Editing Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9 (section)

=== Below is an outline (not a full program, but a reliable scaffold). ===

<syntaxhighlight>import json
import csv
from openai import OpenAI

client = OpenAI()

=== Load taxonomy and reference tables ===
taxonomy = json.load(open("taxonomy.json"))
ref_tables = {
    "materials": json.load(open("materials.json")),
    "biology": json.load(open("biology.json")),
    "deps": json.load(open("dependencies.json")),
}

=== Main output storage ===
output = []

COLUMN_BLOCKS = [
    ["tech_name","description","category","sub_category","era"],

    ["natural_resource_prerequisites",
     "natural_resource_prerequisites_explanations",
     "biological_prerequisites",
     "biological_prerequisites_explanations",
     "geographical_prerequisites",
     "geographical_prerequisites_explanations"],

    ["previous_tech_prerequisites","related_or_parallel_technologies",
     "enabler_results","enabler_description"],

    ["primary_energy_source","tools_used","weapons_enabled",
     "transport_required"],

    ["education_level_and_type","social_complexity",
     "population_density","population_density_explanations"],

    ["ecological_impact","sustainability",
     "productivity_multiplier","innovation_rate",
     "innovation_rate_explanations"],

    ["tech_spread_rate","tech_spread_rate_explanations",
     "stability_dependency","interconnectivity_index",
     "diffusion_method","notes"]
]

def generate_block(row_meta, context, block_columns):
    # Construct the prompt using the model’s strengths:
    # - schema
    # - definitions
    # - reference tables
    # - previous outputs for this row
    # - global constraints
    prompt = build_prompt(row_meta, context, block_columns)
    response = client.chat.completions.create(
        model="your-model",
        messages=[{"role":"user","content":prompt}],
        temperature=0
    )
    return parse_model_output(response, block_columns)

for row in taxonomy:
    row_output = {}
    row_meta = extract_row_metadata(row)

    context = {
        "completed_rows": output,
        "ref_tables": ref_tables
    }

    for block in COLUMN_BLOCKS:
        partial = generate_block(row_meta, context, block)
        row_output.update(partial)

    output.append(row_output)

=== Save final output ===
with open("full_table.csv","w",newline="") as f:
    writer = csv.DictWriter(f, fieldnames=sum(COLUMN_BLOCKS,[]))
    writer.writeheader()
    writer.writerows(output)

</syntaxhighlight>

This gives you full control and avoids any model refusal.