---
license: other
language:
- en
pretty_name: "Microsoft Fabric Docs — rlat knowledge model"
tags:
- retrieval
- knowledge-model
- resonance-lattice
- rlat
- embeddings
- microsoft-fabric
- data-engineering
- power-bi
- lakehouse
- warehouse
size_categories:
- 10K<n<100K
---

# Microsoft Fabric Docs — rlat knowledge model

Prebuilt [Resonance Lattice](https://github.com/tenfingerseddy/resonance-lattice)
knowledge model over **[MicrosoftDocs/fabric-docs](https://github.com/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`](./fabric-docs-bundled.rlat) | 587 MB | **Bundled** — source files packed inside the `.rlat` as zstd frames. Fully self-contained. Works offline. |

```bash
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

Single file, every passage cited back to its source with drift checking,
and no LLM in the retrieval loop. On the project's 63-question Fabric
bench, `rlat deep-search` scored **92.2% answerable accuracy at 0%
hallucination** vs. an LLM-with-grep baseline at 7.8x fewer tokens — full
methodology and run artifacts at the
[benchmarks page](https://tenfingerseddy.github.io/resonance-lattice/benchmarks.html).

This archive carries the single base band (`gte-modernbert-base`, 768d).
An earlier build shipped an experimental "optimised" projection band; it
was retired after benchmarking ([falsification record](https://tenfingerseddy.github.io/resonance-lattice/benchmarks.html))
and this artifact now ships base-band only.

## 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`](https://github.com/MicrosoftDocs/fabric-docs/commit/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](https://github.com/MicrosoftDocs/fabric-docs)**.

- **Copyright:** © Microsoft Corporation. All rights reserved.
- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (documentation); code samples are under [MIT](https://opensource.org/license/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](https://mariadb.com/bsl11/), the same license as the [Resonance Lattice project](https://github.com/tenfingerseddy/resonance-lattice). 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}}
}
```