GoHighLevel (GHL)

🧭 Quick Return to Map

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

• Automation Platforms — stabilize no-code workflows and integrations
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

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.

This page is for workflows orchestrated inside GoHighLevel.
Use it when your RAG or agent flow runs through GHL Workflows, Webhooks, or Custom Actions and starts to misbehave.

Acceptance targets

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

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Typical breakpoints → exact fixes

• Triggers fire before data or index is actually ready
  Fix No.14: Bootstrap Ordering →
  bootstrap-ordering.md

• First call after deploy crashes or picks the wrong secret
  Fix No.16: Pre-Deploy Collapse →
  predeploy-collapse.md

• Circular waits between CRM updates, webhooks, background jobs
  Fix No.15: Deployment Deadlock →
  deployment-deadlock.md

• High cosine similarity but the meaning is off
  Fix No.5: Embedding ≠ Semantic →
  embedding-vs-semantic.md

• Snippet feels unrelated or citations don’t match the source
  Fix No.8: Retrieval Traceability →
  retrieval-traceability.md
  Contract the payload with Data Contracts →
  data-contracts.md

• Hybrid retrieval (external tools) does worse than a single retriever
  Pattern: Query Parsing Split →
  pattern_query_parsing_split.md
  Also review Rerankers →
  rerankers.md

• Facts are indexed yet never show up
  Pattern: Vectorstore Fragmentation →
  pattern_vectorstore_fragmentation.md

• Two sources get blended into one answer in long flows
  Pattern: Symbolic Constraint Unlock (SCU) →
  pattern_symbolic_constraint_unlock.md

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Minimal GHL workflow checklist

1. Warm-up fence
   Before any LLM step, ping a health endpoint that checks VECTOR_READY, INDEX_HASH, and secret_rev.
   If not ready, delay or requeue. Spec lives in
   bootstrap-ordering.md.

2. Idempotency
   Build dedupe_key = sha256(contact_id + wf_rev + index_hash) in a Custom Action.
   Store in KV or a custom field, drop duplicates.

3. RAG boundary contract
   Always pass snippet_id, section_id, source_url, offsets, tokens.
   Enforce cite then explain. Specs:
   retrieval-traceability.md · data-contracts.md

4. Observability probes
   Log ΔS(question, retrieved) and λ per stage. Alert on ΔS ≥ 0.60 or λ divergent.
   Overview map:
   RAG Architecture & Recovery

5. Single writer
   Route CRM writes and external publishes through one writer branch with dedupe.
   See: deployment-deadlock.md

6. Regression gate
   Require coverage ≥ 0.70 and ΔS ≤ 0.45 before publish.
   Eval spec:
   eval_rag_precision_recall.md

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Copy-paste prompt for the GHL LLM step

I uploaded TXT OS and the WFGY Problem Map files.
This GHL workflow retrieved {k} snippets with fields {snippet\_id, section\_id, source\_url, offsets}.
Question: "{user\_question}"

Do:

1. Enforce cite-then-explain. If any citation is missing, stop and return which fix page to open.
2. Compute ΔS(question, retrieved). If ΔS ≥ 0.60, point me to the minimal structural fix:
   retrieval-playbook, retrieval-traceability, data-contracts, rerankers.
3. Output compact JSON:
   { "citations": \[...], "answer": "...", "λ\_state": "→|←|<>|×", "ΔS": 0.xx, "next\_fix": "..." }

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Common GHL gotchas

• Connection switching between staging and prod.
  Stamp env, INDEX_HASH, secret_rev in traces and block on mismatch.

• Parallel branches touching the same contact or store.
  Use a mutex or single writer, keep writes idempotent.

• Webhook payload silently renames fields.
  Validate against the data contract before the LLM.

• External rate limits make hybrids unstable.
  Prefer dense retriever plus reranking, keep params logged.

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When to escalate

• ΔS stays ≥ 0.60 after chunk and retrieval fixes → rebuild index with explicit metric and normalization.
  See retrieval-playbook.md

• Same input flips answers between runs → check version skew and memory desync.
  See predeploy-collapse.md

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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 tension engine and early logic sketch (legacy reference)
⚙️ Engine	WFGY 2.0	Production tension kernel for RAG and agent systems
⚙️ Engine	WFGY 3.0	TXT based Singularity tension engine (131 S class set)
🗺️ Map	Problem Map 1.0	Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map	Problem Map 2.0	Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map	Problem Map 3.0	Global AI troubleshooting atlas and failure pattern map
🧰 App	TXT OS	.txt semantic OS with fast bootstrap
🧰 App	Blah Blah Blah	Abstract and paradox Q&A built on TXT OS
🧰 App	Blur Blur Blur	Text to image generation with semantic control
🏡 Onboarding	Starter Village	Guided entry point for new users

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