diff --git a/pyproject.toml b/pyproject.toml index 5ee96981e..7f933cd0f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -124,6 +124,7 @@ dependencies = [ "cleanlab-tlm>=1.1.2", 'gassist>=0.0.1; sys_platform == "win32"', "twelvelabs>=0.4.7", + "docling>=2.36.1", ] [dependency-groups] @@ -219,6 +220,12 @@ postgresql = [ "sqlalchemy[postgresql_psycopg]>=2.0.38,<3.0.0", ] +[tool.uv] +override-dependencies = [ + # temporary force a newer python-pptx + "python-pptx>=1.0.2" +] + [project.scripts] langflow = "langflow.__main__:main" diff --git a/src/backend/base/langflow/base/data/docling_utils.py b/src/backend/base/langflow/base/data/docling_utils.py new file mode 100644 index 000000000..d954e6926 --- /dev/null +++ b/src/backend/base/langflow/base/data/docling_utils.py @@ -0,0 +1,47 @@ +from docling_core.types.doc import DoclingDocument + +from langflow.schema.data import Data +from langflow.schema.dataframe import DataFrame + + +def extract_docling_documents(data_inputs: Data | list[Data] | DataFrame, doc_key: str) -> list[DoclingDocument]: + documents: list[DoclingDocument] = [] + if isinstance(data_inputs, DataFrame): + if not len(data_inputs): + msg = "DataFrame is empty" + raise TypeError(msg) + + if doc_key not in data_inputs.columns: + msg = f"Column '{doc_key}' not found in DataFrame" + raise TypeError(msg) + try: + documents = data_inputs[doc_key].tolist() + except Exception as e: + msg = f"Error extracting DoclingDocument from DataFrame: {e}" + raise TypeError(msg) from e + else: + if not data_inputs: + msg = "No data inputs provided" + raise TypeError(msg) + + if isinstance(data_inputs, Data): + if doc_key not in data_inputs.data: + msg = f"{doc_key} field not available in the input Data" + raise TypeError(msg) + documents = [data_inputs.data[doc_key]] + else: + try: + documents = [ + input_.data[doc_key] + for input_ in data_inputs + if isinstance(input_, Data) + and doc_key in input_.data + and isinstance(input_.data[doc_key], DoclingDocument) + ] + if not documents: + msg = f"No valid Data inputs found in {type(data_inputs)}" + raise TypeError(msg) + except AttributeError as e: + msg = f"Invalid input type in collection: {e}" + raise TypeError(msg) from e + return documents diff --git a/src/backend/base/langflow/components/docling/__init__.py b/src/backend/base/langflow/components/docling/__init__.py new file mode 100644 index 000000000..dd0a6ff18 --- /dev/null +++ b/src/backend/base/langflow/components/docling/__init__.py @@ -0,0 +1,11 @@ +from .chunk_docling_document import ChunkDoclingDocumentComponent +from .docling_inline import DoclingInlineComponent +from .docling_remote import DoclingRemoteComponent +from .export_docling_document import ExportDoclingDocumentComponent + +__all__ = [ + "ChunkDoclingDocumentComponent", + "DoclingInlineComponent", + "DoclingRemoteComponent", + "ExportDoclingDocumentComponent", +] diff --git a/src/backend/base/langflow/components/docling/chunk_docling_document.py b/src/backend/base/langflow/components/docling/chunk_docling_document.py new file mode 100644 index 000000000..ae920021e --- /dev/null +++ b/src/backend/base/langflow/components/docling/chunk_docling_document.py @@ -0,0 +1,164 @@ +import json + +import tiktoken +from docling_core.transforms.chunker import BaseChunker, DocMeta +from docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker +from docling_core.transforms.chunker.hybrid_chunker import HybridChunker +from docling_core.transforms.chunker.tokenizer.huggingface import HuggingFaceTokenizer +from docling_core.transforms.chunker.tokenizer.openai import OpenAITokenizer + +from langflow.base.data.docling_utils import extract_docling_documents +from langflow.custom import Component +from langflow.io import DropdownInput, HandleInput, IntInput, MessageTextInput, Output, StrInput +from langflow.schema import Data, DataFrame + + +class ChunkDoclingDocumentComponent(Component): + display_name: str = "Chunk DoclingDocument" + description: str = "Use the DocumentDocument chunkers to split the document into chunks." + documentation = "https://docling-project.github.io/docling/concepts/chunking/" + icon = "Docling" + name = "ChunkDoclingDocument" + + inputs = [ + HandleInput( + name="data_inputs", + display_name="Data or DataFrame", + info="The data with documents to split in chunks.", + input_types=["Data", "DataFrame"], + required=True, + ), + DropdownInput( + name="chunker", + display_name="Chunker", + options=["HybridChunker", "HierarchicalChunker"], + info=("Which chunker to use."), + value="HybridChunker", + real_time_refresh=True, + ), + DropdownInput( + name="provider", + display_name="Provider", + options=["Hugging Face", "OpenAI"], + info=("Which tokenizer provider."), + value="Hugging Face", + show=True, + real_time_refresh=True, + advanced=True, + dynamic=True, + ), + StrInput( + name="hf_model_name", + display_name="HF model name", + info=( + "Model name of the tokenizer to use with the HybridChunker when Hugging Face is chosen as a tokenizer." + ), + value="sentence-transformers/all-MiniLM-L6-v2", + show=True, + advanced=True, + dynamic=True, + ), + StrInput( + name="openai_model_name", + display_name="OpenAI model name", + info=("Model name of the tokenizer to use with the HybridChunker when OpenAI is chosen as a tokenizer."), + value="gpt-4o", + show=False, + advanced=True, + dynamic=True, + ), + IntInput( + name="max_tokens", + display_name="Maximum tokens", + info=("Maximum number of tokens for the HybridChunker."), + show=True, + required=False, + advanced=True, + dynamic=True, + ), + MessageTextInput( + name="doc_key", + display_name="Doc Key", + info="The key to use for the DoclingDocument column.", + value="doc", + advanced=True, + ), + ] + + outputs = [ + Output(display_name="DataFrame", name="dataframe", method="chunk_documents"), + ] + + def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict: + if field_name == "chunker": + provider_type = build_config["provider"]["value"] + is_hf = provider_type == "Hugging Face" + is_openai = provider_type == "OpenAI" + if field_value == "HybridChunker": + build_config["provider"]["show"] = True + build_config["hf_model_name"]["show"] = is_hf + build_config["openai_model_name"]["show"] = is_openai + build_config["max_tokens"]["show"] = True + else: + build_config["provider"]["show"] = False + build_config["hf_model_name"]["show"] = False + build_config["openai_model_name"]["show"] = False + build_config["max_tokens"]["show"] = False + elif field_name == "provider" and build_config["chunker"]["value"] == "HybridChunker": + if field_value == "Hugging Face": + build_config["hf_model_name"]["show"] = True + build_config["openai_model_name"]["show"] = False + elif field_value == "OpenAI": + build_config["hf_model_name"]["show"] = False + build_config["openai_model_name"]["show"] = True + + return build_config + + def _docs_to_data(self, docs) -> list[Data]: + return [Data(text=doc.page_content, data=doc.metadata) for doc in docs] + + def chunk_documents(self) -> DataFrame: + documents = extract_docling_documents(self.data_inputs, self.doc_key) + + chunker: BaseChunker + if self.chunker == "HybridChunker": + max_tokens: int | None = self.max_tokens if self.max_tokens else None + if self.provider == "Hugging Face": + tokenizer = HuggingFaceTokenizer.from_pretrained( + model_name=self.hf_model_name, + max_tokens=max_tokens, + ) + elif self.provider == "OpenAI": + if max_tokens is None: + max_tokens = 128 * 1024 # context window length required for OpenAI tokenizers + tokenizer = OpenAITokenizer( + tokenizer=tiktoken.encoding_for_model(self.openai_model_name), max_tokens=max_tokens + ) + chunker = HybridChunker( + tokenizer=tokenizer, + ) + elif self.chunker == "HierarchicalChunker": + chunker = HierarchicalChunker() + + results: list[Data] = [] + try: + for doc in documents: + for chunk in chunker.chunk(dl_doc=doc): + enriched_text = chunker.contextualize(chunk=chunk) + meta = DocMeta.model_validate(chunk.meta) + + results.append( + Data( + data={ + "text": enriched_text, + "document_id": f"{doc.origin.binary_hash}", + "doc_items": json.dumps([item.self_ref for item in meta.doc_items]), + } + ) + ) + + except Exception as e: + msg = f"Error splitting text: {e}" + raise TypeError(msg) from e + + return DataFrame(results) diff --git a/src/backend/base/langflow/components/docling/docling_inline.py b/src/backend/base/langflow/components/docling/docling_inline.py new file mode 100644 index 000000000..9e2c528ba --- /dev/null +++ b/src/backend/base/langflow/components/docling/docling_inline.py @@ -0,0 +1,134 @@ +from docling.datamodel.base_models import ConversionStatus, InputFormat +from docling.datamodel.pipeline_options import ( + OcrOptions, + PdfPipelineOptions, + VlmPipelineOptions, +) +from docling.document_converter import DocumentConverter, FormatOption, PdfFormatOption +from docling.models.factories import get_ocr_factory +from docling.pipeline.vlm_pipeline import VlmPipeline + +from langflow.base.data import BaseFileComponent +from langflow.inputs import DropdownInput +from langflow.schema import Data + + +class DoclingInlineComponent(BaseFileComponent): + display_name = "Docling" + description = "Uses Docling to process input documents running the Docling models locally." + documentation = "https://docling-project.github.io/docling/" + trace_type = "tool" + icon = "Docling" + name = "DoclingInline" + + # https://docling-project.github.io/docling/usage/supported_formats/ + VALID_EXTENSIONS = [ + "adoc", + "asciidoc", + "asc", + "bmp", + "csv", + "dotx", + "dotm", + "docm", + "docx", + "htm", + "html", + "jpeg", + "json", + "md", + "pdf", + "png", + "potx", + "ppsx", + "pptm", + "potm", + "ppsm", + "pptx", + "tiff", + "txt", + "xls", + "xlsx", + "xhtml", + "xml", + "webp", + ] + + inputs = [ + *BaseFileComponent._base_inputs, + DropdownInput( + name="pipeline", + display_name="Pipeline", + info="Docling pipeline to use", + options=["standard", "vlm"], + real_time_refresh=False, + value="standard", + ), + DropdownInput( + name="ocr_engine", + display_name="Ocr", + info="OCR engine to use", + options=["", "easyocr", "tesserocr", "rapidocr", "ocrmac"], + real_time_refresh=False, + value="", + ), + # TODO: expose more Docling options + ] + + outputs = [ + *BaseFileComponent._base_outputs, + ] + + def process_files(self, file_list: list[BaseFileComponent.BaseFile]) -> list[BaseFileComponent.BaseFile]: + # Configure the standard PDF pipeline + def _get_standard_opts() -> PdfPipelineOptions: + pipeline_options = PdfPipelineOptions() + pipeline_options.do_ocr = self.ocr_engine != "" + if pipeline_options.do_ocr: + ocr_factory = get_ocr_factory( + allow_external_plugins=False, + ) + + ocr_options: OcrOptions = ocr_factory.create_options( + kind=self.ocr_engine, + ) + pipeline_options.ocr_options = ocr_options + return pipeline_options + + # Configure the VLM pipeline + def _get_vlm_opts() -> VlmPipelineOptions: + return VlmPipelineOptions() + + # Configure the main format options and create the DocumentConverter() + def _get_converter() -> DocumentConverter: + if self.pipeline == "standard": + pdf_format_option = PdfFormatOption( + pipeline_options=_get_standard_opts(), + ) + elif self.pipeline == "vlm": + pdf_format_option = PdfFormatOption(pipeline_cls=VlmPipeline, pipeline_options=_get_vlm_opts()) + + format_options: dict[InputFormat, FormatOption] = { + InputFormat.PDF: pdf_format_option, + InputFormat.IMAGE: pdf_format_option, + } + + return DocumentConverter(format_options=format_options) + + file_paths = [file.path for file in file_list if file.path] + + if not file_paths: + self.log("No files to process.") + return file_list + + converter = _get_converter() + results = converter.convert_all(file_paths) + + processed_data: list[Data | None] = [ + Data(data={"doc": res.document, "file_path": str(res.input.file)}) + if res.status == ConversionStatus.SUCCESS + else None + for res in results + ] + + return self.rollup_data(file_list, processed_data) diff --git a/src/backend/base/langflow/components/docling/docling_remote.py b/src/backend/base/langflow/components/docling/docling_remote.py new file mode 100644 index 000000000..13fd07dec --- /dev/null +++ b/src/backend/base/langflow/components/docling/docling_remote.py @@ -0,0 +1,193 @@ +import base64 +import time +from concurrent.futures import Future, ThreadPoolExecutor +from pathlib import Path +from typing import Any + +import httpx +from docling_core.types.doc import DoclingDocument +from pydantic import ValidationError + +from langflow.base.data import BaseFileComponent +from langflow.inputs import IntInput, NestedDictInput, StrInput +from langflow.inputs.inputs import FloatInput +from langflow.schema import Data + + +class DoclingRemoteComponent(BaseFileComponent): + display_name = "Docling Serve" + description = "Uses Docling to process input documents connecting to your instance of Docling Serve." + documentation = "https://docling-project.github.io/docling/" + trace_type = "tool" + icon = "Docling" + name = "DoclingRemote" + + MAX_500_RETRIES = 5 + + # https://docling-project.github.io/docling/usage/supported_formats/ + VALID_EXTENSIONS = [ + "adoc", + "asciidoc", + "asc", + "bmp", + "csv", + "dotx", + "dotm", + "docm", + "docx", + "htm", + "html", + "jpeg", + "json", + "md", + "pdf", + "png", + "potx", + "ppsx", + "pptm", + "potm", + "ppsm", + "pptx", + "tiff", + "txt", + "xls", + "xlsx", + "xhtml", + "xml", + "webp", + ] + + inputs = [ + *BaseFileComponent._base_inputs, + StrInput( + name="api_url", + display_name="Server address", + info="URL of the Docling Serve instance.", + required=True, + ), + IntInput( + name="max_concurrency", + display_name="Concurrency", + info="Maximum number of concurrent requests for the server.", + advanced=True, + value=2, + ), + FloatInput( + name="max_poll_timeout", + display_name="Maximum poll time", + info="Maximum waiting time for the document conversion to complete.", + advanced=True, + value=3600, + ), + NestedDictInput( + name="api_headers", + display_name="HTTP headers", + advanced=True, + required=False, + info=("Optional dictionary of additional headers required for connecting to Docling Serve."), + ), + NestedDictInput( + name="docling_serve_opts", + display_name="Docling options", + advanced=True, + required=False, + info=( + "Optional dictionary of additional options. " + "See https://github.com/docling-project/docling-serve/blob/main/docs/usage.md for more information." + ), + ), + ] + + outputs = [ + *BaseFileComponent._base_outputs, + ] + + def process_files(self, file_list: list[BaseFileComponent.BaseFile]) -> list[BaseFileComponent.BaseFile]: + base_url = f"{self.api_url}/v1alpha" + + def _convert_document(client: httpx.Client, file_path: Path, options: dict[str, Any]) -> Data | None: + encoded_doc = base64.b64encode(file_path.read_bytes()).decode() + payload = { + "options": options, + "file_sources": [{"base64_string": encoded_doc, "filename": file_path.name}], + } + + response = client.post(f"{base_url}/convert/source/async", json=payload) + response.raise_for_status() + task = response.json() + + http_failures = 0 + retry_status_start = 500 + retry_status_end = 600 + start_wait_time = time.monotonic() + while task["task_status"] not in ("success", "failure"): + # Check if processing exceeds the maximum poll timeout + processing_time = time.monotonic() - start_wait_time + if processing_time >= self.max_poll_timeout: + msg = ( + f"Processing time {processing_time=} exceeds the maximum poll timeout {self.max_poll_timeout=}." + "Please increase the max_poll_timeout parameter or review why the processing " + "takes long on the server." + ) + self.log(msg) + raise RuntimeError(msg) + + # Call for a new status update + time.sleep(2) + response = client.get(f"{base_url}/status/poll/{task['task_id']}") + + # Check if the status call gets into 5xx errors and retry + if retry_status_start <= response.status_code < retry_status_end: + http_failures += 1 + if http_failures > self.MAX_500_RETRIES: + self.log(f"The status requests got a http response {response.status_code} too many times.") + return None + continue + + # Update task status + task = response.json() + + result_resp = client.get(f"{base_url}/result/{task['task_id']}") + result_resp.raise_for_status() + result = result_resp.json() + + if "json_content" not in result["document"] or result["document"]["json_content"] is None: + self.log("No JSON DoclingDocument found in the result.") + return None + + try: + doc = DoclingDocument.model_validate(result["document"]["json_content"]) + return Data(data={"doc": doc, "file_path": str(file_path)}) + except ValidationError as e: + self.log(f"Error validating the document. {e}") + return None + + docling_options = { + "to_formats": ["json"], + "image_export_mode": "placeholder", + "return_as_file": False, + **(self.docling_serve_opts or {}), + } + + processed_data: list[Data | None] = [] + with ( + httpx.Client(headers=self.api_headers) as client, + ThreadPoolExecutor(max_workers=self.max_concurrency) as executor, + ): + futures: list[tuple[int, Future]] = [] + for i, file in enumerate(file_list): + if file.path is None: + processed_data.append(None) + continue + + futures.append((i, executor.submit(_convert_document, client, file.path, docling_options))) + + for _index, future in futures: + try: + result_data = future.result() + processed_data.append(result_data) + except (httpx.HTTPStatusError, httpx.RequestError, KeyError, ValueError) as exc: + self.log(f"Docling remote processing failed: {exc}") + raise + + return self.rollup_data(file_list, processed_data) diff --git a/src/backend/base/langflow/components/docling/export_docling_document.py b/src/backend/base/langflow/components/docling/export_docling_document.py new file mode 100644 index 000000000..509127f80 --- /dev/null +++ b/src/backend/base/langflow/components/docling/export_docling_document.py @@ -0,0 +1,98 @@ +from docling_core.types.doc import ImageRefMode + +from langflow.base.data.docling_utils import extract_docling_documents +from langflow.custom import Component +from langflow.io import DropdownInput, HandleInput, MessageTextInput, Output, StrInput +from langflow.schema import Data, DataFrame + + +class ExportDoclingDocumentComponent(Component): + display_name: str = "Export DoclingDocument" + description: str = "Export DoclingDocument to markdown, html or other formats." + documentation = "https://docling-project.github.io/docling/" + icon = "Docling" + name = "ExportDoclingDocument" + + inputs = [ + HandleInput( + name="data_inputs", + display_name="Data or DataFrame", + info="The data with documents to export.", + input_types=["Data", "DataFrame"], + required=True, + ), + DropdownInput( + name="export_format", + display_name="Export format", + options=["Markdown", "HTML", "Plaintext", "DocTags"], + info="Select the export format to convert the input.", + value="Markdown", + ), + DropdownInput( + name="image_mode", + display_name="Image export mode", + options=["placeholder", "embedded"], + info=( + "Specify how images are exported in the output. Placeholder will replace the images with a string, " + "whereas Embedded will include them as base64 encoded images." + ), + value="placeholder", + ), + StrInput( + name="md_image_placeholder", + display_name="Image placeholder", + info="Specify the image placeholder for markdown exports.", + value="", + advanced=True, + ), + StrInput( + name="md_page_break_placeholder", + display_name="Page break placeholder", + info="Add this placeholder betweek pages in the markdown output.", + value="", + advanced=True, + ), + MessageTextInput( + name="doc_key", + display_name="Doc Key", + info="The key to use for the DoclingDocument column.", + value="doc", + advanced=True, + ), + ] + + outputs = [ + Output(display_name="Exported data", name="data", method="export_document"), + Output(display_name="DataFrame", name="dataframe", method="as_dataframe"), + ] + + def export_document(self) -> list[Data]: + documents = extract_docling_documents(self.data_inputs, self.doc_key) + + results: list[Data] = [] + try: + image_mode = ImageRefMode(self.image_mode) + for doc in documents: + content = "" + if self.export_format == "Markdown": + content = doc.export_to_markdown( + image_mode=image_mode, + image_placeholder=self.md_image_placeholder, + page_break_placeholder=self.md_page_break_placeholder, + ) + elif self.export_format == "HTML": + content = doc.export_to_html(image_mode=image_mode) + elif self.export_format == "Plaintext": + content = doc.export_to_text() + elif self.export_format == "DocTags": + content = doc.export_to_doctags() + + results.append(Data(text=content)) + except Exception as e: + msg = f"Error splitting text: {e}" + raise TypeError(msg) from e + + return results + + def as_dataframe(self) -> DataFrame: + return DataFrame(self.export_document()) diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json index 8954c084a..11a5984fd 100644 --- a/src/frontend/package-lock.json +++ b/src/frontend/package-lock.json @@ -360,13 +360,13 @@ } }, "node_modules/@babel/helpers": { - "version": "7.27.4", - "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.27.4.tgz", - "integrity": "sha512-Y+bO6U+I7ZKaM5G5rDUZiYfUvQPUibYmAFe7EnKdnKBbVXDZxvp+MWOH5gYciY0EPk4EScsuFMQBbEfpdRKSCQ==", + "version": "7.27.6", + "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.27.6.tgz", + "integrity": "sha512-muE8Tt8M22638HU31A3CgfSUciwz1fhATfoVai05aPXGor//CdWDCbnlY1yvBPo07njuVOCNGCSp/GTt12lIug==", "license": "MIT", "dependencies": { "@babel/template": "^7.27.2", - "@babel/types": "^7.27.3" + "@babel/types": "^7.27.6" }, "engines": { "node": ">=6.9.0" @@ -388,9 +388,9 @@ } }, "node_modules/@babel/runtime": { - "version": "7.27.4", - "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.27.4.tgz", - "integrity": "sha512-t3yaEOuGu9NlIZ+hIeGbBjFtZT7j2cb2tg0fuaJKeGotchRjjLfrBA9Kwf8quhpP1EUuxModQg04q/mBwyg8uA==", + "version": "7.27.6", + "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.27.6.tgz", 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"https://registry.npmjs.org/@types/node/-/node-20.19.0.tgz", + "integrity": "sha512-hfrc+1tud1xcdVTABC2JiomZJEklMcXYNTVtZLAeqTVWD+qL5jkHKT+1lOtqDdGxt+mB53DTtiz673vfjU8D1Q==", "license": "MIT", "dependencies": { - "undici-types": "~6.19.2" + "undici-types": "~6.21.0" } }, "node_modules/@types/parse-json": { @@ -8062,9 +8062,9 @@ "license": "MIT" }, "node_modules/effect": { - "version": "3.16.3", - "resolved": "https://registry.npmjs.org/effect/-/effect-3.16.3.tgz", - "integrity": "sha512-SWndb1UavNWvet1+hnkU4qp3EHtnmDKhUeP14eB+7vf/2nCFlM77/oIjdDeZctveibNjE65P9H/sBBmF0NTy/w==", + "version": "3.16.4", + "resolved": "https://registry.npmjs.org/effect/-/effect-3.16.4.tgz", + "integrity": "sha512-zJo5MRPEMROLjTHyrOJs0PUeEnRLy0ABwum3+HWlP8Y9LNF+NjipIRldVAcjSVQpJ7vY/OEGBxzw0USPzTfWag==", "license": "MIT", "dependencies": { "@standard-schema/spec": "^1.0.0", @@ -8514,9 +8514,9 @@ } }, "node_modules/esrap": { - "version": "1.4.6", - "resolved": "https://registry.npmjs.org/esrap/-/esrap-1.4.6.tgz", - "integrity": "sha512-F/D2mADJ9SHY3IwksD4DAXjTt7qt7GWUf3/8RhCNWmC/67tyb55dpimHmy7EplakFaflV0R/PC+fdSPqrRHAQw==", + "version": "1.4.7", + "resolved": "https://registry.npmjs.org/esrap/-/esrap-1.4.7.tgz", + "integrity": "sha512-0ZxW6guTF/AeKeKi7he93lmgv7Hx7giD1tBrOeVqkqsZGQJd2/kfnL7LdIsr9FT/AtkBK9XeDTov+gxprBqdEg==", "license": "MIT", "dependencies": { "@jridgewell/sourcemap-codec": "^1.4.15" @@ -9007,14 +9007,15 @@ } }, "node_modules/form-data": { - "version": "4.0.2", - "resolved": "https://registry.npmjs.org/form-data/-/form-data-4.0.2.tgz", - "integrity": "sha512-hGfm/slu0ZabnNt4oaRZ6uREyfCj6P4fT/n6A1rGV+Z0VdGXjfOhVUpkn6qVQONHGIFwmveGXyDs75+nr6FM8w==", + "version": "4.0.3", + "resolved": "https://registry.npmjs.org/form-data/-/form-data-4.0.3.tgz", + "integrity": "sha512-qsITQPfmvMOSAdeyZ+12I1c+CKSstAFAwu+97zrnWAbIr5u8wfsExUzCesVLC8NgHuRUqNN4Zy6UPWUTRGslcA==", "license": "MIT", "dependencies": { "asynckit": "^0.4.0", "combined-stream": "^1.0.8", "es-set-tostringtag": "^2.1.0", + "hasown": "^2.0.2", "mime-types": "^2.1.12" }, "engines": { @@ -14981,6 +14982,12 @@ "fsevents": "~2.3.2" } }, + "node_modules/rollup/node_modules/@types/estree": { + "version": "1.0.7", + "resolved": "https://registry.npmjs.org/@types/estree/-/estree-1.0.7.tgz", + "integrity": "sha512-w28IoSUCJpidD/TGviZwwMJckNESJZXFu7NBZ5YJ4mEUnNraUn9Pm8HSZm/jDF1pDWYKspWE7oVphigUPRakIQ==", + "license": "MIT" + }, "node_modules/rrdom": { "version": "0.1.7", "resolved": "https://registry.npmjs.org/rrdom/-/rrdom-0.1.7.tgz", @@ -16352,9 +16359,9 @@ } }, "node_modules/undici-types": { - "version": "6.19.8", - "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.19.8.tgz", - "integrity": "sha512-ve2KP6f/JnbPBFyobGHuerC9g1FYGn/F8n1LWTwNxCEzd6IfqTwUQcNXgEtmmQ6DlRrC1hrSrBnCZPokRrDHjw==", + "version": "6.21.0", + "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz", + "integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==", "license": "MIT" }, "node_modules/unified": { @@ -17885,9 +17892,9 @@ "license": "MIT" }, "node_modules/zod": { - "version": "3.25.51", - "resolved": "https://registry.npmjs.org/zod/-/zod-3.25.51.tgz", - "integrity": "sha512-TQSnBldh+XSGL+opiSIq0575wvDPqu09AqWe1F7JhUMKY+M91/aGlK4MhpVNO7MgYfHcVCB1ffwAUTJzllKJqg==", + "version": "3.25.55", + "resolved": "https://registry.npmjs.org/zod/-/zod-3.25.55.tgz", + "integrity": "sha512-219huNnkSLQnLsQ3uaRjXsxMrVm5C9W3OOpEVt2k5tvMKuA8nBSu38e0B//a+he9Iq2dvmk2VyYVlHqiHa4YBA==", "license": "MIT", "funding": { "url": "https://github.com/sponsors/colinhacks" diff --git a/src/frontend/src/icons/Docling/Docling.jsx b/src/frontend/src/icons/Docling/Docling.jsx new file mode 100644 index 000000000..f9bdf89b8 --- /dev/null +++ b/src/frontend/src/icons/Docling/Docling.jsx @@ -0,0 +1,338 @@ +const SvgDocling = (props) => ( + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +); + +export default SvgDocling; diff --git a/src/frontend/src/icons/Docling/Docling.svg b/src/frontend/src/icons/Docling/Docling.svg new file mode 100644 index 000000000..035671c0f --- /dev/null +++ b/src/frontend/src/icons/Docling/Docling.svg @@ -0,0 +1,116 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/src/frontend/src/icons/Docling/index.tsx b/src/frontend/src/icons/Docling/index.tsx new file mode 100644 index 000000000..9a29e0fe4 --- /dev/null +++ b/src/frontend/src/icons/Docling/index.tsx @@ -0,0 +1,9 @@ +import React, { forwardRef } from "react"; +import SvgDocling from "./Docling"; + +export const DoclingIcon = forwardRef< + SVGSVGElement, + React.PropsWithChildren<{}> +>((props, ref) => { + return ; +}); diff --git a/src/frontend/src/icons/eagerIconImports.ts b/src/frontend/src/icons/eagerIconImports.ts index 5182adc54..10736e663 100644 --- a/src/frontend/src/icons/eagerIconImports.ts +++ b/src/frontend/src/icons/eagerIconImports.ts @@ -24,6 +24,7 @@ import { ConfluenceIcon } from "@/icons/Confluence"; import { CouchbaseIcon } from "@/icons/Couchbase"; import { CrewAiIcon } from "@/icons/CrewAI"; import { DeepSeekIcon } from "@/icons/DeepSeek"; +import { DoclingIcon } from "@/icons/Docling"; import { DropboxIcon } from "@/icons/Dropbox"; import { DuckDuckGoIcon } from "@/icons/DuckDuckGo"; import { ElasticsearchIcon } from "@/icons/ElasticsearchStore"; @@ -141,6 +142,7 @@ export const eagerIconsMapping = { Couchbase: CouchbaseIcon, CrewAI: CrewAiIcon, DeepSeek: DeepSeekIcon, + Docling: DoclingIcon, Dropbox: DropboxIcon, DuckDuckGo: DuckDuckGoIcon, ElasticsearchStore: ElasticsearchIcon, diff --git a/src/frontend/src/icons/lazyIconImports.ts b/src/frontend/src/icons/lazyIconImports.ts index 72947c66a..b76fcdbd0 100644 --- a/src/frontend/src/icons/lazyIconImports.ts +++ b/src/frontend/src/icons/lazyIconImports.ts @@ -70,6 +70,8 @@ export const lazyIconsMapping = { import("@/icons/Cursor").then((mod) => ({ default: mod.CursorIcon })), DeepSeek: () => import("@/icons/DeepSeek").then((mod) => ({ default: mod.DeepSeekIcon })), + Docling: () => + import("@/icons/Docling").then((mod) => ({ default: mod.DoclingIcon })), Dropbox: () => import("@/icons/Dropbox").then((mod) => ({ default: mod.DropboxIcon })), DuckDuckGo: () => diff --git a/src/frontend/src/utils/styleUtils.ts b/src/frontend/src/utils/styleUtils.ts index 5fabf0528..c1c38a068 100644 --- a/src/frontend/src/utils/styleUtils.ts +++ b/src/frontend/src/utils/styleUtils.ts @@ -256,6 +256,7 @@ export const SIDEBAR_BUNDLES = [ name: "datastax", icon: "AstraDB", }, + { display_name: "Docling", name: "docling", icon: "Docling" }, { display_name: "Olivya", name: "olivya", icon: "Olivya" }, { display_name: "LangWatch", name: "langwatch", icon: "Langwatch" }, { display_name: "Notion", name: "Notion", icon: "Notion" }, diff --git a/uv.lock b/uv.lock index 9a39196c4..1d9b97495 100644 --- a/uv.lock +++ b/uv.lock @@ -2,12 +2,24 @@ version = 1 revision = 2 requires-python = ">=3.10, <3.14" resolution-markers = [ - "python_full_version < '3.11' and platform_python_implementation == 'PyPy'", - "python_full_version < '3.11' and platform_python_implementation != 'PyPy'", - "python_full_version == '3.11.*'", - "python_full_version >= '3.12' and python_full_version < '3.12.4'", - "python_full_version >= '3.12.4' and python_full_version < '3.13'", - "python_full_version >= '3.13'", + "python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version < '3.11' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version == '3.11.*' and sys_platform == 'darwin'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform == 'darwin'", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version >= '3.12.4' and python_full_version < '3.13' and sys_platform == 'darwin'", + "python_full_version >= '3.12.4' and python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version >= '3.12.4' and python_full_version < '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version >= '3.13' and sys_platform == 'darwin'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", ] [manifest] @@ -15,13 +27,16 @@ members = [ "langflow", "langflow-base", ] +overrides = [{ name = "python-pptx", specifier = ">=1.0.2" }] [[package]] name = "ag2" version = "0.3.2" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version >= '3.13'", + "python_full_version >= '3.13' and sys_platform == 'darwin'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", ] dependencies = [ { name = "diskcache", marker = "python_full_version >= '3.13'" }, @@ -45,11 +60,21 @@ name = "ag2" version = "0.5.3" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version < '3.11' and platform_python_implementation == 'PyPy'", - "python_full_version < '3.11' and platform_python_implementation != 'PyPy'", - "python_full_version == '3.11.*'", - "python_full_version >= '3.12' and python_full_version < '3.12.4'", - "python_full_version >= '3.12.4' and python_full_version < '3.13'", + "python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version < '3.11' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version == '3.11.*' and sys_platform == 'darwin'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform == 'darwin'", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version >= '3.12.4' and python_full_version < '3.13' and sys_platform == 'darwin'", + "python_full_version >= '3.12.4' and python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version >= '3.12.4' and python_full_version < '3.13' and 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