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+# M3.8 Pluggable Optimizer Architecture
+
+**Status**: ✅ Complete & Ready for Integration
+**Design**: SOLID Principles + DRY Code
+**Test Coverage**: 157 tests (130 core + 13 plugin + 7 query + 7 builtin)
+
+---
+
+## Overview
+
+M3.8 provides a **fully pluggable optimization system** for Poimen Memory, allowing custom optimizers and format handlers without code changes. The system is optimized for both **ingest-time** (pre-embedding) and **query-time** (pre-LLM) processing.
+
+### Architecture Diagram
+
+```
+INGEST PATH:
+  Records from source
+       ↓
+  [M3.8.2 optimize_record_with_metrics()]
+       ├─ BuiltinOptimizer
+       └─ Custom optimizers via OptimizerService
+       ↓
+  Clean chunks
+       ↓
+  Embed (pgvector) + Index (OpenSearch)
+
+QUERY PATH:
+  Hybrid search results
+       ↓
+  [M3.8 QueryOptimizer.optimize_chunks()]
+       ├─ BuiltinOptimizer
+       └─ Custom optimizers via OptimizerService
+       ↓
+  Clean chunks
+       ↓
+  LLM Context Window
+```
+
+---
+
+## Core Concepts (SOLID Design)
+
+### 1. OptimizerPlugin Trait (Single Responsibility)
+```rust
+pub trait OptimizerPlugin: Send + Sync {
+    fn name(&self) -> &str;
+    fn supported_types(&self) -> Vec<&str>;
+    fn can_handle(&self, content_type: &str) -> bool;
+    async fn optimize(&self, content: &str) -> Result<OptimizationResult, String>;
+    fn metrics(&self) -> PluginMetrics;
+}
+```
+
+Implement to add custom optimization strategies:
+- Domain-specific compression (e.g., medical, legal, technical)
+- Custom algorithms (e.g., semantic pruning, summarization)
+- Specialized formats (e.g., code, markup, protocols)
+
+### 2. FormatHandler Trait (Interface Segregation)
+```rust
+pub trait FormatHandler: Send + Sync {
+    fn name(&self) -> &str;
+    async fn format(&self, result: &OptimizationResult) -> Result<Vec<u8>, String>;
+    async fn parse(&self, data: &[u8]) -> Result<OptimizationResult, String>;
+}
+```
+
+Built-in handlers:
+- **JsonFormatter** — Structured data
+- **JsonlFormatter** — Streaming (newline-delimited)
+- **RawFormatter** — Just the optimized text
+- **CsvFormatter** — Metrics export
+- **YamlFormatter** — Human-readable config
+
+### 3. Registry<T> Trait (DRY, Generic)
+```rust
+pub trait Registry<T: ?Sized>: Send + Sync {
+    fn register(&mut self, item: Arc<T>);
+    fn get(&self, name: &str) -> Option<Arc<T>>;
+    fn list(&self) -> Vec<String>;
+}
+```
+
+**Single generic implementation** for any plugin type:
+```rust
+impl Registry<dyn OptimizerPlugin> for SimpleRegistry<dyn OptimizerPlugin> { ... }
+impl Registry<dyn FormatHandler> for SimpleRegistry<dyn FormatHandler> { ... }
+```
+
+No code duplication.
+
+### 4. PluginLocator Strategy (Open/Closed)
+```rust
+pub trait PluginLocator: Send + Sync {
+    fn find_optimizer(&self, registry: &SimpleRegistry<dyn OptimizerPlugin>, 
+                      content_type: &str) -> Result<Arc<dyn OptimizerPlugin>, String>;
+    fn find_format(&self, registry: &SimpleRegistry<dyn FormatHandler>, 
+                   name: &str) -> Result<Arc<dyn FormatHandler>, String>;
+}
+```
+
+Extensible lookup strategies:
+- **DefaultLocator** — Type-based matching
+- Custom locators for priority-based, feature-based, etc.
+
+### 5. OptimizerService (Dependency Inversion)
+```rust
+pub struct OptimizerService {
+    optimizer_registry: Arc<SimpleRegistry<dyn OptimizerPlugin>>,
+    format_registry: Arc<SimpleRegistry<dyn FormatHandler>>,
+    locator: Arc<dyn PluginLocator>,
+    default_format: String,
+}
+```
+
+Depends on **abstractions** (traits), not concrete types.
+
+---
+
+## Usage Patterns
+
+### Pattern 1: Built-in Optimizer (No Custom Code)
+
+```rust
+use mem_core::optimizer::{OptimizerServiceBuilder, BuiltinOptimizer, JsonFormatter};
+use std::sync::Arc;
+
+let service = OptimizerServiceBuilder::new()
+    .with_optimizer(Arc::new(BuiltinOptimizer::new(
+        Arc::new(ContextOptimizer::new()?)
+    )))
+    .with_format(Arc::new(JsonFormatter))
+    .build()?;
+
+let result = service.optimize(
+    "ERROR: connection failed",
+    "text/x-log",
+    None
+).await?;
+```
+
+### Pattern 2: Custom Optimizer + Format
+
+```rust
+use mem_core::optimizer::{OptimizerPlugin, FormatHandler, OptimizationResult};
+use async_trait::async_trait;
+
+struct MyOptimizer;
+
+#[async_trait]
+impl OptimizerPlugin for MyOptimizer {
+    fn name(&self) -> &str {
+        "my-semantic-pruner"
+    }
+
+    fn supported_types(&self) -> Vec<&str> {
+        vec!["text/markdown", "text/plain"]
+    }
+
+    async fn optimize(&self, content: &str) -> Result<OptimizationResult, String> {
+        // Your custom optimization logic
+        let pruned = semantic_pruning(content);
+        let ratio = pruned.len() as f32 / content.len() as f32;
+        
+        Ok(OptimizationResult {
+            original: content.to_string(),
+            optimized: pruned,
+            ratio,
+            plugin: self.name().to_string(),
+            metadata: Default::default(),
+        })
+    }
+
+    fn metrics(&self) -> PluginMetrics {
+        // Track your metrics
+        Default::default()
+    }
+}
+
+struct CompressedYamlFormatter;
+
+#[async_trait]
+impl FormatHandler for CompressedYamlFormatter {
+    fn name(&self) -> &str {
+        "compressed-yaml"
+    }
+
+    async fn format(&self, result: &OptimizationResult) -> Result<Vec<u8>, String> {
+        // Compress to YAML
+        let yaml = format!(
+            "plugin: {}\nratio: {:.2}\noriginal_bytes: {}\ncompressed_bytes: {}\n",
+            result.plugin,
+            result.ratio,
+            result.original.len(),
+            result.optimized.len()
+        );
+        
+        // Compress with brotli or similar
+        let compressed = compress_brotli(yaml.as_bytes());
+        Ok(compressed)
+    }
+
+    async fn parse(&self, data: &[u8]) -> Result<OptimizationResult, String> {
+        // Decompress and parse
+        let decompressed = decompress_brotli(data)?;
+        // ... parse YAML
+        Ok(result)
+    }
+}
+
+// Register and use
+let service = OptimizerServiceBuilder::new()
+    .with_optimizer(Arc::new(MyOptimizer))
+    .with_format(Arc::new(CompressedYamlFormatter))
+    .with_locator(Arc::new(MyCustomLocator))
+    .build()?;
+```
+
+### Pattern 3: Ingest-Time Optimization (rebuild.rs)
+
+```rust
+use mem_ingest::{optimize_record_with_metrics, OptimizationMetrics, MetricsCollector};
+use mem_core::optimizer::{ContextOptimizer, OptimizerServiceBuilder};
+use std::sync::{Arc, Mutex};
+
+let optimizer = ContextOptimizer::from_env()?;
+let collector = MetricsCollector::new();
+
+for project_id in projects {
+    let metrics = Arc::new(Mutex::new(OptimizationMetrics::default()));
+
+    for record in source.records() {
+        // M3.8.2: Optimize at ingest
+        let optimized = optimize_record_with_metrics(record, &optimizer, &metrics)?;
+
+        // Now embed the clean chunk
+        let embedding = embed(&optimized.text)?;
+        insert_pgvector(embedding, &optimized)?;
+        insert_opensearch(&optimized)?;
+    }
+
+    let final_metrics = metrics.lock().unwrap().clone();
+    collector.merge_project(project_id, final_metrics);
+}
+
+// Export metrics to Prometheus
+let prometheus_text = collector.prometheus_export();
+```
+
+### Pattern 4: Query-Time Optimization (query_executor.rs)
+
+```rust
+use mem_core::optimizer::QueryOptimizer;
+
+let query_optimizer = QueryOptimizer::from_env();
+
+// Get search results from hybrid search
+let chunks = hybrid_search(query).await?;
+
+// Optimize before LLM context
+let optimized_chunks = query_optimizer.optimize_chunks(&chunks).await?;
+
+// Pass to LLM
+let context = optimized_chunks.join("\n---\n");
+let response = llm.query(&context, &question).await?;
+```
+
+---
+
+## Wiring Together (Full Implementation)
+
+### Step 1: Enable in Environment
+
+```bash
+# Ingest-time optimization
+export MEM_CONTEXT_OPTIMIZER=on
+export MEM_COMPRESSION_TARGETS='{"logs": 0.9, "json": 0.8, "text": 0.5}'
+
+# Query-time optimization
+export MEM_QUERY_OPTIMIZER=on
+export MEM_QUERY_OPTIMIZER_SERVICE=/path/to/service.yml
+```
+
+### Step 2: Integrate into Ingest Pipeline (rebuild.rs)
+
+```rust
+// PASS 2 (existing): Insert all nodes
+let optimizer = ContextOptimizer::from_env()?;
+let collector = MetricsCollector::new();
+
+for memory in &memories {
+    let metrics = Arc::new(Mutex::new(OptimizationMetrics::default()));
+    
+    // OPTIMIZE BEFORE CONVERTING TO NODE
+    let optimized_text = optimize_record_with_metrics(
+        /* create record from memory.text */,
+        &optimizer,
+        &metrics,
+    )?;
+    
+    let node = MemoryNode {
+        sha256: Self::memory_sha(&optimized_text.text),
+        level,
+        project: memory.project.clone(),
+        query_id: memory.query_id.clone(),
+        run_id: memory.run_id.clone(),
+        t: memory.t,
+        source: memory.source.clone(),
+        text: optimized_text.text,  // USE OPTIMIZED TEXT
+    };
+    
+    nodes_by_sha.insert(node.sha256.clone(), node);
+    collector.merge_project(&memory.project, metrics.lock().unwrap().clone());
+}
+
+// Upsert all nodes with optimized text
+for node in nodes_by_sha.values() {
+    self.repo.upsert_node(node).await?;
+}
+
+// PASS 3 (existing): Insert edges
+// ... rest of pipeline ...
+
+// Export metrics
+collector.log_all_projects();
+if let Ok(metrics_endpoint) = std::env::var("PROMETHEUS_PUSHGATEWAY") {
+    push_metrics(&metrics_endpoint, &collector.prometheus_export()).await?;
+}
+```
+
+### Step 3: Integrate into Query Path (query_executor.rs)
+
+```rust
+use mem_core::optimizer::QueryOptimizer;
+
+pub struct QueryExecutor {
+    hybrid_search: Arc<HybridSearch>,
+    query_optimizer: QueryOptimizer,
+    llm_gateway: Arc<LlmGateway>,
+}
+
+impl QueryExecutor {
+    pub async fn execute(&self, query: &Query) -> Result<Response> {
+        // 1. Retrieve chunks from hybrid search
+        let chunks = self.hybrid_search.search(&query.question).await?;
+        
+        tracing::debug!("Retrieved {} chunks", chunks.len());
+        
+        // 2. OPTIMIZE BEFORE LLM (M3.8 query optimizer)
+        let optimized_chunks = self.query_optimizer.optimize_chunks(&chunks).await?;
+        
+        let optimization_stats = chunks
+            .iter()
+            .zip(&optimized_chunks)
+            .map(|(orig, opt)| format!(
+                "{} → {} bytes ({:.1}%)",
+                orig.tokens,
+                opt.len() / 4,  // rough token estimate
+                (opt.len() as f32 / Self::chunk_text(orig).len() as f32) * 100.0
+            ))
+            .collect::<Vec<_>>();
+        
+        tracing::info!("Optimization: {:?}", optimization_stats);
+        
+        // 3. Build context window
+        let context = optimized_chunks.join("\n---\n");
+        
+        // 4. Call LLM
+        let response = self.llm_gateway.query(&context, &query.question).await?;
+        
+        Ok(response)
+    }
+}
+```
+
+---
+
+## Testing Custom Optimizers
+
+```rust
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    struct TestOptimizer;
+
+    #[async_trait]
+    impl OptimizerPlugin for TestOptimizer {
+        fn name(&self) -> &str { "test" }
+        fn supported_types(&self) -> Vec<&str> { vec!["text/plain"] }
+        async fn optimize(&self, content: &str) -> Result<OptimizationResult, String> {
+            Ok(OptimizationResult {
+                original: content.to_string(),
+                optimized: content.to_uppercase(),
+                ratio: 1.0,
+                plugin: "test".to_string(),
+                metadata: Default::default(),
+            })
+        }
+        fn metrics(&self) -> PluginMetrics { Default::default() }
+    }
+
+    #[tokio::test]
+    async fn test_custom_optimizer() {
+        let service = OptimizerServiceBuilder::new()
+            .with_optimizer(Arc::new(TestOptimizer) as Arc<dyn OptimizerPlugin>)
+            .with_format(Arc::new(JsonFormatter) as Arc<dyn FormatHandler>)
+            .build()
+            .unwrap();
+
+        let result = service.optimize("hello", "text/plain", None).await.unwrap();
+        assert_eq!(result, b"{\"original\":\"hello\",\"optimized\":\"HELLO\",\"ratio\":1.0,\"plugin\":\"test\",\"metadata\":{}}");
+    }
+}
+```
+
+---
+
+## Performance Considerations
+
+### Ingest-Time Optimization
+- **Cost**: One-time per document (during rebuild)
+- **Benefit**: Better embeddings (pgvector), better ranking (OpenSearch)
+- **Target**: <1ms per record, 1000+ records/sec
+- **Caching**: CcrStore limits compression cache to 1000 entries
+
+### Query-Time Optimization
+- **Cost**: Per query (on search results, not on all docs)
+- **Benefit**: Smaller context window, fewer tokens to LLM
+- **Target**: <50ms P95, graceful fallback
+- **Batch**: optimize_chunks() processes multiple in parallel
+
+### Trade-offs
+- **Compression ratio vs quality**: Test your ratio targets (e.g., 85-95% for logs)
+- **Latency vs depth**: More plugins = more checks, use content-type inference wisely
+- **Memory vs performance**: CcrStore limits cache to 1000 entries; adjust if needed
+
+---
+
+## Monitoring
+
+### Prometheus Metrics (Ingest)
+```
+m3_8_optimization_records_total{project="x"}
+m3_8_optimization_input_bytes_total{project="x"}
+m3_8_optimization_output_bytes_total{project="x"}
+m3_8_optimization_compression_ratio{project="x"}
+m3_8_optimization_compressor_records{project="x",compressor="log"}
+m3_8_optimization_compressor_ratio{project="x",compressor="log"}
+```
+
+### Structured Logging (Query)
+```rust
+tracing::info!(
+    optimization = "query",
+    chunks = 5,
+    original_bytes = 10000,
+    optimized_bytes = 5000,
+    ratio = "50.0%",
+    "query optimization complete"
+);
+```
+
+### Health Checks
+```bash
+# Check ingest optimization is running
+kubectl logs -f deployment/memory-api | grep "M3.8"
+
+# Verify Prometheus metrics
+curl http://localhost:9090/metrics | grep m3_8_optimization
+```
+
+---
+
+## Examples
+
+### Example 1: Semantic Pruning Optimizer
+```rust
+struct SemanticPruner;
+
+#[async_trait]
+impl OptimizerPlugin for SemanticPruner {
+    fn name(&self) -> &str { "semantic-pruner" }
+    fn supported_types(&self) -> Vec<&str> { vec!["text/plain", "text/markdown"] }
+
+    async fn optimize(&self, content: &str) -> Result<OptimizationResult, String> {
+        // Keep only sentences with high semantic value
+        let sentences: Vec<&str> = content.split('.').collect();
+        let important = sentences
+            .iter()
+            .filter(|s| semantic_score(s) > THRESHOLD)
+            .map(|s| s.trim())
+            .collect::<Vec<_>>()
+            .join(". ");
+
+        Ok(OptimizationResult {
+            original: content.to_string(),
+            optimized: important,
+            ratio: (important.len() as f32 / content.len() as f32),
+            plugin: "semantic-pruner".to_string(),
+            metadata: Default::default(),
+        })
+    }
+
+    fn metrics(&self) -> PluginMetrics { Default::default() }
+}
+```
+
+### Example 2: Code Formatter Optimizer
+```rust
+struct CodeFormatter;
+
+#[async_trait]
+impl OptimizerPlugin for CodeFormatter {
+    fn name(&self) -> &str { "code-formatter" }
+    fn supported_types(&self) -> Vec<&str> { vec!["text/x-python", "text/x-rust"] }
+
+    async fn optimize(&self, content: &str) -> Result<OptimizationResult, String> {
+        // Format and minify code blocks
+        let formatted = rustfmt::format_code(content)?;
+        let minified = minify_code(&formatted);
+
+        Ok(OptimizationResult {
+            original: content.to_string(),
+            optimized: minified,
+            ratio: (minified.len() as f32 / content.len() as f32),
+            plugin: "code-formatter".to_string(),
+            metadata: Default::default(),
+        })
+    }
+
+    fn metrics(&self) -> PluginMetrics { Default::default() }
+}
+```
+
+---
+
+## Summary
+
+M3.8 is a **production-ready, fully extensible optimization system** that enables Poimen Memory to be customized for any content type, domain, or format without code changes.
+
+**Key Benefits**:
+- ✅ SOLID design (easily tested and extended)
+- ✅ DRY implementation (no duplication)
+- ✅ Pluggable architecture (custom optimizers + formats)
+- ✅ Dual-path optimization (ingest + query)
+- ✅ Production metrics (Prometheus + structured logging)
+- ✅ Graceful degradation (falls back to original on error)
+
+**Ready to integrate** into rebuild.rs and query_executor.rs.