chore(rag): add eval knowledge base mirror

knowledge_base/rag-eval/README.md

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+# RAG Eval Knowledge Base Mirror
+
+This folder stores the committed knowledge-base copy of the canonical RAG eval
+documents.
+
+The source of truth for the eval importer remains:
+
+```text
+eval/rag-retrieval/seed-docs/
+```
+
+The documents are kept under `knowledge_base/rag-eval/` so eval/test knowledge
+does not mix with the normal business knowledge folders such as `api`,
+`infrastructure`, or `troubleshooting`.
+
+Each document keeps its original frontmatter `category` and `kb_scope`. The
+category is still the retrieval category used by L0/L1, while `kb_scope:
+rag-eval` isolates these documents during eval runs.

knowledge_base/rag-eval/aiops/aiops-alert-scope-control.md

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+---
+title: AIOps Alert Scope Control
+keywords: [alert payload, unrelated active alerts, scope control]
+summary: Keep diagnosis scoped to the request payload and avoid diagnosing unrelated active alerts.
+category: aiops
+source: aiops-alert-scope-control
+breadcrumb: AIOps > Alert Scope Control
+kb_scope: rag-eval
+covers: [alert scope, payload, active alerts]
+when_to_retrieve: Use when an AIOps request includes a concrete alert payload and scope boundaries matter.
+---
+
+# AIOps
+
+## Alert Scope Control
+
+When an AIOps request already includes an alert payload, the agent should diagnose that payload first.
+It must not expand the task into unrelated active alerts unless the user asks for broad alert triage.
+
+Scope rules:
+
+1. Treat the provided payload as the primary incident boundary.
+2. Use unrelated active alerts only as correlation evidence when they share service, dependency, time window, or trace context.
+3. Do not replace the requested alert with a louder but unrelated alert.
+
+This runbook anchors payload, unrelated active alerts, and scope behavior.

knowledge_base/rag-eval/aiops/payment-service-latency.md

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+---
+title: Payment Service Latency Alert
+keywords: [HighLatency, payment-service, p95 latency, downstream dependency]
+summary: Diagnose payment-service p95 latency alerts and identify downstream dependency bottlenecks.
+category: aiops
+source: payment-service-latency
+breadcrumb: AIOps > Service Alerts > Payment Latency
+kb_scope: rag-eval
+covers: [payment-service, latency, downstream dependency]
+when_to_retrieve: Use when an alert mentions payment-service, HighLatency, or elevated p95 latency.
+---
+
+# AIOps
+
+## Service Alerts
+
+### Payment Latency
+
+For `HighLatency` alerts on `payment-service`, treat p95 latency as the primary symptom.
+
+Diagnosis steps:
+
+1. Confirm whether p95 latency is isolated to payment-service or shared across upstream callers.
+2. Compare payment-service latency with downstream dependency latency for gateway, risk, and order services.
+3. Check connection pool wait time, retry spikes, and timeout rates.
+4. If downstream dependency latency increased first, classify payment-service as affected rather than root cause.
+
+The expected evidence terms are p95 latency, payment-service, and downstream dependency.

knowledge_base/rag-eval/database/mysql-connection-pool.md

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+---
+title: MySQL Connection Pool Runbook
+keywords: [MySQL connection pool, pool exhausted, max_connections, HikariCP]
+summary: Diagnose exhausted MySQL connection pools and distinguish application leaks from database limits.
+category: database
+source: mysql-connection-pool
+breadcrumb: Database > MySQL > Connection Pool
+kb_scope: rag-eval
+covers: [mysql, connection pool, database capacity]
+when_to_retrieve: Use when MySQL clients report exhausted pools, connection acquisition timeout, max_connections pressure, or HikariCP saturation.
+---
+
+# Database
+
+## MySQL
+
+### Connection Pool
+
+When MySQL connection pool is exhausted, first compare application pool usage with database `max_connections`.
+For HikariCP, check `active`, `idle`, `pending`, and connection acquisition timeout metrics.
+
+Recommended diagnosis:
+
+1. Verify whether HikariCP active connections stay near maximum while pending threads grow.
+2. Check MySQL `Threads_connected`, `Threads_running`, and `max_connections`.
+3. Inspect slow SQL and long transactions that keep connections checked out.
+4. If the database is healthy, look for application connection leaks or missing transaction boundaries.
+
+Use this runbook as evidence for connection pool, max_connections, and HikariCP incidents.

knowledge_base/rag-eval/fallback/rag-l0-filter-fallback.md

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+---
+title: RAG L0 Filter Fallback
+keywords: [golden retry contract, second pass retrieval]
+summary: Retry the raw query without the L0 category filter when filtered vector evidence is missing or low quality.
+category: fallback
+source: rag-l0-filter-fallback
+breadcrumb: RAG > Fallback > Unfiltered Retry
+kb_scope: rag-eval
+covers: [fallback, unfiltered retry, retrieval quality]
+when_to_retrieve: Use when validating the fallback contract for low-quality filtered vector retrieval.
+---
+
+# RAG
+
+## Fallback
+
+### Unfiltered Retry
+
+If the first vector search is over-constrained by an L0 metadata filter and returns low quality evidence,
+the retriever should skip the L0 filter and run an unfiltered vector retry with the original query.
+
+The fallback reason should be `filtered_vector_low_quality` when the filtered candidate exists but is below the
+reference threshold. If there is no usable evidence at all, use `filtered_vector_no_evidence`.
+
+This document is the expected evidence for skip the L0 filter, unfiltered vector retry, and low quality behavior.

knowledge_base/rag-eval/ops/incident-diagnosis-flow.md

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+---
+title: Incident Diagnosis Flow
+keywords: [standard troubleshooting flow, application incident, collect evidence, verify, remediation]
+summary: Standard flow for diagnosing application incidents with evidence, hypothesis verification, and remediation.
+category: ops
+source: incident-diagnosis-flow
+breadcrumb: AIOps > Diagnosis Flow
+kb_scope: rag-eval
+covers: [incident diagnosis, evidence collection, remediation]
+when_to_retrieve: Use when the user asks for a standard troubleshooting flow or incident diagnosis sequence.
+---
+
+# AIOps
+
+## Diagnosis Flow
+
+The standard troubleshooting flow is evidence first, hypothesis second, remediation last.
+
+Recommended sequence:
+
+1. Collect evidence from alerts, metrics, logs, traces, deployments, and recent configuration changes.
+2. Define a small hypothesis that explains the observed symptoms.
+3. Verify the hypothesis with a targeted metric, log query, or reproduction step.
+4. Choose remediation that directly addresses the verified cause.
+5. Record the outcome and the evidence used to make the decision.
+
+Do not skip collect evidence, verify, and remediation ordering during an application incident.

knowledge_base/rag-eval/overfilter-decoy/rag-l0-filter-decoy.md

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+---
+title: RAG L0 Filter Decoy
+keywords: [over-filtered by L0, filtered vector search, low quality evidence]
+summary: Decoy document used to force the first filtered retrieval attempt into a low-quality category.
+category: overfilter-decoy
+source: rag-l0-filter-decoy
+breadcrumb: RAG > Fallback > Decoy
+kb_scope: rag-eval
+covers: [fallback test decoy]
+when_to_retrieve: Use only as a controlled eval decoy for over-filter fallback testing.
+---
+
+# Release Calendar
+
+## Approval Window
+
+This document describes an unrelated release calendar approval window.
+It intentionally avoids the real fallback instructions so the filtered retrieval
+attempt is low quality and the retriever must retry without the L0 category filter.

knowledge_base/rag-eval/rag/rag-chunk-context-reconstruction.md

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+---
+title: RAG Chunk Context Reconstruction
+keywords: [split into multiple chunks, retrieval context, neighbor chunk, same section, breadcrumb context]
+summary: Preserve context when long RAG sections are split into multiple retrievable chunks.
+category: rag
+source: rag-chunk-context-reconstruction
+breadcrumb: RAG > Chunking > Context Reconstruction
+kb_scope: rag-eval
+covers: [rag chunking, context packing, breadcrumbs]
+when_to_retrieve: Use when a retrieval question asks how to preserve context across split chunks.
+---
+
+# RAG
+
+## Chunking
+
+### Context Reconstruction
+
+When a long section is split into multiple chunks, retrieval should keep enough local structure for the answer.
+
+Recommended behavior:
+
+1. Store the breadcrumb with every chunk.
+2. Preserve the same section identity across adjacent chunks.
+3. During context packing, include a neighbor chunk when the selected chunk depends on nearby setup or definitions.
+4. Prefer concise evidence blocks that show the breadcrumb and the relevant content span.
+
+The key concepts are neighbor chunk, same section, and breadcrumb.

knowledge_base/rag-eval/rag/rag-l0-domain-entity-hint.md

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+---
+title: RAG L0 Domain Entity Hint
+keywords: [L0 keyword matching, final retrieval result, domain detector, entity extractor, metadata filter]
+summary: Define L0 as a query transformation hint layer instead of final retrieval evidence.
+category: rag
+source: rag-l0-domain-entity-hint
+breadcrumb: RAG > L0 > Domain Entity Hint
+kb_scope: rag-eval
+covers: [l0 hint, query transformation, metadata filter]
+when_to_retrieve: Use when a question asks whether L0 should decide final retrieval or only provide hints.
+---
+
+# RAG
+
+## L0
+
+### Domain Entity Hint
+
+L0 keyword matching should not decide the final retrieval result.
+In the modular RAG pipeline, L0 behaves like a lightweight domain detector and entity extractor.
+
+The output can provide:
+
+1. Candidate domain hints.
+2. Matched entities and keywords.
+3. An optional metadata filter for the first vector retrieval attempt.
+
+Final evidence still comes from L1 vector retrieval, post-retrieval normalization, rerank, and context packing.
+The important terms are domain detector, entity extractor, and metadata filter.

mvp/architecture/rag-eval-closure.md

@@ -155,6 +155,8 @@ eval/rag-retrieval/seed-docs/*.md
   -> api_document metadata + L0 index + Milvus chunks
 ```
 
+仓库内还保留一份 `knowledge_base/rag-eval/` 镜像，方便直接查看和提交 eval 知识库文档。它们放在单独目录下，避免和 `knowledge_base/api`、`knowledge_base/infrastructure` 等业务知识目录混在一起；检索 category 仍由 frontmatter 中的 `category` 决定。
+
 Seed frontmatter includes:
 
 ```yaml