README.md

metadata

pretty_name: Commonplace Knowledge — Simple English Wikipedia
language:
  - en
license: other
license_name: component-specific-apache-2.0-and-cc-by-sa
license_link: >-
  https://huggingface.co/datasets/DevParker/Commonplace-Knowledge/blob/main/LICENSES.md
task_categories:
  - text-retrieval
tags:
  - wikipedia
  - embeddings
  - local-research
  - source-linked-memory
size_categories:
  - 100K<n<1M
configs:
  - config_name: articles
    data_files:
      - split: train
        path: source/simplewiki.parquet
  - config_name: passages
    data_files:
      - split: train
        path: index/chunks.parquet

Commonplace Knowledge

A reproducible starting knowledge pack for Commonplace, a local research and memory tool. Published as a Hugging Face dataset repository at DevParker/Commonplace-Knowledge. It includes the pinned encoder as a component; it is not a newly trained model.

The payload contains 241,787 Simple English Wikipedia articles, 344,558 passages, 5,772 narrow extracted assertions, a vector index, and a clean writable-memory seed with no custom additions. No personal notes, saved model thoughts, research transcripts, generation-model weights or credentials are included.

Contents

Folder	Contents
source/	Original pinned Simple English snapshot Parquet
index/	Source-offset passages, normalized 512-dimensional FAISS index, metadata
catalog/	Immutable lexical/entity/assertion SQLite catalog and active manifest
seed/	Empty writable-memory schema; no custom additions
encoder/	Pinned Google EmbeddingGemma 2 model, tokenizer and configuration
provenance/	Original dataset card, identities, chunking and seed audit
licenses/	Applicable license texts

manifest.json lists SHA-256 and byte size for every payload file. All payloads are real local files, with no links into another project. Large files have Git LFS attributes. The manifest records the exact total; the pack is approximately 3.3 GiB.

Pinned sources

• Dataset: wikimedia/wikipedia, snapshot 20231101.simple, revision b04c8d1ceb2f5cd4588862100d08de323dccfbaa.
• Encoder: google/embeddinggemma-2, revision 914f7f89142e33e77833254d9c9b90c3cef7303b.

The index uses exact inner-product search over normalized FP32 vectors. The metadata's storage_dtype: float16 describes the original input shards, not the internal FAISS index representation. Redundant input shards are excluded.

Use

Install the independent Commonplace MCP/API. It does not include or require Bantam Factory or a generation model. From the software checkout, after installing its Python and Node dependencies:

.venv/bin/python scripts/download_pack.py --dest ../Commonplace-Knowledge
.venv/bin/python scripts/prepare_store.py --pack ../Commonplace-Knowledge
node scripts/commonplace.mjs serve

The software's release descriptor pins an exact dataset commit and pack identity. No Hugging Face sign-in is required to download this public pack. See the installation and MCP connection guide.

Setup verifies checksums and copies the public seed into a separate writable local store. Subsequent research changes that store, not this public pack. The encoder runs on CPU by default. A local generation model is needed only for the optional full researcher, not for direct memory access.

The dataset card declares two separate Parquet configurations, articles and passages. If using the Hugging Face datasets library after publication, choose one explicitly; they have different schemas. Commonplace itself downloads the whole pinned pack and uses its local SQLite/FAISS files, without that library.

Coverage and limitations

This is Simple English Wikipedia from November 2023. It does not contain the complete English Wikipedia and is not a current world-knowledge database. Sparse articles, missing topics, ambiguous entities and extraction errors remain. Similarity scores do not establish truth. A source citation does not prove that a model correctly interpreted the source.

Wikipedia's editorial and geographic coverage is uneven. Short, simplified articles can omit important qualifications, and the source may contain errors, outdated information or sensitive material. This release does not independently fact-check every article or extracted assertion.

The seed contains no model interpretations; these accrue only in a user's local writable memory. No promise of automatic improvement or universal answer-quality gains is made. See the software repository's validation and design documents.

Attribution, transformations and component licenses: LICENSES.md.