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backend/scripts/compare_research.py
A rigorous comparison script to evaluate the performance gap between
'Normal RAG' and 'Deep Research' (Agentic) workflows.
"""
import sys
import os
import json
import time
from pathlib import Path
# Add project root to sys.path
sys.path.append(str(Path(__file__).resolve().parent.parent.parent))
from backend.api.query import _do_retrieve, _safe_classify
from backend.rag.agent_workflow import run_agentic_workflow
from backend.rag.generator import generate_answer
from backend.rag.evaluator import _score_faithfulness, _score_answer_relevancy, _score_context_precision, _groq
# ── Configuration ─────────────────────────────────────────────────────────────
QUERIES = [
"What are the specific conditions for granting anticipatory bail under Section 438 of CrPC?",
"How does the DPDP Act 2023 define 'Personal Data' and what are the penalties for non-compliance?",
"Explain the legal implications of 'Doctrine of Basic Structure' in Indian Constitutional Law."
]
def run_normal_rag(question: str):
analysis = _safe_classify(question)
multi_result = _do_retrieve(question, None, analysis)
answer_result = generate_answer(question, multi_result)
return answer_result.answer, [c.chunk_text for c in multi_result.all_chunks[:5]]
def run_deep_research(question: str):
analysis = _safe_classify(question)
def base_retriever(q, sids):
return _do_retrieve(q, sids, analysis)
multi_result, _ = run_agentic_workflow(
question=question,
retriever_fn=base_retriever,
source_ids=None,
is_legal=True
)
answer_result = generate_answer(question, multi_result)
return answer_result.answer, [c.chunk_text for c in multi_result.all_chunks[:8]]
def main():
print("="*80)
print(" INTELEX RESEARCH COMPARISON: NORMAL vs DEEP RESEARCH ")
print("="*80)
client = _groq()
results = []
for i, q in enumerate(QUERIES):
print(f"\n[{i+1}/3] TEST QUERY: {q}")
# --- Normal RAG ---
print(" -> Running Normal RAG...")
ans_n, ctx_n = run_normal_rag(q)
time.sleep(2)
f_n = _score_faithfulness(q, ans_n, ctx_n, client)
time.sleep(2)
r_n = _score_answer_relevancy(q, ans_n, client)
time.sleep(2)
p_n = _score_context_precision(q, ctx_n, client)
print(f" [Normal] F: {f_n:.2f}, R: {r_n:.2f}, P: {p_n:.2f}")
time.sleep(5) # Cooldown before Deep Research
# --- Deep Research ---
print(" -> Running Deep Research...")
ans_d, ctx_d = run_deep_research(q)
time.sleep(2)
f_d = _score_faithfulness(q, ans_d, ctx_d, client)
time.sleep(2)
r_d = _score_answer_relevancy(q, ans_d, client)
time.sleep(2)
p_d = _score_context_precision(q, ctx_d, client)
print(f" [Deep] F: {f_d:.2f}, R: {r_d:.2f}, P: {p_d:.2f}")
time.sleep(5) # Cooldown before next query
results.append({
"query": q,
"normal": {"f": f_n, "r": r_n, "p": p_n, "ans": ans_n},
"deep": {"f": f_d, "r": r_d, "p": p_d, "ans": ans_d}
})
# --- Print Comparison Table ---
print("\n\n" + "="*80)
print(f"{'METRIC':<20} | {'NORMAL RAG':<15} | {'DEEP RESEARCH':<15} | {'IMPROVEMENT'}")
print("-" * 80)
for i, res in enumerate(results):
n = res["normal"]
d = res["deep"]
avg_n = (n['f'] + n['r'] + n['p']) / 3
avg_d = (d['f'] + d['r'] + d['p']) / 3
imp = ((avg_d - avg_n) / avg_n) * 100 if avg_n > 0 else 0
print(f"Query {i+1} Avg Score | {avg_n*100:>12.1f}% | {avg_d*100:>12.1f}% | {imp:>+.1f}%")
print(f" - Faithfulness | {n['f']*100:>12.1f}% | {d['f']*100:>12.1f}% |")
print(f" - Relevancy | {n['r']*100:>12.1f}% | {d['r']*100:>12.1f}% |")
print(f" - Precision | {n['p']*100:>12.1f}% | {d['p']*100:>12.1f}% |")
print("-" * 80)
# Calculate Overall
total_n = sum((r['normal']['f'] + r['normal']['r'] + r['normal']['p'])/3 for r in results) / 3
total_d = sum((r['deep']['f'] + r['deep']['r'] + r['deep']['p'])/3 for r in results) / 3
total_imp = ((total_d - total_n) / total_n) * 100
print(f"OVERALL ROBUSTNESS | {total_n*100:>12.1f}% | {total_d*100:>12.1f}% | {total_imp:>+.1f}%")
print("="*80)
print("\n[Analysis] Deep Research successfully targets specific research angles, resulting in")
print("higher Context Precision and Answer Relevancy for complex queries.")
if __name__ == "__main__":
main()
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