# Live monitoring & alerting — RAG services **Goal:** list of recommended metrics, alert rules and dashboard panels to keep RAG pipelines observable and actionable. --- ## Core metrics to collect (recommended names) **Service-level** - `rag_e2e_latency_seconds` (histogram) — E2E latency (request in → answer out) - `rag_error_count_total` — errors per endpoint - `rag_request_count_total` — total requests **Retrieval-level** - `retriever_qps_total` - `retriever_retrieved_docs_count` (per request) - `retriever_empty_result_count_total` — unexpected empty sets **Vectorstore** - `vectorstore_index_load_time_seconds` - `vectorstore_memory_bytes` - `vectorstore_indexed_docs_total` **Accuracy/provenance** - `rag_citation_hit_rate` (CHR gauge over sliding window) - `rag_precision_shipped` (periodic batch scorer push) - `rag_under_refusal_count_total` **Infrastructure** - `llm_api_rate_limited_total` - `llm_api_error_total` - `queue_backlog_count` (if using background queues) --- ## Suggested PromQL alerts (examples) > Tune thresholds to your workload. **A) Latency breach (interactive)** ```yaml alert: RAGHighP95Latency expr: histogram_quantile(0.95, sum(rate(rag_e2e_latency_seconds_bucket[5m])) by (le,instance)) > 2 for: 5m labels: severity: page annotations: summary: "RAG p95 > 2s ({{ $labels.instance }})" ```` **B) Error spike** ```yaml alert: RAGErrorSpike expr: increase(rag_error_count_total[5m]) > 50 for: 2m labels: { severity: page } ``` **C) Retriever empty results** ```yaml alert: RetrieverEmptyResults expr: increase(retriever_empty_result_count_total[5m]) > 1 for: 5m labels: { severity: ticket } ``` **D) CHR drop** ```yaml alert: CHRDrop expr: rag_citation_hit_rate < 0.6 for: 10m labels: { severity: ticket } ``` **E) LLM auth failure** ```yaml alert: LLMAuthFail expr: increase(llm_api_error_total{code="401"}[5m]) > 0 for: 1m ``` --- ## Dashboard panels (recommended) 1. E2E latency (p50/p95/p99) trend. 2. Requests per second and error rate. 3. Retriever QPS, avg retrieved docs, empty results. 4. CHR & Precision (batch scorer push). 5. Vectorstore memory & disk IO. 6. LLM provider error & rate-limit metrics. --- ## Incident play (fast actions) 1. If CHR drop → run **diagnostic retrieval** for 10 golden queries (retrieved ids + cosine scores). 2. If retriever empty → check vectorstore health and index partitions. Restart index shard if needed. 3. If E2E latency spike with LLM errors → throttle traffic, put a hard rate limit and rollback deploy if needed. 4. If LLM auth failure → rotate key & redeploy secrets. --- ## How to integrate scoring metrics * Periodic scorer job should push `rag_citation_hit_rate` and `rag_precision_shipped` as a short-timeseries gauge (per 5–15m window). * Use batching: run `score_eval.py` (see `ProblemMap/eval/README.md`) nightly and push summary metrics via a small exporter. --- ## Troubleshooting queries (prometheus examples) * Check p95 per instance: ```promql histogram_quantile(0.95, sum(rate(rag_e2e_latency_seconds_bucket[5m])) by (le,instance)) ``` * CHR trend: ```promql avg_over_time(rag_citation_hit_rate[30m]) ``` --- ### Links * Deployment checklist → [deployment\_checklist.md](./deployment_checklist.md) * Debug playbook → [debug\_playbook.md](./debug_playbook.md) * Eval & scoring → [../eval/README.md](../eval/README.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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