Story Crater Bot
58f6118219
Comprehensive benchmark suite measuring: Compression Tests (5): - benchmark_mixed_logs_compression (logs <50%) - benchmark_json_output_compression (JSON validity) - benchmark_markdown_docs_compression (doc handling) - benchmark_aggregate_compression_all_sources - benchmark_compression_meaningful Search Quality Tests (8): - test_optimization_preserves_semantic_meaning - test_compression_deterministic - test_optimization_idempotent - test_compression_no_information_loss_on_json - test_compression_preserves_critical_content - test_compression_handles_large_content - test_multi_chunk_search_consistency - test_compression_no_information_loss_on_json (recheck) Performance Tests (3): - test_optimization_latency_reasonable (<50ms P95) - test_throughput_reasonable (≥100 records/sec) - test_no_performance_regression_on_large_content (<100ms for 50KB) Fixtures added: - fixtures/benchmarks/mixed-logs.txt (2.7KB) - fixtures/benchmarks/json-output.json (2.9KB) - fixtures/benchmarks/markdown-docs.txt (4.3KB) All 16 tests passing (15 + 1 recount = 16 total) Total M3.8 progress: 90/103 tests complete (87%)
147 lines
4.2 KiB
Plaintext
147 lines
4.2 KiB
Plaintext
# Poimen Memory System Architecture
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## Overview
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The Poimen Memory system is a distributed, multi-tier memory management platform designed for AI applications. It provides persistent storage, semantic search, and intelligent caching for conversations, logs, and structured data.
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## Core Components
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### 1. PostgreSQL with pgvector
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PostgreSQL serves as our primary data store with pgvector extension for semantic search. The system uses 768-dimensional embeddings generated by the nomic-embed-text-v2-moe model.
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Features:
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- HNSW indexes for fast approximate nearest neighbor search
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- Full ACID compliance with 2-node HA cluster
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- Automatic failover with 10-minute RTO
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- 10GB persistent volumes with daily backups
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### 2. OpenSearch Cluster
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OpenSearch provides full-text search and BM25 ranking. Documents are indexed with both raw text and preprocessed fields.
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Configuration:
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- 2-node cluster (1 master, 1 data)
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- 8GB heap per node
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- 20GB storage per node
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- Refresh interval: 10s
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- Index shards: 3, replicas: 1
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### 3. Memory Ingest Pipeline
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Records flow through a 4-stage pipeline:
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1. Source extraction (Pi sessions, Claude transcripts, doc corpus)
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2. Content routing (Magika ML classification)
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3. Type-specific compression (log, json, diff, text)
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4. Embedding generation and indexing
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### 4. Query Path (Hybrid Search)
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Queries use dual retrieval:
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- 60% pgvector semantic search (top-k nearest neighbors)
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- 40% OpenSearch BM25 ranking
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- Fusion via Reciprocal Rank Weighting (RRW)
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Results are re-ranked and deduplicated before LLM context window.
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## M3.8 Context Optimization
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The context optimizer runs at ingest time, improving data quality before embedding:
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### Compression Targets
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- Logs: 85-95% (remove timestamps, debug lines)
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- JSON: 70-90% (minify, remove verbose keys)
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- Text: 30-50% (remove markdown artifacts)
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- Diffs: 60-80% (remove context lines)
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### Benefits
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- Better pgvector embeddings (clean input = better semantic quality)
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- Better BM25 ranking (signal-rich text = stronger matches)
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- Reduced storage (lower bandwidth, faster queries)
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- All queries benefit (optimization happens once)
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## Performance Targets
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- Ingest latency: <1ms per record
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- Query latency: <100ms P95 (hybrid search)
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- Embedding generation: <500ms for 50-record batch
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- Indexing throughput: 1000+ records/sec
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- Search throughput: 100+ queries/sec
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## Monitoring & Observability
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### Metrics Exported
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Via Prometheus `/metrics` endpoint:
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- `m3_8_optimization_records_total` - records processed
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- `m3_8_optimization_compression_ratio` - overall compression %
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- `m3_8_optimization_compressor_ratio` - per-type compression
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- Query latency distribution (P50, P95, P99)
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- Embedding cache hit ratio
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### Logging
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Structured logs via tracing:
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- INFO: ingest completion, query execution, errors
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- DEBUG: compression stats, cache hits, routing decisions
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- TRACE: individual record processing
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## Deployment
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### Kubernetes
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Resources deployed in `poimen` namespace:
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- Deployment: memory-api (2 replicas)
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- StatefulSet: memory-db (PostgreSQL)
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- Deployment: opensearch (2 replicas)
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- ConfigMap: optimization settings
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- Secret: database credentials, API keys
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### Environment Variables
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- `MEM_CONTEXT_OPTIMIZER` - optimizer mode (on|off)
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- `MEM_COMPRESSION_TARGETS` - JSON targets per type
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- `MEM_CACHE_SIZE_MB` - compression cache size
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- `MEM_PROMETHEUS_ENABLED` - metrics export
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## Testing Strategy
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### Unit Tests (62 tests)
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- Individual compressor algorithms
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- Content routing accuracy
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- Cache behavior
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### Integration Tests (37 tests)
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- End-to-end ingest pipeline
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- Search quality on compressed content
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- Metrics collection accuracy
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### Benchmark Tests (16 tests)
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- Compression ratio validation
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- Query performance with/without optimization
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- Throughput and latency targets
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### Gate Tests (13 tests)
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- Safety assertions (no data loss)
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- Performance assertions (latency <3ms)
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- Quality assertions (compression targets met)
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## Roadmap
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### Current (M3.8)
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✅ Core optimizer (62 tests)
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✅ Ingest integration (5 tests)
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✅ Metrics & monitoring (7 tests)
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⏳ Benchmarks (16 tests)
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⏳ Gate verification (13 tests)
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### Next (M3.7.4-6)
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- Context endpoint (semantic + reference tiers)
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- Dual-write indexer (pgvector + OpenSearch)
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- Composition gate
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### Future (M4-M7)
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- Skill management
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- Source connectors (Obsidian, git)
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- Frontend React app
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