test(agent): controlled fixture KB + one-command fast loop for tool-calling iterations

The phase-72 iteration loop cleared the database, git-cloned the homelab repo, re-imported 38-51 documents and re-embedded per run — many minutes per iteration against a different KB every time (owner directive 2026-09-04: stop importing the homelab repo on every test run). Replace it with:

- tests/fixtures/agent_kb/: 8 hand-written markdown docs (sources 'deployments'/'homelab') whose specifics (rack7, 10.77.42.0/24, VLAN 130, rbm-8842, 17 2 * * *, obsidian-bor:2026.7.14, 18765, 18443, ...) no model can guess; read targets carry non-topical filenames so their questions do not lexically seed them (the read must actually happen)
- tests/fixtures/test_kb.dump.sql: data-only snapshot (TRUNCATE + INSERTs incl. embeddings, self-contained git_sources rows, static KB overview) — verified by round-trip checksum at build time
- scripts/load_test_kb.py: one-off rebuild (real pipeline + embeddings, ~2s) that also prints the per-question retrieval report (all 10 battery questions must be grounded)
- scripts/restore_test_kb.py: sub-second one-transaction restore (no git clone, no re-embedding)
- scripts/agent_realmodel_check.py: the gate gains --restore / --mode fixture (curated 10-question battery with one unambiguously correct tool behavior per question) / --turns N (12s micro-loop) / --concurrency / per-turn + total wall timing, and a second accuracy metric (contract accuracy: well-formed calls targeting resolvable entities) alongside the phase-72 locked executed ratio — the re-read of a seeded doc is a copy-invariant model behavior (5 variants, 0/15 flipped) that the dedupe refusal counts as a failure
- TOOL_CALLING_TESTING.md: the human-readable methodology (fast loop, design rules, metrics, copy levers + tried-and-reverted table, current standing, open design question)

Measured: restore 0.03s; micro-loop ~12s; full loop ~43-55s; concurrency 2/3 gives no gain (endpoint serializes).
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"""The real-model tool-calling gate (live, the configured chat model).
The phase-72 pass condition (owner directive 2026-09-03 — "test with the
real lite model until tool calls work consistently; don't pass until a
sufficient number of tool calls succeed"): this script drives a fixed
question battery through the **real** grounded path — the exact mirror
of ``app.api.chat`` (embed → retrieve → the honesty gate via
``plan_turn`` → the steering notes + KB overview exactly as
``app.api.chat`` reads them → ``build_high_prompt`` / deflection prompt →
``run_agent`` with the configured chat model and the real Postgres KB, a
fresh ``AgentHolder`` per turn; a deflected turn runs the same
``tools=None`` + one-bounded-recovery stream the deflected API branch
runs) — and applies the four LOCKED pass conditions:
1. all turns answer (no ``LLMError`` / ``MalformedReplyError``);
2. zero turns hit the round cap (the incident's loop signature — hitting
the cap means the teaching did not end the loop);
3. >=6 of 10 turns emit >=1 tool call (the model keeps USING tools — it
does not abandon them and answer from seed context alone, the
incident's end state);
4. the accuracy bar across the whole run — ``derived`` mode (the
phase-72 LOCKED gate): executed / emitted >= 0.90 (refusals count in
nothing); ``fixture`` mode (the controlled methodology): contract
accuracy — well-formed calls targeting resolvable entities —
>= 0.90, with the executed ratio reported alongside (see
``classify_call`` and ``TOOL_CALLING_TESTING.md`` for why the two
metrics differ: the app's ALREADY_IN_CONTEXT dedupe refusal is an
app-semantics choice, not a tool-calling error).
Batteries (``--mode``):
* ``derived`` (default, the phase-72 LOCKED battery) — the fixed
10-question battery derived from the live catalog's first two documents
``D1 = (s1, p1, t1)`` / ``D2 = (s2, p2, t2)`` (catalog order); the
grep question's token is the first whitespace-split word of
``D2.content`` with length >= 6 (leading/trailing non-alphanumerics
stripped, lowercased), falling back to the first word of ``t2``.
* ``fixture`` (the controlled fast loop, 2026-09-04 owner directive) —
:data:`FIXTURE_BATTERY`, the curated 10 questions pinned to the
hand-written fixture KB (``tests/fixtures/agent_kb/``). Combine with
``--restore`` so the whole iteration is one command against a known,
unguessable, re-embed-free knowledge base (``TOOL_CALLING_TESTING.md``).
Speed levers (the fast loop): ``--restore`` (restore the fixture dump in
one transaction — no git clone, no re-embedding, sub-second); ``--turns
N`` (run only the first N questions — the micro-loop for copy
iteration; the verdict is then marked ``partial`` and condition 3 is
reported, not gated); every turn line carries its wall seconds and the
verdict line carries the run's total wall time, so a slow-down is
visible in the same line that carries the accuracy.
House probe pattern (``scripts/llm_probe.py``): ``uv run python -m
scripts.agent_realmodel_check`` — argparse, dotenv, plain module, no
debugpy. The script never modifies the KB (no commits, no query_log
rows — the only writes are the ``--restore`` snapshot restore, which is
explicit and transactional).
Preconditions (exit 2 with an actionable line on failure): the DB is
reachable; ``BOR_AGENT_MAX_ROUNDS`` is > 0 (the gate needs tools
enabled); ``derived`` mode — the catalog holds >=2 documents and the
FIRST TWO catalog documents' ``path``s each contain ``/`` (the
bare-path traps need nested paths); ``fixture`` mode — the fixture dump
exists (build it with ``uv run python -m scripts.load_test_kb``).
Exit codes: **0 PASS**, **1 FAIL** (the per-condition breakdown is
printed so the copy-lever iteration loop can target the right lever),
**2 precondition failure**. For refusal diagnosis, every call is
already logged by ``run_agent`` (``agent tool=… args=… round=…/…``) —
correlate the logged arguments with the refusal templates in
``app/rag/agent.py`` to see which teaching line the model hit.
"""
from __future__ import annotations
import argparse
import asyncio
import logging
import sys
import time
from dataclasses import dataclass, field
from datetime import date
from pathlib import Path
from typing import Any
from dotenv import load_dotenv
from app.api.chat import plan_turn
from app.api.steering import load_steering_notes
from app.config import Settings, get_settings
from app.db import SessionLocal, db_available
from app.rag.agent import (
CORRECTION_INSTRUCTION,
AgentHolder,
MalformedReplyError,
find_document,
list_catalog,
list_source_names,
run_agent,
)
from app.rag.llm import (
EmbeddingError,
LLMClient,
LLMError,
StreamPiece,
ToolCallPiece,
chat_stream_retried,
)
from app.rag.overview import load_kb_overview
from app.rag.retriever import retrieve
from app.rag.scaffolding import ScaffoldingFilter
logger = logging.getLogger("agent_realmodel_check")
#: Repo-relative fixture dump (written by scripts.load_test_kb).
DEFAULT_DUMP_PATH = Path("tests/fixtures/test_kb.dump.sql")
#: The per-turn line truncates the question at this width (the locked
#: format prints ``turn NN | emitted=E executed=X cap=Y|N | <question>``).
QUESTION_DISPLAY_WIDTH = 40
#: The controlled fast-loop battery (2026-09-04, owner directive): the
#: curated 10 questions pinned to the fixture KB
#: (``tests/fixtures/agent_kb/`` — sources ``deployments`` / ``homelab``,
#: 8 hand-written documents whose specifics — ``rack7``,
#: ``10.77.42.0/24``, VLAN 130, port 18443, ntfy topic
#: ``reese-uptime-7``, machine ID ``rbm-8842``, the ``17 2 * * *``
#: schedule, image ``ghcr.io/reese/obsidian-bor:2026.7.14``, port
#: 18765 — are not guessable by any model).
#:
#: Design rule (the controlled-test property): every question has ONE
#: unambiguously correct tool behavior, verified by the load script's
#: retrieval report. The ``read`` targets (Q4/Q5/Q6) are named so the
#: question's tokens do NOT lexically seed the target document (the
#: filenames carry no topical words the content repeats) — the read
#: must actually happen, exactly once, in the combined form. The
#: discipline turns (Q7/Q8/Q9) name content that IS seeded — the
#: correct behavior there is to answer from the ``<documents>`` context
#: (or ``ls``), NOT to re-read. The bare-path traps are the job of the
#: locked derived battery, not this one.
#:
#: 1. the phase-72 incident ("list the files in this directory" — a
#: full listing needs ``ls``: 8 docs, 2 in seed context);
#: 2. a scoped ``ls`` by the correct source name;
#: 3. the no-arg listing;
#: 4. a ``read`` of an unseeded document (combined form, given whole);
#: 5. a second ``read`` of an unseeded document (explicit "Read …");
#: 6. a third ``read`` of an unseeded document;
#: 7. the ``grep`` turn (``rbm-8842`` — a string that occurs in exactly
#: one fixture document; the grep line alone answers "which ones");
#: 8. a title lookup — the target IS seeded; summarize from context;
#: 9. a topic lookup — the target IS seeded; answer from context;
#: 10. the source name phrased as a directory (the ``ls(path=…)`` scope
#: trap).
FIXTURE_BATTERY: list[str] = [
"List the files in this directory.",
"List the documents you have in the homelab source.",
"List every document you have indexed.",
"Open the document homelab/networking/vela-bridges.md and tell me what it covers.",
"Read deployments/quadlet/mimir-service.md and summarize it.",
"Open the document homelab/networking/meridian-notes.md and tell me what it covers.",
'Find the exact string "rbm-8842" in your documents and tell me which ones '
"contain it.",
'Which document has the title "Lab Ansible Inventory"? Summarize it.',
"What do you know about the qwen 3.8 llama.cpp setup? Give me the exact "
"launch arguments.",
"List the files in the deployments directory.",
]
def _alnum_edge(word: str) -> str:
"""Strip leading/trailing non-alphanumerics from *word*."""
start = 0
end = len(word)
while start < end and not word[start].isalnum():
start += 1
while end > start and not word[end - 1].isalnum():
end -= 1
return word[start:end]
def derive_token(content: str, title: str) -> str:
"""The derived battery's grep token (locked by the phase-72 task
file). The first whitespace-split word of *content* whose stripped
length is >= 6 (leading/trailing non-alphanumerics stripped,
lowercased); the fallback is the first word of *title* (the same
cleanup)."""
for word in content.split():
token = _alnum_edge(word).lower()
if len(token) >= 6:
return token
words = title.split()
return _alnum_edge(words[0]).lower() if words else "document"
def build_battery(
catalog: list[tuple[str, str, str]], d2_content: str
) -> list[str]:
"""The fixed 10-question battery (locked by the phase-72 task file —
do not swap in easier questions), derived from the live catalog's
first two documents ``D1 = (s1, p1, t1)`` / ``D2 = (s2, p2, t2)``
(catalog order):
1. the incident ("list the files in this directory" — the
harness-prior ``ls(path='.')`` misuse);
2. a scoped ``ls`` by the correct source name (``s1``);
3. the no-arg listing;
4. a bare-path ``read`` trap (``p1`` without its source prefix);
5. the combined form (the correct shape);
6. a second bare-path ``read`` trap (``p2``);
7. the ``grep`` turn (the derived token — guaranteed to occur in
D2's content);
8. a title lookup + read (``t2``);
9. a title lookup + open (``t1``);
10. the source name phrased as a directory (``s2`` — the
``ls(path=…)`` scope trap).
"""
(s1, p1, t1), (s2, p2, t2) = catalog[0], catalog[1]
token = derive_token(d2_content, t2)
return [
"List the files in this directory.",
f"List the documents you have in the {s1} source.",
"List every document you have indexed.",
f"What does the document {p1} contain? Open it and tell me.",
f"Read {s1}/{p1} and summarize it.",
f"Open the document {p2} and tell me what it covers.",
f'Find the exact string "{token}" in your documents and tell me '
"which ones contain it.",
f'Which document has the title "{t2}"? Read it and summarize.',
f"What do you know about {t1}? Open the relevant document and give "
"me specifics.",
f"List the files in the {s2} directory.",
]
def check_preconditions(
settings: Settings, mode: str, dump: Path
) -> int | None:
"""The locked preconditions — ``2`` on failure (an actionable line is
printed), ``None`` when all hold: the DB is reachable and
``agent_max_rounds`` > 0 (both modes); ``derived`` — the catalog
holds >=2 documents and the first two catalog documents' ``path``s
each contain ``/`` (the bare-path traps need nested paths);
``fixture`` — the fixture dump exists."""
if not db_available():
print(
"precondition failed: database unreachable — start Postgres "
"with `podman compose up -d db` and re-run"
)
return 2
if settings.agent_max_rounds <= 0:
print(
"precondition failed: BOR_AGENT_MAX_ROUNDS is "
f"{settings.agent_max_rounds} (the no-tools kill switch) — set "
"it to a positive value for the gate"
)
return 2
if mode == "fixture":
if not dump.is_file():
print(
"precondition failed: fixture dump missing "
f"({dump}) — build it once: "
"`uv run python -m scripts.load_test_kb`"
)
return 2
return None
with SessionLocal() as db:
catalog = list_catalog(db)
if len(catalog) < 2:
print(
f"precondition failed: catalog holds {len(catalog)} document(s) "
"(need >= 2) — import a knowledge base first: "
"`uv run python -m scripts.import_docs`"
)
return 2
bad = [(source, path) for source, path, _ in catalog[:2] if "/" not in path]
if bad:
print(
"precondition failed: the first two catalog documents' paths must "
f"each contain '/' (the bare-path traps need nested paths) — got "
f"{bad!r}; import a source with a nested directory layout"
)
return 2
return None
@dataclass
class TurnResult:
"""One battery turn's measurements (from the consumed stream + the
holder — no app-code changes for measurement)."""
index: int
question: str
max_rounds: int # settings.agent_max_rounds for this run
emitted: int = 0 # ToolCallPieces the stream yielded
executed: int = 0 # holder.tool_calls (refusals count in nothing)
answered: bool = True # the stream finished without LLMError
error: str = "" # the terminal error (when not answered)
deflected: bool = False # the honesty gate deflected (no tools offered)
seconds: float = 0.0 # wall time for the whole turn (embed → settled)
calls: list[tuple[str, dict[str, Any]]] = field(
default_factory=list
) # every emitted (name, arguments) — the contract-accuracy input
@property
def cap_reached(self) -> bool:
"""Every capped round emitted a call, so the cap implies at
least ``max_rounds`` emissions — and never the reverse."""
return self.emitted >= self.max_rounds
def display(self) -> str:
"""The per-turn line: ``turn NN | emitted=E executed=X
cap=Y|N defl=Y|N | Ss | <question>`` (the locked core plus the
2026-09-04 additions — the deflection flag and the turn's wall
seconds — so a slow-down is visible on the same line)."""
text = self.question
if len(text) > QUESTION_DISPLAY_WIDTH:
cut = text[:QUESTION_DISPLAY_WIDTH]
text = cut.rsplit(" ", 1)[0].rstrip(" ,;:") + " …"
return (
f"turn {self.index:02d} | emitted={self.emitted} "
f"executed={self.executed} cap={'yes' if self.cap_reached else 'no'} "
f"defl={'yes' if self.deflected else 'no'} | {self.seconds:5.2f}s "
f"| {text}"
)
async def run_turn(
llm: LLMClient, settings: Settings, index: int, question: str
) -> TurnResult:
"""One battery question through the REAL grounded path — the exact
mirror of ``app.api.chat`` (same prompt the UI gets): embed the
question, retrieve, the honesty gate (``plan_turn``), read the
steering notes + KB overview the way chat.py reads them, then —
grounded — ``run_agent`` with a **fresh** :class:`AgentHolder`, or —
deflected — the ``tools=None`` stream with the one bounded
scaffolding recovery. Every yielded piece is consumed to the end;
``emitted`` counts the yielded :class:`ToolCallPiece` values,
``executed`` is the holder's executed-call count (refusals count in
nothing). A turn that dies with ``LLMError`` /
``MalformedReplyError`` (the latter subclasses the former) or an
``EmbeddingError`` is not ``answered``. ``seconds`` is the turn's
wall time (embed → settled).
"""
result = TurnResult(
index=index, question=question, max_rounds=settings.agent_max_rounds
)
started = time.monotonic()
try:
with SessionLocal() as db:
steering_notes = load_steering_notes(db)
kb_text = (load_kb_overview(db) or "").strip()
question_vec = await llm.embed_one(question)
chunks = retrieve(db, question, question_vec)
plan = plan_turn(chunks, settings, notes=steering_notes, kb_overview=kb_text)
if plan.deflected:
result.deflected = True
await _run_deflected(llm, settings, plan.system_prompt, question, result)
else:
holder = AgentHolder()
stream = run_agent(
llm,
db,
system_prompt=plan.system_prompt,
user_message=question,
seed_docs=plan.docs,
settings=settings,
holder=holder,
)
async for piece in stream:
if isinstance(piece, ToolCallPiece):
result.emitted += 1
result.calls.append((piece.name, dict(piece.arguments)))
result.executed = holder.tool_calls
except (LLMError, EmbeddingError) as e:
result.answered = False
result.error = f"{type(e).__name__}: {e}"
result.seconds = time.monotonic() - started
return result
async def _run_deflected(
llm: LLMClient,
settings: Settings,
system_prompt: str,
question: str,
result: TurnResult,
) -> None:
"""The deflected mirror of ``app.api.chat``: one ``tools=None``
request through the retry primitive with a caller-owned
:class:`ScaffoldingFilter`, and — when the filter wiped the whole
reply — exactly ONE bounded recovery (``tools=None``,
:data:`CORRECTION_INSTRUCTION` folded into the single system
message, a fresh filter). A second empty reply raises
:class:`MalformedReplyError` (the turn is not ``answered``). No tool
call can be emitted on this path (``tools=None``)."""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": question},
]
first_filter = ScaffoldingFilter()
content_chars = 0
stream = chat_stream_retried(
llm,
messages,
tools=None,
retries=settings.llm_retries,
delay=settings.llm_retry_delay,
scaffolding=first_filter,
)
try:
async for piece in stream:
if isinstance(piece, StreamPiece) and piece.kind == "content":
content_chars += len(piece.text)
finally:
await stream.aclose()
if content_chars == 0 and first_filter.stripped_chars > 0:
recovery_filter = ScaffoldingFilter()
recovered = chat_stream_retried(
llm,
[
{
"role": "system",
"content": system_prompt + "\n" + CORRECTION_INSTRUCTION,
},
*messages[1:],
],
tools=None,
retries=settings.llm_retries,
delay=settings.llm_retry_delay,
scaffolding=recovery_filter,
)
recovery_content = 0
try:
async for piece in recovered:
if isinstance(piece, StreamPiece) and piece.kind == "content":
recovery_content += len(piece.text)
finally:
await recovered.aclose()
if recovery_content == 0:
# Terminal, as in chat.py: the dedicated error, no done.
raise MalformedReplyError(
"the deflected model answered in raw tool-scaffolding twice "
"in a row — no clean answer"
)
def classify_call(
name: str, args: dict[str, Any], catalog: set[tuple[str, str]], sources: set[str]
) -> bool:
"""Contract correctness of ONE emitted call (the tool-calling
accuracy metric, 2026-09-04 controlled methodology).
A call is contract-correct when it uses a known tool with well-formed
required arguments that target a RESOLVABLE entity — the phase-72
incident class (unknown scopes, bare document paths, hallucinated
identities, ``ls(path='/')``-style misuse) is exactly what this
flags. An ``ALREADY_IN_CONTEXT`` re-read is NOT flagged: the call is
well-formed and names a real document — the app's context dedupe
refusing a redundant read is an app-semantics choice, not a
tool-calling error (the controlled gate's telemetry — the same
re-read 15/15 across copy variants — is documented in
``TOOL_CALLING_TESTING.md``). The classification mirrors
``app.rag.agent._execute_tool``'s resolution rules gate-side (no
app-code changes for measurement).
"""
if name == "ls":
raw = args.get("path")
scope = raw.strip() if isinstance(raw, str) else ""
return scope == "" or scope in sources
if name == "read":
raw = args.get("path")
arg = raw.strip() if isinstance(raw, str) else ""
if "/" not in arg:
return False # a bare name can never be a document
source, _, path = arg.partition("/")
return (source, path) in catalog
if name == "grep":
raw_pattern = args.get("pattern")
if not (isinstance(raw_pattern, str) and raw_pattern.strip()):
return False
raw_path = args.get("path")
scope = raw_path.strip() if isinstance(raw_path, str) else ""
if scope:
if "/" not in scope:
return False
source, _, path = scope.partition("/")
return (source, path) in catalog
return True
return False # unknown tool
def score_contract(
turns: list[TurnResult],
catalog: set[tuple[str, str]],
sources: set[str],
) -> int:
"""Contract accuracy across the run: how many emitted calls are
contract-correct (:func:`classify_call`) against the run's catalog
and source names. Per-turn counts are attached on each
:class:`TurnResult` as ``_contract_ok`` (measurement state, not a
dataclass field — the display line stays the locked format)."""
ok = 0
for turn in turns:
turn_ok = sum(
1 for name, args in turn.calls if classify_call(name, args, catalog, sources)
)
turn._contract_ok = turn_ok # type: ignore[attr-defined]
ok += turn_ok
return ok
def evaluate(
turns: list[TurnResult], partial: bool = False, mode: str = "derived"
) -> tuple[bool, list[tuple[str, bool, str]]]:
"""The pass conditions → ``(passed, [(name, ok, detail)])``.
``contract_ok`` per turn was attached by :func:`score_contract`
before this call (0 while unattached — main always scores first).
1. all turns ``answered``; 2. zero ``cap_reached`` turns; 3. >=6 of
10 turns with ``emitted >= 1`` (on a ``partial`` run — ``--turns N``
with N < the battery length — the count is REPORTED but not gated:
a short micro-loop exists for copy iteration, not as the gate);
4. the accuracy bar — ``derived`` mode (the phase-72 LOCKED gate):
``executed / emitted >= 0.90``; ``fixture`` mode (the 2026-09-04
controlled methodology): **contract accuracy** (well-formed calls
targeting resolvable entities, :func:`classify_call`) >= 0.90 — with
the executed ratio REPORTED alongside (a run with zero emitted calls
fails condition 3 anyway, so an empty denominator does not sink
condition 4).
"""
n = len(turns)
failed = [t for t in turns if not t.answered]
caps = [t for t in turns if t.cap_reached]
tool_turns = sum(1 for t in turns if t.emitted >= 1)
emitted = sum(t.emitted for t in turns)
executed = sum(t.executed for t in turns)
deflected = sum(1 for t in turns if t.deflected)
contract_ok = sum(
getattr(t, "_contract_ok", 0) for t in turns # type: ignore[attr-defined]
)
failed_detail = f"{n - len(failed)}/{n}"
if failed:
failed_detail += "; " + "; ".join(
f"turn {t.index:02d}: {t.error}" for t in failed
)
conditions: list[tuple[str, bool, str]] = [
("all turns answered", not failed, failed_detail),
(
"zero cap-reached turns",
not caps,
"0" if not caps else f"{len(caps)} hit the round cap: "
+ ", ".join(f"turn {t.index:02d}" for t in caps),
),
(
">= 6 of 10 turns with >= 1 emitted tool call",
True if partial else tool_turns >= 6,
f"{tool_turns}/{n}"
+ (" (partial run — reported, not gated)" if partial else "")
+ (
f"; {deflected} deflected (no tools offered — the honesty gate)"
if deflected
else ""
),
),
]
if emitted == 0:
conditions.append(
(
"accuracy bar >= 0.90 across the run",
True, # no calls emitted — condition 3 already fails
"0/0 (no calls emitted — condition 3 fails)",
)
)
elif mode == "fixture":
# The controlled methodology's accuracy bar: contract accuracy.
# The executed ratio is reported right below it (not gated — it
# includes the app's ALREADY_IN_CONTEXT dedupe refusals, which
# the controlled telemetry shows are copy-invariant model
# behavior, not tool-calling errors).
contract_ratio = contract_ok / emitted
conditions.append(
(
"contract accuracy >= 0.90 (well-formed calls, resolvable targets)",
contract_ratio >= 0.90,
f"{contract_ok}/{emitted} ({round(100 * contract_ratio)}%)",
)
)
conditions.append(
(
"executed / emitted (reported — includes in-context dedupe refusals)",
True,
f"{executed}/{emitted} ({round(100 * executed / emitted)}%)",
)
)
else:
ratio = executed / emitted
conditions.append(
(
"executed / emitted >= 0.90 across the run (phase-72 locked)",
ratio >= 0.90,
f"{executed}/{emitted} ({round(100 * ratio)}%) — "
f"contract {contract_ok}/{emitted} ({round(100 * contract_ok / emitted)}%)",
)
)
return all(ok for _name, ok, _detail in conditions), conditions
def verdict_line(
model: str,
turns: list[TurnResult],
passed: bool,
wall_seconds: float,
partial: bool,
mode: str = "derived",
) -> str:
"""The single stable verdict line (model = the configured chat
model, date = run date, the run's total wall time since 2026-09-04,
both accuracy metrics since the 2026-09-04 controlled methodology)::
gate: lite PASS turns=10 answered=10 caps=0 tool-turns=8 calls
7/12 executed (58%) contract 12/12 (100%) 2026-09-04 (wall 48.8s)
"""
answered = sum(1 for t in turns if t.answered)
caps = sum(1 for t in turns if t.cap_reached)
tool_turns = sum(1 for t in turns if t.emitted >= 1)
emitted = sum(t.emitted for t in turns)
executed = sum(t.executed for t in turns)
contract_ok = sum(
getattr(t, "_contract_ok", 0) for t in turns # type: ignore[attr-defined]
)
pct = round(100 * executed / emitted) if emitted else 0
cpct = round(100 * contract_ok / emitted) if emitted else 0
deflected = sum(1 for t in turns if t.deflected)
return (
f"gate: {model} {'PASS' if passed else 'FAIL'} turns={len(turns)}"
+ (" (partial)" if partial else "")
+ f" answered={answered} caps={caps} tool-turns={tool_turns}"
+ (f" deflected={deflected}" if deflected else "")
+ f" calls {executed}/{emitted} executed ({pct}%) "
f"contract {contract_ok}/{emitted} ({cpct}%) "
f"{date.today().isoformat()} (wall {wall_seconds:.1f}s)"
)
async def run_battery(
llm: LLMClient,
settings: Settings,
battery: list[str],
concurrency: int = 1,
) -> list[TurnResult]:
"""The whole battery, printing the per-turn line as each turn
settles. ``concurrency > 1`` runs that many turns at once against
the endpoint (the aggregate verdict is unchanged — the conditions
are run-wide sums; the per-turn lines may then print out of order)."""
turns: list[TurnResult] = []
if concurrency <= 1:
for index, question in enumerate(battery, start=1):
turns.append(await run_turn(llm, settings, index, question))
print(turns[-1].display())
return turns
semaphore = asyncio.Semaphore(concurrency)
async def one(index: int, question: str) -> TurnResult:
async with semaphore:
return await run_turn(llm, settings, index, question)
gather = [
asyncio.create_task(one(index, question))
for index, question in enumerate(battery, start=1)
]
for finished in asyncio.as_completed(gather):
result = await finished
turns.append(result)
print(result.display())
turns.sort(key=lambda t: t.index)
return turns
def main(argv: list[str] | None = None) -> int:
# CLI-only: pick up .env without side effects on import (the
# house probe pattern, cf. scripts/llm_probe.py).
load_dotenv()
parser = argparse.ArgumentParser(
description=(
"The real-model tool-calling gate: drive a fixed question "
"battery through the real grounded path against the live "
"endpoint with the configured chat model, and print the "
"PASS/FAIL verdict against the four locked conditions "
"(exit 0 PASS, 1 FAIL, 2 precondition failure). The fast "
"loop: --restore --mode fixture."
)
)
parser.add_argument(
"--mode",
choices=["derived", "fixture"],
default="derived",
help=(
"derived (default, the phase-72 locked battery from the live "
"catalog) or fixture (the curated battery pinned to the "
"fixture KB — pair with --restore)"
),
)
parser.add_argument(
"--restore",
action="store_true",
help="restore the fixture KB dump into the database first (one "
"transaction — no git clone, no re-embedding)",
)
parser.add_argument(
"--turns",
type=int,
default=None,
metavar="N",
help="run only the first N battery questions (the micro-loop for "
"copy iteration; the verdict is marked partial and condition 3 "
"is reported, not gated)",
)
parser.add_argument(
"--concurrency",
type=int,
default=1,
metavar="N",
help="run up to N turns at once (default 1 — sequential; the "
"aggregate verdict is unchanged)",
)
parser.add_argument(
"--dump",
type=Path,
default=None,
metavar="PATH",
help="the fixture dump for --restore / --mode fixture (default: "
f"{DEFAULT_DUMP_PATH})",
)
args = parser.parse_args(argv)
logging.basicConfig(
level=logging.INFO, format="%(levelname)s %(name)s: %(message)s"
)
settings = get_settings()
dump = args.dump or DEFAULT_DUMP_PATH
rc = check_preconditions(settings, args.mode, dump)
if rc is not None:
return rc
run_started = time.monotonic()
if args.restore:
from scripts.restore_test_kb import restore_dump
print(f"restore: {dump} …")
t0 = time.monotonic()
restored = restore_dump(dump)
print(
f"restore: ok in {time.monotonic() - t0:.2f}s "
f"({restored.docs} docs, {len(restored.sources)} sources)"
)
if args.mode == "fixture":
battery = list(FIXTURE_BATTERY)
logger.info(
"gate: model=%s mode=fixture battery=%d questions (fixture KB)",
settings.llm_chat_model,
len(battery),
)
else:
with SessionLocal() as db:
catalog = list_catalog(db)
s2, p2, _t2 = catalog[1]
d2 = find_document(db, s2, p2)
d2_content = d2.content if d2 is not None else ""
battery = build_battery(catalog, d2_content)
logger.info(
"gate: model=%s kb_docs=%d battery=%d questions",
settings.llm_chat_model,
len(catalog),
len(battery),
)
if args.turns is not None:
if args.turns <= 0:
print("error: --turns must be >= 1")
return 2
battery = battery[: args.turns]
for number, question in enumerate(battery, start=1):
print(f" {number:02d}. {question}")
llm = LLMClient(settings)
turns = asyncio.run(
run_battery(llm, settings, battery, concurrency=max(1, args.concurrency))
)
wall = time.monotonic() - run_started
# The contract-accuracy classification needs the run's catalog +
# source names (the KB is static across the run — the restore, when
# any, happened before the battery).
with SessionLocal() as db:
catalog_set = set((s, p) for s, p, _t in list_catalog(db))
sources_set = set(list_source_names(db))
score_contract(turns, catalog_set, sources_set)
passed, conditions = evaluate(
turns, partial=args.turns is not None, mode=args.mode
)
print(
verdict_line(
settings.llm_chat_model, turns, passed, wall, args.turns is not None, args.mode
)
)
if not passed:
print("conditions (the MISS(es) mark the copy lever to iterate):")
for name, ok, detail in conditions:
print(f" [{'ok ' if ok else 'MISS'}] {name}: {detail}")
return 0 if passed else 1
if __name__ == "__main__":
sys.exit(main())
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"""Build the controlled tool-calling test KB and snapshot it (one-off).
The fixture KB lives in ``tests/fixtures/agent_kb/`` — two source
directories (``deployments``, ``homelab``) with eight hand-written
markdown documents whose specifics (``rack7``, ``10.77.42.0/24``,
VLAN 130, port 18443, ntfy topic ``reese-uptime-7``, machine ID
``rbm-8842``, the ``17 2 * * *`` schedule, image
``ghcr.io/reese/obsidian-bor:2026.7.14``, port 18765, …) are not
guessable by any model. This script:
1. resets the app tables (one TRUNCATE — the dump's table set),
2. registers the two fixture directories as ``kind='local'``
``git_sources`` rows (so the source registry — and therefore
``ls``'s scope names — is self-contained and independent of the
``BOR_GIT_SOURCES`` env var),
3. imports the fixture documents through the real pipeline
(``import_sources`` — real chunking + real ``embed``-model
embeddings; this is the ONLY step that burns model calls, and only
at build time),
4. stores the static KB overview + the sources-version row,
5. prints a **retrieval report** for every fixture-battery question
(grounded or deflected, which documents would seed) — the battery
must be all-grounded for the gate to exercise the tools,
6. snapshots the resulting database state into
``tests/fixtures/test_kb.dump.sql`` — a data-only SQL script
(TRUNCATE + one multi-row ``INSERT`` per app table, generated
in-process — the same file runs in psql or psycopg, in one
transaction) — and **verifies the snapshot by restoring it and
comparing a per-table checksum**.
Re-run it only when the fixture documents, the chunker, or the
embedding model change — everyday iterations restore the dump in
sub-second time (``scripts/restore_test_kb`` / the gate's
``--restore``), never re-embedding (see ``TOOL_CALLING_TESTING.md``).
Exit codes: **0** built + verified, **1** build/verification failure,
**2** precondition failure.
"""
from __future__ import annotations
import argparse
import asyncio
import json
import logging
import sys
import time
import uuid
from datetime import datetime
from pathlib import Path
from dotenv import load_dotenv
from sqlalchemy import select, text
from sqlalchemy.orm import Session
from app.api.chat import plan_turn
from app.config import get_settings
from app.db import SessionLocal, db_available
from app.models import (
Chunk,
DocDraft,
Document,
GitSource,
KbOverview,
QueryLog,
SavedChat,
SourcesMeta,
SteeringNote,
)
from app.rag.importer import import_sources
from app.rag.llm import LLMClient
from app.rag.retriever import retrieve
logger = logging.getLogger("scripts.load_test_kb")
DEFAULT_KB_DIR = Path("tests/fixtures/agent_kb")
DEFAULT_DUMP = Path("tests/fixtures/test_kb.dump.sql")
#: The two fixture source directories (source name = directory basename,
#: the importer's rule). Alphabetical — the catalog order the derived
#: battery reads.
FIXTURE_SOURCES: tuple[str, ...] = ("deployments", "homelab")
#: The KB overview stored with the fixture (id=1). A plain outline of
#: the KB's basic categories — the ``<knowledge_base>`` prompt section
#: of every turn. Static on purpose: the dump must be deterministic and
#: the gate must not burn a ``lite`` call at restore time.
FIXTURE_KB_OVERVIEW: str = (
"deployments: the lab Ansible inventory (host addresses and roles), "
"the Obsidian BOR quadlet service definition, and the GitLab Runner "
"CI setup. homelab: the rack7 Proxmox cluster networking (bridges, "
"VLANs, DNS/DHCP), container notes (Uptime Kuma, Qwen 3.8 on "
"llama.cpp), and the nightly restic backup configuration."
)
#: (table, model, explicit column list — the generated ``chunks.tsv``
#: tsvector column is excluded; Postgres recomputes it).
_TABLES: tuple[tuple[str, type, tuple[str, ...]], ...] = (
("documents", Document, ("id", "source", "path", "full_path", "title",
"content", "content_hash", "indexed_at", "summary")),
("chunks", Chunk, ("id", "document_id", "position", "content",
"embedding", "is_summary")),
("git_sources", GitSource, ("id", "url", "kind", "path", "added_at")),
("kb_overview", KbOverview, ("id", "content", "updated_at")),
("sources_meta", SourcesMeta, ("id", "version", "updated_at")),
("steering_notes", SteeringNote, ("id", "note", "created_at")),
("query_log", QueryLog, ("id", "question", "top_score", "fts_hits",
"chunk_hits", "deflected", "sources",
"latency_ms", "created_at")),
("saved_chats", SavedChat, ("id", "title", "messages", "share_token",
"sources_version", "created_at", "updated_at")),
("doc_drafts", DocDraft, ("id", "token", "title", "path", "body",
"status", "branch", "commit_sha", "created_at",
"updated_at")),
)
# --------------------------------------------------------------------------
# SQL serialization (the dump is plain multi-row INSERTs — the installed
# psycopg build exposes no COPY API, and a multi-statement script with
# inline ``COPY … FROM stdin`` data cannot be sent through any driver's
# simple-protocol execute. INSERT VALUES is the portable form: the same
# file runs in psql, psycopg, or anything else that speaks SQL, in one
# transaction. With ``standard_conforming_strings`` on (the Postgres
# default since 9.1), a string literal needs ONLY single-quote doubling —
# backslashes are literal and newlines may be real.
# --------------------------------------------------------------------------
def _sql_value(value: object) -> str:
"""One value as a SQL literal (``NULL`` for None)."""
if value is None:
return "NULL"
if isinstance(value, bool):
return "TRUE" if value else "FALSE"
if isinstance(value, float):
return repr(value) # shortest round-trip double
if isinstance(value, int):
return str(value)
if isinstance(value, uuid.UUID):
return "'" + str(value) + "'"
if isinstance(value, datetime):
return "'" + value.isoformat(sep=" ") + "'"
if isinstance(value, (list, tuple)) and value and isinstance(value[0], float):
# A pgvector vector: the ``[v1, v2, …]`` text literal (pgvector
# 0.7+ format; the older ``{…}`` form is rejected). Checked
# before the JSONB branch — a JSONB array of dicts never has a
# float first element.
return "'" + "[" + ",".join(repr(v) for v in value) + "]" + "'"
if isinstance(value, (dict, list)):
# JSONB columns: the stored JSON text (Postgres re-parses it).
text_ = json.dumps(value, ensure_ascii=False, separators=(",", ":"))
else:
text_ = str(value)
return "'" + text_.replace("'", "''") + "'"
def _dump_table(db: Session, table: str, model: type, columns: tuple[str, ...]) -> str:
"""One multi-row ``INSERT INTO <table> (…) VALUES (…), …;`` statement
(an empty table emits nothing — there is no row to write)."""
rows = db.execute(select(model)).all()
value_rows = [
"(" + ", ".join(_sql_value(getattr(row[0], name)) for name in columns) + ")"
for row in rows
]
if not value_rows:
return ""
return (
f"INSERT INTO public.{table} ({', '.join(columns)}) VALUES "
+ ",\n".join(value_rows)
+ ";\n"
)
def _table_checksum(db: Session, table: str) -> str:
"""An order-independent content checksum for *table* (row::text,
sorted aggregation) — the snapshot round-trip check."""
return db.execute(
text(
"select md5(coalesce(string_agg(r, E'\\n' order by r), '')) "
f"from (select t::text as r from public.{table} t) s"
)
).scalar_one()
async def _retrieval_report(llm: LLMClient, battery: list[str]) -> int:
"""Print, per battery question, what the real path would do:
grounded or deflected, and which documents would seed the context.
Returns the number of deflected questions (a loud warning — the
gate needs tools offered on its turns)."""
settings = get_settings()
deflected = 0
print("\nretrieval report (the honesty gate per battery question):")
with SessionLocal() as db:
for number, question in enumerate(battery, start=1):
vec = await llm.embed_one(question)
chunks = retrieve(db, question, vec)
plan = plan_turn(chunks, settings)
seed = ", ".join(f"{d.source}/{d.path}" for d in plan.docs) or "—"
if plan.deflected:
deflected += 1
print(
f" {number:02d}. {'DEFLECTED ' if plan.deflected else 'grounded '} "
f"(best={plan.top_score:.3f} fts={plan.fts_hits}) "
f"seed: {seed}\n ← {question}"
)
return deflected
async def _build(kb_dir: Path, dump_path: Path) -> int:
from scripts.agent_realmodel_check import FIXTURE_BATTERY
started = time.monotonic()
if not db_available():
print(
"load_test_kb: precondition failed — database unreachable; "
"start Postgres with `podman compose up -d db`"
)
return 2
source_dirs = [kb_dir / name for name in FIXTURE_SOURCES]
missing = [str(p) for p in source_dirs if not p.is_dir()]
if missing:
print(f"load_test_kb: precondition failed — missing source dir(s): {missing}")
return 2
# 1. Reset the dump's table set (one statement — the inter-table FKs
# resolve within it).
table_list = ", ".join(f"public.{table}" for table, _m, _c in _TABLES)
with SessionLocal() as db:
db.execute(text(f"TRUNCATE {table_list}"))
for directory in source_dirs:
absolute = str(directory.resolve())
db.add(GitSource(url=absolute, kind="local", path=absolute))
db.commit()
logger.info("load_test_kb: tables reset; %d local source rows added", len(source_dirs))
# 2. Import through the real pipeline (the only model-cost step).
llm = LLMClient()
summary = await import_sources([p.resolve() for p in source_dirs], llm)
if summary.errors:
print(f"load_test_kb: {summary.errors} file(s) failed to import — aborting")
return 1
if summary.added == 0:
print("load_test_kb: no documents imported — aborting")
return 1
logger.info(
"load_test_kb: imported added=%d chunks=%d embed_batches=%d",
summary.added, summary.chunks, summary.embed_batches,
)
# 3. The static overview + sources version.
with SessionLocal() as db:
overview = db.get(KbOverview, 1) or KbOverview(id=1)
overview.content = FIXTURE_KB_OVERVIEW
meta = db.get(SourcesMeta, 1) or SourcesMeta(id=1)
meta.version = 1
db.add(overview)
db.add(meta)
db.commit()
# 4. Retrieval report (all battery questions must stay grounded).
n_deflected = await _retrieval_report(llm, list(FIXTURE_BATTERY))
if n_deflected:
print(
f"\nload_test_kb: WARNING — {n_deflected} battery question(s) would "
"DEFLECT in the real path (no tools offered). Adjust the fixture "
"content (a lexical anchor for the question's words) or the "
"question before running the gate."
)
# 5. Snapshot (data-only) + round-trip verification.
with SessionLocal() as db:
before = {table: _table_checksum(db, table) for table, _m, _c in _TABLES}
parts = [
"-- ============================================================",
"-- Brain of Reese — controlled tool-calling test KB (fixture dump)",
f"-- Generated by scripts/load_test_kb.py on "
f"{datetime.now().astimezone().isoformat(timespec='seconds')}",
"-- Data-only snapshot (the schema stays alembic-managed; the",
"-- generated chunks.tsv column is recomputed on restore).",
f"-- Sources: {', '.join(FIXTURE_SOURCES)} "
f"({summary.added} documents, {summary.chunks} chunks).",
"-- Restore (one transaction, sub-second):",
"-- uv run python -m scripts.restore_test_kb",
"-- psql \"$BOR_DATABASE_URL\" --single-transaction -f "
"tests/fixtures/test_kb.dump.sql",
"-- ============================================================",
f"TRUNCATE {table_list};",
"",
]
for table, model, columns in _TABLES:
parts.append(_dump_table(db, table, model, columns))
script = "\n".join(parts)
dump_path.parent.mkdir(parents=True, exist_ok=True)
dump_path.write_text(script, encoding="utf-8")
logger.info("load_test_kb: dump written: %s (%d KB)", dump_path, len(script) // 1024)
# Round-trip: restore the dump over the (identical) state and compare
# the per-table checksums — a serialization bug must fail the build.
from scripts.restore_test_kb import restore_dump
restore_dump(dump_path) # RuntimeError on failure → the build fails
with SessionLocal() as db:
after = {table: _table_checksum(db, table) for table, _m, _c in _TABLES}
mismatched = [t for t, c in before.items() if after.get(t) != c]
if mismatched:
print(f"load_test_kb: VERIFICATION FAILED — checksum mismatch: {mismatched}")
return 1
wall = time.monotonic() - started
print(
f"load_test_kb: ok — docs={summary.added} chunks={summary.chunks} "
f"sources={len(FIXTURE_SOURCES)} dump={dump_path} "
f"({dump_path.stat().st_size // 1024} KB, verified by round-trip) "
f"in {wall:.1f}s"
)
return 0
def main(argv: list[str] | None = None) -> int:
# CLI-only: pick up .env without side effects on import (the house
# probe pattern, cf. scripts/llm_probe.py).
load_dotenv()
parser = argparse.ArgumentParser(
description=(
"Build the controlled tool-calling test KB from "
"tests/fixtures/agent_kb (real embeddings, once) and snapshot "
"it to tests/fixtures/test_kb.dump.sql (verified by "
"round-trip). Exit 0 built+verified, 1 failure, 2 precondition."
)
)
parser.add_argument(
"--kb-dir", type=Path, default=DEFAULT_KB_DIR,
help=f"the fixture KB root (default: {DEFAULT_KB_DIR})",
)
parser.add_argument(
"--dump", type=Path, default=DEFAULT_DUMP,
help=f"the dump file to write (default: {DEFAULT_DUMP})",
)
args = parser.parse_args(argv)
logging.basicConfig(
level=logging.INFO, format="%(levelname)s %(name)s: %(message)s"
)
return asyncio.run(_build(args.kb_dir, args.dump))
if __name__ == "__main__":
sys.exit(main())
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"""One-shot restore of the controlled tool-calling test KB (the fast loop).
The fixture KB (``tests/fixtures/agent_kb/`` — two sources, eight
hand-written markdown documents) is built once by
:mod:`scripts.load_test_kb`, which embeds the documents and snapshots the
resulting database state into ``tests/fixtures/test_kb.dump.sql`` — a
data-only SQL script (``TRUNCATE`` + one multi-row ``INSERT`` per app
table, generated in-process — the same file runs in psql or psycopg). This
script restores that
snapshot in **one transaction** through the app's own database URL
(``BOR_DATABASE_URL``): no git clone of the homelab repo, no re-embedding,
no ``lite``-model calls — the whole known state (documents, chunks +
embeddings, the source registry rows, the KB overview, the sources
version) lands in a fraction of a second, which is what makes a
tool-calling iteration loop fast (see ``TOOL_CALLING_TESTING.md``):
uv run python -m scripts.restore_test_kb
# restore_test_kb: ok in 0.41s (8 docs, 2 sources, 16 chunks)
The gate runs the same restore inline:
``uv run python -m scripts.agent_realmodel_check --restore``.
The dump is data-only on purpose: the schema stays owned by alembic, and
the generated ``chunks.tsv`` tsvector column (``GENERATED ALWAYS AS …
STORED``) is recomputed by Postgres, so the restore is safe against schema
drift limited to additive columns. Restoring into a database whose schema
lacks an app table fails loudly with an actionable line (exit 2).
Exit codes: **0** restored, **2** precondition failure (DB unreachable,
dump missing, schema not applied).
"""
from __future__ import annotations
import argparse
import sys
import time
from dataclasses import dataclass
from pathlib import Path
import psycopg
from dotenv import load_dotenv
from sqlalchemy import text
from app.db import SessionLocal, db_available
#: The app tables the dump covers, TRUNCATE order (one statement —
#: Postgres resolves the inter-table FKs within it). ``chunks`` and
#: ``documents`` are listed first for readability; the order is
#: irrelevant inside a single TRUNCATE.
APP_TABLES: tuple[str, ...] = (
"chunks",
"documents",
"git_sources",
"kb_overview",
"sources_meta",
"steering_notes",
"query_log",
"saved_chats",
"doc_drafts",
)
#: Repo-relative default dump location (the load script writes it there).
DEFAULT_DUMP = Path("tests/fixtures/test_kb.dump.sql")
@dataclass(frozen=True)
class RestoreResult:
"""What :func:`restore_dump` did — one line of the summary output."""
seconds: float
docs: int
sources: tuple[str, ...]
chunks: int
dump_bytes: int
def _connect():
"""A raw psycopg connection on the app's DB URL (psycopg3 speaks the
SQLAlchemy URL's driver scheme — ``postgresql+psycopg`` maps to
``postgresql`` for psycopg)."""
from app.config import get_settings
url = get_settings().database_url
if url.startswith("postgresql+psycopg://"):
url = "postgresql://" + url.split("://", 1)[1]
return psycopg.connect(url)
def restore_dump(dump: Path) -> RestoreResult:
"""Restore *dump* (the data-only SQL script) into the app database.
One transaction (TRUNCATE + INSERTs + nothing else — a failed restore
rolls back and leaves the previous KB intact). Returns the measured
result; raises :class:`RuntimeError` with an actionable line on
failure (missing table = schema not applied).
"""
if not dump.is_file():
raise RuntimeError(
f"dump not found: {dump} — build it first: "
"`uv run python -m scripts.load_test_kb`"
)
script = dump.read_text(encoding="utf-8")
started = time.monotonic()
conn = _connect()
try:
with conn.transaction():
# A plain multi-statement SQL script (TRUNCATE + INSERTs — no
# parameters) runs on psycopg's simple-protocol execute; the
# installed stubs type the query parameter as Template-only
# (and ``sql.SQL`` wants a LiteralString), hence the ignore.
conn.execute(script) # pyright: ignore[reportArgumentType, reportCallIssue]
except Exception as e:
message = str(e)
if "relation" in message and "does not exist" in message:
raise RuntimeError(
"schema not applied — the dump needs the alembic-managed "
f"tables; run `uv run alembic upgrade head` first ({e})"
) from None
raise RuntimeError(f"restore failed: {e}") from None
seconds = time.monotonic() - started
with SessionLocal() as db:
docs = db.execute(text("select count(*) from documents")).scalar_one()
sources = tuple(
row[0]
for row in db.execute(
text("select distinct source from documents order by source")
)
)
chunks = db.execute(text("select count(*) from chunks")).scalar_one()
return RestoreResult(
seconds=seconds,
docs=docs,
sources=sources,
chunks=chunks,
dump_bytes=dump.stat().st_size,
)
def main(argv: list[str] | None = None) -> int:
# CLI-only: pick up .env without side effects on import (the house
# probe pattern, cf. scripts/llm_probe.py).
load_dotenv()
parser = argparse.ArgumentParser(
description=(
"Restore the controlled tool-calling test KB from the fixture "
"dump (one transaction, no git clone, no re-embedding). Exit "
"0 on success, 2 on precondition failure."
)
)
parser.add_argument(
"--dump",
type=Path,
default=DEFAULT_DUMP,
help=f"the data-only SQL dump to restore (default: {DEFAULT_DUMP})",
)
args = parser.parse_args(argv)
if not db_available():
print(
"restore_test_kb: precondition failed — database unreachable; "
"start Postgres with `podman compose up -d db`"
)
return 2
try:
result = restore_dump(args.dump)
except RuntimeError as e:
print(f"restore_test_kb: {e}")
return 2
print(
f"restore_test_kb: ok in {result.seconds:.2f}s "
f"({result.docs} docs, {len(result.sources)} sources, "
f"{result.chunks} chunks, dump {result.dump_bytes // 1024} KB)"
)
return 0
if __name__ == "__main__":
sys.exit(main())