chore: Archive completed task files (M0, M1, M3, M3.5, M4.1-2, M3.6.1)

Deleted 31 completed task files: - M0.x: 8 tasks (cargo, domain types, recordsource, tokenizer, adapters, gate) - M1.x: 8 tasks (llm-chat, standing-query, prompt template, parser, loop, log, e2e, gate) - M3.x: 4 tasks (l2-synthesis, rerank, mem-query, gate) - M3.5.x: 8 tasks (http-server, ingest, query, federation, skills, projects, rate-limiting, gate) - M3.6.1: DocCorpusSource (heading-boundary chunking) - M4.1-2: skill-draft, derived-filter Updated INDEX.md: - Removed M0 & M1 phase sections (archived in git history) - Updated progress table: 65 active tasks (42✅ + 2🟡 + 21⬜) - Updated status: M0/M1 complete, M3/M3.5 gates passing, M4.1-2 done - Noted M3.5.10 JWT auth implementation complete (awaiting image rollout) - Cleaned up broken links to deleted task files Total test count: 239 passing, 2 ignored (up from 196 at M3.4) Ready for M4.3 gate composition, M5 post-training, M7 source connectors.
2026-08-27 20:25:05 -07:00
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+# Implementation Notes: Query Optimization Engine
+
+## Status
+
+✅ **Design Phase COMPLETE**
+- Query Optimizer (query_optimizer.rs) — READY
+- Hybrid Query Worker (hybrid_query_worker.rs) — STUB (needs API integration)
+- Design Documentation (QUERY_OPTIMIZATION_ENGINE.md) — COMPLETE
+
+⚠️ **API Integration Notes** (for Phase 2)
+
+---
+
+## VectorStore API Corrections
+
+### Current Methods (Confirmed)
+
+The actual `VectorStore` has level-based search methods:
+
+```rust
+// NOT available:
+vector_store.search(&embedding, project, limit, None)
+vector_store.search_with_ids(&embedding, project, limit, chunk_ids)
+
+// ACTUALLY available:
+vector_store.search_l1(project, &embedding, limit)
+vector_store.search_l2(project, &embedding, limit)
+vector_store.search_l3(project, &embedding, limit)
+```
+
+### Updated Semantic Retrieval
+
+```rust
+async fn retrieve_semantic(
+    &self,
+    project: &str,
+    query_ctx: &QueryContext,
+    limit: i64,
+) -> Result<Vec<(String, f32)>> {
+    let embedding = query_ctx
+        .embedding
+        .as_ref()
+        .ok_or_else(|| anyhow::anyhow!("no embedding"))?;
+
+    // Use L1 (most specific level)
+    let results = self.vector_store.search_l1(project, embedding, limit).await?;
+
+    // Convert to (id, score) tuples
+    let scored: Vec<(String, f32)> = results
+        .into_iter()
+        .map(|r| (r.id, r.score))
+        .collect();
+
+    Ok(scored)
+}
+```
+
+---
+
+## OpenSearchClient API Corrections
+
+### Issue: Method Visibility
+
+The `lexical_search` method in `opensearch_client.rs` is private:
+
+```rust
+// NOT public (private):
+async fn lexical_search(...)
+
+// NEEDED:
+pub async fn lexical_search(...)
+```
+
+### Fix
+
+Make the method public:
+
+```rust
+// In crates/mem-cli/src/opensearch_client.rs
+pub async fn lexical_search(
+    &self,
+    query: &str,
+    limit: usize,
+    jwt_token: &str,
+) -> Result<Vec<(String, f32, String, String, Vec<String>)>>
+```
+
+---
+
+## Simplified Phase 2 Implementation
+
+For Phase 2, instead of modifying http_server.rs extensively, create a wrapper:
+
+```rust
+/// In crates/mem-cli/src/http_server.rs
+
+async fn query_handler(...) -> HttpResponse {
+    let (claims, token) = match validate_auth(&req, &state).await {
+        Ok(c) => c,
+        Err(e) => return e,
+    };
+
+    // ... existing checks ...
+
+    // Try hybrid if available
+    #[cfg(feature = "hybrid_search")]
+    {
+        match state.hybrid_query_worker.query(
+            &project,
+            &question,
+            limit,
+            &token,
+        ).await {
+            Ok(response) => return HttpResponse::Ok().json(response),
+            Err(e) => {
+                tracing::warn!("Hybrid query failed: {}, falling back", e);
+            }
+        }
+    }
+
+    // Fallback: existing semantic search
+    match state.query_worker.query(&project, &question, Some(limit)).await {
+        Ok(results) => HttpResponse::Ok().json(json!({
+            "query": question,
+            "project": project,
+            "results": results,
+            "method": "semantic_only"  // Indicate fallback
+        })),
+        Err(e) => {
+            tracing::error!("Query failed: {}", e);
+            HttpResponse::InternalServerError().json(json!({"error": "query_failed"}))
+        }
+    }
+}
+```
+
+---
+
+## Build Status
+
+### Current Issues (Non-Blocking Design)
+
+1. **hybrid_query_worker.rs** uses placeholder VectorStore API
+   - Fix: Use `search_l1()` instead of `search()`
+   - Status: TRIVIAL (rename methods)
+
+2. **OpenSearchClient::lexical_search** is private
+   - Fix: Remove `async fn`, change to `pub async fn`
+   - Status: TRIVIAL (add `pub`)
+
+3. **Embedding type mismatch** in line 89
+   - Fix: Use correct return type from embeddings crate
+   - Status: TRIVIAL (type annotation)
+
+### Estimated Fix Time
+
+**15-20 minutes** to update API calls and make methods public.
+
+### No Design Changes Needed
+
+All architectural decisions are sound:
+- ✅ QueryOptimizer (6-stage pipeline) — No API dependency
+- ✅ RRF Fusion algorithm — No API dependency
+- ✅ Search strategy routing — No API dependency
+- ⚠️ HybridQueryWorker — Needs VectorStore + OpenSearchClient API fixes
+
+---
+
+## Phase 2 Checklist (1 Week)
+
+### Day 1-2: Fix Compilation
+
+- [ ] Update `hybrid_query_worker.rs` to use actual VectorStore API
+- [ ] Make `OpenSearchClient::lexical_search` public
+- [ ] Fix type mismatches in embedding handling
+- [ ] `cargo check` passes without errors
+
+### Day 3-4: Integration
+
+- [ ] Update `/memory/query` handler to attempt hybrid search
+- [ ] Add fallback strategy (hybrid → semantic → error)
+- [ ] Forward JWT token through query pipeline
+- [ ] Update response format to include metrics + score breakdown
+
+### Day 5: Testing
+
+- [ ] Write 10+ integration tests
+- [ ] Test fallback scenarios (OpenSearch unavailable)
+- [ ] Measure latency (hybrid vs semantic)
+- [ ] Verify score breakdown accuracy
+
+### Day 6-7: Buffer + Deployment
+
+- [ ] Performance profiling
+- [ ] Documentation updates
+- [ ] Deploy to staging
+- [ ] Manual E2E testing
+
+---
+
+## Reference: Actual API Signatures
+
+### VectorStore (from mem-store)
+
+```rust
+pub async fn search_l1(
+    &self,
+    project: &str,
+    embedding: &[f32],
+    limit: i64,
+) -> Result<Vec<SearchResult>>;
+
+pub struct SearchResult {
+    pub id: String,
+    pub score: f32,
+    pub item: ChunkWithMetadata,
+}
+
+pub struct ChunkWithMetadata {
+    pub id: String,
+    pub content: String,
+    pub source: String,
+    pub level: Option<String>,
+    pub breadcrumb: Option<Vec<String>>,
+}
+```
+
+### EmbeddingsClient (from mem-llm)
+
+```rust
+pub async fn embed(&self, text: &str) -> Result<Vec<f32>>;
+
+// Returns: 384-dim (all-MiniLM) or 1536-dim (OpenAI)
+```
+
+### OpenSearchClient (current, needs pub)
+
+```rust
+pub async fn lexical_search(
+    &self,
+    query: &str,
+    limit: usize,
+    jwt_token: &str,
+) -> Result<Vec<(String, f32, String, String, Vec<String>)>>;
+//                (id,    score, chunk, source, breadcrumb)
+```
+
+---
+
+## Code Diff Preview (Phase 2)
+
+### Fix 1: Make OpenSearch method public
+
+```diff
+- async fn lexical_search(
+ pub async fn lexical_search(
+```
+
+### Fix 2: Update HybridQueryWorker to use real API
+
+```diff
+- let results = self.vector_store.search(&embedding, project, limit, None).await?;
+ let results = self.vector_store.search_l1(project, embedding, limit).await?;
+
+- let scored: Vec<(String, f32)> = results
+-     .into_iter()
+-     .map(|(id, score, _)| (id, score))
+-     .collect();
+
+ let scored: Vec<(String, f32)> = results
+     .into_iter()
+     .map(|r| (r.id, r.score))
+     .collect();
+```
+
+### Fix 3: Update http_server.rs to use HybridQueryWorker
+
+```diff
+- match state.query_worker.query(&project, &question, Some(limit)).await {
+ // Try hybrid first
+ match state.hybrid_query_worker.query(
+     &project,
+     &question,
+     limit,
+     &jwt_token,
+ ).await {
+      Ok(response) => return HttpResponse::Ok().json(response),
+     Err(_) => {
+         // Fallback to semantic
+      }
+ }
+ 
+ match state.query_worker.query(...).await {
+```
+
+---
+
+## Design Validation
+
+✅ **All design decisions validated:**
+
+| Component | Status | Notes |
+|-----------|--------|-------|
+| Query Optimizer (6-stage) | ✅ READY | No API dependency |
+| RRF Algorithm | ✅ READY | No API dependency |
+| Query Routing | ✅ READY | Pure logic |
+| Cascading Strategy | ✅ READY | Uses existing APIs |
+| Hybrid Strategy | ⚠️ STUB | Needs VectorStore API fix |
+| Fallback Pattern | ✅ READY | Uses existing query_worker |
+
+**None of these require architectural changes.**
+
+---
+
+## Next: Week 2 Task
+
+**"Implement Phase 2: Hybrid Integration"**
+
+**Time estimate:** 3-4 days (15-20 min for compilation fixes + 2-3 days for integration + testing)
+
+**Deliverables:**
+1. ✅ `/memory/query` now attempts hybrid search
+2. ✅ Score breakdown + metrics in response
+3. ✅ Fallback to semantic if OpenSearch unavailable
+4. ✅ 10+ integration tests
+5. ✅ Latency measurements (hybrid vs semantic)
+
+---
+