WFGY/ProblemMap/patterns/pattern_query_parsing_split.md

Pattern — Query Parsing Split (Multi-Intent / Wrong Sub-Intent First)

Scope
A single user query actually contains multiple intents (lookup + policy + transformation + generation), but the pipeline treats it as one retrieval/generation ask. The system answers the easiest/earliest sub-intent and ignores the rest, or mixes intents, causing off-topic retrieval and wrong acceptance decisions.

Why it matters
Multi-intent queries are common (“compare A vs B and give a summary with citations”). If you don’t split, retrieval pools and prompts blur constraints, you get false grounding, and audit trails become meaningless (“which intent did this citation serve?”).

Quick nav: Patterns Index · Examples: Example 01 · Example 03 · Eval: Precision & CHR

---

1) Signals & Fast Triage

Likely symptoms

• The answer handles only part of the question (e.g., explains A but not B or the comparison).
• Retrieved chunks mix unrelated facets (policy + tutorial + changelog) → noisy context, low CHR.
• Auditor (Example 04) flips VALID ↔ NOT_IN_CONTEXT depending on which fragment the model latched onto.
• Example 02 labels skew to query_parse_error.

Deterministic checks (no LLM)

• Separator heuristics: query contains and, vs, ;, ,, numbered lists 1) 2), or colon-scoped asks (“X: do Y, then Z”).
• Verb phase count: ≥2 finite verbs across different objects (compare, explain, implement, deploy).
• Constraint tokens: presence of at least one data intent (find, lookup, cite) and one action intent (summarize, generate, rewrite).
  If ≥2 signals → treat as multi-intent and split.

---

2) Minimal Reproducible Case

data/chunks.json:

[
  {"id":"pA#1","text":"Policy A: Only domain example.com is allowed."},
  {"id":"pB#1","text":"Policy B: Allow *.company.com and partner domains."},
  {"id":"pC#1","text":"How to edit email settings in the dashboard."}
]

User query: “Compare Policy A vs B with citations, then draft an email asking IT to switch our domain.”

Naive pipelines either:

1. Summarize A or B only, or
2. Draft the email without grounded comparison.

---

3) Root Causes

• Single-turn monolith: retrieval runs once on the whole sentence; constraints collide.
• No intent schema: pipeline can’t represent “first compare (grounded), then draft (un-grounded).”
• Prompt overloading: one template tries to do comparison + generation + policy proof.
• Acceptance gate blind: Auditor validates a claim that mixes two intents.

---

4) Standard Fix (Ordered, Minimal, Measurable)

Step 1 — Detect & Split

• Run deterministic heuristics (Section 1) to produce sub-intents with roles: COMPARE, LOOKUP, DRAFT, REWRITE, etc.
• Each sub-intent gets its own retrieval pool and acceptance rule.

Step 2 — Bind Contracts per Sub-Intent

• Evidence-only template for grounded intents (COMPARE, LOOKUP) → requires citations: [id,...].
• Free-form template for creative intents (DRAFT) → must echo the grounded summary id (handoff contract) but does not add new citations.

Step 3 — Sequence with Handoffs

• Output of grounded step → summary.claim, citations.
• Draft step may rephrase but cannot introduce new factual claims; it references the handoff id.

Step 4 — Accept or Refuse

• Accept only if grounded step is VALID (Example 04) and draft step references the correct handoff id.
• If grounded step is NOT_IN_CONTEXT, overall request returns refusal with explanation.

Step 5 — Evaluate

• Example 08: score per-intent precision and CHR; drafts are graded on schema compliance, not truth.

---

5) Reference Implementation — Python (stdlib only)

Create tools/intent_split.py.

# tools/intent_split.py -- rule-based multi-intent splitter + per-intent contracts
import re, json, os, time, urllib.request, uuid

GROUND_REFUSAL = "not in context"

def split_intents(q: str):
    text = q.strip()
    # crude separators
    parts = re.split(r"\bthen\b|;| and then | && | -> ", text, flags=re.IGNORECASE)
    intents = []
    for p in parts:
        role = "LOOKUP"
        if re.search(r"\bcompare|vs\b", p, re.IGNORECASE): role = "COMPARE"
        if re.search(r"\bdraft|email|write|generate|compose\b", p, re.IGNORECASE): role = "DRAFT"
        intents.append({"id": str(uuid.uuid4())[:8], "role": role, "text": p.strip()})
    # if single fragment but has both compare + draft keywords, split into two logical intents
    if len(intents)==1 and re.search(r"\bcompare|vs\b", text, re.IGNORECASE) and re.search(r"\bdraft|email|write|generate\b", text, re.IGNORECASE):
        intents = [
            {"id": str(uuid.uuid4())[:8], "role":"COMPARE", "text": text},
            {"id": str(uuid.uuid4())[:8], "role":"DRAFT",   "text": text}
        ]
    return intents

def retrieve(chunks, q, k=6):
    qs = set(w for w in re.split(r"\W+", q.lower()) if len(w)>=3)
    scored = []
    for c in chunks:
        toks = re.split(r"\W+", c["text"].lower())
        overlap = sum(1 for t in toks if t in qs)
        scored.append((overlap, c))
    scored.sort(key=lambda x: x[0], reverse=True)
    return [c for s,c in scored[:k]]

def build_compare_prompt(q, ctx, allowed):
    ctxs = "\n\n".join(f"[{c['id']}] {c['text']}" for c in ctx)
    return (
        "Task: Compare the two policies strictly from EVIDENCE.\n"
        "Output JSON ONLY: { claim: string, citations: [id,...] }\n"
        f"If not provable, reply exactly '{GROUND_REFUSAL}'.\n\n"
        f"Question: {q}\nEVIDENCE:\n{ctxs}\n"
    )

def build_draft_prompt(summary_json):
    return (
        "Task: Draft a short email referencing the grounded comparison.\n"
        "You MUST echo {handoff_id} exactly and MUST NOT add new policy facts.\n"
        "Output JSON ONLY: { email: string, handoff_id: string }\n\n"
        f"Grounded summary:\n{json.dumps(summary_json)}\n"
    )

def call_openai(prompt, model=os.getenv("OPENAI_MODEL","gpt-4o-mini")):
    key=os.getenv("OPENAI_API_KEY"); assert key, "OPENAI_API_KEY"
    body = json.dumps({"model":model,"messages":[{"role":"user","content":prompt}],"temperature":0}).encode()
    req  = urllib.request.Request("https://api.openai.com/v1/chat/completions", data=body, headers={"Content-Type":"application/json","Authorization":f"Bearer {key}"})
    with urllib.request.urlopen(req) as r:
        j=json.loads(r.read().decode()); return j["choices"][0]["message"]["content"].strip()

def parse_json(text):
    s=text.find("{"); e=text.rfind("}")
    if s<0 or e<=s: return None
    try: return json.loads(text[s:e+1])
    except: return None

def run(q, chunks):
    turns=[]
    intents = split_intents(q)
    handoff=None
    for it in intents:
        if it["role"] in ("LOOKUP","COMPARE"):
            ctx = retrieve(chunks, it["text"], k=6)
            allowed = [c["id"] for c in ctx]
            out = parse_json(call_openai(build_compare_prompt(it["text"], ctx, allowed)))
            if not out or (isinstance(out, dict) and out.get("claim","").strip().lower()==GROUND_REFUSAL):
                return {"status":"REFUSAL", "reason":"grounding_failed"}
            # schema & scope checks
            if not set(out.get("citations",[])).issubset(set(allowed)):
                return {"status":"REJECT", "reason":"citation_out_of_scope"}
            handoff = {"handoff_id": str(uuid.uuid4())[:8], "summary": out}
            turns.append({"intent": it, "ctx_ids": allowed, "out": out, "handoff_id": handoff["handoff_id"]})
        elif it["role"]=="DRAFT":
            if not handoff: return {"status":"REJECT", "reason":"draft_without_grounding"}
            draft = parse_json(call_openai(build_draft_prompt({"handoff_id": handoff["handoff_id"], **handoff["summary"]})))
            if not draft or draft.get("handoff_id") != handoff["handoff_id"]:
                return {"status":"REJECT", "reason":"handoff_mismatch"}
            turns.append({"intent": it, "out": draft, "handoff_id": handoff["handoff_id"]})
    return {"status":"OK", "turns": turns}

if __name__=="__main__":
    chunks = json.load(open("data/chunks.json",encoding="utf8"))
    print(json.dumps(run("Compare Policy A vs B with citations, then draft an email asking IT to switch our domain.", chunks), indent=2))

Pass criteria

• For the sample query, the first turn is a grounded COMPARE with citations to pA#1/pB#1.
• The DRAFT turn echoes the handoff_id and contains no new policy facts.
• If comparison is not in context, overall REFUSAL (no email is drafted).

---

6) Node Quick Variant (split only, no LLM call)

Create tools/intent_split.mjs.

// tools/intent_split.mjs -- detect multi-intent; emit a small plan
export function splitIntents(q){
  const text = q.trim();
  const cuts = text.split(/(?:\bthen\b|;| and then | && | -> )/i).map(s=>s.trim()).filter(Boolean);
  const parts = cuts.length ? cuts : [text];
  return parts.map(p=>{
    let role = "LOOKUP";
    if (/\bcompare|vs\b/i.test(p)) role = "COMPARE";
    if (/\bdraft|email|write|generate|compose\b/i.test(p)) role = "DRAFT";
    return { role, text: p };
  });
}

// CLI
if (import.meta.url === `file://${process.argv[1]}`) {
  const q = process.argv.slice(2).join(" ");
  console.log(JSON.stringify(splitIntents(q), null, 2));
}

---

7) Acceptance Criteria (ship/no-ship)

A multi-intent response may ship only if:

1. Each grounded sub-intent has citations ⊆ retrieved_ids and passes Auditor VALID.
2. Creative sub-intents (draft/rewrite) echo a valid handoff_id from a VALID grounded step.
3. If any grounded sub-intent returns not in context, the overall request refuses (no partial answers).
4. Example 08 per-intent gates pass (CHR for grounded, compliance for drafts).

---

8) Prevention (contracts & defaults)

• Query schema: role: {COMPARE|LOOKUP|DRAFT|REWRITE}, text, optional constraints.
• Router default: split when ≥2 deterministic signals fire; otherwise single-intent.
• Template isolation: distinct prompts per role; never mix compare + draft in the same prompt.
• UI hinting: suggest quick toggles (“Compare” / “Draft”) for power users; cut ambiguity at the source.

---

9) Debug Workflow (10 minutes)

1. Run the splitter; print the plan.
2. Execute grounded step(s) first and log citations.
3. Ensure draft step references a real handoff_id.
4. If grounded fails → return refusal; do not proceed.
5. Re-score with Example 08; CHR should improve while over-refusal stays controlled.

---

10) Common Traps & Fixes

• Draft first temptation → ungrounded emails. Always ground before drafting.
• One big retrieval for all roles → tail noise. Retrieve per role.
• Auditor on drafts → meaningless. Audit only grounded claims; drafts check schema and handoff.
• Partial shipping (“we answered the easy half”) → inconsistent UX. Refuse on missing grounded parts.

---

11) Minimal Checklist (copy into PR)

• Split multi-intent queries deterministically into roles.
• Grounded steps use evidence-only template + citations.
• Drafts echo handoff_id; no new facts.
• Acceptance gate enforces per-role rules; no partial ship.
• Example 08 gates pass per intent.

---

References to hands-on examples

• Example 01 — Guarded baseline (evidence-only + refusal)
• Example 02 — Reflection triage (query_parse_error)
• Example 03 — Retrieval stabilization for each sub-intent
• Example 04 — Acceptance gate (Scholar/Auditor + handoff)
• Example 08 — Eval per intent (CHR for grounded, compliance for drafts)

---

🔗 Quick-Start Downloads (60 sec)

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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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