Datasets:

tenfingers
/

powershell-docs-rlat

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----
-license: other
-language:
-- en
-pretty_name: "PowerShell Docs — rlat knowledge model"
-tags:
-- retrieval
-- knowledge-model
-- resonance-lattice
-- rlat
-- embeddings
-- powershell
-- windows
-- scripting
-- devops
-- automation
-size_categories:
-- 1M<n<10M
----
-# PowerShell Docs — rlat knowledge model
-Prebuilt [Resonance Lattice](https://github.com/tenfingerseddy/resonance-lattice-cli)
-knowledge model over **[MicrosoftDocs/PowerShell-Docs](https://github.com/MicrosoftDocs/PowerShell-Docs)**, built for
-retrieval-augmented coding assistants and LLM context injection.
-**Audience:** Windows admins, DevOps engineers, and developers scripting with PowerShell 5.1 or 7.x.
-## Two variants — which do you want?
-| File | Size | When to pick |
-|------|------|------|
-| [`powershell-docs.rlat`](./powershell-docs.rlat) | 260 MB | **Remote** — knowledge model pins to a GitHub commit SHA. Source text is fetched from `https://github.com/MicrosoftDocs/PowerShell-Docs` on first query and cached locally. Freshest; smaller download. Needs network on first query. |
-| [`powershell-docs-bundled.rlat`](./powershell-docs-bundled.rlat) | 556 MB | **Bundled** — fully self-contained. Source text is packed inside the `.rlat` as zstd frames. Works offline forever. Larger download; no drift tracking. |
-**Pick bundled** if you care about reliability, offline use, or air-gapped environments.
-**Pick remote** if you want automatic freshness via `rlat freshness` / `rlat sync`, or if you plan to query from CI where cold-cache fetches are fine.
-Both variants serve **identical retrieval quality** — same field, same registry, same encoder (BGE-large-en-v1.5 with random projection heads, 5 bands × 2048 dim, 8-bit quantized registry, zstd compressed).
-## Usage
-```bash
-# Install the CLI (one-time)
-pip install resonance-lattice
-# Download one of the variants (pick bundled for zero-network queries):
-huggingface-cli download tenfingers/powershell-docs-rlat powershell-docs-bundled.rlat --local-dir .
-# Query:
-rlat search powershell-docs-bundled.rlat "how do I filter objects with Where-Object"
-rlat ask powershell-docs-bundled.rlat "how do I filter objects with Where-Object"
-# Use as Claude / Cursor / ChatGPT context:
-rlat resonate powershell-docs-bundled.rlat "how do I filter objects with Where-Object" --format context | pbcopy
-```
-## Sample queries
-- `how do I filter objects with Where-Object`
-- `convert JSON to a PowerShell object`
-- `write a function with pipeline input`
-- `sign a PowerShell script`
-## Corpus provenance
-- **Upstream repo:** https://github.com/MicrosoftDocs/PowerShell-Docs
-- **Pinned commit SHA:** `4d079111904d56e4e9982ca283c66595e19519e3`
-- **Files indexed:** 2,719
-- **Chunks (semantic passages):** 17,369
-- **Encoder:** `BAAI/bge-large-en-v1.5` with 5 random projection heads (2048 dim × 5 bands)
-- **Registry compression:** 8-bit quantized (≈75% size reduction, ≥0.99 per-band cosine similarity vs fp32)
-- **Build flags:** `--quantize-registry 8 --compression zstd`
-- **Build date:** 2026-04-22
-## Licensing
-### Source content
-This knowledge model indexes (and in the **bundled** variant, redistributes) content from **[MicrosoftDocs/PowerShell-Docs](https://github.com/MicrosoftDocs/PowerShell-Docs)**.
-- **Copyright:** © Microsoft Corporation. All rights reserved.
-- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (for documentation content); code samples are licensed under the [MIT License](https://opensource.org/license/mit)
-- **Modifications:** Source files are chunked into retrieval-sized passages and indexed into a semantic field + registry. In the **bundled** variant, the source files themselves are packed unchanged as zstd frames inside the `.rlat` (lossless; re-chunking at query time produces the same bytes as the upstream file at the pinned SHA). In the **remote** variant, no source content is redistributed — queries fetch directly from the upstream repo on first access.
-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* — semantic field tensor, source registry, manifest, encoder configuration — is licensed under the [Business Source License 1.1](https://mariadb.com/bsl11/), the same license as the [Resonance Lattice project](https://github.com/tenfingerseddy/resonance-lattice-cli). Each release converts to MPL 2.0 four years after it's first published.
-BSL 1.1 applies to the *structure* of the knowledge model (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. See the [BSL 1.1 plain-language summary](https://github.com/tenfingerseddy/resonance-lattice-cli/blob/main/docs/LICENSE_FAQ.md).
-### Trademark notice
-Microsoft Fabric, Power BI, PowerShell, SQL Server, Windows, Azure, and related product names are trademarks or registered trademarks of Microsoft Corporation in the United States and/or other countries. This project is not affiliated with, endorsed by, or sponsored by Microsoft Corporation.
-## Keeping it fresh (remote variant only)
-```bash
-# Read-only drift check — one GitHub API call
-rlat freshness powershell-docs.rlat
-# Pull upstream diff into the local knowledge model
-rlat sync powershell-docs.rlat
-```
-`rlat sync` fetches only the files that changed between the pinned SHA and upstream HEAD, rechunks them, and reconciles against the existing registry. Cost is proportional to the diff, not the full corpus.
-## Citation
-If you use this rlat in research, please cite:
-```
-@misc{resonance-lattice-rlats-2026,
-  title  = {Resonance Lattice launch rlats},
-  author = {Snyder, Kane},
-  year   = {2026},
-  howpublished = {HuggingFace Hub, \url{https://huggingface.co/datasets/tenfingers/powershell-docs-rlat}}
-}
-```

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