Datasets:

tablinum
/

beam-engram-tables

"""engram_eval.py {aa|sqa} {base|engram} OUT.jsonl [--effort minimal] [--par 8] [--limit N] [--k 3] - paired knowledge eval of
Beam with and without the engram (10-19). aa = AA-Omniscience-Public (600; Beam FP8 Omniscience Index 8.8 on its card),
sqa = SimpleQA-verified (1000, Wikipedia-sourced). Same AA answer prompt for both (abstaining is allowed and scored 0).
engram arm: top-k re-ranked passages injected into the user turn before the question (retrievals cached in
qa/<ds>_retrieval.jsonl, shared by every run). Resumable; grade with grade.py. Items are identical across arms (paired)."""
import argparse, csv, hashlib, json, os, re, sys, threading, time, urllib.request, concurrent.futures as cf
E = "/mnt/optane/engram"
ap = argparse.ArgumentParser(); ap.add_argument("ds"); ap.add_argument("arm"); ap.add_argument("out")
ap.add_argument("--effort", default="minimal"); ap.add_argument("--par", type=int, default=8); ap.add_argument("--limit", type=int, default=0)
ap.add_argument("--k", type=int, default=3); ap.add_argument("--retrieve-only", action="store_true"); ap.add_argument("--max-tokens", type=int, default=4096)
ap.add_argument("--inject", default="v0", help="v0: plain reference block; v1: use a passage only if it states the answer (10-20 near-miss fix)")
ap.add_argument("--domains", default="", help="comma list of domains to keep (after --limit sampling)")
ap.add_argument("--no-scrub", action="store_true", help="keep passages sharing a 13-gram with the question (benchmark copies)")
ap.add_argument("--tables", default="wiki", help="comma list, or 'auto' = route by domain (ROUTE) over the tables that exist")
ap.add_argument("--route", default="auto", choices=["auto", "release"], help="auto = all verticals; release = Software+Law only, closed-book elsewhere")
ap.add_argument("--split", default="all", choices=["all", "dev", "test"], help="all, the hash/round-robin dev 200, or the untouched test 400")
ap.add_argument("--dry-run", action="store_true", help="print the selected split/route counts and exit")
ap.add_argument("--backend", default="local", choices=["local", "acp"], help="acp: opencode ACP (FP8 Beam = opencode/beam-free), tools/acp_client.py")
ap.add_argument("--acp-model", default="opencode/beam-free"); ap.add_argument("--acp-effort", default="low")
ap.add_argument("--url", default="http://100.74.68.37:18360/v1/chat/completions"); A = ap.parse_args()
SYS = """You are answering questions about {domain}, and in particular {subtopic}.
You will be given a question, answer with JUST the answer (no explanation).
If you do not know the answer, or you need more context or tools to answer the question,
be clear about this - it is better that you say this than get the wrong answer."""
if A.ds == "aa":
    items = [{"id": r["question_id"], "q": r["question"], "gold": r["answer"], "domain": r["domain"], "sub": r["subtopic"]}
             for r in csv.DictReader(open(f"{E}/qa/AA-Omniscience_dataset_public.csv"))]
else:
    items = [{"id": r["original_index"], "q": r["problem"], "gold": r["answer"], "domain": r["topic"], "sub": r["answer_type"], "urls": r["urls"]}
             for r in csv.DictReader(open(f"{E}/qa/simpleqa_verified.csv"))]
def sample(src, n):
    """The fixed hash sample: AA round-robins its six domains, so dev is balanced."""
    rr = sorted(src, key=lambda it: hashlib.sha1(f"{A.ds}:{it['id']}".encode()).hexdigest())
    if A.ds == "aa":
        by = {}
        for it in rr:
            by.setdefault(it["domain"], []).append(it)
        rr = [it for grp in zip(*by.values()) for it in grp]
    return rr[:n] if n else rr

DEV_N = 200
if A.split == "dev":
    items = sample(items, DEV_N)
elif A.split == "test":
    dev_ids = {it["id"] for it in sample(items, DEV_N)}
    items = sorted((it for it in items if it["id"] not in dev_ids),
                   key=lambda it: hashlib.sha1(f"{A.ds}:{it['id']}".encode()).hexdigest())
    if A.limit:
        items = items[:A.limit]
elif A.limit:   # a fixed sample, the same items for every arm
    items = sample(items, A.limit)
if A.domains:   # after the split, so a domain subset is the same items as in the full run (paired)
    items = [it for it in items if it["domain"] in A.domains.split(",")]
if A.dry_run:
    by = {}
    for it in items: by[it["domain"]] = by.get(it["domain"], 0) + 1
    print(f"split={A.split} route={A.route} tables={A.tables} n={len(items)} domains={by}")
    print("first ids", [it["id"] for it in items[:5]], "last ids", [it["id"] for it in items[-5:]])
    sys.exit(0)
# oer is the shared open-textbook table for Finance, STEM and Humanities; health/law/software have dedicated tables.
ROUTE_AUTO = {"Health": ["health", "wiki"], "Law": ["law", "wiki"], "Finance": ["oer", "wiki"], "Software Engineering": ["software", "wiki"],
              "Science Engineering and Mathematics": ["oer", "wiki"], "Humanities and Social Sciences": ["oer", "wiki"]}
ROUTE_RELEASE = {"Health": [], "Law": ["law", "wiki"], "Finance": [], "Software Engineering": ["software", "wiki"],
                 "Science Engineering and Mathematics": [], "Humanities and Social Sciences": []}
ROUTE = ROUTE_RELEASE if A.route == "release" else ROUTE_AUTO
HAVE = {"wiki"} | {t for t in os.listdir(f"{E}/tables") if os.path.exists(f"{E}/tables/{t}/index/engram.index")} if os.path.isdir(f"{E}/tables") else {"wiki"}
_default = ["wiki"] if A.route == "auto" else []
tables_for = lambda it: [t for t in ROUTE.get(it["domain"], _default) if t in HAVE] if A.tables == "auto" else A.tables.split(",")
RET = {}
if A.arm == "engram":
    # include the resolved route in the cache key: a route edit must not silently reuse old retrievals
    route_sig = hashlib.sha1(json.dumps({d: [t for t in ts if t in HAVE] for d, ts in sorted(ROUTE.items())}).encode()).hexdigest()[:8]
    tag = "" if A.tables == "wiki" else "_" + (("release" if A.route == "release" else "auto") + "-" + "-".join(sorted(HAVE)) + "-" + route_sig if A.tables == "auto" else A.tables.replace(",", "-"))
    rf = f"{E}/qa/{A.ds}_retrieval{tag}.jsonl"
    if os.path.exists(rf):
        RET = {d["id"]: d["hits"] for d in map(json.loads, open(rf))}
    need = [it for it in items if it["id"] not in RET and tables_for(it)]
    if need:
        sys.path.insert(0, f"{E}/tools"); from engram import Engram
        eg = Engram(tables=sorted({t for it in need for t in tables_for(it)})); t0 = time.time()
        with open(rf, "a") as f:
            for it in need:
                hits = [{k: h[k] for k in ("table", "pid", "title", "section", "text", "score")} for h in eg.retrieve(it["q"], k=10, tables=tables_for(it))]
                RET[it["id"]] = hits; f.write(json.dumps({"id": it["id"], "hits": hits}) + "\n")
        print(f"retrieved {len(need)} in {time.time() - t0:.0f} s", flush=True)
if A.retrieve_only:
    sys.exit(0)
done = {d["id"] for d in map(json.loads, open(A.out))} if os.path.exists(A.out) else set()
lock = threading.Lock()


INJECT = {
    "v0": "Reference passages (retrieved automatically; they may be incomplete or irrelevant):\n{refs}\n\nQuestion: {q}",
    # v1 (10-20): AA's engram losses were near-misses copied from passages about a neighbouring fact (1958 vs 1959, rule -14 vs -15)
    "v1": "Reference passages (retrieved automatically; they may be incomplete or irrelevant):\n{refs}\n\n"
          "Use a passage only if it states the answer to this exact question. A passage about a related but different item, "
          "version, year, rule or case does not answer it: then rely on your own knowledge, or say you don't know.\n\nQuestion: {q}",
}
_w = lambda s: re.sub(r"[^a-z0-9 ]+", " ", s.lower()).split()
grams = lambda s, n=13: {tuple(x[i:i + n]) for x in [_w(s)] for i in range(len(x) - n + 1)}


def refs_for(it):   # 10-20 leak guard: a passage that copies 13+ words of the question is a benchmark copy, not knowledge
    if not tables_for(it):
        return []
    g = set() if A.no_scrub else grams(it["q"])
    return [h for h in RET.get(it["id"], []) if not (g & grams(h["text"]))][:A.k]


def ask(it):
    q = it["q"]
    if A.arm == "engram":
        refs = "\n".join(f"[{i + 1}] {h['text']}" for i, h in enumerate(refs_for(it)))
        if refs:   # no table for this domain = exactly the closed-book prompt
            q = INJECT[A.inject].format(refs=refs, q=it["q"])
    body = {"model": "beam-1.0", "max_tokens": A.max_tokens, "temperature": 0.7, "top_p": 0.99,
            "chat_template_kwargs": ({} if A.effort == "default" else {"reasoning_effort": A.effort}),
            "messages": [{"role": "system", "content": SYS.format(domain=it["domain"], subtopic=it["sub"])}, {"role": "user", "content": q}]}
    t0 = time.time(); tools_used = 0
    if A.backend == "acp":   # system prompt goes at the top of the user turn (ACP has no system slot)
        r = ACP_.ask(body["messages"][0]["content"] + "\n\n" + body["messages"][1]["content"], model=A.acp_model, effort=A.acp_effort)
        ans, fin, n, err, tools_used = r["text"], ("error" if r["err"] else r["stop"]), r["usage"].get("outputTokens", 0) + r["usage"].get("thoughtTokens", 0), r["err"], r["tool_calls"]
    else:
      try:
        d = json.load(urllib.request.urlopen(urllib.request.Request(A.url, json.dumps(body).encode(), {"Content-Type": "application/json"}), timeout=7200))
        m = d["choices"][0]["message"]; ans, fin, n, err = (m.get("content") or "").strip(), d["choices"][0].get("finish_reason"), d["usage"]["completion_tokens"], None
      except Exception as e:
        ans, fin, n, err = "", "error", 0, str(e)[:300]
    rec = {"id": it["id"], "ds": A.ds, "arm": A.arm, "effort": A.effort, "k": A.k, "inject": A.inject, "tables": A.tables if A.arm == "engram" else "",
           "route": A.route if A.arm == "engram" else "", "split": A.split, "q": it["q"], "gold": it["gold"], "domain": it["domain"],
           "answer": ans, "finish": fin, "tokens": n, "backend": A.backend if A.backend == "local" else A.acp_model + "@" + A.acp_effort, "tool_calls": tools_used, "s": round(time.time() - t0, 1), "err": err}
    with lock:
        with open(A.out, "a") as f:
            f.write(json.dumps(rec) + "\n")
        print(f"{time.strftime('%H:%M:%S')} {it['id']} {n} tok {rec['s']} s | {ans[:60]!r} | gold {it['gold'][:40]!r}", flush=True)


if A.backend == "acp":
    sys.path.insert(0, f"{E}/tools"); from acp_client import ACP; ACP_ = ACP()
todo = [it for it in items if it["id"] not in done]
print(f"{A.ds}/{A.arm}: {len(items)} items, {len(done)} done, {len(todo)} to go, effort {A.effort}, par {A.par}", flush=True)
with cf.ThreadPoolExecutor(A.par) as ex:
    list(ex.map(ask, todo))

	"""engram_eval.py {aa\|sqa} {base\|engram} OUT.jsonl [--effort minimal] [--par 8] [--limit N] [--k 3] - paired knowledge eval of
	Beam with and without the engram (10-19). aa = AA-Omniscience-Public (600; Beam FP8 Omniscience Index 8.8 on its card),
	sqa = SimpleQA-verified (1000, Wikipedia-sourced). Same AA answer prompt for both (abstaining is allowed and scored 0).
	engram arm: top-k re-ranked passages injected into the user turn before the question (retrievals cached in
	qa/<ds>_retrieval.jsonl, shared by every run). Resumable; grade with grade.py. Items are identical across arms (paired)."""
	import argparse, csv, hashlib, json, os, re, sys, threading, time, urllib.request, concurrent.futures as cf
	E = "/mnt/optane/engram"
	ap = argparse.ArgumentParser(); ap.add_argument("ds"); ap.add_argument("arm"); ap.add_argument("out")
	ap.add_argument("--effort", default="minimal"); ap.add_argument("--par", type=int, default=8); ap.add_argument("--limit", type=int, default=0)
	ap.add_argument("--k", type=int, default=3); ap.add_argument("--retrieve-only", action="store_true"); ap.add_argument("--max-tokens", type=int, default=4096)
	ap.add_argument("--inject", default="v0", help="v0: plain reference block; v1: use a passage only if it states the answer (10-20 near-miss fix)")
	ap.add_argument("--domains", default="", help="comma list of domains to keep (after --limit sampling)")
	ap.add_argument("--no-scrub", action="store_true", help="keep passages sharing a 13-gram with the question (benchmark copies)")
	ap.add_argument("--tables", default="wiki", help="comma list, or 'auto' = route by domain (ROUTE) over the tables that exist")
	ap.add_argument("--route", default="auto", choices=["auto", "release"], help="auto = all verticals; release = Software+Law only, closed-book elsewhere")
	ap.add_argument("--split", default="all", choices=["all", "dev", "test"], help="all, the hash/round-robin dev 200, or the untouched test 400")
	ap.add_argument("--dry-run", action="store_true", help="print the selected split/route counts and exit")
	ap.add_argument("--backend", default="local", choices=["local", "acp"], help="acp: opencode ACP (FP8 Beam = opencode/beam-free), tools/acp_client.py")
	ap.add_argument("--acp-model", default="opencode/beam-free"); ap.add_argument("--acp-effort", default="low")
	ap.add_argument("--url", default="http://100.74.68.37:18360/v1/chat/completions"); A = ap.parse_args()
	SYS = """You are answering questions about {domain}, and in particular {subtopic}.
	You will be given a question, answer with JUST the answer (no explanation).
	If you do not know the answer, or you need more context or tools to answer the question,
	be clear about this - it is better that you say this than get the wrong answer."""
	if A.ds == "aa":
	items = [{"id": r["question_id"], "q": r["question"], "gold": r["answer"], "domain": r["domain"], "sub": r["subtopic"]}
	for r in csv.DictReader(open(f"{E}/qa/AA-Omniscience_dataset_public.csv"))]
	else:
	items = [{"id": r["original_index"], "q": r["problem"], "gold": r["answer"], "domain": r["topic"], "sub": r["answer_type"], "urls": r["urls"]}
	for r in csv.DictReader(open(f"{E}/qa/simpleqa_verified.csv"))]
	def sample(src, n):
	"""The fixed hash sample: AA round-robins its six domains, so dev is balanced."""
	rr = sorted(src, key=lambda it: hashlib.sha1(f"{A.ds}:{it['id']}".encode()).hexdigest())
	if A.ds == "aa":
	by = {}
	for it in rr:
	by.setdefault(it["domain"], []).append(it)
	rr = [it for grp in zip(*by.values()) for it in grp]
	return rr[:n] if n else rr

	DEV_N = 200
	if A.split == "dev":
	items = sample(items, DEV_N)
	elif A.split == "test":
	dev_ids = {it["id"] for it in sample(items, DEV_N)}
	items = sorted((it for it in items if it["id"] not in dev_ids),
	key=lambda it: hashlib.sha1(f"{A.ds}:{it['id']}".encode()).hexdigest())
	if A.limit:
	items = items[:A.limit]
	elif A.limit: # a fixed sample, the same items for every arm
	items = sample(items, A.limit)
	if A.domains: # after the split, so a domain subset is the same items as in the full run (paired)
	items = [it for it in items if it["domain"] in A.domains.split(",")]
	if A.dry_run:
	by = {}
	for it in items: by[it["domain"]] = by.get(it["domain"], 0) + 1
	print(f"split={A.split} route={A.route} tables={A.tables} n={len(items)} domains={by}")
	print("first ids", [it["id"] for it in items[:5]], "last ids", [it["id"] for it in items[-5:]])
	sys.exit(0)
	# oer is the shared open-textbook table for Finance, STEM and Humanities; health/law/software have dedicated tables.
	ROUTE_AUTO = {"Health": ["health", "wiki"], "Law": ["law", "wiki"], "Finance": ["oer", "wiki"], "Software Engineering": ["software", "wiki"],
	"Science Engineering and Mathematics": ["oer", "wiki"], "Humanities and Social Sciences": ["oer", "wiki"]}
	ROUTE_RELEASE = {"Health": [], "Law": ["law", "wiki"], "Finance": [], "Software Engineering": ["software", "wiki"],
	"Science Engineering and Mathematics": [], "Humanities and Social Sciences": []}
	ROUTE = ROUTE_RELEASE if A.route == "release" else ROUTE_AUTO
	HAVE = {"wiki"} \| {t for t in os.listdir(f"{E}/tables") if os.path.exists(f"{E}/tables/{t}/index/engram.index")} if os.path.isdir(f"{E}/tables") else {"wiki"}
	_default = ["wiki"] if A.route == "auto" else []
	tables_for = lambda it: [t for t in ROUTE.get(it["domain"], _default) if t in HAVE] if A.tables == "auto" else A.tables.split(",")
	RET = {}
	if A.arm == "engram":
	# include the resolved route in the cache key: a route edit must not silently reuse old retrievals
	route_sig = hashlib.sha1(json.dumps({d: [t for t in ts if t in HAVE] for d, ts in sorted(ROUTE.items())}).encode()).hexdigest()[:8]
	tag = "" if A.tables == "wiki" else "_" + (("release" if A.route == "release" else "auto") + "-" + "-".join(sorted(HAVE)) + "-" + route_sig if A.tables == "auto" else A.tables.replace(",", "-"))
	rf = f"{E}/qa/{A.ds}_retrieval{tag}.jsonl"
	if os.path.exists(rf):
	RET = {d["id"]: d["hits"] for d in map(json.loads, open(rf))}
	need = [it for it in items if it["id"] not in RET and tables_for(it)]
	if need:
	sys.path.insert(0, f"{E}/tools"); from engram import Engram
	eg = Engram(tables=sorted({t for it in need for t in tables_for(it)})); t0 = time.time()
	with open(rf, "a") as f:
	for it in need:
	hits = [{k: h[k] for k in ("table", "pid", "title", "section", "text", "score")} for h in eg.retrieve(it["q"], k=10, tables=tables_for(it))]
	RET[it["id"]] = hits; f.write(json.dumps({"id": it["id"], "hits": hits}) + "\n")
	print(f"retrieved {len(need)} in {time.time() - t0:.0f} s", flush=True)
	if A.retrieve_only:
	sys.exit(0)
	done = {d["id"] for d in map(json.loads, open(A.out))} if os.path.exists(A.out) else set()
	lock = threading.Lock()


	INJECT = {
	"v0": "Reference passages (retrieved automatically; they may be incomplete or irrelevant):\n{refs}\n\nQuestion: {q}",
	# v1 (10-20): AA's engram losses were near-misses copied from passages about a neighbouring fact (1958 vs 1959, rule -14 vs -15)
	"v1": "Reference passages (retrieved automatically; they may be incomplete or irrelevant):\n{refs}\n\n"
	"Use a passage only if it states the answer to this exact question. A passage about a related but different item, "
	"version, year, rule or case does not answer it: then rely on your own knowledge, or say you don't know.\n\nQuestion: {q}",
	}
	_w = lambda s: re.sub(r"[^a-z0-9 ]+", " ", s.lower()).split()
	grams = lambda s, n=13: {tuple(x[i:i + n]) for x in [_w(s)] for i in range(len(x) - n + 1)}


	def refs_for(it): # 10-20 leak guard: a passage that copies 13+ words of the question is a benchmark copy, not knowledge
	if not tables_for(it):
	return []
	g = set() if A.no_scrub else grams(it["q"])
	return [h for h in RET.get(it["id"], []) if not (g & grams(h["text"]))][:A.k]


	def ask(it):
	q = it["q"]
	if A.arm == "engram":
	refs = "\n".join(f"[{i + 1}] {h['text']}" for i, h in enumerate(refs_for(it)))
	if refs: # no table for this domain = exactly the closed-book prompt
	q = INJECT[A.inject].format(refs=refs, q=it["q"])
	body = {"model": "beam-1.0", "max_tokens": A.max_tokens, "temperature": 0.7, "top_p": 0.99,
	"chat_template_kwargs": ({} if A.effort == "default" else {"reasoning_effort": A.effort}),
	"messages": [{"role": "system", "content": SYS.format(domain=it["domain"], subtopic=it["sub"])}, {"role": "user", "content": q}]}
	t0 = time.time(); tools_used = 0
	if A.backend == "acp": # system prompt goes at the top of the user turn (ACP has no system slot)
	r = ACP_.ask(body["messages"][0]["content"] + "\n\n" + body["messages"][1]["content"], model=A.acp_model, effort=A.acp_effort)
	ans, fin, n, err, tools_used = r["text"], ("error" if r["err"] else r["stop"]), r["usage"].get("outputTokens", 0) + r["usage"].get("thoughtTokens", 0), r["err"], r["tool_calls"]
	else:
	try:
	d = json.load(urllib.request.urlopen(urllib.request.Request(A.url, json.dumps(body).encode(), {"Content-Type": "application/json"}), timeout=7200))
	m = d["choices"][0]["message"]; ans, fin, n, err = (m.get("content") or "").strip(), d["choices"][0].get("finish_reason"), d["usage"]["completion_tokens"], None
	except Exception as e:
	ans, fin, n, err = "", "error", 0, str(e)[:300]
	rec = {"id": it["id"], "ds": A.ds, "arm": A.arm, "effort": A.effort, "k": A.k, "inject": A.inject, "tables": A.tables if A.arm == "engram" else "",
	"route": A.route if A.arm == "engram" else "", "split": A.split, "q": it["q"], "gold": it["gold"], "domain": it["domain"],
	"answer": ans, "finish": fin, "tokens": n, "backend": A.backend if A.backend == "local" else A.acp_model + "@" + A.acp_effort, "tool_calls": tools_used, "s": round(time.time() - t0, 1), "err": err}
	with lock:
	with open(A.out, "a") as f:
	f.write(json.dumps(rec) + "\n")
	print(f"{time.strftime('%H:%M:%S')} {it['id']} {n} tok {rec['s']} s \| {ans[:60]!r} \| gold {it['gold'][:40]!r}", flush=True)


	if A.backend == "acp":
	sys.path.insert(0, f"{E}/tools"); from acp_client import ACP; ACP_ = ACP()
	todo = [it for it in items if it["id"] not in done]
	print(f"{A.ds}/{A.arm}: {len(items)} items, {len(done)} done, {len(todo)} to go, effort {A.effort}, par {A.par}", flush=True)
	with cf.ThreadPoolExecutor(A.par) as ex:
	list(ex.map(ask, todo))