feat: add nvidia ingest component (#6333)
* initial * cleanup * [autofix.ci] apply automated fixes * ruff * add else * update deps * uv lock * Make nv-ingest an optional dep * revert change to validate * rebase fixes * [autofix.ci] apply automated fixes * Update language * add extra args to make target * [autofix.ci] apply automated fixes * update error language * lockfile update * rebase lockfile: * [autofix.ci] apply automated fixes * Adds nv-ingest by default to -ep docker image * caps fixes * update uv lock * revert ruff upgrade * ruff * Fix lint * [autofix.ci] apply automated fixes * No code changes made. * fix: update ruff configuration to ignore additional linting rule and allow built-in modules * fix: update ruff command to ignore linting rule A005 during autofix * fix: update Ruff check command to ignore linting rule A005 --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>
This commit is contained in:
Jordan Frazier
GitHub
parent
41edba73c9
commit
400572342f
8 changed files with 1587 additions and 1213 deletions
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@ -1,3 +1,4 @@
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from .nvidia_ingest import NvidiaIngestComponent
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from .nvidia_rerank import NvidiaRerankComponent
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__all__ = ["NvidiaRerankComponent"]
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__all__ = ["NvidiaIngestComponent", "NvidiaRerankComponent"]
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235
src/backend/base/langflow/components/nvidia/nvidia_ingest.py
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235
src/backend/base/langflow/components/nvidia/nvidia_ingest.py
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from pathlib import Path
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from urllib.parse import urlparse
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from loguru import logger
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from langflow.custom import Component
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from langflow.io import BoolInput, DropdownInput, FileInput, IntInput, MessageTextInput, Output
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from langflow.schema import Data
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class NvidiaIngestComponent(Component):
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display_name = "NVIDIA Ingest"
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description = "Process, transform, and store data."
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documentation: str = "https://github.com/NVIDIA/nv-ingest/tree/main/docs"
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icon = "NVIDIA"
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name = "NVIDIAIngest"
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beta = True
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try:
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from nv_ingest_client.util.file_processing.extract import EXTENSION_TO_DOCUMENT_TYPE
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file_types = list(EXTENSION_TO_DOCUMENT_TYPE.keys())
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supported_file_types_info = f"Supported file types: {', '.join(file_types)}"
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except ImportError:
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msg = (
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"NVIDIA Ingest dependencies missing. "
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"Please install them using your package manager. (e.g. uv sync --extra nv-ingest)"
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)
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logger.warning(msg)
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file_types = [msg]
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supported_file_types_info = msg
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inputs = [
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MessageTextInput(
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name="base_url",
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display_name="NVIDIA Ingestion URL",
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info="The URL of the NVIDIA Ingestion API.",
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),
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FileInput(
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name="path",
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display_name="Path",
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file_types=file_types,
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info=supported_file_types_info,
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required=True,
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),
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BoolInput(
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name="extract_text",
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display_name="Extract Text",
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info="Extract text from documents",
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value=True,
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),
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BoolInput(
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name="extract_charts",
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display_name="Extract Charts",
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info="Extract text from charts",
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value=False,
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),
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BoolInput(
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name="extract_tables",
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display_name="Extract Tables",
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info="Extract text from tables",
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value=True,
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),
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DropdownInput(
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name="text_depth",
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display_name="Text Depth",
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info=(
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"Level at which text is extracted (applies before splitting). "
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"Support for 'block', 'line', 'span' varies by document type."
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),
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options=["document", "page", "block", "line", "span"],
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value="document", # Default value
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advanced=True,
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),
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BoolInput(
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name="split_text",
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display_name="Split Text",
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info="Split text into smaller chunks",
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value=True,
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),
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DropdownInput(
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name="split_by",
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display_name="Split By",
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info="How to split into chunks ('size' splits by number of characters)",
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options=["page", "sentence", "word", "size"],
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value="word", # Default value
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advanced=True,
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),
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IntInput(
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name="split_length",
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display_name="Split Length",
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info="The size of each chunk based on the 'split_by' method",
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value=200,
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advanced=True,
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),
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IntInput(
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name="split_overlap",
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display_name="Split Overlap",
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info="Number of segments (as determined by the 'split_by' method) to overlap from previous chunk",
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value=20,
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advanced=True,
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),
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IntInput(
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name="max_character_length",
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display_name="Max Character Length",
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info="Maximum number of characters in each chunk",
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value=1000,
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advanced=True,
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),
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IntInput(
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name="sentence_window_size",
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display_name="Sentence Window Size",
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info="Number of sentences to include from previous and following chunk (when split_by='sentence')",
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value=0,
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advanced=True,
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),
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]
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outputs = [
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Output(display_name="Data", name="data", method="load_file"),
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]
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def load_file(self) -> list[Data]:
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try:
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from nv_ingest_client.client import Ingestor
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except ImportError as e:
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msg = (
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"NVIDIA Ingest dependencies missing. "
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"Please install them using your package manager. (e.g. uv sync --extra nv-ingest)"
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)
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raise ImportError(msg) from e
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self.base_url: str | None = self.base_url.strip() if self.base_url else None
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if not self.path:
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err_msg = "Upload a file to use this component."
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self.log(err_msg, name="NVIDIAIngestComponent")
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raise ValueError(err_msg)
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resolved_path = self.resolve_path(self.path)
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extension = Path(resolved_path).suffix[1:].lower()
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if extension not in self.file_types:
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err_msg = f"Unsupported file type: {extension}"
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self.log(err_msg, name="NVIDIAIngestComponent")
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raise ValueError(err_msg)
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try:
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parsed_url = urlparse(self.base_url)
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if not parsed_url.hostname or not parsed_url.port:
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err_msg = "Invalid URL: Missing hostname or port."
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self.log(err_msg, name="NVIDIAIngestComponent")
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raise ValueError(err_msg)
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except Exception as e:
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self.log(f"Error parsing URL: {e}", name="NVIDIAIngestComponent")
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raise
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self.log(
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f"Creating Ingestor for host: {parsed_url.hostname!r}, port: {parsed_url.port!r}",
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name="NVIDIAIngestComponent",
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)
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try:
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from nv_ingest_client.client import Ingestor
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ingestor = (
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Ingestor(message_client_hostname=parsed_url.hostname, message_client_port=parsed_url.port)
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.files(resolved_path)
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.extract(
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extract_text=self.extract_text,
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extract_tables=self.extract_tables,
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extract_charts=self.extract_charts,
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extract_images=False, # Currently not supported
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text_depth=self.text_depth,
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)
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)
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except Exception as e:
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self.log(f"Error creating Ingestor: {e}", name="NVIDIAIngestComponent")
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raise
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if self.split_text:
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ingestor = ingestor.split(
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split_by=self.split_by,
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split_length=self.split_length,
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split_overlap=self.split_overlap,
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max_character_length=self.max_character_length,
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sentence_window_size=self.sentence_window_size,
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)
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try:
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result = ingestor.ingest()
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except Exception as e:
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self.log(f"Error during ingestion: {e}", name="NVIDIAIngestComponent")
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raise
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self.log(f"Results: {result}", name="NVIDIAIngestComponent")
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data = []
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document_type_text = "text"
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document_type_structured = "structured"
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# Result is a list of segments as determined by the text_depth option (if "document" then only one segment)
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# each segment is a list of elements (text, structured, image)
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for segment in result:
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for element in segment:
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document_type = element.get("document_type")
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metadata = element.get("metadata", {})
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source_metadata = metadata.get("source_metadata", {})
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content_metadata = metadata.get("content_metadata", {})
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if document_type == document_type_text:
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data.append(
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Data(
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text=metadata.get("content", ""),
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file_path=source_metadata.get("source_name", ""),
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document_type=document_type,
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description=content_metadata.get("description", ""),
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)
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)
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# Both charts and tables are returned as "structured" document type,
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# with extracted text in "table_content"
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elif document_type == document_type_structured:
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table_metadata = metadata.get("table_metadata", {})
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data.append(
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Data(
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text=table_metadata.get("table_content", ""),
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file_path=source_metadata.get("source_name", ""),
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document_type=document_type,
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description=content_metadata.get("description", ""),
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)
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)
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else:
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# image is not yet supported; skip if encountered
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self.log(f"Unsupported document type: {document_type}", name="NVIDIAIngestComponent")
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self.status = data if data else "No data"
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return data
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