Implementation:Mbzuai oryx Awesome LLM Post training Json Dump Progressive

Knowledge Sources	Awesome-LLM-Post-training Python json module
Domains	Trend_Analysis, Fault_Tolerance
Last Updated	2026-02-08 07:30 GMT

Overview

Concrete tool for progressively saving trend analysis results to a JSON file after each keyword is fully processed.

Description

Within the main processing loop of future_research_data.py, after all years have been queried for a keyword and the results stored in results_dict, the entire dictionary is written to results/research_trends.json using json.dump with indent=4 formatting. The file is overwritten each time, providing a crash-recovery checkpoint that contains complete results for all keywords processed so far.

Usage

This save logic runs automatically after each keyword's yearly counts are collected. It is embedded in the main processing loop and requires no explicit invocation.

Code Reference

Source Location

Repository: Awesome-LLM-Post-training
File: scripts/future_research_data.py
Lines: 63-65

Signature

# Progressive save after each keyword
with open(json_path, "w") as json_file:
    json.dump(results_dict, json_file, indent=4)

Import

import json

I/O Contract

Inputs

Name	Type	Required	Description
results_dict	dict	Yes	Accumulated results dict keyed by keyword, each containing Category and Data (list of year-count dicts)
json_path	str	Yes	Output path (hardcoded as "results/research_trends.json")

Outputs

Name	Type	Description
research_trends.json	File	JSON file containing all keyword results processed so far, overwritten after each keyword

Usage Examples

Progressive Save in Processing Loop

import json
import os

output_dir = "results"
os.makedirs(output_dir, exist_ok=True)
json_path = os.path.join(output_dir, "research_trends.json")

results_dict = {}
for keyword, category in keyword_category_pairs:
    # ... query API for yearly counts ...
    results_dict[keyword] = {
        "Category": category,
        "Data": [{"Year": y, "Papers Published": c} for y, c in zip(years, counts)]
    }

    # Progressive save after each keyword completes
    with open(json_path, "w") as json_file:
        json.dump(results_dict, json_file, indent=4)

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