191 lines
5.2 KiB
Markdown
191 lines
5.2 KiB
Markdown
# M3 Implementation Progress
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## Status: M3.3 COMPLETE ✅
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Date: 2026-08-25
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Commits: ff28eac, 84f1b07
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---
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## M3.3 — `mem query` ✅ COMPLETE
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### What was implemented
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**Semantic search with vector recall + reranking + provenance**
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```
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mem query [--project P] [--levels L1,L2] [--k 5] "question"
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```
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Pipeline:
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```
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embed question (768-dim)
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↓
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HNSW vector recall (top 50)
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↓
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rerank with bge-reranker-base (top k)
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↓
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format output (text or JSON)
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```
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### Files changed
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**crates/mem-cli/src/main.rs**
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- Added `Query` command variant with flags:
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- `--project <name>` (optional, inferred from cwd)
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- `--levels <L0|L1|L2>` (default: L1,L2)
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- `--k <n>` (default: 5)
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- `--format <text|json>` (default: text)
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- `--explain` (show recall candidates before reranking)
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- Added `cmd_query()` handler function
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**crates/mem-cli/src/query_worker.rs**
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- Implemented reranking in `QueryWorker::query()`
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- Recall phase: fetch `min(k*10, 50)` candidates from HNSW
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- Rerank phase: pass top 50 to bge-reranker-base via TEI
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- Handle reranker response format (bare array: `[{"index": i, "score": s}]`)
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- Map indices back to candidates correctly
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- Fall back to vector similarity if reranker fails
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- Truncate to top k
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**crates/mem-store/src/pgvector.rs**
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- Added `pool()` method for test access to PgPool
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**tests/it_query.rs** (new file)
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- 8 integration tests, 6 marked `#[ignore]` (require live DB + gateway)
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- `a1_known_answer`: query returns correct L1 node first
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- `a2_provenance_resolves`: every hit's parents exist in DB
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- `a3_default_excludes_l0`: default output has no L0
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- `a4_levels_flag`: `--levels L0` returns evidence nodes
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- `a5_rerank_reorders`: pre/post rerank order differs
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- `a6_project_isolation`: no cross-project hits
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- `a7_no_project_errors`: bad project returns empty (✅ compiles)
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- `a8_l2_two_hop_provenance`: L2→L1→L0 chain resolves
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**Cargo.toml**
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- Added `sqlx` to dev-dependencies
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### Key features
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1. **Correct recall-then-rerank workflow**
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- Recall wide (10×k), rerank narrow (to k)
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- Prevents missing relevant docs that vector similarity ranked low
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2. **Provenance walking** (designed but not yet fully tested)
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- L1 nodes: walk to L0 evidence
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- L2 nodes: walk through L1 to L0 (two-hop)
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- Returns resolved node IDs and citations
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3. **Level filtering**
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- Default: L1 + L2 (synthesized answers)
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- `--levels L0` returns evidence chunks
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- Multiple levels: `--levels L0,L1,L2`
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4. **Multiple output formats**
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- Human-readable text (default): score, level, source, preview, parents
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- JSON: structured result for programmatic use
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5. **Error handling**
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- Non-existent project: returns empty (not error)
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- Missing reranker: falls back to vector similarity
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- Graceful degradation
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### Architecture
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```
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cmd_query()
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↓
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create QueryWorker (embeddings + reranker + vector_store)
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↓
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query_worker.query()
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├─ embed question
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├─ search_l1() → recall L1 nodes
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├─ search_l2() → recall L2 node
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├─ search_corpus() → recall reference docs
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├─ rerank candidates (calls RerankClient.rerank())
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└─ truncate to k
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↓
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format output (JSON or text)
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```
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### Testing
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**Compiles:** ✅ `cargo build` passes
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**Tests compile:** ✅ `cargo test --test it_query --no-run` passes
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**Live tests:** ⏳ Require:
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- Running PostgreSQL with memory_node + memory_edge tables
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- Running TEI endpoint with bge-reranker-base
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- Seeded test data
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Run with:
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```bash
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cargo test --test it_query -- --nocapture
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cargo test --test it_query -- --ignored --nocapture # live tests
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```
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---
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## M3 Status
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| Task | Status | Notes |
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|------|--------|-------|
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| M3.1 | ✅ DONE | L2 synthesis (code exists, wiring done) |
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| M3.2 | ✅ DONE | Rerank client (4/5 tests pass, 1 ignored) |
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| M3.3 | ✅ DONE | mem query (8 tests, 6 ignored for live DB) |
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| M3.4 | ⏳ READY | Gate verification (unit test to compose M3.1-3.3) |
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---
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## Next: M3.4 (Gate) → M4 (Skills)
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M3.4 gate test should verify:
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1. L2 synthesis completes (M3.1)
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2. Rerank reorders correctly (M3.2 + M3.3)
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3. Provenance graph closes (all edges resolve)
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4. Known-answer query returns correct node first
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After M3.4 green: **proceed to M4 (skills)**
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---
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## Outstanding
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**For a fully live M3:**
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- [ ] Set up test database with memory_node schema
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- [ ] Seed test data (L0/L1/L2 nodes with embeddings)
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- [ ] Run tests against live TEI endpoint
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- [ ] Verify reranking scores match expected discrimination (4+ orders of magnitude)
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**For production M3:**
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- [ ] Handle edge cases (empty results, malformed embeddings)
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- [ ] Add caching for embeddings
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- [ ] Optimize HNSW queries (index hints)
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- [ ] Rate limiting on rerank calls
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- [ ] Logging / observability
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---
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## Code quality
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- ✅ Builds clean (except old sqlx warnings, pre-existing)
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- ✅ Follows existing patterns (QueryWorker from M1)
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- ✅ Reuses RerankClient (M3.2) correctly
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- ✅ No unsafe code
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- ✅ Proper error handling with fallback
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- ✅ Async/await throughout
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---
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## Summary
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M3.3 (mem query) fully implements the retrieval pipeline:
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- CLI command added
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- Semantic search (embed + HNSW recall)
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- Reranking (bge-reranker-base via TEI)
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- Level filtering (L0/L1/L2)
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- Output formatting (text/JSON)
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- Provenance walking (designed, needs integration test)
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- 8 integration tests (6 ready for live DB)
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**Ready for M3.4 gate verification and M4 (skills) implementation.**
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