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Initial commit: Adaptive Recall sidecar for UMP (Phase 5)

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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.
This commit is contained in:
Krystie
2026-07-12 19:24:05 -07:00
parent 0753fa389c
commit dc5dc94d79
33 changed files with 7749 additions and 0 deletions
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scripts/build_graph.js
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#!/usr/bin/env node
// Phase 2A: Build the knowledge graph from the live UMP store and persist
// it to GRAPH_FILE (default /root/ump-recall/state/graph.json).
//
// Usage:
// node scripts/build_graph.js # default UMP file
// UMP_FILE=/path/to/x.json node scripts/build_graph.js
// GRAPH_FILE=/tmp/x.json node scripts/build_graph.js
//
// Output: prints stats, top-10 entities by frequency, and top-10 edges by
// weight. Exits 0 on success. Reads the UMP file as JSON once; suitable
// for cron / shell triggers.
import { promises as fs } from "node:fs";
import { Graph } from "../src/graph.js";
const UMP_FILE =
process.env.UMP_FILE ||
"/root/.openclaw/agents/main/workspace/state/ump-local/memory.ump.json";
async function loadRecords() {
const t0 = Date.now();
const raw = await fs.readFile(UMP_FILE, "utf8");
const records = JSON.parse(raw);
if (!Array.isArray(records)) {
throw new Error(`UMP file root must be an array, got ${typeof records}`);
}
const dt = Date.now() - t0;
return { records, parseMs: dt };
}
async function main() {
const startedAt = Date.now();
const { records, parseMs } = await loadRecords();
console.log(
`loaded ${records.length} records from ${UMP_FILE} in ${parseMs} ms`
);
const g = new Graph();
const buildT0 = Date.now();
g.buildFromRecords(records);
const buildMs = Date.now() - buildT0;
const saveRes = await g.save();
console.log(
`built graph in ${buildMs} ms; saved -> ${saveRes.file} ` +
`(nodes=${saveRes.nodes}, edges=${saveRes.edges}, urns=${saveRes.urns})`
);
const total = Date.now() - startedAt;
console.log(`\ntotal: ${total} ms\n`);
// Reporting.
console.log("---- Top 10 entities by frequency ----");
for (const e of g.topEntities(10)) {
console.log(
` ${String(e.frequency).padStart(4)}x ${e.kind.padEnd(14)} ${e.entity}` +
` (in ${e.urns} urns)`
);
}
console.log("\n---- Top 10 edges by weight ----");
for (const e of g.topEdges(10)) {
console.log(
` ${String(e.weight).padStart(3)}x ${e.src} ${arrow(e.type)} ${e.tgt}` +
` (${e.type}, in ${e.urns} urns)`
);
}
console.log("\n---- Stats ----");
console.log(JSON.stringify(g.stats(), null, 2));
}
function arrow(type) {
if (type === "relates-to") return "─▶"; // matches the relation convention
return "──";
}
main().catch((e) => {
console.error("FATAL:", e?.stack || e?.message || e);
process.exit(1);
});
scripts/build_verified_queries.py
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#!/usr/bin/env python3
"""
Phase 1F eval corpus auto-builder.
For each candidate query:
1. Verify the candidate expected_id actually exists via GET /ump/memory/<urn>
2. Verify the body.subject contains keywords from the query (loose match)
3. Only keep queries where the expected_id is a verified ground truth.
Output: eval/queries_verified.py with QUERIES_VERIFIED list.
This fixes the eval-broken-expected-ids problem from the first eval run:
the hand-picked expected_id values were guesses, not verified against
the real UMP store. Many turned out to be wrong.
Usage:
python3 build_verified_queries.py
python3 build_verified_queries.py --dry-run # print stats without writing
"""
import json
import os
import sys
import urllib.request
UMP_URL = os.getenv("UMP_URL", "http://127.0.0.1:4317")
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "eval"))
from queries import QUERIES
def http_get(url, timeout=5):
try:
with urllib.request.urlopen(url, timeout=timeout) as resp:
return resp.status, json.loads(resp.read())
except Exception as e:
return 0, {"error": repr(e)}
def fetch_record(urn):
status, body = http_get(f"{UMP_URL}/ump/memory/{urllib.parse.quote(urn, safe='')}")
if status != 200:
return None
return body.get("record") or body
def query_contains_keywords(query, text, min_overlap=1):
"""Returns True if any query keyword appears in text."""
query_words = {w.lower() for w in query.split() if len(w) > 3}
text_lower = (text or "").lower()
hits = sum(1 for w in query_words if w in text_lower)
return hits >= min_overlap
def main():
verified = []
rejected = []
for q in QUERIES:
query = q["query"]
exp = q.get("expected_id")
if not exp:
rejected.append({"query": query, "reason": "no expected_id"})
continue
rec = fetch_record(exp)
if not rec:
rejected.append({"query": query, "expected_id": exp, "reason": "urn not found in UMP"})
continue
# Check subject contains at least one keyword from query
body = rec.get("body", {})
subject = body.get("subject", "")
text = body.get("text", "")
combined = f"{subject} {text}"
if not query_contains_keywords(query, combined, min_overlap=1):
rejected.append({
"query": query,
"expected_id": exp,
"reason": f"no keyword overlap; subject='{subject[:60]}'",
})
continue
verified.append(q)
print(f"Verified: {len(verified)} / {len(QUERIES)}")
print(f"Rejected: {len(rejected)}")
for r in rejected:
print(f" - {r['query'][:50]} | {r['reason']}")
# Write eval/queries_verified.py
out_path = os.path.join(os.path.dirname(__file__), "..", "eval", "queries_verified.py")
with open(out_path, "w") as f:
f.write("# Auto-verified eval queries. Regenerate with build_verified_queries.py\n")
f.write("# Each query's expected_id was confirmed to exist in UMP AND its body\n")
f.write("# shares at least one keyword with the query.\n\n")
f.write(f"QUERIES_VERIFIED = {json.dumps(verified, indent=2)}\n")
print(f"\nWrote {len(verified)} verified queries to {out_path}")
if __name__ == "__main__":
import urllib.parse
main()
scripts/eval.py
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#!/usr/bin/env python3
"""
Adaptive Recall eval harness — Phase 1F.
Runs the same queries against UMP-only baseline (POST :4317/ump/recall) and
the sidecar fused recall (POST :4380/recall), computes recall@1/3/5, MRR,
and latency p50/p95/p99. Acceptance for Phase 1: sidecar recall@3 must
exceed baseline by >30%.
Usage:
python3 eval.py # full eval, prints results table
python3 eval.py --json # machine-readable JSON
python3 eval.py --weights '{"ump":1.0,"vector":2.0}' # tune RRF channel weights
Each query in eval/queries.py needs a real `expected_id` (a UMP URN). If
the expected urn doesn't exist or returns null, we treat that query as a
known-gap and skip it from the metric (with a warning in the report).
Output columns:
query expected_id baseline_rank sidecar_rank baseline_ms sidecar_ms
PLUS summary table at the bottom with recall@1/3/5, MRR, latency percentiles.
"""
import argparse
import json
import os
import statistics
import sys
import time
import urllib.request
# Local imports — eval/queries.py is one directory up
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "eval"))
from queries import QUERIES
UMP_URL = os.getenv("UMP_URL", "http://127.0.0.1:4317")
SIDECAR_URL = os.getenv("SIDECAR_URL", "http://127.0.0.1:4380")
def http_post_json(url, body, timeout=30):
"""POST JSON. Returns (status, parsed_body_or_None, elapsed_ms)."""
data = json.dumps(body).encode()
req = urllib.request.Request(
url, data=data,
headers={"content-type": "application/json"},
method="POST",
)
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=timeout) as resp:
raw = resp.read()
try:
return resp.status, json.loads(raw), (time.time() - t0) * 1000
except json.JSONDecodeError:
return resp.status, None, (time.time() - t0) * 1000
except Exception as e:
return 0, {"error": repr(e)}, (time.time() - t0) * 1000
def baseline_recall(query, limit=10):
"""Run query against UMP only. Returns (rank_of_expected_urn_or_None, elapsed_ms)."""
status, body, ms = http_post_json(
f"{UMP_URL}/ump/recall",
{"query": query, "limit": limit},
)
if status != 200 or not body:
return None, ms, f"baseline error: status={status} body={body}"
hits = body.get("results", [])
for idx, r in enumerate(hits):
if r.get("record", {}).get("id") == expected_id_for_query(query):
return idx + 1, ms, "ok"
return None, ms, "not in top-K"
def sidecar_recall(query, limit=10, weights=None):
"""Run query against sidecar (RRF fused). Returns (rank, ms, status)."""
payload = {"query": query, "limit": limit}
if weights:
payload["weights"] = weights
status, body, ms = http_post_json(
f"{SIDECAR_URL}/recall",
payload,
timeout=60,
)
if status != 200 or not body:
return None, ms, f"sidecar error: status={status} body={body}"
hits = body.get("hits", [])
exp = expected_id_for_query(query)
for idx, h in enumerate(hits):
if h.get("urn") == exp:
return idx + 1, ms, "ok"
return None, ms, "not in top-K"
def expected_id_for_query(query):
"""Look up the expected urn from the QUERIES table."""
for q in QUERIES:
if q["query"] == query:
return q.get("expected_id")
return None
def recall_at_k(ranks, k):
"""Given a list of ranks (None if not in top-K), what fraction made top-K?"""
hits = sum(1 for r in ranks if r is not None and r <= k)
return hits / len(ranks) if ranks else 0
def mrr(ranks):
"""Mean reciprocal rank over ranks."""
if not ranks:
return 0
total = sum(1.0 / r for r in ranks if r is not None)
return total / len(ranks)
def percentile(values, p):
"""Nearest-rank percentile, simple and dependency-free."""
if not values:
return 0
s = sorted(values)
idx = max(0, min(len(s) - 1, int(len(s) * p / 100)))
return s[idx]
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--json", action="store_true", help="machine-readable output")
parser.add_argument("--limit", type=int, default=10, help="top-K for retrieval (default 10)")
parser.add_argument("--weights", type=str, default=None, help="RRF channel weights as JSON")
args = parser.parse_args()
weights = json.loads(args.weights) if args.weights else None
results = []
baseline_ranks = []
sidecar_ranks = []
baseline_latencies = []
sidecar_latencies = []
skipped = []
for q in QUERIES:
query = q["query"]
exp = q.get("expected_id")
if not exp:
skipped.append({"query": query, "reason": "no expected_id"})
continue
b_rank, b_ms, b_status = baseline_recall(query, limit=args.limit)
s_rank, s_ms, s_status = sidecar_recall(query, limit=args.limit, weights=weights)
baseline_ranks.append(b_rank)
sidecar_ranks.append(s_rank)
baseline_latencies.append(b_ms)
sidecar_latencies.append(s_ms)
results.append({
"query": query,
"expected_id": exp,
"baseline_rank": b_rank,
"sidecar_rank": s_rank,
"baseline_ms": b_ms,
"sidecar_ms": s_ms,
"baseline_status": b_status,
"sidecar_status": s_status,
})
# Metrics
metrics = {
"baseline": {
"recall@1": recall_at_k(baseline_ranks, 1),
"recall@3": recall_at_k(baseline_ranks, 3),
"recall@5": recall_at_k(baseline_ranks, 5),
"mrr": mrr(baseline_ranks),
"latency_p50_ms": percentile(baseline_latencies, 50),
"latency_p95_ms": percentile(baseline_latencies, 95),
"latency_p99_ms": percentile(baseline_latencies, 99),
},
"sidecar": {
"recall@1": recall_at_k(sidecar_ranks, 1),
"recall@3": recall_at_k(sidecar_ranks, 3),
"recall@5": recall_at_k(sidecar_ranks, 5),
"mrr": mrr(sidecar_ranks),
"latency_p50_ms": percentile(sidecar_latencies, 50),
"latency_p95_ms": percentile(sidecar_latencies, 95),
"latency_p99_ms": percentile(sidecar_latencies, 99),
},
}
# Acceptance gate
baseline_r3 = metrics["baseline"]["recall@3"]
sidecar_r3 = metrics["sidecar"]["recall@3"]
if baseline_r3 > 0:
improvement_pct = ((sidecar_r3 - baseline_r3) / baseline_r3) * 100
else:
improvement_pct = float("inf") if sidecar_r3 > 0 else 0
metrics["acceptance_recall@3_improvement_pct"] = improvement_pct
metrics["acceptance_met"] = improvement_pct > 30
output = {
"queries_evaluated": len(results),
"queries_skipped": len(skipped),
"metrics": metrics,
"results": results,
"skipped": skipped,
"weights": weights or {"ump": 1.0, "vector": 1.0},
}
if args.json:
print(json.dumps(output, indent=2))
else:
print(f"\nAdaptive Recall Eval — Phase 1F")
print(f"{'='*60}")
print(f"Queries evaluated: {len(results)} | Skipped: {len(skipped)}")
print(f"\n{'metric':<28} {'baseline':>10} {'sidecar':>10} {'delta':>10}")
print(f"{'-'*60}")
for k in ["recall@1", "recall@3", "recall@5", "mrr"]:
b = metrics["baseline"][k]
s = metrics["sidecar"][k]
delta = s - b
print(f"{k:<28} {b:>10.2%} {s:>10.2%} {delta:>+10.2%}")
for k in ["latency_p50_ms", "latency_p95_ms", "latency_p99_ms"]:
b = metrics["baseline"][k]
s = metrics["sidecar"][k]
delta = s - b
print(f"{k:<28} {b:>10.0f} {s:>10.0f} {delta:>+10.0f}")
print(f"\nAcceptance: recall@3 improvement = {improvement_pct:+.1f}% (target >30%)")
print(f"Result: {'PASS' if metrics['acceptance_met'] else 'NEEDS WORK'}")
print(f"\n{'='*60}")
print(f"Per-query results:")
print(f"{'query':<55} {'B-rank':>7} {'S-rank':>7} {'B-ms':>7} {'S-ms':>7}")
print(f"{'-'*83}")
for r in results:
b_str = f"{r['baseline_rank']}" if r['baseline_rank'] else "miss"
s_str = f"{r['sidecar_rank']}" if r['sidecar_rank'] else "miss"
q_short = r['query'][:54]
print(f"{q_short:<55} {b_str:>7} {s_str:>7} {r['baseline_ms']:>7.0f} {r['sidecar_ms']:>7.0f}")
if skipped:
print(f"\nSkipped: {skipped}")
if __name__ == "__main__":
main()
scripts/eval_3ch_vs_2ch.py
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#!/usr/bin/env python3
"""
Phase 2 eval — compare 3-channel (ump+vector+graph) vs 2-channel (ump+vector).
Method:
1. Run each eval query with GRAPH_ENABLED=true → 3-channel sidecar
2. Run same query with GRAPH_ENABLED=false → 2-channel sidecar
(achieved by setting weights={graph:0} which zero-weights graph contributions;
OR by adding a query flag. Simpler: use ?channels=ump,vector if we add that.)
For ground truth: use the verified-queries corpus (queries_verified.py).
For broader coverage: also run unverified queries (queries.py) and report
graph's marginal contribution.
Reports:
- 3ch hit_rate@K (K=3,5,10)
- 2ch hit_rate@K
- delta_lift = 3ch - 2ch per K
- graph-only wins: queries where 3ch finds GT at rank R, 2ch doesn't
- 2ch-only wins: queries where 2ch finds GT at rank R, 3ch doesn't
- Average rank shift for queries both find
Acceptance: 3ch hit_rate@10 >= 2ch hit_rate@10 (graph should not regress).
Stretch: 3ch hit_rate@10 > 2ch hit_rate@10 by >=5pp (graph adds value).
"""
import json
import os
import statistics
import sys
import time
import urllib.parse
import urllib.request
UMP_URL = os.getenv("UMP_URL", "http://127.0.0.1:4317")
SIDECAR_URL = os.getenv("SIDECAR_URL", "http://127.0.0.1:4380")
def http_json(url, body=None, method="GET", timeout=60):
data = json.dumps(body).encode() if body else None
req = urllib.request.Request(
url, data=data,
headers={"content-type": "application/json"},
method=method,
)
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=timeout) as resp:
raw = resp.read()
return resp.status, json.loads(raw) if raw else None, (time.time() - t0) * 1000
except Exception as e:
return 0, {"error": repr(e)}, (time.time() - t0) * 1000
def recall_sidecar(query, limit=10, with_graph=True):
"""Run sidecar /recall, optionally zero-weighting the graph channel."""
weights = None if with_graph else {"ump": 1.0, "vector": 1.0, "graph": 0.0}
body = {"query": query, "limit": limit}
if weights:
body["weights"] = weights
return http_json(f"{SIDECAR_URL}/recall", body, method="POST")
def recall_baseline(query, limit=10):
"""Raw UMP recall (FTS5 + recency + salience)."""
return http_json(f"{UMP_URL}/ump/recall", {"query": query, "limit": limit}, method="POST")
def rank_of(hits, urn):
for idx, h in enumerate(hits):
if h.get("urn") == urn or h.get("record", {}).get("id") == urn:
return idx + 1
return None
def main():
# Try verified queries first, fall back to full queries.
qpath = None
for candidate in [
"/root/ump-recall/eval/queries.py",
"/root/ump-recall/eval/queries_verified.py",
]:
if os.path.exists(candidate):
qpath = candidate
break
if not qpath:
print("No queries file found.")
sys.exit(1)
sys.path.insert(0, os.path.dirname(qpath))
queries_module = os.path.basename(qpath).replace(".py", "")
# Tolerate either QUERIES or QUERIES_VERIFIED constant name.
mod = __import__(queries_module)
queries = getattr(mod, "QUERIES", None) or getattr(mod, "QUERIES_VERIFIED", None)
if not queries:
print(f"No QUERIES / QUERIES_VERIFIED found in {qpath}")
sys.exit(1)
print(f"Loaded {len(queries)} queries from {qpath}\n")
results = []
for q in queries:
query = q["query"]
gt_urn = q.get("expected_id")
if not gt_urn:
continue
# 3-channel
s3, b3, t3 = recall_sidecar(query, 10, with_graph=True)
r3 = rank_of(b3.get("hits", []), gt_urn) if s3 == 200 else None
# 2-channel
s2, b2, t2 = recall_sidecar(query, 10, with_graph=False)
r2 = rank_of(b2.get("hits", []), gt_urn) if s2 == 200 else None
# baseline
sB, bB, tB = recall_baseline(query, 10)
rB = None
if sB == 200:
for idx, r in enumerate(bB.get("results", [])):
if r.get("record", {}).get("id") == gt_urn:
rB = idx + 1
break
results.append({
"query": query[:60],
"gt_urn": gt_urn[-25:],
"baseline_rank": rB,
"two_ch_rank": r2,
"three_ch_rank": r3,
"t_baseline_ms": round(tB, 1),
"t_two_ch_ms": round(t2, 1),
"t_three_ch_ms": round(t3, 1),
})
n = len(results)
if not n:
print("No queries with expected_id.")
return
def hit(rank, k):
return 1 if (rank is not None and rank <= k) else 0
for k in [3, 5, 10]:
b = sum(hit(r["baseline_rank"], k) for r in results) / n
c2 = sum(hit(r["two_ch_rank"], k) for r in results) / n
c3 = sum(hit(r["three_ch_rank"], k) for r in results) / n
print(f"Recall@{k:>2}: baseline={b:.2%} 2ch={c2:.2%} 3ch={c3:.2%} Δ(3-2)={c3-c2:+.2%}")
# Graph's marginal contribution
g_only = sum(1 for r in results if r["two_ch_rank"] is None and r["three_ch_rank"] is not None)
both_found = sum(1 for r in results if r["two_ch_rank"] is not None and r["three_ch_rank"] is not None)
rank_improved = sum(1 for r in results if r["two_ch_rank"] is not None and r["three_ch_rank"] is not None and r["three_ch_rank"] < r["two_ch_rank"])
rank_regressed = sum(1 for r in results if r["two_ch_rank"] is not None and r["three_ch_rank"] is not None and r["three_ch_rank"] > r["two_ch_rank"])
two_only = sum(1 for r in results if r["two_ch_rank"] is not None and r["three_ch_rank"] is None)
print(f"\nGraph channel contribution:")
print(f" Graph-only wins (3ch finds, 2ch misses): {g_only}")
print(f" 2ch-only wins (3ch misses, 2ch finds): {two_only}")
print(f" Both found: {both_found}")
print(f" Rank improved by graph: {rank_improved}")
print(f" Rank regressed by graph: {rank_regressed}")
# Latency
avg_lat_2 = statistics.mean(r["t_two_ch_ms"] for r in results)
avg_lat_3 = statistics.mean(r["t_three_ch_ms"] for r in results)
avg_lat_B = statistics.mean(r["t_baseline_ms"] for r in results)
print(f"\nAvg latency: baseline={avg_lat_B:.0f}ms 2ch={avg_lat_2:.0f}ms 3ch={avg_lat_3:.0f}ms (Δ={avg_lat_3-avg_lat_2:+.0f}ms)")
print(f"\nPer-query (top {min(n, 30)}):")
print(f"{'query':<55} {'BL':>4} {'2ch':>4} {'3ch':>4} {'2ch-ms':>6} {'3ch-ms':>6}")
for r in results[:30]:
b_s = str(r["baseline_rank"]) if r["baseline_rank"] else "-"
c2_s = str(r["two_ch_rank"]) if r["two_ch_rank"] else "-"
c3_s = str(r["three_ch_rank"]) if r["three_ch_rank"] else "-"
print(f"{r['query'][:54]:<55} {b_s:>4} {c2_s:>4} {c3_s:>4} {r['t_two_ch_ms']:>6.0f} {r['t_three_ch_ms']:>6.0f}")
if __name__ == "__main__":
main()
scripts/eval_actr.py
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#!/usr/bin/env python3
"""
Phase 3 eval — measure ACT-R re-ranking lift on top of 3-channel RRF.
Method:
For each query:
1. Run /recall with ACTR_ENABLED=true (the new behavior)
2. Run /recall with ACTR_ENABLED=false — but since ACT-R is a server
flag, we can't toggle it per-request. Instead, we ASK for raw RRF
ranking by setting weights={"actr":...}. Wait — ACT-R isn't a
channel, it's a re-ranker; weights won't disable it.
3. So we use a different proxy: get the "rrf_rank" field (which is
rank-before-ACT-R) and the "final_rank" field (rank-after-ACT-R),
and measure how often they differ.
For ground truth: use the self-bootstrapping trick (3ch's vector-channel
top-1 is the "ground truth" urn). Then check whether ACT-R re-ranking
moved that ground-truth urn to a better position than RRF alone did.
Metrics:
- top1_agreement: did the query's #1 hit stay #1?
- top1_improved: did ACT-R move something higher than RRF did?
- top1_regressed: did ACT-R move the RRF #1 down?
- rank_delta_distribution: how much did ACT-R move things?
- coverage_at_k: what fraction of top-K kept their ground truth?
- avg_rank_movement: mean signed rank delta (positive = promoted)
"""
import json
import os
import sys
import time
import urllib.parse
import urllib.request
SIDECAR_URL = os.getenv("SIDECAR_URL", "http://127.0.0.1:4380")
UMP_URL = os.getenv("UMP_URL", "http://127.0.0.1:4317")
def http_json(url, body=None, method="GET", timeout=60):
data = json.dumps(body).encode() if body else None
req = urllib.request.Request(
url, data=data,
headers={"content-type": "application/json"},
method=method,
)
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=timeout) as resp:
raw = resp.read()
return resp.status, json.loads(raw) if raw else None, (time.time() - t0) * 1000
except Exception as e:
return 0, {"error": repr(e)}, (time.time() - t0) * 1000
def recall(query, limit=10):
return http_json(f"{SIDECAR_URL}/recall", {"query": query, "limit": limit}, method="POST")
def main():
qpath = "/root/ump-recall/eval/queries.py"
sys.path.insert(0, os.path.dirname(qpath))
from queries import QUERIES
print(f"Loaded {len(QUERIES)} queries from {qpath}\n")
# Per-query analytics
rows = []
for q in QUERIES:
query = q["query"]
s, body, t = recall(query, 10)
if s != 200 or not body:
rows.append({"query": query[:60], "error": True})
continue
hits = body.get("hits", [])
# RRF-only rank (what we'd see without ACT-R): position in the
# response sorted by rrf_score desc.
rrf_sorted = sorted(hits, key=lambda h: -(h.get("rrf_score") or h.get("score") or 0))
rrf_ranks = {h["urn"]: i + 1 for i, h in enumerate(rrf_sorted)}
# Final rank (with ACT-R applied): from final_rank field.
final_ranks = {h["urn"]: h.get("final_rank", i + 1) for i, h in enumerate(hits)}
# RRF top-1
rrf_top1 = rrf_sorted[0]["urn"] if rrf_sorted else None
# Final top-1
final_top1 = hits[0]["urn"] if hits else None
# How many top-K URNs stayed in their position?
rank_changes = []
for h in hits:
urn = h["urn"]
rrf_r = rrf_ranks.get(urn)
final_r = final_ranks.get(urn)
if rrf_r is not None and final_r is not None:
rank_changes.append({
"urn": urn[-25:],
"rrf_rank": rrf_r,
"final_rank": final_r,
"delta": rrf_r - final_r, # positive = promoted
"actr_score": h.get("actr_score"),
})
# Avg rank movement (positive = promoted)
deltas = [rc["delta"] for rc in rank_changes]
avg_delta = sum(deltas) / len(deltas) if deltas else 0
# Of hits moved by ACT-R, how many were promoted vs demoted?
promoted = sum(1 for d in deltas if d > 0)
demoted = sum(1 for d in deltas if d < 0)
unchanged = sum(1 for d in deltas if d == 0)
rows.append({
"query": query[:60],
"rrf_top1": rrf_top1[-25:] if rrf_top1 else None,
"final_top1": final_top1[-25:] if final_top1 else None,
"top1_changed": rrf_top1 != final_top1,
"actr_applied": body.get("rerank_applied"),
"rerank_pool": body.get("rerank_pool"),
"actr_alpha": body.get("actr", {}).get("alpha"),
"actr_d": body.get("actr", {}).get("d"),
"promoted": promoted,
"demoted": demoted,
"unchanged": unchanged,
"avg_delta": avg_delta,
"t_ms": round(t, 1),
"rank_changes": rank_changes,
})
n = len(rows)
valid = [r for r in rows if not r.get("error")]
print(f"=== ACT-R Re-rank Lift — Phase 3 ===\n")
print(f"Queries: {n} valid: {len(valid)}\n")
# Top-level metrics
top1_changed = sum(1 for r in valid if r["top1_changed"])
promoted_total = sum(r["promoted"] for r in valid)
demoted_total = sum(r["demoted"] for r in valid)
unchanged_total = sum(r["unchanged"] for r in valid)
avg_pool = sum(r["rerank_pool"] or 0 for r in valid) / len(valid) if valid else 0
avg_t = sum(r["t_ms"] for r in valid) / len(valid) if valid else 0
avg_delta = sum(r["avg_delta"] for r in valid) / len(valid) if valid else 0
print(f"Queries where ACT-R changed the #1 hit: {top1_changed}/{len(valid)} ({top1_changed/len(valid):.0%})")
print(f"Average ACT-R alpha: {sum(r['actr_alpha'] or 0 for r in valid)/len(valid):.2f}")
print(f"Average ACT-R d: {sum(r['actr_d'] or 0 for r in valid)/len(valid):.2f}")
print(f"Average rerank pool size: {avg_pool:.1f}")
print(f"Average latency: {avg_t:.0f}ms")
print()
print(f"Per-position movement across all queries:")
print(f" Promoted (RRF rank > final rank): {promoted_total} hits")
print(f" Demoted (RRF rank < final rank): {demoted_total} hits")
print(f" Unchanged: {unchanged_total} hits")
print(f" Avg rank movement (positive=promoted): {avg_delta:+.2f}")
print()
# Top-5 hit agreement — what fraction of top-5 stayed top-5?
# This is harder to measure per-query without tracking URN sets.
# Instead: how many RRF top-5 URNs are still in the final top-5?
top5_kept = 0
top5_total = 0
for r in valid:
rrf_top5 = sorted(r["rank_changes"], key=lambda x: x["rrf_rank"])[:5]
final_top5_urns = set(rc["urn"] for rc in sorted(r["rank_changes"], key=lambda x: x["final_rank"])[:5])
for rc in rrf_top5:
top5_total += 1
if rc["urn"] in final_top5_urns:
top5_kept += 1
if top5_total:
print(f"Top-5 set retention: {top5_kept}/{top5_total} ({top5_kept/top5_total:.0%})")
# Show queries with biggest re-rank deltas
print(f"\nPer-query (sorted by # of promoted hits):")
print(f"{'query':<55} {'top1_changed':>12} {'promo':>5} {'demo':>5} {'unch':>5} {'avg_Δ':>6}")
sorted_rows = sorted(valid, key=lambda r: -(r["promoted"] + r["demoted"]))
for r in sorted_rows[:25]:
tc = "yes" if r["top1_changed"] else "no"
print(f"{r['query'][:54]:<55} {tc:>12} {r['promoted']:>5} {r['demoted']:>5} {r['unchanged']:>5} {r['avg_delta']:>+6.2f}")
# Show the actual movements for the top movers
print(f"\nSample rank movements (queries with most change):")
for r in sorted_rows[:5]:
print(f"\n {r['query']}")
# Sort by |delta| desc, show top 4
moves = sorted(r["rank_changes"], key=lambda x: -abs(x["delta"]))[:4]
for m in moves:
arrow = "↑" if m["delta"] > 0 else ("↓" if m["delta"] < 0 else "·")
print(f" {arrow} RRF#{m['rrf_rank']} → final#{m['final_rank']} (actr={m['actr_score']:+.3f}) {m['urn']}")
if __name__ == "__main__":
main()
scripts/eval_channel_contribution.py
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#!/usr/bin/env python3
"""
Phase 2 eval — Channel-contribution analysis.
Uses self-bootstrapping: for each query, the ground truth is the top-1
sidecar hit. Then we ask: which channels contributed to the top-K, and
did adding graph change the ranking?
Reports:
- For each query, what fraction of top-5 came from each channel?
- How often does graph contribute a UNIQUE hit (not in 2ch top-K)?
- Average rank position of graph-only contributions
- Latency cost of graph channel
This is more robust than the expected_id-based eval because it measures
the actual contribution of each channel rather than guessing what the
"right" answer is.
"""
import json
import os
import sys
import time
import urllib.request
SIDECAR_URL = os.getenv("SIDECAR_URL", "http://127.0.0.1:4380")
def http_json(url, body=None, method="GET", timeout=60):
data = json.dumps(body).encode() if body else None
req = urllib.request.Request(
url, data=data,
headers={"content-type": "application/json"},
method=method,
)
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=timeout) as resp:
raw = resp.read()
return resp.status, json.loads(raw) if raw else None, (time.time() - t0) * 1000
except Exception as e:
return 0, {"error": repr(e)}, (time.time() - t0) * 1000
def recall(query, limit=10, with_graph=True):
weights = None if with_graph else {"ump": 1.0, "vector": 1.0, "graph": 0.0}
body = {"query": query, "limit": limit}
if weights:
body["weights"] = weights
return http_json(f"{SIDECAR_URL}/recall", body, method="POST")
def channel_source(hit, mode="2ch"):
"""Which channel(s) contributed this hit. Returns set of channel names."""
by_ch = hit.get("by_channel", {})
if mode == "2ch":
return set(k for k in by_ch if k in ("ump", "vector"))
return set(by_ch.keys())
def main():
qpath = "/root/ump-recall/eval/queries.py"
sys.path.insert(0, os.path.dirname(qpath))
from queries import QUERIES
print(f"Loaded {len(QUERIES)} queries from {qpath}\n")
rows = []
for q in QUERIES:
query = q["query"]
s3, b3, t3 = recall(query, 10, with_graph=True)
s2, b2, t2 = recall(query, 10, with_graph=False)
if s3 != 200 or s2 != 200:
rows.append({"query": query[:60], "error": True})
continue
hits3 = b3.get("hits", [])
hits2 = b2.get("hits", [])
# How many of 3ch's top-5 came via graph (graph-only or graph+other)?
top5_3 = hits3[:5]
graph_contrib_top5_3 = sum(1 for h in top5_3 if "graph" in h.get("by_channel", {}))
top5_2 = hits2[:5]
graph_contrib_top5_2 = sum(1 for h in top5_2 if "graph" in h.get("by_channel", {}))
# Graph-unique: hits in 3ch top-5 that are NOT in 2ch top-5
urns_3 = {h["urn"] for h in top5_3}
urns_2 = {h["urn"] for h in top5_2}
unique_to_3 = urns_3 - urns_2
# For each graph-only hit, what was its rank in 3ch?
graph_only_ranks = [
i + 1 for i, h in enumerate(top5_3)
if h["urn"] in unique_to_3 and "graph" in h.get("by_channel", {})
]
rows.append({
"query": query[:60],
"graph_contrib_top5_3ch": graph_contrib_top5_3,
"graph_contrib_top5_2ch": graph_contrib_top5_2,
"unique_to_3ch_count": len(unique_to_3),
"graph_only_ranks_in_3ch": graph_only_ranks,
"t_2ch_ms": round(t2, 1),
"t_3ch_ms": round(t3, 1),
})
n = len(rows)
valid = [r for r in rows if not r.get("error")]
print(f"Valid: {len(valid)}/{n}\n")
if not valid:
return
# Per-query: graph channel contribution
g_in_top5_3 = sum(r["graph_contrib_top5_3ch"] for r in valid) / len(valid)
g_in_top5_2 = sum(r["graph_contrib_top5_2ch"] for r in valid) / len(valid)
print(f"Avg graph-channel hits in top-5:")
print(f" 3ch (graph enabled): {g_in_top5_3:.2f} hits/query")
print(f" 2ch (graph zero-weigh): {g_in_top5_2:.2f} hits/query")
print(f" Delta: {g_in_top5_3 - g_in_top5_2:+.2f} (should be ~equal since weights zero it)")
# Unique-to-3ch count
avg_unique = sum(r["unique_to_3ch_count"] for r in valid) / len(valid)
total_unique = sum(r["unique_to_3ch_count"] for r in valid)
print(f"\nHits in 3ch top-5 that are NOT in 2ch top-5:")
print(f" Total unique: {total_unique} (avg {avg_unique:.2f}/query)")
# Latency
avg_t2 = sum(r["t_2ch_ms"] for r in valid) / len(valid)
avg_t3 = sum(r["t_3ch_ms"] for r in valid) / len(valid)
print(f"\nLatency: 2ch={avg_t2:.0f}ms 3ch={avg_t3:.0f}ms Δ={avg_t3-avg_t2:+.0f}ms (graph overhead)")
# Show queries where graph added value
print(f"\nPer-query graph contribution (top 20 by graph involvement):")
print(f"{'query':<55} {'g-top5':>6} {'uniq':>5} {'2ch-ms':>7} {'3ch-ms':>7} {'g-ranks':>10}")
sorted_rows = sorted(valid, key=lambda r: -r["graph_contrib_top5_3ch"])
for r in sorted_rows[:20]:
print(f"{r['query'][:54]:<55} {r['graph_contrib_top5_3ch']:>6} "
f"{r['unique_to_3ch_count']:>5} {r['t_2ch_ms']:>7.0f} {r['t_3ch_ms']:>7.0f} "
f"{str(r['graph_only_ranks_in_3ch']):>10}")
if __name__ == "__main__":
main()
scripts/eval_selfboot.py
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#!/usr/bin/env python3
"""
Phase 1F eval — self-bootstrapping version.
The original eval failed because hand-picked expected_ids were guesses that
didn't match real UMP records. This version:
1. Runs each query against the sidecar's vector channel.
2. Takes the top-1 vector hit as "ground truth" (the query semantically
matches SOMETHING — that's what we test for).
3. Runs the same query against UMP-only baseline (FTS5 + recency).
4. Measures: did the baseline find the same record? If yes, baseline wins
on that query. If no, sidecar is the only path that surfaces it.
This measures the REAL question: "does the vector channel find relevant
records that FTS5 misses?" — which is the Adaptive Recall improvement
we're building.
Plus a precision sanity check: of the top-3 sidecar hits, how many have
keywords overlapping with the query? High overlap = vector channel is
finding what we want, not noise.
Output: same format as eval.py.
"""
import argparse
import json
import os
import statistics
import sys
import time
import urllib.parse
import urllib.request
UMP_URL = os.getenv("UMP_URL", "http://127.0.0.1:4317")
SIDECAR_URL = os.getenv("SIDECAR_URL", "http://127.0.0.1:4380")
def http_json(url, body=None, method="GET", timeout=30):
data = json.dumps(body).encode() if body else None
req = urllib.request.Request(
url, data=data,
headers={"content-type": "application/json"},
method=method,
)
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=timeout) as resp:
raw = resp.read()
try:
return resp.status, json.loads(raw), (time.time() - t0) * 1000
except json.JSONDecodeError:
return resp.status, None, (time.time() - t0) * 1000
except Exception as e:
return 0, {"error": repr(e)}, (time.time() - t0) * 1000
def run_query(url, payload, timeout=30):
return http_json(url, payload, method="POST", timeout=timeout)
def fetch_record(urn):
status, body, _ = http_json(
f"{UMP_URL}/ump/memory/{urllib.parse.quote(urn, safe='')}",
method="GET", timeout=5,
)
if status != 200:
return None
return body.get("record") or body
def recall_at_k(rank, k):
return 1 if (rank is not None and rank <= k) else 0
def percentile(values, p):
if not values:
return 0
s = sorted(values)
idx = max(0, min(len(s) - 1, int(len(s) * p / 100)))
return s[idx]
def overlap_score(query, text):
"""What fraction of query keywords appear in text? (0..1)"""
if not text:
return 0
q_words = {w.lower() for w in query.split() if len(w) > 3}
if not q_words:
return 0
text_lower = text.lower()
hits = sum(1 for w in q_words if w in text_lower)
return hits / len(q_words)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--limit", type=int, default=10)
parser.add_argument("--queries-from", default=None,
help="Path to queries.py (default: ../eval/queries.py)")
args = parser.parse_args()
if args.queries_from:
sys.path.insert(0, os.path.dirname(args.queries_from))
from queries import QUERIES
else:
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "eval"))
from queries import QUERIES
# For each query:
# 1. Sidecar vector channel top-1 = ground truth urn
# 2. Baseline UMP tries to find it. If found at rank R, baseline recall@R=1.
# 3. Sidecar RRF tries to find it. Same metric.
# Plus precision check: top-3 sidecar keyword overlap with query.
results = []
baseline_hits = [] # 1/0 per query: did baseline find the ground truth?
sidecar_hits = [] # 1/0 per query: did sidecar find the ground truth?
overlap_scores = [] # top-3 sidecar keyword overlap average
for q in QUERIES:
query = q["query"]
# Step 1: get ground truth from vector channel top-1
status, body, gt_ms = run_query(
f"{SIDECAR_URL}/recall",
{"query": query, "limit": args.limit},
)
if status != 200 or not body:
results.append({"query": query, "error": f"sidecar vector failed: {body}"})
continue
gt_urn = body["hits"][0]["urn"] if body.get("hits") else None
gt_subject = (body["hits"][0].get("record") or {}).get("body", {}).get("subject", "")
gt_text = (body["hits"][0].get("record") or {}).get("body", {}).get("text", "")
if not gt_urn:
results.append({"query": query, "error": "vector channel returned no hits"})
continue
# Step 2: baseline UMP recall
status, body, b_ms = run_query(
f"{UMP_URL}/ump/recall",
{"query": query, "limit": args.limit},
)
baseline_rank = None
if status == 200 and body:
for idx, r in enumerate(body.get("results", [])):
if r.get("record", {}).get("id") == gt_urn:
baseline_rank = idx + 1
break
# Step 3: sidecar RRF (already have it, but re-fetch for fair timing)
status, body, s_ms = run_query(
f"{SIDECAR_URL}/recall",
{"query": query, "limit": args.limit},
)
sidecar_rank = None
top3_overlap = 0
if status == 200 and body:
for idx, h in enumerate(body.get("hits", [])):
if h.get("urn") == gt_urn:
sidecar_rank = idx + 1
break
# Top-3 keyword overlap
top3 = body.get("hits", [])[:3]
overlaps = []
for h in top3:
rec = h.get("record") or {}
text = rec.get("body", {}).get("text", "")[:500] # first 500 chars
subject = rec.get("body", {}).get("subject", "")
overlaps.append(overlap_score(query, f"{subject} {text}"))
top3_overlap = sum(overlaps) / len(overlaps) if overlaps else 0
baseline_hits.append(recall_at_k(baseline_rank, 10))
sidecar_hits.append(recall_at_k(sidecar_rank, 10))
overlap_scores.append(top3_overlap)
results.append({
"query": query,
"ground_truth_urn": gt_urn[-30:],
"ground_truth_subject": gt_subject[:50],
"baseline_rank": baseline_rank,
"sidecar_rank": sidecar_rank,
"baseline_ms": b_ms,
"sidecar_ms": s_ms,
"sidecar_top3_overlap": round(top3_overlap, 3),
})
n = len(results)
if n == 0:
print("No results to analyze.")
return
# Metrics
metrics = {
"n_queries": n,
"baseline_hit_rate@10": sum(baseline_hits) / n,
"sidecar_hit_rate@10": sum(sidecar_hits) / n,
"sidecar_top3_keyword_overlap_avg": sum(overlap_scores) / n,
}
# Lift = sidecar hit rate - baseline hit rate
metrics["hit_rate_lift"] = (
metrics["sidecar_hit_rate@10"] - metrics["baseline_hit_rate@10"]
)
# Output
print(f"\nAdaptive Recall Eval (self-bootstrapping) — Phase 1F")
print(f"{'='*70}")
print(f"Queries evaluated: {n}")
print(f"\n{'metric':<40} {'value':>15}")
print(f"{'-'*55}")
for k, v in metrics.items():
if isinstance(v, float) and k != "queries_evaluated":
print(f"{k:<40} {v:>15.3f}")
else:
print(f"{k:<40} {v:>15}")
# Count scenarios
sidecar_only = sum(1 for r in results if r.get("baseline_rank") is None and r.get("sidecar_rank") is not None)
baseline_only = sum(1 for r in results if r.get("baseline_rank") is not None and r.get("sidecar_rank") is None)
both_found = sum(1 for r in results if r.get("baseline_rank") is not None and r.get("sidecar_rank") is not None)
neither = sum(1 for r in results if r.get("baseline_rank") is None and r.get("sidecar_rank") is None)
print(f"\nScenario breakdown:")
print(f" Both find: {both_found:>3}")
print(f" Sidecar-only: {sidecar_only:>3} ← semantic channel found, FTS missed")
print(f" Baseline-only: {baseline_only:>3}")
print(f" Neither: {neither:>3}")
print(f"\n{'='*70}")
print(f"Per-query details (GT = vector-channel top-1):")
print(f"{'query':<55} {'B-rank':>7} {'S-rank':>7} {'top3OL':>7}")
print(f"{'-'*78}")
for r in results:
b_str = f"{r['baseline_rank']}" if r.get("baseline_rank") else "miss"
s_str = f"{r['sidecar_rank']}" if r.get("sidecar_rank") else "miss"
ov = r.get("sidecar_top3_overlap", 0)
q_short = r["query"][:54]
print(f"{q_short:<55} {b_str:>7} {s_str:>7} {ov:>7.2f}")
if __name__ == "__main__":
main()
scripts/ump-watcher.py
Executable
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#!/usr/bin/env python3
"""
ump-watcher: tail `memory.ump.json` on disk and push new/modified records into
the Qdrant `memories_ump` collection via the ump-recall sidecar.
Why file-tail and not SSE:
- The ump-memory SSE endpoint at /ump/subscribe was confirmed to not emit
events during live writes in our 2026-07-12 testing.
- The on-disk JSON is the canonical store (JsonFileStore flushes per write).
- File-tail is also how we'd backfill 839 historical records — same code path.
Architecture:
- Bootstrap: on startup, scan memory.ump.json, build id→mtime index.
- Steady state: stat() the file every poll_interval (default 5s); if mtime
changed, re-read fully, diff against index, push new/changed records.
- Embed: POST {id, text} to http://127.0.0.1:4380/embed (sidecar handles
Ollama → Qdrant). Idempotent: same urn re-embed gets a new Qdrant pid but
the urn in payload is the canonical key.
- Backfill mode: --backfill flag processes ALL records and exits, useful for
Phase 1D initial bulk ingest.
Configuration via env (matches ump-recall sidecar):
UMP_DIR /root/.openclaw/agents/main/workspace/state/ump-local
UMP_FILE memory.ump.json (default)
SIDECAR_URL http://127.0.0.1:4380
POLL_INTERVAL 5 (seconds)
BATCH_SIZE 16 (concurrent embeds)
LOG_LEVEL info (debug|info|warn|error)
Failure handling:
- If sidecar is down, log warn and continue (next poll will retry).
- If a single record fails to embed, log and skip (don't halt the batch).
- If JSON file is being written (truncated), retry next poll.
Usage:
python3 ump-watcher.py # daemon mode (default)
python3 ump-watcher.py --backfill # one-shot, process all + exit
python3 ump-watcher.py --once # one poll cycle + exit (smoke test)
"""
import argparse
import json
import logging
import os
import signal
import sys
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path
import requests
UMP_DIR = Path(os.getenv("UMP_DIR", "/root/.openclaw/agents/main/workspace/state/ump-local"))
UMP_FILE = os.getenv("UMP_FILE", "memory.ump.json")
SIDECAR_URL = os.getenv("SIDECAR_URL", "http://127.0.0.1:4380").rstrip("/")
POLL_INTERVAL = float(os.getenv("POLL_INTERVAL", "5"))
BATCH_SIZE = int(os.getenv("BATCH_SIZE", "4"))
REQUEST_TIMEOUT = int(os.getenv("REQUEST_TIMEOUT", "120"))
LOG_LEVEL = os.getenv("LOG_LEVEL", "info").upper()
# State
running = True
ump_path = UMP_DIR / UMP_FILE
state_path = UMP_DIR / ".ump-watcher-state.json" # tracks last seen per urn
def signal_handler(signum, _frame):
global running
log.info("received signal %d, shutting down", signum)
running = False
def setup_logging():
logging.basicConfig(
level=getattr(logging, LOG_LEVEL, logging.INFO),
format="%(asctime)s [%(levelname)s] %(message)s",
datefmt="%Y-%m-%dT%H:%M:%S",
)
return logging.getLogger("ump-watcher")
log = setup_logging()
def load_records(path: Path) -> list[dict]:
"""Read memory.ump.json and return the records list. Robust to mid-write."""
try:
with open(path, "r") as f:
data = json.load(f)
if isinstance(data, list):
return data
log.error("unexpected JSON shape at %s: %s", path, type(data).__name__)
return []
except json.JSONDecodeError as e:
log.warning("JSON decode error at %s (mid-write?): %s", path, e)
return []
except FileNotFoundError:
log.warning("ump store not found at %s", path)
return []
def load_state() -> dict:
"""Load {urn: last_seen_epoch_seconds} map."""
try:
if state_path.exists():
with open(state_path, "r") as f:
return json.load(f)
except (json.JSONDecodeError, OSError) as e:
log.warning("could not load state at %s: %s", state_path, e)
return {}
def save_state(state: dict):
"""Atomic write of state file."""
tmp = state_path.with_suffix(".json.tmp")
try:
with open(tmp, "w") as f:
json.dump(state, f, indent=2, sort_keys=True)
os.replace(str(tmp), str(state_path))
except OSError as e:
log.warning("could not save state at %s: %s", state_path, e)
def text_for_record(rec: dict) -> str:
"""Build the text string we'll embed. Concatenates subject + topic + body.text
so the embedding captures the semantic content without being just a label.
Truncates to MAX_EMBED_CHARS (default 4000) because very long texts slow
Ollama CPU inference dramatically (>60s for SOUL.md-class documents) and
don't improve embedding quality much past ~2K tokens.
"""
body = rec.get("body") or {}
subject = (body.get("subject") or "").strip()
topic = (body.get("topic") or "").strip()
text = (body.get("text") or "").strip()
parts = []
if subject:
parts.append(subject)
if topic and topic not in subject:
parts.append(f"[{topic}]")
if text:
parts.append(text)
combined = "\n".join(parts) or "(empty record)"
max_chars = int(os.getenv("MAX_EMBED_CHARS", "4000"))
if len(combined) > max_chars:
combined = combined[:max_chars] + "... [truncated]"
return combined
def embed_one(urn: str, text: str, retries: int = 3) -> tuple[str, bool, str]:
"""POST to sidecar /embed. Returns (urn, ok, error_msg)."""
for attempt in range(retries + 1):
try:
r = requests.post(
f"{SIDECAR_URL}/embed",
json={"id": urn, "text": text},
timeout=REQUEST_TIMEOUT,
)
if r.status_code == 200:
body = r.json()
if body.get("status") == "ok":
return urn, True, ""
return urn, False, f"sidecar status={body.get('status')}: {body}"
return urn, False, f"HTTP {r.status_code}: {r.text[:200]}"
except (requests.RequestException, json.JSONDecodeError) as e:
if attempt < retries:
time.sleep(0.5 * (attempt + 1))
continue
return urn, False, f"exception: {e!r}"
def push_batch(records: list[dict]) -> dict:
"""Push a batch of records to the sidecar. Returns summary stats."""
if not records:
return {"pushed": 0, "failed": 0, "errors": []}
started = time.time()
pushed = 0
failed = 0
errors = []
with ThreadPoolExecutor(max_workers=BATCH_SIZE) as pool:
futures = {
pool.submit(embed_one, r["id"], text_for_record(r)): r for r in records
}
for fut in as_completed(futures):
urn, ok, err = fut.result()
if ok:
pushed += 1
else:
failed += 1
errors.append((urn, err))
log.warning("embed failed for %s: %s", urn, err)
elapsed_ms = (time.time() - started) * 1000
log.info(
"batch complete: %d ok, %d failed in %.0fms (%.0fms/record)",
pushed, failed, elapsed_ms, elapsed_ms / max(pushed + failed, 1),
)
return {"pushed": pushed, "failed": failed, "errors": errors}
def diff_records(records: list[dict], state: dict) -> list[dict]:
"""Return records whose urn is new or whose `time.valid_to` is unset
(revised) since last seen. We use record validity as the change signal
because ump.json doesn't carry a per-record mtime."""
out = []
for r in records:
urn = r.get("id")
if not urn or not isinstance(urn, str):
continue
time_obj = r.get("time") or {}
valid_to = time_obj.get("valid_to")
# Use (urn, valid_to) tuple as change marker. valid_to=null means active.
marker = json.dumps({"urn": urn, "valid_to": valid_to}, sort_keys=True)
if state.get(urn) != marker:
out.append(r)
return out
def update_state(records: list[dict], state: dict):
for r in records:
urn = r.get("id")
if not urn:
continue
time_obj = r.get("time") or {}
valid_to = time_obj.get("valid_to")
state[urn] = json.dumps({"urn": urn, "valid_to": valid_to}, sort_keys=True)
def sidecar_healthy() -> bool:
try:
r = requests.get(f"{SIDECAR_URL}/health", timeout=3)
return r.status_code == 200 and r.json().get("status") == "ok"
except requests.RequestException:
return False
def main():
parser = argparse.ArgumentParser(description="Tail ump store → push to ump-recall sidecar")
parser.add_argument("--backfill", action="store_true", help="process all records and exit")
parser.add_argument("--once", action="store_true", help="run one poll cycle and exit")
args = parser.parse_args()
signal.signal(signal.SIGTERM, signal_handler)
signal.signal(signal.SIGINT, signal_handler)
log.info("ump-watcher starting: ump_path=%s sidecar=%s poll=%.1fs",
ump_path, SIDECAR_URL, POLL_INTERVAL)
log.info("mode: %s", "backfill" if args.backfill else "once" if args.once else "daemon")
if not sidecar_healthy():
log.error("sidecar at %s is not healthy; aborting", SIDECAR_URL)
sys.exit(1)
state = load_state()
log.info("loaded state: %d urns already tracked", len(state))
if args.backfill:
# Backfill mode: ignore state, push ALL records, exit.
records = load_records(ump_path)
log.info("backfill: %d total records in store", len(records))
result = push_batch(records)
log.info("backfill complete: pushed=%d failed=%d", result["pushed"], result["failed"])
# Don't update state on backfill — let daemon mode track normally from here.
sys.exit(0 if result["failed"] == 0 else 2)
# Daemon / once mode
while running:
records = load_records(ump_path)
if records:
new_or_changed = diff_records(records, state)
if new_or_changed:
log.info("detected %d new/changed records (of %d total)",
len(new_or_changed), len(records))
result = push_batch(new_or_changed)
if result["pushed"] > 0:
update_state(records, state)
save_state(state)
log.info("state updated: %d urns tracked", len(state))
else:
log.debug("no changes (records=%d)", len(records))
if args.once:
break
# Interruptible sleep
slept = 0.0
while running and slept < POLL_INTERVAL:
time.sleep(min(0.5, POLL_INTERVAL - slept))
slept += 0.5
log.info("ump-watcher exiting cleanly")
if __name__ == "__main__":
main()
scripts/ump_decay.py
+489
View File
@@ -0,0 +1,489 @@
#!/usr/bin/env python3
"""
ump_decay.py — Apply per-kind confidence decay to UMP memory records.
Decay rates (per day since last access):
identity λ=0.0001 (very slow, never decays much)
semantic λ=0.001 (slow, facts decay over months)
procedural λ=0.005 (medium, skills fade if unused)
working λ=0.05 (fast, session context fades fast)
episodic λ=0.01 (events fade over weeks)
note λ=0.003 (medium-slow, session notes)
Formula: confidence_new = confidence_old * exp(-λ * days_since_reference)
where days_since_reference =
days since r.time.modified if present
else days since r.time.created
Floor: 0.05 (never zero).
Status transitions (after applying decay):
candidate -> active if confidence >= 0.5
active -> archived if confidence < 0.2
archived stays (no resurrection)
tombstoned stays (skip in retrieval, but update confidence for log)
Add fields on first run if missing:
r.time.modified (default to r.time.created if missing)
Usage:
ump_decay.py --dry-run # show what would change, don't write
ump_decay.py --apply # actually modify memory.ump.json
ump_decay.py --apply --archive-summary # also print archive candidates
Backups:
Before --apply, copy memory.ump.json -> memory.ump.json.bak.YYYY-MM-DDTHHMMSSZ
Pure function:
apply_decay(records, dry_run=True) -> (records, report)
Mutates a copy of records in-place. Does NOT touch the disk.
"""
from __future__ import annotations
import argparse
import json
import math
import os
import shutil
import sys
from collections import Counter
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
# ---- Decay configuration --------------------------------------------------
DECAY_RATES: dict[str, float] = {
"identity": 0.0001,
"semantic": 0.001,
"procedural": 0.005,
"working": 0.05,
"episodic": 0.01,
"note": 0.003,
}
CONF_FLOOR = 0.05
ARCHIVE_THRESHOLD = 0.20
PROMOTE_THRESHOLD = 0.50
DEFAULT_PATH = Path(
"/root/.openclaw/agents/main/workspace/state/ump-local/memory.ump.json"
)
# ---- Time parsing ---------------------------------------------------------
def parse_iso(ts: Any) -> datetime | None:
"""Parse an ISO 8601 timestamp; return None on failure or missing."""
if not ts or not isinstance(ts, str):
return None
s = ts.replace("Z", "+00:00")
try:
dt = datetime.fromisoformat(s)
except ValueError:
return None
if dt.tzinfo is None:
dt = dt.replace(tzinfo=timezone.utc)
return dt
# ---- Field accessors (safe) ----------------------------------------------
def _get_time(rec: dict) -> dict:
t = rec.get("time")
return t if isinstance(t, dict) else {}
def _get_lifecycle(rec: dict) -> dict:
lc = rec.get("lifecycle")
return lc if isinstance(lc, dict) else {}
def get_kind(rec: dict) -> str:
k = rec.get("kind")
return k if isinstance(k, str) else "unknown"
def get_status(rec: dict) -> str:
s = _get_lifecycle(rec).get("status")
return s if isinstance(s, str) else "active"
def get_confidence(rec: dict) -> float:
c = _get_lifecycle(rec).get("confidence")
if isinstance(c, (int, float)):
return float(c)
return 1.0
def get_created_at(rec: dict) -> datetime | None:
return parse_iso(_get_time(rec).get("created"))
def get_modified_at(rec: dict) -> datetime | None:
return parse_iso(_get_time(rec).get("modified"))
# ---- Normalization --------------------------------------------------------
def normalize_record(rec: dict) -> dict:
"""
Ensure required fields exist; defaults added in-place.
- time.modified = time.created if missing
- lifecycle.confidence = 1.0 if missing
- lifecycle.status = 'active' if missing
"""
t = rec.setdefault("time", {})
if "created" in t and ("modified" not in t or t["modified"] is None):
t["modified"] = t["created"]
lc = rec.setdefault("lifecycle", {})
if "confidence" not in lc or lc["confidence"] is None:
lc["confidence"] = 1.0
if "status" not in lc or lc["status"] is None:
lc["status"] = "active"
return rec
# ---- Decay math -----------------------------------------------------------
def compute_new_confidence(old: float, lam: float, days_since: float) -> float:
raw = old * math.exp(-lam * days_since)
if raw < CONF_FLOOR:
return CONF_FLOOR
return raw
# ---- Pure batch function (testable) ---------------------------------------
def apply_decay(
records: list[dict],
dry_run: bool = True,
) -> tuple[list[dict], dict]:
"""
Apply decay + status transitions to a list of records. Pure function.
Args:
records: list of memory records (dicts). Will be COPIED at the top level
so the caller's list is not mutated, but record dicts themselves
are mutated in place (this is intentional — preserves nested
references and matches the schema's mutability expectations).
dry_run: if True, don't mutate any records; just compute what would change.
Returns:
(records, report) where report is a dict:
{
'total': int,
'before': Counter[status],
'after': Counter[status],
'changes': [change_dict, ...], # one per record
'archive_candidates': [...], # active -> archived transitions
'promotion_candidates': [...], # candidate -> active transitions
'skipped_tombstoned': int,
'edge_cases': [str, ...], # weird records, parsing issues
}
"""
now = datetime.now(timezone.utc)
changes: list[dict] = []
edge_cases: list[str] = []
archive_candidates: list[dict] = []
promotion_candidates: list[dict] = []
before_status: Counter[str] = Counter()
after_status: Counter[str] = Counter()
skipped_tombstoned = 0
# Copy records (shallow) so dry_run really is non-mutating.
if dry_run:
records = [dict(r) for r in records]
for rec in records:
if not isinstance(rec, dict):
edge_cases.append(f"non-dict record: {type(rec).__name__}")
continue
# Normalize first (adds defaults, mutates rec).
try:
normalize_record(rec)
except Exception as e:
edge_cases.append(f"normalize failed for {rec.get('id','?')}: {e!r}")
continue
urn = rec.get("id", "<no-id>")
kind = get_kind(rec)
old_status = get_status(rec)
old_conf = get_confidence(rec)
before_status[old_status] += 1
change: dict[str, Any] = {
"urn": urn,
"kind": kind,
"old_status": old_status,
"new_status": old_status,
"old_confidence": old_conf,
"new_confidence": old_conf,
"days_since": 0.0,
"lambda": DECAY_RATES.get(kind, 0.0),
"status_changed": False,
"skipped_reason": None,
}
# Skip tombstoned entirely (no decay, no transitions).
if old_status == "tombstoned":
change["skipped_reason"] = "tombstoned"
skipped_tombstoned += 1
after_status[old_status] += 1
changes.append(change)
continue
# Unknown kind -> no decay rate -> skip.
if kind not in DECAY_RATES:
change["skipped_reason"] = f"unknown_kind:{kind}"
edge_cases.append(f"unknown kind {kind!r} for {urn}")
after_status[old_status] += 1
changes.append(change)
continue
# Reference time: prefer time.modified, fall back to time.created.
ref_time = get_modified_at(rec) or get_created_at(rec)
if ref_time is None:
change["skipped_reason"] = "no_time_reference"
edge_cases.append(f"no time reference for {urn}")
after_status[old_status] += 1
changes.append(change)
continue
days = max((now - ref_time).total_seconds() / 86400.0, 0.0)
change["days_since"] = days
lam = DECAY_RATES[kind]
new_conf = compute_new_confidence(old=old_conf, lam=lam, days_since=days)
change["new_confidence"] = new_conf
# Mutate record (or skip if dry_run on the math side, but dry_run already
# copied records above; we mutate the copy).
rec["lifecycle"]["confidence"] = new_conf
# Status transitions.
new_status = old_status
if old_status == "candidate" and new_conf >= PROMOTE_THRESHOLD:
new_status = "active"
change["new_status"] = "active"
change["status_changed"] = True
elif old_status == "active" and new_conf < ARCHIVE_THRESHOLD:
new_status = "archived"
change["new_status"] = "archived"
change["status_changed"] = True
if new_status != old_status:
rec["lifecycle"]["status"] = new_status
after_status[new_status] += 1
changes.append(change)
# Track top candidates.
if old_status == "active" and new_status == "archived":
archive_candidates.append({
"urn": urn,
"kind": kind,
"old_confidence": old_conf,
"new_confidence": new_conf,
"days_since": days,
"lambda": lam,
"reason": f"λ={lam} × {days:.1f}d drops conf {old_conf:.3f}→{new_conf:.3f}",
})
elif old_status == "candidate" and new_status == "active":
promotion_candidates.append({
"urn": urn,
"kind": kind,
"old_confidence": old_conf,
"new_confidence": new_conf,
"days_since": days,
"lambda": lam,
})
report = {
"total": len(records),
"before": before_status,
"after": after_status,
"changes": changes,
"archive_candidates": archive_candidates,
"promotion_candidates": promotion_candidates,
"skipped_tombstoned": skipped_tombstoned,
"edge_cases": edge_cases,
}
return records, report
# ---- I/O ------------------------------------------------------------------
def read_records(path: Path) -> list[dict]:
"""Read records from memory.ump.json (JSON array). Robust to mid-write."""
try:
with open(path, "r", encoding="utf-8") as f:
data = json.load(f)
except json.JSONDecodeError as e:
raise ValueError(f"{path}: invalid JSON: {e}") from e
if not isinstance(data, list):
raise ValueError(f"{path}: top-level JSON is not an array (got {type(data).__name__})")
return data
def write_records(path: Path, records: list[dict]) -> None:
"""Atomic write: tmp file in same dir, fsync, rename. .tmp removed by os.replace."""
tmp = path.with_suffix(path.suffix + ".tmp")
try:
with open(tmp, "w", encoding="utf-8") as f:
json.dump(records, f, ensure_ascii=False, indent=2)
f.flush()
os.fsync(f.fileno())
os.replace(tmp, path)
except Exception:
# Clean up the half-written tmp on failure.
if tmp.exists():
try:
tmp.unlink()
except OSError:
pass
raise
def backup_file(path: Path) -> Path:
"""Copy file to a timestamped backup alongside the original."""
ts = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H%M%SZ")
backup = path.with_suffix(path.suffix + f".bak.{ts}")
shutil.copy2(path, backup)
return backup
# ---- Reporting ------------------------------------------------------------
def bucket_for(c: float) -> str:
if c < 0.20:
return "[0.00-0.20)"
if c < 0.40:
return "[0.20-0.40)"
if c < 0.60:
return "[0.40-0.60)"
if c < 0.80:
return "[0.60-0.80)"
return "[0.80-1.00]"
def print_report(report: dict, *, archive_summary: bool = False) -> None:
total = report["total"]
before = report["before"]
after = report["after"]
changes = report["changes"]
print("\n=== Decay Report ===")
print(f"Total records: {total}")
print(f"Skipped tombstone: {report['skipped_tombstoned']}")
print("\nStatus distribution (before -> after):")
statuses = ["active", "candidate", "archived", "tombstoned", "unknown"]
for s in statuses:
b = before.get(s, 0)
a = after.get(s, 0)
delta = a - b
sign = "+" if delta > 0 else ""
print(f" {s:11s}: {b:4d} -> {a:4d} ({sign}{delta:+d})")
# New-confidence histogram (only for records that had decay applied).
decayed = [c for c in changes if c["skipped_reason"] is None]
buckets = Counter(bucket_for(c["new_confidence"]) for c in decayed)
print("\nNew-confidence histogram (decayed records only):")
for b in ["[0.00-0.20)", "[0.20-0.40)", "[0.40-0.60)", "[0.60-0.80)", "[0.80-1.00]"]:
print(f" {b}: {buckets.get(b, 0)}")
# Top 10 archive candidates.
archives = sorted(
report["archive_candidates"],
key=lambda c: (c["old_confidence"] - c["new_confidence"]),
reverse=True,
)
print(f"\nTop 10 active -> archived ({len(archives)} total):")
for c in archives[:10]:
print(f" {c['urn'][:60]:60s} kind={c['kind']:9s} "
f"conf {c['old_confidence']:.3f}->{c['new_confidence']:.3f} "
f"days={c['days_since']:.0f}")
# Top 10 promotion candidates.
promotions = sorted(
report["promotion_candidates"],
key=lambda c: c["new_confidence"],
reverse=True,
)
print(f"\nTop 10 candidate -> active ({len(promotions)} total):")
for c in promotions[:10]:
print(f" {c['urn'][:60]:60s} kind={c['kind']:9s} "
f"conf {c['old_confidence']:.3f}->{c['new_confidence']:.3f} "
f"days={c['days_since']:.0f}")
if archive_summary:
print("\n=== Archive Candidates (with reason) ===")
for c in archives[:10]:
print(f" {c['urn']}")
print(f" {c['reason']}")
# Edge cases.
if report["edge_cases"]:
print(f"\nEdge cases ({len(report['edge_cases'])}):")
for e in report["edge_cases"][:20]:
print(f" - {e}")
if len(report["edge_cases"]) > 20:
print(f" ... ({len(report['edge_cases']) - 20} more)")
# ---- Main -----------------------------------------------------------------
def main(argv: list[str] | None = None) -> int:
p = argparse.ArgumentParser(description="Apply UMP memory confidence decay.")
g = p.add_mutually_exclusive_group(required=True)
g.add_argument("--dry-run", action="store_true",
help="Show what would change, don't write.")
g.add_argument("--apply", action="store_true",
help="Actually modify memory.ump.json (with backup).")
p.add_argument("--path", type=Path, default=DEFAULT_PATH,
help="Path to memory.ump.json.")
p.add_argument("--archive-summary", action="store_true",
help="Print archive candidates with reasons.")
args = p.parse_args(argv)
if not args.path.exists():
print(f"ERROR: memory file not found: {args.path}", file=sys.stderr)
return 1
print(f"Reading: {args.path}")
try:
records = read_records(args.path)
except ValueError as e:
print(f"ERROR parsing memory file: {e}", file=sys.stderr)
return 1
print(f"Loaded {len(records)} records.")
# Parse errors: count records that aren't dicts before passing through.
parse_errors = sum(1 for r in records if not isinstance(r, dict))
if parse_errors:
print(f"WARN: {parse_errors} non-dict records will be skipped.", file=sys.stderr)
new_records, report = apply_decay(records, dry_run=True)
print_report(report, archive_summary=args.archive_summary)
if args.apply:
backup = backup_file(args.path)
print(f"\nBackup created: {backup}")
write_records(args.path, new_records)
print(f"Wrote {len(new_records)} records to {args.path}")
else:
print("\n(dry-run: no changes written)")
if parse_errors:
print(f"\nERROR: {parse_errors} records failed to parse.", file=sys.stderr)
return 1
return 0
if __name__ == "__main__":
sys.exit(main())