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README.md
README.md
 
 
betweenness_centrality.ipynb
betweenness_centrality.ipynb
 
 
case-biobank.ipynb
case-biobank.ipynb
 
 
check_config.py
check_config.py
 
 
closeness_centrality.ipynb
closeness_centrality.ipynb
 
 
country.ipynb
country.ipynb
 
 
degree_distribution.csv
degree_distribution.csv
 
 
sample-export-engine-advanced.py
sample-export-engine-advanced.py
 
 
sample-export-engine-simple.py
sample-export-engine-simple.py
 
 
sample_calculate_llm_call.py
sample_calculate_llm_call.py
 
 
sample_complete_pipeline.py
sample_complete_pipeline.py
 
 
sample_import_ris_file.py
sample_import_ris_file.py
 
 
sample_recall_llm.py
sample_recall_llm.py
 
 
sample_reset_flag.py
sample_reset_flag.py
 
 
sample_reset_llm_flag_with_fx.py
sample_reset_llm_flag_with_fx.py
 
 
sample_reset_status.py
sample_reset_status.py
 
 
sample_update_response.py
sample_update_response.py
 
 
sample_update_response_advance.py
sample_update_response_advance.py
 
 
sample_update_response_jsondecodererror.py
sample_update_response_jsondecodererror.py
 
 
selection-sampling.ipynb
selection-sampling.ipynb
 
 
test.ipynb
test.ipynb
 
 

README.md

CookBook

Example code for accomplishing common tasks with the LangChain library

Check Configuration

Settings in the TripleA are very important. Before running long and important processes, make sure that the configuration variables are correct. The check_config.py is a simple example of checking settings

Complete Pipeline

sample_complete_pipeline.py

Export to multiple CSV format

After performing various steps on the collection of articles in TripleA, a lot of information is collected. If we want to get an output from this information that includes information related to questions and answers from artificial intelligence, the export_engine function should be used. This function allows you to write the functions related to filtering and transformation of the data model and outputs as you wish. and provides a list of normalized dictionaries. You can convert this list to csv files by using convert_unified2csv_dynamically function in Class Converter.

sample-export-engine-advanced.py

Calculate calling of LLM

sample_calculate_llm_call.py

Reset LLM flag with function

sample_reset_llm_flag_with_fx.py

Reset LLM flag and recall another LLM QA

sample_recall_llm.py

Update LLM response

[sample_update_response.py]

Update LLM response with determined JSONDecodeError

sample_update_response_jsondecodererror.py

Update LLM response with advanced technique

In this method, special methods are used, which can be used in general for any structured LLM answer. The remodel_llm_response function is produced as a dynamic engine that allows changing the LLM response model using another customer order function such as fx_response_remodel.

sample_update_response_advance.py