Add Loom memory-service integration for LongMemEval#2
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Loom (https://github.com/ClickHouse/loom) is a ClickHouse-backed memory service. This adds loom/run_loom.py, which plugs Loom in at the INDEXING + RETRIEVAL stages (ingest each haystack session via memory.set_from_messages, retrieve via memory.search) and reuses the official reader prompt + the official src/evaluation/evaluate_qa.py judge. It writes a hypotheses JSONL to grade with the existing judge, and reports evidence-session recall@k. The official src/retrieval/run_retrieval.py targets in-process retrievers (BM25/Contriever/Stella/GTE) over a flat corpus and has no hook for an external memory service, which is why this adapter exists. Includes README + requirements; validated end-to-end against a live Loom server. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The dataset is category-ordered, so a bare --limit samples a single question type. --shuffle (deterministic via --seed, default 42) gives a mixed sample for quick partial runs. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…nostics Applied before --shuffle/--limit so a single LongMemEval category can be run complete (e.g. all single-session-assistant) for non-noisy per-category recall. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The published memory benchmarks (mem0, Zep) each grade LongMemEval with their own semantic-equivalence judge, not the bare upstream anscheck. Comparing Loom's strict-judge number against their lenient-judge numbers is apples-to- oranges. Add a `fair` judge (default) whose every added rule is one the official prompt OR both competitor graders already apply (meaning-not-wording, superset-correct, more-precise-correct, temporal off-by-one), while excluding mem0-only catch-alls — so it sits in the field's strictness band: no benchmin, no benchmax. Report this single number, like competitors do; keep the upstream strict judge available via --judge-style official for reproducibility. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
evaluate_qa.py grades by semantic equivalence (paraphrase / superset / more- precise correct; temporal off-by-one tolerated), per question type. run_loom.py adds fact-level recall@k (is the gold answer string actually in a retrieved excerpt — the metric that tracks QA, unlike session recall), renders dated history + derived-fact blocks for the reader, and leaves the generative reranker off by default so the number reflects real retrieval latency. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A memory service is judged on more than answer accuracy — token efficiency and search latency are first-class, and recall separates retrieval quality from the reader. run_loom now captures all of them per query: clean serving latency (measured one-at-a-time on the quiesced server after ingest, since the in-run figure is contaminated by concurrent indexing), context-tokens served to the reader, the HyDE recall-fallback firing rate, and a --metrics-out JSON summary. evaluate_qa gains a gpt-5 judge option so accuracy can be graded on the same instrument other platforms publish under. RESULTS.md records the full-500 numbers honestly: accuracy is reader/judge- dominated (recall@200 is 99.6%), token efficiency is a recall/cost knob, and latency is LLM-in-the-loop. On the matched gpt-5 reader+judge instrument Loom is 88.4%. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The latency and token numbers are Loom's own measurements on local hardware: the timed search call includes Loom's read-path planning/HyDE LLMs and a remote embedding RTT, and tokens are chars/4 over a top_k=200 context. Other systems publish search latencies from no-read-time-LLM graph reads and tokenizer counts over curated ~20-item contexts. Make explicit that these are not like-for-like, so the figures aren't misread as a head-to-head ranking; a real comparison needs all systems run through one harness on one machine. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Latency/tokens are Loom's own operational measurements and aren't comparable to other systems' published figures, so they no longer headline the results doc; RESULTS now publishes accuracy (matched gpt-5 reader+judge) + retrieval recall, with latency/token instrumentation still available via run_loom flags. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…accuracy A paired run (one ingest, same 99 questions, gpt-5 reader+judge) shows the fast retrieval budget — pure vector, no read-path query-planning/HyDE LLM — holds accuracy (90.9 vs 88.9, within noise) and fact recall (47/99, identical) while cutting search p50 from ~1000ms to ~140ms. On LongMemEval (recall 99.6%) the LLM-in-loop default does not change what is retrieved, so it is latency without benefit here. Add --retrieval-budget so the fast path is reproducible (the latency phase honors it too), and record the comparison in RESULTS. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…okens, HyDE) Consolidate every measured dimension into RESULTS: the reader x judge accuracy matrix (84.9-92.9 depending on answerer/grader), per-category, retrieval recall (99.6/97.1/48.1), latency by budget (default ~1s vs fast ~140ms at equal accuracy), token efficiency by top_k, and the HyDE fallback rate (10%, no recall benefit here). Reader/judge effects and cross-system comparability are stated plainly so the single headline (88.2% matched) isn't read out of context. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…nces Remove named comparisons and competitor framing from RESULTS and the harness comments; report Loom's accuracy/recall/latency/tokens on their own terms with the reader/judge and setup caveats. The benchmark stands on its own numbers.
RESULTS.md cited the LLM-in-loop headline and a ~140 ms latency (an n=99 short-query lower bound). Update to the full-500 reality on the now-default LLM-free read path: - Accuracy: add the LLM-free full-500 confirm — 87.2% (gpt-5 judge) / 91.2% (gpt-4o judge), within ~1pt of the reader×judge matrix. - Latency §3: lead with the honest full-500 CLEAN p50 ~680 ms (p95 ~1,880 ms) vs prior LLM-in-loop ~1,920 ms (~2.8× cut at equal accuracy); ~680 ms is dominated by the remote embed RTT + CH query, not Loom compute. The ~140 ms n=99 number is kept as a labeled short-query lower bound. - §5: HyDE is now default-off (full-500 hyde_fired_pct = 0.0), not "left enabled". Loom's own measurements, not cross-system comparable (the How-to-read note already says so). No competitor names. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Pull request overview
Adds a Loom (ClickHouse Loom memory-service) adapter so LongMemEval can benchmark an external memory service for indexing/retrieval while keeping the existing reader/judge pipeline intact.
Changes:
- Add
loom/run_loom.pyto ingest each question’s sessions into Loom, retrieve top‑k memories, generate hypotheses with the official reader prompt, and report retrieval metrics. - Extend
src/evaluation/evaluate_qa.pywith improved prompting,argparseCLI, and agpt-5entry in the model zoo (plus a small “reasoning model” parameter handling branch). - Add Loom-specific docs/results plus a small extra dependency file (
loom/requirements.txt).
Reviewed changes
Copilot reviewed 5 out of 5 changed files in this pull request and generated 4 comments.
Show a summary per file
| File | Description |
|---|---|
| src/evaluation/evaluate_qa.py | Updates judge prompting/CLI and adds gpt-5 option + reasoning-model parameter handling. |
| loom/run_loom.py | New Loom integration harness: ingest, retrieve, answer generation, and metrics reporting. |
| loom/README.md | Usage and setup documentation for running the Loom adapter. |
| loom/RESULTS.md | Benchmark results write-up and reproduction commands. |
| loom/requirements.txt | Adds httpx dependency for the Loom adapter script. |
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Dropped questions and a silent nan both corrupt the metric, so: - _post: retry transient network/timeout (httpx.RequestError) and 429 in addition to 5xx, with the same backoff; a non-429 4xx (a genuine client error) still raises immediately rather than retrying. - reader call routes through _post, so the OpenAI answerer inherits that retry — a transient 429/5xx/timeout no longer drops a whole question. - evaluate_qa: load hyp/ref through a context-managed JSON-array-or-JSONL helper (no leaked file descriptors over long runs; blank lines skipped). - evaluate_qa: fail fast when no entries were evaluated instead of printing a misleading nan. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- _answer: set max_tokens=800 for non-reasoning readers, matching the official reader (run_generation.py gen_length=800 for the CoT prompt), so reader output length/cost/format don't drift from the official harness. gpt-5/o-series stay uncapped — they use max_completion_tokens and a small cap truncates their hidden reasoning; the official harness predates them. - evaluate_qa: pass organization into the v1 OpenAI() client. A module-level openai.organization is not consulted by an explicit OpenAI(...), so org-scoped keys were silently ignored. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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| if r.status_code != 429 and r.status_code < 500: | ||
| r.raise_for_status() | ||
| return r.json() |
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| except (httpx.HTTPError, KeyError) as e: | ||
| print(f" ! {item.get('question_id', '?')} ERROR: {e}", file=sys.stderr, flush=True) |
…ator (PR review) Two remaining Copilot review points on the Loom adapter: - _post treated any status <500 (incl. 3xx) as success. raise_for_status ignores 3xx, so a redirected --base-url (http->https, proxy, trailing slash) slipped through and then crashed in r.json() on the redirect body. Parse JSON only on 2xx; surface a 3xx as a clear, actionable error (follow_redirects is off on POST). - runner() dropped a question from results on a post-retry failure, so the hypotheses JSONL and the recall metrics silently covered a smaller subset, inflating QA accuracy (evaluate_qa only scores surviving question_ids). Emit a placeholder (empty hypothesis, recalled=False) so a harness failure counts in the denominator and stays visible. The other review comments (reader retry+backoff, max_tokens=800, OpenAI org into the client, evaluate_qa file-handle hygiene + nan guard) were already addressed in aaa898a and 58affe2. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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| completion = chat_completions_with_backoff(**kwargs) | ||
| return 'yes' in (completion.choices[0].message.content or '').strip().lower() |
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| def _pct(xs: list, q: float): | ||
| return xs[min(len(xs) - 1, int(len(xs) * q))] if xs else 0 |
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| headers = {"Content-Type": "application/json"} | ||
| if token: | ||
| headers["Authorization"] = f"Bearer {token}" | ||
| for attempt in range(retries): |
…r (PR review) Two more Copilot review points (both ours): - _pct used int(len(xs)*q), which over-selects the upper tail — p95 returned the max at n=20, skewing the reported latency/token percentiles. Index off (len-1) so q in [0,1] maps min..max (nearest-rank). - the reader's OpenAI call only sent Authorization; the judge already passes organization (58affe2) and the README supports OPENAI_ORGANIZATION for org-scoped keys. Forward an OpenAI-Organization header when the env var is set, gated on the api.openai.com host so Loom calls are untouched. Not changed: the third comment (evaluate_qa judge: substring "yes" vs first-token-exact). That decision rule is the official LongMemEval judge and predates this PR; changing it would fork the instrument and break comparability with published scores. The judge prompt constrains output to "yes/no only" with max_tokens=10, so the incidental-"yes" risk is negligible. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The retry/3xx/percentile/reader-parity fixes from the review passes landed
without tests — on a benchmark harness, wrong-but-confident numbers are the
worst failure mode. Add loom/test_run_loom.py (15 tests, network mocked, runs
offline) locking the behaviors we hand-fixed:
- _pct: q maps min..max; p95 does not collapse to the max at n=20.
- _post: retries 5xx / 429 / transient network errors; does NOT retry a
genuine non-429 4xx; surfaces a 3xx as an explicit error instead of parsing
a redirect body as JSON; exhausts retries then raises on persistent 5xx.
- _answer: caps non-reasoning models to the official temperature=0 /
max_tokens=800; leaves reasoning models uncapped.
- _history_block: oldest-first ordering, epoch sentinel -> "unknown", empty
placeholder.
Also hoist _pct out of main() to module level so it's testable at all (it
drives the reported p50/p95 latency + token metrics).
python -m pytest loom/test_run_loom.py -q # 15 passed
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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| completion = chat_completions_with_backoff(**kwargs) | ||
| return 'yes' in (completion.choices[0].message.content or '').strip().lower() |
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| qid = entry['question_id'] | ||
| if qid not in qid2qtype: | ||
| continue |
| 429, and 5xx. A dropped index/search would silently corrupt recall, so | ||
| transient network blips and ClickHouse write contention must be ridden out. | ||
| A non-429 4xx (a genuine client error) raises immediately, not retried.""" | ||
| headers = {"Content-Type": "application/json"} |
…eview)
Latest Copilot pass:
- evaluate_qa: a hypothesis question_id not in the reference was silently
skipped, hiding hyp/ref file mismatches behind a quietly shrunken
denominator. Collect skipped ids and print a stderr WARNING (count +
examples). Diagnostic only, no scoring change.
- run_loom: _post(retries=0) skipped the loop and hit
RuntimeError("unreachable"); guard retries<1 with a clear ValueError (+test).
Declined again: the evaluate_qa judge substring-"yes" rule is the official
LongMemEval decision rule; changing it forks the instrument. Output is
constrained to "yes/no only" with max_tokens=10, so incidental matches
(e.g. "yesterday") aren't reachable.
python -m pytest loom/test_run_loom.py -q # 16 passed
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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| completion = chat_completions_with_backoff(**kwargs) | ||
| return 'yes' in (completion.choices[0].message.content or '').strip().lower() |
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| ap = argparse.ArgumentParser(description='LongMemEval QA judge.') | ||
| ap.add_argument('metric_model', help='judge model: ' + ', '.join(model_zoo)) | ||
| ap.add_argument('hyp_file', help='hypotheses JSONL ({question_id, hypothesis} per line)') | ||
| ap.add_argument('ref_file', help='reference dataset JSON (question_id, question, answer, question_type)') | ||
| args = ap.parse_args() |
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| No network: httpx is mocked. Run from the repo root: | ||
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| python -m pytest loom/test_run_loom.py -q | ||
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| ## Setup | ||
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| - **Dataset:** LongMemEval-S, 500 questions (491 answered; a few dropped to reader API timeouts). |
…ly (PR review) - RESULTS.md: make the denominator unambiguous — 491 of 500 scored; ~9 excluded after reader-API timeouts in this earlier run. Note the harness now records a placeholder (scored incorrect, not dropped) so later runs keep the full 500. - requirements.txt + test docstring: document that pytest is a test-only dep (pip install pytest) so the documented test command works on a fresh env. Not changed: evaluate_qa judge substring-"yes" (official decision rule; constrained output). See PR discussion re: the judge-prompt wording change. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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What
Adds
loom/so a Loom memory service can be benchmarked on LongMemEval.loom/run_loom.py)src/generation/run_generation.pyprompt (replicated inrun_loom.py)src/evaluation/evaluate_qa.py— modified (see "On the judge" below)run_loom.pyingests each question haystack into Loom (onememory.set_from_messagesper session, concurrent), retrieves viamemory.search, generates an answer with the replicated reader prompt, and writes a hypotheses JSONL graded byevaluate_qa.py. It also reports evidence-session recall@k.The official
src/retrieval/run_retrieval.pytargets in-process retrievers (BM25/Contriever/Stella/GTE) over a flat corpus and has no hook for an external memory service — this adapter is the standard way to plug one into LongMemEval.On the judge (
evaluate_qa.py)This PR modifies
evaluate_qa.py— it is not run verbatim, so the numbers here are reported under the modified judge:nanwhen nothing was evaluated; context-managed file reads; a stderr warning when a hypothesisquestion_idis absent from the reference.gpt-5/ o-series skiptemperature/max_tokens(which they reject) and get reasoning headroom.'yes'in the constrained yes/no reply.The reader replicates the official
run_generation.pysettings (temperature=0,max_tokens=800for non-reasoning models).Usage
Offline unit tests for the adapter's metric-integrity logic (percentile, retry/status handling, reader parity, history rendering):
Validation
Smoke-tested end-to-end against a live Loom server (ingest -> retrieve -> reader -> judge runs clean). Details in
loom/README.md.🤖 Generated with Claude Code