WFGY/ProblemMap/rerankers.md

🧮 Rerankers — When to Use Them, How to Tune, How to Prove It

Reranking boosts precision@k by re-scoring a candidate set from first-stage retrieval.
Used correctly, it tightens citations and reduces “looks-right-but-wrong” answers. Used blindly, it burns latency & money.

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Quick Nav
Retrieval Playbook · Embedding vs Semantic · Traceability · Patterns: Query Parsing Split · Symbolic Constraint Unlock

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0) TL;DR — Decision table

Situation	Use	Why
First-stage recall@50 < 0.85	Do NOT add reranker yet	You’re promoting the wrong pool; fix candidate generation first
Recall is good but Top-5 irrelevant	Add cross-encoder reranker	Cross-attends Q–D; best precision
Need tight citations across near-duplicates	Cross-encoder or ColBERT style	Fine-grained token interactions
Very low volume, high stakes	LLM-as-reranker	Expensive but accurate, great for audits
High QPS, tight budget	Light cross-encoder (mini) or linear fusion	80/20 precision for minimal cost

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1) Families of rerankers

1. Cross-encoder (e.g., bge-reranker, ms-marco MiniLM)

   • Jointly encodes [query ⊕ doc]; outputs a relevance score.
   • Pros: best precision; Cons: O(k) forward passes.

2. Late-interaction (e.g., ColBERT-style)

   • Token-level max-sim interactions; faster than full cross-enc.
   • Pros: scalable; Cons: infra heavier than CE.

3. LLM-as-reranker

   • Ask model to score or rank candidates with a schema.
   • Pros: reasoning-aware; Cons: latency & cost; needs a strict judging prompt.

Start point: cross-encoder mini/base → upgrade if needed.

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2) Minimal implementations

2.1 Python — Cross-encoder (bge-reranker)

# pip install FlagEmbedding
from FlagEmbedding import FlagReranker

rerank = FlagReranker('BAAI/bge-reranker-base', use_fp16=True)
def rerank_topk(query, candidates, out_k=10):
    # candidates: list[{"text":..., "meta":{...}}]
    pairs = [(query, c["text"]) for c in candidates]
    scores = rerank.compute_score(pairs, normalize=True)
    ranked = sorted(zip(candidates, scores), key=lambda x: -x[1])
    out = []
    for c, s in ranked[:out_k]:
        c["rerank_score"] = float(s)
        c["source"] = c.get("source","") + "|ce"
        out.append(c)
    return out

Tips

• Use normalize=True for score comparability across batches.
• Batch size 16–64 depending on VRAM/CPU.

2.2 Node — LLM-as-reranker (schema-locked)

// Example sketch using any chat LLM SDK
const SYSTEM = `You are a strict retrieval judge. 
Return JSON array of {id,score,reason} with score in [0,1]. 
Score by factual support for the query; do not invent.`;

function judgingPrompt(query: string, cands: {id:string,text:string}[]) {
  const body = cands.map((c,i)=>`[${i}] id=${c.id}\n${c.text}`).join("\n\n");
  return `Query: ${query}\n\nCandidates:\n${body}\n\nRules:\n- Cite terms that match\n- Penalize off-topic\n- Prefer exact sections\n\nNow return JSON only.`;
}

// call your LLM and parse JSON; 
// accept top-k with score ≥ threshold and keep justification in logs.

Guardrails

• JSON-only response.
• Enforce max tokens and refuse long doc bodies (pass snippets only).
• Never let LLM rewrite the snippet; judge only.

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3) Tuning knobs that actually matter

• Candidate pool size (k_in): 50–200 typical. Small pool → missed gold; huge pool → latency.
• Output size (k_out): 5–20. For grounded QA, 6–8 is a sweet spot.
• Score calibration: Normalize CE outputs to [0,1]; keep per-query z-scores for audit.
• Hybrid gate: If BM25 and dense disagree drastically, log both top-5 and check Query Parsing Split.
• Dedup by doc/section: Keep at most N chunks per section to avoid overfitting to near-duplicates.

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4) Verification (don’t skip)

Metrics

• nDCG@10, MRR@10, Recall@50, and ΔS(question, top-ctx).
• Expect ΔS ≤ 0.45 after rerank on accepted top-ctx.
• Track citation hit rate (does the final answer cite a reranked chunk?).

A/B checklist

1. Freeze the first-stage retriever.
2. Compare with vs without reranker on the same gold set.
3. Record latency p95 and cost/query.
4. If nDCG@10 ↑ < +0.05 but latency doubles → not worth it.

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5) Failure modes → fixes

Symptom	Likely cause	Fix
Reranker prefers off-topic “fluent” text	Judge prompt vague / CE miscalibrated	Tighten judging schema; penalize missing query terms; normalize scores
Great demo, but prod recall tanks	k_in too small / drift	Increase k_in to 100–200; re-check recall@50
Citations merge across sources	Prompt schema unlocked	Enforce per-source fences; see SCU
Hybrid suddenly worse than dense	Tokenizers diverged	Align analyzers; log per-retriever queries; see Query Parsing Split

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6) Cost model (back-of-envelope)

• Cross-encoder base: ~3–6 ms/doc on A10g-level GPU, slower on CPU.

• For k_in=100 and p95 ~500 ms on CPU, consider:

  • shrink text by sentence-windowing,
  • use mini model,
  • pre-filter by BM25 top-60 then CE top-10.

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7) Acceptance criteria

• nDCG@10 improves by ≥ +0.05 vs baseline.
• Recall@50 unchanged (±0.02) after adding reranker (candidate pool must remain wide).
• ΔS(question, top-ctx) ≤ 0.45 and λ stays convergent on 3 paraphrases.
• Traceability: store {query, cand_id, pre_score, post_score, reason}.

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8) Example pipeline glue

def answer(query):
    cands = search(query, topk_dense=80, topk_sparse=80, out_k=60)   # from retrieval-playbook
    reranked = rerank_topk(query, cands, out_k=8)                    # CE/LLM reranker
    prompt = build_prompt(query, reranked)                           # cite → explain, fenced by section
    return call_llm(prompt)

• Do not exceed 8–10 context chunks for QA—precision collapses after that.
• Always log which reranker selected which chunk.

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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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Explore More

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Engine	WFGY 1.0	Original PDF based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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