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Production path on BIRD Mini-Dev (SQLite, n=200, seed=0): A → C → D → G → hybrid.
Empirical EA lift trace at codestral free tier:
A (full_schema) 47.0%
C (dense_cards + sort) 51.0% +4.0pp
D (+ fewshot k=3 BIRD train) 55.5% +4.5pp
G (+ verify_retry on empty) 56.5% +1.0pp
G + Sonnet challenging hybrid 57.0% +0.5pp (challenging tier only)
Config B (BM25) is documented as an enum member but `run_config_b` raises
NotImplementedError — dense retrieval (config C) was strictly superior in
pilot runs and BM25 only widens the prompt with no recall lift.
Configs E and F remain implemented for ablation completeness.
"""
from __future__ import annotations
import time
from collections.abc import Callable, Iterable, Sequence
from dataclasses import dataclass, field
from enum import StrEnum
from typing import Any
from sqlalchemy import Engine
from sqlalchemy.exc import SQLAlchemyError
from nl_sql.agent import PipelineConfig, build_pipeline, run_pipeline
from nl_sql.agent.nodes._support import (
parse_generate_sql_output,
render_fewshot_block,
render_schema_block,
)
from nl_sql.agent.prompts import load_prompt
from nl_sql.db.connection import Dialect, execute_readonly
from nl_sql.db.registry import DatabaseRegistry
from nl_sql.eval.dataset import BirdExample, extract_gold_tables
from nl_sql.eval.metrics.execution_accuracy import (
ResultComparison,
compare_results,
execution_accuracy,
)
from nl_sql.eval.metrics.schema_recall import schema_recall_at_k
from nl_sql.eval.self_consistency import Candidate, vote
from nl_sql.execution.errors import ExecutionErrorKind
from nl_sql.execution.runner import ExecutionOutcome, execute_validated
from nl_sql.llm.providers.base import GenerateRequest, LLMProvider
from nl_sql.schema_index.chunker import SchemaChunk, to_chunks
from nl_sql.schema_index.indexer import SchemaIndex, SchemaQueryHit
from nl_sql.schema_index.introspector import introspect
from nl_sql.schema_index.retriever import ContextBundle
class Configuration(StrEnum):
"""The 5 configurations from docs/03_eval_methodology.md §4.1."""
A_FULL_SCHEMA = "A_full_schema"
B_BM25 = "B_bm25_cards"
C_DENSE = "C_dense_cards"
D_FEWSHOT = "D_dense_fewshot"
E_FINAL = "E_dense_fewshot_repair"
F_SELF_CONSISTENCY = "F_self_consistency"
G_VERIFY_RETRY = "G_dense_fewshot_verify_retry"
@dataclass(frozen=True, slots=True)
class EvalRecord:
"""Per-example outcome. `match` is the EA bit."""
question_id: int
db_id: str
difficulty: str
dialect: str
question: str
gold_sql: str
pred_sql: str
match: bool
schema_recall: bool
error_kind: str | None
error_message: str
repair_attempted: bool
first_pass_match: bool
latency_ms: float
input_tokens: int
output_tokens: int
gold_tables: tuple[str, ...]
retrieved_tables: tuple[str, ...]
pred_row_count: int
gold_row_count: int
comparison_reason: str
@dataclass(slots=True)
class EvalSummary:
"""Aggregates per a slice (overall, per-difficulty, etc)."""
n: int
ea: float
validity_rate: float
schema_recall_at_k: float
repair_success_rate: float
first_pass_ea: float
empty_result_rate: float
latency_p50_ms: float
latency_p95_ms: float
tokens_p50: float
tokens_p95: float
@dataclass(slots=True)
class EvalRun:
"""Result of running one configuration against a list of examples."""
configuration: Configuration
sql_model: str
overall: EvalSummary
per_difficulty: dict[str, EvalSummary] = field(default_factory=dict)
records: list[EvalRecord] = field(default_factory=list)
# ---------------------------------------------------------------------------
# Public entry point — only Configuration.A is implemented in milestone 1.
# ---------------------------------------------------------------------------
def run_config_a(
examples: Sequence[BirdExample],
*,
sql_provider: LLMProvider,
registry: DatabaseRegistry,
statement_timeout_ms: int = 60_000,
row_cap: int = 10_000,
sample_size: int = 3,
max_tokens: int = 1024,
progress: Callable[[int, int, EvalRecord], None] | None = None,
) -> EvalRun:
"""Run configuration A (full_schema baseline) against `examples`.
`progress` (optional): called after every example as
`progress(idx, total, record)` — used by `scripts/eval_baseline.py` to
print live status without polluting the runner with stdout.
"""
schema_cache: dict[str, list[SchemaChunk]] = {}
records: list[EvalRecord] = []
for idx, example in enumerate(examples, start=1):
record = _run_one_config_a(
example,
sql_provider=sql_provider,
registry=registry,
schema_cache=schema_cache,
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
sample_size=sample_size,
max_tokens=max_tokens,
)
records.append(record)
if progress is not None:
progress(idx, len(examples), record)
return _summarise(
configuration=Configuration.A_FULL_SCHEMA,
sql_model=getattr(sql_provider, "model", "unknown"),
records=records,
)
def run_config_b(*_: Any, **__: Any) -> EvalRun:
raise NotImplementedError("Configuration B (BM25) ships in stage 6.b")
def run_config_c(
examples: Sequence[BirdExample],
*,
sql_provider: LLMProvider,
explain_provider: LLMProvider,
schema_index: SchemaIndex,
registry: DatabaseRegistry,
schema_top_k: int = 5,
fk_hops: int = 1,
table_budget: int = 12,
statement_timeout_ms: int = 60_000,
row_cap: int = 10_000,
max_tokens: int = 1024,
sort_schema_block: bool = False,
primary_sample_size: int = 3,
extended_sample_size: int = 0,
progress: Callable[[int, int, EvalRecord], None] | None = None,
) -> EvalRun:
"""Run configuration C (dense schema cards + FK 1-hop, no fewshot, no repair).
Reuses the production LangGraph pipeline so the eval signal directly
measures the same code path the API will serve. `disable_repair=True`
flips the route_after_validate/execute conditional edges to fall through
to deterministic_format on first failure, so we measure first-pass EA.
"""
pipeline = build_pipeline(
PipelineConfig(
sql_provider=sql_provider,
explain_provider=explain_provider,
schema_index=schema_index,
registry=registry,
schema_top_k=schema_top_k,
fewshot_top_k=0,
fk_hops=fk_hops,
table_budget=table_budget,
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
sort_schema_block=sort_schema_block,
primary_sample_size=primary_sample_size,
extended_sample_size=extended_sample_size,
)
)
records: list[EvalRecord] = []
for idx, example in enumerate(examples, start=1):
record = _run_one_via_pipeline(
example,
pipeline=pipeline,
registry=registry,
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
disable_repair=True,
)
records.append(record)
if progress is not None:
progress(idx, len(examples), record)
return _summarise(
configuration=Configuration.C_DENSE,
sql_model=getattr(sql_provider, "model", "unknown"),
records=records,
)
def run_config_d(
examples: Sequence[BirdExample],
*,
sql_provider: LLMProvider,
explain_provider: LLMProvider,
schema_index: SchemaIndex,
registry: DatabaseRegistry,
schema_top_k: int = 5,
fewshot_top_k: int = 3,
fk_hops: int = 1,
table_budget: int = 12,
statement_timeout_ms: int = 60_000,
row_cap: int = 10_000,
max_tokens: int = 1024,
sort_schema_block: bool = True,
primary_sample_size: int = 3,
extended_sample_size: int = 0,
cross_db_fewshot: bool = True,
progress: Callable[[int, int, EvalRecord], None] | None = None,
) -> EvalRun:
"""Run configuration D (config C + cross-db fewshot, no repair).
Fewshot pool is built from BIRD *train* (~9.4k Q→SQL pairs over 69 dbs;
see `scripts/build_fewshot_index.py`). Dev questions reach for the
most semantically similar train question regardless of db_id since
train and dev share zero databases — see the `cross_db_fewshot` flag
on `PipelineConfig` for the leakage-prevention reasoning.
"""
pipeline = build_pipeline(
PipelineConfig(
sql_provider=sql_provider,
explain_provider=explain_provider,
schema_index=schema_index,
registry=registry,
schema_top_k=schema_top_k,
fewshot_top_k=fewshot_top_k,
fk_hops=fk_hops,
table_budget=table_budget,
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
sort_schema_block=sort_schema_block,
primary_sample_size=primary_sample_size,
extended_sample_size=extended_sample_size,
cross_db_fewshot=cross_db_fewshot,
)
)
records: list[EvalRecord] = []
for idx, example in enumerate(examples, start=1):
record = _run_one_via_pipeline(
example,
pipeline=pipeline,
registry=registry,
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
disable_repair=True,
)
records.append(record)
if progress is not None:
progress(idx, len(examples), record)
return _summarise(
configuration=Configuration.D_FEWSHOT,
sql_model=getattr(sql_provider, "model", "unknown"),
records=records,
)
def run_config_e(
examples: Sequence[BirdExample],
*,
sql_provider: LLMProvider,
explain_provider: LLMProvider,
schema_index: SchemaIndex,
registry: DatabaseRegistry,
schema_top_k: int = 5,
fk_hops: int = 1,
table_budget: int = 12,
statement_timeout_ms: int = 60_000,
row_cap: int = 10_000,
max_tokens: int = 1024,
sort_schema_block: bool = False,
primary_sample_size: int = 3,
extended_sample_size: int = 0,
progress: Callable[[int, int, EvalRecord], None] | None = None,
) -> EvalRun:
"""Run configuration E (config C + repair_once enabled) — final v2 config.
The only difference from C is that the repair branch fires on the first
validate/execute failure. Results capture both first-pass and final EA
so the methodology report can isolate the repair contribution.
"""
pipeline = build_pipeline(
PipelineConfig(
sql_provider=sql_provider,
explain_provider=explain_provider,
schema_index=schema_index,
registry=registry,
schema_top_k=schema_top_k,
fewshot_top_k=0,
fk_hops=fk_hops,
table_budget=table_budget,
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
sort_schema_block=sort_schema_block,
primary_sample_size=primary_sample_size,
extended_sample_size=extended_sample_size,
)
)
records: list[EvalRecord] = []
for idx, example in enumerate(examples, start=1):
record = _run_one_via_pipeline(
example,
pipeline=pipeline,
registry=registry,
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
disable_repair=False,
)
records.append(record)
if progress is not None:
progress(idx, len(examples), record)
return _summarise(
configuration=Configuration.E_FINAL,
sql_model=getattr(sql_provider, "model", "unknown"),
records=records,
)
def run_config_f(
examples: Sequence[BirdExample],
*,
sql_provider: LLMProvider,
explain_provider: LLMProvider,
schema_index: SchemaIndex,
registry: DatabaseRegistry,
sql_candidate_temperatures: Sequence[float] = (0.2, 0.4, 0.6, 0.8),
schema_top_k: int = 5,
fewshot_top_k: int = 0,
fk_hops: int = 1,
table_budget: int = 12,
statement_timeout_ms: int = 60_000,
row_cap: int = 10_000,
max_tokens: int = 1024,
sort_schema_block: bool = True,
primary_sample_size: int = 3,
extended_sample_size: int = 0,
cross_db_fewshot: bool = False,
progress: Callable[[int, int, EvalRecord], None] | None = None,
) -> EvalRun:
"""Run configuration F (self-consistency execution-based voting).
For each example, runs the pipeline N times at the supplied
temperatures, executes every candidate against the live engine, and
picks the winner via `eval.self_consistency.vote` (largest
execution-result cluster, ties broken by max LLM confidence, then
lowest temperature). Repair is disabled per-candidate — voting is the
error-correction mechanism for this configuration.
Fewshot support: pass `fewshot_top_k > 0` (and `cross_db_fewshot=True`
for BIRD) to enable the cross-domain fewshot block on top of voting.
Stacking is roughly additive on challenging tier: F lifts challenging
via vote, fewshot lifts it via better first-pass; combining gets the
best-of-both.
"""
if not sql_candidate_temperatures:
raise ValueError("sql_candidate_temperatures must be non-empty")
pipelines = [
build_pipeline(
PipelineConfig(
sql_provider=sql_provider,
explain_provider=explain_provider,
schema_index=schema_index,
registry=registry,
schema_top_k=schema_top_k,
fewshot_top_k=fewshot_top_k,
fk_hops=fk_hops,
table_budget=table_budget,
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
sort_schema_block=sort_schema_block,
primary_sample_size=primary_sample_size,
extended_sample_size=extended_sample_size,
sql_temperature=t,
cross_db_fewshot=cross_db_fewshot,
)
)
for t in sql_candidate_temperatures
]
records: list[EvalRecord] = []
for idx, example in enumerate(examples, start=1):
record = _run_one_self_consistency(
example,
pipelines=pipelines,
temperatures=tuple(sql_candidate_temperatures),
registry=registry,
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
)
records.append(record)
if progress is not None:
progress(idx, len(examples), record)
return _summarise(
configuration=Configuration.F_SELF_CONSISTENCY,
sql_model=getattr(sql_provider, "model", "unknown"),
records=records,
)
def run_config_g(
examples: Sequence[BirdExample],
*,
sql_provider: LLMProvider,
explain_provider: LLMProvider,
schema_index: SchemaIndex,
registry: DatabaseRegistry,
schema_top_k: int = 5,
fewshot_top_k: int = 3,
fk_hops: int = 1,
table_budget: int = 12,
statement_timeout_ms: int = 60_000,
row_cap: int = 10_000,
max_tokens: int = 1024,
sort_schema_block: bool = True,
primary_sample_size: int = 3,
extended_sample_size: int = 0,
cross_db_fewshot: bool = True,
progress: Callable[[int, int, EvalRecord], None] | None = None,
) -> EvalRun:
"""Run configuration G (config D + verify-retry on empty/error).
Layers a one-shot retry on top of D for outcomes that execute but
return zero rows OR fail at runtime. Empty-result is treated as a
soft-fail because it usually means the model picked a wrong filter
value (case mismatch, missing LIKE pattern, NULL handling); the
repair_once node sees a custom hint (set by execute_node when
`verify_retry_on_empty` is on) and gets one more try.
Invalid-SQL repair still happens — same as E — so the validity floor
only goes up. Repair_attempted guard caps total LLM calls per
question at most one above config D.
"""
pipeline = build_pipeline(
PipelineConfig(
sql_provider=sql_provider,
explain_provider=explain_provider,
schema_index=schema_index,
registry=registry,
schema_top_k=schema_top_k,
fewshot_top_k=fewshot_top_k,
fk_hops=fk_hops,
table_budget=table_budget,
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
sort_schema_block=sort_schema_block,
primary_sample_size=primary_sample_size,
extended_sample_size=extended_sample_size,
cross_db_fewshot=cross_db_fewshot,
verify_retry_on_empty=True,
)
)
records: list[EvalRecord] = []
for idx, example in enumerate(examples, start=1):
record = _run_one_via_pipeline(
example,
pipeline=pipeline,
registry=registry,
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
disable_repair=False,
verify_retry_on_empty=True,
)
records.append(record)
if progress is not None:
progress(idx, len(examples), record)
return _summarise(
configuration=Configuration.G_VERIFY_RETRY,
sql_model=getattr(sql_provider, "model", "unknown"),
records=records,
)
# ---------------------------------------------------------------------------
# Internal helpers
# ---------------------------------------------------------------------------
def _run_one_config_a(
example: BirdExample,
*,
sql_provider: LLMProvider,
registry: DatabaseRegistry,
schema_cache: dict[str, list[SchemaChunk]],
statement_timeout_ms: int,
row_cap: int,
sample_size: int,
max_tokens: int,
) -> EvalRecord:
started = time.perf_counter()
spec = registry.get(example.registry_db_id)
engine = spec.make_engine()
try:
chunks = _full_schema_chunks(
engine, db_id=example.registry_db_id, cache=schema_cache, sample_size=sample_size
)
bundle = _bundle_from_chunks(
chunks, question=example.question, db_id=example.registry_db_id
)
prompt = load_prompt(
"generate_sql",
dialect=example.dialect,
schema_block=render_schema_block(bundle),
fewshot_block=render_fewshot_block(bundle),
plan_block="(no plan — generate SQL directly from question)",
question=_compose_question(example),
)
response = sql_provider.generate(
GenerateRequest(prompt=prompt, max_tokens=max_tokens, temperature=0.0)
)
parsed = parse_generate_sql_output(response.text)
pred_sql = parsed.sql
outcome = execute_validated(
engine,
pred_sql,
dialect=_to_dialect(example.dialect),
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
)
gold_rows, _gold_columns, gold_failed = _execute_gold_with_status(
engine,
example.sql,
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
)
comparison = _compare_outcome(
outcome, gold_rows, gold_sql=example.sql, gold_failed=gold_failed
)
gold_tables = tuple(extract_gold_tables(example.sql))
retrieved = tuple(c.table_name for c in chunks)
recall = schema_recall_at_k(gold_tables, retrieved)
elapsed_ms = (time.perf_counter() - started) * 1000.0
return EvalRecord(
question_id=example.question_id,
db_id=example.db_id,
difficulty=example.difficulty,
dialect=example.dialect,
question=example.question,
gold_sql=example.sql,
pred_sql=pred_sql,
match=comparison.match,
schema_recall=recall,
error_kind=outcome.error_kind.value if outcome.error_kind else None,
error_message=outcome.error_message,
repair_attempted=False,
first_pass_match=comparison.match, # config A has no repair
latency_ms=elapsed_ms,
input_tokens=response.input_tokens,
output_tokens=response.output_tokens,
gold_tables=gold_tables,
retrieved_tables=retrieved,
pred_row_count=comparison.pred_rows,
gold_row_count=comparison.gold_rows,
comparison_reason=comparison.reason,
)
finally:
engine.dispose()
def _run_one_via_pipeline(
example: BirdExample,
*,
pipeline: Any,
registry: DatabaseRegistry,
statement_timeout_ms: int,
row_cap: int,
disable_repair: bool,
verify_retry_on_empty: bool = False,
) -> EvalRecord:
"""Drive one example through the compiled LangGraph pipeline.
Used by configurations C/D/E (and any future config that wants the
production code path with knobs flipped). EA is computed against the
same gold engine via `_execute_gold` to keep parity with config A.
"""
started = time.perf_counter()
spec = registry.get(example.registry_db_id)
gold_engine = spec.make_engine()
try:
try:
result = run_pipeline(
pipeline,
question=_compose_question(example),
db_id=example.registry_db_id,
dialect=_to_dialect(example.dialect),
disable_repair=disable_repair,
verify_retry_on_empty=verify_retry_on_empty,
)
except Exception as exc:
elapsed_ms = (time.perf_counter() - started) * 1000.0
return EvalRecord(
question_id=example.question_id,
db_id=example.db_id,
difficulty=example.difficulty,
dialect=example.dialect,
question=example.question,
gold_sql=example.sql,
pred_sql="",
match=False,
schema_recall=False,
error_kind="pipeline_exception",
error_message=str(exc),
repair_attempted=False,
first_pass_match=False,
latency_ms=elapsed_ms,
input_tokens=0,
output_tokens=0,
gold_tables=tuple(extract_gold_tables(example.sql)),
retrieved_tables=(),
pred_row_count=0,
gold_row_count=0,
comparison_reason=f"pipeline raised: {exc!r}",
)
gold_rows, _, gold_failed = _execute_gold_with_status(
gold_engine,
example.sql,
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
)
# The pipeline's outcome is what `match` should reflect — but the
# comparison runs against the gold rows we just fetched. Build a
# synthetic outcome view for `_compare_outcome`, or pull rows out.
if gold_failed:
comparison = ResultComparison(
match=False,
reason="gold execution failed",
gold_rows=0,
pred_rows=(
len(result.outcome.result.rows)
if result.outcome is not None and result.outcome.result is not None
else 0
),
)
elif result.outcome is not None and result.outcome.result is not None:
comparison = compare_results(
gold_rows,
result.outcome.result.rows,
gold_sql=example.sql,
)
else:
comparison = ResultComparison(
match=False,
reason=(
f"pred failed: {result.error_kind.value if result.error_kind else 'unknown'}"
),
gold_rows=len(gold_rows),
pred_rows=0,
)
gold_tables = tuple(extract_gold_tables(example.sql))
retrieved = _retrieved_from_trace(result.trace)
recall = schema_recall_at_k(gold_tables, retrieved)
in_tok, out_tok = _tokens_from_trace(result.trace)
elapsed_ms = (time.perf_counter() - started) * 1000.0
return EvalRecord(
question_id=example.question_id,
db_id=example.db_id,
difficulty=example.difficulty,
dialect=example.dialect,
question=example.question,
gold_sql=example.sql,
pred_sql=result.sql,
match=comparison.match,
schema_recall=recall,
error_kind=result.error_kind.value if result.error_kind else None,
error_message=result.error_message,
# `disable_repair=True` seeds repair_attempted in initial state to
# short-circuit routing — that's not a "repair happened" signal,
# so suppress it in the record. When repair is enabled, trust the
# pipeline's flag.
repair_attempted=_repair_actually_fired(result, disable_repair),
# First-pass EA: if repair fired, the first generate definitely
# produced bad SQL → first_pass = False. If repair did not fire,
# the first SQL *was* the final SQL, so first_pass = final match.
first_pass_match=(
False if _repair_actually_fired(result, disable_repair) else comparison.match
),
latency_ms=elapsed_ms,
input_tokens=in_tok,
output_tokens=out_tok,
gold_tables=gold_tables,
retrieved_tables=tuple(retrieved),
pred_row_count=comparison.pred_rows,
gold_row_count=comparison.gold_rows,
comparison_reason=comparison.reason,
)
finally:
gold_engine.dispose()
def _run_one_self_consistency(
example: BirdExample,
*,
pipelines: Sequence[Any],
temperatures: tuple[float, ...],
registry: DatabaseRegistry,
statement_timeout_ms: int,
row_cap: int,
) -> EvalRecord:
"""Run N pipelines (one per temperature), vote on the result, score the winner."""
started = time.perf_counter()
spec = registry.get(example.registry_db_id)
gold_engine = spec.make_engine()
try:
candidates: list[Candidate] = []
for pipe, temp in zip(pipelines, temperatures, strict=True):
try:
run_result = run_pipeline(
pipe,
question=_compose_question(example),
db_id=example.registry_db_id,
dialect=_to_dialect(example.dialect),
disable_repair=True,
)
candidates.append(Candidate(result=run_result, temperature=temp))
except Exception:
# A single crashed candidate is not fatal — voting handles partials.
continue
if not candidates:
elapsed_ms = (time.perf_counter() - started) * 1000.0
return EvalRecord(
question_id=example.question_id,
db_id=example.db_id,
difficulty=example.difficulty,
dialect=example.dialect,
question=example.question,
gold_sql=example.sql,
pred_sql="",
match=False,
schema_recall=False,
error_kind="pipeline_exception",
error_message="all candidates raised",
repair_attempted=False,
first_pass_match=False,
latency_ms=elapsed_ms,
input_tokens=0,
output_tokens=0,
gold_tables=tuple(extract_gold_tables(example.sql)),
retrieved_tables=(),
pred_row_count=0,
gold_row_count=0,
comparison_reason="all candidates raised",
)
winner = vote(candidates)
result = winner.result
gold_rows, _, gold_failed = _execute_gold_with_status(
gold_engine,
example.sql,
statement_timeout_ms=statement_timeout_ms,
row_cap=row_cap,
)
if gold_failed:
comparison = ResultComparison(
match=False,
reason="gold execution failed",
gold_rows=0,
pred_rows=(
len(result.outcome.result.rows)
if result.outcome is not None and result.outcome.result is not None
else 0
),
)
elif result.outcome is not None and result.outcome.result is not None:
comparison = compare_results(
gold_rows, result.outcome.result.rows, gold_sql=example.sql
)
else:
comparison = ResultComparison(
match=False,
reason=(
f"pred failed: {result.error_kind.value if result.error_kind else 'unknown'}"
),
gold_rows=len(gold_rows),
pred_rows=0,
)
gold_tables = tuple(extract_gold_tables(example.sql))
retrieved = _retrieved_from_trace(result.trace)
recall = schema_recall_at_k(gold_tables, retrieved)
# Token cost = sum across all candidates (the real serving cost of voting).
in_tok = 0
out_tok = 0
for c in candidates:
ci, co = _tokens_from_trace(c.result.trace)
in_tok += ci
out_tok += co
elapsed_ms = (time.perf_counter() - started) * 1000.0
return EvalRecord(
question_id=example.question_id,
db_id=example.db_id,
difficulty=example.difficulty,
dialect=example.dialect,
question=example.question,
gold_sql=example.sql,
pred_sql=result.sql,
match=comparison.match,
schema_recall=recall,
error_kind=result.error_kind.value if result.error_kind else None,
error_message=result.error_message,
repair_attempted=False,
first_pass_match=comparison.match,
latency_ms=elapsed_ms,
input_tokens=in_tok,
output_tokens=out_tok,
gold_tables=gold_tables,
retrieved_tables=tuple(retrieved),
pred_row_count=comparison.pred_rows,
gold_row_count=comparison.gold_rows,
comparison_reason=comparison.reason,
)
finally:
gold_engine.dispose()
def _repair_actually_fired(result: Any, disable_repair: bool) -> bool:
"""True iff the repair_once node ran during this pipeline invocation.
`disable_repair=True` seeds the flag in the initial state, so we can't
just trust `result.repair_attempted` — that returns True whether repair
fired or not. When disable_repair=True we know repair could not fire
(routing falls through), so the answer is False.
"""
if disable_repair:
return False
return bool(result.repair_attempted)
def _retrieved_from_trace(trace: list[dict[str, object]]) -> tuple[str, ...]:
"""Pull `tables` from the context_builder trace step (set by node)."""
for step in trace:
if step.get("node") == "context_builder":
tables = step.get("tables")
if isinstance(tables, list):
return tuple(str(t) for t in tables)
break
return ()
def _tokens_from_trace(trace: list[dict[str, object]]) -> tuple[int, int]:
"""Sum input + output tokens across all generate-style trace steps."""
in_tok = 0
out_tok = 0
for step in trace:
i = step.get("input_tokens")
o = step.get("output_tokens")
in_tok += int(i) if isinstance(i, (int, float)) else 0
out_tok += int(o) if isinstance(o, (int, float)) else 0
return in_tok, out_tok
def _full_schema_chunks(
engine: Engine,
*,
db_id: str,
cache: dict[str, list[SchemaChunk]],
sample_size: int,
) -> list[SchemaChunk]:
if db_id in cache:
return cache[db_id]
tables = introspect(engine, sample_size=sample_size)
chunks = to_chunks(tables, db_id=db_id)
cache[db_id] = chunks
return chunks
def _bundle_from_chunks(
chunks: list[SchemaChunk],
*,
question: str,
db_id: str,
) -> ContextBundle:
"""Synthesize a ContextBundle that puts every table into `schema_hits`.
distance=inf marks each as graph-derived rather than dense-retrieved —
`render_schema_block` doesn't care about distance, but downstream tracing
can still tell config A bundles apart from config C/D bundles.
"""
hits = [
SchemaQueryHit(
chunk_id=c.chunk_id,
table_name=c.table_name,
db_id=c.db_id,
text=c.text,
distance=float("inf"),
metadata=dict(c.metadata),
)
for c in chunks
]
return ContextBundle(
db_id=db_id,
question=question,
schema_hits=hits,
fk_neighbours=[],
fewshots=[],
truncated=False,
notes=["config-A: full schema, no retrieval"],
)
def _compose_question(example: BirdExample) -> str:
"""Embed BIRD `evidence` (external knowledge) inline with the question.
BIRD's leaderboard runs the evaluation_ex baseline *with* evidence —
the gold SQL often relies on definitions that only appear in evidence.
Dropping it would underestimate model capability across the board.
"""
if not example.evidence:
return example.question
return f"{example.question}\n\nHint: {example.evidence}"
def _execute_gold_with_status(
engine: Engine,
sql: str,
*,
statement_timeout_ms: int,
row_cap: int,
) -> tuple[list[tuple[Any, ...]], list[str], bool]:
"""Run gold SQL and return `(rows, columns, gold_failed)`.
Mirror of `_execute_gold` that surfaces the failure flag. Used by the
runner internals so `_compare_outcome` can short-circuit gold-failure
instead of letting `compare_results([], [])` bless an empty pred as
match=True (Codex audit 2026-05-25 #1, same defect class as the qid 518
pred-side bug already fixed in `safe_compare_pred`).
"""
try:
with execute_readonly(
engine, sql, statement_timeout_ms=statement_timeout_ms, row_cap=row_cap
) as result:
return list(result.rows), list(result.columns), False
except (SQLAlchemyError, MemoryError):
# Last-resort: try the raw connection to surface gold-SQL bugs in
# logs without crashing the runner. BIRD ships ~1% gold SQLs that
# fail under sqlite default settings (e.g. cross joins blowing up
# before the row cap kicks in → MemoryError); we count them as
# gold-failure rather than pred-failure.
try:
with engine.connect() as conn:
cursor = conn.exec_driver_sql(sql)
cols = list(cursor.keys())
rows = [tuple(r) for r in cursor.fetchmany(row_cap)]
cursor.close()
return rows, cols, False
except (SQLAlchemyError, MemoryError):
return [], [], True
def _execute_gold(
engine: Engine,
sql: str,
*,
statement_timeout_ms: int,
row_cap: int,
) -> tuple[list[tuple[Any, ...]], list[str]]:
"""Run gold SQL with the same row cap / timeout as predictions.
Bypasses the validator (gold is trusted, BIRD ships it). Errors propagate
as empty result + sentinel — the EA comparison will then fail naturally.
Legacy 2-tuple wrapper retained for the dozen+ scripts that import this
name; new runner-internal callsites should use `_execute_gold_with_status`
so the gold-failure flag can route to `safe_compare_pred(gold_failed=True)`.
"""
rows, cols, _gold_failed = _execute_gold_with_status(
engine, sql, statement_timeout_ms=statement_timeout_ms, row_cap=row_cap
)
return rows, cols
def _compare_outcome(
outcome: ExecutionOutcome,
gold_rows: list[tuple[Any, ...]],
*,
gold_sql: str,
gold_failed: bool = False,
) -> ResultComparison:
if gold_failed:
return ResultComparison(
match=False,
reason="gold execution failed",
gold_rows=0,
pred_rows=0 if outcome.result is None else len(outcome.result.rows),
)
if outcome.result is None:
return ResultComparison(
match=False,
reason=f"pred failed: {outcome.error_kind.value if outcome.error_kind else 'unknown'}",
gold_rows=len(gold_rows),
pred_rows=0,
)
return compare_results(gold_rows, outcome.result.rows, gold_sql=gold_sql)
def _to_dialect(dialect: str) -> Dialect:
if dialect in ("sqlite", "postgresql"):
return dialect # type: ignore[return-value]
return "sqlite"
# ---------------------------------------------------------------------------
# Aggregation
# ---------------------------------------------------------------------------
def _summarise(
*,
configuration: Configuration,
sql_model: str,
records: list[EvalRecord],
) -> EvalRun:
overall = _summary_for(records)
per_difficulty = {
diff: _summary_for([r for r in records if r.difficulty == diff])
for diff in ("simple", "moderate", "challenging")
}
return EvalRun(
configuration=configuration,
sql_model=sql_model,
overall=overall,
per_difficulty=per_difficulty,
records=records,
)
def _summary_for(records: Iterable[EvalRecord]) -> EvalSummary:
rs = list(records)
if not rs:
return EvalSummary(
n=0,
ea=0.0,
validity_rate=0.0,
schema_recall_at_k=0.0,
repair_success_rate=0.0,
first_pass_ea=0.0,
empty_result_rate=0.0,
latency_p50_ms=0.0,
latency_p95_ms=0.0,
tokens_p50=0.0,
tokens_p95=0.0,
)
matches = [r.match for r in rs]
valid = [r.error_kind != ExecutionErrorKind.INVALID_SQL.value for r in rs]
repair_success = [r.match for r in rs if r.repair_attempted]
empty = [r.error_kind == ExecutionErrorKind.EMPTY_RESULT.value for r in rs]
latencies = sorted(r.latency_ms for r in rs)
tokens = sorted((r.input_tokens + r.output_tokens) for r in rs)
return EvalSummary(
n=len(rs),
ea=execution_accuracy(matches),
validity_rate=sum(valid) / len(rs),
schema_recall_at_k=sum(1 for r in rs if r.schema_recall) / len(rs),
repair_success_rate=(sum(repair_success) / len(repair_success)) if repair_success else 0.0,
first_pass_ea=sum(1 for r in rs if r.first_pass_match) / len(rs),
empty_result_rate=sum(empty) / len(rs),
latency_p50_ms=_percentile(latencies, 0.5),
latency_p95_ms=_percentile(latencies, 0.95),
tokens_p50=_percentile(tokens, 0.5),
tokens_p95=_percentile(tokens, 0.95),
)
def _percentile(sorted_values: Sequence[float | int], q: float) -> float:
if not sorted_values:
return 0.0
if len(sorted_values) == 1:
return float(sorted_values[0])
pos = q * (len(sorted_values) - 1)
low = int(pos)
high = min(low + 1, len(sorted_values) - 1)
frac = pos - low
return float(sorted_values[low]) * (1 - frac) + float(sorted_values[high]) * frac
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