Apache Airflow — Guardrails and Fix Patterns

Use this when your workflow is orchestrated by Apache Airflow (DAGs with PythonOperators, Sensors, KubernetesPodOperator, etc.). If pipelines succeed but the answers are wrong, citations miss, or behavior differs between ad-hoc runs and scheduled runs, anchor your diagnosis here.

Acceptance targets

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

Typical breakpoints → exact fixes

Output sounds right but cites the wrong snippet/section
Fix No.1: Hallucination & Chunk Drift →
Hallucination ·
Retrieval Playbook
High vector similarity yet wrong meaning
Fix No.5: Embedding ≠ Semantic →
Embedding ≠ Semantic
Facts are indexed but never show up in top-k (prod vs. local)
Pattern: Vectorstore Fragmentation →
Vectorstore Fragmentation
“Why this snippet?” is untraceable across tasks/pods
Fix No.8: Retrieval Traceability + snippet/citation schema →
Retrieval Traceability ·
Data Contracts
Long multi-task chains drift or flatten over time
Fix No.3/No.9: Context Drift and Entropy Collapse →
Context Drift ·
Entropy Collapse
DAG runs succeed but first “cold start” call fails after deploy
Infra family: Pre-Deploy / Bootstrap / Deadlock →
Pre-Deploy Collapse ·
Bootstrap Ordering ·
Deployment Deadlock
Confident tone with wrong claims
Fix No.4: Bluffing / Overconfidence →
Bluffing

Minimal Airflow pattern with WFGY checks

A compact pattern that preserves cite-first schema, observable retrieval, and ΔS/λ validation across tasks and pods.

Task A — Ingest/OCR (PythonOperator or KubernetesPodOperator)
- Normalize PDFs/images. Drop low-confidence OCR lines.
- Emit: doc_id, section_id, text, anchors

Task B — Chunk & Index (PythonOperator)
- Semantic chunking by section/sentence. Unit-normalize embeddings.
- Persist with explicit metric flag (cosine vs inner product).

Task C — Retrieve (PythonOperator)
- Input: { question, k }
- Output: snippets[] = { snippet_id, text, source, section_id }
- Store in durable cache keyed by run_id/request_id.

Task D — Assemble & Call LLM (PythonOperator)
SYSTEM:
  Cite lines before explanation. Per-source fences. No cross-section reuse.
CONTEXT:
  <joined snippets with snippet_id + source + text>
QUESTION:
  <user question>

Task E — WFGY Post-check (PythonOperator)
- Body: { question, context, answer }
- Compute ΔS and λ; measure coverage.
- Emit: { deltaS, lambda, coverage, notes }

Task F — Gate & Notify (BranchPythonOperator)
IF deltaS ≥ 0.60 OR lambda != "→"
  → route to remediation with trace table (snippet_id↔citation)
ELSE
  → deliver { answer, citations[], deltaS, lambda, coverage }

Reference specs: RAG Architecture & Recovery · Retrieval Playbook · Retrieval Traceability · Data Contracts

Airflow-specific gotchas

K8sPodOperator image skew: embeddings or tokenizers differ between images. Pin exact versions and verify unit normalization at write/read. See Embedding ≠ Semantic
Race between retriever warm-up and first LLM call: scheduled runs start before vectorstore is hydrated. Add bootstrap fences and health checks. See Bootstrap Ordering · Pre-Deploy Collapse
Unbounded prompt assembly inside operators: prompt fields drift. Centralize schema in one utility and import it across tasks to keep order stable. See Retrieval Traceability
Large artifacts in XCom: truncate prompts or lose attachments. Store blobs (snippets, PDFs) in object storage; pass only request_id through XCom.
MMR/rerank differences by environment: ensure identical tokenizer/analyzer across retriever and reranker, then lock ordering after per-source ΔS ≤ 0.50. See Rerankers

When to escalate

ΔS remains ≥ 0.60 after chunk/retrieval fixes → rebuild index with explicit metric flag, verify cosine vs. IP end-to-end, and re-probe ΔS vs k (aim ≤ 0.45). Retrieval Playbook
Session-to-session flips between backfills and scheduled runs → stamp and check mem_rev/mem_hash and pin image digests per task. Patterns: Memory Desync

🔗 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

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 —
⭐ WFGY Engine 2.0 is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the Unlock Board.

9.6 KiB Raw Blame History Unescape Escape