Principle:Mbzuai oryx Awesome LLM Post training Keyword Data Loading
| Knowledge Sources | |
|---|---|
| Domains | Data_Collection, Data_Ingestion |
| Last Updated | 2026-02-08 07:30 GMT |
Overview
A data ingestion pattern that loads structured keyword lists from tabular files to drive parameterized API queries.
Description
Keyword Data Loading is the initial step of a trend analysis pipeline where a set of research keywords and their categories are read from an external tabular file (typically CSV). Each keyword-category pair defines a separate query to be issued against an academic API, and the categories provide grouping for downstream visualization and export.
This pattern separates the query definition from the query execution, allowing researchers to modify the set of tracked keywords without changing any code. It also enables reproducibility: the same keyword file produces the same analysis.
Usage
Use this principle when:
- The set of queries to execute is externally defined and may change between runs
- Keywords need to be grouped by category for organized reporting
- The query list should be version-controlled independently of the analysis script
Theoretical Basis
Pseudo-code Logic:
# Abstract keyword loading pattern (NOT real implementation)
keyword_table = load_tabular_file("keywords.csv")
for row in keyword_table:
category = row["Category"]
keyword = row["Research Keyword"]
results = query_api(keyword, year_range)
store_results(keyword, category, results)
The pattern enforces a schema contract: the input file must contain specific column names that the downstream pipeline depends on.