Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<httpMethod: string, path: string, responseMode: string, options: struct<temperature: double>, jsCode: string, resource: string, operation: string, modelId: string, prompt: string, conditions: struct<options: struct<caseSensitive: bool, leftValue: string, typeValidation: string>, conditions: list<item: struct<leftValue: string, rightValue: string, operator: struct<type: string, operation: string>>>, combinator: string>, channel: string, text: string, respondWith: string, responseBody: string>
to
{'httpMethod': Value('string'), 'path': Value('string'), 'responseMode': Value('string'), 'options': {'temperature': Value('float64')}, 'jsCode': Value('string'), 'resource': Value('string'), 'operation': Value('string'), 'modelId': Value('string'), 'prompt': Value('string'), 'conditions': {'options': {'caseSensitive': Value('bool'), 'leftValue': Value('string'), 'typeValidation': Value('string')}, 'conditions': List({'leftValue': Value('string'), 'rightValue': Value('int64'), 'operator': {'type': Value('string'), 'operation': Value('string')}}), 'combinator': Value('string')}, 'channel': Value('string'), 'text': Value('string'), 'sheetName': Value('string'), 'columns': {'value': {'name': Value('string'), 'email': Value('string'), 'score': Value('string'), 'reason': Value('string')}}}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2118, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<httpMethod: string, path: string, responseMode: string, options: struct<temperature: double>, jsCode: string, resource: string, operation: string, modelId: string, prompt: string, conditions: struct<options: struct<caseSensitive: bool, leftValue: string, typeValidation: string>, conditions: list<item: struct<leftValue: string, rightValue: string, operator: struct<type: string, operation: string>>>, combinator: string>, channel: string, text: string, respondWith: string, responseBody: string>
to
{'httpMethod': Value('string'), 'path': Value('string'), 'responseMode': Value('string'), 'options': {'temperature': Value('float64')}, 'jsCode': Value('string'), 'resource': Value('string'), 'operation': Value('string'), 'modelId': Value('string'), 'prompt': Value('string'), 'conditions': {'options': {'caseSensitive': Value('bool'), 'leftValue': Value('string'), 'typeValidation': Value('string')}, 'conditions': List({'leftValue': Value('string'), 'rightValue': Value('int64'), 'operator': {'type': Value('string'), 'operation': Value('string')}}), 'combinator': Value('string')}, 'channel': Value('string'), 'text': Value('string'), 'sheetName': Value('string'), 'columns': {'value': {'name': Value('string'), 'email': Value('string'), 'score': Value('string'), 'reason': Value('string')}}}Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
n8n Workflow Patterns: LLM-Orchestrated Automation
Three annotated n8n workflow templates for common LLM-in-the-loop automation patterns: lead qualification, support ticket triage, and multilingual chatbot handoff. Built by Ömer Batuhan Tandoğan, founder of Noderan (AI agent + no-code automation platform).
These are structural templates, not drop-in-and-run workflows — they use real n8n node types and connection patterns, but credential-bound nodes (OpenAI, Slack, Google Sheets, Zendesk, Gmail) need your own connected accounts before they'll execute. Import them into n8n via Workflows → Import from File to inspect and adapt the structure.
Files
| File | Pattern |
|---|---|
lead-qualification.json |
Webhook → LLM scoring → conditional routing (Slack for hot leads, sheet for nurture) |
support-ticket-routing.json |
Email trigger → LLM classification (category/urgency/language) → parallel urgency + translation branches → helpdesk ticket |
multilingual-chatbot-handoff.json |
Webhook → language-agnostic LLM reply → self-reported low-confidence handoff to a human agent |
Why these three
Every workflow here answers a question that comes up constantly when teams start
LLM-orchestrating n8n automations: how do you route on an LLM's judgment without the whole
pipeline breaking when the model doesn't respond in the exact shape you expected? Each file's
meta.commonFailureModes field documents the specific failure mode that pattern hits in
production (defensive JSON parsing, urgency-inflation without few-shot calibration, sentinel
strings leaking into hedged responses) — these are the details that don't show up in a basic
"connect node A to node B" tutorial.
Usage
from huggingface_hub import hf_hub_download
import json
path = hf_hub_download(
repo_id="omerbatutandogan/n8n-workflow-patterns",
filename="lead-qualification.json",
repo_type="dataset",
)
workflow = json.load(open(path))
Or download directly from the Files tab and import into your n8n instance.
License
MIT — adapt freely, no attribution required (though a link back is always appreciated).
- Downloads last month
- 38