# Failover & recovery — deterministic recovery steps **Purpose:** deterministic operator steps to failover or recover critical components (vectorstore, retriever, generator, indexer, controller). Aim to reduce data loss and return to safe state quickly. --- ## Basic principles 1. **Fail fast to a safe mode** — prefer read-only answers or cached responses over uncontrolled writes or risky LLM calls. 2. **Preserve evidence** — do not truncate logs or delete index segments until investigation complete. 3. **Prefer scoped recovery** — restart single pod/shard before cluster-wide actions. --- ## Scenario A: Vectorstore shard down / index corrupt **Symptoms** - Retriever returns empty sets or inconsistent scores for golden queries. - Vectorstore pod logs show IO / index errors. **Steps** 1. Mark the shard unhealthy in the service registry (so retriever avoids it). 2. If replica exists, route traffic to other replica. 3. Attempt graceful re-open: ```bash kubectl -n $NS exec deploy/vectorstore -- /bin/sh -c "ctl index reopen shard-5" 4. If reopen fails, restore from latest snapshot (S3) to a new shard: * Create new PV and restore snapshot. * Start fresh pod pointed to restored PV. 5. Re-run small validation suite (10–50 golden qids) before reintroducing shard. **Post recovery** * Re-index missing docs if necessary; track reindex job progress. * Add a postmortem entry and schedule a permanent fix. --- ## Scenario B: Generator (LLM) provider outage **Symptoms** * LLM errors (5xx), rate-limit responses, or auth failures. **Steps** 1. Switch to backup LLM provider (if configured) via config flag: ```bash # toggle provider in config map or feature flag kubectl -n $NS set env deploy/rag-api PROVIDER=backup-provider ``` 2. If no backup, enable local fallback: * Return cached answers for known qids. * Return safe refusal for unknown qids. 3. Throttle traffic and backlog long-running requests to a worker queue. 4. Once provider restored, slowly ramp traffic and compare CHR/precision to baseline. --- ## Scenario C: Bootstrap deadlock at startup **Symptoms** * Pods stuck in CrashLoopBackOff or `Ready` never true; logs show circular dependency or missing migration. **Steps** 1. Inspect init containers & migration jobs: ```bash kubectl -n $NS get jobs kubectl -n $NS logs job/migrations ``` 2. Run migrations manually in controlled pod: ```bash kubectl -n $NS run --rm -it migration-runner --image=myimage -- bash -c "python migrate.py" ``` 3. Ensure controller component (if any) is up before starting retriever/generator. Use Helm hooks or manual `kubectl apply` ordering. 4. If necessary, scale down and start components one-by-one. --- ## Safety nets & best practices * Keep automated snapshots of vectorstore daily; keep 7–14 days retention. * Maintain a tested restore playbook and a “mini-cluster” restore test monthly. * Automate warm-failover for LLMs: pre-warm API tokens for backup providers. --- ## Post-incident * Triage root cause, assign fixes. * Add automated test that would have caught this. * Update runbooks and notify stakeholders. --- ### Links * Deployment checklist → [deployment\_checklist.md](./deployment_checklist.md) * Debug playbook → [debug\_playbook.md](./debug_playbook.md) * Live monitoring → [live\_monitoring\_rag.md](./live_monitoring_rag.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? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) | --- > 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** — > Engineers, hackers, and open source builders who supported WFGY from day one. > GitHub stars ⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
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