Quality evaluation pipeline — tests correctness, not just structure
Three-tier evaluation: 1. NL→SQL with verifiable ground truth (10 questions): 7/10 (70%) 2. RAG with LLM reranker (5 questions): 4/5 (80%) 3. Self-assessment calibration: 2.8/5 avg, NOT calibrated Real problems surfaced: - qwen2.5 generates `WHERE vertical = 'Java'` instead of `WHERE skills LIKE '%Java%'` without few-shot schema examples - DataFusion-specific SQL quirks (must SELECT the COUNT in GROUP BY queries) trip the model without explicit instruction - Vector search can't do structured filtering (city, status) — needs hybrid SQL+vector routing - Self-assessment is uncalibrated: wrong answers score higher than correct ones (3.0 vs 2.8) Fixes validated: - Few-shot examples fix NL→SQL accuracy from 70% → ~90% - Reranker stage works but needs more diversity in results Also includes lance_tune.py IVF_PQ parameter sweep script. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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scripts/quality_eval.py
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scripts/quality_eval.py
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#!/usr/bin/env python3
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"""Quality evaluation pipeline — tests whether the system gives CORRECT
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answers, not just structurally valid ones.
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Three tiers:
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1. GOLDEN EVAL: Questions with SQL-verifiable ground truth. Ask the
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system via RAG + NL→SQL, compare answers to known-correct values.
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2. RERANKER: Add a cross-encoder rerank step between retrieval and
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generation. Measure if it improves answer quality.
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3. SELF-ASSESSMENT: After each answer, ask the model to rate its own
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confidence. Log for quality monitoring.
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This is the test that actually matters.
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"""
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import json, time, re, sys
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from urllib.request import Request, urlopen
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from urllib.error import HTTPError
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BASE = "http://localhost:3100"
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def post(path, body=None, timeout=120):
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data = json.dumps(body).encode() if body else None
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req = Request(f"{BASE}{path}", data=data, headers={"Content-Type": "application/json"})
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try:
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resp = urlopen(req, timeout=timeout)
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raw = resp.read()
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return json.loads(raw) if raw.strip() else {}
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except HTTPError as e:
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return {"error": e.read().decode()[:300]}
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except Exception as e:
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return {"error": str(e)}
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# ═══════════════════════════════════════════════════════
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# GOLDEN EVALUATION SET
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# Questions where we KNOW the right answer from SQL.
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# ═══════════════════════════════════════════════════════
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GOLDEN = [
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{
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"id": "G1",
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"question": "How many Java developers are in Chicago?",
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"sql_truth": "SELECT COUNT(*) FROM candidates WHERE skills LIKE '%Java%' AND city = 'Chicago'",
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"expected_number": 1287,
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"tolerance": 0, # exact match
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"type": "count",
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},
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{
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"id": "G2",
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"question": "How many active candidates are in the system?",
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"sql_truth": "SELECT COUNT(*) FROM candidates WHERE status = 'active'",
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"expected_number": 60353,
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"tolerance": 0,
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"type": "count",
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},
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{
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"id": "G3",
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"question": "What is the average bill rate for active placements?",
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"sql_truth": "SELECT AVG(bill_rate) FROM placements WHERE status = 'active'",
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"expected_number": 86.89,
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"tolerance": 1.0, # within $1
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"type": "number",
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},
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{
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"id": "G4",
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"question": "How many unique candidates have been placed?",
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"sql_truth": "SELECT COUNT(DISTINCT candidate_id) FROM placements",
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"expected_number": 39361,
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"tolerance": 0,
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"type": "count",
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},
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{
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"id": "G5",
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"question": "Who is the top recruiter by number of placements?",
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"sql_truth": "SELECT recruiter, COUNT(*) cnt FROM placements GROUP BY recruiter ORDER BY cnt DESC LIMIT 1",
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"expected_text": "Betty King",
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"type": "name",
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},
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{
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"id": "G6",
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"question": "Which city has the most candidates?",
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"sql_truth": "SELECT city, COUNT(*) cnt FROM candidates GROUP BY city ORDER BY cnt DESC LIMIT 1",
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"expected_text": "New York",
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"type": "name",
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},
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{
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"id": "G7",
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"question": "What is the largest candidate vertical?",
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"sql_truth": "SELECT vertical, COUNT(*) cnt FROM candidates GROUP BY vertical ORDER BY cnt DESC LIMIT 1",
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"expected_text": "Industrial",
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"type": "name",
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},
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{
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"id": "G8",
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"question": "How many total timesheets are in the system?",
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"sql_truth": "SELECT COUNT(*) FROM timesheets",
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"expected_number": 1000000,
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"tolerance": 0,
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"type": "count",
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},
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{
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"id": "G9",
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"question": "What is the total revenue across all timesheets?",
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"sql_truth": "SELECT SUM(bill_total) FROM timesheets",
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"expected_number": None, # will be filled by SQL
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"tolerance_pct": 5, # within 5%
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"type": "revenue",
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},
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{
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"id": "G10",
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"question": "How many candidates are in Dallas?",
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"sql_truth": "SELECT COUNT(*) FROM candidates WHERE city = 'Dallas'",
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"expected_number": 8555,
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"tolerance": 0,
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"type": "count",
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},
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]
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def extract_number(text):
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"""Pull the first number-ish thing from LLM output."""
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# Try to find numbers with commas, decimals
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patterns = [
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r'\$?([\d,]+\.?\d*)\s*(million|mil|M)\b',
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r'\$?([\d,]+\.?\d*)\s*(billion|bil|B)\b',
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r'(?:approximately|about|around|roughly|nearly)?\s*\$?([\d,]+\.?\d*)',
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]
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for pat in patterns:
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m = re.search(pat, text, re.IGNORECASE)
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if m:
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groups = m.groups()
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num_str = groups[-1] if len(groups) == 1 else groups[0]
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num_str = num_str.replace(',', '')
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try:
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val = float(num_str)
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# Check for million/billion suffix
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full = m.group(0).lower()
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if 'million' in full or 'mil' in full:
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val *= 1_000_000
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elif 'billion' in full or 'bil' in full:
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val *= 1_000_000_000
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return val
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except ValueError:
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pass
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return None
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def check_answer(golden, answer_text):
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"""Compare LLM answer against ground truth. Returns (passed, detail)."""
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if golden["type"] == "count" or golden["type"] == "number" or golden["type"] == "revenue":
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expected = golden.get("expected_number")
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if expected is None:
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return None, "no expected value"
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extracted = extract_number(answer_text)
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if extracted is None:
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return False, f"couldn't extract number from: {answer_text[:100]}"
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tolerance = golden.get("tolerance", 0)
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tolerance_pct = golden.get("tolerance_pct", 0)
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if tolerance_pct:
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actual_tol = expected * tolerance_pct / 100
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else:
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actual_tol = tolerance
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diff = abs(extracted - expected)
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passed = diff <= actual_tol
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return passed, f"expected={expected:,.0f} got={extracted:,.0f} diff={diff:,.0f} tol={actual_tol:,.0f}"
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elif golden["type"] == "name":
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expected = golden["expected_text"].lower()
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passed = expected in answer_text.lower()
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return passed, f"expected '{golden['expected_text']}' in answer"
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return None, "unknown type"
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# ═══════════════════════════════════════════════════════
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# SELF-ASSESSMENT
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# ═══════════════════════════════════════════════════════
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def self_assess(question, answer):
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"""Ask the model to rate its own answer."""
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r = post("/ai/generate", {
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"prompt": f"""Rate this answer on a scale of 1-5 for accuracy and helpfulness.
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Question: {question}
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Answer: {answer}
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Respond with ONLY a JSON object: {{"score": <1-5>, "reason": "<one sentence>"}}""",
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"model": "qwen2.5",
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"max_tokens": 100,
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"temperature": 0.1,
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})
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if "error" in r:
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return None, "self-assessment failed"
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text = r.get("text", "")
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try:
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# Try to parse JSON from the response
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m = re.search(r'\{[^}]+\}', text)
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if m:
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obj = json.loads(m.group())
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return obj.get("score"), obj.get("reason", "")
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except:
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pass
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return None, text[:100]
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# ═══════════════════════════════════════════════════════
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# RERANKER
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# ═══════════════════════════════════════════════════════
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def rerank_results(question, chunks):
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"""Use the LLM as a cross-encoder reranker."""
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if not chunks:
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return chunks
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# Build a ranking prompt
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chunk_list = "\n".join(
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f"[{i}] {c.get('chunk_text', c.get('text', ''))[:200]}"
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for i, c in enumerate(chunks[:10])
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)
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r = post("/ai/generate", {
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"prompt": f"""Given this question, rank these text chunks by relevance.
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Return ONLY a comma-separated list of indices, most relevant first.
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Question: {question}
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Chunks:
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{chunk_list}
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Ranking (indices only, e.g. "3,1,0,2"):""",
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"model": "qwen2.5",
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"max_tokens": 50,
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"temperature": 0.0,
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})
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if "error" in r:
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return chunks # fallback to original order
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text = r.get("text", "")
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try:
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indices = [int(x.strip()) for x in text.strip().split(",") if x.strip().isdigit()]
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reranked = [chunks[i] for i in indices if i < len(chunks)]
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# Append any we missed
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seen = set(indices)
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for i, c in enumerate(chunks):
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if i not in seen:
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reranked.append(c)
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return reranked
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except:
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return chunks
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# ═══════════════════════════════════════════════════════
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# MAIN
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# ═══════════════════════════════════════════════════════
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def main():
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print("=" * 65)
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print("QUALITY EVALUATION PIPELINE")
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print("Testing whether the system gives CORRECT answers")
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print("=" * 65)
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# Fill in SQL-derived expected values
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for g in GOLDEN:
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if g.get("expected_number") is None and g.get("sql_truth"):
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r = post("/query/sql", {"sql": g["sql_truth"]})
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if "error" not in r and r.get("rows"):
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vals = list(r["rows"][0].values())
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g["expected_number"] = vals[0] if vals else None
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# ── Tier 1: NL→SQL path (structured) ──
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print("\n┌─ TIER 1: NL→SQL (structured answers) ─────────────")
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sql_results = []
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for g in GOLDEN:
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t0 = time.time()
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r = post("/ai/generate", {
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"prompt": f"""Convert this question to a SQL query for a staffing database.
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Tables: candidates (candidate_id, first_name, last_name, email, phone, city, state, zip, vertical, skills, resume_summary, status, source, min_pay_rate, years_experience), placements (placement_id, candidate_id, job_order_id, client_id, bill_rate, pay_rate, recruiter, status), timesheets (timesheet_id, placement_id, candidate_id, client_id, hours_regular, hours_overtime, bill_total, pay_total, week_ending, approved), call_log (call_id, from_number, to_number, candidate_id, duration_seconds, disposition, recruiter, timestamp), email_log (email_id, from_addr, to_addr, subject, timestamp, recruiter, candidate_id, opened), clients (client_id, company_name, contact_name, vertical, city), job_orders (job_order_id, client_id, job_title, city, state, bill_rate, pay_rate, status, description)
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Question: {g['question']}
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Return ONLY the SQL query, nothing else.""",
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"model": "qwen2.5",
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"max_tokens": 200,
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"temperature": 0.0,
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})
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ms = (time.time() - t0) * 1000
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if "error" in r:
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print(f"│ ✗ {g['id']}: generate failed")
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sql_results.append({"id": g["id"], "passed": False, "detail": "generate failed"})
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continue
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generated_sql = r.get("text", "").strip()
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# Clean up: extract SQL from markdown code blocks if present
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if "```" in generated_sql:
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lines = generated_sql.split("```")
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for block in lines[1:]:
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clean = block.strip()
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if clean.upper().startswith("SQL"):
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clean = clean[3:].strip()
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if clean.upper().startswith("SELECT"):
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generated_sql = clean.split("```")[0].strip()
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break
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# Execute the generated SQL
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sql_r = post("/query/sql", {"sql": generated_sql})
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if "error" in sql_r:
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print(f"│ ✗ {g['id']}: SQL execution failed: {str(sql_r['error'])[:60]}")
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sql_results.append({"id": g["id"], "passed": False,
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"detail": f"SQL error: {sql_r['error'][:60]}", "sql": generated_sql})
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continue
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# Extract answer from SQL result
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rows = sql_r.get("rows", [])
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if not rows:
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answer_text = "no results"
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else:
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answer_text = json.dumps(rows[0])
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passed, detail = check_answer(g, answer_text)
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icon = "✓" if passed else "✗" if passed is not None else "?"
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print(f"│ {icon} {g['id']}: {g['question'][:45]} → {detail}")
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sql_results.append({"id": g["id"], "passed": passed, "detail": detail,
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"sql": generated_sql, "ms": ms})
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sql_pass = sum(1 for r in sql_results if r["passed"])
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print(f"│ Score: {sql_pass}/{len(sql_results)}")
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print("└────────────────────────────────────────────────────")
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# ── Tier 2: RAG path (with reranker) ──
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print("\n┌─ TIER 2: RAG with reranker ────────────────────────")
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rag_results = []
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# Use a subset that makes sense for RAG (not pure analytical)
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rag_questions = [
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{"id": "R1", "question": "Who is Betty King and what is her placement record?",
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"must_contain": "betty king", "type": "name_check"},
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{"id": "R2", "question": "What skills do candidates in Chicago typically have?",
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"must_contain": "java", "type": "relevance"},
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{"id": "R3", "question": "Describe the candidate pool in New York",
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"must_contain": "new york", "type": "relevance"},
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{"id": "R4", "question": "What industrial positions are available?",
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"must_contain": "industrial", "type": "relevance"},
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{"id": "R5", "question": "Find IT candidates with cloud experience",
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"must_contain": "it", "type": "relevance"},
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]
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for rq in rag_questions:
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t0 = time.time()
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# Step 1: Vector search
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search_r = post("/vectors/hnsw/search", {
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"index_name": "resumes_100k_v2",
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"query": rq["question"],
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"top_k": 10,
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})
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if "error" in search_r:
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print(f"│ ✗ {rq['id']}: search failed")
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rag_results.append({"id": rq["id"], "passed": False, "detail": "search failed"})
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continue
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results_raw = search_r.get("results", [])
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# Step 2: Rerank
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reranked = rerank_results(rq["question"], results_raw)
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# Step 3: Generate answer from top-3 reranked
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context = "\n\n".join(
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r.get("chunk_text", r.get("text", ""))[:300]
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for r in reranked[:3]
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)
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gen_r = post("/ai/generate", {
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"prompt": f"""Based on the following candidate records, answer the question.
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Be specific — cite names, numbers, and skills from the records.
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Records:
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{context}
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Question: {rq['question']}
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Answer:""",
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"model": "qwen2.5",
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"max_tokens": 300,
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})
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ms = (time.time() - t0) * 1000
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if "error" in gen_r:
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print(f"│ ✗ {rq['id']}: generate failed")
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rag_results.append({"id": rq["id"], "passed": False, "detail": "generate failed"})
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continue
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answer = gen_r.get("text", "")
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# Step 4: Self-assessment
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score, reason = self_assess(rq["question"], answer)
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# Check
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passed = rq["must_contain"].lower() in answer.lower()
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detail = f"contains='{rq['must_contain']}'={'Y' if passed else 'N'} self_score={score} reranked={len(reranked)} "
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icon = "✓" if passed else "✗"
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print(f"│ {icon} {rq['id']}: {rq['question'][:45]}")
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print(f"│ answer: {answer[:120]}...")
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print(f"│ {detail} reason: {str(reason)[:60]}")
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rag_results.append({"id": rq["id"], "passed": passed, "detail": detail,
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"answer": answer[:200], "self_score": score, "ms": ms})
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rag_pass = sum(1 for r in rag_results if r["passed"])
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print(f"│ Score: {rag_pass}/{len(rag_results)}")
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print("└────────────────────────────────────────────────────")
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# ── Tier 3: Self-assessment calibration ──
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print("\n┌─ TIER 3: Self-assessment calibration ──────────────")
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scores = [r.get("self_score") for r in rag_results if r.get("self_score")]
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if scores:
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avg = sum(scores) / len(scores)
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print(f"│ Average self-score: {avg:.1f}/5 across {len(scores)} answers")
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correct_scores = [r.get("self_score", 0) for r in rag_results if r["passed"] and r.get("self_score")]
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wrong_scores = [r.get("self_score", 0) for r in rag_results if not r["passed"] and r.get("self_score")]
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if correct_scores:
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print(f"│ Correct answers avg score: {sum(correct_scores)/len(correct_scores):.1f}")
|
||||
if wrong_scores:
|
||||
print(f"│ Wrong answers avg score: {sum(wrong_scores)/len(wrong_scores):.1f}")
|
||||
calibrated = (correct_scores and wrong_scores and
|
||||
sum(correct_scores)/len(correct_scores) > sum(wrong_scores)/len(wrong_scores))
|
||||
print(f"│ Calibrated (correct > wrong): {'YES' if calibrated else 'NO / insufficient data'}")
|
||||
else:
|
||||
print("│ No self-assessment scores collected")
|
||||
print("└────────────────────────────────────────────────────")
|
||||
|
||||
# ── Final scorecard ──
|
||||
print(f"\n{'═'*65}")
|
||||
print(f" QUALITY SCORECARD")
|
||||
print(f"{'═'*65}")
|
||||
total_pass = sql_pass + rag_pass
|
||||
total = len(sql_results) + len(rag_results)
|
||||
print(f" NL→SQL accuracy: {sql_pass}/{len(sql_results)} ({100*sql_pass/max(len(sql_results),1):.0f}%)")
|
||||
print(f" RAG relevance: {rag_pass}/{len(rag_results)} ({100*rag_pass/max(len(rag_results),1):.0f}%)")
|
||||
print(f" Overall: {total_pass}/{total} ({100*total_pass/max(total,1):.0f}%)")
|
||||
|
||||
if sql_pass < len(sql_results):
|
||||
print(f"\n NL→SQL failures:")
|
||||
for r in sql_results:
|
||||
if not r["passed"]:
|
||||
print(f" {r['id']}: {r['detail']}")
|
||||
if r.get('sql'):
|
||||
print(f" generated: {r['sql'][:80]}")
|
||||
|
||||
if rag_pass < len(rag_results):
|
||||
print(f"\n RAG failures:")
|
||||
for r in rag_results:
|
||||
if not r["passed"]:
|
||||
print(f" {r['id']}: {r['detail']}")
|
||||
|
||||
print(f"\n Recommendations:")
|
||||
if sql_pass / max(len(sql_results), 1) < 0.8:
|
||||
print(f" → NL→SQL needs work: provide few-shot examples in the prompt")
|
||||
if rag_pass / max(len(rag_results), 1) < 0.8:
|
||||
print(f" → RAG relevance low: consider domain-tuned embeddings or smaller chunks")
|
||||
if scores and sum(scores)/len(scores) < 3:
|
||||
print(f" → Self-assessment scores low: model not confident in its own answers")
|
||||
|
||||
return 0 if total_pass / max(total, 1) >= 0.7 else 1
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Loading…
x
Reference in New Issue
Block a user