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Remove outdated documentation in docs and refer to https://docs.qlever.dev #2631

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13f8d53
Migrate docs to docs.qlever.dev
ullingerc Jan 7, 2026
76fa853
link docs
ullingerc Jan 7, 2026
a00785d
remove docs folder
ullingerc Jan 8, 2026
d7a2831
Move up mention of documentation
Jan 23, 2026
776e4d7
Move up mention of use as embedded database
Jan 23, 2026
417b490
Remove section "Using QLever without the qlever CLI"
Jan 23, 2026
967f258
Merge https://github.com/ad-freiburg/qlever into migrate-docs
ullingerc Jan 29, 2026
adf6b0e
adjust readme
ullingerc Jan 29, 2026
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55 changes: 7 additions & 48 deletions README.md
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Expand Up @@ -19,8 +19,10 @@ outstanding performance, QLever offers a variety of unique features: advanced
text search capabilities, context-sensitive autocompletion of SPARQL queries,
live query analysis, very efficient spatial queries, and the interactive
visualization of very large numbers of geometric objects on a map.
QLever can also be used as an embedded database, that is, without the standard
client-server setup but running it in-process inside your own C++ program.

[Here are demos of QLever](http://qlever.cs.uni-freiburg.de) on a variety of
[Here are demos of QLever](http://qlever.dev/) on a variety of
large datasets, including the complete Wikidata, Wikimedia Commons,
OpenStreetMap, UniProt, PubChem, and DBLP. Those demos also feature QLever's
context-sensitive autocompletion, which makes SPARQL query construction so much
Expand All @@ -46,52 +48,9 @@ or if there is anything else you want to tell us, please [open an
issue](https://github.com/ad-freiburg/qlever/issues) or [open a
discussion](https://github.com/ad-freiburg/qlever/discussions).

# Quickstart

Use QLever via the `qlever` command-line interface (CLI), which can be
installed via `pip install qlever`. It is self-documenting via `qlever --help`
(for an overview of all commands) and `qlever <command> --help` (for details on
any specific command). For more information and example use cases, see
https://github.com/ad-freiburg/qlever-control .

You can control everything `qlever` does via a single configuration file, the
so-called `Qleverfile`. You can fetch any of a number of example `Qleverfile`s
(in particular, one for each of the demos mentioned above) via `qlever
setup-config <config-name>`. To write a `Qleverfile` for your own data, pick
one of these configurations as a starting point and edit the `Qleverfile` as
you see fit. Every option from the `Qleverfile` can also be set (and
overridden) via a command-line option with the same name, see `qlever <command>
--help`.


# Using QLever without the `qlever` CLI

This is not recommended but can be useful or necessary in certain (in
particular, non-interactive) environments. QLever's main binaries are called
`qlever-index` (for loading and indexing data) and `qlever-server` (for
querying the data). Each of these has a `--help` option that describes the
available options.

The easiest way to find out the right command line is to use the `qlever` CLI,
which for each command prints the exact command line it is going to execute.
With the option `--show`, it will print the command-line without executing it,
e.g., `qlever start --show`.

# Using QLever as an embedded database

QLever can also be used as an embedded database, that is, without the standard
client-server setup but running it in-process inside your own C++ program.
See https://github.com/ad-freiburg/qlever/pull/2100 for details and a link to a
small example program.

# Wiki and older documentation
# Quickstart and documentation

The [Qlever Wiki](https://github.com/ad-freiburg/qlever/wiki) provides
high-level descriptions of how Qlever works, as well as performance evaluations,
experiences with some concrete datasets, and further details.
To get started with QLever you may use our native packages released for [Debian, Ubuntu](https://docs.qlever.dev/quickstart/#debian-and-ubuntu) and [macOS](https://docs.qlever.dev/quickstart/#macos-apple-silicon). Additionally, a platform-independent version of QLever is available as an [image for Docker and Podman](https://hub.docker.com/r/adfreiburg/qlever), which can be used through our Python-based `qlever` command-line interface (CLI). Please refer to our [Quickstart documentation](https://docs.qlever.dev/quickstart/) for details.

There is quite a bit of additional documentation in the [docs](docs) folder of
this repository. The documents in that folder are not well maintained and may
be outdated. We are currently working on an own `qlever-docs` repository that
will provide extensive documentation and tutorials. However, for the RDF/SPARQL
specialist, the self-documenting `qlever` CLI should be sufficient.
For the official documentation, see [docs.qlever.dev](https://docs.qlever.dev/). Additional
information can also be found in the [QLever Wiki](https://github.com/ad-freiburg/qlever/wiki).
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