Vayikra 9:24 — vatetzei esh milifnei Hashem vatokhal al hamizbeach.
Phase 4 of the architecture is now complete. The remaining three steps
that were marked "blocked on external infrastructure" are built —
discipline-respecting, eish-zarah-aware, and tested.
## Step 22 — Korban Processor external model integration
(korban_processor/src/korban_processor/external_offering.py)
The single legitimate entry point for external model consultation.
Wraps Claude API calls through the offering protocol so external
output is treated as korban (raw material), never authoritative:
- semichah — provenance is tagged (avodah zarah Form 1 prevention)
- shechitah — assistant voice, hedging, formatting stripped
- zerikah — vital content extracted
- haktarah — converted to Prolog propositions
- route — sent to internal component per offering type
The raw Korban is preserved on the ProcessedKorban (gilui arayot
Form 3 prevention — input/output coupling never severed).
No internal component should call call_claude() directly.
11 tests, mocked external calls. Live API test opt-in via
SHORASHIM_LIVE_API=1.
## Step 24 — L2 Urim v'Tumim interface
(vav_pipeline/lib/vav_pipeline/urim_v_tumim.ex)
PRD §4.7: housed in the kohen gadol's breastplate; consulted only
on high-stakes decisions where normal deliberation is insufficient.
Bamidbar 27:21 — al-piv yetz'u v'al-piv yavo'u.
Three escalation steps, three honest outcomes:
1. resolved_by_specification — re-read produced an answer
2. resolved_by_external — external testimony recorded (NOT binding)
3. deferred_to_human — the system stops, asks for human judgment
Pre-condition gating refuses cases that didn't reach Sanhedrin teiku
first (no preempting deliberation) or that aren't flagged high-stakes
(ordinary teiku stays teiku). Records every consultation with steps
taken — there is no quiet escalation.
Per PRD: "The Urim v'Tumim never fabricates certainty where none
exists." When external also fails, the system stops. It does not
proceed on a guess.
Added to VavPipeline supervision tree. 14 tests.
## Step 21 — Kushya generator scaffolding
(kushya_generator/)
Directory layout per Architecture §3.6 with three layers:
- data/training_pairs/seed_pairs.jsonl — 10 hand-curated bootstrap
pairs covering 9 kushya types (scope_narrowing, internal_tension,
harmonization, exception_test, edge_case, scope_application,
rabbinic_constraint, method_question, subject_identification)
- data/prepare_training.py — validates the four kushya requirements
(specific Torah passage, identified weakness, question form,
non-trivial), produces train.jsonl + eval.jsonl with stratified
80/20 split by type
- fine_tune/config.yaml + train.py — QLoRA configuration and
training script. Operator's task — needs GPU. Script is
complete and self-contained.
- inference/kushya_api.py — three-backend interface
(fine_tuned → external → fallback). When fine-tuned model isn't
deployed, falls back to external via Korban Processor (Step 22).
The fallback is honest: returns no kushya rather than inventing
one. The Talmudic Engine's rule-based challenge.pl remains the
primary kushya source.
17 tests covering dataset validation, backend selection, and
reference extraction.
## .gitignore fix
The earlier "data/" line was too broad — it caught
kushya_generator/data/. Scoped to /data/ (root-level only).
## Test counts
- Elixir: 274 (was 260; +14 for UrimVTumim)
- Korban Python: 196 + 1 skipped (was 195; +1 module = 11 tests)
- Kushya Python: 17 (new)
- Haskell: 33 (unchanged)
- Prolog: 195 (unchanged)
- Total: 715 + 19 L3 excluded
## Phase 4 status
All 12 steps now have implementations:
- Steps 23, 25, 26, 27, 28, 29, 30, 31, 32: complete
- Steps 21, 22, 24: complete with discipline preserved
Step 21's fine-tuning run remains the operator's GPU task; the
pipeline is built such that a deployed adapter slots into the
inference backend without touching the Talmudic Engine or anything
above.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
148 lines
4.8 KiB
Python
148 lines
4.8 KiB
Python
"""
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Validate the kushya training dataset structure.
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Architecture §3.6 — every training pair must satisfy the four requirements
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or be rejected. These tests run prepare_training.py's validator against
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the seed pairs to catch dataset regressions.
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"""
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import json
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import sys
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from pathlib import Path
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# Add parent data/ dir to path so we can import prepare_training
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "data"))
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from prepare_training import ( # noqa: E402
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REQUIRED_TYPES,
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ValidationError,
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load_jsonl,
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to_training_format,
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validate_pair,
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)
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SEED_PATH = (
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Path(__file__).resolve().parent.parent / "data" / "training_pairs" / "seed_pairs.jsonl"
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)
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def test_seed_pairs_file_exists():
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"""The seed pairs file must exist — it bootstraps the training pipeline."""
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assert SEED_PATH.exists(), f"seed_pairs.jsonl not found at {SEED_PATH}"
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def test_seed_pairs_validate():
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"""Every seed pair must pass validation. If a pair fails, the dataset
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has regressed and the training pipeline would silently degrade."""
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failures = []
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for line_no, pair in load_jsonl(SEED_PATH):
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try:
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validate_pair(pair, "seed_pairs.jsonl", line_no)
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except ValidationError as e:
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failures.append(str(e))
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assert not failures, "seed_pairs.jsonl has invalid records:\n" + "\n".join(failures)
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def test_seed_pairs_cover_all_kushya_types():
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"""The seed set should exercise every kushya type, so the training
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split is stratified across the categories."""
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types_seen = set()
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for _, pair in load_jsonl(SEED_PATH):
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types_seen.add(pair["type"])
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# We don't require ALL types to be in the small seed (10 pairs),
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# but we do require at least 3 types to ensure stratification works.
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assert len(types_seen) >= 3, (
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f"Only {len(types_seen)} types in seed: {types_seen}. "
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f"Need at least 3 for stratified split."
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)
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# Every type used must be a known type (validator already checks this,
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# but we want a clear failure if a typo creeps in).
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unknown = types_seen - REQUIRED_TYPES
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assert not unknown, f"Unknown types in seed: {unknown}"
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def test_to_training_format_produces_expected_shape():
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"""The training format must follow Architecture §3.6:
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input: 'Proposed interpretation: ...'
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output: 'Kushya: ...'
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"""
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sample = {
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"interpretation": "Cooking on Shabbat is forbidden.",
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"kushya": "What about cooking by direct sunlight where no kindling occurs in Shemot 35:3?",
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"torah_refs": ["Shemot 35:3"],
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"type": "scope_narrowing",
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}
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formatted = to_training_format(sample)
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assert formatted["input"].startswith("Proposed interpretation:")
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assert formatted["output"].startswith("Kushya:")
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assert formatted["metadata"]["type"] == "scope_narrowing"
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assert formatted["metadata"]["torah_refs"] == ["Shemot 35:3"]
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# ============================================================
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# Validator regression tests — these should REJECT bad pairs.
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# ============================================================
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def _expect_rejection(pair, must_contain):
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try:
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validate_pair(pair, "test", 1)
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except ValidationError as e:
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assert must_contain in str(e), (
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f"Expected error containing {must_contain!r}, got: {e}"
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)
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else:
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raise AssertionError(f"Pair should have been rejected: {pair}")
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def test_validator_rejects_empty_kushya():
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pair = {
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"interpretation": "Some interp.",
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"kushya": "",
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"torah_refs": ["Shemot 21:1"],
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"type": "internal_tension",
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}
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_expect_rejection(pair, "kushya must be a non-empty string")
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def test_validator_rejects_kushya_without_question_form():
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pair = {
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"interpretation": "Some interp.",
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"kushya": "There is a problem with this interpretation that needs clarification.",
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"torah_refs": ["Shemot 21:1"],
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"type": "internal_tension",
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}
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_expect_rejection(pair, "expressible as a question")
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def test_validator_rejects_too_short_kushya():
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pair = {
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"interpretation": "Some interp.",
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"kushya": "Why?",
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"torah_refs": ["Shemot 21:1"],
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"type": "internal_tension",
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}
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_expect_rejection(pair, "too short")
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def test_validator_rejects_no_torah_reference():
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pair = {
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"interpretation": "Some interp.",
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"kushya": "What does this even mean in context — there is internal tension here is there not?",
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"torah_refs": [],
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"type": "internal_tension",
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}
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_expect_rejection(pair, "must cite a specific Torah passage")
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def test_validator_rejects_unknown_type():
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pair = {
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"interpretation": "Some interp.",
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"kushya": "What does this mean in Shemot 21:1, given the apparent tension?",
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"torah_refs": ["Shemot 21:1"],
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"type": "made_up_type",
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}
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_expect_rejection(pair, "not in known types")
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