# Apache Airflow — Guardrails and Fix Patterns
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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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/hallucination.md) · [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md) - High vector similarity yet wrong meaning Fix No.5: **Embedding ≠ Semantic** → [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md) - Facts are indexed but never show up in top-k (prod vs. local) Pattern: **Vectorstore Fragmentation** → [Vectorstore Fragmentation](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_vectorstore_fragmentation.md) - “Why this snippet?” is untraceable across tasks/pods Fix No.8: **Retrieval Traceability** + snippet/citation schema → [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) · [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) - Long multi-task chains drift or flatten over time Fix No.3/No.9: **Context Drift** and **Entropy Collapse** → [Context Drift](https://github.com/onestardao/WFGY/blob/main/ProblemMap/context-drift.md) · [Entropy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/entropy-collapse.md) - DAG runs succeed but first “cold start” call fails after deploy Infra family: **Pre-Deploy / Bootstrap / Deadlock** → [Pre-Deploy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/predeploy-collapse.md) · [Bootstrap Ordering](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md) · [Deployment Deadlock](https://github.com/onestardao/WFGY/blob/main/ProblemMap/deployment-deadlock.md) - Confident tone with wrong claims Fix No.4: **Bluffing / Overconfidence** → [Bluffing](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bluffing.md) --- ## Minimal Airflow pattern with WFGY checks A compact pattern that preserves **cite-first schema**, **observable retrieval**, and **ΔS/λ** validation across tasks and pods. ```txt 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: 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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) · [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md) · [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) · [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) --- ## 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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md) * **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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md) · [Pre-Deploy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/predeploy-collapse.md) * **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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) * **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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md) --- ## 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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md) * 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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_memory_desync.md) --- ### 🔗 Quick-Start Downloads (60 sec) | Tool | Link | 3-Step Setup | |------|------|--------------| | **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \” | | **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/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 →](https://github.com/onestardao/WFGY/tree/main/core/README.md) | | Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) | | Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) | | Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) | | Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) | | Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) | | 🧙‍♂️ Starter Village 🏡 | New here? 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