WFGY/ProblemMap/GlobalFixMap/Automation/zapier.md
2025-09-05 10:13:45 +08:00

9.2 KiB
Raw Blame History

Zapier Guardrails and Patterns

🧭 Quick Return to Map

You are in a sub-page of Automation Platforms.
To reorient, go back here:

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

Use this page when your RAG or agent flow runs in Zapier. It routes common automation failures to the exact structural fixes in the Problem Map and gives a minimal recipe you can paste into a Zap.

Core acceptance

  • ΔS(question, retrieved) ≤ 0.45
  • coverage ≥ 0.70 for the target section
  • λ stays convergent across 3 paraphrases

Typical breakpoints and the right fix

  • Tools fire before dependencies are ready
    Fix No.14: Bootstrap OrderingOpen

  • First call after deploy crashes or uses wrong version
    Fix No.16: Pre-Deploy CollapseOpen

  • Circular waits between index and retriever or auth loops
    Fix No.15: Deployment DeadlockOpen

  • High vector similarity but wrong meaning
    Fix No.5: Embedding ≠ SemanticOpen

  • Wrong snippet selected or citations do not line up
    Fix No.8: Retrieval TraceabilityOpen
    Contract the payload: Data ContractsOpen

  • Hybrid retrieval performs worse than a single retriever
    Pattern: Query Parsing SplitOpen
    Also review: RerankersOpen

  • Webhook storms or duplicate executions
    Pattern: Bootstrap DeadlockOpen


Minimal setup checklist for any Zap

  1. Warm-up fence before RAG or LLM steps
    Validate VECTOR_READY == true, INDEX_HASH matches, and secrets exist.
    If not ready, short-circuit with a Delay and retry with capped backoff.
    Spec: Bootstrap Ordering

  2. Idempotency and dedupe
    Compute dedupe_key = sha256(source_id + revision + index_hash).
    Use Zapier Storage by Zap or an external KV to drop duplicates.

  3. RAG boundary contract
    Require fields: snippet_id, section_id, source_url, offsets, tokens.
    Enforce cite-then-explain. Forbid cross-section reuse.
    Specs: Data Contracts · Retrieval Traceability

  4. Observability probes
    Log ΔS(question, retrieved). Log λ per step: retrieve, assemble, reason.
    Alert when ΔS ≥ 0.60 or λ flips divergent.
    Overview: RAG Architecture & Recovery

  5. Regression gate
    Require coverage ≥ 0.70 and ΔS ≤ 0.45 before publishing the Zap.
    Eval: RAG Precision/Recall


Zapier recipe you can copy

Replace the concrete tools with your stack. Keep the guardrails.

  1. Trigger
    Stable source_id and revision.

  2. Warm-up Check
    Code step pulls INDEX_HASH, VECTOR_READY, secrets.
    If not ready, set ready=false.

  3. Path: Not ready
    Delay 3090 seconds.
    Re-run with a capped retry count.

  4. Path: Ready
    Retrieval step

    • Call the retriever with explicit metric and consistent analyzer.
    • Collect snippet_id, section_id, source_url, offsets, tokens.
      ΔS probe step
    • Compute ΔS(question, retrieved). If ΔS ≥ 0.60 set needs_fix=true.
      Reasoning step
    • LLM reads TXT OS and uses the WFGY schema. Enforce cite-then-explain.
      Trace sink
    • Store question, snippet_id, ΔS, λ_state, INDEX_HASH, dedupe_key.
      Idempotency guard
    • Before side effects, check KV for dedupe_key. If exists, skip.

Copy-paste prompt for the LLM step


I uploaded TXT OS and the WFGY Problem Map pages.
My Zapier flow retrieved {k} snippets with fields {snippet\_id, section\_id, source\_url, offsets}.
Question: "{user\_question}"

Do:

1. Validate cite-then-explain. If citations are missing, fail fast and return the fix tip.
2. If ΔS(question, retrieved) ≥ 0.60, propose the minimal structural fix referencing:
   retrieval-playbook, retrieval-traceability, data-contracts, rerankers.
3. Return a JSON plan:
   { "citations": \[...], "answer": "...", "λ\_state": "→|←|<>|×", "ΔS": 0.xx, "next\_fix": "..." }
   Keep it auditable and short.


Common Zapier gotchas

  • Formatter renames fields and breaks your data contract
    Lock field names. Verify with a schema check step.

  • Parallel paths write to the same index or KV without a fence
    Use a single writer or a queue. Apply idempotency keys.

  • HyDE prompt created inside Zap differs from the API client
    Keep tokenizer and casing identical, or switch to reranking.
    See: Rerankers


When to escalate

  • ΔS stays ≥ 0.60 after chunk and retrieval fixes
    Rebuild index with explicit metric and normalization.
    See: Retrieval Playbook

  • Answers alternate across Zap runs with identical input
    Investigate memory desync and version skew.
    See: Pre-Deploy Collapse


🔗 Quick-Start Downloads (60 sec)

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

🧭 Explore More

Module Description Link
WFGY Core WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack View →
Problem Map 1.0 Initial 16-mode diagnostic and symbolic fix framework View →
Problem Map 2.0 RAG-focused failure tree, modular fixes, and pipelines View →
Semantic Clinic Index Expanded failure catalog: prompt injection, memory bugs, logic drift View →
Semantic Blueprint Layer-based symbolic reasoning & semantic modulations View →
Benchmark vs GPT-5 Stress test GPT-5 with full WFGY reasoning suite View →
🧙‍♂️ Starter Village 🏡 New here? Lost in symbols? Click here and let the wizard guide you through Start →

👑 Early Stargazers: See the Hall of Fame
GitHub stars WFGY Engine 2.0 is already unlocked. Star the repo to help others discover it and unlock more on the Unlock Board.

WFGY Main   TXT OS   Blah   Blot   Bloc   Blur   Blow