vrr/WFGY
2
0
Fork 0
mirror of https://github.com/onestardao/WFGY.git synced 2026-04-26 10:40:55 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 4
WFGY/ProblemMap/GlobalFixMap/Automation/airtable.md

8.7 KiB
Raw Blame History

Airtable — Guardrails and Fix 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 when your pipeline uses Airtable as the control plane or as the source-of-truth table for RAG/agents, and you see record drift, duplicated actions, or citations that don’t map back to records.

Acceptance targets

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

Typical breakpoints → exact fixes

  • Automations/webhooks fire before embeddings/index finish updating
    Fix No.14: Bootstrap Ordering →
    Bootstrap Ordering

  • First run after deploy reads wrong base or missing secret
    Fix No.16: Pre-Deploy Collapse →
    Pre-Deploy Collapse

  • Cross-table syncs create circular waits (record-upsert → external job → back to record)
    Fix No.15: Deployment Deadlock →
    Deployment Deadlock

  • High cosine similarity, wrong meaning (good vector match, bad semantic match)
    Fix No.5: Embedding ≠ Semantic →
    Embedding ≠ Semantic

  • “Why this snippet?” cannot be explained; citations don’t line up with source cells
    Fix No.8: Retrieval Traceability →
    Retrieval Traceability
    Standardize fields with Data Contracts →
    Data Contracts

  • Hybrid retrieval (dense + formula/filter views + external reranker) gets worse than single retriever
    Pattern: Query Parsing Split →
    Query Parsing Split
    Also review Rerankers →
    Rerankers

  • Facts are in the base but never retrieved
    Pattern: Vectorstore Fragmentation →
    Vectorstore Fragmentation

  • Two different records are merged into one narrative in the summary
    Pattern: Symbolic Constraint Unlock (SCU) →
    Symbolic Constraint Unlock

Minimal Airtable workflow checklist

  1. Warm-up fence
    Verify VECTOR_READY, INDEX_HASH, secret_rev, and that base_id/table_id/view_id resolve before any LLM step.
    Spec: Bootstrap Ordering

  2. Idempotency
    Create dedupe_key = sha256(record_id + wf_rev + index_hash) and store it (hidden field or external KV).
    Reject duplicate writes/retries.

  3. RAG boundary contract
    Pass record_id, base_id, table_id, view_id, field_map, source_url, offsets, tokens.
    Enforce cite-then-explain. Specs:
    Retrieval Traceability · Data Contracts

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

  5. Schema stability
    Avoid free-form field renames that break downstream contracts. Pin with schema_rev and check it at runtime.

  6. Regression gate
    Require coverage ≥ 0.70 and ΔS ≤ 0.45 before posting back into Airtable.
    Eval spec: RAG Precision/Recall

Copy-paste prompt for the Airtable LLM step


I uploaded TXT OS and the WFGY Problem Map files.
Airtable context:

* base\_id: {base}
* table\_id: {table}
* view\_id: {view}
* record\_id(s): {rids}
* fields: {field\_map}
  Question: "{user\_question}"

Do:

1. Enforce cite-then-explain. If any citation lacks record\_id/section/offsets, stop and tell me 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":\[{"record\_id":"...", "field":"...", "offsets":\[s,e]}],
   "answer":"...", "λ\_state":"→|←|<>|×", "ΔS":0.xx, "next\_fix":"..." }

Common Airtable gotchas

  • Formula fields or lookup/rollup not updated yet when webhook fires
    Add a delay or readiness probe; gate on schema_rev + index_hash.

  • Pagination/backfill causes missed embeddings
    Log the cursor; re-ingest until the cursor is exhausted; compare counts vs. expected.

  • Field renames break contracts silently
    Pin schema_rev; fail fast if it changes; include field_map in traces.

  • Attachment/text mix leads to partial content**
    Normalize: extract attachments to text with a fixed OCR gate before embedding.

  • Rate limits destabilize hybrid retrieval
    Prefer dense retriever + reranking; keep per-retriever params in logs.

When to escalate

  • ΔS stays ≥ 0.60 after chunk/retrieval fixes → rebuild index with explicit metric/normalization.
    See: Retrieval Playbook

  • Same inputs, different answers on different runs → check version skew and memory desync.
    See: Pre-Deploy Collapse

🔗 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

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

If this repository helped, starring it improves discovery so more builders can find the docs and tools.
GitHub Repo stars