Deployment checklist

Purpose: a short, rigorous checklist to verify your environment and reduce bootstrap/dependency issues during deployment.

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Before you deploy (pre-flight)

1) Environment & prerequisites

• Kubernetes cluster accessible; kubectl points to correct context.

  kubectl config current-context
  kubectl get nodes
  
  
  

• Ensure cluster resources: CPU / memory / ephemeral storage for vectorstore. Confirm quotas.
• Secrets: API keys (LLM), db credentials, vectorstore creds in k8s Secret or vault.
• Helm chart / manifests: reviewed and values set for production (replicas, resources, liveness/readiness).

2) Configuration sanity

• values.yaml contains:

  • resources.requests and limits for retriever/generator.
  • replicaCount >= 2 for critical services (if expected load > small).
  • readinessProbe and livenessProbe configured.

• Vector store sizing: index_shards, disk IOPS, memory (embedding index memory).

• Network egress rules for model API (if external LLM).

3) Observability & alarms

• Prometheus scraping configured for app metrics endpoints (/metrics).
• Default dashboards in Grafana (latency, error-rate, retriever QPS, CHR).
• Alerts configured (see live_monitoring_rag.md for suggested alerts).

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Deploy steps

1. Create namespace & secrets:

   kubectl create ns rag-prod || true
   kubectl -n rag-prod apply -f k8s/secrets.yaml
   

2. Install/upgrade Helm chart:

   helm upgrade --install rag . -n rag-prod -f values.prod.yaml
   

3. Wait for pods to be ready (watch):

   kubectl -n rag-prod rollout status deploy/rag-api -w
   kubectl -n rag-prod get pods -o wide
   

4. Smoke tests (simple requests):

   curl -fsS http://<ingress>/healthz
   curl -fsS -X POST http://<ingress>/api/qa -d '{"qid":"smoke-1","q":"Who is the CEO of WFGY?" }' | jq
   

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Post-deploy checks (first 15 minutes)

• Confirm retriever returns docs for 10 sample queries:

  • Use your retrieval debug endpoint to inspect retrieved_ids.

• Confirm p95 E2E latency ≤ target (by env). Collect from Grafana or kubectl logs.

• Confirm CHR on 10 smoke items ≥ expected baseline (manually assert correctness).

• Check for error spikes in logs:

  kubectl -n rag-prod logs -l app=rag --since=10m | egrep "ERROR|WARN" | head -n 200
  

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Common config gotchas (double-check)

• Vectorstore read-only mode accidentally set? (affects writes)
• LLM rate-limiting / auth errors (wrong key or quota).
• Wrong index/namespace names between chunker and retriever (off-by-one).
• Probes misconfigured — containers get restarted continuously.

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Rollback criteria

Rollback if any of:

• P95 > target and sustained for 10m.
• Error rate > 3× baseline and not transient.
• Retrieval failures (empty pool) > 1% of requests.

Rollback command example:

helm rollback rag <previous_revision> -n rag-prod

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Quick checklist (copy/paste)

• Namespace created, secrets applied
• Helm values validated (resources, probes)
• Prometheus/Grafana dashboards in place
• Smoke tests passed (health & basic QA)
• Alerts deployed
• Canary traffic small → monitor 10–30 min

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Links

• Debug playbook → debug_playbook.md
• Live monitoring → live_monitoring_rag.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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