ndl-core-collection
Collection
A collection of UK government structured datasets and textual sources for research, analysis, and AI applications. • 6 items • Updated • 3
Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
@context: string
@type: string
name: string
description: string
creator: struct<@type: string, name: string>
publisher: struct<@type: string, name: string>
license: struct<@type: string, name: string, url: string>
conditionsOfAccess: string
keywords: list<item: string>
distribution: list<item: struct<@type: string, name: string, description: string, contentUrl: string, encodingFormat: string>>
isBasedOn: struct<@type: string, name: string, url: string>
intendedUse: list<item: string>
limitations: string
conformsTo: list<item: string>
vs
index_type: string
faiss_index: struct<type: string, metric: string>
embedding: struct<model_name: string, provider: string, dimension: int64, normalised: bool>
chunking: struct<strategy: string, unit: string, chunk_size: int64, chunk_overlap: int64>
source: struct<dataset: string, id_field: string>
created_at: timestamp[s]
version: string
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
for key, example in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 563, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
@context: string
@type: string
name: string
description: string
creator: struct<@type: string, name: string>
publisher: struct<@type: string, name: string>
license: struct<@type: string, name: string, url: string>
conditionsOfAccess: string
keywords: list<item: string>
distribution: list<item: struct<@type: string, name: string, description: string, contentUrl: string, encodingFormat: string>>
isBasedOn: struct<@type: string, name: string, url: string>
intendedUse: list<item: string>
limitations: string
conformsTo: list<item: string>
vs
index_type: string
faiss_index: struct<type: string, metric: string>
embedding: struct<model_name: string, provider: string, dimension: int64, normalised: bool>
chunking: struct<strategy: string, unit: string, chunk_size: int64, chunk_overlap: int64>
source: struct<dataset: string, id_field: string>
created_at: timestamp[s]
version: stringNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
NDL Core RAG Index
This dataset contains a FAISS index and associated chunk metadata to support retrieval-augmented generation (RAG) use cases on ndl-core-corpus.
Overview
- Model: sentence-transformers/all-MiniLM-L6-v2
- Dimension: 384
- Normalisation: L2
- Similarity: cosine (inner product)
Chunking
- Strategy: recursive character-based chunking
- Chunk size: 800 characters
- Overlap: 100 characters
Index–Metadata Alignment
The FAISS index (index.faiss) and the chunk metadata file
(data/ndl_core_rag_index.parquet) are strictly index-aligned.
This means:
- The n-th embedding vector in
index.faisscorresponds exactly to the n-th row indata/ndl_core_rag_index.parquet - Retrieved FAISS indices can be used directly to look up chunk text, source identifiers, and metadata in the parquet file
This guarantees deterministic and reliable mapping from similarity search results back to their original source records.
LanceDB Search Index
A LanceDB-based search index has been added to support searching for NDL Core datasets by topic and downloading them. This index uses the same all-MiniLM-L6-v2 model and generates embedding vectors based on the concatenation of the title, description, and the first 500 characters of the text. The LanceDB index includes the full records for retrieval.
Source data
Chunks reference records are in the dataset: ndl-core-corpus
See rag_config.json for full, machine-readable configuration.
Example Application
This index is used in a live retrieval-augmented chat application:
🔗 NDL Core RAG Chat
https://huggingface.co/spaces/theodi/ndl-core-rag-chat
The application demonstrates:
- Semantic retrieval over UK public sector data
- Deterministic citation of source records
- End-to-end RAG using the published FAISS index and metadata
- Downloads last month
- 37