Command-line interface¶
Graphbrain provides a command-line interface that can be used to execute a variety of tasks. You can access it either by using python to run the graphbrain root module as a script:
python -m graphbrain ...
or using the provided command:
graphbrain ...
All cases below work with both.
Here’s an overview of the interface:
graphbrain [-h] [--hg HG] [--infile INFILE] [--outfile OUTFILE]
[--fields FIELDS] [--show_namespaces] [--lang LANG]
[--pattern PATTERN] [--agent AGENT]
command
positional arguments:
command command to execute
optional arguments:
-h, --help show this help message and exit
--hg HG hypergraph db file path
--infile INFILE input file
--outfile OUTFILE output file
--fields FIELDS field names
--show_namespaces show namespaces
--lang LANG language
--pattern PATTERN hyperedge pattern
--agent AGENT agent name
The only obligatory argument, command, is used to specify the task to perform. Each command uses a subset of the optional arguments. Presented below are the details for each command.
Commands¶
run¶
Run a knowledge agent:
graphbrain --hg <hypergraph> --agent <agent name> run
A knowledge agent is a program that manipulates an hypergraph in some way. It can be introspective, working only on the current contents of the hypergraph to derive new knowledge. For example, the taxonomy agent infers simple taxonomies from concepts. It can infer that ‘black cat’ is a type of ‘cat’ or that ‘city of Berlin’ is a type of ‘city’. You can run it like this:
graphbrain --hg <hypergraph> --agent taxonomy run
It produces new hyperedges such as:
(type_of/P/. city/C (of/B city/C berlin/C))
Certain agents use outside sources to introduce knowledge into hypergraphs. For example, the txt_parser agent receives as input a simple text file and converters each sentence that it detects in it into an hyperedge. You can run it like this:
graphbrain --infile some_test_file.txt --hg <hypergraph> --agent txt_parser run
You can find the full list of agents that are distributed with Graphbrain here: