tenfingers/fabric-docs-rlat

Microsoft Fabric Docs — rlat knowledge model

Prebuilt Resonance Lattice knowledge model over MicrosoftDocs/fabric-docs, ready for grounded retrieval in AI coding assistants and LLM context injection.

Audience: Data engineers and Fabric users building lakehouses, pipelines, and warehouses on Microsoft Fabric.

What you get

File	Size	Mode
fabric-docs-bundled.rlat	682 MB	Bundled — source files packed inside the .rlat as zstd frames. Fully self-contained. Works offline.

pip install rlat
huggingface-cli download tenfingers/fabric-docs-rlat fabric-docs-bundled.rlat --local-dir .
rlat search fabric-docs-bundled.rlat "how do I create a lakehouse"

Why use this instead of grepping the docs

Matched A/B benchmark — 63 Fabric questions, Sonnet 4.6 judge, single-shot retrieval (single_knowledge lane):

Metric	Base band only	+ Optimised band	Δ
Answerable accuracy	60.8 %	62.7 %	+1.96 pp
Answerable hallucination	7.8 %	2.0 %	−5.88 pp (≈4× fewer)
Distractor refusal rate	58.3 %	58.3 %	unchanged
Latency / query	7.19 s	7.66 s	+0.47 s

The optimised band is an MRL-trained projection of the base band specialised on this corpus's query distribution. Both bands ship together — rlat search auto-selects optimised.

For full multi-hop (deep_knowledge lane) on the older v1 of this corpus, the same harness scored 92.2 % answerable accuracy at 0 % hallucination vs. 94.1 % at 25 % distractor-hallucination for an LLM-with-grep baseline, at 7.8× fewer tokens and ~30 % faster wall-time. See the project benchmark gate for methodology.

Sample queries

• how do I create a lakehouse
• what is a shortcut in OneLake
• setup row-level security in a warehouse
• integrate dataflows with a pipeline
• Spark VCore admission decisions
• Direct Lake mode vs import mode trade-offs

Corpus provenance

• Upstream repo: https://github.com/MicrosoftDocs/fabric-docs
• Pinned commit SHA: 97e7246ad6 (2026-05-13)
• Files indexed: 2,435
• Passages: 67,503 (chunked to 200–3,200 chars, semantic boundaries)
• Encoder: Alibaba-NLP/gte-modernbert-base, 768-dim, CLS-pooled + L2-normalised, pinned revision e7f32e3c00f91d699e8c43b53106206bcc72bb22
• Bands:
  • base (768d) — primary retrieval field
  • optimised (MRL 512d, nested [64, 128, 256, 512]) — corpus-specialised projection trained on synthesised hard negatives. rlat search auto-selects this band when present.
• ANN: FAISS HNSW (M=32, efConstruction=200) on both bands
• Storage mode: bundled — source markdown packed inside the .rlat ZIP
• Build date: 2026-05-13

Licensing

Source content

This knowledge model indexes and (in the bundled variant) redistributes content from MicrosoftDocs/fabric-docs.

• Copyright: © Microsoft Corporation. All rights reserved.
• License: CC BY 4.0 (documentation); code samples are under MIT.
• Modifications: Source files are chunked into retrieval-sized passages and indexed into a dense field + registry. Source bytes are packed unchanged as zstd frames inside the .rlat (lossless; re-chunking at query time reproduces the same bytes as upstream at the pinned SHA).

All retrieved passages retain their upstream license. Commercial use is permitted under the upstream license; attribution to the upstream repo is required when redistributing passages.

Artifact structure

The knowledge-model structure — bands, registry, manifest, encoder configuration — is licensed under Business Source License 1.1, the same license as the Resonance Lattice project. Each release converts to MPL 2.0 four years after first publication.

BSL 1.1 applies to the structure (how it indexes and retrieves), not to the embedded content. You can use this .rlat commercially for retrieval and grounded AI workflows over the indexed docs; what BSL restricts is building a competing Resonance Lattice implementation from the lattice structure itself.

Trademark notice

Microsoft Fabric, Power BI, PowerShell, SQL Server, Windows, Azure, and related product names are trademarks or registered trademarks of Microsoft Corporation. This project is not affiliated with, endorsed by, or sponsored by Microsoft Corporation.

Citation

@misc{resonance-lattice-rlats-2026,
  title  = {Resonance Lattice launch rlats},
  author = {Snyder, Kane},
  year   = {2026},
  howpublished = {HuggingFace Hub, \url{https://huggingface.co/datasets/tenfingers/fabric-docs-rlat}}
}