deepsearch-glm 0.2.2

Graph Language Models

Graph Language Models

Install

Python installation

To use the python interface, first make sure all dependencies are installed. We use poetry for that. To install all the dependent python packages and get the python bindings, simply execute,

poetry install

CXX compilation

To compile from scratch, simply run the following command in the deepsearch-glm root folder to create the build directory,

cmake -B ./build; 

Next, compile the code from scratch,

cmake --build ./build -j

Run using the Python Interface

NLP and GLM examples

To run the examples, simply do execute the scripts as poetry run python <script> <input>. Examples are,

1. apply NLP on document(s)

poetry run python ./deepsearch_glm/nlp_apply_on_docs.py --pdf './data/documents/articles/2305.*.pdf' --models 'language;term'

2. analyse NLP on document(s)

poetry run python ./deepsearch_glm/nlp_apply_on_docs.py --json './data/documents/articles/2305.*.nlp.json' 

3. create GLM from document(s)

poetry run python ./deepsearch_glm/glm_create_from_docs.py --pdf ./data/documents/reports/2022-ibm-annual-report.pdf

Deep Search utilities

1. Query and download document(s)

poetry run python ./deepsearch_glm/utils/ds_query.py --index patent-uspto --query "\"global warming potential\" AND \"etching\""

2. Converting PDF document(s) into JSON

poetry run python ./deepsearch_glm/utils/ds_convert.py --pdf './data/documents/articles/2305.*.pdf'"

Run using CXX executables

If you like to be bare-bones, you can also use the executables for NLP and GLM's directly. In general, we follow a simple scheme of the form

./nlp.exe -m <mode> -c <JSON-config file>
./glm.exe -m <mode> -c <JSON-config file>

In both cases, the modes can be queried directly via the -h or --help

./nlp.exe -h
./glm.exe -h

and the configuration files can be generated,

./nlp.exe -m create-configs
./glm.exe -m create-configs

Natural Language Processing (NLP)

After you have generated the configuration files (see above), you can

1. train simple NLP models

./nlp.exe -m train -c nlp_train_config.json

2. leverage pre-trained models

./nlp.exe -m predict -c nlp_predict.example.json

Graph Language Models (GLM)

1. create a GLM

./glm.exe -m create -c glm_config_create.json

2. explore interactively the GLM

./glm.exe -m explore -c glm_config_explore.json