Wikidata

Wikidata stores knowledge as data rather than prose. Each of its 100 million-plus items, whether a person, a chemical element, a city, or a film, has a stable identifier (like Q42) and a set of statements linking it to other items and to hundreds of outside databases. Because the facts are structured, you can ask precise questions, such as which cities are capitals of European countries, and get exact answers rather than hoping the right sentence turns up in an article.

You have several ways in. The full dumps come in JSON and RDF (a format that describes things as subject, predicate, object triples), and the JSON dump is the usual choice for bulk processing, though it is large and best streamed rather than loaded whole. For targeted work you can run a live SPARQL query against the public endpoint (SPARQL is a query language built for knowledge graphs) or fetch individual items through the REST API. Labels and descriptions come in hundreds of languages, so the same item serves multilingual pipelines.

For RAG, Wikidata works best as a fact layer beside your text corpus rather than as the corpus itself. Resolve an entity mentioned in a document to its Wikidata item, then pull reliable attributes, dates, and links to ground the answer and disambiguate names. Its stable identifiers also act as a shared key across the open data world, since it absorbed much of the old Freebase project and now anchors entities for Wikipedia and many other datasets.

Two caveats. Coverage and depth are uneven, being community edited, so popular topics are rich while niche ones can be sparse or partly unsourced. On licensing it is refreshingly simple: the data is released into the public domain under CC0, so you can use it commercially with no attribution or share-alike obligation. Pair it with Wikipedia when you need the prose behind the facts.