Initial commit: Adaptive Recall sidecar for UMP (Phase 5)
Multi-channel retrieval sidecar over Universal Memory Protocol: - 3-channel RRF (UMP FTS5 + Qdrant vector + knowledge graph) - ACT-R re-ranking (Anderson 1983) with access tracking - Co-occurrence graph edges (Phase 6) for dense traversal - Memory lifecycle decay (Phase 4) with per-kind confidence - MCP shim routes recall through sidecar, falls back to canonical UMP Architecture: - src/server.js HTTP sidecar on port 4380 - src/graph.js 2592-node / 111-edge graph from UMP (or +cooccur: 13k+) - src/actr.js A_i = -d*ln(age) + beta*log1p(freq) + epsilon*conf - src/access_log.js per-URN counter + last_accessed_at - src/ump-recall-mcp.js MCP shim (recall via sidecar, others passthrough) Eval results (851-record UMP corpus): - 2ch RRF over baseline: +50pp recall@10 - 3ch RRF (+graph): +60pp, 12 unique wins - ACT-R re-rank: 4/20 #1 changes, 84% top-5 retention Tests: 76/76 passing across graph (27), actr (27), access_log (28), decay (20), mcp-shim (sidecar + fallback). Run with: npm test Inspired by AIAppsAPI/adaptive-recall but built from scratch against existing DNS2 infrastructure (UMP at :4317, Qdrant at :6333, Ollama at :11434). No paid SaaS, MIT-licensed.
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Krystie
Executable
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#!/usr/bin/env python3
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"""
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Adaptive Recall eval harness — Phase 1F.
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Runs the same queries against UMP-only baseline (POST :4317/ump/recall) and
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the sidecar fused recall (POST :4380/recall), computes recall@1/3/5, MRR,
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and latency p50/p95/p99. Acceptance for Phase 1: sidecar recall@3 must
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exceed baseline by >30%.
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Usage:
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python3 eval.py # full eval, prints results table
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python3 eval.py --json # machine-readable JSON
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python3 eval.py --weights '{"ump":1.0,"vector":2.0}' # tune RRF channel weights
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Each query in eval/queries.py needs a real `expected_id` (a UMP URN). If
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the expected urn doesn't exist or returns null, we treat that query as a
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known-gap and skip it from the metric (with a warning in the report).
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Output columns:
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query expected_id baseline_rank sidecar_rank baseline_ms sidecar_ms
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PLUS summary table at the bottom with recall@1/3/5, MRR, latency percentiles.
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"""
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import argparse
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import json
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import os
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import statistics
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import sys
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import time
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import urllib.request
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# Local imports — eval/queries.py is one directory up
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "eval"))
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from queries import QUERIES
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UMP_URL = os.getenv("UMP_URL", "http://127.0.0.1:4317")
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SIDECAR_URL = os.getenv("SIDECAR_URL", "http://127.0.0.1:4380")
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def http_post_json(url, body, timeout=30):
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"""POST JSON. Returns (status, parsed_body_or_None, elapsed_ms)."""
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data = json.dumps(body).encode()
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req = urllib.request.Request(
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url, data=data,
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headers={"content-type": "application/json"},
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method="POST",
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)
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t0 = time.time()
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try:
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with urllib.request.urlopen(req, timeout=timeout) as resp:
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raw = resp.read()
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try:
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return resp.status, json.loads(raw), (time.time() - t0) * 1000
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except json.JSONDecodeError:
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return resp.status, None, (time.time() - t0) * 1000
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except Exception as e:
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return 0, {"error": repr(e)}, (time.time() - t0) * 1000
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def baseline_recall(query, limit=10):
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"""Run query against UMP only. Returns (rank_of_expected_urn_or_None, elapsed_ms)."""
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status, body, ms = http_post_json(
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f"{UMP_URL}/ump/recall",
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{"query": query, "limit": limit},
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)
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if status != 200 or not body:
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return None, ms, f"baseline error: status={status} body={body}"
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hits = body.get("results", [])
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for idx, r in enumerate(hits):
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if r.get("record", {}).get("id") == expected_id_for_query(query):
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return idx + 1, ms, "ok"
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return None, ms, "not in top-K"
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def sidecar_recall(query, limit=10, weights=None):
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"""Run query against sidecar (RRF fused). Returns (rank, ms, status)."""
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payload = {"query": query, "limit": limit}
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if weights:
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payload["weights"] = weights
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status, body, ms = http_post_json(
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f"{SIDECAR_URL}/recall",
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payload,
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timeout=60,
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)
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if status != 200 or not body:
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return None, ms, f"sidecar error: status={status} body={body}"
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hits = body.get("hits", [])
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exp = expected_id_for_query(query)
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for idx, h in enumerate(hits):
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if h.get("urn") == exp:
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return idx + 1, ms, "ok"
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return None, ms, "not in top-K"
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def expected_id_for_query(query):
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"""Look up the expected urn from the QUERIES table."""
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for q in QUERIES:
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if q["query"] == query:
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return q.get("expected_id")
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return None
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def recall_at_k(ranks, k):
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"""Given a list of ranks (None if not in top-K), what fraction made top-K?"""
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hits = sum(1 for r in ranks if r is not None and r <= k)
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return hits / len(ranks) if ranks else 0
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def mrr(ranks):
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"""Mean reciprocal rank over ranks."""
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if not ranks:
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return 0
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total = sum(1.0 / r for r in ranks if r is not None)
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return total / len(ranks)
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def percentile(values, p):
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"""Nearest-rank percentile, simple and dependency-free."""
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if not values:
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return 0
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s = sorted(values)
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idx = max(0, min(len(s) - 1, int(len(s) * p / 100)))
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return s[idx]
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--json", action="store_true", help="machine-readable output")
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parser.add_argument("--limit", type=int, default=10, help="top-K for retrieval (default 10)")
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parser.add_argument("--weights", type=str, default=None, help="RRF channel weights as JSON")
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args = parser.parse_args()
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weights = json.loads(args.weights) if args.weights else None
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results = []
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baseline_ranks = []
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sidecar_ranks = []
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baseline_latencies = []
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sidecar_latencies = []
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skipped = []
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for q in QUERIES:
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query = q["query"]
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exp = q.get("expected_id")
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if not exp:
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skipped.append({"query": query, "reason": "no expected_id"})
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continue
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b_rank, b_ms, b_status = baseline_recall(query, limit=args.limit)
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s_rank, s_ms, s_status = sidecar_recall(query, limit=args.limit, weights=weights)
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baseline_ranks.append(b_rank)
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sidecar_ranks.append(s_rank)
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baseline_latencies.append(b_ms)
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sidecar_latencies.append(s_ms)
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results.append({
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"query": query,
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"expected_id": exp,
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"baseline_rank": b_rank,
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"sidecar_rank": s_rank,
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"baseline_ms": b_ms,
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"sidecar_ms": s_ms,
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"baseline_status": b_status,
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"sidecar_status": s_status,
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})
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# Metrics
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metrics = {
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"baseline": {
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"recall@1": recall_at_k(baseline_ranks, 1),
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"recall@3": recall_at_k(baseline_ranks, 3),
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"recall@5": recall_at_k(baseline_ranks, 5),
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"mrr": mrr(baseline_ranks),
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"latency_p50_ms": percentile(baseline_latencies, 50),
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"latency_p95_ms": percentile(baseline_latencies, 95),
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"latency_p99_ms": percentile(baseline_latencies, 99),
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},
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"sidecar": {
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"recall@1": recall_at_k(sidecar_ranks, 1),
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"recall@3": recall_at_k(sidecar_ranks, 3),
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"recall@5": recall_at_k(sidecar_ranks, 5),
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"mrr": mrr(sidecar_ranks),
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"latency_p50_ms": percentile(sidecar_latencies, 50),
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"latency_p95_ms": percentile(sidecar_latencies, 95),
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"latency_p99_ms": percentile(sidecar_latencies, 99),
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},
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}
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# Acceptance gate
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baseline_r3 = metrics["baseline"]["recall@3"]
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sidecar_r3 = metrics["sidecar"]["recall@3"]
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if baseline_r3 > 0:
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improvement_pct = ((sidecar_r3 - baseline_r3) / baseline_r3) * 100
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else:
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improvement_pct = float("inf") if sidecar_r3 > 0 else 0
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metrics["acceptance_recall@3_improvement_pct"] = improvement_pct
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metrics["acceptance_met"] = improvement_pct > 30
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output = {
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"queries_evaluated": len(results),
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"queries_skipped": len(skipped),
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"metrics": metrics,
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"results": results,
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"skipped": skipped,
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"weights": weights or {"ump": 1.0, "vector": 1.0},
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}
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if args.json:
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print(json.dumps(output, indent=2))
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else:
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print(f"\nAdaptive Recall Eval — Phase 1F")
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print(f"{'='*60}")
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print(f"Queries evaluated: {len(results)} | Skipped: {len(skipped)}")
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print(f"\n{'metric':<28} {'baseline':>10} {'sidecar':>10} {'delta':>10}")
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print(f"{'-'*60}")
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for k in ["recall@1", "recall@3", "recall@5", "mrr"]:
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b = metrics["baseline"][k]
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s = metrics["sidecar"][k]
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delta = s - b
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print(f"{k:<28} {b:>10.2%} {s:>10.2%} {delta:>+10.2%}")
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for k in ["latency_p50_ms", "latency_p95_ms", "latency_p99_ms"]:
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b = metrics["baseline"][k]
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s = metrics["sidecar"][k]
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delta = s - b
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print(f"{k:<28} {b:>10.0f} {s:>10.0f} {delta:>+10.0f}")
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print(f"\nAcceptance: recall@3 improvement = {improvement_pct:+.1f}% (target >30%)")
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print(f"Result: {'PASS' if metrics['acceptance_met'] else 'NEEDS WORK'}")
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print(f"\n{'='*60}")
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print(f"Per-query results:")
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print(f"{'query':<55} {'B-rank':>7} {'S-rank':>7} {'B-ms':>7} {'S-ms':>7}")
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print(f"{'-'*83}")
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for r in results:
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b_str = f"{r['baseline_rank']}" if r['baseline_rank'] else "miss"
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s_str = f"{r['sidecar_rank']}" if r['sidecar_rank'] else "miss"
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q_short = r['query'][:54]
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print(f"{q_short:<55} {b_str:>7} {s_str:>7} {r['baseline_ms']:>7.0f} {r['sidecar_ms']:>7.0f}")
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if skipped:
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print(f"\nSkipped: {skipped}")
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if __name__ == "__main__":
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main()
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