WFGY/ProblemMap/retrieval-playbook.md

🔎 Retrieval Playbook — Practical, Measurable, Fix-first

The goal: consistent, explainable retrieval that makes reasoning easy.
This playbook gives you a minimal, testable setup across OCR → chunk → embed → index → retrieve → prompt, with failure probes and repair steps. No hype—only what ships.

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Quick Nav
OCR/Parsing Checklist · Chunking Checklist · Embedding vs Semantic · Traceability · Rerankers · Patterns: Query Parsing Split · Vectorstore Fragmentation · Symbolic Constraint Unlock

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0) Executive summary

1. Generate clean candidates (dense/sparse/hybrid) with traceable IDs.
2. Measure with ΔS + recall@k before adding complexity.
3. Add reranking only when first-stage recall is consistently ≥0.85 for your task.
4. Lock prompt schema (cite → explain) and forbid cross-source merges.
5. Regression-guard with small golden sets (10–50 Q/A).

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1) Candidate generation (first-stage)

1.1 Choose a primary retriever

• Dense (embeddings): good default for semantic matches across paraphrases.
• Sparse (BM25/SPLADE): strong for exact terms, code, and rare tokens.
• Hybrid: reciprocal rank fusion (RRF) of dense + sparse is robust to query style.

Rule of thumb

• Start dense for general docs; add BM25 for code/legal/IDs.
• If hybrid hurts recall, check tokenization & analyzer drift → see Query Parsing Split.

1.2 Index hygiene (FAISS/Elasticsearch/Qdrant/Chroma)

• Normalize vectors on both write & read if using cosine.
• Pin the metric type (cosine vs inner product) in code & metadata.
• Persist doc_id / section_id / line_span with each vector.
• Verify index cardinality = sum(chunks); add a one-liner count check in CI.

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2) Minimal reference pipelines

2.1 Python — FAISS (dense) + BM25 (hybrid via RRF)

# pip install sentence-transformers rank_bm25 faiss-cpu numpy
from sentence_transformers import SentenceTransformer
from rank_bm25 import BM25Okapi
import faiss, numpy as np

# 1) data
chunks = [...]                  # list[str], pre-chunked sentences/sections
meta   = [...]                  # list[dict], each with {doc_id, section_id, span}

# 2) encoder (cosine → L2-normalize)
enc = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
X = enc.encode(chunks, normalize_embeddings=True)
index = faiss.IndexFlatIP(X.shape[1])         # inner product == cosine on normalized vectors
index.add(X.astype(np.float32))

# 3) sparse side
tokenized = [c.split() for c in chunks]
bm25 = BM25Okapi(tokenized)

def rrf(ranks, k=60):
    # ranks: list of lists of (idx, rank_position starting at 1)
    from collections import defaultdict
    score = defaultdict(float)
    for rr in ranks:
        for idx, rp in rr:
            score[idx] += 1.0 / (k + rp)
    return sorted(score.items(), key=lambda x: -x[1])

def search(query, topk_dense=50, topk_sparse=50, out_k=20):
    qv = enc.encode([query], normalize_embeddings=True).astype(np.float32)
    d_s, d_i = index.search(qv, topk_dense)
    # ranks as (idx, rankpos)
    dense_rank  = [(int(i), r+1) for r, i in enumerate(d_i[0])]
    sparse_rank = []
    for r, idx in enumerate(bm25.get_top_n(query.split(), list(range(len(chunks))), n=topk_sparse)):
        sparse_rank.append((idx, r+1))
    fused = rrf([dense_rank, sparse_rank])
    out = []
    for idx, _ in fused[:out_k]:
        out.append({"text": chunks[idx], "meta": meta[idx], "source":"hybrid"})
    return out

res = search("how to reset billing cycle")

Sanity checks

• After encoding: assert np.abs(np.linalg.norm(X[0]) - 1.0) < 1e-3
• After add: index.ntotal == len(chunks)
• For sparse-only: if IDs/code outperform dense, keep hybrid.

2.2 Node (TypeScript) — Elastic BM25 + Dense store

// pnpm add @elastic/elasticsearch @huggingface/inference cosine-similarity
import { Client } from "@elastic/elasticsearch";
import { HfInference } from "@huggingface/inference";
import cosine from "cosine-similarity";

const es = new Client({ node: process.env.ES_URL! });
const hf = new HfInference(process.env.HF_TOKEN!);

// 1) BM25 query
async function bm25(query: string, topk=50) {
  const { hits } = await es.search({
    index: "chunks",
    size: topk,
    query: { match: { text: { query, operator: "and" } } },
    _source: ["text", "doc_id", "section_id", "span"]
  });
  return hits.hits.map((h, i) => ({ id: h._id, score: hits.max_score, r:i+1, src: h._source }));
}

// 2) Dense side (use same model for write+read)
async function embed(s: string) {
  const out = await hf.featureExtraction({
    model: "sentence-transformers/all-MiniLM-L6-v2",
    inputs: s
  });
  // L2 normalize
  const v = (out as number[]); 
  const norm = Math.sqrt(v.reduce((a,b)=>a+b*b,0)); 
  return v.map(x=>x/norm);
}

Keep dense vectors in your KV/DB keyed by chunk_id; at query time compute cosine vs a small candidate pool (top-200 BM25), then RRF.

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3) Retrieval observability (ΔS & λ)

• ΔS(question, retrieved) = 1 − cos(I, G) with I = retrieved snippet embedding, G = anchor (title/expected section or gold answer).
• Thresholds: <0.40 stable · 0.40–0.60 transitional · ≥0.60 action.
• λ states: → convergent · ← divergent · <> recursive · × chaotic.

Probe recipe

1. Vary k ∈ {5, 10, 20}; plot ΔS vs k.
2. If curve flat & high → metric/normalization/index mismatch.
3. If sharp drop at higher k → retriever filter too strict; consider MMR or hybrid.

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4) Prompt assembly: cite → explain (lock constraints)

• Keep per-source fences (no cross-source merges).
• Order: system → task → constraints → citations → answer.
• Force cite-first; explanation must reference citation IDs/lines.
• See: Traceability and SCU Pattern.

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5) Reranking: when & how

Add a reranker only if:

• Recall@50 ≥ 0.85, but Top-5 precision is weak, or
• You need tight citation alignment across near-duplicates.

Start with:

• Cross-encoder (bge-reranker-mini/base) for accuracy;
• Or LLM rerank for low volume, high precision needs. See: Rerankers.

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6) Acceptance criteria

• Retrieval sanity: ΔS(question, top-ctx) ≤ 0.45, coverage ≥ 0.70 of target section.
• Traceability: snippet ↔ citation table reproducible.
• Stability: same inputs over 3 paraphrases keep λ → convergent.
• No SCU: who-said-what does not merge across sources.

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7) Common failures → repair

Symptom	Likely cause	Fix
Hybrid worse than single	Analyzer/tokenizer split	Align analyzers; log per-retriever queries; see Query Parsing Split
Some facts never retrieved	Fragmented store / id skew	Rebuild + shard audit; see Vectorstore Fragmentation
Citations cross-bleed	Prompt schema unlocked	Per-source fences + cite-first; see SCU
ΔS flat & high vs k	Metric/normalization mismatch	Normalize embeddings; pin FAISS metric; see Embedding vs Semantic

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8) Tiny gold set (do this!)

Create 10–50 realistic Q/A with citation lines. Commit a goldset.jsonl:

{"q":"How to reset billing cycle?","doc_id":"a","section":"billing","lines":[120,145],"a":"..."}

Run recall@50, nDCG@10, and ΔS on each PR.

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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

Layer	Page	What it’s for
Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
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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