Resilient Data Gathering

import json

# Verify persistence
with open("intermediate_analysis.json", "r") as f:
   验证数据 = json.load(f)
    assert "results" in 验证数据
    assert "metadata" in 验证数据
    print(f"✓ Persisted {len(验证数据['results'])} records")

import json

# Load previous intermediate results
with open("intermediate_analysis.json", "r") as f:
    previous_results = json.load(f)["results"]

# Build on previous work
for sku, data in previous_results.items():
    # Continue analysis using persisted data
    pass

{
  "metadata": {
    "generated_at": "2024-01-15T10:30:00",
    "source": "embedded_domain_data",
    "version": "1.0"
  },
  "results": {...}
}

# After each significant transformation
with open(f"step_{step_num}_results.json", "w") as f:
    json.dump(current_state, f, indent=2)

print(f"ARTIFACT_PATH:{os.path.abspath('output.json')}")

try:
    results = complex_analysis(data)
except Exception as e:
    print(f"Warning: {e}, saving partial results")
    results = partial_results

with open("results.json", "w") as f:
    json.dump(results, f, indent=2)

"metadata": {
    "fallback_reason": "search_web and shell_agent failed 3x",
    "original_approach": "web_research_then_analysis",
    "fallback_approach": "embedded_data_direct_analysis"
}

# ===== FALLBACK DATA GATHERING SCRIPT =====
import json
import os
from datetime import datetime

# Known data embedded directly (no external calls)
KNOWN_COMPETITORS = {
    "competitor_a": {"product_x": 24.99, "product_y": 34.99},
    "competitor_b": {"product_x": 26.99, "product_y": 32.99}
}

OUR_PRODUCTS = ["product_x", "product_y"]

# Step 1: Calculate benchmarks
benchmarks = {}
for product in OUR_PRODUCTS:
    prices = [KNOWN_COMPETITORS[c][product] for c in KNOWN_COMPETITORS if product in KNOWN_COMPETITORS[c]]
    benchmarks[product] = {
        "min_price": min(prices),
        "max_price": max(prices),
        "avg_price": sum(prices) / len(prices)
    }

# Step 2: Persist Step 1 results
with open("step1_benchmarks.json", "w") as f:
    json.dump(benchmarks, f, indent=2)
print("ARTIFACT_PATH:step1_benchmarks.json")

# Step 3: Generate recommendations
recommendations = {}
for product, benchmark in benchmarks.items():
    recommendations[product] = {
        "recommended_price": round(benchmark["avg_price"] * 0.95, 2),
        "rationale": f"5% below competitor average of ${benchmark['avg_price']:.2f}"
    }

# Step 4: Persist final results
final_output = {
    "metadata": {
        "created": datetime.now().isoformat(),
        "method": "embedded_data_fallback",
        "preceding_artifact": "step1_benchmarks.json"
    },
    "benchmarks": benchmarks,
    "recommendations": recommendations
}

with open("final_recommendations.json", "w") as f:
    json.dump(final_output, f, indent=2)
print("ARTIFACT_PATH:final_recommendations.json")

print(json.dumps(final_output, indent=2))

# List all generated artifacts
ls -la *.json

# Validate JSON structure
python3 -c "import json; [json.load(open(f)) for f in ['step1_benchmarks.json', 'final_recommendations.json']]; print('✓ All JSON files valid')"

# Review metadata for fallback context
python3 -c "import json; print(json.load(open('final_recommendations.json'))['metadata'])"