorigin href="/webui/assets/feature-documents-XY-qP1x8.js">
+    <link rel="modulepreload" crossorigin href="/webui/assets/mermaid-vendor-pCi8nQvB.js">
     <link rel="modulepreload" crossorigin href="/webui/assets/markdown-vendor-DmIvJdn7.js">
-    <link rel="modulepreload" crossorigin href="/webui/assets/feature-retrieval-Q3pnpDUM.js">
+    <link rel="modulepreload" crossorigin href="/webui/assets/feature-retrieval-lTh8tRF9.js">
     <link rel="stylesheet" crossorigin href="/webui/assets/feature-graph-BipNuM18.css">
     <link rel="stylesheet" crossorigin href="/webui/assets/index-CafJWW1u.css">
   </head>

lightrag/lightrag.py

@@ -971,11 +971,10 @@ class LightRAG:
         """
         Pipeline for Processing Documents
 
-        1. Validate ids if provided or generate MD5 hash IDs
-        2. Remove duplicate contents
-        3. Generate document initial status
-        4. Filter out already processed documents
-        5. Enqueue document in status
+        1. Validate ids if provided or generate MD5 hash IDs and remove duplicate contents
+        2. Generate document initial status
+        3. Filter out already processed documents
+        4. Enqueue document in status
 
         Args:
             input: Single document string or list of document strings
@@ -1008,7 +1007,7 @@ class LightRAG:
             # If no file paths provided, use placeholder
             file_paths = ["unknown_source"] * len(input)
 
-        # 1. Validate ids if provided or generate MD5 hash IDs
+        # 1. Validate ids if provided or generate MD5 hash IDs and remove duplicate contents
         if ids is not None:
             # Check if the number of IDs matches the number of documents
             if len(ids) != len(input):
@@ -1018,22 +1017,25 @@ class LightRAG:
             if len(ids) != len(set(ids)):
                 raise ValueError("IDs must be unique")
 
-            # Generate contents dict of IDs provided by user and documents
+            # Generate contents dict and remove duplicates in one pass
+            unique_contents = {}
+            for id_, doc, path in zip(ids, input, file_paths):
+                cleaned_content = clean_text(doc)
+                if cleaned_content not in unique_contents:
+                    unique_contents[cleaned_content] = (id_, path)
+
+            # Reconstruct contents with unique content
             contents = {
-                id_: {"content": doc, "file_path": path}
-                for id_, doc, path in zip(ids, input, file_paths)
+                id_: {"content": content, "file_path": file_path}
+                for content, (id_, file_path) in unique_contents.items()
             }
         else:
-            # Clean input text and remove duplicates
-            cleaned_input = [
-                (clean_text(doc), path) for doc, path in zip(input, file_paths)
-            ]
+            # Clean input text and remove duplicates in one pass
             unique_content_with_paths = {}
-
-            # Keep track of unique content and their paths
-            for content, path in cleaned_input:
-                if content not in unique_content_with_paths:
-                    unique_content_with_paths[content] = path
+            for doc, path in zip(input, file_paths):
+                cleaned_content = clean_text(doc)
+                if cleaned_content not in unique_content_with_paths:
+                    unique_content_with_paths[cleaned_content] = path
 
             # Generate contents dict of MD5 hash IDs and documents with paths
             contents = {
@@ -1044,21 +1046,7 @@ class LightRAG:
                 for content, path in unique_content_with_paths.items()
             }
 
-        # 2. Remove duplicate contents
-        unique_contents = {}
-        for id_, content_data in contents.items():
-            content = content_data["content"]
-            file_path = content_data["file_path"]
-            if content not in unique_contents:
-                unique_contents[content] = (id_, file_path)
-
-        # Reconstruct contents with unique content
-        contents = {
-            id_: {"content": content, "file_path": file_path}
-            for content, (id_, file_path) in unique_contents.items()
-        }
-
-        # 3. Generate document initial status (without content)
+        # 2. Generate document initial status (without content)
         new_docs: dict[str, Any] = {
             id_: {
                 "status": DocStatus.PENDING,
@@ -1074,22 +1062,24 @@ class LightRAG:
             for id_, content_data in contents.items()
         }
 
-        # 4. Filter out already processed documents
+        # 3. Filter out already processed documents
         # Get docs ids
         all_new_doc_ids = set(new_docs.keys())
-        # Exclude IDs of documents that are already in progress
+        # Exclude IDs of documents that are already enqueued
         unique_new_doc_ids = await self.doc_status.filter_keys(all_new_doc_ids)
 
-        # Log ignored document IDs
-        ignored_ids = [
-            doc_id for doc_id in unique_new_doc_ids if doc_id not in new_docs
-        ]
+        # Log ignored document IDs (documents that were filtered out because they already exist)
+        ignored_ids = list(all_new_doc_ids - unique_new_doc_ids)
         if ignored_ids:
-            logger.warning(
-                f"Ignoring {len(ignored_ids)} document IDs not found in new_docs"
-            )
             for doc_id in ignored_ids:
-                logger.warning(f"Ignored document ID: {doc_id}")
+                file_path = new_docs.get(doc_id, {}).get("file_path", "unknown_source")
+                logger.warning(
+                    f"Ignoring document ID (already exists): {doc_id} ({file_path})"
+                )
+            if len(ignored_ids) > 3:
+                logger.warning(
+                    f"Total Ignoring {len(ignored_ids)} document IDs that already exist in storage"
+                )
 
         # Filter new_docs to only include documents with unique IDs
         new_docs = {
@@ -1099,11 +1089,11 @@ class LightRAG:
         }
 
         if not new_docs:
-            logger.info("No new unique documents were found.")
+            logger.warning("No new unique documents were found.")
             return
 
-        # 5. Store document content in full_docs and status in doc_status
-        # Store full document content separately
+        # 4. Store document content in full_docs and status in doc_status
+        #    Store full document content separately
         full_docs_data = {
             doc_id: {"content": contents[doc_id]["content"]}
             for doc_id in new_docs.keys()
@@ -1118,23 +1108,114 @@ class LightRAG:
 
         return track_id
 
+    async def apipeline_enqueue_error_documents(
+        self,
+        error_files: list[dict[str, Any]],
+        track_id: str | None = None,
+    ) -> None:
+        """
+        Record file extraction errors in doc_status storage.
+
+        This function creates error document entries in the doc_status storage for files
+        that failed during the extraction process. Each error entry contains information
+        about the failure to help with debugging and monitoring.
+
+        Args:
+            error_files: List of dictionaries containing error information for each failed file.
+                Each dictionary should contain:
+                - file_path: Original file name/path
+                - error_description: Brief error description (for content_summary)
+                - original_error: Full error message (for error_msg)
+                - file_size: File size in bytes (for content_length, 0 if unknown)
+            track_id: Optional tracking ID for grouping related operations
+
+        Returns:
+            None
+        """
+        if not error_files:
+            logger.debug("No error files to record")
+            return
+
+        # Generate track_id if not provided
+        if track_id is None or track_id.strip() == "":
+            track_id = generate_track_id("error")
+
+        error_docs: dict[str, Any] = {}
+        current_time = datetime.now(timezone.utc).isoformat()
+
+        for error_file in error_files:
+            file_path = error_file.get("file_path", "unknown_file")
+            error_description = error_file.get(
+                "error_description", "File extraction failed"
+            )
+            original_error = error_file.get("original_error", "Unknown error")
+            file_size = error_file.get("file_size", 0)
+
+            # Generate unique doc_id with "error-" prefix
+            doc_id_content = f"{file_path}-{error_description}"
+            doc_id = compute_mdhash_id(doc_id_content, prefix="error-")
+
+            error_docs[doc_id] = {
+                "status": DocStatus.FAILED,
+                "content_summary": error_description,
+                "content_length": file_size,
+                "error_msg": original_error,
+                "chunks_count": 0,  # No chunks for failed files
+                "created_at": current_time,
+                "updated_at": current_time,
+                "file_path": file_path,
+                "track_id": track_id,
+                "metadata": {
+                    "error_type": "file_extraction_error",
+                },
+            }
+
+        # Store error documents in doc_status
+        if error_docs:
+            await self.doc_status.upsert(error_docs)
+            # Log each error for debugging
+            for doc_id, error_doc in error_docs.items():
+                logger.error(
+                    f"File processing error: - ID: {doc_id} {error_doc['file_path']}"
+                )
+
     async def _validate_and_fix_document_consistency(
         self,
         to_process_docs: dict[str, DocProcessingStatus],
         pipeline_status: dict,
         pipeline_status_lock: asyncio.Lock,
     ) -> dict[str, DocProcessingStatus]:
-        """Validate and fix document data consistency by deleting inconsistent entries"""
+        """Validate and fix document data consistency by deleting inconsistent entries, but preserve failed documents"""
         inconsistent_docs = []
+        failed_docs_to_preserve = []
 
         # Check each document's data consistency
         for doc_id, status_doc in to_process_docs.items():
             # Check if corresponding content exists in full_docs
             content_data = await self.full_docs.get_by_id(doc_id)
             if not content_data:
-                inconsistent_docs.append(doc_id)
+                # Check if this is a failed document that should be preserved
+                if (
+                    hasattr(status_doc, "status")
+                    and status_doc.status == DocStatus.FAILED
+                ):
+                    failed_docs_to_preserve.append(doc_id)
+                else:
+                    inconsistent_docs.append(doc_id)
 
-        # Delete inconsistent document entries one by one
+        # Log information about failed documents that will be preserved
+        if failed_docs_to_preserve:
+            async with pipeline_status_lock:
+                preserve_message = f"Preserving {len(failed_docs_to_preserve)} failed document entries for manual review"
+                logger.info(preserve_message)
+                pipeline_status["latest_message"] = preserve_message
+                pipeline_status["history_messages"].append(preserve_message)
+
+            # Remove failed documents from processing list but keep them in doc_status
+            for doc_id in failed_docs_to_preserve:
+                to_process_docs.pop(doc_id, None)
+
+        # Delete inconsistent document entries(excluding failed documents)
         if inconsistent_docs:
             async with pipeline_status_lock:
                 summary_message = (
@@ -1156,7 +1237,9 @@ class LightRAG:
 
                     # Log successful deletion
                     async with pipeline_status_lock:
-                        log_message = f"Deleted entry: {doc_id} ({file_path})"
+                        log_message = (
+                            f"Deleted inconsistent entry: {doc_id} ({file_path})"
+                        )
                         logger.info(log_message)
                         pipeline_status["latest_message"] = log_message
                         pipeline_status["history_messages"].append(log_message)
@@ -1174,7 +1257,7 @@ class LightRAG:
 
             # Final summary log
             async with pipeline_status_lock:
-                final_message = f"Data consistency cleanup completed: successfully deleted {successful_deletions} entries"
+                final_message = f"Data consistency cleanup completed: successfully deleted {successful_deletions} inconsistent entries, preserved {len(failed_docs_to_preserve)} failed documents"
                 logger.info(final_message)
                 pipeline_status["latest_message"] = final_message
                 pipeline_status["history_messages"].append(final_message)

lightrag/llm/bedrock.py

@@ -15,11 +15,25 @@ from tenacity import (
     retry_if_exception_type,
 )
 
+import sys
+
+if sys.version_info < (3, 9):
+    from typing import AsyncIterator
+else:
+    from collections.abc import AsyncIterator
+from typing import Union
+
 
 class BedrockError(Exception):
     """Generic error for issues related to Amazon Bedrock"""
 
 
+def _set_env_if_present(key: str, value):
+    """Set environment variable only if a non-empty value is provided."""
+    if value is not None and value != "":
+        os.environ[key] = value
+
+
 @retry(
     stop=stop_after_attempt(5),
     wait=wait_exponential(multiplier=1, max=60),
@@ -34,17 +48,35 @@ async def bedrock_complete_if_cache(
     aws_secret_access_key=None,
     aws_session_token=None,
     **kwargs,
-) -> str:
-    os.environ["AWS_ACCESS_KEY_ID"] = os.environ.get(
-        "AWS_ACCESS_KEY_ID", aws_access_key_id
-    )
-    os.environ["AWS_SECRET_ACCESS_KEY"] = os.environ.get(
-        "AWS_SECRET_ACCESS_KEY", aws_secret_access_key
-    )
-    os.environ["AWS_SESSION_TOKEN"] = os.environ.get(
-        "AWS_SESSION_TOKEN", aws_session_token
-    )
+) -> Union[str, AsyncIterator[str]]:
+    # Respect existing env; only set if a non-empty value is available
+    access_key = os.environ.get("AWS_ACCESS_KEY_ID") or aws_access_key_id
+    secret_key = os.environ.get("AWS_SECRET_ACCESS_KEY") or aws_secret_access_key
+    session_token = os.environ.get("AWS_SESSION_TOKEN") or aws_session_token
+    _set_env_if_present("AWS_ACCESS_KEY_ID", access_key)
+    _set_env_if_present("AWS_SECRET_ACCESS_KEY", secret_key)
+    _set_env_if_present("AWS_SESSION_TOKEN", session_token)
+    # Region handling: prefer env, else kwarg (optional)
+    region = os.environ.get("AWS_REGION") or kwargs.pop("aws_region", None)
     kwargs.pop("hashing_kv", None)
+    # Capture stream flag (if provided) and remove from kwargs since it's not a Bedrock API parameter
+    # We'll use this to determine whether to call converse_stream or converse
+    stream = bool(kwargs.pop("stream", False))
+    # Remove unsupported args for Bedrock Converse API
+    for k in [
+        "response_format",
+        "tools",
+        "tool_choice",
+        "seed",
+        "presence_penalty",
+        "frequency_penalty",
+        "n",
+        "logprobs",
+        "top_logprobs",
+        "max_completion_tokens",
+        "response_format",
+    ]:
+        kwargs.pop(k, None)
     # Fix message history format
     messages = []
     for history_message in history_messages:
@@ -77,21 +109,131 @@ async def bedrock_complete_if_cache(
                 kwargs.pop(param)
             )
 
-    # Call model via Converse API
-    session = aioboto3.Session()
-    async with session.client("bedrock-runtime") as bedrock_async_client:
-        try:
-            response = await bedrock_async_client.converse(**args, **kwargs)
-        except Exception as e:
-            raise BedrockError(e)
+    # Import logging for error handling
+    import logging
 
-    return response["output"]["message"]["content"][0]["text"]
+    # For streaming responses, we need a different approach to keep the connection open
+    if stream:
+        # Create a session that will be used throughout the streaming process
+        session = aioboto3.Session()
+        client = None
+
+        # Define the generator function that will manage the client lifecycle
+        async def stream_generator():
+            nonlocal client
+
+            # Create the client outside the generator to ensure it stays open
+            client = await session.client(
+                "bedrock-runtime", region_name=region
+            ).__aenter__()
+            event_stream = None
+            iteration_started = False
+
+            try:
+                # Make the API call
+                response = await client.converse_stream(**args, **kwargs)
+                event_stream = response.get("stream")
+                iteration_started = True
+
+                # Process the stream
+                async for event in event_stream:
+                    # Validate event structure
+                    if not event or not isinstance(event, dict):
+                        continue
+
+                    if "contentBlockDelta" in event:
+                        delta = event["contentBlockDelta"].get("delta", {})
+                        text = delta.get("text")
+                        if text:
+                            yield text
+                    # Handle other event types that might indicate stream end
+                    elif "messageStop" in event:
+                        break
+
+            except Exception as e:
+                # Log the specific error for debugging
+                logging.error(f"Bedrock streaming error: {e}")
+
+                # Try to clean up resources if possible
+                if (
+                    iteration_started
+                    and event_stream
+                    and hasattr(event_stream, "aclose")
+                    and callable(getattr(event_stream, "aclose", None))
+                ):
+                    try:
+                        await event_stream.aclose()
+                    except Exception as close_error:
+                        logging.warning(
+                            f"Failed to close Bedrock event stream: {close_error}"
+                        )
+
+                raise BedrockError(f"Streaming error: {e}")
+
+            finally:
+                # Clean up the event stream
+                if (
+                    iteration_started
+                    and event_stream
+                    and hasattr(event_stream, "aclose")
+                    and callable(getattr(event_stream, "aclose", None))
+                ):
+                    try:
+                        await event_stream.aclose()
+                    except Exception as close_error:
+                        logging.warning(
+                            f"Failed to close Bedrock event stream in finally block: {close_error}"
+                        )
+
+                # Clean up the client
+                if client:
+                    try:
+                        await client.__aexit__(None, None, None)
+                    except Exception as client_close_error:
+                        logging.warning(
+                            f"Failed to close Bedrock client: {client_close_error}"
+                        )
+
+        # Return the generator that manages its own lifecycle
+        return stream_generator()
+
+    # For non-streaming responses, use the standard async context manager pattern
+    session = aioboto3.Session()
+    async with session.client(
+        "bedrock-runtime", region_name=region
+    ) as bedrock_async_client:
+        try:
+            # Use converse for non-streaming responses
+            response = await bedrock_async_client.converse(**args, **kwargs)
+
+            # Validate response structure
+            if (
+                not response
+                or "output" not in response
+                or "message" not in response["output"]
+                or "content" not in response["output"]["message"]
+                or not response["output"]["message"]["content"]
+            ):
+                raise BedrockError("Invalid response structure from Bedrock API")
+
+            content = response["output"]["message"]["content"][0]["text"]
+
+            if not content or content.strip() == "":
+                raise BedrockError("Received empty content from Bedrock API")
+
+            return content
+
+        except Exception as e:
+            if isinstance(e, BedrockError):
+                raise
+            else:
+                raise BedrockError(f"Bedrock API error: {e}")
 
 
 # Generic Bedrock completion function
 async def bedrock_complete(
     prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
-) -> str:
+) -> Union[str, AsyncIterator[str]]:
     kwargs.pop("keyword_extraction", None)
     model_name = kwargs["hashing_kv"].global_config["llm_model_name"]
     result = await bedrock_complete_if_cache(
@@ -117,18 +259,21 @@ async def bedrock_embed(
     aws_secret_access_key=None,
     aws_session_token=None,
 ) -> np.ndarray:
-    os.environ["AWS_ACCESS_KEY_ID"] = os.environ.get(
-        "AWS_ACCESS_KEY_ID", aws_access_key_id
-    )
-    os.environ["AWS_SECRET_ACCESS_KEY"] = os.environ.get(
-        "AWS_SECRET_ACCESS_KEY", aws_secret_access_key
-    )
-    os.environ["AWS_SESSION_TOKEN"] = os.environ.get(
-        "AWS_SESSION_TOKEN", aws_session_token
-    )
+    # Respect existing env; only set if a non-empty value is available
+    access_key = os.environ.get("AWS_ACCESS_KEY_ID") or aws_access_key_id
+    secret_key = os.environ.get("AWS_SECRET_ACCESS_KEY") or aws_secret_access_key
+    session_token = os.environ.get("AWS_SESSION_TOKEN") or aws_session_token
+    _set_env_if_present("AWS_ACCESS_KEY_ID", access_key)
+    _set_env_if_present("AWS_SECRET_ACCESS_KEY", secret_key)
+    _set_env_if_present("AWS_SESSION_TOKEN", session_token)
+
+    # Region handling: prefer env
+    region = os.environ.get("AWS_REGION")
 
     session = aioboto3.Session()
-    async with session.client("bedrock-runtime") as bedrock_async_client:
+    async with session.client(
+        "bedrock-runtime", region_name=region
+    ) as bedrock_async_client:
         if (model_provider := model.split(".")[0]) == "amazon":
             embed_texts = []
             for text in texts:

lightrag_webui/src/components/documents/ClearDocumentsDialog.tsx

@@ -1,4 +1,4 @@
-import { useState, useCallback, useEffect } from 'react'
+import { useState, useCallback, useEffect, useRef } from 'react'
 import Button from '@/components/ui/Button'
 import {
   Dialog,
@@ -15,10 +15,10 @@ import { toast } from 'sonner'
 import { errorMessage } from '@/lib/utils'
 import { clearDocuments, clearCache } from '@/api/lightrag'
 
-import { EraserIcon, AlertTriangleIcon } from 'lucide-react'
+import { EraserIcon, AlertTriangleIcon, Loader2Icon } from 'lucide-react'
 import { useTranslation } from 'react-i18next'
 
-// 简单的Label组件
+// Simple Label component
 const Label = ({
   htmlFor,
   className,
@@ -43,18 +43,51 @@ export default function ClearDocumentsDialog({ onDocumentsCleared }: ClearDocume
   const [open, setOpen] = useState(false)
   const [confirmText, setConfirmText] = useState('')
   const [clearCacheOption, setClearCacheOption] = useState(false)
+  const [isClearing, setIsClearing] = useState(false)
+  const timeoutRef = useRef<ReturnType<typeof setTimeout> | null>(null)
   const isConfirmEnabled = confirmText.toLowerCase() === 'yes'
 
-  // 重置状态当对话框关闭时
+  // Timeout constant (30 seconds)
+  const CLEAR_TIMEOUT = 30000
+
+  // Reset state when dialog closes
   useEffect(() => {
     if (!open) {
       setConfirmText('')
       setClearCacheOption(false)
+      setIsClearing(false)
+
+      // Clear timeout timer
+      if (timeoutRef.current) {
+        clearTimeout(timeoutRef.current)
+        timeoutRef.current = null
+      }
     }
   }, [open])
 
+  // Cleanup when component unmounts
+  useEffect(() => {
+    return () => {
+      // Clear timeout timer when component unmounts
+      if (timeoutRef.current) {
+        clearTimeout(timeoutRef.current)
+      }
+    }
+  }, [])
+
   const handleClear = useCallback(async () => {
-    if (!isConfirmEnabled) return
+    if (!isConfirmEnabled || isClearing) return
+
+    setIsClearing(true)
+
+    // Set timeout protection
+    timeoutRef.current = setTimeout(() => {
+      if (isClearing) {
+        toast.error(t('documentPanel.clearDocuments.timeout'))
+        setIsClearing(false)
+        setConfirmText('') // Reset confirmation text after timeout
+      }
+    }, CLEAR_TIMEOUT)
 
     try {
       const result = await clearDocuments()
@@ -81,13 +114,20 @@ export default function ClearDocumentsDialog({ onDocumentsCleared }: ClearDocume
         onDocumentsCleared().catch(console.error)
       }
 
-      // 所有操作成功后关闭对话框
+      // Close dialog after all operations succeed
       setOpen(false)
     } catch (err) {
       toast.error(t('documentPanel.clearDocuments.error', { error: errorMessage(err) }))
       setConfirmText('')
+    } finally {
+      // Clear timeout timer
+      if (timeoutRef.current) {
+        clearTimeout(timeoutRef.current)
+        timeoutRef.current = null
+      }
+      setIsClearing(false)
     }
-  }, [isConfirmEnabled, clearCacheOption, setOpen, t, onDocumentsCleared])
+  }, [isConfirmEnabled, isClearing, clearCacheOption, setOpen, t, onDocumentsCleared, CLEAR_TIMEOUT])
 
   return (
     <Dialog open={open} onOpenChange={setOpen}>
@@ -125,6 +165,7 @@ export default function ClearDocumentsDialog({ onDocumentsCleared }: ClearDocume
               onChange={(e: React.ChangeEvent<HTMLInputElement>) => setConfirmText(e.target.value)}
               placeholder={t('documentPanel.clearDocuments.confirmPlaceholder')}
               className="w-full"
+              disabled={isClearing}
             />
           </div>
 
@@ -133,6 +174,7 @@ export default function ClearDocumentsDialog({ onDocumentsCleared }: ClearDocume
               id="clear-cache"
               checked={clearCacheOption}
               onCheckedChange={(checked: boolean | 'indeterminate') => setClearCacheOption(checked === true)}
+              disabled={isClearing}
             />
             <Label htmlFor="clear-cache" className="text-sm font-medium cursor-pointer">
               {t('documentPanel.clearDocuments.clearCache')}
@@ -141,15 +183,26 @@ export default function ClearDocumentsDialog({ onDocumentsCleared }: ClearDocume
         </div>
 
         <DialogFooter>
-          <Button variant="outline" onClick={() => setOpen(false)}>
+          <Button
+            variant="outline"
+            onClick={() => setOpen(false)}
+            disabled={isClearing}
+          >
             {t('common.cancel')}
           </Button>
           <Button
             variant="destructive"
             onClick={handleClear}
-            disabled={!isConfirmEnabled}
+            disabled={!isConfirmEnabled || isClearing}
           >
-            {t('documentPanel.clearDocuments.confirmButton')}
+            {isClearing ? (
+              <>
+                <Loader2Icon className="mr-2 h-4 w-4 animate-spin" />
+                {t('documentPanel.clearDocuments.clearing')}
+              </>
+            ) : (
+              t('documentPanel.clearDocuments.confirmButton')
+            )}
           </Button>
         </DialogFooter>
       </DialogContent>

lightrag_webui/src/features/DocumentManager.tsx

@@ -532,15 +532,25 @@ export default function DocumentManager() {
       // Reset health check timer with 1 second delay to avoid race condition
       useBackendState.getState().resetHealthCheckTimerDelayed(1000);
 
-      // Schedule a health check 2 seconds after successful scan
+      // Start fast refresh with 2-second interval immediately after scan
       startPollingInterval(2000);
+
+      // Set recovery timer to restore normal polling interval after 15 seconds
+      setTimeout(() => {
+        if (isMountedRef.current && currentTab === 'documents' && health) {
+          // Restore intelligent polling interval based on document status
+          const hasActiveDocuments = (statusCounts.processing || 0) > 0 || (statusCounts.pending || 0) > 0;
+          const normalInterval = hasActiveDocuments ? 5000 : 30000;
+          startPollingInterval(normalInterval);
+        }
+      }, 15000); // Restore after 15 seconds
     } catch (err) {
       // Only show error if component is still mounted
       if (isMountedRef.current) {
         toast.error(t('documentPanel.documentManager.errors.scanFailed', { error: errorMessage(err) }));
       }
     }
-  }, [t, startPollingInterval])
+  }, [t, startPollingInterval, currentTab, health, statusCounts])
 
   // Handle page size change - update state and save to store
   const handlePageSizeChange = useCallback((newPageSize: number) => {

lightrag_webui/src/locales/ar.json

@@ -50,6 +50,8 @@
       "confirmPlaceholder": "اكتب yes للتأكيد",
       "clearCache": "مسح كاش نموذج اللغة",
       "confirmButton": "نعم",
+      "clearing": "جارٍ المسح...",
+      "timeout": "انتهت مهلة عملية المسح، يرجى المحاولة مرة أخرى",
       "success": "تم مسح المستندات بنجاح",
       "cacheCleared": "تم مسح ذاكرة التخزين المؤقت بنجاح",
       "cacheClearFailed": "فشل مسح ذاكرة التخزين المؤقت:\n{{error}}",

lightrag_webui/src/locales/en.json

@@ -50,6 +50,8 @@
       "confirmPlaceholder": "Type yes to confirm",
       "clearCache": "Clear LLM cache",
       "confirmButton": "YES",
+      "clearing": "Clearing...",
+      "timeout": "Clear operation timed out, please try again",
       "success": "Documents cleared successfully",
       "cacheCleared": "Cache cleared successfully",
       "cacheClearFailed": "Failed to clear cache:\n{{error}}",

lightrag_webui/src/locales/fr.json

@@ -50,6 +50,8 @@
       "confirmPlaceholder": "Tapez yes pour confirmer",
       "clearCache": "Effacer le cache LLM",
       "confirmButton": "OUI",
+      "clearing": "Effacement en cours...",
+      "timeout": "L'opération d'effacement a expiré, veuillez réessayer",
       "success": "Documents effacés avec succès",
       "cacheCleared": "Cache effacé avec succès",
       "cacheClearFailed": "Échec de l'effacement du cache :\n{{error}}",

lightrag_webui/src/locales/zh.json

@@ -50,6 +50,8 @@
       "confirmPlaceholder": "输入 yes 确认",
       "clearCache": "清空LLM缓存",
       "confirmButton": "确定",
+      "clearing": "正在清除...",
+      "timeout": "清除操作超时，请重试",
       "success": "文档清空成功",
       "cacheCleared": "缓存清空成功",
       "cacheClearFailed": "清空缓存失败：\n{{error}}",

lightrag_webui/src/locales/zh_TW.json

@@ -50,6 +50,8 @@
       "confirmPlaceholder": "輸入 yes 以確認",
       "clearCache": "清空 LLM 快取",
       "confirmButton": "確定",
+      "clearing": "正在清除...",
+      "timeout": "清除操作逾時，請重試",
       "success": "文件清空成功",
       "cacheCleared": "快取清空成功",
       "cacheClearFailed": "清空快取失敗：\n{{error}}",