code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def _update_audio_metadata(row: pd.Series, audio_client: Any, trace_info: Dict) -> Dict:
"""
Modifies the metadata of a row if the conditions for table extraction are met.
Parameters
----------
row : pd.Series
A row from the DataFrame containing metadata for the audio extraction.
audio... |
Modifies the metadata of a row if the conditions for table extraction are met.
Parameters
----------
row : pd.Series
A row from the DataFrame containing metadata for the audio extraction.
audio_client : Any
The client used to call the audio inference model.
trace_info : Dict
... | _update_audio_metadata | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/audio/audio_extraction.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/audio/audio_extraction.py | Apache-2.0 |
def extract_text_from_audio_internal(
df_extraction_ledger: pd.DataFrame,
task_config: Dict[str, Any],
extraction_config: AudioExtractorSchema,
execution_trace_log: Optional[Dict] = None,
) -> Tuple[pd.DataFrame, Dict]:
"""
Extracts audio data from a DataFrame.
Parameters
----------
... |
Extracts audio data from a DataFrame.
Parameters
----------
df_extraction_ledger : pd.DataFrame
DataFrame containing the content from which audio data is to be extracted.
task_config : Dict[str, Any]
Dictionary containing task properties and configurations.
extraction_config ... | extract_text_from_audio_internal | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/audio/audio_extraction.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/audio/audio_extraction.py | Apache-2.0 |
def _prepare_task_props(
task_config: Union[Dict[str, Any], BaseModel], base64_row: pd.Series
) -> (Dict[str, Any], Optional[str]):
"""
Prepares the task properties by converting a Pydantic model to a dictionary (if needed)
and injecting row-specific data.
Parameters
----------
task_config ... |
Prepares the task properties by converting a Pydantic model to a dictionary (if needed)
and injecting row-specific data.
Parameters
----------
task_config : Union[Dict[str, Any], BaseModel]
A dictionary or Pydantic model containing instructions and parameters for extraction.
base64_row... | _prepare_task_props | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/docx_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/docx_extractor.py | Apache-2.0 |
def _decode_and_extract_from_docx(
base64_row: pd.Series,
task_config: Union[Dict[str, Any], BaseModel],
extraction_config: Any,
execution_trace_log: Optional[Dict[str, Any]] = None,
) -> Any:
"""
Decodes base64 content from a DataFrame row and extracts data using the specified extraction method... |
Decodes base64 content from a DataFrame row and extracts data using the specified extraction method.
The function decodes the base64-encoded content from the "content" key in the row, prepares
extraction parameters (including additional row data and configuration), and invokes the extraction
function ... | _decode_and_extract_from_docx | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/docx_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/docx_extractor.py | Apache-2.0 |
def extract_primitives_from_docx_internal(
df_extraction_ledger: pd.DataFrame,
task_config: Union[Dict[str, Any], BaseModel],
extraction_config: DocxExtractorSchema,
execution_trace_log: Optional[Dict[str, Any]] = None,
) -> pd.DataFrame:
"""
Processes a pandas DataFrame containing DOCX files en... |
Processes a pandas DataFrame containing DOCX files encoded in base64, extracting text from
each document and replacing the original content with the extracted text.
This function applies a decoding and extraction routine to each row of the input DataFrame.
The routine is provided via the `decode_and_e... | extract_primitives_from_docx_internal | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/docx_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/docx_extractor.py | Apache-2.0 |
def __init__(self, document: Document, source_metadata: Dict):
"""
Copy over some of the docx core properties
"""
self.document = document
self.source_metadata = source_metadata
# Extract core properties with None checks
core_properties = self.document.core_prope... |
Copy over some of the docx core properties
| __init__ | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | Apache-2.0 |
def _update_source_meta_data(self):
"""
Update the source metadata with the document's core properties
"""
# Only update metadata if dates are available
metadata_updates = {}
if self.created is not None:
metadata_updates["date_created"] = self.created.isoform... |
Update the source metadata with the document's core properties
| _update_source_meta_data | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | Apache-2.0 |
def format_text(self, text: str, bold: bool, italic: bool, underline: bool) -> str:
"""
Apply markdown styling (bold, italic, underline) to the given text.
Parameters
----------
text : str
The text to format.
bold : bool
Whether to apply bold styl... |
Apply markdown styling (bold, italic, underline) to the given text.
Parameters
----------
text : str
The text to format.
bold : bool
Whether to apply bold styling.
italic : bool
Whether to apply italic styling.
underline : boo... | format_text | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | Apache-2.0 |
def format_paragraph(self, paragraph: "Paragraph") -> Tuple[str, List["Image"]]:
"""
Format a paragraph into styled text and extract associated images.
Parameters
----------
paragraph : Paragraph
The paragraph to format. This includes text and potentially embedded im... |
Format a paragraph into styled text and extract associated images.
Parameters
----------
paragraph : Paragraph
The paragraph to format. This includes text and potentially embedded images.
Returns
-------
tuple of (str, list of Image)
- T... | format_paragraph | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | Apache-2.0 |
def format_cell(self, cell: "_Cell") -> Tuple[str, List["Image"]]:
"""
Format a table cell into Markdown text and extract associated images.
Parameters
----------
cell : _Cell
The table cell to format.
Returns
-------
tuple of (str, list of I... |
Format a table cell into Markdown text and extract associated images.
Parameters
----------
cell : _Cell
The table cell to format.
Returns
-------
tuple of (str, list of Image)
- The formatted text of the cell with markdown styling appli... | format_cell | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | Apache-2.0 |
def format_table(self, table: "Table") -> Tuple[Optional[str], List["Image"], DataFrame]:
"""
Format a table into text, extract images, and represent it as a DataFrame.
Parameters
----------
table : Table
The table to format.
Returns
-------
... |
Format a table into text, extract images, and represent it as a DataFrame.
Parameters
----------
table : Table
The table to format.
Returns
-------
tuple of (str or None, list of Image, DataFrame)
- The formatted table as text, using the... | format_table | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | Apache-2.0 |
def apply_text_style(style: str, text: str, level: int = 0) -> str:
"""
Apply a specific text style (e.g., heading, list, title, subtitle) to the given text.
Parameters
----------
style : str
The style to apply. Supported styles include headings ("Heading 1" to "Head... |
Apply a specific text style (e.g., heading, list, title, subtitle) to the given text.
Parameters
----------
style : str
The style to apply. Supported styles include headings ("Heading 1" to "Heading 9"),
list items ("List"), and document structures ("Title", "Su... | apply_text_style | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | Apache-2.0 |
def _construct_image_metadata(
self, para_idx: int, caption: str, base_unified_metadata: Dict, base64_img: str
) -> List[Union[str, dict]]:
"""
Build metadata for an image in a DOCX file.
Parameters
----------
para_idx : int
The paragraph index containing... |
Build metadata for an image in a DOCX file.
Parameters
----------
para_idx : int
The paragraph index containing the image.
caption : str
The caption associated with the image.
base_unified_metadata : dict
The base metadata to build up... | _construct_image_metadata | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | Apache-2.0 |
def _extract_para_images(
self, images: List["Image"], para_idx: int, caption: str, base_unified_metadata: Dict
) -> None:
"""
Collect images from a paragraph and store them for metadata construction.
Parameters
----------
images : list of Image
The image... |
Collect images from a paragraph and store them for metadata construction.
Parameters
----------
images : list of Image
The images found in the paragraph.
para_idx : int
The index of the paragraph containing the images.
caption : str
T... | _extract_para_images | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | Apache-2.0 |
def _construct_text_metadata(
self, accumulated_text: List[str], para_idx: int, text_depth: "TextTypeEnum", base_unified_metadata: Dict
) -> List[Union[str, dict]]:
"""
Build metadata for text content in a DOCX file.
Parameters
----------
accumulated_text : list of s... |
Build metadata for text content in a DOCX file.
Parameters
----------
accumulated_text : list of str
The accumulated text to include in the metadata.
para_idx : int
The paragraph index containing the text.
text_depth : TextTypeEnum
Th... | _construct_text_metadata | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | Apache-2.0 |
def _extract_para_text(
self,
paragraph,
paragraph_text,
base_unified_metadata: Dict,
text_depth: str,
para_idx: int,
) -> None:
"""
Process the text, images, and styles in a DOCX paragraph.
Parameters
----------
paragraph: Par... |
Process the text, images, and styles in a DOCX paragraph.
Parameters
----------
paragraph: Paragraph
The paragraph to process.
paragraph_text: str
The text content of the paragraph.
base_unified_metadata : dict
The base metadata to as... | _extract_para_text | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | Apache-2.0 |
def _finalize_images(self, extract_tables: bool, extract_charts: bool, **kwargs) -> None:
"""
Build and append final metadata for each pending image in batches.
Parameters
----------
extract_tables : bool
Whether to attempt table detection in images.
extract_... |
Build and append final metadata for each pending image in batches.
Parameters
----------
extract_tables : bool
Whether to attempt table detection in images.
extract_charts : bool
Whether to attempt chart detection in images.
**kwargs
... | _finalize_images | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | Apache-2.0 |
def _extract_table_data(
self,
child,
base_unified_metadata: Dict,
) -> None:
"""
Process the text and images in a DOCX table.
Parameters
----------
child : element
The table element to process.
base_unified_metadata : dict
... |
Process the text and images in a DOCX table.
Parameters
----------
child : element
The table element to process.
base_unified_metadata : dict
The base metadata to associate with extracted data.
text_depth : TextTypeEnum
The depth of t... | _extract_table_data | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docxreader.py | Apache-2.0 |
def python_docx(
*,
docx_stream: IO,
extract_text: bool,
extract_images: bool,
extract_infographics: bool,
extract_tables: bool,
extract_charts: bool,
extraction_config: dict,
execution_trace_log: Optional[List] = None,
):
"""
Helper function that use python-docx to extract t... |
Helper function that use python-docx to extract text from a bytestream document
A document has three levels - document, paragraphs and runs. To align with the
pdf extraction paragraphs are aliased as block. python-docx leaves the page number
and line number to the renderer so we assume that the entire... | python_docx | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docx_helper.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/docx/engines/docxreader_helpers/docx_helper.py | Apache-2.0 |
def _filter_valid_chart_images(
base64_images: List[str],
) -> Tuple[List[str], List[np.ndarray], List[int], List[Tuple[str, Optional[Dict]]]]:
"""
Filter base64-encoded images based on minimum dimensions for chart extraction.
Returns:
- valid_images: Base64 strings meeting size requirements.
... |
Filter base64-encoded images based on minimum dimensions for chart extraction.
Returns:
- valid_images: Base64 strings meeting size requirements.
- valid_arrays: Corresponding numpy arrays.
- valid_indices: Original indices of valid images.
- results: Initial results list where invalid... | _filter_valid_chart_images | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/chart_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/chart_extractor.py | Apache-2.0 |
def _run_chart_inference(
yolox_client: Any,
paddle_client: Any,
valid_arrays: List[np.ndarray],
valid_images: List[str],
trace_info: Dict,
) -> Tuple[List[Any], List[Any]]:
"""
Run concurrent inference for chart extraction using YOLOX and Paddle.
Returns a tuple of (yolox_results, padd... |
Run concurrent inference for chart extraction using YOLOX and Paddle.
Returns a tuple of (yolox_results, paddle_results).
| _run_chart_inference | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/chart_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/chart_extractor.py | Apache-2.0 |
def _validate_chart_inference_results(
yolox_results: Any, paddle_results: Any, valid_arrays: List[Any], valid_images: List[str]
) -> Tuple[List[Any], List[Any]]:
"""
Ensure inference results are lists and have expected lengths.
Raises:
ValueError if results do not match expected types or lengths... |
Ensure inference results are lists and have expected lengths.
Raises:
ValueError if results do not match expected types or lengths.
| _validate_chart_inference_results | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/chart_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/chart_extractor.py | Apache-2.0 |
def _merge_chart_results(
base64_images: List[str],
valid_indices: List[int],
yolox_results: List[Any],
paddle_results: List[Any],
initial_results: List[Tuple[str, Optional[Dict]]],
) -> List[Tuple[str, Optional[Dict]]]:
"""
Merge inference results into the initial results list using the ori... |
Merge inference results into the initial results list using the original indices.
For each valid image, processes the results from both inference calls and updates the
corresponding entry in the results list.
| _merge_chart_results | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/chart_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/chart_extractor.py | Apache-2.0 |
def _update_chart_metadata(
base64_images: List[str],
yolox_client: Any,
paddle_client: Any,
trace_info: Dict,
worker_pool_size: int = 8, # Not currently used.
) -> List[Tuple[str, Optional[Dict]]]:
"""
Given a list of base64-encoded chart images, concurrently call both YOLOX and Paddle
... |
Given a list of base64-encoded chart images, concurrently call both YOLOX and Paddle
inference services to extract chart data.
For each base64-encoded image, returns:
(original_image_str, joined_chart_content_dict)
Images that do not meet minimum size requirements are marked as skipped.
| _update_chart_metadata | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/chart_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/chart_extractor.py | Apache-2.0 |
def _decode_and_extract_from_image(
base64_row: pd.Series,
task_config: Dict[str, Any],
validated_extraction_config: ImageExtractorSchema,
execution_trace_log: Optional[List[Any]] = None,
) -> Any:
"""
Decode base64-encoded image content from a DataFrame row and extract data using a specified ex... |
Decode base64-encoded image content from a DataFrame row and extract data using a specified extraction method.
This function extracts the "content" (base64 string) from the row, prepares additional task parameters by
inserting the remaining row data under "row_data", and decodes the base64 content into a ... | _decode_and_extract_from_image | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/image_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/image_extractor.py | Apache-2.0 |
def _filter_infographic_images(
base64_images: List[str],
) -> Tuple[List[str], List[int], List[Tuple[str, Optional[Any], Optional[Any]]]]:
"""
Filters base64-encoded images based on minimum size requirements.
Parameters
----------
base64_images : List[str]
List of base64-encoded image ... |
Filters base64-encoded images based on minimum size requirements.
Parameters
----------
base64_images : List[str]
List of base64-encoded image strings.
Returns
-------
Tuple[List[str], List[int], List[Tuple[str, Optional[Any], Optional[Any]]]]
- valid_images: List of image... | _filter_infographic_images | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/infographic_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/infographic_extractor.py | Apache-2.0 |
def _update_infographic_metadata(
base64_images: List[str],
paddle_client: NimClient,
worker_pool_size: int = 8, # Not currently used
trace_info: Optional[Dict] = None,
) -> List[Tuple[str, Optional[Any], Optional[Any]]]:
"""
Filters base64-encoded images and uses PaddleOCR to extract infograph... |
Filters base64-encoded images and uses PaddleOCR to extract infographic data.
For each image that meets the minimum size, calls paddle_client.infer to obtain
(text_predictions, bounding_boxes). Invalid images are marked as skipped.
Parameters
----------
base64_images : List[str]
List ... | _update_infographic_metadata | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/infographic_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/infographic_extractor.py | Apache-2.0 |
def _meets_infographic_criteria(row: pd.Series) -> bool:
"""
Determines if a DataFrame row meets the criteria for infographic extraction.
A row qualifies if:
- It contains a 'metadata' dictionary.
- The 'content_metadata' in metadata has type "structured" and subtype "infographic".
- The ... |
Determines if a DataFrame row meets the criteria for infographic extraction.
A row qualifies if:
- It contains a 'metadata' dictionary.
- The 'content_metadata' in metadata has type "structured" and subtype "infographic".
- The 'table_metadata' is not None.
- The 'content' is not None ... | _meets_infographic_criteria | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/infographic_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/infographic_extractor.py | Apache-2.0 |
def extract_infographic_data_from_image_internal(
df_extraction_ledger: pd.DataFrame,
task_config: Dict[str, Any],
extraction_config: InfographicExtractorSchema,
execution_trace_log: Optional[Dict] = None,
) -> Tuple[pd.DataFrame, Dict]:
"""
Extracts infographic data from a DataFrame in bulk, fo... |
Extracts infographic data from a DataFrame in bulk, following the chart extraction pattern.
Parameters
----------
df_extraction_ledger : pd.DataFrame
DataFrame containing the content from which infographic data is to be extracted.
task_config : Dict[str, Any]
Dictionary containing ... | extract_infographic_data_from_image_internal | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/infographic_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/infographic_extractor.py | Apache-2.0 |
def _filter_valid_images(base64_images: List[str]) -> Tuple[List[str], List[np.ndarray], List[int]]:
"""
Filter base64-encoded images by their dimensions.
Returns three lists:
- valid_images: The base64 strings that meet minimum size requirements.
- valid_arrays: The corresponding numpy arrays.... |
Filter base64-encoded images by their dimensions.
Returns three lists:
- valid_images: The base64 strings that meet minimum size requirements.
- valid_arrays: The corresponding numpy arrays.
- valid_indices: The original indices in the input list.
| _filter_valid_images | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/table_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/table_extractor.py | Apache-2.0 |
def _run_inference(
enable_yolox: bool,
yolox_client: Any,
paddle_client: Any,
valid_arrays: List[np.ndarray],
valid_images: List[str],
trace_info: Optional[Dict] = None,
) -> Tuple[List[Any], List[Any]]:
"""
Run inference concurrently for YOLOX (if enabled) and Paddle.
Returns a tu... |
Run inference concurrently for YOLOX (if enabled) and Paddle.
Returns a tuple of (yolox_results, paddle_results).
| _run_inference | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/table_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/table_extractor.py | Apache-2.0 |
def _validate_inference_results(
yolox_results: Any,
paddle_results: Any,
valid_arrays: List[Any],
valid_images: List[str],
) -> Tuple[List[Any], List[Any]]:
"""
Validate that both inference results are lists and have the expected lengths.
If not, default values are assigned. Raises a Value... |
Validate that both inference results are lists and have the expected lengths.
If not, default values are assigned. Raises a ValueError if the lengths do not match.
| _validate_inference_results | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/table_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/table_extractor.py | Apache-2.0 |
def _update_table_metadata(
base64_images: List[str],
yolox_client: Any,
paddle_client: Any,
worker_pool_size: int = 8, # Not currently used
enable_yolox: bool = False,
trace_info: Optional[Dict] = None,
) -> List[Tuple[str, Any, Any, Any]]:
"""
Given a list of base64-encoded images, th... |
Given a list of base64-encoded images, this function filters out images that do not meet
the minimum size requirements and then calls the PaddleOCR model via paddle_client.infer
to extract table data.
For each base64-encoded image, the result is a tuple:
(base64_image, yolox_result, paddle_tex... | _update_table_metadata | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/table_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/table_extractor.py | Apache-2.0 |
def extract_table_data_from_image_internal(
df_extraction_ledger: pd.DataFrame,
task_config: Union[IngestTaskTableExtraction, Dict[str, Any]],
extraction_config: TableExtractorSchema,
execution_trace_log: Optional[Dict] = None,
) -> Tuple[pd.DataFrame, Dict]:
"""
Extracts table data from a DataF... |
Extracts table data from a DataFrame in a bulk fashion rather than row-by-row,
following the chart extraction pattern.
Parameters
----------
df_extraction_ledger : pd.DataFrame
DataFrame containing the content from which table data is to be extracted.
task_config : Dict[str, Any]
... | extract_table_data_from_image_internal | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/table_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/table_extractor.py | Apache-2.0 |
def load_and_preprocess_image(image_stream: io.BytesIO) -> np.ndarray:
"""
Loads and preprocesses a JPEG, JPG, or PNG image from a bytestream.
Parameters
----------
image_stream : io.BytesIO
A bytestream of the image file.
Returns
-------
np.ndarray
Preprocessed image a... |
Loads and preprocesses a JPEG, JPG, or PNG image from a bytestream.
Parameters
----------
image_stream : io.BytesIO
A bytestream of the image file.
Returns
-------
np.ndarray
Preprocessed image as a numpy array.
| load_and_preprocess_image | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/image_helpers/common.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/image_helpers/common.py | Apache-2.0 |
def convert_svg_to_bitmap(image_stream: io.BytesIO) -> np.ndarray:
"""
Converts an SVG image from a bytestream to a bitmap format.
Parameters
----------
image_stream : io.BytesIO
A bytestream of the SVG file.
Returns
-------
np.ndarray
Preprocessed image as a numpy arra... |
Converts an SVG image from a bytestream to a bitmap format.
Parameters
----------
image_stream : io.BytesIO
A bytestream of the SVG file.
Returns
-------
np.ndarray
Preprocessed image as a numpy array in bitmap format.
| convert_svg_to_bitmap | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/image_helpers/common.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/image_helpers/common.py | Apache-2.0 |
def extract_page_element_images(
annotation_dict: Dict[str, List[List[float]]],
original_image: np.ndarray,
page_idx: int,
page_elements: List[Tuple[int, "CroppedImageWithContent"]],
) -> None:
"""
Handle the extraction of tables and charts from the inference results and run additional model inf... |
Handle the extraction of tables and charts from the inference results and run additional model inference.
Parameters
----------
annotation_dict : dict of {str : list of list of float}
A dictionary containing detected objects and their bounding boxes. Keys should include "table" and "chart",
... | extract_page_element_images | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/image_helpers/common.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/image_helpers/common.py | Apache-2.0 |
def extract_page_elements_from_images(
images: List[np.ndarray],
config: ImageConfigSchema,
trace_info: Optional[List] = None,
) -> List[Tuple[int, object]]:
"""
Detect and extract tables/charts from a list of NumPy images using YOLOX.
Parameters
----------
images : List[np.ndarray]
... |
Detect and extract tables/charts from a list of NumPy images using YOLOX.
Parameters
----------
images : List[np.ndarray]
List of images in NumPy array format.
config : ImageConfigSchema
Configuration object containing YOLOX endpoints, auth token, etc.
trace_info : Optional[Lis... | extract_page_elements_from_images | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/image/image_helpers/common.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/image/image_helpers/common.py | Apache-2.0 |
def extract_primitives_from_pdf_internal(
df_extraction_ledger: pd.DataFrame,
task_config: Dict[str, Any],
extractor_config: Any,
execution_trace_log: Optional[List[Any]] = None,
) -> Tuple[pd.DataFrame, Dict]:
"""
Process a DataFrame of PDF documents by orchestrating extraction for each row.
... |
Process a DataFrame of PDF documents by orchestrating extraction for each row.
This function applies the row-level orchestration function to every row in the
DataFrame, aggregates the results, and returns a new DataFrame with the extracted
data along with any trace information.
Parameters
---... | extract_primitives_from_pdf_internal | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pdf/pdf_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pdf/pdf_extractor.py | Apache-2.0 |
def adobe_extractor(
pdf_stream: io.BytesIO,
extract_text: bool,
extract_images: bool,
extract_infographics: bool,
extract_tables: bool,
extractor_config: dict,
execution_trace_log: Optional[List[Any]] = None,
) -> pd.DataFrame:
"""
Helper function to use unstructured-io REST API to ... |
Helper function to use unstructured-io REST API to extract text from a bytestream PDF.
Parameters
----------
pdf_stream : io.BytesIO
A bytestream PDF.
extract_text : bool
Specifies whether to extract text.
extract_images : bool
Specifies whether to extract images.
e... | adobe_extractor | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pdf/engines/adobe.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pdf/engines/adobe.py | Apache-2.0 |
def llama_parse_extractor(
pdf_stream: io.BytesIO,
extract_text: bool,
extract_images: bool,
extract_infographics: bool,
extract_tables: bool,
extractor_config: dict,
execution_trace_log: Optional[List[Any]] = None,
) -> List[Dict[ContentTypeEnum, Dict[str, Any]]]:
"""
Helper functio... |
Helper function to use LlamaParse API to extract text from a bytestream PDF.
Parameters
----------
pdf_stream : io.BytesIO
A bytestream PDF.
extract_text : bool
Specifies whether to extract text.
extract_images : bool
Specifies whether to extract images.
extract_tab... | llama_parse_extractor | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pdf/engines/llama.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pdf/engines/llama.py | Apache-2.0 |
async def async_llama_parse(
pdf_stream: io.BytesIO,
api_key: str,
file_name: str = DEFAULT_FILE_NAME,
result_type: str = DEFAULT_RESULT_TYPE,
check_interval_seconds: int = DEFAULT_CHECK_INTERVAL_SECONDS,
max_timeout_seconds: int = DEFAULT_MAX_TIMEOUT_SECONDS,
) -> str:
"""Uses the LlamaPars... | Uses the LlamaParse API to extract text from bytestream PDF.
Parameters
----------
pdf_stream : io.BytesIO
A bytestream PDF.
api_key: str
API key from https://cloud.llamaindex.ai.
file_name: str
Name of the PDF file.
result_type: str
The result type for the parse... | async_llama_parse | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pdf/engines/llama.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pdf/engines/llama.py | Apache-2.0 |
def nemoretriever_parse_extractor(
pdf_stream: io.BytesIO,
extract_text: bool,
extract_images: bool,
extract_infographics: bool,
extract_tables: bool,
extract_charts: bool,
extractor_config: dict,
execution_trace_log: Optional[List[Any]] = None,
) -> str:
"""
Helper function to u... |
Helper function to use nemoretriever_parse to extract text from a bytestream PDF.
Parameters
----------
pdf_stream : io.BytesIO
A bytestream PDF.
extract_text : bool
Specifies whether to extract text.
extract_images : bool
Specifies whether to extract images.
extrac... | nemoretriever_parse_extractor | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pdf/engines/nemoretriever.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pdf/engines/nemoretriever.py | Apache-2.0 |
def _extract_page_elements_using_image_ensemble(
pages: List[Tuple[int, np.ndarray, Tuple[int, int]]],
yolox_client,
yolox_model_name: str = "yolox",
execution_trace_log: Optional[List] = None,
) -> List[Tuple[int, object]]:
"""
Given a list of (page_index, image) tuples and a YOLOX client, this... |
Given a list of (page_index, image) tuples and a YOLOX client, this function performs
inference to extract page element annotations from all pages.
Parameters
----------
pages : List[Tuple[int, np.ndarray, Tuple[int, int]]]
List of tuples containing page index, image data as numpy array,
... | _extract_page_elements_using_image_ensemble | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pdf/engines/pdfium.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pdf/engines/pdfium.py | Apache-2.0 |
def _extract_page_element_images(
annotation_dict,
original_image,
page_idx,
page_elements,
padding_offset=(0, 0),
):
"""
Handle the extraction of page elements from the inference results and run additional model inference.
Parameters
----------
annotation_dict : dict/
A... |
Handle the extraction of page elements from the inference results and run additional model inference.
Parameters
----------
annotation_dict : dict/
A dictionary containing detected objects and their bounding boxes.
original_image : np.ndarray
The original image from which objects w... | _extract_page_element_images | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pdf/engines/pdfium.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pdf/engines/pdfium.py | Apache-2.0 |
def _extract_page_images(
extract_images_method: str,
page,
page_idx: int,
page_width: float,
page_height: float,
page_count: int,
source_metadata: dict,
base_unified_metadata: dict,
**extract_images_params,
) -> list:
"""
Always extract images from the given page and return ... |
Always extract images from the given page and return a list of image metadata items.
The caller decides whether to call this based on a flag.
| _extract_page_images | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pdf/engines/pdfium.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pdf/engines/pdfium.py | Apache-2.0 |
def _extract_page_elements(
pages: list,
page_count: int,
source_metadata: dict,
base_unified_metadata: dict,
extract_tables: bool,
extract_charts: bool,
extract_infographics: bool,
paddle_output_format: str,
yolox_endpoints: Tuple[Optional[str], Optional[str]],
yolox_infer_proto... |
Extract page elements from the given pages using YOLOX-based inference.
This function creates a YOLOX client using the provided parameters, extracts elements
from pages, and builds metadata for each extracted element based on the specified
extraction flags.
Parameters
----------
pages : l... | _extract_page_elements | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pdf/engines/pdfium.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pdf/engines/pdfium.py | Apache-2.0 |
def unstructured_io_extractor(
pdf_stream: io.BytesIO,
extract_text: bool,
extract_images: bool,
extract_infographics: bool,
extract_charts: bool,
extract_tables: bool,
extractor_config: Dict[str, Any],
execution_trace_log: Optional[List[Any]] = None,
) -> pd.DataFrame:
"""
Helpe... |
Helper function to use unstructured-io REST API to extract text from a bytestream PDF.
This function sends the provided PDF stream to the unstructured-io API and
returns the extracted text. Additional parameters for the extraction are
provided via the extractor_config dictionary. Note that although fl... | unstructured_io_extractor | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pdf/engines/unstructured_io.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pdf/engines/unstructured_io.py | Apache-2.0 |
def _work_extract_pdf(
*,
pdf_stream: io.BytesIO,
extract_text: bool,
extract_images: bool,
extract_infographics: bool,
extract_tables: bool,
extract_charts: bool,
extractor_config: dict,
execution_trace_log=None,
) -> Any:
"""
Perform PDF extraction on a decoded PDF stream u... |
Perform PDF extraction on a decoded PDF stream using the given extraction parameters.
| _work_extract_pdf | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pdf/engines/pdf_helpers/__init__.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pdf/engines/pdf_helpers/__init__.py | Apache-2.0 |
def _orchestrate_row_extraction(
row: pd.Series,
task_config: Dict[str, Any],
extractor_config: Any,
execution_trace_log: Optional[List[Any]] = None,
) -> Any:
"""
Orchestrate extraction for a single DataFrame row by decoding the PDF stream,
building an extractor_config, and then delegating ... |
Orchestrate extraction for a single DataFrame row by decoding the PDF stream,
building an extractor_config, and then delegating to the work function.
| _orchestrate_row_extraction | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pdf/engines/pdf_helpers/__init__.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pdf/engines/pdf_helpers/__init__.py | Apache-2.0 |
def _prepare_task_properties(
base64_row: pd.Series, task_props: Union[Dict[str, Any], BaseModel]
) -> Tuple[Dict[str, Any], Optional[str]]:
"""
Prepare and return the task properties dictionary and source identifier from a DataFrame row.
This function converts task properties to a dictionary (if provi... |
Prepare and return the task properties dictionary and source identifier from a DataFrame row.
This function converts task properties to a dictionary (if provided as a Pydantic model),
extracts row data (excluding the "content" field), and stores it under the "row_data" key within
the task properties. ... | _prepare_task_properties | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pptx/pptx_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pptx/pptx_extractor.py | Apache-2.0 |
def _decode_and_extract_from_pptx(
base64_row: pd.Series,
task_props: Union[Dict[str, Any], BaseModel],
extraction_config: Any,
trace_info: Dict[str, Any],
) -> Any:
"""
Decode base64-encoded PPTX content from a DataFrame row and extract data using the specified method.
The function prepare... |
Decode base64-encoded PPTX content from a DataFrame row and extract data using the specified method.
The function prepares task properties (using `_prepare_task_properties`), decodes the base64 content
into a byte stream, determines extraction parameters, and calls the extraction function (e.g. `python_pp... | _decode_and_extract_from_pptx | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pptx/pptx_extractor.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pptx/pptx_extractor.py | Apache-2.0 |
def _finalize_images(
pending_images: List[Tuple[Shape, int, int, int, dict, dict, dict]],
extracted_data: List,
pptx_extraction_config: PPTXConfigSchema,
extract_tables: bool = False,
extract_charts: bool = False,
trace_info: Optional[Dict] = None,
):
"""
Post-process all pending images... |
Post-process all pending images.
- Convert shape image -> NumPy or base64
- If `extract_tables` or `extract_charts`, do detection (table/chart)
- Build the appropriate metadata, either table/chart or image.
This mimics the docx approach, but adapted for python-pptx shapes.
| _finalize_images | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pptx/engines/pptx_helper.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pptx/engines/pptx_helper.py | Apache-2.0 |
def process_shape(
shape, shape_idx, slide_idx, slide_count, pending_images, page_nearby_blocks, source_metadata, base_unified_metadata
):
"""
Recursively process a shape:
- If the shape is a group, iterate over its child shapes.
- If it is a picture or a placeholder with an embedded image, appe... |
Recursively process a shape:
- If the shape is a group, iterate over its child shapes.
- If it is a picture or a placeholder with an embedded image, append it to pending_images.
| process_shape | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pptx/engines/pptx_helper.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pptx/engines/pptx_helper.py | Apache-2.0 |
def python_pptx(
*,
pptx_stream: IO,
extract_text: bool,
extract_images: bool,
extract_infographics: bool,
extract_tables: bool,
extract_charts: bool,
extraction_config: dict,
execution_trace_log: Optional[List] = None,
):
"""
Uses python-pptx to extract text from a PPTX byte... |
Uses python-pptx to extract text from a PPTX bytestream, while deferring image
classification into tables/charts if requested.
| python_pptx | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/extract/pptx/engines/pptx_helper.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/extract/pptx/engines/pptx_helper.py | Apache-2.0 |
def _hash_content(x: Any, algorithm: str = "md5") -> bytes:
"""
Compute a hash of the content using the specified algorithm.
Parameters
----------
x : dict
A dictionary containing the content under the key "content".
algorithm : str, optional
Hashing algorithm to use (default "m... |
Compute a hash of the content using the specified algorithm.
Parameters
----------
x : dict
A dictionary containing the content under the key "content".
algorithm : str, optional
Hashing algorithm to use (default "md5").
Returns
-------
bytes
The computed hash.... | _hash_content | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/mutate/deduplicate.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/mutate/deduplicate.py | Apache-2.0 |
def deduplicate_images_internal(
df_ledger: pd.DataFrame,
task_config: Dict[str, Any],
mutate_config: ImageDedupSchema = ImageDedupSchema(),
execution_trace_log: Optional[List[Any]] = None,
) -> pd.DataFrame:
"""
Deduplicate images in a DataFrame based on content hashes.
The function proces... |
Deduplicate images in a DataFrame based on content hashes.
The function processes rows where the 'document_type' is IMAGE, computes a content hash for each,
and then either removes duplicates or marks them based on the 'filter' flag in task_config.
A 'hash_algorithm' flag in task_config determines the... | deduplicate_images_internal | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/mutate/deduplicate.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/mutate/deduplicate.py | Apache-2.0 |
def filter_images_internal(
df_ledger: pd.DataFrame,
task_config: Dict[str, Any],
mutate_config: ImageFilterSchema = ImageFilterSchema(),
execution_trace_log: Optional[List[Any]] = None,
) -> pd.DataFrame:
"""
Apply an image filtering operation to a DataFrame based on average image size and aspe... |
Apply an image filtering operation to a DataFrame based on average image size and aspect ratio.
Parameters
----------
df_ledger : pd.DataFrame
DataFrame to be filtered. Must contain 'document_type' and 'metadata' columns.
task_config : dict
Dictionary with the following keys:
... | filter_images_internal | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/mutate/filter.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/mutate/filter.py | Apache-2.0 |
def remove_task_by_type(ctrl_msg, task: str):
"""
Remove a task from the control message by matching its type.
This function iterates over the tasks in the control message, and if it finds a task
whose type matches the provided task string, it removes that task (using its unique id)
and returns the... |
Remove a task from the control message by matching its type.
This function iterates over the tasks in the control message, and if it finds a task
whose type matches the provided task string, it removes that task (using its unique id)
and returns the task's properties.
Parameters
----------
... | remove_task_by_type | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | Apache-2.0 |
def add_task(self, task: ControlMessageTask):
"""
Add a task to the control message, keyed by the task's unique 'id'.
Raises
------
ValueError
If a task with the same 'id' already exists.
"""
if task.id in self._tasks:
raise ValueError(f"T... |
Add a task to the control message, keyed by the task's unique 'id'.
Raises
------
ValueError
If a task with the same 'id' already exists.
| add_task | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | Apache-2.0 |
def remove_task(self, task_id: str) -> ControlMessageTask:
"""
Remove a task from the control message. Logs a warning if the task does not exist.
"""
if task_id in self._tasks:
_task = self._tasks[task_id]
del self._tasks[task_id]
return _task
... |
Remove a task from the control message. Logs a warning if the task does not exist.
| remove_task | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | Apache-2.0 |
def config(self, config: Dict[str, Any] = None) -> Dict[str, Any]:
"""
Get or update the control message configuration.
If 'config' is provided, it must be a dictionary. The configuration is updated with the
provided values. If no argument is provided, returns a copy of the current conf... |
Get or update the control message configuration.
If 'config' is provided, it must be a dictionary. The configuration is updated with the
provided values. If no argument is provided, returns a copy of the current configuration.
Raises
------
ValueError
If th... | config | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | Apache-2.0 |
def get_metadata(self, key: Union[str, re.Pattern] = None, default_value: Any = None) -> Any:
"""
Retrieve metadata. If 'key' is None, returns a copy of all metadata.
Parameters
----------
key : str or re.Pattern, optional
If a string is provided, returns the value f... |
Retrieve metadata. If 'key' is None, returns a copy of all metadata.
Parameters
----------
key : str or re.Pattern, optional
If a string is provided, returns the value for that exact key.
If a regex pattern is provided, returns a dictionary of all metadata key-v... | get_metadata | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | Apache-2.0 |
def has_metadata(self, key: Union[str, re.Pattern]) -> bool:
"""
Check if a metadata key exists.
Parameters
----------
key : str or re.Pattern
If a string is provided, checks for the exact key.
If a regex pattern is provided, returns True if any metadata ... |
Check if a metadata key exists.
Parameters
----------
key : str or re.Pattern
If a string is provided, checks for the exact key.
If a regex pattern is provided, returns True if any metadata key matches the regex.
Returns
-------
bool
... | has_metadata | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | Apache-2.0 |
def filter_timestamp(self, regex_filter: str) -> Dict[str, datetime]:
"""
Retrieve timestamps whose keys match the regex filter.
"""
pattern = re.compile(regex_filter)
timestamps_snapshot = self._timestamps.copy()
return {key: ts for key, ts in timestamps_snapshot.items()... |
Retrieve timestamps whose keys match the regex filter.
| filter_timestamp | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | Apache-2.0 |
def get_timestamp(self, key: str, fail_if_nonexist: bool = False) -> datetime:
"""
Retrieve a timestamp for a given key.
Raises
------
KeyError
If the key is not found and 'fail_if_nonexist' is True.
"""
if key in self._timestamps:
return ... |
Retrieve a timestamp for a given key.
Raises
------
KeyError
If the key is not found and 'fail_if_nonexist' is True.
| get_timestamp | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | Apache-2.0 |
def set_timestamp(self, key: str, timestamp: Any) -> None:
"""
Set a timestamp for a given key. Accepts either a datetime object or an ISO format string.
Raises
------
ValueError
If the provided timestamp is neither a datetime object nor a valid ISO format string.
... |
Set a timestamp for a given key. Accepts either a datetime object or an ISO format string.
Raises
------
ValueError
If the provided timestamp is neither a datetime object nor a valid ISO format string.
| set_timestamp | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | Apache-2.0 |
def payload(self, payload: pd.DataFrame = None) -> pd.DataFrame:
"""
Get or set the payload DataFrame.
Raises
------
ValueError
If the provided payload is not a pandas DataFrame.
"""
if payload is None:
return self._payload
if not... |
Get or set the payload DataFrame.
Raises
------
ValueError
If the provided payload is not a pandas DataFrame.
| payload | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/ingest_control_message.py | Apache-2.0 |
def __init__(
self,
model_interface,
protocol: str,
endpoints: Tuple[str, str],
auth_token: Optional[str] = None,
timeout: float = 120.0,
max_retries: int = 5,
):
"""
Initialize the NimClient with the specified model interface, protocol, and se... |
Initialize the NimClient with the specified model interface, protocol, and server endpoints.
Parameters
----------
model_interface : ModelInterface
The model interface implementation to use.
protocol : str
The protocol to use ("grpc" or "http").
... | __init__ | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/nim_client.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/nim_client.py | Apache-2.0 |
def _fetch_max_batch_size(self, model_name, model_version: str = "") -> int:
"""Fetch the maximum batch size from the Triton model configuration in a thread-safe manner."""
if model_name in self._max_batch_sizes:
return self._max_batch_sizes[model_name]
with self._lock:
... | Fetch the maximum batch size from the Triton model configuration in a thread-safe manner. | _fetch_max_batch_size | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/nim_client.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/nim_client.py | Apache-2.0 |
def _process_batch(self, batch_input, *, batch_data, model_name, **kwargs):
"""
Process a single batch input for inference using its corresponding batch_data.
Parameters
----------
batch_input : Any
The input data for this batch.
batch_data : Any
... |
Process a single batch input for inference using its corresponding batch_data.
Parameters
----------
batch_input : Any
The input data for this batch.
batch_data : Any
The corresponding scratch-pad data for this batch as returned by format_input.
... | _process_batch | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/nim_client.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/nim_client.py | Apache-2.0 |
def infer(self, data: dict, model_name: str, **kwargs) -> Any:
"""
Perform inference using the specified model and input data.
Parameters
----------
data : dict
The input data for inference.
model_name : str
The model name.
kwargs : dict
... |
Perform inference using the specified model and input data.
Parameters
----------
data : dict
The input data for inference.
model_name : str
The model name.
kwargs : dict
Additional parameters for inference.
Returns
-... | infer | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/nim_client.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/nim_client.py | Apache-2.0 |
def _grpc_infer(self, formatted_input: np.ndarray, model_name: str) -> np.ndarray:
"""
Perform inference using the gRPC protocol.
Parameters
----------
formatted_input : np.ndarray
The input data formatted as a numpy array.
model_name : str
The na... |
Perform inference using the gRPC protocol.
Parameters
----------
formatted_input : np.ndarray
The input data formatted as a numpy array.
model_name : str
The name of the model to use for inference.
Returns
-------
np.ndarray
... | _grpc_infer | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/nim_client.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/nim_client.py | Apache-2.0 |
def _http_infer(self, formatted_input: dict) -> dict:
"""
Perform inference using the HTTP protocol, retrying for timeouts or 5xx errors up to 5 times.
Parameters
----------
formatted_input : dict
The input data formatted as a dictionary.
Returns
---... |
Perform inference using the HTTP protocol, retrying for timeouts or 5xx errors up to 5 times.
Parameters
----------
formatted_input : dict
The input data formatted as a dictionary.
Returns
-------
dict
The output of the model as a dictio... | _http_infer | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/nim_client.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/nim_client.py | Apache-2.0 |
def prepare_data_for_inference(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""
Decode base64-encoded images into NumPy arrays, storing them in `data["image_arrays"]`.
Parameters
----------
data : dict of str -> Any
The input data containing either:
- ... |
Decode base64-encoded images into NumPy arrays, storing them in `data["image_arrays"]`.
Parameters
----------
data : dict of str -> Any
The input data containing either:
- "base64_image": a single base64-encoded image, or
- "base64_images": a list ... | prepare_data_for_inference | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/cached.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/cached.py | Apache-2.0 |
def format_input(self, data: Dict[str, Any], protocol: str, max_batch_size: int, **kwargs) -> Any:
"""
Format input data for the specified protocol ("grpc" or "http"), handling batched images.
Additionally, returns batched data that coalesces the original image arrays and their dimensions
... |
Format input data for the specified protocol ("grpc" or "http"), handling batched images.
Additionally, returns batched data that coalesces the original image arrays and their dimensions
in the same order as provided.
Parameters
----------
data : dict of str -> Any
... | format_input | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/cached.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/cached.py | Apache-2.0 |
def parse_output(self, response: Any, protocol: str, data: Optional[Dict[str, Any]] = None, **kwargs: Any) -> Any:
"""
Parse the output from the Cached model's inference response.
Parameters
----------
response : Any
The raw response from the model inference.
... |
Parse the output from the Cached model's inference response.
Parameters
----------
response : Any
The raw response from the model inference.
protocol : str
The protocol used ("grpc" or "http").
data : dict of str -> Any, optional
Addi... | parse_output | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/cached.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/cached.py | Apache-2.0 |
def _extract_content_from_nim_response(self, json_response: Dict[str, Any]) -> Any:
"""
Extract content from the JSON response of a NIM (HTTP) API request.
Parameters
----------
json_response : dict of str -> Any
The JSON response from the NIM API.
Returns
... |
Extract content from the JSON response of a NIM (HTTP) API request.
Parameters
----------
json_response : dict of str -> Any
The JSON response from the NIM API.
Returns
-------
Any
The extracted content from the response.
Raises... | _extract_content_from_nim_response | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/cached.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/cached.py | Apache-2.0 |
def multiprocessing_cache(max_calls):
"""
A decorator that creates a global cache shared between multiple processes.
The cache is invalidated after `max_calls` number of accesses.
Args:
max_calls (int): The number of calls after which the cache is cleared.
Returns:
function: The de... |
A decorator that creates a global cache shared between multiple processes.
The cache is invalidated after `max_calls` number of accesses.
Args:
max_calls (int): The number of calls after which the cache is cleared.
Returns:
function: The decorated function with global cache and invali... | multiprocessing_cache | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/decorators.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/decorators.py | Apache-2.0 |
def prepare_data_for_inference(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""
Prepare input data by decoding one or more base64-encoded images into NumPy arrays.
Parameters
----------
data : dict
The input data containing either 'base64_image' (single image)
... |
Prepare input data by decoding one or more base64-encoded images into NumPy arrays.
Parameters
----------
data : dict
The input data containing either 'base64_image' (single image)
or 'base64_images' (multiple images).
Returns
-------
di... | prepare_data_for_inference | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/deplot.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/deplot.py | Apache-2.0 |
def _prepare_deplot_payload(
base64_list: list,
max_tokens: int = 500,
temperature: float = 0.5,
top_p: float = 0.9,
) -> Dict[str, Any]:
"""
Prepare an HTTP payload for Deplot that includes one message per image,
matching the original single-image style:
... |
Prepare an HTTP payload for Deplot that includes one message per image,
matching the original single-image style:
messages = [
{
"role": "user",
"content": "Generate ... <img src="data:image/png;base64,..." />"
},
{
... | _prepare_deplot_payload | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/deplot.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/deplot.py | Apache-2.0 |
def _extract_content_from_deplot_response(json_response: Dict[str, Any]) -> Any:
"""
Extract content from the JSON response of a Deplot HTTP API request.
The original code expected a single choice with a single textual content.
"""
if "choices" not in json_response or not json_re... |
Extract content from the JSON response of a Deplot HTTP API request.
The original code expected a single choice with a single textual content.
| _extract_content_from_deplot_response | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/deplot.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/deplot.py | Apache-2.0 |
def preprocess_image_for_paddle(array: np.ndarray, image_max_dimension: int = 960) -> np.ndarray:
"""
Preprocesses an input image to be suitable for use with PaddleOCR by resizing, normalizing, padding,
and transposing it into the required format.
This function is intended for preprocessing images to b... |
Preprocesses an input image to be suitable for use with PaddleOCR by resizing, normalizing, padding,
and transposing it into the required format.
This function is intended for preprocessing images to be passed as input to PaddleOCR using GRPC.
It is not necessary when using the HTTP endpoint.
Ste... | preprocess_image_for_paddle | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/helpers.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/helpers.py | Apache-2.0 |
def is_ready(http_endpoint: str, ready_endpoint: str) -> bool:
"""
Check if the server at the given endpoint is ready.
Parameters
----------
http_endpoint : str
The HTTP endpoint of the server.
ready_endpoint : str
The specific ready-check endpoint.
Returns
-------
... |
Check if the server at the given endpoint is ready.
Parameters
----------
http_endpoint : str
The HTTP endpoint of the server.
ready_endpoint : str
The specific ready-check endpoint.
Returns
-------
bool
True if the server is ready, False otherwise.
| is_ready | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/helpers.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/helpers.py | Apache-2.0 |
def get_version(http_endpoint: str, metadata_endpoint: str = "/v1/metadata", version_field: str = "version") -> str:
"""
Get the version of the server from its metadata endpoint.
Parameters
----------
http_endpoint : str
The HTTP endpoint of the server.
metadata_endpoint : str, optional... |
Get the version of the server from its metadata endpoint.
Parameters
----------
http_endpoint : str
The HTTP endpoint of the server.
metadata_endpoint : str, optional
The metadata endpoint to query (default: "/v1/metadata").
version_field : str, optional
The field conta... | get_version | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/helpers.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/helpers.py | Apache-2.0 |
def get_model_name(
http_endpoint: str,
default_model_name,
metadata_endpoint: str = "/v1/metadata",
model_info_field: str = "modelInfo",
) -> str:
"""
Get the model name of the server from its metadata endpoint.
Parameters
----------
http_endpoint : str
The HTTP endpoint of... |
Get the model name of the server from its metadata endpoint.
Parameters
----------
http_endpoint : str
The HTTP endpoint of the server.
metadata_endpoint : str, optional
The metadata endpoint to query (default: "/v1/metadata").
model_info_field : str, optional
The field... | get_model_name | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/helpers.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/helpers.py | Apache-2.0 |
def format_input(self, data: Dict[str, Any], protocol: str, max_batch_size: int, **kwargs) -> Any:
"""
Format input data for the specified protocol.
Parameters
----------
data : dict
The input data to format.
protocol : str
The protocol to use ("g... |
Format input data for the specified protocol.
Parameters
----------
data : dict
The input data to format.
protocol : str
The protocol to use ("grpc" or "http").
**kwargs : dict
Additional parameters for HTTP payload formatting.
... | format_input | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/nemoretriever_parse.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/nemoretriever_parse.py | Apache-2.0 |
def parse_output(self, response: Any, protocol: str, data: Optional[Dict[str, Any]] = None, **kwargs) -> Any:
"""
Parse the output from the model's inference response.
Parameters
----------
response : Any
The response from the model inference.
protocol : str
... |
Parse the output from the model's inference response.
Parameters
----------
response : Any
The response from the model inference.
protocol : str
The protocol used ("grpc" or "http").
data : dict, optional
Additional input data passed ... | parse_output | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/nemoretriever_parse.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/nemoretriever_parse.py | Apache-2.0 |
def _extract_content_from_nemoretriever_parse_response(self, json_response: Dict[str, Any]) -> Any:
"""
Extract content from the JSON response of a Deplot HTTP API request.
Parameters
----------
json_response : dict
The JSON response from the Deplot API.
Ret... |
Extract content from the JSON response of a Deplot HTTP API request.
Parameters
----------
json_response : dict
The JSON response from the Deplot API.
Returns
-------
Any
The extracted content from the response.
Raises
-... | _extract_content_from_nemoretriever_parse_response | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/nemoretriever_parse.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/nemoretriever_parse.py | Apache-2.0 |
def prepare_data_for_inference(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""
Decode one or more base64-encoded images into NumPy arrays, storing them
alongside their dimensions in `data`.
Parameters
----------
data : dict of str -> Any
The input data con... |
Decode one or more base64-encoded images into NumPy arrays, storing them
alongside their dimensions in `data`.
Parameters
----------
data : dict of str -> Any
The input data containing either:
- 'base64_image': a single base64-encoded image, or
... | prepare_data_for_inference | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/paddle.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/paddle.py | Apache-2.0 |
def format_input(self, data: Dict[str, Any], protocol: str, max_batch_size: int, **kwargs) -> Any:
"""
Format input data for the specified protocol ("grpc" or "http"), supporting batched data.
Parameters
----------
data : dict of str -> Any
The input data dictionary,... |
Format input data for the specified protocol ("grpc" or "http"), supporting batched data.
Parameters
----------
data : dict of str -> Any
The input data dictionary, expected to contain "image_arrays" (list of np.ndarray)
and "image_dims" (list of (height, width)... | format_input | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/paddle.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/paddle.py | Apache-2.0 |
def parse_output(self, response: Any, protocol: str, data: Optional[Dict[str, Any]] = None, **kwargs: Any) -> Any:
"""
Parse the model's inference response for the given protocol. The parsing
may handle batched outputs for multiple images.
Parameters
----------
response ... |
Parse the model's inference response for the given protocol. The parsing
may handle batched outputs for multiple images.
Parameters
----------
response : Any
The raw response from the PaddleOCR model.
protocol : str
The protocol used for inferenc... | parse_output | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/paddle.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/paddle.py | Apache-2.0 |
def _prepare_paddle_payload(self, base64_img: str) -> Dict[str, Any]:
"""
DEPRECATED by batch logic in format_input. Kept here if you need single-image direct calls.
Parameters
----------
base64_img : str
A single base64-encoded image string.
Returns
... |
DEPRECATED by batch logic in format_input. Kept here if you need single-image direct calls.
Parameters
----------
base64_img : str
A single base64-encoded image string.
Returns
-------
dict of str -> Any
The payload in either legacy or n... | _prepare_paddle_payload | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/paddle.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/paddle.py | Apache-2.0 |
def _extract_content_from_paddle_http_response(
self,
json_response: Dict[str, Any],
dimensions: List[Dict[str, Any]],
) -> List[Tuple[str, str]]:
"""
Extract content from the JSON response of a PaddleOCR HTTP API request.
Parameters
----------
json_r... |
Extract content from the JSON response of a PaddleOCR HTTP API request.
Parameters
----------
json_response : dict of str -> Any
The JSON response returned by the PaddleOCR endpoint.
table_content_format : str or None
The specified format for table conte... | _extract_content_from_paddle_http_response | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/paddle.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/paddle.py | Apache-2.0 |
def _extract_content_from_paddle_grpc_response(
self,
response: np.ndarray,
dimensions: List[Dict[str, Any]],
) -> List[Tuple[str, str]]:
"""
Parse a gRPC response for one or more images. The response can have two possible shapes:
- (3,) for batch_size=1
-... |
Parse a gRPC response for one or more images. The response can have two possible shapes:
- (3,) for batch_size=1
- (3, n) for batch_size=n
In either case:
response[0, i]: byte string containing bounding box data
response[1, i]: byte string containing text predic... | _extract_content_from_paddle_grpc_response | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/paddle.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/paddle.py | Apache-2.0 |
def _postprocess_paddle_response(
bounding_boxes: List[Any],
text_predictions: List[str],
dims: Optional[List[Dict[str, Any]]] = None,
img_index: int = 0,
) -> Tuple[List[Any], List[str]]:
"""
Convert bounding boxes with normalized coordinates to pixel cooridnates by ... |
Convert bounding boxes with normalized coordinates to pixel cooridnates by using
the dimensions. Also shift the coorindates if the inputs were padded. For multiple images,
the correct image dimensions (height, width) are retrieved from `dims[img_index]`.
Parameters
----------
... | _postprocess_paddle_response | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/paddle.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/paddle.py | Apache-2.0 |
def __init__(
self,
endpoint: str,
auth_token: Optional[str] = None,
function_id: Optional[str] = None,
use_ssl: Optional[bool] = None,
ssl_cert: Optional[str] = None,
):
"""
Initialize the ParakeetClient.
Parameters
----------
... |
Initialize the ParakeetClient.
Parameters
----------
endpoint : str
The URL of the Parakeet service endpoint.
auth_token : Optional[str], default=None
The authentication token for accessing the service.
function_id: Optional[str]
The ... | __init__ | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/parakeet.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/parakeet.py | Apache-2.0 |
def transcribe(
self,
audio_content: str,
language_code: str = "en-US",
automatic_punctuation: bool = True,
word_time_offsets: bool = True,
max_alternatives: int = 1,
profanity_filter: bool = False,
verbatim_transcripts: bool = True,
speaker_diariz... |
Transcribe an audio file using Riva ASR.
Parameters
----------
audio_content : str
Base64-encoded audio content to be transcribed.
language_code : str, default="en-US"
The language code for transcription.
automatic_punctuation : bool, default=Tru... | transcribe | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/parakeet.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/parakeet.py | Apache-2.0 |
def convert_to_mono_wav(audio_bytes):
"""
Convert an audio file to mono WAV format using Librosa and SciPy.
Parameters
----------
audio_bytes : bytes
The raw audio data in bytes.
Returns
-------
bytes
The processed audio in mono WAV format.
"""
if librosa is No... |
Convert an audio file to mono WAV format using Librosa and SciPy.
Parameters
----------
audio_bytes : bytes
The raw audio data in bytes.
Returns
-------
bytes
The processed audio in mono WAV format.
| convert_to_mono_wav | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/parakeet.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/parakeet.py | Apache-2.0 |
def process_transcription_response(response):
"""
Process a Riva transcription response (a protobuf message) to extract:
- final_transcript: the complete transcript.
- segments: a list of segments with start/end times and text.
Parameters:
response: The Riva transcription response message... |
Process a Riva transcription response (a protobuf message) to extract:
- final_transcript: the complete transcript.
- segments: a list of segments with start/end times and text.
Parameters:
response: The Riva transcription response message.
Returns:
segments (list): Each segment i... | process_transcription_response | python | NVIDIA/nv-ingest | api/src/nv_ingest_api/internal/primitives/nim/model_interface/parakeet.py | https://github.com/NVIDIA/nv-ingest/blob/master/api/src/nv_ingest_api/internal/primitives/nim/model_interface/parakeet.py | Apache-2.0 |
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