The Staging brick is being deprecated in favor of the new and more comprehensive Destination Connectors. To explore the complete list and usage, please refer to Destination Connectors documentation.
Note: We are constantly expanding our collection of destination connectors. If you wish to request a specific Destination Connector, you’re encouraged to submit a Feature Request on the Unstructured GitHub repository.
Staging functions in the unstructured package help prepare your data for ingestion into downstream systems. A staging function accepts a list of document elements as input and return an appropriately formatted dictionary as output. In the example below, we get our narrative text samples prepared for ingestion into LabelStudio using the stage_for_label_studio function. We can take this data and directly upload it into LabelStudio to quickly get started with an NLP labeling task.
import json
from unstructured.staging.label_studio import stage_for_label_studio
output = stage_for_label_studio(narrative_text)
print(json.dumps(output[:2], indent=4))
convert_to_csv
Converts outputs to the initial structured data (ISD) format as a CSV string.
Examples:
from unstructured.documents.elements import Title, NarrativeText
from unstructured.staging.base import convert_to_csv
elements = [Title(text="Title"), NarrativeText(text="Narrative")]
isd_csv = convert_to_csv(elements)
For more information about the convert_to_csv function, you can check the source code here.
convert_to_dataframe
Converts a list of document Element objects to a pandas dataframe. The dataframe will have a text column with the text from the element and a type column indicating the element type, such as NarrativeText or Title.
Examples:
from unstructured.documents.elements import Title, NarrativeText
from unstructured.staging.base import convert_to_dataframe
elements = [Title(text="Title"), NarrativeText(text="Narrative")]
df = convert_to_dataframe(elements)
For more information about the convert_to_dataframe function, you can check the source code here.
convert_to_dict
Converts a list of Element objects to a dictionary. This is the default format for representing documents in unstructured.
Examples:
from unstructured.documents.elements import Title, NarrativeText
from unstructured.staging.base import convert_to_dict
elements = [Title(text="Title"), NarrativeText(text="Narrative")]
isd = convert_to_dict(elements)
For more information about the convert_to_dict function, you can check the source code here.
dict_to_elements
Converts a dictionary of the format produced by convert_to_dict back to a list of Element objects.
Examples:
from unstructured.staging.base import dict_to_elements
isd = [
{"text": "My Title", "type": "Title"},
{"text": "My Narrative", "type": "NarrativeText"}
]
elements = dict_to_elements(isd)
For more information about the dict_to_elements function, you can check the source code here.
stage_csv_for_prodigy
Formats outputs in CSV format for use with Prodigy. After running stage_csv_for_prodigy, you can write the results to a CSV file that is ready to be used with Prodigy.
Examples:
from unstructured.documents.elements import Title, NarrativeText
from unstructured.staging.prodigy import stage_csv_for_prodigy
elements = [Title(text="Title"), NarrativeText(text="Narrative")]
metadata = [{"type": "title"}, {"source": "news"}]
prodigy_csv_data = stage_csv_for_prodigy(elements, metadata)
with open("prodigy.csv", "w") as csv_file:
csv_file.write(prodigy_csv_data)
For more information about the stage_csv_for_prodigy function, you can check the source code here.
stage_for_argilla
Convert a list of Text elements to an Argilla Dataset. The type of Argilla dataset to be generated can be specified with argilla_task parameter. Valid values for argilla_task are "text_classification", "token_classification", and "text2text". If "token_classification" is selected and tokens is not included in the optional kwargs, the nltk word tokenizer is used by default.
Examples:
import json
from unstructured.documents.elements import Title, NarrativeText
from unstructured.staging.argilla import stage_for_argilla
elements = [Title(text="Title"), NarrativeText(text="Narrative")]
metadata = [{"type": "title"}, {"type": "text"}]
argilla_dataset = stage_for_argilla(elements, "text_classification", metadata=metadata)
For more information about the stage_for_argilla function, you can check the source code here.
stage_for_baseplate-
The stage_for_baseplate staging function prepares a list of Element objects for ingestion into Baseplate, an LLM backend with a spreadsheet interface. After running the stage_for_baseplate function, you can use the Baseplate API to upload the documents to Baseplate. The following example code shows how to use the stage_for_baseplate function.
from unstructured.documents.elements import ElementMetadata, NarrativeText, Title
from unstructured.staging.baseplate import stage_for_baseplate
metadata = ElementMetadata(filename="fox.epub")
elements = [
Title("A Wonderful Story About A Fox", metadata=metadata),
NarrativeText(
"A fox ran into the chicken coop and the chickens flew off!",
metadata=metadata,
),
]
rows = stage_for_baseplate(elements)
The output will look like:
{
"rows": [
{
"data": {
"element_id": "ad270eefd1cc68d15f4d3e51666d4dc8",
"text": "A Wonderful Story About A Fox",
"type": "Title",
},
"metadata": {"filename": "fox.epub"},
},
{
"data": {
"element_id": "8275769fdd1804f9f2b55ad3c9b0ef1b",
"text": "A fox ran into the chicken coop and the chickens flew off!",
"type": "NarrativeText",
},
"metadata": {"filename": "fox.epub"},
},
],
}
For more information about the stage_for_baseplate function, you can check the source code here.
stage_for_datasaur
Formats a list of Text elements as input to token based tasks in Datasaur.
Example:
from unstructured.documents.elements import Text
from unstructured.staging.datasaur import stage_for_datasaur
elements = [Text("Text1"),Text("Text2")]
datasaur_data = stage_for_datasaur(elements)
The output is a list of dictionaries, each one with two keys: “text” with the content of the element and “entities” with an empty list.
You can also specify entities in the stage_for_datasaur function. Entities you specify in the input will be included in the entities key in the output. The list of entities is a list of dictionaries and must have all of the keys in the example below. The list of entities must be the same length as the list of elements. Use an empty list for any elements that do not have any entities.
Example:
from unstructured.documents.elements import Text
from unstructured.staging.datasaur import stage_for_datasaur
elements = [Text("Hi my name is Matt.")]
entities = [[{"text": "Matt", "type": "PER", "start_idx": 11, "end_idx": 15}]]
datasaur_data = stage_for_datasaur(elements, entities)
For more information about the stage_for_datasaur function, you can check the source code here.
stage_for_label_box
Formats outputs for use with LabelBox. LabelBox accepts cloud-hosted data and does not support importing text directly. The stage_for_label_box does the following:
-
Stages the data files in the output_directory specified in function arguments to be uploaded to a cloud storage service.
-
Returns a config of type List[Dict[str, Any]] that can be written to a json file and imported into LabelBox.
Note: stage_for_label_box does not upload the data to remote storage such as S3. Users can upload the data to S3 using aws s3 sync ${output_directory} ${url_prefix} after running the stage_for_label_box staging function.
Examples:
The following example demonstrates generating a config.json file that can be used with LabelBox and uploading the staged data files to an S3 bucket.
import os
import json
from unstructured.documents.elements import Title, NarrativeText
from unstructured.staging.label_box import stage_for_label_box
S3_BUCKET_NAME = "labelbox-staging-bucket"
S3_BUCKET_KEY_PREFIX = "data/"
S3_URL_PREFIX = f"https://{S3_BUCKET_NAME}.s3.amazonaws.com/{S3_BUCKET_KEY_PREFIX}"
LOCAL_OUTPUT_DIRECTORY = "/tmp/labelbox-staging"
elements = [Title(text="Title"), NarrativeText(text="Narrative")]
labelbox_config = stage_for_label_box(
elements,
output_directory=LOCAL_OUTPUT_DIRECTORY,
url_prefix=S3_URL_PREFIX,
external_ids=["id1", "id2"],
attachments=[[{"type": "RAW_TEXT", "value": "Title description"}], [{"type": "RAW_TEXT", "value": "Narrative Description"}]],
create_directory=True,
)
with open("config.json", "w+") as labelbox_config_file:
json.dump(labelbox_config, labelbox_config_file, indent=4)
def upload_staged_files():
from s3fs import S3FileSystem
fs = S3FileSystem()
for filename in os.listdir(LOCAL_OUTPUT_DIRECTORY):
filepath = os.path.join(LOCAL_OUTPUT_DIRECTORY, filename)
upload_key = os.path.join(S3_BUCKET_KEY_PREFIX, filename)
fs.put_file(lpath=filepath, rpath=os.path.join(S3_BUCKET_NAME, upload_key))
upload_staged_files()
For more information about the stage_for_label_box function, you can check the source code here.
stage_for_label_studio
Formats outputs for upload to LabelStudio. After running stage_for_label_studio, you can write the results to a JSON folder that is ready to be included in a new LabelStudio project.
Examples:
import json
from unstructured.documents.elements import Title, NarrativeText
from unstructured.staging.label_studio import stage_for_label_studio
elements = [Title(text="Title"), NarrativeText(text="Narrative")]
label_studio_data = stage_for_label_studio(elements, text_field="my_text", id_field="my_id")
with open("label_studio.json", "w") as f:
json.dump(label_studio_data, f, indent=4)
You can also include pre-annotations and predictions as part of your LabelStudio upload.
The annotations kwarg is a list of lists. If annotations is specified, there must be a list of annotations for each element in the elements list. If an element does not have any annotations, use an empty list. The following shows an example of how to upload annotations for the “Text Classification” task in LabelStudio:
import json
from unstructured.documents.elements import NarrativeText
from unstructured.staging.label_studio import (
stage_for_label_studio,
LabelStudioAnnotation,
LabelStudioResult,
)
elements = [NarrativeText(text="Narrative")]
annotations = [[
LabelStudioAnnotation(
result=[
LabelStudioResult(
type="choices",
value={"choices": ["Positive"]},
from_name="sentiment",
to_name="text",
)
]
)
]]
label_studio_data = stage_for_label_studio(
elements,
annotations=annotations,
text_field="my_text",
id_field="my_id"
)
with open("label_studio.json", "w") as f:
json.dump(label_studio_data, f, indent=4)
Similar to annotations, the predictions kwarg is also a list of lists. A prediction is an annotation with the addition of a score value. If predictions is specified, there must be a list of predictions for each element in the elements list. If an element does not have any predictions, use an empty list. The following shows an example of how to upload predictions for the “Text Classification” task in LabelStudio:
import json
from unstructured.documents.elements import NarrativeText
from unstructured.staging.label_studio import (
stage_for_label_studio,
LabelStudioPrediction,
LabelStudioResult,
)
elements = [NarrativeText(text="Narrative")]
predictions = [[
LabelStudioPrediction(
result=[
LabelStudioResult(
type="choices",
value={"choices": ["Positive"]},
from_name="sentiment",
to_name="text",
)
],
score=0.68
)
]]
label_studio_data = stage_for_label_studio(
elements,
predictions=predictions,
text_field="my_text",
id_field="my_id"
)
with open("label_studio.json", "w") as f:
json.dump(label_studio_data, f, indent=4)
The following shows an example of how to upload annotations for the “Named Entity Recognition” task in LabelStudio:
import json
from unstructured.documents.elements import NarrativeText
from unstructured.staging.label_studio import (
stage_for_label_studio,
LabelStudioAnnotation,
LabelStudioResult,
)
elements = [NarrativeText(text="Narrative")]
annotations = [[
LabelStudioAnnotation(
result=[
LabelStudioResult(
type="labels",
value={"start": 0, "end": 9, "text": "Narrative", "labels": ["MISC"]},
from_name="label",
to_name="text",
)
]
)
]]
label_studio_data = stage_for_label_studio(
elements,
annotations=annotations,
text_field="my_text",
id_field="my_id"
)
with open("label_studio.json", "w") as f:
json.dump(label_studio_data, f, indent=4)
See the LabelStudio docs for a full list of options for labels and annotations.
For more information about the stage_for_label_studio function, you can check the source code here.
stage_for_prodigy
Formats outputs in JSON format for use with Prodigy. After running stage_for_prodigy, you can write the results to a JSON file that is ready to be used with Prodigy.
Examples:
import json
from unstructured.documents.elements import Title, NarrativeText
from unstructured.staging.prodigy import stage_for_prodigy
elements = [Title(text="Title"), NarrativeText(text="Narrative")]
metadata = [{"type": "title"}, {"type": "text"}]
prodigy_data = stage_for_prodigy(elements, metadata)
with open("prodigy.json", "w") as f:
json.dump(prodigy_data, f, indent=4)
Note: Prodigy recommends .jsonl format for feeding data to API loaders. After running stage_for_prodigy, you can use the save_as_jsonl utility function to save the formatted data to a .jsonl file that is ready to be used with Prodigy.
from unstructured.documents.elements import Title, NarrativeText
from unstructured.staging.prodigy import stage_for_prodigy
from unstructured.utils import save_as_jsonl
elements = [Title(text="Title"), NarrativeText(text="Narrative")]
metadata = [{"type": "title"}, {"type": "text"}]
prodigy_data = stage_for_prodigy(elements, metadata)
save_as_jsonl(prodigy_data, "prodigy.jsonl")
For more information about the stage_for_prodigy function, you can check the source code here.
Prepares Text elements for processing in transformers pipelines by splitting the elements into chunks that fit into the model’s attention window.
Examples:
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
from unstructured.documents.elements import NarrativeText
from unstructured.staging.huggingface import stage_for_transformers
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
text = """From frost advisories this morning to a strong cold front expected later this week, the chance of fall showing up is real.
There's a refreshing crispness to the air, and it looks to get only more pronounced as the week goes on.
Frost advisories were in place this morning across portions of the Appalachians and coastal Maine as temperatures dropped into the 30s.
Temperatures this morning were in the 40s as far south as the Florida Panhandle.
And Maine even had a few reports of their first snow of the season Sunday. More cities could see their first snow later this week.
Yes, hello fall!
As temperatures moderate during the next few days, much of the east will stay right around seasonal norms, but the next blast of cold air will be strong and come with the potential for hazardous conditions.
"A more active fall weather pattern is expected to evolve by the end of this week and continuing into the weekend as a couple of cold fronts move across the central and eastern states," the Weather Prediction Center said.
The potent cold front will come in from Canada with a punch of chilly air, heavy rain and strong wind.
The Weather Prediction Center has a slight risk of excessive rainfall for much of the Northeast and New England on Thursday, including places like New York City, Buffalo and Burlington, so we will have to look out for flash flooding in these areas.
"More impactful weather continues to look likely with confidence growing that our region will experience the first real fall-like system with gusty to strong winds and a period of moderate to heavy rain along and ahead of a cold front passage," the National Weather Service office in Burlington wrote.
The potential for very heavy rain could accompany the front, bringing up to two inches of rain for much of the area, and isolated locations could see even more.
"Ensembles [forecast models] show median rainfall totals by Wednesday night around a half inch, with a potential for some spots to see around one inch, our first substantial rainfall in at least a couple of weeks," the weather service office in Grand Rapids noted, adding, "It may also get cold enough for some snow to mix in Thursday night to Friday morning, especially in the higher terrain north of Grand Rapids toward Cadillac."
There is also a chance for very strong winds to accompany the system.
The weather service is forecasting winds of 30-40 mph ahead of the cold front, which could cause some tree limbs to fall and sporadic power outages.
Behind the front, temperatures will fall.
"East Coast, with highs about 5-15 degrees below average to close out the workweek and going into next weekend, with highs only in the 40s and 50s from the Great Lakes to the Northeast on most days," the Weather Prediction Center explained.
By the weekend, a second cold front will drop down from Canada and bring a reinforcing shot of chilly air across the eastern half of the country."""
elements = stage_for_transformers([NarrativeText(text=text)], tokenizer)
The following optional keyword arguments can be specified in stage_for_transformers:
-
buffer: Indicates the number of tokens to leave as a buffer for the attention window. This is to account for special tokens like [CLS] that can appear at the beginning or end of an input sequence.
-
max_input_size: The size of the attention window for the model. If not specified, the default is the model_max_length attribute on the tokenizer object.
-
split_function: The function used to split the text into chunks to consider for adding to the attention window. Splits on spaces be default.
-
chunk_separator: The string used to concat adjacent chunks when reconstructing the text. Uses spaces by default.
If you need to operate on text directly instead of unstructured Text objects, use the chunk_by_attention_window helper function. Simply modify the example above to include the following:
from unstructured.staging.huggingface import chunk_by_attention_window
chunks = chunk_by_attention_window(text, tokenizer)
results = [nlp(chunk) for chunk in chunks]
For more information about the stage_for_transformers function, you can check the source code here.
stage_for_weaviate
The stage_for_weaviate staging function prepares a list of Element objects for ingestion into the Weaviate vector database. You can create a schema in Weaviate for the unstructured outputs using the following workflow:
from unstructured.staging.weaviate import create_unstructured_weaviate_class
import weaviate
unstructured_class = create_unstructured_weaviate_class(class_name="UnstructuredDocument")
schema = {"classes": [unstructured_class]}
client = weaviate.Client("http://localhost:8080")
client.schema.create(schema)
Once the schema is created, you can batch upload documents to Weaviate using the following workflow. See the Weaviate documentation for more details on options for uploading data and querying data once it has been uploaded.
from unstructured.partition.pdf import partition_pdf
from unstructured.staging.weaviate import stage_for_weaviate
import weaviate
from weaviate.util import generate_uuid5
filename = "example-docs/pdf/layout-parser-paper-fast.pdf"
elements = partition_pdf(filename=filename, strategy="fast")
data_objects = stage_for_weaviate(elements)
client = weaviate.Client("http://localhost:8080")
with client.batch(batch_size=10) as batch:
for data_object in tqdm.tqdm(data_objects):
batch.add_data_object(
data_object,
unstructured_class_name,
uuid=generate_uuid5(data_object),
)
For more information about the stage_for_weaviate function, you can check the source code here.
