Compare commits
2
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 rock
|
8b01e04569 | ||
|
rock
|
8ac2bd580b |
@@ -36,10 +36,12 @@ jobs:
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run: cargo clippy --all --all-targets -- -D warnings 2>&1 | tail -50 || true
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- name: Get short SHA
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if: github.event_name == 'push' || github.event_name == 'workflow_dispatch'
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id: sha
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run: echo "short_sha=$(git rev-parse --short HEAD)" >> $GITHUB_OUTPUT
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- name: Registry login
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if: github.event_name == 'push' || github.event_name == 'workflow_dispatch'
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run: |
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echo "${REGISTRY_TOKEN}" | docker login "${REGISTRY}" \
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--username "${REGISTRY_USER}" --password-stdin
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@@ -48,6 +50,7 @@ jobs:
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REGISTRY_TOKEN: ${{ secrets.FORGEJO_REGISTRY_TOKEN }}
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- name: Build Docker image
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if: github.event_name == 'push' || github.event_name == 'workflow_dispatch'
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run: |
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docker build --no-cache --progress=plain \
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-t "${IMAGE}:${{ steps.sha.outputs.short_sha }}" \
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@@ -62,4 +65,5 @@ jobs:
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echo "✓ Pushed: ${IMAGE}:${{ steps.sha.outputs.short_sha }}"
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|
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- name: Prune unused images
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if: github.event_name == 'push' || github.event_name == 'workflow_dispatch'
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run: docker image prune -a --force 2>&1 | tail -3 || true
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Generated
-1
@@ -2106,7 +2106,6 @@ dependencies = [
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"mem-chunk",
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"mem-core",
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"regex",
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"reqwest",
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"serde",
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"serde_json",
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"serde_yaml",
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@@ -1,263 +0,0 @@
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# CRITICAL FIXES NEEDED - Poimen Memory Service
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## STATUS: Service Non-Functional ❌
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|
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**Root Issues Blocking Service**:
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1. ✅ HTTP handler deadlock fixed (schema init error handling)
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2. ❌ Server initialization hangs during schema or startup (logs stop after `l2_l1_edges`)
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3. ❌ Ingest pipeline NOT implemented (just raw vector storage, no entities/edges)
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4. ❌ Temporal schema missing (no t_valid, t_invalid, version tracking)
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5. ❌ GRM gate not integrated (no memorability scores, confidence)
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6. ❌ Query doesn't use knowledge graph (just vector search)
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7. ❌ Compaction disabled
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8. ❌ Verification gates missing
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|
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---
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## STEP 1: Fix Server Startup Hang ⚠️
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**Current Issue**: Server hangs during initialization after schema creation.
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**Suspected causes**:
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- OptimizerServiceBuilder.build() getting stuck
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- AccessGuard creation blocking
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- Background task spawning deadlock
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**Fix**:
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```rust
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// In http_server.rs:316-325
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// Wrap in timeout or disable non-essentials
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let optimizer_service = match tokio::time::timeout(
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Duration::from_secs(5),
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async { mem_core::optimizer::OptimizerServiceBuilder::new().build() }
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).await {
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Ok(Ok(service)) => Some(Arc::new(service)),
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_ => {
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tracing::warn!("Optimizer initialization skipped (timeout or error)");
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None
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}
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};
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```
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**Test**: `./target/release/mem serve --port 9999` should reach "Starting HTTP server" within 10s
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|
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---
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## STEP 2: Implement Ingest Pipeline (HIGH PRIORITY)
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**Current Implementation** (`ingest_worker.rs`):
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```rust
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// Just stores raw chunks + embeddings
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store_chunk_l0(&l0_chunk)
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store_memory_l1(&l1_memory, &embedding)
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```
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**Expected Implementation**:
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```rust
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// 1. Extract entities (entity_extractor)
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let entities = entity_extractor.extract(&content).await?;
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// 2. Extract facts + edges (fact_extractor)
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let facts = fact_extractor.extract(&content, entities).await?;
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// 3. Create temporal edges with GRM gate
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for fact in facts {
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let edge = TemporalEdge {
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source: fact.source_entity,
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target: fact.target_entity,
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relation: fact.relation,
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fact: fact.text,
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t_valid: now(),
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t_invalid: None,
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confidence: grm_gate.score(&fact)?, // ← GRM gate
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version: 1,
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};
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edge_repo.insert(&edge).await?;
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}
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// 4. Check contradictions + queue for review
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for edge in edges {
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if contradiction_detector.detect(&edge, existing_edges)? {
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review_queue.enqueue(&edge).await?;
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}
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}
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```
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**Files to modify**:
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- `crates/mem-cli/src/ingest_worker.rs` (core ingest logic)
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- `crates/mem-ingest/src/ingest_pipeline.rs` (entity + fact extraction)
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- `crates/mem-ingest/src/contradiction_detector.rs` (pre-filter + review)
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---
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## STEP 3: Update Storage Schema (MEDIUM PRIORITY)
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**Missing fields**:
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```sql
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ALTER TABLE memories_l1 ADD COLUMN (
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t_valid TIMESTAMP NOT NULL DEFAULT NOW(),
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t_invalid TIMESTAMP,
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confidence FLOAT DEFAULT 0.5,
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version INT DEFAULT 1,
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memorability_score INT,
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contribution_date TIMESTAMP
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);
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ALTER TABLE l1_l0_edges MODIFY TO (
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l1_id UUID,
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l0_id UUID,
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relation_type VARCHAR,
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fact TEXT,
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t_valid TIMESTAMP DEFAULT NOW(),
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t_invalid TIMESTAMP,
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confidence FLOAT,
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contradiction_flag BOOL DEFAULT FALSE,
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review_queue_id UUID,
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version INT DEFAULT 1,
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PRIMARY KEY (l1_id, l0_id, version)
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);
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```
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**Migration script**: `crates/mem-store/migrations/003_temporal_grm_schema.sql`
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---
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## STEP 4: Wire Query Handler to Knowledge Graph (MEDIUM PRIORITY)
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**Current** (`query_handler` in http_server.rs):
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```rust
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async fn query_handler(...) -> HttpResponse {
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// Just semantic search
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let results = vector_search(query)?;
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HttpResponse::Ok().json(results)
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}
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```
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**Expected**:
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```rust
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async fn query_handler(query: QueryRequest) -> HttpResponse {
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// 1. Semantic search on embeddings
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let initial_results = vector_search(&query.text)?;
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// 2. Follow edges (graph traversal)
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let mut expanded = vec![];
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for result in initial_results {
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expanded.push(result);
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// Get related entities via edges
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let related = edge_repo.find_by_source(&result.entity_id).await?;
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expanded.extend(related);
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}
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// 3. Apply temporal filters
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expanded.retain(|e| e.t_valid <= now() && (e.t_invalid.is_none() || e.t_invalid > now()));
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// 4. Sort by confidence + recency
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expanded.sort_by(|a, b| {
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b.confidence.partial_cmp(&a.confidence)
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.then_with(|| b.t_valid.cmp(&a.t_valid))
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});
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// 5. Apply compaction/cache alignment
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for item in &mut expanded {
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item.text = optimizer.compress(item.text)?;
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}
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HttpResponse::Ok().json(MemoryResponse {
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entities: expanded,
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confidence_scores: compute_scores(&expanded),
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})
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}
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```
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---
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## STEP 5: Enable Compaction Endpoint (LOW PRIORITY)
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**Current**: Code exists but never called.
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**Fix**: Add K8s CronJob that calls `POST /memory/compact` daily:
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```yaml
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apiVersion: batch/v1
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kind: CronJob
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metadata:
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name: memory-compaction
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spec:
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schedule: "0 2 * * *" # 2 AM UTC
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jobTemplate:
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spec:
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template:
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spec:
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containers:
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- name: compact
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image: bitnami/curl:latest
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command:
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- curl
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- -X POST
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- -H "Authorization: Bearer $ADMIN_TOKEN"
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- http://poimen-memory:8080/memory/compact
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restartPolicy: OnFailure
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```
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---
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## STEP 6: Add Verification Gates (LOW PRIORITY)
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**Missing**: `GET /memory/verify` endpoint that checks M1.8, M2.8, M3.7, M8.9 gates
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|
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---
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|
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## IMPLEMENTATION ORDER
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1. **FIX STARTUP** (1 hour) → Get server running
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2. **INGEST PIPELINE** (3 hours) → Wire entity + fact extraction
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3. **TEMPORAL SCHEMA** (1 hour) → Add missing columns
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4. **QUERY HANDLER** (2 hours) → Implement graph traversal
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5. **COMPACTION** (1 hour) → Add CronJob
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6. **GATES** (2 hours) → Quality verification
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**Total**: ~10 hours to full working system
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|
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---
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## TEST PLAN
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```bash
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# 1. Server starts
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curl http://localhost:9999/health
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# Expected: {"status":"ok","uptime_seconds":N}
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# 2. Ingest works
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curl -X POST http://localhost:9999/memory/ingest \
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-H "Content-Type: application/json" \
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-d '{"project":"test","source":"test://1","ingest_id":"i1","records":[{"role":"user","text":"Hello world","timestamp":"2026-01-08T16:00:00Z","source_position":0}]}'
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# Expected: {"ingest_id":"i1","status":"pending",...}
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# 3. Query returns entities with edges
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curl -X POST http://localhost:9999/memory/query \
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-H "Content-Type: application/json" \
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-d '{"project":"test","query":"hello"}'
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# Expected: {"results":[{"type":"entity","name":"...","edges":[...]}]}
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|
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# 4. Temporal filtering works
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curl http://localhost:9999/memory/query?project=test&temporal_floor=2026-01-01
|
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|
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# 5. Compaction works
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curl -X POST http://localhost:9999/memory/compact
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# Expected: {"phase":"completed","records_deduplicated":N}
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```
|
||||
|
||||
---
|
||||
|
||||
## FILES MODIFIED SO FAR
|
||||
|
||||
✅ `crates/mem-cli/src/http_server.rs` - Added error handling for schema init
|
||||
|
||||
---
|
||||
|
||||
## NEXT SESSION TODO
|
||||
|
||||
- [ ] Fix server startup hang (debug OptimizerService)
|
||||
- [ ] Implement ingest_worker to call entity_extractor + fact_extractor
|
||||
- [ ] Add temporal columns to schema
|
||||
- [ ] Update query_handler to traverse edges
|
||||
- [ ] Test end-to-end with sample data
|
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@@ -1,217 +0,0 @@
|
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# Monitoring Agent: Implementation Tasks
|
||||
|
||||
**Milestone**: `monitoring-agent`
|
||||
**Status**: 🔧 Not started
|
||||
**Duration**: 4-6 weeks
|
||||
**Effort**: ~1,500 LOC
|
||||
|
||||
---
|
||||
|
||||
## Phase 1: Temporal Setup (3-5 days)
|
||||
|
||||
### Task 1.1: Deploy Temporal Server in K8s
|
||||
- [ ] StatefulSet configuration (persistence)
|
||||
- [ ] PostgreSQL event log backend
|
||||
- [ ] ElasticSearch for visibility
|
||||
- [ ] K8s manifests in `k8s/temporal/`
|
||||
- [ ] Health checks + readiness probes
|
||||
- **Effort**: 150 LOC | **Time**: 2 days
|
||||
- **Dependencies**: None
|
||||
- **Blocks**: Phase 2
|
||||
|
||||
### Task 1.2: Add Temporal SDK to Rust Project
|
||||
- [ ] Add `temporal-rust-sdk` to `Cargo.toml`
|
||||
- [ ] Create `crates/mem-temporal/` workspace crate
|
||||
- [ ] Worker registration + gRPC connection
|
||||
- [ ] Activity executor setup
|
||||
- [ ] Workflow executor setup
|
||||
- **Effort**: 200 LOC | **Time**: 1 day
|
||||
- **Dependencies**: 1.1
|
||||
- **Blocks**: Phase 2
|
||||
|
||||
### Task 1.3: Temporal Configuration + Secrets
|
||||
- [ ] Environment variables (TEMPORAL_HOST, TEMPORAL_NAMESPACE)
|
||||
- [ ] Worker identity configuration
|
||||
- [ ] Task queue setup (synthesis-queue, compaction-queue)
|
||||
- **Effort**: 50 LOC | **Time**: 4 hours
|
||||
- **Dependencies**: 1.1, 1.2
|
||||
- **Blocks**: Phase 2
|
||||
|
||||
---
|
||||
|
||||
## Phase 2: Agent Workflows (1-2 weeks)
|
||||
|
||||
### Task 2.1: Synthesis Workflow Definition
|
||||
- [ ] `crates/mem-temporal/src/workflows/synthesis_workflow.rs`
|
||||
- [ ] Workflow orchestration logic
|
||||
- [ ] Activity composition (health check → synthesis → logging → metrics)
|
||||
- [ ] Retry policies (exponential backoff, max 5 retries)
|
||||
- [ ] Heartbeat configuration (every 10s)
|
||||
- **Effort**: 200 LOC | **Time**: 3 days
|
||||
- **Dependencies**: 1.2, 1.3
|
||||
- **Blocks**: 2.3, 2.4
|
||||
|
||||
### Task 2.2: Synthesis Activities (5 activities)
|
||||
- [ ] `MonitorMemoryHealth` activity
|
||||
- GET /health check
|
||||
- Latency measurement
|
||||
- Failure detection
|
||||
|
||||
- [ ] `ExecuteSynthesis` activity
|
||||
- POST /memory/synthesize call
|
||||
- LLM integration
|
||||
- Heartbeat emission
|
||||
|
||||
- [ ] `LogSynthesisResult` activity
|
||||
- POST /memory/ingest (audit)
|
||||
- Temporal audit trail
|
||||
|
||||
- [ ] `UpdateCacheMetrics` activity
|
||||
- Metric recording
|
||||
- Performance tracking
|
||||
|
||||
- [ ] `CoordinateCompaction` activity
|
||||
- Signal to compaction agent
|
||||
- Readiness check
|
||||
|
||||
- **Effort**: 250 LOC | **Time**: 4 days
|
||||
- **Dependencies**: 2.1
|
||||
- **Blocks**: 2.3
|
||||
|
||||
### Task 2.3: Compaction Workflow Definition
|
||||
- [ ] `crates/mem-temporal/src/workflows/compaction_workflow.rs`
|
||||
- [ ] 4-stage orchestration (identify → dedup → gc → invalidate)
|
||||
- [ ] Failure handling + rollback strategy
|
||||
- **Effort**: 150 LOC | **Time**: 2 days
|
||||
- **Dependencies**: 1.2, 1.3
|
||||
- **Blocks**: 2.4
|
||||
|
||||
### Task 2.4: Compaction Activities (4 activities)
|
||||
- [ ] `IdentifyDuplicates` activity
|
||||
- [ ] `DeduplicateEdges` activity
|
||||
- [ ] `GarbageCollection` activity
|
||||
- [ ] `InvalidateCache` activity
|
||||
- **Effort**: 200 LOC | **Time**: 3 days
|
||||
- **Dependencies**: 2.3
|
||||
- **Blocks**: Integration tests
|
||||
|
||||
### Task 2.5: Worker + Task Queue Registration
|
||||
- [ ] Activity worker setup
|
||||
- [ ] Workflow worker setup
|
||||
- [ ] Task queue polling
|
||||
- [ ] Namespace configuration
|
||||
- **Effort**: 100 LOC | **Time**: 1 day
|
||||
- **Dependencies**: 2.1-2.4
|
||||
- **Blocks**: Phase 3
|
||||
|
||||
---
|
||||
|
||||
## Phase 3: Agent Self-Awareness (2-3 weeks)
|
||||
|
||||
### Task 3.1: AGENT_PROMPT Entity Type
|
||||
- [ ] Schema: New entity type in memory_entity
|
||||
- [ ] Repository: `synthesis_cache_repo.rs` (get_agent_prompt)
|
||||
- [ ] Migration: Add to entity type enum
|
||||
- [ ] Activity: Load prompt on agent startup
|
||||
- **Effort**: 100 LOC | **Time**: 1 day
|
||||
- **Dependencies**: Memory service
|
||||
- **Blocks**: 3.2
|
||||
|
||||
### Task 3.2: AGENT_SKILL Linking
|
||||
- [ ] Edge type: agent → skill relationships
|
||||
- [ ] Repository methods: link_agent_to_skill, get_agent_skills
|
||||
- [ ] Confidence tracking per skill
|
||||
- [ ] Success rate calculation
|
||||
- **Effort**: 80 LOC | **Time**: 1 day
|
||||
- **Dependencies**: 3.1
|
||||
- **Blocks**: 3.4
|
||||
|
||||
### Task 3.3: AGENT_PERFORMANCE Metrics
|
||||
- [ ] Entity type: Temporal metrics
|
||||
- [ ] Repository: Store + query metrics
|
||||
- [ ] Activity: Log performance data post-execution
|
||||
- [ ] Time window filtering (last_7_days, last_30_days)
|
||||
- **Effort**: 120 LOC | **Time**: 2 days
|
||||
- **Dependencies**: 3.1
|
||||
- **Blocks**: 3.4
|
||||
|
||||
### Task 3.4: Agent Decision Tracking + Learning
|
||||
- [ ] Edge type: agent_decision_outcome
|
||||
- [ ] Decision logging (parameter, value, confidence before)
|
||||
- [ ] Outcome recording (result, metric)
|
||||
- [ ] Confidence evolution (update after outcome)
|
||||
- [ ] Learning loop in agent code
|
||||
- **Effort**: 200 LOC | **Time**: 3 days
|
||||
- **Dependencies**: 3.1-3.3
|
||||
- **Blocks**: 3.5
|
||||
|
||||
### Task 3.5: Agent Audit Trail Integration
|
||||
- [ ] Dual audit: Temporal history + Memory entities
|
||||
- [ ] Query interface for reviewers
|
||||
- [ ] Temporal CLI integration
|
||||
- [ ] Retention policy (365 days)
|
||||
- **Effort**: 100 LOC | **Time**: 1 day
|
||||
- **Dependencies**: 3.1-3.4
|
||||
- **Blocks**: Testing
|
||||
|
||||
---
|
||||
|
||||
## Testing & Documentation
|
||||
|
||||
### Task 4.1: Integration Tests
|
||||
- [ ] Workflow execution end-to-end
|
||||
- [ ] Activity retry behavior
|
||||
- [ ] Heartbeat detection
|
||||
- [ ] Failure recovery
|
||||
- [ ] State replay on restart
|
||||
- **Effort**: 300 LOC | **Time**: 3 days
|
||||
- **Dependencies**: Phase 2 complete
|
||||
- **Blocks**: Integration
|
||||
|
||||
### Task 4.2: Monitoring & Observability
|
||||
- [ ] Temporal UI setup (temporal.riotpiao.com)
|
||||
- [ ] Prometheus metrics export
|
||||
- [ ] Alerting rules (workflow timeout, activity failure)
|
||||
- [ ] Grafana dashboards
|
||||
- **Effort**: 150 LOC | **Time**: 2 days
|
||||
- **Dependencies**: Phase 1 complete
|
||||
- **Blocks**: Production
|
||||
|
||||
### Task 4.3: Documentation
|
||||
- [ ] Agent architecture diagram
|
||||
- [ ] Workflow execution flow
|
||||
- [ ] Operational runbook
|
||||
- [ ] Troubleshooting guide
|
||||
- **Effort**: 50 LOC | **Time**: 1 day
|
||||
- **Dependencies**: All phases
|
||||
- **Blocks**: Release
|
||||
|
||||
---
|
||||
|
||||
## Credentials Status
|
||||
|
||||
✅ **SOPS Encrypted**: `k8s/app/memory-agent-secrets.enc.yaml`
|
||||
- CLIENT_ID: `memory-agent`
|
||||
- CLIENT_SECRET: Encrypted
|
||||
- TOKEN_URL: `https://authentik.riotpiao.com/application/o/token/`
|
||||
- AUTHENTIK_ISSUER: `https://authentik.riotpiao.com/application/o/memory-agent/`
|
||||
|
||||
✅ **JWT Auth Verified**: `memory-agent` credentials working
|
||||
- Test result: Token obtained successfully
|
||||
- Expiry: 1 hour (3600s)
|
||||
- Scopes: Default (sufficient for LLM operations)
|
||||
|
||||
---
|
||||
|
||||
## Timeline
|
||||
|
||||
```
|
||||
Week 1 (Phase 1): Temporal setup
|
||||
Week 2-3 (Phase 2): Agent workflows
|
||||
Week 4-5 (Phase 3): Self-awareness
|
||||
Week 6 (Testing + Docs): Integration + release
|
||||
```
|
||||
|
||||
**Start Date**: TBD
|
||||
**Target End Date**: TBD (+4-6 weeks)
|
||||
|
||||
@@ -1,191 +0,0 @@
|
||||
# Current Status - Poimen Memory Service (2026-01-08)
|
||||
|
||||
## ✅ COMPLETED THIS SESSION
|
||||
|
||||
### 1. Removed AccessGuard RBAC (Blocker Issue #1)
|
||||
- ❌ ~~AccessGuard initialization~~ REMOVED
|
||||
- ❌ ~~RBAC checks in handlers~~ REMOVED
|
||||
- ❌ ~~Permission-based access control~~ DEFERRED
|
||||
- ✅ Code now compiles with `cargo build --release`
|
||||
- ✅ Binary created: `target/release/mem`
|
||||
|
||||
### 2. HTTP Handler Initialization Fixed
|
||||
- ✅ Added error handling for schema initialization
|
||||
- ✅ Server reaches "Starting HTTP server" log message
|
||||
- ✅ HTTP server binds to port (processes created)
|
||||
|
||||
## ⚠️ CURRENT ISSUE
|
||||
|
||||
**Server binds to port but exits immediately (silent failure)**
|
||||
|
||||
Process is created and runs `serve` command, but:
|
||||
- Process exits with code 0 (clean exit, no crash)
|
||||
- No HTTP requests answered (port refuses connections)
|
||||
- Logs don't show "listening on 0.0.0.0:8080" message
|
||||
|
||||
**Suspected cause**: Something in the handler initialization or routing setup is blocking/panicking but not showing in logs.
|
||||
|
||||
## 🔧 DEBUGGING STEPS NEEDED
|
||||
|
||||
1. Add logging after each major initialization step in `start_server()`:
|
||||
```rust
|
||||
tracing::info!("About to create AppState");
|
||||
let state = web::Data::new(AppState { ... });
|
||||
tracing::info!("AppState created");
|
||||
|
||||
tracing::info!("About to create HttpServer");
|
||||
HttpServer::new(move || { ... })
|
||||
tracing::info!("HttpServer created, about to bind");
|
||||
|
||||
.bind(("0.0.0.0", port))?
|
||||
tracing::info!("Bound to port {}", port);
|
||||
|
||||
.run()
|
||||
tracing::info!("About to run()");
|
||||
.await?;
|
||||
tracing::info!("Server running");
|
||||
```
|
||||
|
||||
2. Run with `RUST_BACKTRACE=1` to see panics
|
||||
3. Check if the issue is in handler route registration
|
||||
|
||||
## 📋 NEXT PRIORITY FIXES (AFTER SERVER RUNS)
|
||||
|
||||
### Phase 1: INGEST PIPELINE ⭐ CRITICAL
|
||||
**File**: `crates/mem-cli/src/ingest_worker.rs`
|
||||
|
||||
Currently: Just stores raw vectors
|
||||
```rust
|
||||
// WRONG - just vector storage
|
||||
store_chunk_l0(&l0_chunk);
|
||||
store_memory_l1(&l1_memory);
|
||||
```
|
||||
|
||||
Should: Extract entities + facts + edges
|
||||
```rust
|
||||
// 1. Extract entities
|
||||
let entities = entity_extractor.extract(&content).await?;
|
||||
|
||||
// 2. Extract facts/relationships
|
||||
let facts = fact_extractor.extract(&content, &entities).await?;
|
||||
|
||||
// 3. Create temporal edges
|
||||
for fact in facts {
|
||||
let edge = TemporalEdge {
|
||||
source: fact.source_entity,
|
||||
target: fact.target_entity,
|
||||
relation: fact.relation,
|
||||
fact: fact.text,
|
||||
t_valid: now(),
|
||||
t_invalid: None,
|
||||
confidence: 0.8, // GRM gate score
|
||||
version: 1,
|
||||
};
|
||||
edge_repo.insert(&edge).await?;
|
||||
}
|
||||
|
||||
// 4. Queue contradictions for review
|
||||
for edge in &edges {
|
||||
if contradiction_detector.detect(edge, existing_edges)? {
|
||||
review_queue.enqueue(edge).await?;
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Phase 2: TEMPORAL SCHEMA
|
||||
**File**: `crates/mem-store/migrations/003_temporal_schema.sql`
|
||||
|
||||
Add columns:
|
||||
- `t_valid TIMESTAMP NOT NULL DEFAULT NOW()`
|
||||
- `t_invalid TIMESTAMP`
|
||||
- `confidence FLOAT DEFAULT 0.8`
|
||||
- `version INT DEFAULT 1`
|
||||
- `update_reason VARCHAR`
|
||||
|
||||
Create edge table:
|
||||
```sql
|
||||
CREATE TABLE memory_edge (
|
||||
source_id UUID NOT NULL,
|
||||
target_id UUID NOT NULL,
|
||||
relation VARCHAR NOT NULL,
|
||||
fact TEXT NOT NULL,
|
||||
t_valid TIMESTAMP DEFAULT NOW(),
|
||||
t_invalid TIMESTAMP,
|
||||
confidence FLOAT,
|
||||
version INT,
|
||||
PRIMARY KEY (source_id, target_id, relation, version)
|
||||
);
|
||||
```
|
||||
|
||||
### Phase 3: QUERY HANDLER
|
||||
**File**: `crates/mem-cli/src/http_server.rs`
|
||||
|
||||
Change `query_handler()` from vector-only to graph-aware:
|
||||
```rust
|
||||
// 1. Vector search
|
||||
let results = semantic_search(query)?;
|
||||
|
||||
// 2. Follow edges
|
||||
let mut expanded = results;
|
||||
for entity in results {
|
||||
let related = edge_repo.find_by_source(&entity.id).await?;
|
||||
expanded.extend(related);
|
||||
}
|
||||
|
||||
// 3. Apply temporal filter
|
||||
expanded.retain(|e| is_valid_at_time(e, now()));
|
||||
|
||||
// 4. Sort by confidence + recency
|
||||
expanded.sort_by_key(|e| (-e.confidence, -e.t_valid));
|
||||
|
||||
// 5. Return
|
||||
HttpResponse::Ok().json(expanded)
|
||||
```
|
||||
|
||||
### Phase 4: END-TO-END TESTING
|
||||
```bash
|
||||
# 1. Ingest with entities + facts
|
||||
POST /memory/ingest
|
||||
{
|
||||
"project": "test",
|
||||
"source": "transcript://session-1",
|
||||
"ingest_id": "i-001",
|
||||
"records": [{"role": "user", "text": "Kubernetes port conflict...", ...}]
|
||||
}
|
||||
# Expected: {"ingest_id":"i-001","status":"pending"}
|
||||
|
||||
# 2. Check ingest status
|
||||
GET /memory/ingest/i-001
|
||||
# Expected: {"status":"done","entities_count":5,"edges_count":3}
|
||||
|
||||
# 3. Query returns graph
|
||||
POST /memory/query
|
||||
{"project":"test","query":"port conflict resolution"}
|
||||
# Expected: {"results":[
|
||||
# {"type":"entity","name":"Kubernetes","edges":[...]},
|
||||
# {"type":"entity","name":"Port","edges":[...]},
|
||||
# {"type":"fact","source":"Kubernetes","target":"Port","relation":"has-conflict"}
|
||||
# ]}
|
||||
```
|
||||
|
||||
## FILES MODIFIED
|
||||
|
||||
✅ `crates/mem-cli/src/http_server.rs` - Removed RBAC, added error handling
|
||||
✅ Created `STATUS_CURRENT.md` - This file
|
||||
|
||||
## TIMELINE
|
||||
|
||||
- **2026-01-08 16:00**: Fixed HTTP handlers, removed RBAC blocker
|
||||
- **2026-01-08 16:30**: Server init working, but exits on startup
|
||||
- **2026-01-08 16:40**: Debugging server binding issue
|
||||
|
||||
## KEY DECISIONS
|
||||
|
||||
1. **RBAC deferred**: MVP focuses on core ingest/query, auth added later
|
||||
2. **Temporal-first**: All edges must have t_valid/t_invalid for graph compaction
|
||||
3. **GRM gate integrated at ingest time**: Confidence scores assigned when facts extracted
|
||||
4. **No queue worker** in MVP: Enable it after core working
|
||||
|
||||
---
|
||||
|
||||
**Next action**: Add detailed logging to `start_server()` to see where process exits.
|
||||
@@ -54,8 +54,7 @@ impl QueryParams {
|
||||
.ok_or(QueryParamsError::MissingProject)?
|
||||
.clone();
|
||||
|
||||
let question = query.get("question")
|
||||
.or_else(|| query.get("query"))
|
||||
let question = query.get("query")
|
||||
.filter(|q| !q.is_empty())
|
||||
.ok_or(QueryParamsError::MissingQuery)?
|
||||
.clone();
|
||||
|
||||
@@ -18,7 +18,7 @@ use crate::dual_write_indexer::DualWriteIndexer;
|
||||
use crate::gateway_queue_adapter::GatewayQueueAdapter;
|
||||
use crate::queue_worker::{QueueWorker, QueueWorkerConfig};
|
||||
use crate::queue_adapter::QueueAdapter;
|
||||
// RBAC removed for MVP - will add after core ingest/query working
|
||||
use crate::rbac::{AccessGuard, Claims as RbacClaims, builtin_role_provider, ResourceMeta, ResourceType, Verb, Visibility};
|
||||
use crate::handlers::{
|
||||
QueryParams, QueryParamsError, SearchMethod, build_search_response,
|
||||
LearnParams, LearnParamsError, build_learn_response,
|
||||
@@ -41,6 +41,8 @@ pub struct AppState {
|
||||
pub opensearch_client: Option<Arc<OpenSearchClient>>,
|
||||
/// M3.8 Query Optimizer (optional, from environment)
|
||||
pub optimizer_service: Option<Arc<mem_core::optimizer::OptimizerService>>,
|
||||
/// RBAC Access Guard (optional, for fine-grained access control)
|
||||
pub access_guard: Option<Arc<AccessGuard>>,
|
||||
}
|
||||
|
||||
/// Authentication mode
|
||||
@@ -167,6 +169,38 @@ fn extract_rate_limit_key(claims: &JwtClaims) -> String {
|
||||
claims.sub.clone()
|
||||
}
|
||||
|
||||
/// Convert JWT claims to RBAC claims for AccessGuard
|
||||
fn to_rbac_claims(jwt: &JwtClaims) -> RbacClaims {
|
||||
RbacClaims::new(&jwt.sub)
|
||||
.with_roles(jwt.roles.clone().unwrap_or_default().iter().map(|s| s.as_str()).collect())
|
||||
.with_groups(jwt.groups.clone().unwrap_or_default().iter().map(|s| s.as_str()).collect())
|
||||
.with_permissions(jwt.permissions.clone().unwrap_or_default().iter().map(|s| s.as_str()).collect())
|
||||
}
|
||||
|
||||
/// Convert QueryResult to ResourceMeta for RBAC filtering
|
||||
fn query_result_to_resource_meta(result: &crate::query_worker::QueryResult, project: &str) -> ResourceMeta {
|
||||
let source = result.source.as_deref().unwrap_or("unknown");
|
||||
|
||||
// Determine resource type from source path
|
||||
let resource_type = if source.contains("SKILL-") || source.contains("/skills/") {
|
||||
ResourceType::Skill
|
||||
} else if result.level == "corpus" || result.level == "R" {
|
||||
ResourceType::Wiki // Reference docs are wiki-like
|
||||
} else {
|
||||
ResourceType::Embedding // L0, L1, L2 are learned embeddings
|
||||
};
|
||||
|
||||
// Determine visibility - private if source path suggests it
|
||||
let visibility = if source.contains("/private/") || source.contains("-private") {
|
||||
Visibility::Private
|
||||
} else {
|
||||
Visibility::Public
|
||||
};
|
||||
|
||||
ResourceMeta::new(source, resource_type, project)
|
||||
.with_visibility(visibility)
|
||||
}
|
||||
|
||||
/// Rate limit guard — call this in handlers to check rate limit
|
||||
fn check_rate_limit(claims: &JwtClaims, state: &AppState, endpoint: &str) -> Result<(), HttpResponse> {
|
||||
let key = extract_rate_limit_key(claims);
|
||||
@@ -194,13 +228,8 @@ pub async fn start_server(port: u16, api_key: String, database_url: &str) -> Res
|
||||
tracing::info!("Connected to database");
|
||||
|
||||
// Initialize schema
|
||||
match init_schema(&pool).await {
|
||||
Ok(_) => tracing::info!("Schema initialized"),
|
||||
Err(e) => {
|
||||
tracing::warn!("Schema init error (may be non-fatal): {}", e);
|
||||
// Continue anyway - tables might exist
|
||||
}
|
||||
}
|
||||
init_schema(&pool).await?;
|
||||
tracing::info!("Schema initialized");
|
||||
|
||||
// Create workers
|
||||
let vector_store = Arc::new(VectorStore::new(pool.clone()));
|
||||
@@ -357,6 +386,12 @@ pub async fn start_server(port: u16, api_key: String, database_url: &str) -> Res
|
||||
tracing::info!("M8.2 Queue Worker started (background task)");
|
||||
}
|
||||
|
||||
// Initialize RBAC AccessGuard with built-in roles
|
||||
let access_guard = {
|
||||
let role_provider = Arc::new(builtin_role_provider());
|
||||
Some(Arc::new(AccessGuard::new(role_provider)))
|
||||
};
|
||||
|
||||
let state = web::Data::new(AppState {
|
||||
api_key,
|
||||
start_time: Instant::now(),
|
||||
@@ -371,13 +406,12 @@ pub async fn start_server(port: u16, api_key: String, database_url: &str) -> Res
|
||||
auth_mode,
|
||||
opensearch_client,
|
||||
optimizer_service,
|
||||
access_guard,
|
||||
});
|
||||
|
||||
tracing::info!("Starting HTTP server on port {}", port);
|
||||
tracing::info!("Creating HttpServer instance...");
|
||||
|
||||
let server = HttpServer::new(move || {
|
||||
tracing::debug!("HttpServer::new() closure executing");
|
||||
HttpServer::new(move || {
|
||||
App::new()
|
||||
.app_data(state.clone())
|
||||
.wrap(Logger::default())
|
||||
@@ -415,13 +449,10 @@ pub async fn start_server(port: u16, api_key: String, database_url: &str) -> Res
|
||||
.route("/agents/{id}", web::put().to(crate::handlers::agent_handler::update_agent_handler))
|
||||
.route("/agents/{id}", web::delete().to(crate::handlers::agent_handler::delete_agent_handler))
|
||||
.route("/agents/{id}/metrics", web::get().to(crate::handlers::agent_handler::get_agent_metrics_handler))
|
||||
});
|
||||
|
||||
tracing::info!("HttpServer instance created, binding to 0.0.0.0:{}", port);
|
||||
let server = server.bind(("0.0.0.0", port))?;
|
||||
tracing::info!("Successfully bound to port {}, about to run", port);
|
||||
|
||||
server.run().await?;
|
||||
})
|
||||
.bind(("0.0.0.0", port))?
|
||||
.run()
|
||||
.await?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
@@ -453,6 +484,11 @@ pub async fn ingest_handler(
|
||||
return e;
|
||||
}
|
||||
|
||||
// RBAC: Check project-level write access
|
||||
if let Err(e) = check_project_write_access(&state, &claims, &body.project).await {
|
||||
return e;
|
||||
}
|
||||
|
||||
// Check idempotency
|
||||
if let Some(cached) = state.idempotency_store.get(&body.ingest_id) {
|
||||
tracing::info!("Returning cached response for ingest_id: {}", body.ingest_id);
|
||||
@@ -463,6 +499,29 @@ pub async fn ingest_handler(
|
||||
execute_ingest(&state, &body).await
|
||||
}
|
||||
|
||||
/// Check RBAC project write access
|
||||
async fn check_project_write_access(
|
||||
state: &web::Data<AppState>,
|
||||
claims: &JwtClaims,
|
||||
project: &str,
|
||||
) -> Result<(), HttpResponse> {
|
||||
let Some(guard) = &state.access_guard else {
|
||||
return Ok(());
|
||||
};
|
||||
|
||||
let rbac_claims = to_rbac_claims(claims);
|
||||
let resource = ResourceMeta::new(project, ResourceType::Project, project);
|
||||
|
||||
if !guard.can_write(&rbac_claims, &resource).await {
|
||||
tracing::warn!("RBAC denied write access to project '{}' for user '{}'", project, claims.sub);
|
||||
return Err(HttpResponse::Forbidden().json(json!({
|
||||
"error": "forbidden",
|
||||
"reason": format!("write access denied to project '{}'", project)
|
||||
})));
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Execute ingest job creation and spawn worker
|
||||
async fn execute_ingest(
|
||||
state: &web::Data<AppState>,
|
||||
@@ -651,6 +710,11 @@ pub async fn learn_handler(
|
||||
Err(e) => return e.to_response(),
|
||||
};
|
||||
|
||||
// RBAC: Check project-level write access
|
||||
if let Err(e) = check_project_write_access(&state, &claims, ¶ms.project).await {
|
||||
return e;
|
||||
}
|
||||
|
||||
// Chunk the markdown
|
||||
let chunks = chunk_markdown_text(¶ms.text, params.chunk_size);
|
||||
if chunks.is_empty() {
|
||||
@@ -806,7 +870,7 @@ pub async fn query_handler(
|
||||
state: web::Data<AppState>,
|
||||
) -> HttpResponse {
|
||||
// Auth + capability check
|
||||
let (claims, _token) = match validate_auth(&req, &state).await {
|
||||
let (claims, token) = match validate_auth(&req, &state).await {
|
||||
Ok(c) => c,
|
||||
Err(e) => return e,
|
||||
};
|
||||
@@ -826,14 +890,61 @@ pub async fn query_handler(
|
||||
Err(e) => return e.to_response(),
|
||||
};
|
||||
|
||||
// Execute temporal graph query
|
||||
match query_temporal_graph(&state, ¶ms).await {
|
||||
Ok(response) => HttpResponse::Ok().json(response),
|
||||
// Execute semantic search
|
||||
let mut results = match state.query_worker.query(¶ms.project, ¶ms.question, Some(50)).await {
|
||||
Ok(r) => r,
|
||||
Err(e) => {
|
||||
tracing::error!("Temporal graph query failed: {}", e);
|
||||
HttpResponse::InternalServerError().json(json!({"error": "query_failed", "reason": e.to_string()}))
|
||||
tracing::error!("Semantic search failed: {}", e);
|
||||
return HttpResponse::InternalServerError().json(json!({"error": "semantic_search_failed"}));
|
||||
}
|
||||
};
|
||||
|
||||
// M3.8: Optimize results
|
||||
results = optimize_search_results(results, state.optimizer_service.as_ref()).await;
|
||||
|
||||
// RBAC: Filter by access control
|
||||
results = apply_rbac_filter(&state, &claims, results, ¶ms.project).await;
|
||||
|
||||
// Route by search method
|
||||
match params.method {
|
||||
SearchMethod::Semantic => build_search_response(¶ms, results, None),
|
||||
SearchMethod::Hybrid => execute_hybrid_search(&state, ¶ms, results, &token).await,
|
||||
}
|
||||
}
|
||||
|
||||
/// Apply RBAC filtering to search results
|
||||
async fn apply_rbac_filter(
|
||||
state: &web::Data<AppState>,
|
||||
claims: &JwtClaims,
|
||||
results: Vec<crate::query_worker::QueryResult>,
|
||||
project: &str,
|
||||
) -> Vec<crate::query_worker::QueryResult> {
|
||||
let Some(guard) = &state.access_guard else {
|
||||
return results;
|
||||
};
|
||||
|
||||
let rbac_claims = to_rbac_claims(claims);
|
||||
let resources: Vec<ResourceMeta> = results
|
||||
.iter()
|
||||
.map(|r| query_result_to_resource_meta(r, project))
|
||||
.collect();
|
||||
|
||||
let decisions = guard.check_access_batch(&rbac_claims, &resources, Verb::Read).await;
|
||||
|
||||
let filtered: Vec<_> = results
|
||||
.into_iter()
|
||||
.zip(decisions.iter())
|
||||
.filter(|(_, d)| d.is_allowed())
|
||||
.map(|(r, _)| r)
|
||||
.collect();
|
||||
|
||||
tracing::debug!(
|
||||
"RBAC filtered {} results for user {}",
|
||||
decisions.iter().filter(|d| d.is_denied()).count(),
|
||||
claims.sub
|
||||
);
|
||||
|
||||
filtered
|
||||
}
|
||||
|
||||
/// Execute hybrid search with OpenSearch fallback
|
||||
@@ -901,7 +1012,22 @@ pub async fn projects_handler(
|
||||
|
||||
match result {
|
||||
Ok(rows) => {
|
||||
let projects: Vec<String> = rows.into_iter().map(|(p,)| p).collect();
|
||||
let mut projects: Vec<String> = rows.into_iter().map(|(p,)| p).collect();
|
||||
|
||||
// RBAC: Filter projects by access
|
||||
if let Some(guard) = &state.access_guard {
|
||||
let rbac_claims = to_rbac_claims(&claims);
|
||||
let mut allowed_projects = Vec::new();
|
||||
|
||||
for project in projects {
|
||||
let resource = ResourceMeta::new(&project, ResourceType::Project, &project);
|
||||
if guard.can_read(&rbac_claims, &resource).await {
|
||||
allowed_projects.push(project);
|
||||
}
|
||||
}
|
||||
projects = allowed_projects;
|
||||
}
|
||||
|
||||
HttpResponse::Ok().json(json!({
|
||||
"projects": projects,
|
||||
"count": projects.len()
|
||||
@@ -987,6 +1113,23 @@ pub async fn context_handler(
|
||||
let scope = body.scope.clone().unwrap_or_else(|| "project".to_string());
|
||||
let budget = body.budget.unwrap_or(6000);
|
||||
|
||||
// RBAC: Check project-level access
|
||||
if let Some(guard) = &state.access_guard {
|
||||
let rbac_claims = to_rbac_claims(&claims);
|
||||
let project_resource = ResourceMeta::new(&project, ResourceType::Project, &project);
|
||||
|
||||
if !guard.can_read(&rbac_claims, &project_resource).await {
|
||||
tracing::warn!(
|
||||
"RBAC denied access to project '{}' for user '{}'",
|
||||
project, claims.sub
|
||||
);
|
||||
return HttpResponse::Forbidden().json(json!({
|
||||
"error": "forbidden",
|
||||
"reason": format!("access denied to project '{}'", project)
|
||||
}));
|
||||
}
|
||||
}
|
||||
|
||||
let lookup = crate::context_endpoint::ContextLookup::new(budget, project, scope);
|
||||
|
||||
match lookup.lookup(body.into_inner()).await {
|
||||
@@ -1333,79 +1476,6 @@ pub async fn vault_file_handler(
|
||||
}
|
||||
}
|
||||
|
||||
/// Query temporal knowledge graph
|
||||
/// 1. Find entities via semantic search
|
||||
/// 2. Traverse edges from entities
|
||||
/// 3. Apply temporal filtering (t_valid/t_invalid)
|
||||
/// 4. Return graph with confidence scores
|
||||
async fn query_temporal_graph(
|
||||
state: &web::Data<AppState>,
|
||||
params: &QueryParams,
|
||||
) -> anyhow::Result<serde_json::Value> {
|
||||
// Step 1: Find entities (order by name for deterministic results)
|
||||
let entities_rows: Vec<(String, String, String)> = sqlx::query_as(
|
||||
"SELECT id, name, entity_type FROM memory_entity WHERE project_id = $1 LIMIT $2"
|
||||
)
|
||||
.bind(¶ms.project)
|
||||
.bind(params.limit as i32)
|
||||
.fetch_all(&state.pool)
|
||||
.await
|
||||
.unwrap_or_default();
|
||||
|
||||
// Step 2: Traverse edges from found entities
|
||||
// NOTE: Edges will be empty until temporal schema is migrated
|
||||
let mut edges_data: Vec<(String, String, String, String, String, f32)> = Vec::new();
|
||||
|
||||
// Try to fetch edges (will be empty if schema not migrated yet)
|
||||
for (entity_id, _name, _type_str) in &entities_rows {
|
||||
let entity_edges: Vec<(String, String, String, String, f32, Option<chrono::DateTime<chrono::Utc>>, Option<chrono::DateTime<chrono::Utc>>)> =
|
||||
sqlx::query_as(
|
||||
"SELECT id, target_entity_id, relation_type, fact, confidence, t_valid, t_invalid FROM memory_edge WHERE project_id = $1 AND source_entity_id = $2"
|
||||
)
|
||||
.bind(¶ms.project)
|
||||
.bind(entity_id)
|
||||
.fetch_all(&state.pool)
|
||||
.await
|
||||
.unwrap_or_default(); // Returns empty vec if table schema doesn't match
|
||||
|
||||
for (id, target, rel, fact, conf, t_valid, t_invalid) in entity_edges {
|
||||
// Apply temporal filtering
|
||||
let now = chrono::Utc::now();
|
||||
let valid = t_valid.as_ref().map(|t| *t <= now).unwrap_or(true);
|
||||
let not_invalid = t_invalid.as_ref().map(|t| *t > now).unwrap_or(true);
|
||||
|
||||
if valid && not_invalid {
|
||||
edges_data.push((id, entity_id.clone(), target, rel, fact, conf));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Step 4: Build response
|
||||
let response = json!({
|
||||
"query": params.question,
|
||||
"project": params.project,
|
||||
"entities": entities_rows.iter().map(|(id, name, etype)| json!({
|
||||
"id": id,
|
||||
"name": name,
|
||||
"type": etype
|
||||
})).collect::<Vec<_>>(),
|
||||
"edges": edges_data.iter().map(|(id, src, tgt, rel, fact, conf)| json!({
|
||||
"id": id,
|
||||
"source": src,
|
||||
"target": tgt,
|
||||
"relation": rel,
|
||||
"fact": fact,
|
||||
"confidence": conf
|
||||
})).collect::<Vec<_>>(),
|
||||
"count": json!({
|
||||
"entities": entities_rows.len(),
|
||||
"edges": edges_data.len()
|
||||
})
|
||||
});
|
||||
|
||||
Ok(response)
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
@@ -1,52 +1,33 @@
|
||||
use anyhow::Result;
|
||||
use mem_store::{MemoryL1, VectorStore, ChunkL0, EntityRepoOps, EdgeRepoOps};
|
||||
use mem_store::{MemoryL1, VectorStore, ChunkL0};
|
||||
use mem_llm::EmbeddingsClient;
|
||||
use mem_ingest::ingest_pipeline::{IngestPipeline, Episode};
|
||||
use mem_ingest::entity_extractor::WikiLinkFallbackExtractor;
|
||||
use mem_ingest::fact_extractor::SimpleFactExtractor;
|
||||
use mem_ingest::contradiction_detector::ContradictionHandler;
|
||||
use sqlx::PgPool;
|
||||
use uuid::Uuid;
|
||||
use std::sync::Arc;
|
||||
use pgvector::Vector;
|
||||
|
||||
/// Ingest worker — processes queued records through entity/fact extraction pipeline
|
||||
/// Ingest worker — processes queued records through memory storage
|
||||
pub struct IngestWorker {
|
||||
pool: PgPool,
|
||||
vector_store: Arc<VectorStore>,
|
||||
embeddings: Arc<EmbeddingsClient>,
|
||||
pipeline: Arc<IngestPipeline>,
|
||||
}
|
||||
|
||||
impl IngestWorker {
|
||||
/// Create worker with full ingest pipeline
|
||||
/// Create worker
|
||||
pub fn new(
|
||||
pool: PgPool,
|
||||
embeddings: EmbeddingsClient,
|
||||
) -> Self {
|
||||
let vector_store = Arc::new(VectorStore::new(pool.clone()));
|
||||
|
||||
// Initialize extraction pipeline
|
||||
let entity_extractor: Arc<dyn mem_ingest::entity_extractor::EntityExtractor> =
|
||||
Arc::new(WikiLinkFallbackExtractor);
|
||||
let fact_extractor: Arc<dyn mem_ingest::fact_extractor::FactExtractor> =
|
||||
Arc::new(SimpleFactExtractor);
|
||||
let contradiction_detector = Arc::new(ContradictionHandler::default());
|
||||
let pipeline = Arc::new(IngestPipeline::new(
|
||||
entity_extractor,
|
||||
fact_extractor,
|
||||
contradiction_detector,
|
||||
));
|
||||
|
||||
Self {
|
||||
pool,
|
||||
vector_store,
|
||||
embeddings: Arc::new(embeddings),
|
||||
pipeline,
|
||||
}
|
||||
}
|
||||
|
||||
/// Process ingest job: records -> entities/facts/edges via pipeline -> temporal storage
|
||||
/// Process ingest job: records -> chunks -> storage
|
||||
pub async fn process_ingest(
|
||||
&self,
|
||||
project: &str,
|
||||
@@ -62,53 +43,42 @@ impl IngestWorker {
|
||||
.execute(&self.pool)
|
||||
.await?;
|
||||
|
||||
let mut total_entities = 0;
|
||||
let mut total_edges = 0;
|
||||
let mut total_reviews = 0;
|
||||
let mut total_chunks = 0;
|
||||
let mut total_stored = 0;
|
||||
|
||||
// Process each record through the ingest pipeline
|
||||
for (idx, (content, source)) in records.iter().enumerate() {
|
||||
// Create episode from record
|
||||
let episode = Episode {
|
||||
id: format!("{}-{}", ingest_id, idx),
|
||||
project_id: project.to_string(),
|
||||
text: content.clone(),
|
||||
wiki_links: extract_wiki_links(content),
|
||||
// Process each record
|
||||
for (content, source) in &records {
|
||||
let chunk_id = Uuid::new_v4();
|
||||
|
||||
// Store L0 chunk
|
||||
let l0_chunk = ChunkL0 {
|
||||
id: chunk_id,
|
||||
project: project.to_string(),
|
||||
query_id: "ingest".to_string(),
|
||||
source: source.clone(),
|
||||
content: content.clone(),
|
||||
tokens: (content.len() / 4) as i32,
|
||||
};
|
||||
self.vector_store.store_chunk_l0(&l0_chunk).await?;
|
||||
total_chunks += 1;
|
||||
total_stored += 1;
|
||||
|
||||
// Try to embed and create a basic L1 memory
|
||||
if let Ok(embedding) = self.embeddings.embed_one(content).await {
|
||||
let l1 = MemoryL1 {
|
||||
id: Uuid::new_v4(),
|
||||
project: project.to_string(),
|
||||
query_id: "ingest".to_string(),
|
||||
content: content.clone(),
|
||||
tokens: (content.len() / 4) as i32,
|
||||
embedding: Some(embedding.to_vec()),
|
||||
chunks_seen: 1,
|
||||
chunks_used: 1,
|
||||
run_id: ingest_id.to_string(),
|
||||
};
|
||||
|
||||
// Run extraction pipeline (entity + fact extraction + contradiction detection)
|
||||
match self.pipeline.ingest(&episode).await {
|
||||
Ok(result) => {
|
||||
tracing::debug!(
|
||||
"Pipeline extracted {} entities, {} edges for episode {}",
|
||||
result.entities.len(),
|
||||
result.edges.len(),
|
||||
episode.id
|
||||
);
|
||||
|
||||
// Save entities to database (normally via EntityRepo, using direct SQL for now)
|
||||
for entity in &result.entities {
|
||||
if let Err(e) = save_entity_to_db(&self.pool, entity).await {
|
||||
tracing::warn!("Failed to save entity {}: {}", entity.name, e);
|
||||
} else {
|
||||
total_entities += 1;
|
||||
}
|
||||
}
|
||||
|
||||
// Save edges to database (normally via EdgeRepo, using direct SQL for now)
|
||||
for edge in &result.edges {
|
||||
if let Err(e) = save_edge_to_db(&self.pool, edge).await {
|
||||
tracing::warn!("Failed to save edge: {}", e);
|
||||
} else {
|
||||
total_edges += 1;
|
||||
}
|
||||
}
|
||||
|
||||
total_reviews += result.reviews.len();
|
||||
}
|
||||
Err(e) => {
|
||||
tracing::error!("Pipeline failed for episode {}: {}", episode.id, e);
|
||||
// Continue processing other records
|
||||
if let Err(e) = self.vector_store.store_memory_l1(&l1, &embedding).await {
|
||||
tracing::warn!("Failed to store L1 memory: {}", e);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -120,10 +90,7 @@ impl IngestWorker {
|
||||
.execute(&self.pool)
|
||||
.await?;
|
||||
|
||||
tracing::info!(
|
||||
"Ingest completed: {} (entities={}, edges={}, reviews={})",
|
||||
ingest_id, total_entities, total_edges, total_reviews
|
||||
);
|
||||
tracing::info!("Ingest completed: {} (stored {} chunks)", ingest_id, total_stored);
|
||||
Ok(())
|
||||
}
|
||||
|
||||
@@ -142,80 +109,3 @@ impl IngestWorker {
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
/// Extract wiki links from text (e.g., [[Kubernetes]] -> "Kubernetes")
|
||||
fn extract_wiki_links(text: &str) -> Vec<String> {
|
||||
let mut links = Vec::new();
|
||||
let mut chars = text.chars().peekable();
|
||||
|
||||
while let Some(ch) = chars.next() {
|
||||
if ch == '[' && chars.peek() == Some(&'[') {
|
||||
chars.next(); // consume second '['
|
||||
let mut link = String::new();
|
||||
while let Some(c) = chars.next() {
|
||||
if c == ']' && chars.peek() == Some(&']') {
|
||||
chars.next(); // consume second ']'
|
||||
links.push(link);
|
||||
break;
|
||||
}
|
||||
link.push(c);
|
||||
}
|
||||
}
|
||||
}
|
||||
links
|
||||
}
|
||||
|
||||
/// Save entity to database via raw SQL (normally would use EntityRepo trait)
|
||||
async fn save_entity_to_db(pool: &PgPool, entity: &mem_core::entity::Entity) -> Result<()> {
|
||||
// Convert OffsetDateTime to PostgreSQL timestamp format
|
||||
let t_created_str = entity.t_created.to_string();
|
||||
|
||||
sqlx::query(
|
||||
"INSERT INTO memory_entity (id, project_id, name, entity_type, description, t_created, t_updated, confidence)
|
||||
VALUES ($1, $2, $3, $4, $5, $6::TIMESTAMPTZ, $7::TIMESTAMPTZ, $8)
|
||||
ON CONFLICT (id) DO NOTHING"
|
||||
)
|
||||
.bind(&entity.id)
|
||||
.bind(&entity.project_id)
|
||||
.bind(&entity.name)
|
||||
.bind(entity.entity_type.as_str())
|
||||
.bind(entity.summary.as_deref())
|
||||
.bind(&t_created_str)
|
||||
.bind(&t_created_str)
|
||||
.bind(1.0_f32) // default confidence
|
||||
.execute(pool)
|
||||
.await?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Save edge to database via raw SQL (normally would use EdgeRepo trait)
|
||||
/// NOTE: Production DB may have old schema. Gracefully skip if temporal columns missing.
|
||||
async fn save_edge_to_db(pool: &PgPool, edge: &mem_core::edge::Edge) -> Result<()> {
|
||||
// Try temporal schema first (id, project_id, source_entity_id, etc)
|
||||
let result = sqlx::query(
|
||||
"INSERT INTO memory_edge (id, project_id, source_entity_id, target_entity_id, relation_type, fact, t_valid, t_invalid, t_created, confidence)
|
||||
VALUES ($1, $2, $3, $4, $5, $6, $7::TIMESTAMPTZ, $8::TIMESTAMPTZ, $9::TIMESTAMPTZ, $10)
|
||||
ON CONFLICT (id) DO NOTHING"
|
||||
)
|
||||
.bind(&edge.id)
|
||||
.bind(&edge.project_id)
|
||||
.bind(&edge.source_entity_id)
|
||||
.bind(&edge.target_entity_id)
|
||||
.bind(&edge.relation_type)
|
||||
.bind(&edge.fact)
|
||||
.bind(edge.t_valid.map(|t| t.to_string()))
|
||||
.bind(edge.t_invalid.map(|t| t.to_string()))
|
||||
.bind(edge.t_created.to_string())
|
||||
.bind(edge.confidence)
|
||||
.execute(pool)
|
||||
.await;
|
||||
|
||||
match result {
|
||||
Ok(_) => Ok(()),
|
||||
Err(e) => {
|
||||
tracing::debug!("Temporal edge schema not available: {}. Skipping edge save (will be available after schema migration).", e);
|
||||
// This is expected if production DB hasn't migrated to temporal schema yet
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -20,7 +20,6 @@ walkdir = "2.5"
|
||||
sha2 = { workspace = true }
|
||||
regex = { workspace = true }
|
||||
async-trait = { workspace = true }
|
||||
reqwest = { workspace = true }
|
||||
|
||||
[dev-dependencies]
|
||||
time = { workspace = true }
|
||||
|
||||
@@ -1,160 +0,0 @@
|
||||
//! Authentik JWT Token Exchange
|
||||
//!
|
||||
//! Uses OAuth2 client credentials flow to obtain JWT tokens from Authentik
|
||||
//! These tokens are used to authenticate with LLM gateway and S3
|
||||
|
||||
use anyhow::{Result, anyhow};
|
||||
use serde::{Deserialize, Serialize};
|
||||
use std::sync::Arc;
|
||||
use std::sync::Mutex;
|
||||
use std::time::{SystemTime, Duration};
|
||||
|
||||
/// JWT token response from Authentik
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct TokenResponse {
|
||||
pub access_token: String,
|
||||
pub token_type: String,
|
||||
pub expires_in: u64,
|
||||
#[serde(skip)]
|
||||
pub obtained_at: Option<SystemTime>,
|
||||
}
|
||||
|
||||
impl TokenResponse {
|
||||
/// Check if token is still valid
|
||||
pub fn is_expired(&self) -> bool {
|
||||
match self.obtained_at {
|
||||
Some(time) => {
|
||||
let elapsed = time.elapsed().unwrap_or(Duration::from_secs(u64::MAX));
|
||||
elapsed.as_secs() >= self.expires_in - 60 // Refresh 60s before expiry
|
||||
}
|
||||
None => true, // No timestamp = expired
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Authentik JWT issuer client
|
||||
pub struct AuthentikJwtIssuer {
|
||||
issuer_url: String,
|
||||
client_id: String,
|
||||
client_secret: String,
|
||||
cached_token: Arc<Mutex<Option<TokenResponse>>>,
|
||||
}
|
||||
|
||||
impl AuthentikJwtIssuer {
|
||||
pub fn new(issuer_url: &str, client_id: &str, client_secret: &str) -> Self {
|
||||
Self {
|
||||
issuer_url: issuer_url.to_string(),
|
||||
client_id: client_id.to_string(),
|
||||
client_secret: client_secret.to_string(),
|
||||
cached_token: Arc::new(Mutex::new(None)),
|
||||
}
|
||||
}
|
||||
|
||||
/// From environment: AUTHENTIK_ISSUER, AUTHENTIK_CLIENT_ID, AUTHENTIK_CLIENT_SECRET
|
||||
pub fn from_env() -> Result<Self> {
|
||||
let issuer = std::env::var("AUTHENTIK_ISSUER")
|
||||
.map_err(|_| anyhow!("AUTHENTIK_ISSUER not set"))?;
|
||||
let client_id = std::env::var("AUTHENTIK_CLIENT_ID")
|
||||
.map_err(|_| anyhow!("AUTHENTIK_CLIENT_ID not set"))?;
|
||||
let client_secret = std::env::var("AUTHENTIK_CLIENT_SECRET")
|
||||
.map_err(|_| anyhow!("AUTHENTIK_CLIENT_SECRET not set"))?;
|
||||
|
||||
Ok(Self::new(&issuer, &client_id, &client_secret))
|
||||
}
|
||||
|
||||
/// Get valid access token, using cache if available
|
||||
pub async fn get_access_token(&self) -> Result<String> {
|
||||
// Check cache
|
||||
if let Ok(lock) = self.cached_token.lock() {
|
||||
if let Some(token) = lock.as_ref() {
|
||||
if !token.is_expired() {
|
||||
tracing::debug!("Using cached Authentik token");
|
||||
return Ok(token.access_token.clone());
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Fetch new token
|
||||
let mut token = self.fetch_token().await?;
|
||||
token.obtained_at = Some(SystemTime::now());
|
||||
let access_token = token.access_token.clone();
|
||||
|
||||
// Cache it
|
||||
if let Ok(mut lock) = self.cached_token.lock() {
|
||||
*lock = Some(token);
|
||||
}
|
||||
|
||||
Ok(access_token)
|
||||
}
|
||||
|
||||
/// Exchange client credentials for JWT token
|
||||
async fn fetch_token(&self) -> Result<TokenResponse> {
|
||||
let client = reqwest::Client::new();
|
||||
|
||||
// Authentik OAuth2 token endpoint
|
||||
let token_url = format!("{}/token/", self.issuer_url.trim_end_matches('/'));
|
||||
|
||||
let params = [
|
||||
("grant_type", "client_credentials"),
|
||||
("client_id", &self.client_id),
|
||||
("client_secret", &self.client_secret),
|
||||
];
|
||||
|
||||
let response = client
|
||||
.post(&token_url)
|
||||
.form(¶ms)
|
||||
.timeout(Duration::from_secs(10))
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
if !response.status().is_success() {
|
||||
return Err(anyhow!(
|
||||
"Authentik token request failed: {} - {}",
|
||||
response.status(),
|
||||
response.text().await.unwrap_or_default()
|
||||
));
|
||||
}
|
||||
|
||||
let token_resp: TokenResponse = response.json().await?;
|
||||
|
||||
tracing::info!(
|
||||
"Obtained Authentik JWT token (expires in {} seconds)",
|
||||
token_resp.expires_in
|
||||
);
|
||||
|
||||
Ok(token_resp)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_token_expiry_check() {
|
||||
let mut token = TokenResponse {
|
||||
access_token: "test".to_string(),
|
||||
token_type: "Bearer".to_string(),
|
||||
expires_in: 3600,
|
||||
obtained_at: SystemTime::now(),
|
||||
};
|
||||
|
||||
assert!(!token.is_expired());
|
||||
|
||||
// Simulate aged token
|
||||
token.obtained_at = SystemTime::now() - Duration::from_secs(3600);
|
||||
assert!(token.is_expired());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_issuer_creation() {
|
||||
let issuer = AuthentikJwtIssuer::new(
|
||||
"https://example.com",
|
||||
"client_id",
|
||||
"client_secret",
|
||||
);
|
||||
|
||||
assert_eq!(issuer.issuer_url, "https://example.com");
|
||||
assert_eq!(issuer.client_id, "client_id");
|
||||
}
|
||||
}
|
||||
@@ -14,9 +14,6 @@ use async_trait::async_trait;
|
||||
use mem_core::entity::{Entity, EntityType};
|
||||
use serde::{Deserialize, Serialize};
|
||||
use crate::speaker_extractor::SpeakerExtractor;
|
||||
use crate::authentik_jwt::AuthentikJwtIssuer;
|
||||
use std::sync::Arc;
|
||||
use tokio::sync::Mutex;
|
||||
|
||||
/// Extracted entity from LLM (intermediate representation)
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
@@ -43,20 +40,16 @@ pub trait EntityExtractor: Send + Sync {
|
||||
}
|
||||
|
||||
/// LLM-based extractor with reflection verification (stage 1 + 2)
|
||||
/// Uses Authentik JWT tokens for authentication to LLM gateway
|
||||
pub struct LlmEntityExtractor {
|
||||
model_name: String,
|
||||
enable_reflection: bool,
|
||||
jwt_issuer: Option<Arc<Mutex<AuthentikJwtIssuer>>>,
|
||||
}
|
||||
|
||||
impl LlmEntityExtractor {
|
||||
pub fn new(model_name: &str) -> Self {
|
||||
let jwt_issuer = AuthentikJwtIssuer::from_env().ok();
|
||||
Self {
|
||||
model_name: model_name.to_string(),
|
||||
enable_reflection: true,
|
||||
jwt_issuer: jwt_issuer.map(|iss| Arc::new(Mutex::new(iss))),
|
||||
}
|
||||
}
|
||||
|
||||
@@ -87,76 +80,11 @@ impl LlmEntityExtractor {
|
||||
Ok(parsed.verified.into_iter().map(|v| (v.name, v.present)).collect())
|
||||
}
|
||||
|
||||
/// Call LLM via api.riotpiao.com using Authentik JWT
|
||||
/// Token is fetched from Authentik service account and cached
|
||||
async fn call_llm_endpoint(&self, prompt: &str) -> Result<String> {
|
||||
let endpoint = std::env::var("LLM_ENDPOINT")
|
||||
.unwrap_or_else(|_| "http://api-internal.riotpiao.com:8000/v1/chat/completions".to_string());
|
||||
let model = std::env::var("LLM_MODEL")
|
||||
.unwrap_or_else(|_| "qwen:7b".to_string());
|
||||
|
||||
// Get JWT token from Authentik
|
||||
let auth_header = if let Some(jwt_issuer) = &self.jwt_issuer {
|
||||
let issuer = jwt_issuer.lock().await;
|
||||
match issuer.get_access_token().await {
|
||||
Ok(token) => format!("Bearer {}", token),
|
||||
Err(e) => {
|
||||
tracing::warn!("Failed to get Authentik JWT: {}", e);
|
||||
return Err(e);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// Fallback to env var if Authentik not configured
|
||||
let api_key = std::env::var("LLM_API_KEY")
|
||||
.or_else(|_| std::env::var("MEM_API_KEY"))
|
||||
.unwrap_or_else(|_| "default-key".to_string());
|
||||
format!("Bearer {}", api_key)
|
||||
};
|
||||
|
||||
let client = reqwest::Client::new();
|
||||
|
||||
// OpenAI-compatible API call
|
||||
let payload = serde_json::json!({
|
||||
"model": model,
|
||||
"messages": [
|
||||
{"role": "system", "content": "You are an entity extraction specialist. Extract named entities from text in JSON format."},
|
||||
{"role": "user", "content": prompt}
|
||||
],
|
||||
"temperature": 0.3,
|
||||
"max_tokens": 500
|
||||
});
|
||||
|
||||
let response = client
|
||||
.post(&endpoint)
|
||||
.header("Authorization", auth_header)
|
||||
.header("Content-Type", "application/json")
|
||||
.json(&payload)
|
||||
.timeout(std::time::Duration::from_secs(30))
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
if !response.status().is_success() {
|
||||
tracing::warn!(
|
||||
"LLM API error: {} - {}",
|
||||
response.status(),
|
||||
response.text().await.unwrap_or_default()
|
||||
);
|
||||
// Fallback to mock response on error
|
||||
return Ok(r#"{"entities": []}"#.to_string());
|
||||
}
|
||||
|
||||
let data: serde_json::Value = response.json().await?;
|
||||
let content = data["choices"][0]["message"]["content"]
|
||||
.as_str()
|
||||
.unwrap_or("{}")
|
||||
.to_string();
|
||||
|
||||
tracing::debug!("LLM response (via Authentik JWT): {}", content);
|
||||
Ok(content)
|
||||
}
|
||||
|
||||
/// Fallback mock LLM call (for testing without API)
|
||||
fn simulate_llm(&self, _prompt: &str) -> Result<String> {
|
||||
/// Mock LLM call - replace with real API in production
|
||||
/// TODO (Phase 2.6): Integrate with api.riotpiao.com/v1/chat/completions
|
||||
/// TODO (Phase 2.6): Add JWT authentication from Authentik OIDC
|
||||
async fn simulate_llm(&self, _prompt: &str) -> Result<String> {
|
||||
// Production: call api.riotpiao.com with Bearer JWT token
|
||||
// Mock response for testing
|
||||
Ok(r#"{
|
||||
"entities": [
|
||||
@@ -206,12 +134,7 @@ Respond in JSON:
|
||||
text
|
||||
);
|
||||
|
||||
// Try real LLM first, fallback to mock if not configured
|
||||
let extraction_response = if std::env::var("LLM_ENDPOINT").is_ok() {
|
||||
self.call_llm_endpoint(&prompt).await.unwrap_or_else(|_| self.simulate_llm(&prompt).unwrap_or_default())
|
||||
} else {
|
||||
self.simulate_llm(&prompt)?
|
||||
};
|
||||
let extraction_response = self.simulate_llm(&prompt).await?;
|
||||
let extracted = Self::parse_extraction(&extraction_response)?;
|
||||
entities.extend(extracted); // Add LLM-extracted entities after speaker
|
||||
|
||||
@@ -232,11 +155,7 @@ Respond in JSON:
|
||||
text, entities
|
||||
);
|
||||
|
||||
let reflection = if std::env::var("LLM_ENDPOINT").is_ok() {
|
||||
self.call_llm_endpoint(&reflection_prompt).await.unwrap_or_else(|_| self.simulate_llm(&reflection_prompt).unwrap_or_default())
|
||||
} else {
|
||||
self.simulate_llm(&reflection_prompt)?
|
||||
};
|
||||
let reflection = self.simulate_llm(&reflection_prompt).await?;
|
||||
let verified = Self::parse_reflection(&reflection)?;
|
||||
|
||||
// Filter: keep only entities marked present
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
pub mod pi_session;
|
||||
pub mod claude_transcript;
|
||||
pub mod authentik_jwt;
|
||||
pub mod doc_corpus;
|
||||
pub mod derived_filter;
|
||||
pub mod obsidian_ref_source;
|
||||
|
||||
@@ -1,321 +0,0 @@
|
||||
# Poimen Memory: Authentik JWT + SOPS Encryption Setup
|
||||
|
||||
## Overview
|
||||
|
||||
The Poimen Memory service uses:
|
||||
1. **Authentik service account** for OAuth2 client credentials flow
|
||||
2. **SOPS + Age encryption** to encrypt secrets in git
|
||||
3. **JWT tokens** for authentication to LLM gateway, S3, and other services
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ Kubernetes (poimen) │
|
||||
├─────────────────────────────────────────────────────────────┤
|
||||
│ │
|
||||
│ ┌──────────────┐ ┌─────────────────┐ │
|
||||
│ │ ConfigMap │ │ Secret (SOPS) │ │
|
||||
│ │ (unencrypted)│ │ (age-encrypted)│ │
|
||||
│ └──────┬───────┘ └────────┬────────┘ │
|
||||
│ │ │ │
|
||||
│ ├─────────┬───────────┤ │
|
||||
│ │ │ │ │
|
||||
│ ┌────▼─────────▼───────────▼────┐ │
|
||||
│ │ poimen-memory Pod │ │
|
||||
│ │ Environment Variables: │ │
|
||||
│ │ - LLM_ENDPOINT │ │
|
||||
│ │ - AUTHENTIK_ISSUER │ │
|
||||
│ │ - AUTHENTIK_CLIENT_ID │ │
|
||||
│ │ - AUTHENTIK_CLIENT_SECRET │ │
|
||||
│ │ - S3_ACCESS_KEY │ │
|
||||
│ │ - S3_SECRET_KEY │ │
|
||||
│ └────┬────────────────┬──────────┘ │
|
||||
│ │ │ │
|
||||
│ ┌──────▼──┐ ┌──────────▼──────┐ │
|
||||
│ │ Authentik│ │ LLM Endpoint │ │
|
||||
│ │ (JWT) │ │ (api.riotpiao) │ │
|
||||
│ └──────────┘ └─────────────────┘ │
|
||||
│ │
|
||||
│ ┌─────────────────────────────────────┐ │
|
||||
│ │ Entity Extraction Pipeline │ │
|
||||
│ │ ┌────────────────────────────┐ │ │
|
||||
│ │ │ 1. WikiLink fallback │ │ │
|
||||
│ │ │ 2. LLM extraction (JWT auth)│ │ │
|
||||
│ │ │ 3. Reflection verification │ │ │
|
||||
│ │ │ 4. Contradiction detection │ │ │
|
||||
│ │ └────────────────────────────┘ │ │
|
||||
│ └──────────────┬──────────────────────┘ │
|
||||
│ │ │
|
||||
│ ┌───────▼────────┐ │
|
||||
│ │ PostgreSQL │ │
|
||||
│ │ (entities DB) │ │
|
||||
│ └────────────────┘ │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## Step 1: Create Authentik Service Account
|
||||
|
||||
### In Authentik Admin Panel:
|
||||
|
||||
1. Navigate: **Settings** → **Applications** → **Create Application**
|
||||
2. Name: `poimen-memory`
|
||||
3. Slug: `poimen-memory`
|
||||
4. Provider: Create a new OAuth2 Provider
|
||||
- Name: `poimen-memory`
|
||||
- Client type: `confidential`
|
||||
- Client ID: `<auto-generated>`
|
||||
- Client secret: `<auto-generated>`
|
||||
5. Save and note the **Client ID** and **Client Secret**
|
||||
|
||||
### Verify OAuth2 Token Endpoint:
|
||||
```bash
|
||||
curl -X POST https://authentik.riotpiao.com/application/o/token/ \
|
||||
-d "grant_type=client_credentials" \
|
||||
-d "client_id=<CLIENT_ID>" \
|
||||
-d "client_secret=<CLIENT_SECRET>"
|
||||
|
||||
# Response:
|
||||
# {
|
||||
# "access_token": "eyJ0eXAi...",
|
||||
# "token_type": "Bearer",
|
||||
# "expires_in": 3600
|
||||
# }
|
||||
```
|
||||
|
||||
## Step 2: Create Encrypted Secrets File
|
||||
|
||||
### 2.1 Ensure SOPS is configured:
|
||||
|
||||
```bash
|
||||
# Load SOPS_AGE_KEY_FILE
|
||||
export SOPS_AGE_KEY_FILE=~/.sops/key.txt
|
||||
|
||||
# Verify key exists
|
||||
ls -la ~/.sops/key.txt
|
||||
```
|
||||
|
||||
### 2.2 Create unencrypted secrets template:
|
||||
|
||||
```yaml
|
||||
# k8s/app/poimen-memory-secrets.yaml
|
||||
apiVersion: v1
|
||||
kind: Secret
|
||||
metadata:
|
||||
name: poimen-memory-secrets
|
||||
namespace: poimen
|
||||
type: Opaque
|
||||
stringData:
|
||||
# Authentik OAuth2 Credentials
|
||||
AUTHENTIK_ISSUER: "https://authentik.riotpiao.com/application/o/memory"
|
||||
AUTHENTIK_AUDIENCE: "poimen-memory"
|
||||
AUTHENTIK_CLIENT_ID: "<from-authentik-app>"
|
||||
AUTHENTIK_CLIENT_SECRET: "<from-authentik-app>"
|
||||
|
||||
# LLM Gateway API Key (optional fallback)
|
||||
LLM_API_KEY: "<jwt-will-be-auto-generated>"
|
||||
|
||||
# S3/Minio Credentials
|
||||
S3_ACCESS_KEY: "<minio-access-key>"
|
||||
S3_SECRET_KEY: "<minio-secret-key>"
|
||||
```
|
||||
|
||||
### 2.3 Encrypt with SOPS:
|
||||
|
||||
```bash
|
||||
export SOPS_AGE_KEY_FILE=~/.sops/key.txt
|
||||
cd ~/workplace/Poimen/memory
|
||||
|
||||
sops -e k8s/app/poimen-memory-secrets.yaml > k8s/app/poimen-memory-secrets.enc.yaml
|
||||
|
||||
# Verify encryption worked
|
||||
sops -d k8s/app/poimen-memory-secrets.enc.yaml | head -20
|
||||
```
|
||||
|
||||
### 2.4 Commit encrypted file only:
|
||||
|
||||
```bash
|
||||
git add k8s/app/poimen-memory-secrets.enc.yaml
|
||||
git add .sops.yaml
|
||||
git rm k8s/app/poimen-memory-secrets.yaml # Remove plaintext
|
||||
git commit -m "feat: add SOPS-encrypted Authentik secrets"
|
||||
```
|
||||
|
||||
## Step 3: Deploy to Kubernetes
|
||||
|
||||
### 3.1 Install KSOPS plugin (if using ArgoCD):
|
||||
|
||||
```bash
|
||||
# ArgoCD Helm values
|
||||
kustomization:
|
||||
plugins:
|
||||
- name: Kustomize
|
||||
image: ghcr.io/viaduct-ai/kustomize-sops:v4.1.1
|
||||
```
|
||||
|
||||
### 3.2 Apply secrets manifest:
|
||||
|
||||
```bash
|
||||
# With KSOPS: ArgoCD auto-decrypts and applies
|
||||
# Without KSOPS: Manual decryption before apply
|
||||
export SOPS_AGE_KEY_FILE=~/.sops/key.txt
|
||||
sops -d k8s/app/poimen-memory-secrets.enc.yaml | kubectl apply -f -
|
||||
|
||||
# Verify secret created
|
||||
kubectl -n poimen get secret poimen-memory-secrets
|
||||
kubectl -n poimen describe secret poimen-memory-secrets
|
||||
```
|
||||
|
||||
### 3.3 Update deployment envFrom:
|
||||
|
||||
```yaml
|
||||
# k8s/app/deployment.yaml
|
||||
spec:
|
||||
template:
|
||||
spec:
|
||||
containers:
|
||||
- name: poimen-memory
|
||||
envFrom:
|
||||
- configMapRef:
|
||||
name: poimen-memory-config
|
||||
- secretRef:
|
||||
name: poimen-memory-secrets # <-- Add this
|
||||
```
|
||||
|
||||
## Step 4: Entity Extractor JWT Flow
|
||||
|
||||
### Code: `crates/mem-ingest/src/entity_extractor.rs`
|
||||
|
||||
```rust
|
||||
// Initialization
|
||||
pub struct LlmEntityExtractor {
|
||||
jwt_issuer: Option<Arc<Mutex<AuthentikJwtIssuer>>>,
|
||||
}
|
||||
|
||||
impl LlmEntityExtractor {
|
||||
pub fn new(model_name: &str) -> Self {
|
||||
let jwt_issuer = AuthentikJwtIssuer::from_env().ok();
|
||||
Self {
|
||||
jwt_issuer: jwt_issuer.map(|iss| Arc::new(Mutex::new(iss))),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// LLM call with JWT
|
||||
async fn call_llm_endpoint(&self, prompt: &str) -> Result<String> {
|
||||
// Get JWT token from Authentik (cached, auto-refreshed)
|
||||
let auth_header = if let Some(jwt_issuer) = &self.jwt_issuer {
|
||||
let issuer = jwt_issuer.lock().await;
|
||||
let token = issuer.get_access_token().await?;
|
||||
format!("Bearer {}", token)
|
||||
} else {
|
||||
format!("Bearer {}", fallback_api_key)
|
||||
};
|
||||
|
||||
// POST to LLM endpoint with JWT
|
||||
client
|
||||
.post(&endpoint)
|
||||
.header("Authorization", auth_header)
|
||||
.json(&payload)
|
||||
.send()
|
||||
.await?
|
||||
}
|
||||
```
|
||||
|
||||
## Step 5: Runtime Verification
|
||||
|
||||
### 5.1 Check JWT token exchange in logs:
|
||||
|
||||
```bash
|
||||
kubectl -n poimen logs deployment/poimen-memory | grep -i "authentik\|jwt"
|
||||
|
||||
# Expected output:
|
||||
# [2026-01-09T20:30:15Z] Obtained Authentik JWT token (expires in 3600 seconds)
|
||||
# [2026-01-09T20:30:15Z] LLM response (via Authentik JWT): {...}
|
||||
```
|
||||
|
||||
### 5.2 Test entity extraction end-to-end:
|
||||
|
||||
```bash
|
||||
# Port-forward to service
|
||||
kubectl -n poimen port-forward svc/poimen-memory 8080:8080 &
|
||||
|
||||
# Ingest a record
|
||||
curl -X POST http://localhost:8080/memory/ingest \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"project": "homelab",
|
||||
"source": "test://jwt",
|
||||
"ingest_id": "jwt-test-001",
|
||||
"records": [{
|
||||
"role": "architect",
|
||||
"text": "[[Kubernetes]] uses [[Docker]]. [[ArgoCD]] manages deployments.",
|
||||
"timestamp": "2026-01-09T20:30:00Z",
|
||||
"source_position": 0
|
||||
}]
|
||||
}'
|
||||
|
||||
# Check logs for JWT usage
|
||||
kubectl -n poimen logs deployment/poimen-memory | tail -20
|
||||
```
|
||||
|
||||
## Step 6: Monitoring & Maintenance
|
||||
|
||||
### Token Expiry Handling:
|
||||
- JWT tokens are cached with auto-refresh
|
||||
- If token expires during use, new token is fetched automatically
|
||||
- No manual token rotation required
|
||||
|
||||
### Credential Rotation:
|
||||
- Rotate Authentik client secret periodically
|
||||
- Update SOPS secret file and re-encrypt
|
||||
- Redeploy pod to pick up new secret
|
||||
|
||||
### SOPS Key Rotation (Yearly):
|
||||
```bash
|
||||
# Generate new age key
|
||||
age-keygen -o ~/.sops/key.txt.new
|
||||
|
||||
# Re-encrypt all secrets with new key
|
||||
for file in k8s/**/*.enc.yaml; do
|
||||
sops -r $file
|
||||
done
|
||||
|
||||
# Update ArgoCD to use new key
|
||||
# Commit changes
|
||||
git add k8s/**/*.enc.yaml
|
||||
git commit -m "chore: rotate SOPS encryption keys"
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Issue: "AUTHENTIK_ISSUER not set"
|
||||
**Cause**: Secret not mounted properly
|
||||
**Solution**: `kubectl -n poimen get secret poimen-memory-secrets`
|
||||
|
||||
### Issue: "JWT token request failed: 401"
|
||||
**Cause**: Invalid client credentials
|
||||
**Solution**: Verify Client ID/Secret in Authentik, check SOPS decryption
|
||||
|
||||
### Issue: "error loading config: no matching creation rules found"
|
||||
**Cause**: SOPS .sops.yaml not configured correctly
|
||||
**Solution**: Use `.sops.yaml` with explicit age key instead of config-based rules
|
||||
|
||||
### Issue: "LLM API error: 403 Forbidden"
|
||||
**Cause**: JWT token doesn't have permission to LLM gateway
|
||||
**Solution**: Add RBAC role "LLM User" to service account in Authentik
|
||||
|
||||
---
|
||||
|
||||
## Files Modified
|
||||
|
||||
- ✅ `crates/mem-ingest/src/authentik_jwt.rs` — JWT token exchange module
|
||||
- ✅ `crates/mem-ingest/src/entity_extractor.rs` — LLM calls with JWT
|
||||
- ✅ `crates/mem-ingest/src/lib.rs` — Module export
|
||||
- ✅ `k8s/app/poimen-memory-secrets.yaml` — Secret template (plaintext, not committed)
|
||||
- ✅ `k8s/app/poimen-memory-secrets.enc.yaml` — Secret encrypted with SOPS
|
||||
- ✅ `k8s/app/deployment.yaml` — Updated envFrom for secrets
|
||||
- ✅ `k8s/app/config.yaml` — LLM endpoint configuration
|
||||
- ✅ `k8s/.sops.yaml` — SOPS encryption rules
|
||||
|
||||
@@ -1,982 +0,0 @@
|
||||
# Memory Service Observability
|
||||
|
||||
## Why Observe a Knowledge Base System
|
||||
|
||||
A memory service that retrieves wrong facts is worse than one that retrieves nothing — it causes hallucination. Traditional web services measure uptime and latency. A knowledge-base service must also measure **whether the answer was correct**, **whether the stored fact was accurate**, and **whether stale or contradictory information leaked through**.
|
||||
|
||||
Every metric in this document exists to answer one question: **"Did the user get the right information, fast enough, from a source we trust?"**
|
||||
|
||||
---
|
||||
|
||||
## Call Flow: Left to Right
|
||||
|
||||
```
|
||||
INGEST PATH
|
||||
===========
|
||||
|
||||
Client ─── POST /memory/ingest ─── Auth + Rate Limit ─── Dedup Check ─── Embed (768d) ─── Dual Write ─── Done
|
||||
│ │ │ │ │ │
|
||||
│ [I1: req_count] [I2: auth_ms] [I3: dedup_hit] [I4: embed_ms] [I5: write_ms]
|
||||
│ │ │
|
||||
│ pgvector INSERT OpenSearch INDEX
|
||||
│ │ │
|
||||
│ [I6: pg_ms] [I7: os_ms]
|
||||
│ │
|
||||
│ [I8: os_fail_count]
|
||||
│ (eventual consistency)
|
||||
│
|
||||
│
|
||||
QUERY PATH
|
||||
==========
|
||||
|
||||
Client ─── POST /memory/query ─── Auth + Rate Limit ─── Classify Intent ─── Embed Query ─── Search ─── RRF Fusion ─── Rerank ─── Respond
|
||||
│ │ │ │ │ │ │ │ │
|
||||
│ [Q1: req_count] [Q2: auth_ms] [Q3: intent_type] [Q4: embed_ms] │ [Q7: rrf_ms] [Q8: rerank_ms] │
|
||||
│ │ │
|
||||
│ ┌────────────┴──────────┐ │
|
||||
│ pgvector cosine OpenSearch BM25 │
|
||||
│ │ │ │
|
||||
│ [Q5: sem_ms] [Q6: lex_ms] │
|
||||
│ [Q5a: sem_count] [Q6a: lex_count] │
|
||||
│ [Q9: total_ms]
|
||||
│ [Q10: result_count]
|
||||
│
|
||||
│
|
||||
CONTEXT PATH (3-tier retrieval)
|
||||
==============================
|
||||
|
||||
Client ─── POST /memory/context ─── Tier 1: Exact Signature ─── Tier 2: Hybrid Search ─── Tier 3: Reference Fallback ─── Budget Assembly ─── Respond
|
||||
│ │ │ │ │ │ │
|
||||
│ [C1: req_count] [C2: t1_hit] [C3: t2_hit] [C4: t3_hit] [C5: budget_used] [C6: total_ms]
|
||||
│ [C2a: t1_ms] [C3a: t2_ms] [C4a: t3_ms] [C5a: dropped_count]
|
||||
│
|
||||
│
|
||||
RELEVANCE JUDGMENT (offline, periodic)
|
||||
=====================================
|
||||
|
||||
Sampled Query Log ─── Replay Query ─── Retrieve Top-K ─── Qwen-7B Judge ─── Score (0-2) ─── Compute NDCG/MRR/Precision/Recall
|
||||
│ │ │ │ │
|
||||
[R1: sample_size] [R2: replay_ms] [R3: judge_ms] [R4: relevance_dist] [R5: ndcg_10]
|
||||
[R3a: judge_cost] [R6: mrr]
|
||||
[R7: precision_10]
|
||||
[R8: recall_10]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 1. Ingest Observability
|
||||
|
||||
### Why
|
||||
|
||||
Every fact written to memory becomes a retrieval candidate. A bad write — duplicate, contradictory, or malformed — pollutes all future queries. Ingest observability answers: **"How many facts are entering the system, how fast, and are any of them bad?"**
|
||||
|
||||
### Metrics
|
||||
|
||||
| ID | Metric | Type | Unit | Why It Matters |
|
||||
|----|--------|------|------|----------------|
|
||||
| **I1** | `ingest_requests_total` | Counter | requests | Total write demand. Capacity planning baseline. Sudden spikes = upstream behavior change. |
|
||||
| **I2** | `ingest_auth_duration_seconds` | Histogram | seconds | JWT validation overhead. Should be < 5ms. Spike = JWKS fetch or Authentik down. |
|
||||
| **I3** | `ingest_dedup_hits_total` | Counter | requests | Idempotency saves. High ratio = client retry storm or misconfigured source. Low = healthy unique writes. |
|
||||
| **I4** | `ingest_embed_duration_seconds` | Histogram | seconds | Embedding latency per chunk. Budget: < 50ms for single chunk. Spike = model cold start or GPU contention. |
|
||||
| **I5** | `ingest_dual_write_duration_seconds` | Histogram | seconds | Total time to write both stores. SLO: p99 < 500ms. |
|
||||
| **I6** | `ingest_pgvector_duration_seconds` | Histogram | seconds | Postgres INSERT latency. Includes HNSW index update. Degrades as table grows. |
|
||||
| **I7** | `ingest_opensearch_duration_seconds` | Histogram | seconds | OpenSearch bulk index latency. Sensitive to segment merges. |
|
||||
| **I8** | `ingest_opensearch_failures_total` | Counter | failures | OpenSearch write failures. System is eventual-consistent: pgvector is primary. But if this counter grows, lexical search degrades silently. |
|
||||
| **I9** | `ingest_bytes_total` | Counter | bytes | Total data volume written. Growth rate = storage budget burn. |
|
||||
| **I10** | `ingest_chunks_total` | Counter | chunks | Write throughput in logical units. 1 ingest request may produce N chunks after splitting. |
|
||||
| **I11** | `ingest_contradiction_detected_total` | Counter | contradictions | Facts that conflict with existing knowledge. High count = noisy source or domain shift. Each one enters review queue. |
|
||||
| **I12** | `ingest_review_queue_depth` | Gauge | items | Pending human reviews. Growing = reviewers not keeping up. Stale contradictions = latent hallucination risk. |
|
||||
|
||||
### Alerts
|
||||
|
||||
| Condition | Severity | Action |
|
||||
|-----------|----------|--------|
|
||||
| `ingest_opensearch_failures_total` rate > 5/min for 10min | **Warning** | Check OpenSearch cluster health. Lexical search degrading. |
|
||||
| `ingest_review_queue_depth` > 100 for 24h | **Warning** | Unreviewed contradictions. Risk of serving conflicting facts. |
|
||||
| `ingest_pgvector_duration_seconds` p99 > 1s | **Critical** | Postgres overloaded. HNSW index rebuild or VACUUM needed. |
|
||||
| `ingest_dedup_hits_total` / `ingest_requests_total` > 0.5 | **Warning** | More than half of writes are duplicates. Source misconfiguration. |
|
||||
|
||||
---
|
||||
|
||||
## 2. Query Observability
|
||||
|
||||
### Why
|
||||
|
||||
Query latency is what the user feels. But latency alone is insufficient — a fast query returning wrong results is worse than a slow correct one. Query observability answers: **"Did the system respond quickly, and did the search pipeline find the right documents?"**
|
||||
|
||||
### Metrics
|
||||
|
||||
| ID | Metric | Type | Unit | Why It Matters |
|
||||
|----|--------|------|------|----------------|
|
||||
| **Q1** | `query_requests_total` | Counter | requests | Read demand. Ratio to ingest = read/write skew. Memory systems are read-heavy (10:1+). |
|
||||
| **Q2** | `query_auth_duration_seconds` | Histogram | seconds | Same as ingest. Shared auth path. |
|
||||
| **Q3** | `query_intent_classification` | Counter (labeled) | requests | Labels: `bug_fix`, `how_to`, `reference`, `faq`. Distribution reveals what users ask most. If 80% is `bug_fix` but recall is low for that intent, prioritize that retrieval path. |
|
||||
| **Q4** | `query_embed_duration_seconds` | Histogram | seconds | Query embedding latency. Same model as ingest. Should match I4. |
|
||||
| **Q5** | `query_semantic_duration_seconds` | Histogram | seconds | pgvector cosine search. SLO: p99 < 200ms. Degrades with index size. |
|
||||
| **Q5a** | `query_semantic_candidates` | Histogram | count | Number of vectors above similarity floor. Zero = total miss. Hundreds = floor too low. |
|
||||
| **Q6** | `query_lexical_duration_seconds` | Histogram | seconds | OpenSearch BM25 latency. SLO: p99 < 150ms. |
|
||||
| **Q6a** | `query_lexical_candidates` | Histogram | count | BM25 hit count. Zero = query terms not in corpus (vocabulary gap). |
|
||||
| **Q7** | `query_rrf_fusion_duration_seconds` | Histogram | seconds | RRF merge time. Should be < 5ms (in-memory). If slow, too many candidates. |
|
||||
| **Q8** | `query_rerank_duration_seconds` | Histogram | seconds | Cross-encoder reranking. Most expensive step. Budget: < 200ms for top-20. |
|
||||
| **Q9** | `query_total_duration_seconds` | Histogram | seconds | End-to-end latency. SLO: p99 < 500ms. User-facing number. |
|
||||
| **Q10** | `query_results_returned` | Histogram | count | How many results pass all filters. Zero = query miss. Track per-intent. |
|
||||
| **Q11** | `query_empty_results_total` | Counter | requests | Queries that returned nothing. High rate = coverage gap in knowledge base. |
|
||||
| **Q12** | `query_score_distribution` | Histogram | score (0-1) | Top-1 result score distribution. Bimodal = some queries match well, others poorly. Low mean = embedding quality issue. |
|
||||
|
||||
### Alerts
|
||||
|
||||
| Condition | Severity | Action |
|
||||
|-----------|----------|--------|
|
||||
| `query_total_duration_seconds` p99 > 1s | **Critical** | Pipeline bottleneck. Check Q5, Q6, Q8 to isolate which leg is slow. |
|
||||
| `query_empty_results_total` rate > 20% of Q1 | **Warning** | 1 in 5 queries finds nothing. Coverage gap. Check if ingest is running. |
|
||||
| `query_semantic_candidates` p50 = 0 | **Critical** | Embedding search broken. Model mismatch or empty index. |
|
||||
| `query_lexical_duration_seconds` p99 > 500ms | **Warning** | OpenSearch overloaded. Check segment count, heap usage. |
|
||||
|
||||
---
|
||||
|
||||
## 3. Context Endpoint (Three-Tier) Observability
|
||||
|
||||
### Why
|
||||
|
||||
The context endpoint is the primary consumer-facing API. It orchestrates three retrieval tiers with budget constraints. Observing tier hit rates reveals whether the knowledge base has coverage at each level, and whether the budget assembly is dropping important results.
|
||||
|
||||
### Metrics
|
||||
|
||||
| ID | Metric | Type | Unit | Why It Matters |
|
||||
|----|--------|------|------|----------------|
|
||||
| **C1** | `context_requests_total` | Counter | requests | Context lookup demand. Main integration point. |
|
||||
| **C2** | `context_tier1_hits_total` | Counter | hits | Exact signature matches. High = system is learning from repeated failures. SLO: tier-1 hit rate >= 0.80. |
|
||||
| **C2a** | `context_tier1_duration_seconds` | Histogram | seconds | Signature lookup. Should be < 50ms (indexed hash). |
|
||||
| **C3** | `context_tier2_hits_total` | Counter | hits | Hybrid search hits. Bulk of useful results. |
|
||||
| **C3a** | `context_tier2_duration_seconds` | Histogram | seconds | Full hybrid search. Budget: < 500ms. |
|
||||
| **C4** | `context_tier3_hits_total` | Counter | hits | Reference fallback. High ratio = learned knowledge insufficient, falling back to docs. |
|
||||
| **C4a** | `context_tier3_duration_seconds` | Histogram | seconds | Obsidian API + reference retrieval. Slowest tier. |
|
||||
| **C5** | `context_budget_used_bytes` | Histogram | bytes | How much of the token budget was consumed. Full = rich context. Low = sparse knowledge. |
|
||||
| **C5a** | `context_dropped_results_total` | Counter | results | Results dropped to fit budget. High = budget too small or results too verbose. |
|
||||
| **C6** | `context_total_duration_seconds` | Histogram | seconds | End-to-end context assembly. SLO: p99 < 2s. |
|
||||
| **C7** | `context_tier_distribution` | Counter (labeled) | requests | Label: `tier=1\|2\|3`. Which tier served the primary result. Shift from tier-1 to tier-3 over time = knowledge decay. |
|
||||
| **C8** | `context_degraded_total` | Counter | requests | Requests where a leg failed (e.g., Obsidian timeout). Partial results served. |
|
||||
|
||||
### Alerts
|
||||
|
||||
| Condition | Severity | Action |
|
||||
|-----------|----------|--------|
|
||||
| `context_tier1_hits_total` / `context_requests_total` < 0.60 | **Warning** | Signature match rate dropping. System not learning from failures. Check ingest pipeline. |
|
||||
| `context_dropped_results_total` rate > 30% of results | **Warning** | Budget too tight. Users missing relevant context. |
|
||||
| `context_degraded_total` rate > 5% | **Warning** | Partial responses. Check Obsidian API, OpenSearch health. |
|
||||
|
||||
---
|
||||
|
||||
## 4. Relevance Judgment with Qwen-7B
|
||||
|
||||
### Why
|
||||
|
||||
All the metrics above measure speed and volume. None measure **correctness**. A system that returns 10 results in 50ms is useless if those results are wrong. Traditional IR evaluation requires human-labeled relevance judgments — expensive and slow. Instead, we use a **Qwen-7B model as an automated relevance judge** on sampled queries.
|
||||
|
||||
This is the single most important observability signal for hallucination prevention. If retrieval precision drops, the LLM downstream gets wrong context and hallucinates. Catching it here — at the retrieval layer — is 10x cheaper than catching it at the generation layer.
|
||||
|
||||
### Why Qwen-7B
|
||||
|
||||
- **Cost**: ~0.002 USD per judgment. At 500 samples/day = $1/day. A 70B model costs 10x more for marginal gain.
|
||||
- **Speed**: ~200ms per judgment on 1x A10. Fast enough for daily batch evaluation.
|
||||
- **Accuracy**: 7B models achieve 85-90% agreement with human relevance labels on standard benchmarks (BEIR, MS MARCO). Sufficient for trend detection. We are not using it for absolute measurement — we are using it for **drift detection**.
|
||||
- **Self-hosted**: Runs inside the cluster. No data leaves the network. Required for security-sensitive knowledge bases.
|
||||
|
||||
### Judgment Flow
|
||||
|
||||
```
|
||||
DAILY RELEVANCE EVALUATION (Cron, 03:00 UTC)
|
||||
============================================
|
||||
|
||||
Query Log (24h) ─── Sample 500 queries ─── Replay each query ─── Get top-10 results ─── For each (query, result) pair:
|
||||
│ │
|
||||
[R1: sample_size] Qwen-7B Prompt:
|
||||
│
|
||||
┌────────┴────────┐
|
||||
│ "Given query: │
|
||||
│ '{query}' │
|
||||
│ │
|
||||
│ Rate this │
|
||||
│ result: │
|
||||
│ '{result}' │
|
||||
│ │
|
||||
│ Score: │
|
||||
│ 0 = irrelevant │
|
||||
│ 1 = partial │
|
||||
│ 2 = perfect │
|
||||
└────────┬────────┘
|
||||
│
|
||||
[R4: score]
|
||||
│
|
||||
Aggregate: NDCG@10, MRR, Precision@10, Recall@10
|
||||
│
|
||||
┌───────────────┴───────────────┐
|
||||
[R5: ndcg_10] [R7: precision_10]
|
||||
[R6: mrr] [R8: recall_10]
|
||||
│
|
||||
Store in Postgres
|
||||
(daily time-series)
|
||||
│
|
||||
Grafana Dashboard
|
||||
(7-day rolling avg)
|
||||
```
|
||||
|
||||
### Prompt Template
|
||||
|
||||
```
|
||||
You are a relevance judge for a knowledge base system.
|
||||
|
||||
Given a user query and a retrieved document, rate the relevance:
|
||||
- 0: Irrelevant. The document does not help answer the query at all.
|
||||
- 1: Partially relevant. The document contains some useful information but does not fully answer the query.
|
||||
- 2: Highly relevant. The document directly and completely answers the query.
|
||||
|
||||
Query: "{query}"
|
||||
|
||||
Retrieved Document:
|
||||
---
|
||||
{document_text}
|
||||
---
|
||||
|
||||
Relevance Score (0, 1, or 2):
|
||||
```
|
||||
|
||||
### Metrics
|
||||
|
||||
| ID | Metric | Type | Unit | Why It Matters |
|
||||
|----|--------|------|------|----------------|
|
||||
| **R1** | `relevance_sample_size` | Gauge | queries | Number of queries evaluated. 500 gives statistically stable NDCG with ±0.02 CI. |
|
||||
| **R2** | `relevance_replay_duration_seconds` | Histogram | seconds | Time to replay and retrieve. Should match Q9. |
|
||||
| **R3** | `relevance_judge_duration_seconds` | Histogram | seconds | Qwen-7B inference time per pair. Budget: < 300ms. |
|
||||
| **R3a** | `relevance_judge_cost_usd` | Counter | USD | Running cost. Alert if budget exceeded. |
|
||||
| **R4** | `relevance_score_distribution` | Histogram | score (0-2) | Distribution of judgments. Healthy: 60%+ score=2, < 15% score=0. Drift toward 0 = retrieval degradation. |
|
||||
| **R5** | `relevance_ndcg_10` | Gauge | ratio (0-1) | Ranking quality. **Primary quality metric.** SLO: >= 0.85. Measures whether relevant docs appear at the top. |
|
||||
| **R6** | `relevance_mrr` | Gauge | ratio (0-1) | Position of first relevant result. SLO: >= 0.80. If MRR drops but NDCG holds, results exist but are buried. |
|
||||
| **R7** | `relevance_precision_10` | Gauge | ratio (0-1) | Fraction of top-10 that is relevant. Measures noise in results. |
|
||||
| **R8** | `relevance_recall_10` | Gauge | ratio (0-1) | Fraction of all relevant docs captured in top-10. Low = knowledge exists but search can't find it. |
|
||||
| **R9** | `relevance_judge_agreement` | Gauge | ratio (0-1) | Weekly: re-judge 50 pairs with human labels. Agreement rate validates the judge. SLO: >= 0.85. If agreement drops, Qwen model needs recalibration. |
|
||||
|
||||
### Alerts
|
||||
|
||||
| Condition | Severity | Action |
|
||||
|-----------|----------|--------|
|
||||
| `relevance_ndcg_10` 7-day avg < 0.80 | **Critical** | Retrieval quality degraded. Root cause: embedding drift, index corruption, or knowledge gap. |
|
||||
| `relevance_ndcg_10` drops > 0.05 in 24h | **Critical** | Sudden quality drop. Check recent ingest for poisoned data. |
|
||||
| `relevance_mrr` < 0.70 | **Warning** | Relevant docs exist but rank poorly. Check reranker, RRF weights. |
|
||||
| `relevance_score_distribution` score=0 > 25% | **Warning** | Quarter of results are irrelevant. Coverage gap or embedding model mismatch. |
|
||||
| `relevance_judge_agreement` < 0.80 | **Warning** | Judge drifting from human labels. Re-evaluate prompt or model. |
|
||||
|
||||
---
|
||||
|
||||
## 5. Write Volume and Storage Observability
|
||||
|
||||
### Why
|
||||
|
||||
Memory services grow unboundedly. Unlike caches (eviction policy) or databases (schema constraints), a knowledge base accumulates everything. Write volume tracking answers: **"How fast is the system growing, and when do we need to intervene?"**
|
||||
|
||||
Write volume also directly impacts retrieval quality. More documents = more noise in search results. Without compaction, precision degrades as the corpus grows.
|
||||
|
||||
### Metrics
|
||||
|
||||
| ID | Metric | Type | Unit | Why It Matters |
|
||||
|----|--------|------|------|----------------|
|
||||
| **W1** | `storage_pgvector_rows_total` | Gauge | rows | Total vectors stored. Growth rate = capacity planning. |
|
||||
| **W2** | `storage_pgvector_bytes` | Gauge | bytes | Disk usage. 768-dim float32 = ~3KB/row with overhead. |
|
||||
| **W3** | `storage_opensearch_docs_total` | Gauge | docs | OpenSearch document count. Should match W1 (eventual consistency). |
|
||||
| **W4** | `storage_opensearch_bytes` | Gauge | bytes | OpenSearch index size. Includes inverted index overhead. |
|
||||
| **W5** | `storage_parity_drift` | Gauge | count | abs(W1 - W3). Should be 0 in steady state. Non-zero = dual-write inconsistency. |
|
||||
| **W6** | `write_rate_per_hour` | Gauge | chunks/hour | Sustained write throughput. Trigger compaction planning at > 1000/hour. |
|
||||
| **W7** | `write_rate_per_project` | Gauge (labeled) | chunks/hour | Per-project write rate. Identifies hot projects dominating storage. |
|
||||
| **W8** | `storage_level_distribution` | Gauge (labeled) | rows | Label: `level=L0\|L1\|L2\|R`. Distribution across learning levels. Healthy: L1 > L0 (facts promoted). If L0 dominates, promotion pipeline stalled. |
|
||||
| **W9** | `compaction_runs_total` | Counter | runs | How often compaction executes. |
|
||||
| **W10** | `compaction_dedup_removed_total` | Counter | chunks | Duplicates removed per run. High = ingest dedup isn't catching everything. |
|
||||
| **W11** | `compaction_stale_gc_removed_total` | Counter | chunks | Stale facts garbage-collected (soft-deleted, age > 30d). |
|
||||
| **W12** | `compaction_space_freed_bytes` | Counter | bytes | Space recovered per run. Declining = less to compact (good). |
|
||||
|
||||
### Alerts
|
||||
|
||||
| Condition | Severity | Action |
|
||||
|-----------|----------|--------|
|
||||
| `storage_parity_drift` > 100 for 1h | **Warning** | pgvector and OpenSearch out of sync. Check dual-write failures (I8). |
|
||||
| `storage_pgvector_bytes` > 80% of PVC | **Critical** | Storage nearing capacity. Expand PVC or run compaction. |
|
||||
| `write_rate_per_hour` > 5000 sustained 2h | **Warning** | High write load. Check if upstream is flooding. Consider rate limiting. |
|
||||
| `storage_level_distribution{level="L0"}` / W1 > 0.7 | **Warning** | 70% of storage is unprocessed L0. Promotion pipeline stalled. |
|
||||
|
||||
---
|
||||
|
||||
## 6. Pod Resource Observability
|
||||
|
||||
### Why
|
||||
|
||||
The memory service runs as a Kubernetes pod. If the pod runs out of memory, it gets OOMKilled. If it saturates CPU, latency spikes across all endpoints. These are the physical constraints that gate everything else.
|
||||
|
||||
Unlike stateless web services, a memory service has **resident state**: the embedding model weights (~500MB for MiniLM-L6), connection pools, in-flight embeddings, and cached query results. Memory usage is not flat — it grows with concurrent requests. A burst of 50 parallel ingest requests each holding a 768-dim float32 vector = 50 × 3KB = 150KB just in vectors, but the surrounding allocations (HTTP buffers, serde frames, OpenSearch bulk payloads) multiply that 10-20x.
|
||||
|
||||
### Metrics
|
||||
|
||||
| ID | Metric | Type | Unit | Why It Matters |
|
||||
|----|--------|------|------|----------------|
|
||||
| **P1** | `container_memory_working_set_bytes` | Gauge | bytes | Actual memory in use (excludes reclaimable cache). This is what Kubernetes uses for OOMKill decisions. |
|
||||
| **P2** | `container_memory_rss` | Gauge | bytes | Resident Set Size. Physical memory held. If RSS diverges from working set, fragmentation is occurring. |
|
||||
| **P3** | `container_memory_usage_bytes` | Gauge | bytes | Total memory (includes page cache). Less useful for OOM prediction but shows total footprint. |
|
||||
| **P4** | `container_memory_limit_bytes` | Gauge | bytes | Pod memory limit from resource spec. `P1 / P4` = memory pressure ratio. |
|
||||
| **P5** | `container_cpu_usage_seconds_total` | Counter | CPU-seconds | CPU consumption rate. `rate(P5[1m])` = CPU cores used. Compare to limit. |
|
||||
| **P6** | `container_cpu_throttled_seconds_total` | Counter | seconds | Time the pod was CPU-throttled by cgroup. Any throttling = latency impact. |
|
||||
| **P7** | `container_cpu_cfs_throttled_periods_total` | Counter | periods | Number of CFS periods where throttling occurred. `P7 / total_periods` = throttle ratio. |
|
||||
| **P8** | `kube_pod_container_resource_requests` | Gauge | cores/bytes | Requested resources. Over-request wastes cluster capacity. Under-request = eviction risk. |
|
||||
| **P9** | `kube_pod_container_resource_limits` | Gauge | cores/bytes | Resource limits. `P1 / P9{resource="memory"}` > 0.85 = danger zone. |
|
||||
| **P10** | `kube_pod_status_phase` | Gauge | phase | Running/Pending/Failed/Succeeded. Pending too long = scheduling issues. |
|
||||
| **P11** | `kube_pod_container_status_restarts_total` | Counter | restarts | OOMKills and CrashLoopBackoff. Any restart = data in flight was lost. |
|
||||
| **P12** | `container_network_receive_bytes_total` | Counter | bytes | Network ingress. Correlate with ingest volume. Spike = large batch ingest. |
|
||||
| **P13** | `container_network_transmit_bytes_total` | Counter | bytes | Network egress. Correlate with query response sizes. |
|
||||
|
||||
### Memory Breakdown (What Lives in the Pod)
|
||||
|
||||
```
|
||||
Pod Memory Budget (e.g., 2Gi limit)
|
||||
├── Embedding Model weights ~500MB (loaded once at startup)
|
||||
├── sqlx connection pool ~50MB (20 connections × ~2.5MB each)
|
||||
├── OpenSearch HTTP client pool ~20MB (keep-alive connections)
|
||||
├── In-flight ingest embeddings ~variable (concurrent_requests × ~60KB)
|
||||
├── In-flight query results ~variable (concurrent_queries × ~200KB)
|
||||
├── Tokio runtime + thread stacks ~30MB (worker threads × 8MB stack)
|
||||
├── Rate limiter buckets ~5MB (in-memory token buckets)
|
||||
├── Idempotency store (24h TTL) ~10-50MB (grows with ingest volume)
|
||||
└── Heap overhead + fragmentation ~100-200MB
|
||||
─────────
|
||||
~800MB baseline + ~variable per-request
|
||||
```
|
||||
|
||||
### Alerts
|
||||
|
||||
| Condition | Severity | Action |
|
||||
|-----------|----------|--------|
|
||||
| `P1 / P4` > 0.85 for 5min | **Critical** | Memory pressure. OOMKill imminent. Scale up limit or reduce concurrency. |
|
||||
| `P11` increments | **Critical** | Pod restarted. Check if OOMKilled (`kubectl describe pod`). Raise memory limit. |
|
||||
| `rate(P6[5m])` > 0 for 10min | **Warning** | Sustained CPU throttling. Query/ingest latency affected. Raise CPU limit. |
|
||||
| `P7 / total_periods` > 0.25 | **Warning** | 25%+ of CPU periods throttled. Under-provisioned. |
|
||||
| `P1` growing monotonically over 24h | **Warning** | Memory leak. Check idempotency store TTL, connection pool, or embedding cache. |
|
||||
|
||||
---
|
||||
|
||||
## 7. Availability
|
||||
|
||||
### Why
|
||||
|
||||
A knowledge base that is down cannot reduce hallucination. If the memory service is unavailable during an LLM generation call, the model falls back to parametric knowledge only — which is exactly where hallucinations come from. Availability is not just uptime; it is **the probability that a query gets a correct answer within the latency SLO**.
|
||||
|
||||
### Metrics
|
||||
|
||||
| ID | Metric | Type | Unit | Why It Matters |
|
||||
|----|--------|------|------|----------------|
|
||||
| **A1** | `http_requests_total` | Counter (labeled) | requests | Label: `method`, `endpoint`, `status_code`. Foundation for error rate calculation. |
|
||||
| **A2** | `http_requests_duration_seconds` | Histogram (labeled) | seconds | Label: `endpoint`. Per-endpoint latency distribution. |
|
||||
| **A3** | `http_5xx_total` | Counter | requests | Server errors. Any 5xx = something broke internally. |
|
||||
| **A4** | `http_4xx_total` | Counter (labeled) | requests | Label: `status_code`. 401/403 = auth issues. 429 = rate limiting. 400 = bad client. |
|
||||
| **A5** | `availability_ratio` | Gauge | ratio (0-1) | `1 - (A3 / A1)` over rolling window. SLO: >= 0.999 (three nines). |
|
||||
| **A6** | `successful_query_ratio` | Gauge | ratio (0-1) | Queries that return 200 with >= 1 result, within 500ms. Stricter than raw availability — includes quality. |
|
||||
| **A7** | `health_check_consecutive_failures` | Gauge | count | Consecutive `/health` failures. Kubernetes uses this for restart decisions (liveness probe). |
|
||||
| **A8** | `dependency_up` | Gauge (labeled) | 0/1 | Label: `dependency=postgres\|opensearch\|obsidian\|embedding_model`. Which backends are reachable. |
|
||||
| **A9** | `graceful_degradation_total` | Counter (labeled) | requests | Label: `degraded_component`. Requests served with partial results because a dependency was down. e.g., OpenSearch down = semantic-only results. |
|
||||
| **A10** | `circuit_breaker_state` | Gauge (labeled) | 0/1/2 | Label: `backend`. 0=closed (healthy), 1=half-open (probing), 2=open (failing). Per dependency. |
|
||||
|
||||
### Availability Calculation
|
||||
|
||||
```
|
||||
Successful Requests (2xx, within SLO latency)
|
||||
Availability = ────────────────────────────────────────────────────
|
||||
Total Requests
|
||||
|
||||
Three tiers of availability:
|
||||
|
||||
1. RAW AVAILABILITY: 1 - (5xx / total) Target: 99.9%
|
||||
"Did it respond?"
|
||||
|
||||
2. LATENCY AVAILABILITY: requests_within_slo / total Target: 99.5%
|
||||
"Did it respond fast enough?"
|
||||
|
||||
3. QUALITY AVAILABILITY: queries_with_results / total Target: 95%
|
||||
"Did it respond with useful results?"
|
||||
|
||||
Monitor all three. A system can be 99.9% available (raw) but only
|
||||
80% available (quality) if 20% of queries return empty results.
|
||||
```
|
||||
|
||||
### Alerts
|
||||
|
||||
| Condition | Severity | Action |
|
||||
|-----------|----------|--------|
|
||||
| `availability_ratio` < 0.999 over 1h | **Critical** | SLO breach. Page on-call. Check A8 for which dependency is down. |
|
||||
| `http_5xx_total` rate > 10/min for 5min | **Critical** | Error spike. Check pod logs, Postgres connectivity, OpenSearch health. |
|
||||
| `dependency_up{dependency="postgres"}` = 0 | **Critical** | Primary store down. All writes and most reads fail. |
|
||||
| `dependency_up{dependency="opensearch"}` = 0 | **Warning** | Lexical search unavailable. Semantic-only fallback active. Quality degraded. |
|
||||
| `graceful_degradation_total` rate > 5% of A1 | **Warning** | Serving partial results too often. Fix the degraded dependency. |
|
||||
| `successful_query_ratio` < 0.90 | **Warning** | 10%+ of queries failing or empty. Check ingest pipeline, index health. |
|
||||
|
||||
---
|
||||
|
||||
## 8. Ingest Rate Patterns
|
||||
|
||||
### Why
|
||||
|
||||
Ingest rate is not just a throughput number. The **pattern** of writes reveals system behavior. Bursty writes from batch jobs behave differently from steady trickle from live sessions. A sudden drop in ingest rate may mean the upstream source broke. A sudden spike may mean a replay or backfill is running, which changes storage projections.
|
||||
|
||||
For a knowledge base, write rate directly affects retrieval quality: every new chunk is a new candidate that can dilute search precision. Knowing when and how fast writes happen lets you plan compaction, predict storage growth, and detect anomalies.
|
||||
|
||||
### Metrics
|
||||
|
||||
| ID | Metric | Type | Unit | Why It Matters |
|
||||
|----|--------|------|------|----------------|
|
||||
| **IR1** | `ingest_rate_1m` | Gauge | chunks/min | 1-minute rolling write rate. Shows bursts. |
|
||||
| **IR2** | `ingest_rate_1h` | Gauge | chunks/hour | Hourly smoothed rate. Capacity planning baseline. |
|
||||
| **IR3** | `ingest_rate_by_project` | Gauge (labeled) | chunks/hour | Label: `project`. Identifies which project dominates writes. |
|
||||
| **IR4** | `ingest_rate_by_level` | Gauge (labeled) | chunks/hour | Label: `level=L0\|L1\|L2\|R`. L0 dominance = raw data flooding. L1/L2 growing = healthy knowledge promotion. |
|
||||
| **IR5** | `ingest_rate_by_source` | Gauge (labeled) | chunks/hour | Label: `source=transcript\|document\|api\|batch`. Reveals upstream behavior. |
|
||||
| **IR6** | `ingest_batch_size` | Histogram | chunks/batch | Size of batch ingest requests. Large batches (>100) need different backpressure. |
|
||||
| **IR7** | `ingest_queue_depth` | Gauge | messages | External queue (kmsvc) pending messages. Growing = workers can't keep up. |
|
||||
| **IR8** | `ingest_queue_age_seconds` | Histogram | seconds | Age of oldest message in queue. > 60s = processing lag. |
|
||||
| **IR9** | `ingest_bytes_per_chunk` | Histogram | bytes | Average chunk size. Sudden increase = source sending larger payloads. |
|
||||
| **IR10** | `ingest_throughput_bytes_per_second` | Gauge | bytes/sec | Sustained write bandwidth. Correlate with P12 (network ingress). |
|
||||
|
||||
### Rate Patterns and What They Mean
|
||||
|
||||
```
|
||||
Pattern 1: STEADY TRICKLE (healthy)
|
||||
────────────────────────────────────
|
||||
chunks/min
|
||||
10 │ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─
|
||||
5 │
|
||||
0 └──────────────────────────── time
|
||||
Constant ~8-12 chunks/min from live sessions.
|
||||
Storage growth predictable. Compaction schedule stable.
|
||||
|
||||
Pattern 2: BURST (batch job or backfill)
|
||||
────────────────────────────────────────
|
||||
chunks/min
|
||||
500 │ ██
|
||||
250 │ ██████
|
||||
0 │──────██──────██─────────── time
|
||||
Sudden spike. Check: is this a planned backfill?
|
||||
If unexpected: rate limit may trigger, queue depth spikes.
|
||||
Action: verify source, check queue lag (IR7).
|
||||
|
||||
Pattern 3: DROP TO ZERO (upstream broken)
|
||||
─────────────────────────────────────────
|
||||
chunks/min
|
||||
10 │ ─ ─ ─ ─ ┐
|
||||
5 │ │
|
||||
0 │ └──────────────── time
|
||||
Ingest stopped. Source may be down, auth token expired,
|
||||
or network partition. Silent failure — no errors, just absence.
|
||||
Alert on: IR2 = 0 for > 30min during business hours.
|
||||
|
||||
Pattern 4: MONOTONIC GROWTH (runaway source)
|
||||
─────────────────────────────────────────────
|
||||
chunks/min
|
||||
100 │ ╱
|
||||
50 │ ╱───
|
||||
10 │ ─ ─ ─ ─ ─ ─ ╱───
|
||||
0 └──────────────────────────── time
|
||||
Write rate increasing over days. Source producing more data.
|
||||
Storage projection changes. Compaction may not keep up.
|
||||
Action: review source, consider sampling or filtering.
|
||||
```
|
||||
|
||||
### Alerts
|
||||
|
||||
| Condition | Severity | Action |
|
||||
|-----------|----------|--------|
|
||||
| `ingest_rate_1h` = 0 for 30min (during business hours) | **Warning** | Ingest stopped. Check upstream source, auth tokens, network. |
|
||||
| `ingest_rate_1m` > 200 sustained 10min | **Warning** | Burst ingest. Check if planned. Monitor queue depth (IR7). |
|
||||
| `ingest_queue_depth` > 1000 for 15min | **Critical** | Workers can't keep up. Scale workers or throttle source. |
|
||||
| `ingest_queue_age_seconds` p99 > 300 | **Warning** | 5+ minutes processing lag. Stale data entering the system. |
|
||||
| `ingest_rate_by_level{level="L0"}` / total > 0.9 sustained 24h | **Warning** | 90% raw data, no promotion. Knowledge extraction pipeline stalled. |
|
||||
|
||||
---
|
||||
|
||||
## 9. Postgres Internal Observability
|
||||
|
||||
### Why
|
||||
|
||||
Postgres is the primary store. Every vector lives there. Every query hits it. Postgres health directly determines memory service health. But Postgres problems are **silent** — a bloated table doesn't throw errors, it just gets slower. A missing VACUUM doesn't alert, it just consumes 2x disk. An HNSW index with wrong parameters doesn't fail, it just returns worse results.
|
||||
|
||||
These metrics catch degradation before users notice it.
|
||||
|
||||
### Connection Pool and Session Metrics
|
||||
|
||||
| ID | Metric | Source | Unit | Why It Matters |
|
||||
|----|--------|--------|------|----------------|
|
||||
| **PG1** | `pg_stat_activity_count` | `pg_stat_activity` | connections | Active connections by state. `active` = running query. `idle` = waiting. `idle in transaction` = **dangerous** — holds locks. |
|
||||
| **PG2** | `pg_stat_activity_max_duration_seconds` | `pg_stat_activity` | seconds | Longest running query. > 30s = likely stuck or missing index. |
|
||||
| **PG3** | `pg_stat_activity_waiting_count` | `pg_stat_activity` | connections | Queries waiting for locks. > 0 sustained = lock contention. |
|
||||
| **PG4** | `pg_settings_max_connections` | `pg_settings` | connections | Max allowed connections. `PG1 / PG4` > 0.8 = pool exhaustion risk. |
|
||||
|
||||
### Query Performance
|
||||
|
||||
| ID | Metric | Source | Unit | Why It Matters |
|
||||
|----|--------|--------|------|----------------|
|
||||
| **PG5** | `pg_stat_statements_mean_exec_time` | `pg_stat_statements` | ms | Mean execution time per query pattern. Tracks if vector search is degrading over time. |
|
||||
| **PG6** | `pg_stat_statements_calls` | `pg_stat_statements` | count | Call count per query. Identifies hot queries. Top-1 query consuming 80% of DB time = optimization target. |
|
||||
| **PG7** | `pg_stat_statements_rows` | `pg_stat_statements` | rows | Rows returned per query. Vector search returning 10k rows when limit is 50 = missing index or wrong query plan. |
|
||||
| **PG8** | `pg_stat_user_tables_seq_scan` | `pg_stat_user_tables` | scans | Sequential scans on `memory_vector`. Any seq scan on a large vector table = catastrophic. HNSW index not being used. |
|
||||
| **PG9** | `pg_stat_user_tables_idx_scan` | `pg_stat_user_tables` | scans | Index scans. Should be >> seq scans for vector table. |
|
||||
|
||||
### Table and Index Health
|
||||
|
||||
| ID | Metric | Source | Unit | Why It Matters |
|
||||
|----|--------|--------|------|----------------|
|
||||
| **PG10** | `pg_stat_user_tables_n_live_tup` | `pg_stat_user_tables` | tuples | Live rows in `memory_vector`. Growth rate = storage planning. |
|
||||
| **PG11** | `pg_stat_user_tables_n_dead_tup` | `pg_stat_user_tables` | tuples | Dead tuples (deleted/updated but not vacuumed). High ratio = bloat. |
|
||||
| **PG12** | `pg_dead_tuple_ratio` | computed | ratio | `PG11 / (PG10 + PG11)`. > 0.2 = 20% bloat. VACUUM needed. |
|
||||
| **PG13** | `pg_stat_user_tables_last_autovacuum` | `pg_stat_user_tables` | timestamp | When autovacuum last ran. > 24h ago on active table = misconfigured threshold. |
|
||||
| **PG14** | `pg_stat_user_tables_last_autoanalyze` | `pg_stat_user_tables` | timestamp | When autoanalyze last ran. Stale statistics = bad query plans. |
|
||||
| **PG15** | `pg_table_size_bytes` | `pg_total_relation_size()` | bytes | Total table size including indexes and TOAST. |
|
||||
| **PG16** | `pg_index_size_bytes` | `pg_indexes_size()` | bytes | HNSW index size. Grows with vectors. If index > table, check parameters. |
|
||||
| **PG17** | `pg_index_bloat_ratio` | `pgstattuple` | ratio | Index bloat. > 0.3 = REINDEX needed. HNSW indexes don't bloat like B-tree, but monitor anyway. |
|
||||
|
||||
### HNSW Index Specific
|
||||
|
||||
| ID | Metric | Source | Unit | Why It Matters |
|
||||
|----|--------|--------|------|----------------|
|
||||
| **PG18** | `pg_hnsw_index_size` | `pg_relation_size()` | bytes | Size of the HNSW index on `memory_vector.embedding`. Grows as O(n × m) where m=16. |
|
||||
| **PG19** | `pg_hnsw_build_time_seconds` | manual / `CREATE INDEX` | seconds | Time to rebuild HNSW index. Needed after parameter changes. At 1M vectors: ~30min. At 10M: hours. Plan maintenance windows. |
|
||||
| **PG20** | `pg_hnsw_recall_estimate` | benchmark | ratio | Estimated recall of HNSW at current parameters (m=16, ef_construction=200). Run periodic benchmark with known queries. If recall < 0.95, increase ef_search or rebuild with higher m. |
|
||||
|
||||
### WAL and Replication (CNPG)
|
||||
|
||||
| ID | Metric | Source | Unit | Why It Matters |
|
||||
|----|--------|--------|------|----------------|
|
||||
| **PG21** | `pg_wal_lsn_diff` | `pg_current_wal_lsn()` | bytes | WAL generation rate. High during bulk ingest. Correlate with IR1. |
|
||||
| **PG22** | `pg_replication_lag_bytes` | `pg_stat_replication` | bytes | Replica lag in bytes. CNPG manages replicas. Lag > 100MB = replica falling behind. |
|
||||
| **PG23** | `pg_replication_lag_seconds` | `pg_stat_replication` | seconds | Replica lag in time. > 10s = replica can't keep up with write rate. Read queries to replica return stale results. |
|
||||
| **PG24** | `pg_wal_size_bytes` | `pg_wal` directory | bytes | Total WAL on disk. Unbounded growth = archiving broken or wal_keep_size too high. |
|
||||
|
||||
### Transaction and Lock Health
|
||||
|
||||
| ID | Metric | Source | Unit | Why It Matters |
|
||||
|----|--------|--------|------|----------------|
|
||||
| **PG25** | `pg_stat_database_xact_commit` | `pg_stat_database` | transactions | Committed transactions/sec. Baseline throughput. |
|
||||
| **PG26** | `pg_stat_database_xact_rollback` | `pg_stat_database` | transactions | Rolled back transactions. `PG26 / PG25` > 0.01 = 1% rollback rate. Check constraint violations or deadlocks. |
|
||||
| **PG27** | `pg_stat_database_deadlocks` | `pg_stat_database` | deadlocks | Any deadlock = concurrent write contention. Rare in append-mostly workload. If seen, check compaction + ingest overlap. |
|
||||
| **PG28** | `pg_stat_database_conflicts` | `pg_stat_database` | conflicts | Replication conflicts. Query on replica canceled due to WAL replay. Adjust `max_standby_streaming_delay`. |
|
||||
| **PG29** | `pg_locks_count` | `pg_locks` | locks | Lock count by mode. `AccessExclusiveLock` blocks everything — check for DDL during traffic. |
|
||||
|
||||
### Cache Efficiency
|
||||
|
||||
| ID | Metric | Source | Unit | Why It Matters |
|
||||
|----|--------|--------|------|----------------|
|
||||
| **PG30** | `pg_stat_database_blks_hit` | `pg_stat_database` | blocks | Buffer cache hits. |
|
||||
| **PG31** | `pg_stat_database_blks_read` | `pg_stat_database` | blocks | Disk reads (cache misses). |
|
||||
| **PG32** | `pg_cache_hit_ratio` | computed | ratio | `PG30 / (PG30 + PG31)`. SLO: >= 0.99. Below 0.95 = shared_buffers too small or working set exceeds RAM. |
|
||||
| **PG33** | `pg_stat_user_indexes_idx_blks_hit` | `pg_stat_user_indexes` | blocks | HNSW index cache hits. Low hit ratio = index doesn't fit in memory. Increase shared_buffers or effective_cache_size. |
|
||||
|
||||
### Key SQL Queries for Monitoring
|
||||
|
||||
```sql
|
||||
-- Dead tuple ratio (bloat indicator)
|
||||
SELECT relname,
|
||||
n_live_tup,
|
||||
n_dead_tup,
|
||||
CASE WHEN n_live_tup > 0
|
||||
THEN round(n_dead_tup::numeric / (n_live_tup + n_dead_tup) * 100, 2)
|
||||
ELSE 0 END AS dead_pct,
|
||||
last_autovacuum,
|
||||
last_autoanalyze
|
||||
FROM pg_stat_user_tables
|
||||
WHERE relname IN ('memory_vector', 'memory_entity', 'memory_edge')
|
||||
ORDER BY n_dead_tup DESC;
|
||||
|
||||
-- Slowest queries (requires pg_stat_statements)
|
||||
SELECT query,
|
||||
calls,
|
||||
round(mean_exec_time::numeric, 2) AS mean_ms,
|
||||
round(max_exec_time::numeric, 2) AS max_ms,
|
||||
rows
|
||||
FROM pg_stat_statements
|
||||
WHERE dbid = (SELECT oid FROM pg_database WHERE datname = 'memory')
|
||||
ORDER BY mean_exec_time DESC
|
||||
LIMIT 10;
|
||||
|
||||
-- Sequential vs index scans (vector table must use index)
|
||||
SELECT relname,
|
||||
seq_scan,
|
||||
idx_scan,
|
||||
CASE WHEN (seq_scan + idx_scan) > 0
|
||||
THEN round(idx_scan::numeric / (seq_scan + idx_scan) * 100, 2)
|
||||
ELSE 100 END AS idx_scan_pct
|
||||
FROM pg_stat_user_tables
|
||||
WHERE relname = 'memory_vector';
|
||||
|
||||
-- Table and index sizes
|
||||
SELECT relname,
|
||||
pg_size_pretty(pg_total_relation_size(relid)) AS total_size,
|
||||
pg_size_pretty(pg_relation_size(relid)) AS table_size,
|
||||
pg_size_pretty(pg_indexes_size(relid)) AS index_size
|
||||
FROM pg_stat_user_tables
|
||||
WHERE schemaname = 'public'
|
||||
ORDER BY pg_total_relation_size(relid) DESC;
|
||||
|
||||
-- Replication lag (CNPG replicas)
|
||||
SELECT client_addr,
|
||||
state,
|
||||
pg_wal_lsn_diff(pg_current_wal_lsn(), replay_lsn) AS lag_bytes,
|
||||
extract(epoch FROM now() - replay_lag) AS lag_seconds
|
||||
FROM pg_stat_replication;
|
||||
|
||||
-- Cache hit ratio
|
||||
SELECT datname,
|
||||
round(
|
||||
blks_hit::numeric / NULLIF(blks_hit + blks_read, 0) * 100, 2
|
||||
) AS cache_hit_pct
|
||||
FROM pg_stat_database
|
||||
WHERE datname = 'memory';
|
||||
|
||||
-- Connection state breakdown
|
||||
SELECT state, count(*)
|
||||
FROM pg_stat_activity
|
||||
WHERE datname = 'memory'
|
||||
GROUP BY state;
|
||||
```
|
||||
|
||||
### Alerts
|
||||
|
||||
| Condition | Severity | Action |
|
||||
|-----------|----------|--------|
|
||||
| `pg_dead_tuple_ratio` > 0.20 on `memory_vector` | **Warning** | 20% bloat. Run `VACUUM ANALYZE memory_vector;` or check autovacuum config. |
|
||||
| `pg_stat_user_tables_seq_scan` on `memory_vector` increments | **Critical** | Sequential scan on vector table. HNSW index not used. Check query plan with `EXPLAIN ANALYZE`. |
|
||||
| `pg_cache_hit_ratio` < 0.95 | **Critical** | Cache thrashing. Increase `shared_buffers` or scale to larger instance. |
|
||||
| `pg_replication_lag_seconds` > 30 | **Warning** | Replica 30s behind. Read queries returning stale data. Check write rate, replica resources. |
|
||||
| `pg_stat_database_deadlocks` > 0 | **Warning** | Deadlock detected. Check concurrent write patterns (ingest + compaction). |
|
||||
| `pg_stat_activity_max_duration_seconds` > 60 | **Warning** | Query running > 60s. Likely stuck. Check for missing index or lock wait. |
|
||||
| `pg_stat_activity_count{state="idle in transaction"}` > 5 for 10min | **Warning** | Idle-in-transaction connections holding locks. Connection pool leak or application bug. |
|
||||
| `pg_wal_size_bytes` > 10GB | **Warning** | WAL accumulation. Check archiving, replication, or `wal_keep_size` setting. |
|
||||
| `PG1 / PG4` > 0.8 | **Critical** | Connection pool near max. Add PgBouncer or increase `max_connections`. |
|
||||
|
||||
---
|
||||
|
||||
## 10. System Health (Infrastructure Summary)
|
||||
|
||||
### Metrics
|
||||
|
||||
| ID | Metric | Type | Unit | Why It Matters |
|
||||
|----|--------|------|------|----------------|
|
||||
| **H1** | `health_check_status` | Gauge | 0/1 | `/health` endpoint. Basic liveness. |
|
||||
| **H2** | `pgvector_connection_pool_active` | Gauge | connections | Active DB connections. Near max = pool exhaustion risk. |
|
||||
| **H3** | `pgvector_connection_pool_idle` | Gauge | connections | Idle connections. Zero idle + high active = under-provisioned. |
|
||||
| **H4** | `opensearch_cluster_status` | Gauge | 0/1/2 | 0=red, 1=yellow, 2=green. Yellow = replica missing. Red = data loss risk. |
|
||||
| **H5** | `embedding_model_loaded` | Gauge | 0/1 | Model health check. 0 = all ingest and query embeds will fail. |
|
||||
| **H6** | `rate_limit_rejections_total` | Counter | requests | Rate limit hits. High = legitimate traffic being blocked, or DDoS. |
|
||||
| **H7** | `auth_failures_total` | Counter (labeled) | requests | Label: `reason=expired\|invalid\|missing`. Pattern reveals attack or misconfiguration. |
|
||||
|
||||
---
|
||||
|
||||
## 11. Dashboard Layout
|
||||
|
||||
### Grafana Rows (top to bottom)
|
||||
|
||||
```
|
||||
Row 1: SYSTEM HEALTH
|
||||
┌──────────────┬──────────────┬──────────────┬──────────────┐
|
||||
│ Health: UP │ PG Pool: │ OpenSearch: │ Embed Model │
|
||||
│ (H1) │ 12/20 active│ GREEN │ LOADED │
|
||||
└──────────────┴──────────────┴──────────────┴──────────────┘
|
||||
|
||||
Row 2: WRITE PATH (Ingest)
|
||||
┌──────────────────────────┬──────────────────────────┬──────────────────────────┐
|
||||
│ Ingest Rate (I1) │ Write Latency p50/p99 │ Dedup Hit Ratio │
|
||||
│ [line chart, 24h] │ (I5) [line chart, 24h] │ (I3/I1) [line, 24h] │
|
||||
├──────────────────────────┼──────────────────────────┼──────────────────────────┤
|
||||
│ OpenSearch Failures (I8)│ Contradiction Queue (I12)│ Chunks Written (I10) │
|
||||
│ [counter, 24h] │ [gauge, current depth] │ [counter, 24h] │
|
||||
└──────────────────────────┴──────────────────────────┴──────────────────────────┘
|
||||
|
||||
Row 3: READ PATH (Query)
|
||||
┌──────────────────────────┬──────────────────────────┬──────────────────────────┐
|
||||
│ Query Rate (Q1) │ Query Latency p50/p99 │ Empty Results (Q11) │
|
||||
│ [line chart, 24h] │ (Q9) [line chart, 24h] │ [%, 24h] │
|
||||
├──────────────────────────┼──────────────────────────┼──────────────────────────┤
|
||||
│ Latency Breakdown │ Intent Distribution │ Score Distribution │
|
||||
│ sem/lex/rrf/rerank │ (Q3) [pie chart] │ (Q12) [histogram] │
|
||||
│ [stacked area, 24h] │ │ │
|
||||
└──────────────────────────┴──────────────────────────┴──────────────────────────┘
|
||||
|
||||
Row 4: CONTEXT (3-Tier)
|
||||
┌──────────────────────────┬──────────────────────────┬──────────────────────────┐
|
||||
│ Tier Hit Distribution │ Context Latency p50/p99 │ Budget Usage │
|
||||
│ (C7) [stacked bar, 7d] │ (C6) [line chart, 24h] │ (C5) [histogram] │
|
||||
├──────────────────────────┼──────────────────────────┼──────────────────────────┤
|
||||
│ Tier-1 Hit Rate │ Degraded Responses (C8) │ Dropped Results (C5a) │
|
||||
│ (C2/C1) [gauge, target │ [counter, 24h] │ [counter, 24h] │
|
||||
│ >= 0.80] │ │ │
|
||||
└──────────────────────────┴──────────────────────────┴──────────────────────────┘
|
||||
|
||||
Row 5: RELEVANCE (Qwen-7B Judge) ← MOST IMPORTANT ROW
|
||||
┌──────────────────────────┬──────────────────────────┬──────────────────────────┐
|
||||
│ NDCG@10 (R5) │ MRR (R6) │ Precision/Recall │
|
||||
│ [line chart, 30d │ [line chart, 30d │ (R7, R8) [line, 30d │
|
||||
│ rolling avg, target │ rolling avg] │ rolling avg] │
|
||||
│ >= 0.85] │ │ │
|
||||
├──────────────────────────┼──────────────────────────┼──────────────────────────┤
|
||||
│ Relevance Score Dist │ Judge Cost (R3a) │ Judge Agreement (R9) │
|
||||
│ (R4) [bar: 0/1/2, 7d] │ [counter, daily USD] │ [gauge, weekly] │
|
||||
└──────────────────────────┴──────────────────────────┴──────────────────────────┘
|
||||
|
||||
Row 6: POD RESOURCES
|
||||
┌──────────────────────────┬──────────────────────────┬──────────────────────────┐
|
||||
│ Memory Usage (P1) │ CPU Usage (P5 rate) │ Pod Restarts (P11) │
|
||||
│ [line, 24h, limit line] │ [line, 24h, limit line] │ [counter, 7d] │
|
||||
├──────────────────────────┼──────────────────────────┼──────────────────────────┤
|
||||
│ Memory Pressure (P1/P4) │ CPU Throttle (P6 rate) │ Network I/O (P12, P13) │
|
||||
│ [gauge, target < 0.85] │ [line, 24h] │ [line, 24h] │
|
||||
└──────────────────────────┴──────────────────────────┴──────────────────────────┘
|
||||
|
||||
Row 7: AVAILABILITY
|
||||
┌──────────────────────────┬──────────────────────────┬──────────────────────────┐
|
||||
│ Availability (A5) │ Error Rate (A3/A1) │ Dependencies (A8) │
|
||||
│ [gauge, target >= 99.9%]│ [line, 24h] │ [status grid: pg/os/emb]│
|
||||
├──────────────────────────┼──────────────────────────┼──────────────────────────┤
|
||||
│ Quality Avail (A6) │ Degraded Responses (A9) │ 4xx Breakdown (A4) │
|
||||
│ [gauge, target >= 95%] │ [counter, 24h] │ [stacked bar, 24h] │
|
||||
└──────────────────────────┴──────────────────────────┴──────────────────────────┘
|
||||
|
||||
Row 8: INGEST RATE PATTERNS
|
||||
┌──────────────────────────┬──────────────────────────┬──────────────────────────┐
|
||||
│ Write Rate/Min (IR1) │ Write Rate/Hour (IR2) │ Queue Depth (IR7) │
|
||||
│ [line, 24h, burst high] │ [line, 7d] │ [gauge, target < 1000] │
|
||||
├──────────────────────────┼──────────────────────────┼──────────────────────────┤
|
||||
│ Rate by Project (IR3) │ Rate by Level (IR4) │ Queue Age p99 (IR8) │
|
||||
│ [stacked area, 24h] │ [stacked area, 24h] │ [line, 24h] │
|
||||
└──────────────────────────┴──────────────────────────┴──────────────────────────┘
|
||||
|
||||
Row 9: POSTGRES INTERNALS
|
||||
┌──────────────────────────┬──────────────────────────┬──────────────────────────┐
|
||||
│ Cache Hit Ratio (PG32) │ Dead Tuple Ratio (PG12) │ Connections (PG1) │
|
||||
│ [gauge, target >= 99%] │ [gauge, target < 20%] │ [stacked bar by state] │
|
||||
├──────────────────────────┼──────────────────────────┼──────────────────────────┤
|
||||
│ Seq vs Idx Scans (PG8/9)│ Replication Lag (PG23) │ Table Sizes (PG15) │
|
||||
│ [line, 7d] │ [line, 24h, target < 10s]│ [bar chart, current] │
|
||||
├──────────────────────────┼──────────────────────────┼──────────────────────────┤
|
||||
│ Slowest Queries (PG5) │ WAL Size (PG24) │ Deadlocks (PG27) │
|
||||
│ [table, top 5] │ [line, 7d] │ [counter, 30d] │
|
||||
└──────────────────────────┴──────────────────────────┴──────────────────────────┘
|
||||
|
||||
Row 10: STORAGE
|
||||
┌──────────────────────────┬──────────────────────────┬──────────────────────────┐
|
||||
│ Total Vectors (W1) │ Storage Bytes │ Write Rate/Hour (W6) │
|
||||
│ [gauge, current] │ (W2+W4) [line, 30d] │ [line chart, 24h] │
|
||||
├──────────────────────────┼──────────────────────────┼──────────────────────────┤
|
||||
│ Parity Drift (W5) │ Level Distribution (W8) │ Compaction Freed (W12) │
|
||||
│ [gauge, target = 0] │ [stacked bar] │ [counter per run] │
|
||||
└──────────────────────────┴──────────────────────────┴──────────────────────────┘
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 12. Implementation: Prometheus Metrics in Rust
|
||||
|
||||
```rust
|
||||
use prometheus::{
|
||||
register_counter, register_counter_vec, register_gauge, register_gauge_vec,
|
||||
register_histogram, register_histogram_vec,
|
||||
Counter, CounterVec, Gauge, GaugeVec, Histogram, HistogramVec,
|
||||
};
|
||||
use lazy_static::lazy_static;
|
||||
|
||||
lazy_static! {
|
||||
// === INGEST ===
|
||||
pub static ref INGEST_REQUESTS: Counter =
|
||||
register_counter!("memory_ingest_requests_total", "Total ingest requests").unwrap();
|
||||
pub static ref INGEST_CHUNKS: Counter =
|
||||
register_counter!("memory_ingest_chunks_total", "Total chunks written").unwrap();
|
||||
pub static ref INGEST_BYTES: Counter =
|
||||
register_counter!("memory_ingest_bytes_total", "Total bytes ingested").unwrap();
|
||||
pub static ref INGEST_DEDUP_HITS: Counter =
|
||||
register_counter!("memory_ingest_dedup_hits_total", "Deduplicated chunks skipped").unwrap();
|
||||
pub static ref INGEST_CONTRADICTIONS: Counter =
|
||||
register_counter!("memory_ingest_contradictions_total", "Contradictions detected").unwrap();
|
||||
pub static ref INGEST_EMBED_DURATION: Histogram =
|
||||
register_histogram!("memory_ingest_embed_seconds", "Embedding latency per chunk",
|
||||
vec![0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0]).unwrap();
|
||||
pub static ref INGEST_PGVECTOR_DURATION: Histogram =
|
||||
register_histogram!("memory_ingest_pgvector_seconds", "pgvector write latency",
|
||||
vec![0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0]).unwrap();
|
||||
pub static ref INGEST_OPENSEARCH_DURATION: Histogram =
|
||||
register_histogram!("memory_ingest_opensearch_seconds", "OpenSearch index latency",
|
||||
vec![0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0]).unwrap();
|
||||
pub static ref INGEST_OPENSEARCH_FAILURES: Counter =
|
||||
register_counter!("memory_ingest_opensearch_failures_total", "OpenSearch write failures").unwrap();
|
||||
pub static ref REVIEW_QUEUE_DEPTH: Gauge =
|
||||
register_gauge!("memory_review_queue_depth", "Pending contradiction reviews").unwrap();
|
||||
|
||||
// === QUERY ===
|
||||
pub static ref QUERY_REQUESTS: Counter =
|
||||
register_counter!("memory_query_requests_total", "Total query requests").unwrap();
|
||||
pub static ref QUERY_EMPTY_RESULTS: Counter =
|
||||
register_counter!("memory_query_empty_results_total", "Queries returning zero results").unwrap();
|
||||
pub static ref QUERY_TOTAL_DURATION: Histogram =
|
||||
register_histogram!("memory_query_total_seconds", "End-to-end query latency",
|
||||
vec![0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0]).unwrap();
|
||||
pub static ref QUERY_SEMANTIC_DURATION: Histogram =
|
||||
register_histogram!("memory_query_semantic_seconds", "pgvector search latency",
|
||||
vec![0.01, 0.025, 0.05, 0.1, 0.2, 0.5]).unwrap();
|
||||
pub static ref QUERY_LEXICAL_DURATION: Histogram =
|
||||
register_histogram!("memory_query_lexical_seconds", "OpenSearch BM25 latency",
|
||||
vec![0.01, 0.025, 0.05, 0.1, 0.2, 0.5]).unwrap();
|
||||
pub static ref QUERY_INTENT: CounterVec =
|
||||
register_counter_vec!("memory_query_intent_total", "Query intent classification",
|
||||
&["intent"]).unwrap();
|
||||
pub static ref QUERY_RESULTS_COUNT: Histogram =
|
||||
register_histogram!("memory_query_results_count", "Results returned per query",
|
||||
vec![0.0, 1.0, 3.0, 5.0, 10.0, 20.0, 50.0]).unwrap();
|
||||
pub static ref QUERY_TOP1_SCORE: Histogram =
|
||||
register_histogram!("memory_query_top1_score", "Top-1 result similarity score",
|
||||
vec![0.3, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 1.0]).unwrap();
|
||||
|
||||
// === CONTEXT ===
|
||||
pub static ref CONTEXT_REQUESTS: Counter =
|
||||
register_counter!("memory_context_requests_total", "Total context lookups").unwrap();
|
||||
pub static ref CONTEXT_TIER_HITS: CounterVec =
|
||||
register_counter_vec!("memory_context_tier_hits_total", "Hits per tier",
|
||||
&["tier"]).unwrap();
|
||||
pub static ref CONTEXT_TOTAL_DURATION: Histogram =
|
||||
register_histogram!("memory_context_total_seconds", "End-to-end context latency",
|
||||
vec![0.1, 0.25, 0.5, 1.0, 2.0, 5.0]).unwrap();
|
||||
pub static ref CONTEXT_DROPPED: Counter =
|
||||
register_counter!("memory_context_dropped_results_total", "Results dropped for budget").unwrap();
|
||||
|
||||
// === RELEVANCE (updated daily by batch job) ===
|
||||
pub static ref RELEVANCE_NDCG: Gauge =
|
||||
register_gauge!("memory_relevance_ndcg_10", "NDCG@10 from Qwen-7B judge").unwrap();
|
||||
pub static ref RELEVANCE_MRR: Gauge =
|
||||
register_gauge!("memory_relevance_mrr", "Mean Reciprocal Rank").unwrap();
|
||||
pub static ref RELEVANCE_PRECISION: Gauge =
|
||||
register_gauge!("memory_relevance_precision_10", "Precision@10").unwrap();
|
||||
pub static ref RELEVANCE_RECALL: Gauge =
|
||||
register_gauge!("memory_relevance_recall_10", "Recall@10").unwrap();
|
||||
|
||||
// === STORAGE ===
|
||||
pub static ref STORAGE_VECTORS: Gauge =
|
||||
register_gauge!("memory_storage_vectors_total", "Total vectors in pgvector").unwrap();
|
||||
pub static ref STORAGE_BYTES: Gauge =
|
||||
register_gauge!("memory_storage_bytes", "Total storage bytes (pg + os)").unwrap();
|
||||
pub static ref STORAGE_PARITY_DRIFT: Gauge =
|
||||
register_gauge!("memory_storage_parity_drift", "pgvector vs OpenSearch doc count difference").unwrap();
|
||||
pub static ref WRITE_RATE: Gauge =
|
||||
register_gauge!("memory_write_rate_per_hour", "Current write rate (chunks/hour)").unwrap();
|
||||
}
|
||||
```
|
||||
|
||||
### Instrumentation Example (Ingest Handler)
|
||||
|
||||
```rust
|
||||
pub async fn ingest_handler(req: HttpRequest, body: web::Json<IngestRequest>, state: web::Data<AppState>) -> HttpResponse {
|
||||
INGEST_REQUESTS.inc();
|
||||
|
||||
// Auth
|
||||
let auth_timer = INGEST_AUTH_DURATION.start_timer();
|
||||
let (claims, token) = match validate_auth(&req, &state).await { ... };
|
||||
auth_timer.observe_duration();
|
||||
|
||||
// Dedup
|
||||
if state.idempotency_store.is_duplicate(&body.idempotency_key) {
|
||||
INGEST_DEDUP_HITS.inc();
|
||||
return HttpResponse::Ok().json(json!({"status": "duplicate"}));
|
||||
}
|
||||
|
||||
// Embed
|
||||
let embed_timer = INGEST_EMBED_DURATION.start_timer();
|
||||
let embedding = state.embeddings.embed_one(&body.text).await?;
|
||||
embed_timer.observe_duration();
|
||||
|
||||
// pgvector write
|
||||
let pg_timer = INGEST_PGVECTOR_DURATION.start_timer();
|
||||
state.vector_store.insert(&body.project, &body.text, &embedding).await?;
|
||||
pg_timer.observe_duration();
|
||||
|
||||
// OpenSearch write
|
||||
let os_timer = INGEST_OPENSEARCH_DURATION.start_timer();
|
||||
match state.opensearch_client.index_document(...).await {
|
||||
Ok(_) => {},
|
||||
Err(e) => {
|
||||
INGEST_OPENSEARCH_FAILURES.inc();
|
||||
tracing::warn!("OpenSearch write failed (non-blocking): {}", e);
|
||||
}
|
||||
}
|
||||
os_timer.observe_duration();
|
||||
|
||||
INGEST_CHUNKS.inc();
|
||||
INGEST_BYTES.inc_by(body.text.len() as f64);
|
||||
|
||||
HttpResponse::Created().json(...)
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 13. Relevance Evaluation CronJob
|
||||
|
||||
```yaml
|
||||
apiVersion: batch/v1
|
||||
kind: CronJob
|
||||
metadata:
|
||||
name: memory-relevance-eval
|
||||
namespace: poimen
|
||||
spec:
|
||||
schedule: "0 3 * * *" # Daily at 03:00 UTC
|
||||
jobTemplate:
|
||||
spec:
|
||||
template:
|
||||
spec:
|
||||
containers:
|
||||
- name: relevance-eval
|
||||
image: forgejo.riotpiao.com/rock/poimen-memory:latest
|
||||
command: ["mem", "evaluate-relevance"]
|
||||
env:
|
||||
- name: EVAL_SAMPLE_SIZE
|
||||
value: "500"
|
||||
- name: EVAL_JUDGE_MODEL
|
||||
value: "qwen2.5-7b"
|
||||
- name: EVAL_JUDGE_ENDPOINT
|
||||
value: "http://ollama.poimen.svc:11434/api/generate"
|
||||
- name: EVAL_QUERY_LOG_HOURS
|
||||
value: "24"
|
||||
- name: DATABASE_URL
|
||||
valueFrom:
|
||||
secretKeyRef:
|
||||
name: memory-db-credentials
|
||||
key: url
|
||||
resources:
|
||||
requests:
|
||||
cpu: "500m"
|
||||
memory: "512Mi"
|
||||
limits:
|
||||
cpu: "1"
|
||||
memory: "1Gi"
|
||||
restartPolicy: OnFailure
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 14. SLOs Summary
|
||||
|
||||
| Signal | Target | Window | Consequence of Miss |
|
||||
|--------|--------|--------|---------------------|
|
||||
| Ingest p99 latency | < 500ms | 24h rolling | Backpressure on upstream systems |
|
||||
| Query p99 latency | < 500ms | 24h rolling | User-perceived slowness |
|
||||
| Context p99 latency | < 2s | 24h rolling | Agent timeout, degraded assistance |
|
||||
| Query empty rate | < 20% | 24h rolling | Users get no answer, lose trust |
|
||||
| Tier-1 hit rate | >= 80% | 7d rolling | System not learning from failures |
|
||||
| NDCG@10 | >= 0.85 | 7d rolling | Retrieval quality degraded, hallucination risk |
|
||||
| MRR | >= 0.80 | 7d rolling | Relevant results buried in ranking |
|
||||
| Storage parity drift | = 0 | 1h | Dual-write inconsistency, partial search |
|
||||
| Review queue depth | < 100 | 24h | Unreviewed contradictions leaking through |
|
||||
| Relevance judge agreement | >= 85% | Weekly | Automated evaluation unreliable |
|
||||
| Raw availability | >= 99.9% | 24h rolling | Service down, LLM falls back to parametric knowledge |
|
||||
| Quality availability | >= 95% | 24h rolling | Queries succeeding but returning nothing useful |
|
||||
| Pod memory pressure | < 85% of limit | 5min | OOMKill imminent, in-flight requests lost |
|
||||
| CPU throttle ratio | < 25% | 5min | Latency degradation across all endpoints |
|
||||
| PG cache hit ratio | >= 99% | 1h | Disk thrashing, query latency spikes |
|
||||
| PG dead tuple ratio | < 20% | 24h | Table bloat, slower scans, wasted disk |
|
||||
| PG replication lag | < 10s | 5min | Stale reads from replica |
|
||||
| Ingest rate (zero) | > 0 during business hours | 30min | Silent upstream failure, knowledge going stale |
|
||||
| Ingest queue depth | < 1000 | 15min | Workers can't keep up, processing lag |
|
||||
@@ -1,4 +0,0 @@
|
||||
creation_rules:
|
||||
- path_regex: .*\.enc\.ya?ml$
|
||||
encrypted_regex: '^(stringData|data)$'
|
||||
age: age1e5fq3hwxy78psus2nfvmtmua36g0u3suk78ephw6246l974d2utsvn0hla
|
||||
@@ -21,8 +21,3 @@ data:
|
||||
OPENSEARCH_HOST: "opensearch.poimen.svc.cluster.local:9200"
|
||||
# Obsidian
|
||||
OBSIDIAN_URL: "http://obsidian-server.poimen.svc.cluster.local:8080"
|
||||
# LLM Configuration (for entity extraction)
|
||||
LLM_ENDPOINT: "http://api-internal.riotpiao.com:8000/v1/chat/completions"
|
||||
LLM_MODEL: "qwen:7b"
|
||||
LLM_TIMEOUT_SECS: "30"
|
||||
ENABLE_LLM_EXTRACTION: "true"
|
||||
|
||||
@@ -29,7 +29,7 @@ spec:
|
||||
type: RuntimeDefault
|
||||
containers:
|
||||
- name: memory
|
||||
image: forgejo.riotpiao.com/riotpiao-poimen/poimen-memory:latest
|
||||
image: forgejo.riotpiao.com/rock/poimen-memory:latest
|
||||
securityContext:
|
||||
allowPrivilegeEscalation: false
|
||||
readOnlyRootFilesystem: true
|
||||
@@ -76,8 +76,6 @@ spec:
|
||||
name: poimen-memory-config
|
||||
- secretRef:
|
||||
name: poimen-memory-auth
|
||||
- secretRef:
|
||||
name: poimen-memory-secrets
|
||||
args:
|
||||
- serve
|
||||
- --port
|
||||
|
||||
@@ -1,30 +0,0 @@
|
||||
apiVersion: v1
|
||||
kind: Secret
|
||||
metadata:
|
||||
name: memory-agent-auth
|
||||
namespace: poimen
|
||||
type: Opaque
|
||||
stringData:
|
||||
CLIENT_ID: ENC[AES256_GCM,data:qeGqSfcR8mUIkQRd4A==,iv:JZx+tR3Z9Mm8KLJqE8CfGZfZ0q+PdJKJLGT5bOKLjno=,tag:mYpvF5X/+cHXdm8vxqr5dA==,type:str]
|
||||
CLIENT_SECRET: ENC[AES256_GCM,data:8nC3VGevMxzHb0EQeZZ+qEjppZrpNH8lrVBrYTJvVCEqb1gK6lKr4w==,iv:gZUVn+x7K3qHXYYxRTJQzVEZoQkkM1D6yPjFXCK0AWo=,tag:4I8p6LYc1vvNHxUmvVLQCQ==,type:str]
|
||||
TOKEN_URL: ENC[AES256_GCM,data:jlpHwzKNFKlpMJTfPGCDgQS3kWb4pqAVEGJccJzq+xCPXo0=,iv:kX4D6ydoL6V/5V5g1Kzb8PwBZGKvQJZHmxQPRAcVLdo=,tag:sKGCwL3XcWG9sKZqKlEi8g==,type:str]
|
||||
AUTHENTIK_ISSUER: ENC[AES256_GCM,data:ILkQfNBfZ/7L6s7Oy6dE/xRpB91qP23fKHC2Q0IzDxA=,iv:J+2mPqfKmfVaXI0L5cC3j8KhXjcPTMiZaZYvZZEhKFA=,tag:nG3g7FJ4jRgPXhCDzCzXEQ==,type:str]
|
||||
AUTHENTIK_AUDIENCE: ENC[AES256_GCM,data:4d8nw7Mf2Yg=,iv:eEKzB8d6fC1Z+6JMRZ/tWPcQfbOGLFvLwP1B68n3OqI=,tag:WTKJmgXp8WEqJLfz7AQfIw==,type:str]
|
||||
sops:
|
||||
kms: []
|
||||
gcp_kms: []
|
||||
azure_kv: []
|
||||
hc_vault: []
|
||||
age:
|
||||
- recipient: age1e5fq3hwxy78psus2nfvmtmua36g0u3suk78ephw6246l974d2utsvn0hla
|
||||
enc: |
|
||||
-----BEGIN AGE ENCRYPTED FILE-----
|
||||
YWdlLWVuY3J5cHRpb24ub3JnL3YxCi0+IFgyNTUxOSBHSmt6SzRWSENWdFdN
|
||||
L1o4TDZGdlY1UzVZVld1SXU1eHR3RENKSUZGYXdVCnlQQUwwUkxQa1pQRjhE
|
||||
TDM1b2pxRjE5WmRKd3oxZGpkdm1FVkxRaXcKLT4gAhagIFqyQ1hpIVg6
|
||||
-----END AGE ENCRYPTED FILE-----
|
||||
lastmodified: "2025-01-30T15:43:00Z"
|
||||
mac: ENC[AES256_GCM,data:REDACTED,iv:REDACTED,tag:REDACTED,type:str]
|
||||
pgp: []
|
||||
unencrypted_suffix: _unencrypted
|
||||
version: 3.8.1
|
||||
@@ -1,24 +0,0 @@
|
||||
apiVersion: v1
|
||||
kind: Secret
|
||||
metadata:
|
||||
name: poimen-memory-auth
|
||||
namespace: poimen
|
||||
labels:
|
||||
app.kubernetes.io/name: poimen-memory
|
||||
type: Opaque
|
||||
stringData:
|
||||
# Authentik Service Account - OAuth2 client credentials
|
||||
# These are obtained from Authentik admin panel:
|
||||
# Settings → Applications → poimen-memory → Service Account
|
||||
AUTHENTIK_ISSUER: "https://authentik.riotpiao.com/application/o/memory"
|
||||
AUTHENTIK_AUDIENCE: "poimen-memory"
|
||||
AUTHENTIK_CLIENT_ID: "${AUTHENTIK_SERVICE_ACCOUNT_CLIENT_ID}"
|
||||
AUTHENTIK_CLIENT_SECRET: "${AUTHENTIK_SERVICE_ACCOUNT_SECRET}"
|
||||
|
||||
# LLM API Key
|
||||
# Generated by Authentik service account with permissions to LLM gateway
|
||||
LLM_API_KEY: "${LLM_API_KEY_FROM_AUTHENTIK}"
|
||||
|
||||
# S3 Credentials for backups (Velero)
|
||||
S3_ACCESS_KEY: "${MINIO_ACCESS_KEY}"
|
||||
S3_SECRET_KEY: "${MINIO_SECRET_KEY}"
|
||||
Reference in New Issue
Block a user