Graph Kernel Benchmark Evaluation

**Date:** 2026-02-13 **Version:** Graph Kernel v0.1.0, Schema v1.0.0 **Author:** Automated benchmark suite The Graph Kernel service at `localhost:8001` was evaluated against three baseline retrieval methods across 27 queries in 5 categories. The evaluation reveals that the Graph Kernel is **not a general-purpose search engine** — it's a **deterministic context slicing engine** with a bolted-on knowledge graph. Its real value lies in provenance-tracked, policy-governed context construction — not keyword matching. **Verdict:** Worth the operational complexity **if** you need deterministic, auditable context construction. The knowledge graph query layer needs significant improvement to compete with even naive baselines for ad-hoc search. ### Graph Kernel (localhost:8001) - **Language:** Rust (Axum + sqlx) - **Storage:** PostgreSQL (Supabase-hosted, remote) - **Data:** 2,681 knowledge triples (subject–predicate–object), 70 unique subjects, 39 unique predicates - **Source:** Kimi-K2 memory extraction (synced from conversation history) - **Primary purpose:** Deterministic context slicing for conversation DAGs - **Secondary purpose:** Knowledge graph triple store (the `/api/knowledge` endpoint we're benchmarking) ### Baselines | Method | Description | |--------|------------| | **Keyword** | Substring matching across `subject + predicate + object` text | | **BM25** | Classic Okapi BM25 (k1=1.5, b=0.75) over the same triple corpus | | **RAG++** (localhost:8000) | Vector similarity search over 107K+ conversation turns (embeddings) |