Embedding Pipeline Ops

You will deploy pgvector and build an embedding ingestion pipeline with operational instrumentation. Deploy PostgreSQL with pgvector extension in your vCluster. Create embedding tables with proper indexing (HNSW for approximate nearest neighbor). Build `EmbeddingIngestionPipeline`: reads documents from a source queue, chunks documents using configurable strategy, calls embedding model via LiteLLM, and stores vectors in pgvector. Instrument the pipeline: `embedding_pipeline_throughput_docs_per_second`, `embedding_pipeline_latency_seconds{stage}` (chunking, embedding, storage), `embedding_pipeline_errors_total{error_type}`, `embedding_pipeline_queue_depth`. Implement pipeline health checks: verify embedding model is accessible, verify pgvector is writable, verify queue is being drained.

You will build failure detection and automatic reprocessing for the embedding pipeline. Implement failure detection: monitor for embedding model errors (API failures, rate limits), storage errors (pgvector connection failures, disk full), and quality errors (embedding dimension mismatch, NaN values). For each failure type, define detection criteria and alerting threshold. Build reprocessing workflow: maintain a `failed_documents` table tracking documents that failed embedding. Implement `ReprocessingWorkflow` as an Argo CronJob that retries failed documents with exponential backoff (retry after 5min, 30min, 2h, 24h). Implement staleness detection: track document freshness (last embedded timestamp vs source document modified timestamp). Alert when freshness SLA is violated. Track `pipeline_reprocessing_total{reason}`, `pipeline_stale_documents_count`.

You will build a comprehensive embedding pipeline health dashboard. Implement Grafana panels: Pipeline Throughput (documents processed per minute with capacity line), Queue Depth (pending documents over time -- should be bounded), Processing Latency (per-stage latency breakdown), Error Rate (by error type over time), Freshness SLA (percentage of documents within freshness target), Reprocessing Queue (failed documents awaiting retry with age distribution). Implement pipeline SLOs: 99% of documents embedded within 1 hour of source update, < 0.1% embedding failure rate, p95 per-document processing time < 30 seconds. Track SLO compliance on the dashboard. Build pipeline capacity planning: based on throughput trends, project when current capacity will be exceeded.

You will build comprehensive testing and validation for the embedding pipeline operations system. Implement `EmbeddingPipelineOperationsTester`: define test scenarios that verify all critical paths work correctly under normal conditions, edge cases, and failure conditions. Build integration tests that verify the system integrates correctly with upstream and downstream components. Implement regression testing: maintain a test suite that runs on every configuration change to catch regressions. Build `POST /api/v1/embedding-pipeline-operations/test` API that triggers the full test suite and returns results. Run tests as scheduled Argo Workflow CronJobs. Track `test_pass_rate_{system}_total`, `test_duration_seconds`. Build test results dashboard showing pass rates, flaky tests, and coverage.

You will build performance monitoring and optimization for the embedding pipeline operations system. Implement `EmbeddingPipelineOperationsOptimizer`: instrument all critical paths with latency histograms, identify bottlenecks using p95/p99 analysis, and implement optimizations. Build capacity analysis: measure maximum throughput under load, identify scaling limits, and document capacity thresholds. Implement performance SLOs: define acceptable latency and throughput targets, track compliance, and alert on degradation. Build performance benchmarking: run standardized benchmarks on every significant change to detect performance regressions. Track `performance_benchmark_result_{system}`, `performance_slo_compliance_{system}`. Create performance dashboard with trend analysis.

You will build comprehensive operational documentation and runbooks for the embedding pipeline operations system. Implement `EmbeddingPipelineOperationsDocGenerator`: auto-generate architecture diagrams from deployed resources, configuration reference from active configs, and API documentation from FastAPI OpenAPI specs. Build operational runbooks: document common operational tasks (scaling, configuration changes, troubleshooting), emergency procedures (failure recovery, rollback), and maintenance procedures (upgrades, data migrations). Implement documentation freshness: track when documentation was last updated vs when the system was last changed, flag stale docs. Store documentation in Git with version tracking. Build `GET /api/v1/embedding-pipeline-operations/docs` serving current documentation. Track `documentation_freshness_{system}`, `documentation_coverage_{system}`.