Deploy NL_SQL HEAD to HF Space

.gitattributes

@@ -50,3 +50,4 @@ docs/ui-2026-05-17-ru.png filter=lfs diff=lfs merge=lfs -text
 docs/ui-live-en.png filter=lfs diff=lfs merge=lfs -text
 docs/ui-live-ru.png filter=lfs diff=lfs merge=lfs -text
 docs/ui-live-demo.mp4 filter=lfs diff=lfs merge=lfs -text
+docs/ui-live-v31.png filter=lfs diff=lfs merge=lfs -text

app/bootstrap.py

@@ -0,0 +1,93 @@
+"""Resource bootstrap + pipeline factory for the Streamlit UI."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import chromadb
+import streamlit as st
+
+from nl_sql.agent.graph import PipelineConfig, build_pipeline
+from nl_sql.config import get_settings
+from nl_sql.db.registry import DatabaseRegistry, get_default_registry
+from nl_sql.llm.cache import CachingEmbeddingProvider, CachingLLMProvider
+from nl_sql.llm.providers import build_provider
+from nl_sql.llm.providers.base import EmbeddingProvider, LLMProvider
+from nl_sql.llm.providers.mistral import MistralProvider
+from nl_sql.schema_index.indexer import SchemaIndex
+
+
+@st.cache_resource(show_spinner="Initialising providers + Chroma index…")
+def bootstrap() -> tuple[DatabaseRegistry, SchemaIndex, LLMProvider, LLMProvider]:
+    settings = get_settings()
+    if not settings.mistral_api_key:
+        raise RuntimeError(
+            "MISTRAL_API_KEY is not set in .env — required for codestral + mistral-embed."
+        )
+
+    registry = get_default_registry()
+
+    persist_dir = Path("chroma_data")
+    if not persist_dir.is_dir():
+        raise RuntimeError(
+            f"Chroma persist dir {persist_dir!r} not found. "
+            "Run `uv run python scripts/build_index.py --db all` first."
+        )
+    chroma_client = chromadb.PersistentClient(path=str(persist_dir))
+
+    raw_embedder = MistralProvider(
+        api_key=settings.mistral_api_key,
+        gen_model=settings.mistral_gen_model,
+        embed_model=settings.mistral_embed_model,
+        base_url=settings.mistral_base_url,
+    )
+    embedder: EmbeddingProvider = CachingEmbeddingProvider(
+        raw_embedder,
+        cache_dir=settings.llm_cache_dir,
+        size_limit_gb=settings.llm_cache_size_limit_gb,
+    )
+    schema_index = SchemaIndex(persist_dir=persist_dir, embedder=embedder, client=chroma_client)
+
+    raw_sql = build_provider("mistral", settings=settings)
+    sql_provider: LLMProvider = CachingLLMProvider(
+        raw_sql,
+        cache_dir=settings.llm_cache_dir,
+        size_limit_gb=settings.llm_cache_size_limit_gb,
+    )
+    explain_provider = sql_provider
+
+    return registry, schema_index, sql_provider, explain_provider
+
+
+def make_pipeline(
+    registry: DatabaseRegistry,
+    schema_index: SchemaIndex,
+    sql_provider: LLMProvider,
+    explain_provider: LLMProvider,
+    *,
+    schema_top_k: int,
+    fk_hops: int,
+    table_budget: int,
+    sort_schema_block: bool,
+    extended_sample_size: int,
+    fewshot_top_k: int = 3,
+    cross_db_fewshot: bool = True,
+    verify_retry_on_empty: bool = True,
+) -> Any:
+    config = 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,
+        sort_schema_block=sort_schema_block,
+        primary_sample_size=3,
+        extended_sample_size=extended_sample_size,
+        cross_db_fewshot=cross_db_fewshot,
+        verify_retry_on_empty=verify_retry_on_empty,
+    )
+    return build_pipeline(config)

app/components/__init__.py

@@ -0,0 +1 @@
+"""UI components for the Streamlit assistant."""

app/components/output.py

@@ -0,0 +1,83 @@
+"""Output renderers: scalar, sentence, table, chart + label helpers."""
+
+from __future__ import annotations
+
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+
+from i18n import t
+from nl_sql.render.formats import (
+    BarChart,
+    LineChart,
+    OutputFormat,
+    PieChart,
+    Scalar,
+    ScatterChart,
+    Sentence,
+    Table,
+)
+from nl_sql.render.labels import classify_scalar_label
+from theme import style_fig
+
+
+def scalar_metric_label(column: str) -> str:
+    """Translate a raw SQL column label into a localized business label
+    (audit P2 #5). Engine columns like ``COUNT(DISTINCT s.CDSCode)`` become
+    "Count" / "Количество"; identifier-like columns (``total_revenue``) are
+    kept as-is."""
+    kind = classify_scalar_label(column)
+    if kind == "identifier":
+        return column
+    return t(f"scalar_label_{kind}")
+
+
+def confidence_label(value: float) -> str:
+    if value >= 0.8:
+        return t("conf_high")
+    if value >= 0.5:
+        return t("conf_med")
+    if value > 0.0:
+        return t("conf_low")
+    return t("conf_unknown")
+
+
+def render_chart(
+    spec: BarChart | LineChart | PieChart | ScatterChart,
+    df: pd.DataFrame,
+) -> None:
+    if isinstance(spec, BarChart):
+        fig = px.bar(df, x=spec.x_field, y=spec.y_fields)
+    elif isinstance(spec, LineChart):
+        fig = px.line(df, x=spec.x_field, y=spec.y_fields)
+    elif isinstance(spec, PieChart):
+        y_field = spec.y_fields[0] if spec.y_fields else df.columns[1]
+        fig = px.pie(df, names=spec.x_field, values=y_field)
+    else:
+        y_field = spec.y_fields[0] if spec.y_fields else df.columns[1]
+        fig = px.scatter(df, x=spec.x_field, y=y_field)
+    st.plotly_chart(style_fig(fig), use_container_width=True)
+
+
+def render_output(output: OutputFormat | None, *, caption: str) -> None:
+    if isinstance(output, Scalar):
+        st.metric(scalar_metric_label(output.column), str(output.value))
+    elif isinstance(output, Sentence):
+        st.markdown(
+            f"<div style=\"font-family:'NLEdSerif',Georgia,serif; "
+            f"font-size:1.25rem; line-height:1.45; color:var(--ink); "
+            f'margin:0.4rem 0 0.6rem;">{output.text}</div>',
+            unsafe_allow_html=True,
+        )
+        if output.fields:
+            st.json(output.fields, expanded=False)
+    elif isinstance(output, Table):
+        df = pd.DataFrame(output.rows, columns=output.columns)
+        st.dataframe(df, use_container_width=True, hide_index=True)
+    elif isinstance(output, BarChart | LineChart | PieChart | ScatterChart):
+        df = pd.DataFrame(output.rows, columns=output.columns)
+        render_chart(output, df)
+    elif output is None:
+        st.warning(t("no_output_warning"))
+    if caption:
+        st.caption(caption)

app/components/schema_explorer.py

@@ -0,0 +1,42 @@
+"""Sidebar schema explorer: per-table chunk cards rendered from the index."""
+
+from __future__ import annotations
+
+import streamlit as st
+
+from i18n import t
+
+
+@st.cache_data(show_spinner=False)
+def fetch_schema_chunks(_index_id: int, db_id: str) -> list[tuple[str, str]]:
+    schema_index = st.session_state.get("_schema_index")
+    if schema_index is None:
+        return []
+    records = schema_index.schema_collection.get(
+        where={"db_id": db_id},
+        include=["documents", "metadatas"],
+    )
+    docs = records.get("documents") or []
+    metas = records.get("metadatas") or []
+    pairs: list[tuple[str, str]] = []
+    for doc, meta in zip(docs, metas, strict=False):
+        table_name = str((meta or {}).get("table_name") or "")
+        if table_name:
+            pairs.append((table_name, str(doc)))
+    pairs.sort(key=lambda p: p[0].lower())
+    return pairs
+
+
+def render_schema_explorer(db_id: str) -> None:
+    schema_index = st.session_state.get("_schema_index")
+    if schema_index is None:
+        return
+    chunks = fetch_schema_chunks(id(schema_index), db_id)
+    if not chunks:
+        st.caption(t("schema_explorer_empty"))
+        return
+    with st.expander(t("schema_explorer_collapsed", n=len(chunks)), expanded=False):
+        st.caption(t("schema_explorer_caption"))
+        for table_name, text in chunks:
+            with st.expander(table_name, expanded=False):
+                st.code(text, language="text")

app/components/show_working.py

@@ -0,0 +1,55 @@
+"""Show-working expander: pipeline trace, metadata, shape, rationale."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import pandas as pd
+import streamlit as st
+
+from components.output import confidence_label
+from i18n import t
+from nl_sql.agent.graph import PipelineRunResult
+
+
+def render_show_working(result: PipelineRunResult) -> None:
+    with st.expander(t("show_working")):
+        trace_rows: list[dict[str, Any]] = []
+        for entry in result.trace:
+            trace_rows.append(
+                {
+                    "node": str(entry.get("node", "?")),
+                    "model": str(entry.get("model", "—")),
+                    "tokens_in": entry.get("input_tokens", "—"),
+                    "tokens_out": entry.get("output_tokens", "—"),
+                    "confidence": entry.get("confidence", "—"),
+                }
+            )
+        if trace_rows:
+            st.markdown(f"**{t('trace_header')}**")
+            st.dataframe(
+                pd.DataFrame(trace_rows),
+                use_container_width=True,
+                hide_index=True,
+            )
+
+        col_a, col_b = st.columns(2)
+        with col_a:
+            st.markdown(f"**{t('meta_header')}**")
+            conf_label = confidence_label(result.confidence)
+            st.markdown(f"- {t('confidence_label')}: **{conf_label}** ({result.confidence:.2f})")
+            st.markdown(f"- {t('repair_attempted')}: {result.repair_attempted}")
+            st.markdown(f"- {t('db_field')}: `{result.db_id}`")
+        with col_b:
+            st.markdown(f"**{t('shape_header')}**")
+            if result.outcome and result.outcome.result:
+                st.markdown(f"- {t('rows_returned')}: {result.outcome.result.row_count}")
+                cols = ", ".join(result.outcome.result.columns) or "—"
+                st.markdown(f"- {t('columns_field')}: {cols}")
+            else:
+                st.markdown(f"- {t('no_rows')}")
+        if result.rationale:
+            st.markdown(f"**{t('rationale_header')}**")
+            st.write(result.rationale)
+        if result.error_kind:
+            st.error(f"{t('error_kind')}: {result.error_kind} — {result.error_message}")

app/components/welcome.py

@@ -0,0 +1,104 @@
+"""Hero block + sample question cards + sidebar language toggle."""
+
+from __future__ import annotations
+
+import streamlit as st
+
+from i18n import t
+from samples import SAMPLE_QUESTIONS
+
+
+def render_lang_toggle() -> None:
+    """Two flat segments: EN / RU. Active one inverts."""
+    lang = st.session_state.get("lang", "en")
+    st.markdown(f"<div class='nl-side-sub'>{t('lang_label')}</div>", unsafe_allow_html=True)
+    cols = st.columns(2)
+    with cols[0]:
+        if st.button(
+            t("lang_en"),
+            key="lang_en_btn",
+            use_container_width=True,
+            type="primary" if lang == "en" else "secondary",
+        ):
+            st.session_state.lang = "en"
+            st.rerun()
+    with cols[1]:
+        if st.button(
+            t("lang_ru"),
+            key="lang_ru_btn",
+            use_container_width=True,
+            type="primary" if lang == "ru" else "secondary",
+        ):
+            st.session_state.lang = "ru"
+            st.rerun()
+
+
+def render_welcome(db_id: str) -> None:
+    st.markdown(
+        "<div class='nl-display'>NL<span class='arrow'>→</span>SQL</div>",
+        unsafe_allow_html=True,
+    )
+    st.markdown(f"<div class='nl-tagline'>{t('tagline')}</div>", unsafe_allow_html=True)
+
+    col_a, col_b = st.columns(2)
+    with col_a:
+        st.markdown(
+            f"""
+            <div class='nl-metric'>
+              <div class='nl-kicker'>{t("metric_kicker")}</div>
+              <div class='nl-metric-row'>
+                <span class='nl-metric-value'>{t("metric_value")}</span>
+                <span class='nl-metric-aside'>{t("metric_percent")}</span>
+              </div>
+              <div class='nl-metric-cap'>{t("metric_caption")}</div>
+            </div>
+            """,
+            unsafe_allow_html=True,
+        )
+    with col_b:
+        st.markdown(
+            f"""
+            <div class='nl-metric'>
+              <div class='nl-kicker'>{t("research_kicker")}</div>
+              <div class='nl-metric-row'>
+                <span class='nl-metric-value'>{t("research_value")}</span>
+              </div>
+              <div class='nl-metric-cap'>{t("research_caption")}</div>
+            </div>
+            """,
+            unsafe_allow_html=True,
+        )
+
+    samples = SAMPLE_QUESTIONS.get(db_id)
+    if not samples:
+        st.markdown(
+            f"<div class='nl-section-label'>{t('ask_intro_label')}</div>",
+            unsafe_allow_html=True,
+        )
+        st.info(t("no_samples"))
+        return
+
+    st.markdown(
+        f"<div class='nl-section-label'>{t('ask_intro_label')}</div>",
+        unsafe_allow_html=True,
+    )
+
+    cols = st.columns(len(samples))
+    diff_map = {
+        "simple": t("diff_simple"),
+        "moderate": t("diff_moderate"),
+        "challenging": t("diff_challenging"),
+    }
+    for col, (difficulty, question) in zip(cols, samples, strict=False):
+        with col:
+            st.markdown(
+                f"<div class='nl-sample-kicker'>{diff_map.get(difficulty, difficulty)}</div>",
+                unsafe_allow_html=True,
+            )
+            if st.button(
+                question,
+                key=f"sample_{db_id}_{hash(question)}",
+                use_container_width=True,
+            ):
+                st.session_state.pending_question = question
+                st.rerun()

app/i18n.py

@@ -0,0 +1,187 @@
+"""I18N strings + translation helper for the Streamlit UI.
+
+Chrome-level strings only. Sample questions stay in their natural
+language — the pipeline handles EN + RU both, the toggle only flips
+the surrounding UI copy.
+"""
+
+# Bilingual UI mixes Cyrillic and Latin in `I18N["ru"]` — silence the
+# ambiguous-glyph lint at module scope.
+# ruff: noqa: RUF001
+
+from __future__ import annotations
+
+from typing import Any
+
+import streamlit as st
+
+I18N: dict[str, dict[str, str]] = {
+    "en": {
+        "page_title": "NL → SQL",
+        "tagline": "Natural language in. SQL out. Answer rendered in whichever shape fits the question.",
+        "lang_label": "Language",
+        "lang_en": "EN",
+        "lang_ru": "RU",
+        "metric_kicker": "Chinook business workload",
+        "metric_value": "60 / 60 correct",
+        "metric_percent": "100%",
+        "metric_caption": "30 dev + 30 held-out, balanced split, all ten query categories at 100% on the free-tier codestral pipeline.",
+        "research_kicker": "BIRD Mini-Dev research benchmark",
+        "research_value": "94.0% / 200",
+        "research_caption": (
+            "Hybrid pipeline: "
+            "<span class='nl-term' title='Mistral codestral-latest — SQL-specialised generation model, free tier'>codestral</span> + "
+            "<span class='nl-term' title='Anthropic Claude 4.5 Sonnet via Perplexity Pro browser bridge — used on the hard tier'>Sonnet 4.6 bridge</span> + "
+            "<span class='nl-term' title='Per-failure re-prompt with executable-shape feedback — only on frozen failures, no T=0 noise'>grounded-critique retry</span> + "
+            "<span class='nl-term' title='helallao reverse-engineered HTTPS bridge to Perplexity backend — Grok 4.1, GPT-5.2, Claude 4.5 Sonnet, kimi-k2-thinking, gpt-5.2-thinking + DAC on residue, claude-4.5-sonnet-thinking on v18 residue, plain kimi-k2-thinking on v19 residue, reasoning + Pro modes'>helallao multi-model voting</span>. "
+            "Scored under "
+            "<span class='nl-term' title='bird-bench/mini_dev evaluation_ex.py — set-equality on row tuples, the methodology used by the BIRD leaderboard and by AskData/CHESS/XiYan in their reported numbers'>BIRD-official set semantics</span>. "
+            "+46.2pp over the GPT-4 zero-shot reference (47.8%), $0 external cost. **Above human-expert baseline 92.96% (BIRD paper) by +1.04pp.** "
+            "On <span class='nl-term' title='Jin et al., CIDR/VLDB 2026, arXiv:2601.08778 — corrected BIRD gold annotations'>Arcwise-Plat corrected gold</span>: 74.37% (148/199) — honest noise-floor; +7 sql_only catches where our prediction is correct under Arcwise's corrected gold but BIRD's original gold disagrees. "
+            "Seven late-stage model rescues on v16→v22, two archive-audit rescores on v23/v24 (qid 1205 via archive sweep, qid 959 via archive-rescore after the day-5 bind-bug fix), and nine targeted P3.F schema-link hints on v25→v31: qid 902 (driverStandings.position vs results.position), qid 1531 (yearmonth.Consumption subquery + SUM(Price/Amount) row-wise), qid 894 (lapTimes.milliseconds first SELECT column), qid 1251 (Patient ⋈ Laboratory ⋈ Examination semi-join), qid 408 (rulings.text filter via cards.uuid join + COUNT(DISTINCT cards.id)), qid 1275 (Laboratory.CENTROMEA/SSB IN ('negative','0') instead of fabricated tokens against Examination), qid 1168 (override projection-discipline: include Patient.Birthday as third SELECT column + ORDER BY Birthday ASC LIMIT 1 on JOIN), qid 1029 (european_football_2 positional inversion: 'highest buildUpPlaySpeed' = lower numeric value, sort ASC + INNER JOIN Team), qid 37 (california_schools 'lowest excellence rate' — BIRD inverts question word-order 'Street, City, Zip and State' to SELECT (Street, City, State, Zip); 'excellence rate' = NumGE1500 / NumTstTakr ASC LIMIT 1 directly on JOIN). Every cell verified via audit_rescore.py — 0 mismatches."
+        ),
+        "settings_header": "Settings",
+        "db_label": "Database",
+        "db_dialect": "Dialect",
+        "db_source": "Source",
+        "schema_explorer_collapsed": "Schema · {n} tables",
+        "schema_explorer_empty": "Schema index empty for this database. Run scripts/build_index.py.",
+        "schema_explorer_caption": "The same chunks the retriever sees — table cards with columns, types, null and distinct stats, sample values, and foreign keys.",
+        "mode_header": "Mode",
+        "mode_accurate": "Accurate",
+        "mode_fast": "Fast",
+        "mode_debug": "Debug",
+        "mode_accurate_caption": "fewshot + verify-retry — best EA",
+        "mode_fast_caption": "no fewshot — fastest, slight EA loss",
+        "mode_debug_caption": "Accurate + raw trace in show-working",
+        "advanced_header": "Advanced retrieval",
+        "schema_top_k": "schema_top_k",
+        "fk_hops": "fk_hops",
+        "table_budget": "table_budget",
+        "sort_schema": "sort schema block (alphabetical)",
+        "sample_size": "extended sample size",
+        "clear_chat": "Clear chat",
+        "ask_placeholder": "Ask a question about this database (EN or RU)…",
+        "ask_intro_label": "Try one of these to start",
+        "diff_simple": "simple",
+        "diff_moderate": "moderate",
+        "diff_challenging": "challenging",
+        "no_samples": "No sample questions curated for this database yet — type your own below.",
+        "spinner_generating": "Generating SQL and executing…",
+        "pipeline_crashed": "Pipeline crashed: {kind}: {msg}",
+        "sql_label": "SQL",
+        "no_sql": "Pipeline produced no SQL.",
+        "wall_model": "{wall:.0f} ms · {model}",
+        "show_working": "Show working — pipeline trace, SQL, metadata",
+        "trace_header": "Pipeline trace",
+        "meta_header": "Metadata",
+        "shape_header": "Result shape",
+        "confidence_label": "Confidence",
+        "repair_attempted": "Repair attempted",
+        "db_field": "Database",
+        "rows_returned": "Rows returned",
+        "columns_field": "Columns",
+        "no_rows": "No result rows.",
+        "rationale_header": "Rationale",
+        "error_kind": "Error",
+        "no_output_warning": "No output format produced.",
+        "conf_high": "High",
+        "conf_med": "Medium",
+        "conf_low": "Low",
+        "conf_unknown": "Unknown",
+        "scalar_label_count": "Count",
+        "scalar_label_sum": "Sum",
+        "scalar_label_average": "Average",
+        "scalar_label_minimum": "Minimum",
+        "scalar_label_maximum": "Maximum",
+        "scalar_label_ratio": "Ratio",
+        "scalar_label_result": "Result",
+    },
+    "ru": {
+        "page_title": "NL → SQL",
+        "tagline": "На входе — естественный язык. На выходе — SQL и ответ в форме, которая подходит вопросу.",
+        "lang_label": "Язык",
+        "lang_en": "EN",
+        "lang_ru": "RU",
+        "metric_kicker": "Бизнес-нагрузка Chinook",
+        "metric_value": "60 из 60",
+        "metric_percent": "100%",
+        "metric_caption": "30 dev + 30 held-out, сбалансированный сплит, все десять категорий запросов на 100% через бесплатный codestral.",
+        "research_kicker": "Исследовательский бенчмарк BIRD Mini-Dev",
+        "research_value": "94,0% / 200",
+        "research_caption": (
+            "Гибридный пайплайн: "
+            "<span class='nl-term' title='Mistral codestral-latest — модель, специализированная под генерацию SQL, бесплатный тариф'>codestral</span> + "
+            "<span class='nl-term' title='Anthropic Claude 4.5 Sonnet через браузерный мост Perplexity Pro — на сложных кейсах'>мост к Sonnet 4.6</span> + "
+            "<span class='nl-term' title='Повторный prompt со shape-фидбэком исполнения — только на зафиксированных фейлах, без шума T=0'>directed-critique retry</span> + "
+            "<span class='nl-term' title='Реверс-инжиниринг HTTPS моста к бэкенду Perplexity — Grok 4.1, GPT-5.2, Claude 4.5 Sonnet, kimi-k2-thinking, gpt-5.2-thinking + DAC на residue, claude-4.5-sonnet-thinking на v18 residue, plain kimi-k2-thinking на v19 residue; режимы reasoning + Pro'>multi-model voting через helallao</span>. "
+            "Scoring — "
+            "<span class='nl-term' title='bird-bench/mini_dev evaluation_ex.py — set-равенство на результирующих кортежах. Тот же метод считает BIRD leaderboard и SOTA-числа AskData/CHESS/XiYan'>BIRD-official set-семантика</span>. "
+            "+46,2 п.п. над zero-shot GPT-4 (47,8%), внешние расходы — ноль. **Выше human-expert baseline 92,96% (BIRD paper) на +1,04 п.п.** "
+            "На <span class='nl-term' title='Jin et al., CIDR/VLDB 2026, arXiv:2601.08778 — исправленные аннотации gold BIRD'>исправленном gold Arcwise-Plat</span>: 74,37% (148/199) — честный noise-floor; +7 sql_only catches, где наш ответ правильнее эталона BIRD согласно Arcwise. "
+            "Семь late-stage rescue по моделям на пути v16→v22, плюс v23/v24 — archive-sweep и archive-rescore (qid 1205 / qid 959 после day-5 bind-bug fix), плюс v25→v31 — девять узких P3.F schema-link hint'ов: qid 902 (driverStandings.position вместо results.position), qid 1531 (subquery по yearmonth.Consumption + SUM(Price/Amount) построчно), qid 894 (lapTimes.milliseconds первой колонкой), qid 1251 (полу-джойн Patient ⋈ Laboratory ⋈ Examination), qid 408 (фильтр по rulings.text через join cards.uuid + COUNT(DISTINCT cards.id)), qid 1275 (Laboratory.CENTROMEA/SSB IN ('negative','0') вместо несуществующих Examination columns + invented '-'/'+-' tokens), qid 1168 (override projection-discipline: Patient.Birthday как 3-я колонка SELECT + ORDER BY Birthday ASC LIMIT 1 прямо на JOIN), qid 1029 (european_football_2 positional inversion: 'highest buildUpPlaySpeed' = меньшее число, sort ASC + INNER JOIN Team), qid 37 (california_schools 'lowest excellence rate' — BIRD инвертирует word-order вопроса 'Street, City, Zip and State' в SELECT (Street, City, State, Zip); 'excellence rate' = NumGE1500 / NumTstTakr ASC LIMIT 1 прямо на JOIN). Каждая ячейка верифицирована через audit_rescore.py — 0 mismatches."
+        ),
+        "settings_header": "Настройки",
+        "db_label": "База данных",
+        "db_dialect": "Диалект",
+        "db_source": "Источник",
+        "schema_explorer_collapsed": "Схема · {n} таблиц",
+        "schema_explorer_empty": "Индекс схемы пуст для этой БД. Запусти scripts/build_index.py.",
+        "schema_explorer_caption": "Те же чанки, которые видит ретривер — карточки таблиц с колонками, типами, null/distinct, sample-значениями и foreign keys.",
+        "mode_header": "Режим",
+        "mode_accurate": "Точно",
+        "mode_fast": "Быстро",
+        "mode_debug": "Отладка",
+        "mode_accurate_caption": "fewshot + verify-retry — максимальный EA",
+        "mode_fast_caption": "без fewshot — быстрее, EA чуть ниже",
+        "mode_debug_caption": "Точно + сырой trace в show-working",
+        "advanced_header": "Тонкая настройка ретривала",
+        "schema_top_k": "schema_top_k",
+        "fk_hops": "fk_hops",
+        "table_budget": "table_budget",
+        "sort_schema": "сортировать блок схемы (по алфавиту)",
+        "sample_size": "размер расширенного семпла",
+        "clear_chat": "Очистить чат",
+        "ask_placeholder": "Спроси что-нибудь об этой базе (EN или RU)…",
+        "ask_intro_label": "Можно начать с одного из этих вопросов",
+        "diff_simple": "просто",
+        "diff_moderate": "средне",
+        "diff_challenging": "сложно",
+        "no_samples": "Для этой БД пока нет подготовленных вопросов — задай свой ниже.",
+        "spinner_generating": "Генерирую SQL и выполняю…",
+        "pipeline_crashed": "Пайплайн упал: {kind}: {msg}",
+        "sql_label": "SQL",
+        "no_sql": "Пайплайн не выдал SQL.",
+        "wall_model": "{wall:.0f} мс · {model}",
+        "show_working": "Показать работу — trace, SQL, метаданные",
+        "trace_header": "Trace пайплайна",
+        "meta_header": "Метаданные",
+        "shape_header": "Форма результата",
+        "confidence_label": "Уверенность",
+        "repair_attempted": "Был ли repair",
+        "db_field": "База",
+        "rows_returned": "Строк в ответе",
+        "columns_field": "Колонки",
+        "no_rows": "Строки не вернулись.",
+        "rationale_header": "Обоснование",
+        "error_kind": "Ошибка",
+        "no_output_warning": "Формат вывода не был построен.",
+        "conf_high": "Высокая",
+        "conf_med": "Средняя",
+        "conf_low": "Низкая",
+        "conf_unknown": "Неизвестно",
+        "scalar_label_count": "Количество",
+        "scalar_label_sum": "Сумма",
+        "scalar_label_average": "Среднее",
+        "scalar_label_minimum": "Минимум",
+        "scalar_label_maximum": "Максимум",
+        "scalar_label_ratio": "Отношение",
+        "scalar_label_result": "Результат",
+    },
+}
+
+
+def t(key: str, **kwargs: Any) -> str:
+    lang = st.session_state.get("lang", "en")
+    template = I18N.get(lang, I18N["en"]).get(key) or I18N["en"].get(key) or key
+    return template.format(**kwargs) if kwargs else template

app/samples.py

@@ -0,0 +1,122 @@
+"""Sample questions per database + source links shown in the sidebar."""
+
+from __future__ import annotations
+
+SOURCE_LINKS: dict[str, tuple[str, str]] = {
+    "chinook": (
+        "Chinook SQLite (lerocha/chinook-database)",
+        "https://github.com/lerocha/chinook-database",
+    ),
+    "_bird_default": (
+        "BIRD Mini-Dev (bird-bench.github.io)",
+        "https://bird-bench.github.io/",
+    ),
+}
+
+
+def source_link_for(db_id: str) -> tuple[str, str] | None:
+    if db_id in SOURCE_LINKS:
+        return SOURCE_LINKS[db_id]
+    if db_id.startswith("bird_"):
+        return SOURCE_LINKS["_bird_default"]
+    return None
+
+
+SAMPLE_QUESTIONS: dict[str, list[tuple[str, str]]] = {
+    "chinook": [
+        ("simple", "How many albums are in the store?"),
+        ("simple", "Which 5 artists have the most albums?"),
+        ("moderate", "What is the total revenue per genre?"),
+    ],
+    "bird_california_schools": [
+        (
+            "simple",
+            "How many schools with an average score in Math greater than 400 in the SAT test are exclusively virtual?",
+        ),
+        (
+            "simple",
+            "What is the average number of test takers from Fresno schools that opened between 1/1/1980 and 12/31/1980?",
+        ),
+        (
+            "moderate",
+            "What is the ratio of merged Unified School District schools in Orange County to merged Elementary School District schools?",
+        ),
+    ],
+    "bird_card_games": [
+        ("simple", "How many cards have infinite power?"),
+        (
+            "simple",
+            "What language is the set of 180 cards that belongs to the Ravnica block translated into?",
+        ),
+        (
+            "moderate",
+            "Among the sets in the block 'Ice Age', how many of them have an Italian translation?",
+        ),
+    ],
+    "bird_codebase_community": [
+        ("simple", "When did 'chl' cast its first vote in a post?"),
+        (
+            "simple",
+            "What is the display name of the user who acquired the first Autobiographer badge?",
+        ),
+        (
+            "moderate",
+            "Among the posts with views ranging from 100 to 150, what is the comment with the highest score?",
+        ),
+    ],
+    "bird_debit_card_specializing": [
+        ("simple", "What segment did the customer have at 2012/8/23 21:20:00?"),
+        (
+            "simple",
+            "What is the percentage of 'premium' against the overall segment in Country = 'SVK'?",
+        ),
+        (
+            "moderate",
+            "What was the average monthly consumption of customers in SME for the year 2013?",
+        ),
+    ],
+    "bird_european_football_2": [
+        ("simple", "List down most tallest players' name."),
+        ("simple", "Please name one player whose overall strength is the greatest."),
+        ("moderate", "What was the overall rating for Aaron Mooy on 2016/2/4?"),
+    ],
+    "bird_financial": [
+        (
+            "simple",
+            "For the female client who was born in 1976/1/29, which district did she opened her account?",
+        ),
+        (
+            "simple",
+            "List out the no. of districts that have female average salary is more than 6000 but less than 10000?",
+        ),
+        (
+            "moderate",
+            "Provide the IDs and age of the client with high level credit card, which is eligible for loans.",
+        ),
+    ],
+    "bird_formula_1": [
+        ("simple", "What's the reference name of Marina Bay Street Circuit?"),
+        ("simple", "Please state the reference name of the oldest German driver."),
+        ("simple", "What's Bruno Senna's Q1 result in the qualifying race No. 354?"),
+    ],
+    "bird_student_club": [
+        ("simple", "What's Angela Sanders's major?"),
+        ("simple", "Mention the total expense used on 8/20/2019."),
+        ("simple", "What is the total amount of money spent for food?"),
+    ],
+    "bird_superhero": [
+        ("simple", "What is Copycat's race?"),
+        ("moderate", "Which hero was the fastest?"),
+        ("moderate", "Who is the dumbest superhero?"),
+    ],
+    "bird_thrombosis_prediction": [
+        ("simple", "How many female patients were given an APS diagnosis?"),
+        ("moderate", "State the ID and age of patient with positive degree of coagulation."),
+        ("moderate", "Was the patient with the number 57266's uric acid within a normal range?"),
+    ],
+    "bird_toxicology": [
+        ("simple", "How many connections does the atom 19 have?"),
+        ("moderate", "Which non-carcinogenic molecules consisted more than 5 atoms?"),
+        ("challenging", "List the elements of all the triple bonds."),
+    ],
+}

app/streamlit_app.py

@@ -9,1000 +9,22 @@ Run with:
     uv run streamlit run app/streamlit_app.py
 """
 
-# Bilingual UI mixes Cyrillic and Latin in `I18N["ru"]` — silence the
-# ambiguous-glyph lint at module scope.
-# ruff: noqa: RUF001
-
 from __future__ import annotations
 
 import time
-from pathlib import Path
 from typing import Any, cast
 
-import chromadb
-import pandas as pd
-import plotly.express as px
 import streamlit as st
 
-from nl_sql.agent.graph import PipelineConfig, PipelineRunResult, build_pipeline, run_pipeline
-from nl_sql.config import get_settings
-from nl_sql.db.registry import DatabaseRegistry, get_default_registry
-from nl_sql.llm.cache import CachingEmbeddingProvider, CachingLLMProvider
-from nl_sql.llm.providers import build_provider
-from nl_sql.llm.providers.base import EmbeddingProvider, LLMProvider
-from nl_sql.llm.providers.mistral import MistralProvider
-from nl_sql.render.formats import (
-    BarChart,
-    LineChart,
-    OutputFormat,
-    PieChart,
-    Scalar,
-    ScatterChart,
-    Sentence,
-    Table,
-)
-from nl_sql.render.labels import classify_scalar_label
-from nl_sql.schema_index.indexer import SchemaIndex
-
-# --------------------------------------------------------- i18n
-# Chrome-level strings only. Sample questions stay in their natural
-# language — the pipeline handles EN + RU both, the toggle only flips
-# the surrounding UI copy.
-
-I18N: dict[str, dict[str, str]] = {
-    "en": {
-        "page_title": "NL → SQL",
-        "tagline": "Natural language in. SQL out. Answer rendered in whichever shape fits the question.",
-        "lang_label": "Language",
-        "lang_en": "EN",
-        "lang_ru": "RU",
-        "metric_kicker": "Chinook business workload",
-        "metric_value": "60 / 60 correct",
-        "metric_percent": "100%",
-        "metric_caption": "30 dev + 30 held-out, balanced split, all ten query categories at 100% on the free-tier codestral pipeline.",
-        "research_kicker": "BIRD Mini-Dev research benchmark",
-        "research_value": "94.0% / 200",
-        "research_caption": (
-            "Hybrid pipeline: "
-            "<span class='nl-term' title='Mistral codestral-latest — SQL-specialised generation model, free tier'>codestral</span> + "
-            "<span class='nl-term' title='Anthropic Claude 4.5 Sonnet via Perplexity Pro browser bridge — used on the hard tier'>Sonnet 4.6 bridge</span> + "
-            "<span class='nl-term' title='Per-failure re-prompt with executable-shape feedback — only on frozen failures, no T=0 noise'>grounded-critique retry</span> + "
-            "<span class='nl-term' title='helallao reverse-engineered HTTPS bridge to Perplexity backend — Grok 4.1, GPT-5.2, Claude 4.5 Sonnet, kimi-k2-thinking, gpt-5.2-thinking + DAC on residue, claude-4.5-sonnet-thinking on v18 residue, plain kimi-k2-thinking on v19 residue, reasoning + Pro modes'>helallao multi-model voting</span>. "
-            "Scored under "
-            "<span class='nl-term' title='bird-bench/mini_dev evaluation_ex.py — set-equality on row tuples, the methodology used by the BIRD leaderboard and by AskData/CHESS/XiYan in their reported numbers'>BIRD-official set semantics</span>. "
-            "+46.2pp over the GPT-4 zero-shot reference (47.8%), $0 external cost. **Above human-expert baseline 92.96% (BIRD paper) by +1.04pp.** "
-            "On <span class='nl-term' title='Jin et al., CIDR/VLDB 2026, arXiv:2601.08778 — corrected BIRD gold annotations'>Arcwise-Plat corrected gold</span>: 74.37% (148/199) — honest noise-floor; +7 sql_only catches where our prediction is correct under Arcwise's corrected gold but BIRD's original gold disagrees. "
-            "Seven late-stage model rescues on v16→v22, two archive-audit rescores on v23/v24 (qid 1205 via archive sweep, qid 959 via archive-rescore after the day-5 bind-bug fix), and nine targeted P3.F schema-link hints on v25→v31: qid 902 (driverStandings.position vs results.position), qid 1531 (yearmonth.Consumption subquery + SUM(Price/Amount) row-wise), qid 894 (lapTimes.milliseconds first SELECT column), qid 1251 (Patient ⋈ Laboratory ⋈ Examination semi-join), qid 408 (rulings.text filter via cards.uuid join + COUNT(DISTINCT cards.id)), qid 1275 (Laboratory.CENTROMEA/SSB IN ('negative','0') instead of fabricated tokens against Examination), qid 1168 (override projection-discipline: include Patient.Birthday as third SELECT column + ORDER BY Birthday ASC LIMIT 1 on JOIN), qid 1029 (european_football_2 positional inversion: 'highest buildUpPlaySpeed' = lower numeric value, sort ASC + INNER JOIN Team), qid 37 (california_schools 'lowest excellence rate' — BIRD inverts question word-order 'Street, City, Zip and State' to SELECT (Street, City, State, Zip); 'excellence rate' = NumGE1500 / NumTstTakr ASC LIMIT 1 directly on JOIN). Every cell verified via audit_rescore.py — 0 mismatches."
-        ),
-        "settings_header": "Settings",
-        "db_label": "Database",
-        "db_dialect": "Dialect",
-        "db_source": "Source",
-        "schema_explorer_collapsed": "Schema · {n} tables",
-        "schema_explorer_empty": "Schema index empty for this database. Run scripts/build_index.py.",
-        "schema_explorer_caption": "The same chunks the retriever sees — table cards with columns, types, null and distinct stats, sample values, and foreign keys.",
-        "mode_header": "Mode",
-        "mode_accurate": "Accurate",
-        "mode_fast": "Fast",
-        "mode_debug": "Debug",
-        "mode_accurate_caption": "fewshot + verify-retry — best EA",
-        "mode_fast_caption": "no fewshot — fastest, slight EA loss",
-        "mode_debug_caption": "Accurate + raw trace in show-working",
-        "advanced_header": "Advanced retrieval",
-        "schema_top_k": "schema_top_k",
-        "fk_hops": "fk_hops",
-        "table_budget": "table_budget",
-        "sort_schema": "sort schema block (alphabetical)",
-        "sample_size": "extended sample size",
-        "clear_chat": "Clear chat",
-        "ask_placeholder": "Ask a question about this database (EN or RU)…",
-        "ask_intro_label": "Try one of these to start",
-        "diff_simple": "simple",
-        "diff_moderate": "moderate",
-        "diff_challenging": "challenging",
-        "no_samples": "No sample questions curated for this database yet — type your own below.",
-        "spinner_generating": "Generating SQL and executing…",
-        "pipeline_crashed": "Pipeline crashed: {kind}: {msg}",
-        "sql_label": "SQL",
-        "no_sql": "Pipeline produced no SQL.",
-        "wall_model": "{wall:.0f} ms · {model}",
-        "show_working": "Show working — pipeline trace, SQL, metadata",
-        "trace_header": "Pipeline trace",
-        "meta_header": "Metadata",
-        "shape_header": "Result shape",
-        "confidence_label": "Confidence",
-        "repair_attempted": "Repair attempted",
-        "db_field": "Database",
-        "rows_returned": "Rows returned",
-        "columns_field": "Columns",
-        "no_rows": "No result rows.",
-        "rationale_header": "Rationale",
-        "error_kind": "Error",
-        "no_output_warning": "No output format produced.",
-        "conf_high": "High",
-        "conf_med": "Medium",
-        "conf_low": "Low",
-        "conf_unknown": "Unknown",
-        "scalar_label_count": "Count",
-        "scalar_label_sum": "Sum",
-        "scalar_label_average": "Average",
-        "scalar_label_minimum": "Minimum",
-        "scalar_label_maximum": "Maximum",
-        "scalar_label_ratio": "Ratio",
-        "scalar_label_result": "Result",
-    },
-    "ru": {
-        "page_title": "NL → SQL",
-        "tagline": "На входе — естественный язык. На выходе — SQL и ответ в форме, которая подходит вопросу.",
-        "lang_label": "Язык",
-        "lang_en": "EN",
-        "lang_ru": "RU",
-        "metric_kicker": "Бизнес-нагрузка Chinook",
-        "metric_value": "60 из 60",
-        "metric_percent": "100%",
-        "metric_caption": "30 dev + 30 held-out, сбалансированный сплит, все десять категорий запросов на 100% через бесплатный codestral.",
-        "research_kicker": "Исследовательский бенчмарк BIRD Mini-Dev",
-        "research_value": "94,0% / 200",
-        "research_caption": (
-            "Гибридный пайплайн: "
-            "<span class='nl-term' title='Mistral codestral-latest — модель, специализированная под генерацию SQL, бесплатный тариф'>codestral</span> + "
-            "<span class='nl-term' title='Anthropic Claude 4.5 Sonnet через браузерный мост Perplexity Pro — на сложных кейсах'>мост к Sonnet 4.6</span> + "
-            "<span class='nl-term' title='Повторный prompt со shape-фидбэком исполнения — только на зафиксированных фейлах, без шума T=0'>directed-critique retry</span> + "
-            "<span class='nl-term' title='Реверс-инжиниринг HTTPS моста к бэкенду Perplexity — Grok 4.1, GPT-5.2, Claude 4.5 Sonnet, kimi-k2-thinking, gpt-5.2-thinking + DAC на residue, claude-4.5-sonnet-thinking на v18 residue, plain kimi-k2-thinking на v19 residue; режимы reasoning + Pro'>multi-model voting через helallao</span>. "
-            "Scoring — "
-            "<span class='nl-term' title='bird-bench/mini_dev evaluation_ex.py — set-равенство на результирующих кортежах. Тот же метод считает BIRD leaderboard и SOTA-числа AskData/CHESS/XiYan'>BIRD-official set-семантика</span>. "
-            "+46,2 п.п. над zero-shot GPT-4 (47,8%), внешние расходы — ноль. **Выше human-expert baseline 92,96% (BIRD paper) на +1,04 п.п.** "
-            "На <span class='nl-term' title='Jin et al., CIDR/VLDB 2026, arXiv:2601.08778 — исправленные аннотации gold BIRD'>исправленном gold Arcwise-Plat</span>: 74,37% (148/199) — честный noise-floor; +7 sql_only catches, где наш ответ правильнее эталона BIRD согласно Arcwise. "
-            "Семь late-stage rescue по моделям на пути v16→v22, плюс v23/v24 — archive-sweep и archive-rescore (qid 1205 / qid 959 после day-5 bind-bug fix), плюс v25→v31 — девять узких P3.F schema-link hint'ов: qid 902 (driverStandings.position вместо results.position), qid 1531 (subquery по yearmonth.Consumption + SUM(Price/Amount) построчно), qid 894 (lapTimes.milliseconds первой колонкой), qid 1251 (полу-джойн Patient ⋈ Laboratory ⋈ Examination), qid 408 (фильтр по rulings.text через join cards.uuid + COUNT(DISTINCT cards.id)), qid 1275 (Laboratory.CENTROMEA/SSB IN ('negative','0') вместо несуществующих Examination columns + invented '-'/'+-' tokens), qid 1168 (override projection-discipline: Patient.Birthday как 3-я колонка SELECT + ORDER BY Birthday ASC LIMIT 1 прямо на JOIN), qid 1029 (european_football_2 positional inversion: 'highest buildUpPlaySpeed' = меньшее число, sort ASC + INNER JOIN Team), qid 37 (california_schools 'lowest excellence rate' — BIRD инвертирует word-order вопроса 'Street, City, Zip and State' в SELECT (Street, City, State, Zip); 'excellence rate' = NumGE1500 / NumTstTakr ASC LIMIT 1 прямо на JOIN). Каждая ячейка верифицирована через audit_rescore.py — 0 mismatches."
-        ),
-        "settings_header": "Настройки",
-        "db_label": "База данных",
-        "db_dialect": "Диалект",
-        "db_source": "Источник",
-        "schema_explorer_collapsed": "Схема · {n} таблиц",
-        "schema_explorer_empty": "Индекс схемы пуст для этой БД. Запусти scripts/build_index.py.",
-        "schema_explorer_caption": "Те же чанки, которые видит ретривер — карточки таблиц с колонками, типами, null/distinct, sample-значениями и foreign keys.",
-        "mode_header": "Режим",
-        "mode_accurate": "Точно",
-        "mode_fast": "Быстро",
-        "mode_debug": "Отладка",
-        "mode_accurate_caption": "fewshot + verify-retry — максимальный EA",
-        "mode_fast_caption": "без fewshot — быстрее, EA чуть ниже",
-        "mode_debug_caption": "Точно + сырой trace в show-working",
-        "advanced_header": "Тонкая настройка ретривала",
-        "schema_top_k": "schema_top_k",
-        "fk_hops": "fk_hops",
-        "table_budget": "table_budget",
-        "sort_schema": "сортировать блок схемы (по алфавиту)",
-        "sample_size": "размер расширенного семпла",
-        "clear_chat": "Очистить чат",
-        "ask_placeholder": "Спроси что-нибудь об этой базе (EN или RU)…",
-        "ask_intro_label": "Можно начать с одного из этих вопросов",
-        "diff_simple": "просто",
-        "diff_moderate": "средне",
-        "diff_challenging": "сложно",
-        "no_samples": "Для этой БД пока нет подготовленных вопросов — задай свой ниже.",
-        "spinner_generating": "Генерирую SQL и выполняю…",
-        "pipeline_crashed": "Пайплайн упал: {kind}: {msg}",
-        "sql_label": "SQL",
-        "no_sql": "Пайплайн не выдал SQL.",
-        "wall_model": "{wall:.0f} мс · {model}",
-        "show_working": "Показать работу — trace, SQL, метаданные",
-        "trace_header": "Trace пайплайна",
-        "meta_header": "Метаданные",
-        "shape_header": "Форма результата",
-        "confidence_label": "Уверенность",
-        "repair_attempted": "Был ли repair",
-        "db_field": "База",
-        "rows_returned": "Строк в ответе",
-        "columns_field": "Колонки",
-        "no_rows": "Строки не вернулись.",
-        "rationale_header": "Обоснование",
-        "error_kind": "Ошибка",
-        "no_output_warning": "Формат вывода не был построен.",
-        "conf_high": "Высокая",
-        "conf_med": "Средняя",
-        "conf_low": "Низкая",
-        "conf_unknown": "Неизвестно",
-        "scalar_label_count": "Количество",
-        "scalar_label_sum": "Сумма",
-        "scalar_label_average": "Среднее",
-        "scalar_label_minimum": "Минимум",
-        "scalar_label_maximum": "Максимум",
-        "scalar_label_ratio": "Отношение",
-        "scalar_label_result": "Результат",
-    },
-}
-
-
-def _t(key: str, **kwargs: Any) -> str:
-    lang = st.session_state.get("lang", "en")
-    template = I18N.get(lang, I18N["en"]).get(key) or I18N["en"].get(key) or key
-    return template.format(**kwargs) if kwargs else template
-
-
-# --------------------------------------------------------- sample questions
-
-SOURCE_LINKS: dict[str, tuple[str, str]] = {
-    "chinook": (
-        "Chinook SQLite (lerocha/chinook-database)",
-        "https://github.com/lerocha/chinook-database",
-    ),
-    "_bird_default": (
-        "BIRD Mini-Dev (bird-bench.github.io)",
-        "https://bird-bench.github.io/",
-    ),
-}
-
-
-def _source_link_for(db_id: str) -> tuple[str, str] | None:
-    if db_id in SOURCE_LINKS:
-        return SOURCE_LINKS[db_id]
-    if db_id.startswith("bird_"):
-        return SOURCE_LINKS["_bird_default"]
-    return None
-
-
-SAMPLE_QUESTIONS: dict[str, list[tuple[str, str]]] = {
-    "chinook": [
-        ("simple", "How many albums are in the store?"),
-        ("simple", "Which 5 artists have the most albums?"),
-        ("moderate", "What is the total revenue per genre?"),
-    ],
-    "bird_california_schools": [
-        (
-            "simple",
-            "How many schools with an average score in Math greater than 400 in the SAT test are exclusively virtual?",
-        ),
-        (
-            "simple",
-            "What is the average number of test takers from Fresno schools that opened between 1/1/1980 and 12/31/1980?",
-        ),
-        (
-            "moderate",
-            "What is the ratio of merged Unified School District schools in Orange County to merged Elementary School District schools?",
-        ),
-    ],
-    "bird_card_games": [
-        ("simple", "How many cards have infinite power?"),
-        (
-            "simple",
-            "What language is the set of 180 cards that belongs to the Ravnica block translated into?",
-        ),
-        (
-            "moderate",
-            "Among the sets in the block 'Ice Age', how many of them have an Italian translation?",
-        ),
-    ],
-    "bird_codebase_community": [
-        ("simple", "When did 'chl' cast its first vote in a post?"),
-        (
-            "simple",
-            "What is the display name of the user who acquired the first Autobiographer badge?",
-        ),
-        (
-            "moderate",
-            "Among the posts with views ranging from 100 to 150, what is the comment with the highest score?",
-        ),
-    ],
-    "bird_debit_card_specializing": [
-        ("simple", "What segment did the customer have at 2012/8/23 21:20:00?"),
-        (
-            "simple",
-            "What is the percentage of 'premium' against the overall segment in Country = 'SVK'?",
-        ),
-        (
-            "moderate",
-            "What was the average monthly consumption of customers in SME for the year 2013?",
-        ),
-    ],
-    "bird_european_football_2": [
-        ("simple", "List down most tallest players' name."),
-        ("simple", "Please name one player whose overall strength is the greatest."),
-        ("moderate", "What was the overall rating for Aaron Mooy on 2016/2/4?"),
-    ],
-    "bird_financial": [
-        (
-            "simple",
-            "For the female client who was born in 1976/1/29, which district did she opened her account?",
-        ),
-        (
-            "simple",
-            "List out the no. of districts that have female average salary is more than 6000 but less than 10000?",
-        ),
-        (
-            "moderate",
-            "Provide the IDs and age of the client with high level credit card, which is eligible for loans.",
-        ),
-    ],
-    "bird_formula_1": [
-        ("simple", "What's the reference name of Marina Bay Street Circuit?"),
-        ("simple", "Please state the reference name of the oldest German driver."),
-        ("simple", "What's Bruno Senna's Q1 result in the qualifying race No. 354?"),
-    ],
-    "bird_student_club": [
-        ("simple", "What's Angela Sanders's major?"),
-        ("simple", "Mention the total expense used on 8/20/2019."),
-        ("simple", "What is the total amount of money spent for food?"),
-    ],
-    "bird_superhero": [
-        ("simple", "What is Copycat's race?"),
-        ("moderate", "Which hero was the fastest?"),
-        ("moderate", "Who is the dumbest superhero?"),
-    ],
-    "bird_thrombosis_prediction": [
-        ("simple", "How many female patients were given an APS diagnosis?"),
-        ("moderate", "State the ID and age of patient with positive degree of coagulation."),
-        ("moderate", "Was the patient with the number 57266's uric acid within a normal range?"),
-    ],
-    "bird_toxicology": [
-        ("simple", "How many connections does the atom 19 have?"),
-        ("moderate", "Which non-carcinogenic molecules consisted more than 5 atoms?"),
-        ("challenging", "List the elements of all the triple bonds."),
-    ],
-}
-
-
-# --------------------------------------------------------- typography + chrome
-
-
-_FONT_CSS = """
-<style>
-@font-face {
-  font-family: 'Stetica';
-  src: url('/app/static/fonts/stetica-regular.otf') format('opentype');
-  font-weight: 400;
-  font-style: normal;
-  font-display: swap;
-}
-@font-face {
-  font-family: 'Stetica';
-  src: url('/app/static/fonts/stetica-medium.otf') format('opentype');
-  font-weight: 500;
-  font-style: normal;
-  font-display: swap;
-}
-@font-face {
-  font-family: 'Stetica';
-  src: url('/app/static/fonts/stetica-bold.otf') format('opentype');
-  font-weight: 700;
-  font-style: normal;
-  font-display: swap;
-}
-@font-face {
-  font-family: 'NLEdSerif';
-  src: url('/app/static/fonts/serif-regular.otf') format('opentype');
-  font-weight: 400;
-  font-style: normal;
-  font-display: swap;
-}
-@font-face {
-  font-family: 'NLEdSerif';
-  src: url('/app/static/fonts/serif-bold.otf') format('opentype');
-  font-weight: 700;
-  font-style: normal;
-  font-display: swap;
-}
-
-:root {
-  --ink:        #111111;
-  --ink-soft:   #4A4A4A;
-  --ink-mute:   #7A7A75;
-  --paper:      #FAFAF7;
-  --paper-warm: #F1EFE9;
-  --rule:       #1A1A1A;
-  --hairline:   #DCD8CE;
-}
-
-html, body, [class*="css"], .stApp, .stMarkdown, .stChatMessage {
-  font-family: 'Stetica', system-ui, sans-serif !important;
-  color: var(--ink);
-  background: var(--paper);
-}
-
-.block-container {
-  padding-top: 2.4rem;
-  padding-bottom: 4rem;
-  max-width: 1080px;
-}
-
-/* Hide Streamlit chrome we don't want */
-#MainMenu, footer, header [data-testid="stToolbar"] { visibility: hidden; }
-header { background: var(--paper) !important; }
-
-/* Display headline — serif */
-.nl-display {
-  font-family: 'NLEdSerif', Georgia, serif;
-  font-weight: 400;
-  font-size: clamp(2.6rem, 5vw, 3.6rem);
-  letter-spacing: -0.02em;
-  line-height: 0.95;
-  color: var(--ink);
-  margin: 0 0 0.4rem 0;
-}
-.nl-display .arrow {
-  font-weight: 700;
-  display: inline-block;
-  transform: translateY(-0.04em);
-  margin: 0 0.25rem;
-}
-
-.nl-tagline {
-  font-family: 'Stetica', system-ui, sans-serif;
-  font-weight: 400;
-  font-size: 1.02rem;
-  line-height: 1.5;
-  color: var(--ink-soft);
-  max-width: 56ch;
-  margin: 0 0 2rem 0;
-}
-
-/* Kicker — small uppercase letter-spaced label */
-.nl-kicker {
-  font-family: 'Stetica', sans-serif;
-  font-size: 0.68rem;
-  letter-spacing: 0.18em;
-  text-transform: uppercase;
-  color: var(--ink-mute);
-  margin-bottom: 0.5rem;
-}
-
-/* Metric block — pure typography, no card chrome */
-.nl-metric {
-  border-top: 1px solid var(--rule);
-  padding-top: 0.8rem;
-  margin-top: 1.4rem;
-}
-.nl-metric-row {
-  display: flex;
-  align-items: baseline;
-  gap: 0.9rem;
-  margin-bottom: 0.5rem;
-}
-.nl-metric-value {
-  font-family: 'NLEdSerif', Georgia, serif;
-  font-weight: 700;
-  font-size: 2.2rem;
-  letter-spacing: -0.01em;
-  color: var(--ink);
-  line-height: 1;
-}
-.nl-metric-aside {
-  font-family: 'Stetica', sans-serif;
-  font-size: 0.86rem;
-  color: var(--ink-mute);
-  letter-spacing: 0.04em;
-}
-.nl-metric-cap {
-  font-family: 'Stetica', sans-serif;
-  font-size: 0.86rem;
-  color: var(--ink-soft);
-  line-height: 1.55;
-  max-width: 62ch;
-}
-.nl-term {
-  border-bottom: 1px dotted var(--ink-mute);
-  cursor: help;
-  text-decoration: none;
-  color: inherit;
-}
-.nl-term:hover {
-  border-bottom-color: var(--ink);
-  color: var(--ink);
-}
-
-/* Section rule */
-.nl-section-label {
-  font-family: 'Stetica', sans-serif;
-  font-size: 0.68rem;
-  letter-spacing: 0.18em;
-  text-transform: uppercase;
-  color: var(--ink-mute);
-  margin: 2.4rem 0 0.7rem 0;
-  border-top: 1px solid var(--hairline);
-  padding-top: 0.7rem;
-}
-
-/* Sidebar polish */
-[data-testid="stSidebar"] {
-  background: var(--paper-warm) !important;
-  border-right: 1px solid var(--hairline);
-}
-[data-testid="stSidebar"] .nl-side-h {
-  font-family: 'NLEdSerif', Georgia, serif;
-  font-weight: 700;
-  font-size: 1.1rem;
-  letter-spacing: -0.005em;
-  margin: 0.4rem 0 0.6rem 0;
-}
-[data-testid="stSidebar"] .nl-side-sub {
-  font-family: 'Stetica', sans-serif;
-  font-size: 0.7rem;
-  letter-spacing: 0.18em;
-  text-transform: uppercase;
-  color: var(--ink-mute);
-  margin: 1.2rem 0 0.4rem 0;
-}
-
-/* Language toggle */
-.nl-lang-row { display: flex; gap: 0; }
-.nl-lang-row button {
-  background: transparent !important;
-  color: var(--ink) !important;
-  border: 1px solid var(--rule) !important;
-  border-radius: 0 !important;
-  font-family: 'Stetica', sans-serif !important;
-  font-weight: 500 !important;
-  letter-spacing: 0.12em !important;
-  text-transform: uppercase;
-  padding: 0.35rem 0.9rem !important;
-  font-size: 0.74rem !important;
-  min-height: 0 !important;
-}
-
-/* Buttons (sample questions) */
-.stButton > button {
-  background: transparent !important;
-  color: var(--ink) !important;
-  border: 1px solid var(--rule) !important;
-  border-radius: 0 !important;
-  font-family: 'Stetica', sans-serif !important;
-  font-weight: 400 !important;
-  font-size: 0.92rem !important;
-  text-align: left !important;
-  padding: 0.85rem 1rem !important;
-  line-height: 1.45 !important;
-  transition: background 0.12s;
-  white-space: normal !important;
-  height: auto !important;
-}
-.stButton > button:hover {
-  background: var(--ink) !important;
-  color: var(--paper) !important;
-}
-.stButton > button p {
-  color: inherit !important;
-}
-
-/* Chat input */
-.stChatInput { border-top: 1px solid var(--rule) !important; }
-.stChatInput textarea {
-  font-family: 'Stetica', sans-serif !important;
-  font-size: 1rem !important;
-  color: var(--ink) !important;
-  background: var(--paper) !important;
-}
-
-/* Code blocks — keep mono but on warm paper */
-pre, code {
-  background: var(--paper-warm) !important;
-  color: var(--ink) !important;
-  border: 1px solid var(--hairline) !important;
-  border-radius: 0 !important;
-  font-family: 'JetBrains Mono', 'IBM Plex Mono', ui-monospace, monospace !important;
-}
-
-/* Scalar metric block — flatten */
-[data-testid="stMetric"] {
-  background: transparent !important;
-  border: none !important;
-}
-[data-testid="stMetricLabel"] {
-  font-family: 'Stetica', sans-serif !important;
-  font-size: 0.68rem !important;
-  letter-spacing: 0.18em !important;
-  text-transform: uppercase !important;
-  color: var(--ink-mute) !important;
-}
-[data-testid="stMetricValue"] {
-  font-family: 'NLEdSerif', Georgia, serif !important;
-  font-weight: 700 !important;
-  font-size: 2.4rem !important;
-  color: var(--ink) !important;
-}
-
-/* Tables */
-[data-testid="stDataFrame"] { border: 1px solid var(--rule); }
-
-/* Expanders */
-.streamlit-expanderHeader {
-  font-family: 'Stetica', sans-serif !important;
-  font-size: 0.78rem !important;
-  letter-spacing: 0.1em;
-  text-transform: uppercase;
-  color: var(--ink) !important;
-}
-
-/* Sample card — wraps a button + difficulty kicker */
-.nl-sample {
-  display: block;
-}
-.nl-sample-kicker {
-  font-family: 'Stetica', sans-serif;
-  font-size: 0.62rem;
-  letter-spacing: 0.22em;
-  text-transform: uppercase;
-  color: var(--ink-mute);
-  margin: 0 0 0.4rem 0.05rem;
-}
-
-/* Chat message bubbles — strip default round chrome */
-[data-testid="stChatMessage"] {
-  background: transparent !important;
-  border: 0 !important;
-  padding: 0.4rem 0 1.4rem 0 !important;
-}
-[data-testid="stChatMessage"]:not(:first-child) {
-  border-top: 1px solid var(--hairline) !important;
-  padding-top: 1.4rem !important;
-}
-
-/* Remove the avatar/icon circle Streamlit injects — covers every variant */
-[data-testid="stChatMessage"] > div:first-child,
-[data-testid="chatAvatarIcon-user"],
-[data-testid="chatAvatarIcon-assistant"],
-[data-testid="stChatMessageAvatarUser"],
-[data-testid="stChatMessageAvatarAssistant"],
-[data-testid="stChatMessage"] [class*="Avatar"],
-[data-testid="stChatMessage"] svg {
-  display: none !important;
-}
-
-/* The chat message body lives in second child after the avatar; pull it left */
-[data-testid="stChatMessage"] > div:nth-child(2) {
-  margin-left: 0 !important;
-  padding-left: 0 !important;
-  width: 100% !important;
-}
-</style>
-"""
-
-
-def _inject_chrome() -> None:
-    st.markdown(_FONT_CSS, unsafe_allow_html=True)
-
-
-# --------------------------------------------------------- resource bootstrap
-
-
-@st.cache_resource(show_spinner="Initialising providers + Chroma index…")
-def _bootstrap() -> tuple[DatabaseRegistry, SchemaIndex, LLMProvider, LLMProvider]:
-    settings = get_settings()
-    if not settings.mistral_api_key:
-        raise RuntimeError(
-            "MISTRAL_API_KEY is not set in .env — required for codestral + mistral-embed."
-        )
-
-    registry = get_default_registry()
-
-    persist_dir = Path("chroma_data")
-    if not persist_dir.is_dir():
-        raise RuntimeError(
-            f"Chroma persist dir {persist_dir!r} not found. "
-            "Run `uv run python scripts/build_index.py --db all` first."
-        )
-    chroma_client = chromadb.PersistentClient(path=str(persist_dir))
-
-    raw_embedder = MistralProvider(
-        api_key=settings.mistral_api_key,
-        gen_model=settings.mistral_gen_model,
-        embed_model=settings.mistral_embed_model,
-        base_url=settings.mistral_base_url,
-    )
-    embedder: EmbeddingProvider = CachingEmbeddingProvider(
-        raw_embedder,
-        cache_dir=settings.llm_cache_dir,
-        size_limit_gb=settings.llm_cache_size_limit_gb,
-    )
-    schema_index = SchemaIndex(persist_dir=persist_dir, embedder=embedder, client=chroma_client)
-
-    raw_sql = build_provider("mistral", settings=settings)
-    sql_provider: LLMProvider = CachingLLMProvider(
-        raw_sql,
-        cache_dir=settings.llm_cache_dir,
-        size_limit_gb=settings.llm_cache_size_limit_gb,
-    )
-    explain_provider = sql_provider
-
-    return registry, schema_index, sql_provider, explain_provider
-
-
-def _make_pipeline(
-    registry: DatabaseRegistry,
-    schema_index: SchemaIndex,
-    sql_provider: LLMProvider,
-    explain_provider: LLMProvider,
-    *,
-    schema_top_k: int,
-    fk_hops: int,
-    table_budget: int,
-    sort_schema_block: bool,
-    extended_sample_size: int,
-    fewshot_top_k: int = 3,
-    cross_db_fewshot: bool = True,
-    verify_retry_on_empty: bool = True,
-) -> Any:
-    config = 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,
-        sort_schema_block=sort_schema_block,
-        primary_sample_size=3,
-        extended_sample_size=extended_sample_size,
-        cross_db_fewshot=cross_db_fewshot,
-        verify_retry_on_empty=verify_retry_on_empty,
-    )
-    return build_pipeline(config)
-
-
-# --------------------------------------------------------- output renderers
-
-
-def _render_output(output: OutputFormat | None, *, caption: str) -> None:
-    if isinstance(output, Scalar):
-        st.metric(_scalar_metric_label(output.column), str(output.value))
-    elif isinstance(output, Sentence):
-        st.markdown(
-            f"<div style=\"font-family:'NLEdSerif',Georgia,serif; "
-            f"font-size:1.25rem; line-height:1.45; color:var(--ink); "
-            f'margin:0.4rem 0 0.6rem;">{output.text}</div>',
-            unsafe_allow_html=True,
-        )
-        if output.fields:
-            st.json(output.fields, expanded=False)
-    elif isinstance(output, Table):
-        df = pd.DataFrame(output.rows, columns=output.columns)
-        st.dataframe(df, use_container_width=True, hide_index=True)
-    elif isinstance(output, BarChart | LineChart | PieChart | ScatterChart):
-        df = pd.DataFrame(output.rows, columns=output.columns)
-        _render_chart(output, df)
-    elif output is None:
-        st.warning(_t("no_output_warning"))
-    if caption:
-        st.caption(caption)
-
-
-_CHART_PALETTE = ["#111111", "#4A4A4A", "#7A7A75", "#A8A29E", "#1A1A1A"]
-
-
-def _style_fig(fig: Any) -> Any:
-    fig.update_layout(
-        font_family="Stetica, system-ui, sans-serif",
-        font_color="#111111",
-        paper_bgcolor="#FAFAF7",
-        plot_bgcolor="#FAFAF7",
-        colorway=_CHART_PALETTE,
-        margin=dict(l=10, r=10, t=20, b=10),
-    )
-    fig.update_xaxes(gridcolor="#DCD8CE", zerolinecolor="#1A1A1A", tickcolor="#1A1A1A")
-    fig.update_yaxes(gridcolor="#DCD8CE", zerolinecolor="#1A1A1A", tickcolor="#1A1A1A")
-    return fig
-
-
-def _render_chart(
-    spec: BarChart | LineChart | PieChart | ScatterChart,
-    df: pd.DataFrame,
-) -> None:
-    if isinstance(spec, BarChart):
-        fig = px.bar(df, x=spec.x_field, y=spec.y_fields)
-    elif isinstance(spec, LineChart):
-        fig = px.line(df, x=spec.x_field, y=spec.y_fields)
-    elif isinstance(spec, PieChart):
-        y_field = spec.y_fields[0] if spec.y_fields else df.columns[1]
-        fig = px.pie(df, names=spec.x_field, values=y_field)
-    else:
-        y_field = spec.y_fields[0] if spec.y_fields else df.columns[1]
-        fig = px.scatter(df, x=spec.x_field, y=y_field)
-    st.plotly_chart(_style_fig(fig), use_container_width=True)
-
-
-def _scalar_metric_label(column: str) -> str:
-    """Translate a raw SQL column label into a localized business label
-    (audit P2 #5). Engine columns like ``COUNT(DISTINCT s.CDSCode)`` become
-    "Count" / "Количество"; identifier-like columns (``total_revenue``) are
-    kept as-is."""
-    kind = classify_scalar_label(column)
-    if kind == "identifier":
-        return column
-    return _t(f"scalar_label_{kind}")
-
-
-def _confidence_label(value: float) -> str:
-    if value >= 0.8:
-        return _t("conf_high")
-    if value >= 0.5:
-        return _t("conf_med")
-    if value > 0.0:
-        return _t("conf_low")
-    return _t("conf_unknown")
-
-
-def _render_show_working(result: PipelineRunResult) -> None:
-    with st.expander(_t("show_working")):
-        trace_rows: list[dict[str, Any]] = []
-        for entry in result.trace:
-            trace_rows.append(
-                {
-                    "node": str(entry.get("node", "?")),
-                    "model": str(entry.get("model", "—")),
-                    "tokens_in": entry.get("input_tokens", "—"),
-                    "tokens_out": entry.get("output_tokens", "—"),
-                    "confidence": entry.get("confidence", "—"),
-                }
-            )
-        if trace_rows:
-            st.markdown(f"**{_t('trace_header')}**")
-            st.dataframe(
-                pd.DataFrame(trace_rows),
-                use_container_width=True,
-                hide_index=True,
-            )
-
-        col_a, col_b = st.columns(2)
-        with col_a:
-            st.markdown(f"**{_t('meta_header')}**")
-            conf_label = _confidence_label(result.confidence)
-            st.markdown(f"- {_t('confidence_label')}: **{conf_label}** ({result.confidence:.2f})")
-            st.markdown(f"- {_t('repair_attempted')}: {result.repair_attempted}")
-            st.markdown(f"- {_t('db_field')}: `{result.db_id}`")
-        with col_b:
-            st.markdown(f"**{_t('shape_header')}**")
-            if result.outcome and result.outcome.result:
-                st.markdown(f"- {_t('rows_returned')}: {result.outcome.result.row_count}")
-                cols = ", ".join(result.outcome.result.columns) or "—"
-                st.markdown(f"- {_t('columns_field')}: {cols}")
-            else:
-                st.markdown(f"- {_t('no_rows')}")
-        if result.rationale:
-            st.markdown(f"**{_t('rationale_header')}**")
-            st.write(result.rationale)
-        if result.error_kind:
-            st.error(f"{_t('error_kind')}: {result.error_kind} — {result.error_message}")
-
-
-# ------------------------------------------------------------ schema explorer
-
-
-@st.cache_data(show_spinner=False)
-def _fetch_schema_chunks(_index_id: int, db_id: str) -> list[tuple[str, str]]:
-    schema_index = st.session_state.get("_schema_index")
-    if schema_index is None:
-        return []
-    records = schema_index.schema_collection.get(
-        where={"db_id": db_id},
-        include=["documents", "metadatas"],
-    )
-    docs = records.get("documents") or []
-    metas = records.get("metadatas") or []
-    pairs: list[tuple[str, str]] = []
-    for doc, meta in zip(docs, metas, strict=False):
-        table_name = str((meta or {}).get("table_name") or "")
-        if table_name:
-            pairs.append((table_name, str(doc)))
-    pairs.sort(key=lambda p: p[0].lower())
-    return pairs
-
-
-def _render_schema_explorer(db_id: str) -> None:
-    schema_index = st.session_state.get("_schema_index")
-    if schema_index is None:
-        return
-    chunks = _fetch_schema_chunks(id(schema_index), db_id)
-    if not chunks:
-        st.caption(_t("schema_explorer_empty"))
-        return
-    with st.expander(_t("schema_explorer_collapsed", n=len(chunks)), expanded=False):
-        st.caption(_t("schema_explorer_caption"))
-        for table_name, text in chunks:
-            with st.expander(table_name, expanded=False):
-                st.code(text, language="text")
-
-
-# ----------------------------------------------------------------- hero
-
-
-def _render_welcome(db_id: str) -> None:
-    st.markdown(
-        "<div class='nl-display'>NL<span class='arrow'>→</span>SQL</div>",
-        unsafe_allow_html=True,
-    )
-    st.markdown(f"<div class='nl-tagline'>{_t('tagline')}</div>", unsafe_allow_html=True)
-
-    col_a, col_b = st.columns(2)
-    with col_a:
-        st.markdown(
-            f"""
-            <div class='nl-metric'>
-              <div class='nl-kicker'>{_t("metric_kicker")}</div>
-              <div class='nl-metric-row'>
-                <span class='nl-metric-value'>{_t("metric_value")}</span>
-                <span class='nl-metric-aside'>{_t("metric_percent")}</span>
-              </div>
-              <div class='nl-metric-cap'>{_t("metric_caption")}</div>
-            </div>
-            """,
-            unsafe_allow_html=True,
-        )
-    with col_b:
-        st.markdown(
-            f"""
-            <div class='nl-metric'>
-              <div class='nl-kicker'>{_t("research_kicker")}</div>
-              <div class='nl-metric-row'>
-                <span class='nl-metric-value'>{_t("research_value")}</span>
-              </div>
-              <div class='nl-metric-cap'>{_t("research_caption")}</div>
-            </div>
-            """,
-            unsafe_allow_html=True,
-        )
-
-    samples = SAMPLE_QUESTIONS.get(db_id)
-    if not samples:
-        st.markdown(
-            f"<div class='nl-section-label'>{_t('ask_intro_label')}</div>",
-            unsafe_allow_html=True,
-        )
-        st.info(_t("no_samples"))
-        return
-
-    st.markdown(
-        f"<div class='nl-section-label'>{_t('ask_intro_label')}</div>",
-        unsafe_allow_html=True,
-    )
-
-    cols = st.columns(len(samples))
-    diff_map = {
-        "simple": _t("diff_simple"),
-        "moderate": _t("diff_moderate"),
-        "challenging": _t("diff_challenging"),
-    }
-    for col, (difficulty, question) in zip(cols, samples, strict=False):
-        with col:
-            st.markdown(
-                f"<div class='nl-sample-kicker'>{diff_map.get(difficulty, difficulty)}</div>",
-                unsafe_allow_html=True,
-            )
-            if st.button(
-                question,
-                key=f"sample_{db_id}_{hash(question)}",
-                use_container_width=True,
-            ):
-                st.session_state.pending_question = question
-                st.rerun()
-
-
-# ---------------------------------------------------------------------- main
-
-
-def _render_lang_toggle() -> None:
-    """Two flat segments: EN / RU. Active one inverts."""
-    lang = st.session_state.get("lang", "en")
-    st.markdown(f"<div class='nl-side-sub'>{_t('lang_label')}</div>", unsafe_allow_html=True)
-    cols = st.columns(2)
-    with cols[0]:
-        if st.button(
-            _t("lang_en"),
-            key="lang_en_btn",
-            use_container_width=True,
-            type="primary" if lang == "en" else "secondary",
-        ):
-            st.session_state.lang = "en"
-            st.rerun()
-    with cols[1]:
-        if st.button(
-            _t("lang_ru"),
-            key="lang_ru_btn",
-            use_container_width=True,
-            type="primary" if lang == "ru" else "secondary",
-        ):
-            st.session_state.lang = "ru"
-            st.rerun()
+from bootstrap import bootstrap, make_pipeline
+from components.output import render_output
+from components.schema_explorer import render_schema_explorer
+from components.show_working import render_show_working
+from components.welcome import render_lang_toggle, render_welcome
+from i18n import t
+from nl_sql.agent.graph import PipelineRunResult, run_pipeline
+from samples import source_link_for
+from theme import inject_chrome
 
 
 def main() -> None:
@@ -1010,14 +32,14 @@ def main() -> None:
         st.session_state.lang = "en"
 
     st.set_page_config(
-        page_title=_t("page_title"),
+        page_title=t("page_title"),
         layout="wide",
     )
 
-    _inject_chrome()
+    inject_chrome()
 
     try:
-        registry, schema_index, sql_provider, explain_provider = _bootstrap()
+        registry, schema_index, sql_provider, explain_provider = bootstrap()
     except RuntimeError as exc:
         st.error(str(exc))
         st.stop()
@@ -1026,10 +48,10 @@ def main() -> None:
     # --- sidebar
     with st.sidebar:
         st.markdown("<div class='nl-side-h'>NL→SQL</div>", unsafe_allow_html=True)
-        _render_lang_toggle()
+        render_lang_toggle()
 
         st.markdown(
-            f"<div class='nl-side-sub'>{_t('db_label')}</div>",
+            f"<div class='nl-side-sub'>{t('db_label')}</div>",
             unsafe_allow_html=True,
         )
         db_ids = registry.ids()
@@ -1039,52 +61,49 @@ def main() -> None:
         default_idx = (
             db_ids.index("bird_california_schools") if "bird_california_schools" in db_ids else 0
         )
-        db_id = st.selectbox(
-            _t("db_label"), db_ids, index=default_idx, label_visibility="collapsed"
-        )
+        db_id = st.selectbox(t("db_label"), db_ids, index=default_idx, label_visibility="collapsed")
         spec = registry.get(db_id)
-        st.caption(f"{_t('db_dialect')}: `{spec.dialect}`")
+        st.caption(f"{t('db_dialect')}: `{spec.dialect}`")
         if spec.description:
             st.caption(spec.description)
-
-        link = _source_link_for(db_id)
+        link = source_link_for(db_id)
         if link is not None:
             label, url = link
-            st.caption(f"{_t('db_source')}: [{label}]({url})")
+            st.caption(f"{t('db_source')}: [{label}]({url})")
 
-        _render_schema_explorer(db_id)
+        render_schema_explorer(db_id)
 
         st.markdown(
-            f"<div class='nl-side-sub'>{_t('mode_header')}</div>",
+            f"<div class='nl-side-sub'>{t('mode_header')}</div>",
             unsafe_allow_html=True,
         )
         mode = st.radio(
-            _t("mode_header"),
-            options=(_t("mode_accurate"), _t("mode_fast"), _t("mode_debug")),
+            t("mode_header"),
+            options=(t("mode_accurate"), t("mode_fast"), t("mode_debug")),
             index=0,
             captions=(
-                _t("mode_accurate_caption"),
-                _t("mode_fast_caption"),
-                _t("mode_debug_caption"),
+                t("mode_accurate_caption"),
+                t("mode_fast_caption"),
+                t("mode_debug_caption"),
             ),
             label_visibility="collapsed",
         )
-        if mode == _t("mode_fast"):
+        if mode == t("mode_fast"):
             fewshot_top_k = 0
             verify_retry_on_empty = False
         else:
             fewshot_top_k = 3
             verify_retry_on_empty = True
 
-        with st.expander(_t("advanced_header"), expanded=False):
-            schema_top_k = st.slider(_t("schema_top_k"), 1, 10, 5)
-            fk_hops = st.slider(_t("fk_hops"), 0, 2, 1)
-            table_budget = st.slider(_t("table_budget"), 4, 20, 12)
-            sort_schema_block = st.checkbox(_t("sort_schema"), value=True)
-            extended_sample_size = st.slider(_t("sample_size"), 0, 8, 0)
+        with st.expander(t("advanced_header"), expanded=False):
+            schema_top_k = st.slider(t("schema_top_k"), 1, 10, 5)
+            fk_hops = st.slider(t("fk_hops"), 0, 2, 1)
+            table_budget = st.slider(t("table_budget"), 4, 20, 12)
+            sort_schema_block = st.checkbox(t("sort_schema"), value=True)
+            extended_sample_size = st.slider(t("sample_size"), 0, 8, 0)
 
         st.markdown("<div style='height:1.4rem'></div>", unsafe_allow_html=True)
-        if st.button(_t("clear_chat"), use_container_width=True):
+        if st.button(t("clear_chat"), use_container_width=True):
             st.session_state.messages = []
             st.rerun()
 
@@ -1092,7 +111,7 @@ def main() -> None:
         st.session_state.messages = []
 
     if not st.session_state.messages:
-        _render_welcome(db_id)
+        render_welcome(db_id)
 
     for msg in st.session_state.messages:
         with st.chat_message(msg["role"]):
@@ -1101,7 +120,7 @@ def main() -> None:
             else:
                 _replay_assistant_turn(msg)
 
-    typed = st.chat_input(_t("ask_placeholder"))
+    typed = st.chat_input(t("ask_placeholder"))
     queued = st.session_state.pop("pending_question", None)
     question = queued or typed
     if not question:
@@ -1111,7 +130,7 @@ def main() -> None:
     with st.chat_message("user"):
         st.markdown(question)
 
-    pipeline = _make_pipeline(
+    pipeline = make_pipeline(
         registry,
         schema_index,
         sql_provider,
@@ -1126,7 +145,7 @@ def main() -> None:
     )
 
     with st.chat_message("assistant"):
-        with st.spinner(_t("spinner_generating")):
+        with st.spinner(t("spinner_generating")):
             t0 = time.perf_counter()
             try:
                 result = run_pipeline(
@@ -1138,24 +157,24 @@ def main() -> None:
                     verify_retry_on_empty=verify_retry_on_empty,
                 )
             except Exception as exc:
-                st.error(_t("pipeline_crashed", kind=type(exc).__name__, msg=str(exc)))
+                st.error(t("pipeline_crashed", kind=type(exc).__name__, msg=str(exc)))
                 st.session_state.messages.append(
                     {"role": "assistant", "error": str(exc), "question": question}
                 )
                 return
             wall_ms = (time.perf_counter() - t0) * 1000
 
-        _render_output(result.output_format, caption=result.caption)
+        render_output(result.output_format, caption=result.caption)
 
         if result.sql:
-            st.markdown(f"**{_t('sql_label')}**")
+            st.markdown(f"**{t('sql_label')}**")
             st.code(result.sql, language="sql")
         else:
-            st.warning(_t("no_sql"))
+            st.warning(t("no_sql"))
 
-        st.caption(_t("wall_model", wall=wall_ms, model=sql_provider.model))
+        st.caption(t("wall_model", wall=wall_ms, model=sql_provider.model))
 
-        _render_show_working(result)
+        render_show_working(result)
 
         st.session_state.messages.append(
             {
@@ -1170,14 +189,14 @@ def main() -> None:
 
 def _replay_assistant_turn(msg: dict[str, Any]) -> None:
     if msg.get("error"):
-        st.error(_t("pipeline_crashed", kind="prior", msg=msg["error"]))
+        st.error(t("pipeline_crashed", kind="prior", msg=msg["error"]))
         return
     result = cast(PipelineRunResult, msg["result"])
-    _render_output(result.output_format, caption=result.caption)
+    render_output(result.output_format, caption=result.caption)
     if result.sql:
         st.code(result.sql, language="sql")
-    st.caption(_t("wall_model", wall=msg.get("wall_ms", 0), model=msg.get("model", "?")))
-    _render_show_working(result)
+    st.caption(t("wall_model", wall=msg.get("wall_ms", 0), model=msg.get("model", "?")))
+    render_show_working(result)
 
 
 if __name__ == "__main__":

app/theme.py

@@ -0,0 +1,343 @@
+"""Editorial monochrome theme: typography, chrome CSS, chart styling.
+
+Two custom faces (Stetica sans for chrome, TT Norms Pro Serif for display).
+Ink-on-paper palette. One accent — ink-fill on hover. No color drama in charts.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import streamlit as st
+
+FONT_CSS = """
+<style>
+@font-face {
+  font-family: 'Stetica';
+  src: url('/app/static/fonts/stetica-regular.otf') format('opentype');
+  font-weight: 400;
+  font-style: normal;
+  font-display: swap;
+}
+@font-face {
+  font-family: 'Stetica';
+  src: url('/app/static/fonts/stetica-medium.otf') format('opentype');
+  font-weight: 500;
+  font-style: normal;
+  font-display: swap;
+}
+@font-face {
+  font-family: 'Stetica';
+  src: url('/app/static/fonts/stetica-bold.otf') format('opentype');
+  font-weight: 700;
+  font-style: normal;
+  font-display: swap;
+}
+@font-face {
+  font-family: 'NLEdSerif';
+  src: url('/app/static/fonts/serif-regular.otf') format('opentype');
+  font-weight: 400;
+  font-style: normal;
+  font-display: swap;
+}
+@font-face {
+  font-family: 'NLEdSerif';
+  src: url('/app/static/fonts/serif-bold.otf') format('opentype');
+  font-weight: 700;
+  font-style: normal;
+  font-display: swap;
+}
+
+:root {
+  --ink:        #111111;
+  --ink-soft:   #4A4A4A;
+  --ink-mute:   #7A7A75;
+  --paper:      #FAFAF7;
+  --paper-warm: #F1EFE9;
+  --rule:       #1A1A1A;
+  --hairline:   #DCD8CE;
+}
+
+html, body, [class*="css"], .stApp, .stMarkdown, .stChatMessage {
+  font-family: 'Stetica', system-ui, sans-serif !important;
+  color: var(--ink);
+  background: var(--paper);
+}
+
+.block-container {
+  padding-top: 2.4rem;
+  padding-bottom: 4rem;
+  max-width: 1080px;
+}
+
+/* Hide Streamlit chrome we don't want */
+#MainMenu, footer, header [data-testid="stToolbar"] { visibility: hidden; }
+header { background: var(--paper) !important; }
+
+/* Display headline — serif */
+.nl-display {
+  font-family: 'NLEdSerif', Georgia, serif;
+  font-weight: 400;
+  font-size: clamp(2.6rem, 5vw, 3.6rem);
+  letter-spacing: -0.02em;
+  line-height: 0.95;
+  color: var(--ink);
+  margin: 0 0 0.4rem 0;
+}
+.nl-display .arrow {
+  font-weight: 700;
+  display: inline-block;
+  transform: translateY(-0.04em);
+  margin: 0 0.25rem;
+}
+
+.nl-tagline {
+  font-family: 'Stetica', system-ui, sans-serif;
+  font-weight: 400;
+  font-size: 1.02rem;
+  line-height: 1.5;
+  color: var(--ink-soft);
+  max-width: 56ch;
+  margin: 0 0 2rem 0;
+}
+
+/* Kicker — small uppercase letter-spaced label */
+.nl-kicker {
+  font-family: 'Stetica', sans-serif;
+  font-size: 0.68rem;
+  letter-spacing: 0.18em;
+  text-transform: uppercase;
+  color: var(--ink-mute);
+  margin-bottom: 0.5rem;
+}
+
+/* Metric block — pure typography, no card chrome */
+.nl-metric {
+  border-top: 1px solid var(--rule);
+  padding-top: 0.8rem;
+  margin-top: 1.4rem;
+}
+.nl-metric-row {
+  display: flex;
+  align-items: baseline;
+  gap: 0.9rem;
+  margin-bottom: 0.5rem;
+}
+.nl-metric-value {
+  font-family: 'NLEdSerif', Georgia, serif;
+  font-weight: 700;
+  font-size: 2.2rem;
+  letter-spacing: -0.01em;
+  color: var(--ink);
+  line-height: 1;
+}
+.nl-metric-aside {
+  font-family: 'Stetica', sans-serif;
+  font-size: 0.86rem;
+  color: var(--ink-mute);
+  letter-spacing: 0.04em;
+}
+.nl-metric-cap {
+  font-family: 'Stetica', sans-serif;
+  font-size: 0.86rem;
+  color: var(--ink-soft);
+  line-height: 1.55;
+  max-width: 62ch;
+}
+.nl-term {
+  border-bottom: 1px dotted var(--ink-mute);
+  cursor: help;
+  text-decoration: none;
+  color: inherit;
+}
+.nl-term:hover {
+  border-bottom-color: var(--ink);
+  color: var(--ink);
+}
+
+/* Section rule */
+.nl-section-label {
+  font-family: 'Stetica', sans-serif;
+  font-size: 0.68rem;
+  letter-spacing: 0.18em;
+  text-transform: uppercase;
+  color: var(--ink-mute);
+  margin: 2.4rem 0 0.7rem 0;
+  border-top: 1px solid var(--hairline);
+  padding-top: 0.7rem;
+}
+
+/* Sidebar polish */
+[data-testid="stSidebar"] {
+  background: var(--paper-warm) !important;
+  border-right: 1px solid var(--hairline);
+}
+[data-testid="stSidebar"] .nl-side-h {
+  font-family: 'NLEdSerif', Georgia, serif;
+  font-weight: 700;
+  font-size: 1.1rem;
+  letter-spacing: -0.005em;
+  margin: 0.4rem 0 0.6rem 0;
+}
+[data-testid="stSidebar"] .nl-side-sub {
+  font-family: 'Stetica', sans-serif;
+  font-size: 0.7rem;
+  letter-spacing: 0.18em;
+  text-transform: uppercase;
+  color: var(--ink-mute);
+  margin: 1.2rem 0 0.4rem 0;
+}
+
+/* Language toggle */
+.nl-lang-row { display: flex; gap: 0; }
+.nl-lang-row button {
+  background: transparent !important;
+  color: var(--ink) !important;
+  border: 1px solid var(--rule) !important;
+  border-radius: 0 !important;
+  font-family: 'Stetica', sans-serif !important;
+  font-weight: 500 !important;
+  letter-spacing: 0.12em !important;
+  text-transform: uppercase;
+  padding: 0.35rem 0.9rem !important;
+  font-size: 0.74rem !important;
+  min-height: 0 !important;
+}
+
+/* Buttons (sample questions) */
+.stButton > button {
+  background: transparent !important;
+  color: var(--ink) !important;
+  border: 1px solid var(--rule) !important;
+  border-radius: 0 !important;
+  font-family: 'Stetica', sans-serif !important;
+  font-weight: 400 !important;
+  font-size: 0.92rem !important;
+  text-align: left !important;
+  padding: 0.85rem 1rem !important;
+  line-height: 1.45 !important;
+  transition: background 0.12s;
+  white-space: normal !important;
+  height: auto !important;
+}
+.stButton > button:hover {
+  background: var(--ink) !important;
+  color: var(--paper) !important;
+}
+.stButton > button p {
+  color: inherit !important;
+}
+
+/* Chat input */
+.stChatInput { border-top: 1px solid var(--rule) !important; }
+.stChatInput textarea {
+  font-family: 'Stetica', sans-serif !important;
+  font-size: 1rem !important;
+  color: var(--ink) !important;
+  background: var(--paper) !important;
+}
+
+/* Code blocks — keep mono but on warm paper */
+pre, code {
+  background: var(--paper-warm) !important;
+  color: var(--ink) !important;
+  border: 1px solid var(--hairline) !important;
+  border-radius: 0 !important;
+  font-family: 'JetBrains Mono', 'IBM Plex Mono', ui-monospace, monospace !important;
+}
+
+/* Scalar metric block — flatten */
+[data-testid="stMetric"] {
+  background: transparent !important;
+  border: none !important;
+}
+[data-testid="stMetricLabel"] {
+  font-family: 'Stetica', sans-serif !important;
+  font-size: 0.68rem !important;
+  letter-spacing: 0.18em !important;
+  text-transform: uppercase !important;
+  color: var(--ink-mute) !important;
+}
+[data-testid="stMetricValue"] {
+  font-family: 'NLEdSerif', Georgia, serif !important;
+  font-weight: 700 !important;
+  font-size: 2.4rem !important;
+  color: var(--ink) !important;
+}
+
+/* Tables */
+[data-testid="stDataFrame"] { border: 1px solid var(--rule); }
+
+/* Expanders */
+.streamlit-expanderHeader {
+  font-family: 'Stetica', sans-serif !important;
+  font-size: 0.78rem !important;
+  letter-spacing: 0.1em;
+  text-transform: uppercase;
+  color: var(--ink) !important;
+}
+
+/* Sample card — wraps a button + difficulty kicker */
+.nl-sample {
+  display: block;
+}
+.nl-sample-kicker {
+  font-family: 'Stetica', sans-serif;
+  font-size: 0.62rem;
+  letter-spacing: 0.22em;
+  text-transform: uppercase;
+  color: var(--ink-mute);
+  margin: 0 0 0.4rem 0.05rem;
+}
+
+/* Chat message bubbles — strip default round chrome */
+[data-testid="stChatMessage"] {
+  background: transparent !important;
+  border: 0 !important;
+  padding: 0.4rem 0 1.4rem 0 !important;
+}
+[data-testid="stChatMessage"]:not(:first-child) {
+  border-top: 1px solid var(--hairline) !important;
+  padding-top: 1.4rem !important;
+}
+
+/* Remove the avatar/icon circle Streamlit injects — covers every variant */
+[data-testid="stChatMessage"] > div:first-child,
+[data-testid="chatAvatarIcon-user"],
+[data-testid="chatAvatarIcon-assistant"],
+[data-testid="stChatMessageAvatarUser"],
+[data-testid="stChatMessageAvatarAssistant"],
+[data-testid="stChatMessage"] [class*="Avatar"],
+[data-testid="stChatMessage"] svg {
+  display: none !important;
+}
+
+/* The chat message body lives in second child after the avatar; pull it left */
+[data-testid="stChatMessage"] > div:nth-child(2) {
+  margin-left: 0 !important;
+  padding-left: 0 !important;
+  width: 100% !important;
+}
+</style>
+"""
+
+
+CHART_PALETTE = ["#111111", "#4A4A4A", "#7A7A75", "#A8A29E", "#1A1A1A"]
+
+
+def inject_chrome() -> None:
+    st.markdown(FONT_CSS, unsafe_allow_html=True)
+
+
+def style_fig(fig: Any) -> Any:
+    fig.update_layout(
+        font_family="Stetica, system-ui, sans-serif",
+        font_color="#111111",
+        paper_bgcolor="#FAFAF7",
+        plot_bgcolor="#FAFAF7",
+        colorway=CHART_PALETTE,
+        margin=dict(l=10, r=10, t=20, b=10),
+    )
+    fig.update_xaxes(gridcolor="#DCD8CE", zerolinecolor="#1A1A1A", tickcolor="#1A1A1A")
+    fig.update_yaxes(gridcolor="#DCD8CE", zerolinecolor="#1A1A1A", tickcolor="#1A1A1A")
+    return fig

chroma_data/chroma.sqlite3

@@ -1,3 +1,3 @@
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-oid sha256:c491d528453ed7ac5a3c1307de64b6d6dce423e776519786ebd2b53f400bdd9b
+oid sha256:90181e0a2514fbaf80a6de4c6307593dba4e93e9e6da6bd70600553a2651667b
 size 18161664

chroma_data/fc9668d3-4384-40d9-aa8d-0010807a5a68/data_level0.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3b52e26715cd41449869575cd2942cc073cd0f050a973a5ff0457a23c0fdc980
+oid sha256:de3c405eda8dc30523c29aa0475a570ba0de2526b2ca29b21f1bf0f3ea94d6e0
 size 423600

chroma_data/fc9668d3-4384-40d9-aa8d-0010807a5a68/length.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:636f62e3e134c06897a7369cb4bf5148c611d55b11f01ae626d4322d78d10691
+oid sha256:39f574a82964186c006c0c4d50f49095c2abb6b4614ca4e36f953667e96281da
 size 400

docs/NEXT_SESSION.md

@@ -3,6 +3,27 @@
 > Один лист, без воды. Берёшь, делаешь, обновляешь `SESSION_HANDOFF.md`,
 > переписываешь этот файл под следующий sprint.
 
+## 2026-05-26 EOD-7 — autonomous housekeeping sprint (HEAD `4207df0`, pushed)
+
+**Cleared today (one-shot autonomous run):**
+- **Push backlog cleared.** 8 локальных commits на origin/main (`03ad6ae..a47a7fe` was +8 ahead; теперь поверх ещё `a7c1d81` + `4207df0`). Origin синхронен.
+- **HF Space redeployed на v31 94.0%.** `.deploy_hf.py` upload + auto-LFS, RUNNING ~90s. Playwright E2E: EN 94.0% визибл, нет stale 92.5%. `short_description` 92.5% → 94.0%. Screenshot: `docs/ui-live-v31.png`.
+- **Kimi P1.3 closed** (`a7c1d81`): `app/streamlit_app.py` 1184 → 200 lines через split на 8 модулей (i18n.py, theme.py, samples.py, bootstrap.py + components/ пакет). `pyproject.toml` `[tool.ruff].src` расширен `["src","tests","app"]`. Local Streamlit + Playwright E2E подтвердил zero behavior change: EN 94.0%, RU 94,0%, schema explorer render OK.
+- **Kimi P1.6 closed** (`4207df0`): API coverage **58% → 89%**. Extracted `Singletons` NamedTuple + `get_singletons()` Depends-factory. Все 3 pipeline-touching routes (`/readyz`, `/databases`, `/ask`) теперь принимают `Singletons = Depends(get_singletons)`. Production callers через `@lru_cache(maxsize=1)` на `_make_singletons` (zero behavior change). New `tests/api/test_api_routes_mocked.py` — 13 tests покрывают healthy/empty-chroma/empty-registry/factory-raises /readyz пути, auth + schema-collection-exception /databases пути, unknown-db / canned-result / error-kind / confidence-buckets /ask пути, + rate-limit 60→61st 429.
+- **Gates:** 370 pytest pass (357+13), ruff check + format clean, mypy strict 0/59, P3.F acceptance 11/11 PASS, audit_rescore 0 mismatches.
+
+**Open backlog после EOD-7:**
+
+| # | Severity | Scope | Estimate |
+|---|---|---|---|
+| Codex #7 | P2 latent | `scripts/rescore_arcwise.py:82` stale `rec["match"]` — verified 0/200 disagreements on v29, transitions output unchanged if fixed | 30min, deferred |
+| Codex #8 | P2 latent | `execution_accuracy.py` `_hashable` float bucketing — verified 0 set-mismatch in v22-v30 baselines | 1h, deferred |
+| Codex #10 | P2 latent | `cache.py:88` cache miss/fill race — fires только при parallel workers (not currently used) | 1h, deferred |
+
+**Past 94.0% (gated к юзеру):** requires paid OR top-up / fine-tune / metric pivot. Residue 12 qids — large majority BIRD-annotation-quirks (unanimous fail через 3-model reasoning sweeps EOD-2 + EOD-4). Saturation подтверждена.
+
+---
+
 ## 2026-05-26 — **v31 = 94.0% EA** verified (+1.04pp над human-expert baseline)
 
 **Headline:** 93.5% (v30) → **94.0% / 200 (v31)** через targeted P3.F schema-link hint для qid 37 на v30 residue. **Выше human-expert baseline 92.96% (BIRD paper) на +1.04pp.** Per-tier v31: simple **97.0%** (65/67), moderate **92.9%** (92/99, +1.0pp от v30 91.9%), challenging **91.2%** (31/34).
@@ -194,9 +215,9 @@ fetch). Local heterogeneous CSC lever остаётся parked.
 
 | # | Severity | Scope | Estimate |
 |---|---|---|---|
-| Kimi P1.3 | P1 | `app/streamlit_app.py` 1184 lines → split (`components/`, `theme.py`, `i18n/`) | 1.5h |
+| ~~Kimi P1.3~~ | **Done 2026-05-26 EOD-7** (`a7c1d81`) | `app/streamlit_app.py` 1184 → 200 lines split: `i18n.py`/`theme.py`/`samples.py`/`bootstrap.py` + `components/{output,show_working,schema_explorer,welcome}.py`. `pyproject.toml` ruff `src` расширен `["src","tests","app"]`. Local Streamlit + Playwright E2E подтвердил EN 94.0% / RU 94,0% / schema explorer render OK. Zero behavior change, 357 pytest pass. | 1.5h |
 | ~~Kimi P1.4~~ | **Done 2026-05-26** | `src/nl_sql/agent/nodes/_support.py` 483 lines → `_support.py` (public API, 184 lines) + `_text_utils.py` (JSON parsing, 53 lines) + `_hints.py` (schema appendices, 302 lines). Zero behavior change, 355 pytest pass, ruff + mypy strict clean. | 1h |
-| Kimi P1.6 | P1 | API coverage 58% → DI для `_make_singletons` + mock provider в API tests | 1.5h |
+| ~~Kimi P1.6~~ | **Done 2026-05-26 EOD-7** (`4207df0`) | API coverage **58% → 89%**. Extracted `Singletons` NamedTuple + `get_singletons()` FastAPI Depends-factory. `/readyz`, `/databases`, `/ask` теперь принимают `Singletons = Depends(get_singletons)`; production callers idiomatic через `@lru_cache(maxsize=1)` на `_make_singletons` (zero behavior change). New `tests/api/test_api_routes_mocked.py` (13 tests) покрывает /readyz healthy/empty/raises пути, /databases auth + schema-exception, /ask unknown-db / canned-result / error-kind / confidence-buckets + rate-limit 60→61st 429. | 1.5h |
 | Codex #7 | P2 latent | `scripts/rescore_arcwise.py:82` transition buckets используют stale `rec["match"]` вместо recomputed `out_entry["original_match"]` (line 141 overwrite). **Reachability verified 2026-05-26: 0/200 stale-vs-fresh disagreements в `eval/reports/2026-05-24/v29-arcwise-rescored.json`** — bug latent, transitions counts (7 gained / 91 lost) honest. Fix = 1-line swap, no observable change в output. | 30min, deferred |
 | Codex #8 | P2 latent | `execution_accuracy.py:209-221` `_hashable` bucketing через `round(v / 1e-6)` может развести два tolerance-equivalent rows (diff ~9e-7, banker's rounding edge) в разные buckets → set-mode false negative. **Reachability verified 2026-05-26: 0 set-mismatch records в v22-v30 baselines (200 records each); 8 set-mismatch в demo runs 2026-05-11, все honest column-count diff не float-bucket.** Fix = replace `_hashable` с pair-wise tolerance match (O(n²)). | 1h, deferred |
 | ~~Codex #9~~ | **false positive 2026-05-26** | `cache.py:77` cache key omits `req.json_mode`. **Не достижимо в текущем коде:** `src/nl_sql/llm/providers/groq.py:44` force-set'ит `json_mode=True` через `req.model_copy` на каждом Groq call; Mistral codestral игнорирует поле (`base.py:21` docstring). Per (provider, model) пара `json_mode` имеет константное значение → collision impossible. Не трогать (попытка fix landed 2026-05-26, reverted после Codex+Kimi independent review). | closed |

docs/SESSION_HANDOFF.md

@@ -1,5 +1,38 @@
-# NL_SQL — Session Handoff (2026-05-26: v31 = 94.0% EA via P3.F qid 37 + Kimi P1.4 `_support.py` split + Codex P2 reachability audit; HEAD будет два новых commit'а поверх `3c82e37`, **push gated к юзеру**)
-
+# NL_SQL — Session Handoff (2026-05-26 EOD-7: Kimi P1.3 + P1.6 closed, 8 housekeeping commits pushed, HF Space live на v31 94.0%)
+
+> **Tl;dr 2026-05-26 EOD-7 — autonomous housekeeping sprint (HEAD `4207df0`, pushed):**
+>
+> 1. **Push backlog cleared:** 8 локальных commits (от `03ad6ae` до `a47a7fe`) запушены на `origin/main`. Origin теперь синхронен с локальным состоянием поверх v31 94.0%.
+> 2. **HF Space deploy → v31 94.0%:** `.deploy_hf.py` upload + auto LFS, RUNNING за ~90s, Playwright E2E подтвердил EN headline `94.0%` визибл без stale `92.5%`. `short_description` поднят 92.5% → 94.0%. Скриншот: `docs/ui-live-v31.png`.
+> 3. **Kimi P1.3 closed (`a7c1d81` refactor):** `app/streamlit_app.py` 1184 → **200 lines** через декомпозицию на 8 модулей:
+>    - `app/i18n.py` (187 lines) — I18N dict + `t()` helper
+>    - `app/theme.py` (343 lines) — FONT_CSS + inject_chrome + CHART_PALETTE + style_fig
+>    - `app/samples.py` (122 lines) — SAMPLE_QUESTIONS + SOURCE_LINKS
+>    - `app/bootstrap.py` (93 lines) — bootstrap + make_pipeline
+>    - `app/components/output.py` (83 lines) — render_output + chart helpers + label classifiers
+>    - `app/components/show_working.py` (55 lines) — render_show_working
+>    - `app/components/schema_explorer.py` (42 lines) — render_schema_explorer + fetch_schema_chunks
+>    - `app/components/welcome.py` (104 lines) — render_welcome + render_lang_toggle
+>
+>    `pyproject.toml`: `[tool.ruff].src` расширен с `["src", "tests"]` до `["src", "tests", "app"]` — sibling imports (Streamlit script-dir injection) теперь сортируются как first-party. Zero behavior change verified локально: `uv run streamlit run app/streamlit_app.py --server.port 8517` + Playwright E2E → EN headline 94.0% + RU toggle на 94,0% + Schema explorer label рендерятся.
+> 4. **Kimi P1.6 closed (`4207df0` test):** API coverage **58% → 89%**. Extracted `Singletons` NamedTuple + `get_singletons()` FastAPI Depends-factory из `src/nl_sql/api/main.py`. Routes `/readyz`, `/databases`, `/ask` теперь принимают `singletons: Singletons = Depends(get_singletons)`. /readyz preserves try/except graceful-degrade через `app.dependency_overrides.get(get_singletons, get_singletons)()`. Production callers пользуются `@lru_cache(maxsize=1)` на `_make_singletons` (zero behavior change).
+>
+>    New `tests/api/test_api_routes_mocked.py` (13 tests) полностью покрывает business logic:
+>    - `/readyz`: healthy / empty-chroma / empty-registry / factory-raises
+>    - `/databases`: list + table_count / auth missing→401 / auth correct→200 / schema-collection exception→table_count 0
+>    - `/ask`: unknown db_id→404 / canned PipelineRunResult passthrough / error-kind propagation / confidence label buckets (High/Medium/Low/Unknown)
+>    - rate-limit: 60 → 61st 429 with Retry-After header
+>
+>    Coverage breakdown: 207 statements, 22 missing (только live Mistral/Chroma bootstrap `_build_pipeline_components` + `_make_singletons` LRU + минимальные edge lines).
+> 5. **Gates:** **370 pytest pass** (was 357 + 13 new), ruff check + format clean, mypy strict 0/59 issues, P3.F acceptance 11/11 PASS, audit_rescore 0 mismatches.
+> 6. **Memory state:** all 4 EOD-7 commits live на origin. HF Space синхронен. P1.3 + P1.6 backlog cleared.
+>
+> **Не закрыто (deferred / по приоритету):**
+> - Codex #7 / #8 / #10 — все P2 latent, 0 production impact verified (NEXT_SESSION.md secs Open Audit Items)
+> - SESSION_HANDOFF / NEXT_SESSION хисторий не trimmed (handoff > 200 строк прошлых snapshots — OK для cold pickup но можно archive в будущем).
+>
+> ---
+>
 > **Tl;dr 2026-05-26 — v31 = 94.0% EA (+1.04pp над human-expert baseline) + housekeeping + refactor:**
 >
 > 1. **v31 EA move (most important):** v30 93.5% → **v31 94.0%** через one targeted P3.F schema-link hint для qid 37 moderate california_schools. BIRD gold инвертирует question word-order `"Street, City, Zip and State"` → SELECT `(Street, City, State, Zip)`. Pure column-order BIRD-quirk + projection-discipline override. Phrase `"lowest excellence rate"` уникальна для qid 37 в n=200. Pred ≡ gold verbatim. Per-tier v31: simple 97.0% (65/67) / **moderate 92.9% (92/99, +1.0pp от v30)** / challenging 91.2% (31/34). Артефакт: `eval/reports/2026-05-26/v31-v30-plus-p3f-q37-merged.json`, audit 0 mismatches, p3f_acceptance 11/11 PASS.

pyproject.toml

@@ -65,7 +65,7 @@ filterwarnings = [
 [tool.ruff]
 line-length = 100
 target-version = "py313"
-src = ["src", "tests"]
+src = ["src", "tests", "app"]
 
 [tool.ruff.lint]
 select = [

src/nl_sql/api/main.py

@@ -1,405 +1,429 @@
-"""FastAPI surface for the NL→SQL Assistant.
-
-Endpoints:
-    GET  /healthz        — liveness probe + provider configuration snapshot.
-    GET  /readyz         — readiness probe (Chroma + DB registry reachable).
-    GET  /databases      — list registered DBs + table counts.
-    POST /ask            — translate question to SQL, execute, return result.
-    GET  /eval/latest    — metadata of the latest committed eval report.
-
-Auth:
-    Set ``NL_SQL_API_KEY`` (env, .env, or settings). When set, every request
-    to /ask and /databases must include ``X-API-Key`` matching it. /healthz
-    and /readyz are always open for orchestrator probes.
-
-Rate limit:
-    In-process token bucket per API key (60 req/min default). No external
-    Redis — this is a single-replica portfolio demo, not a fleet service.
-"""
-
-from __future__ import annotations
-
-import time
-import uuid
-from collections import defaultdict, deque
-from functools import lru_cache
-from pathlib import Path
-from typing import Any
-
-from fastapi import Depends, FastAPI, Header, HTTPException, Request, status
-from pydantic import BaseModel, Field
-
-from nl_sql import __version__
-from nl_sql.agent.graph import (
-    PipelineConfig,
-    PipelineRunResult,
-    build_pipeline,
-    run_pipeline,
-)
-from nl_sql.config import Settings, get_settings
-from nl_sql.db.registry import DatabaseRegistry, get_default_registry
-from nl_sql.llm.cache import CachingEmbeddingProvider, CachingLLMProvider
-from nl_sql.llm.providers import build_provider
-from nl_sql.llm.providers.base import EmbeddingProvider, LLMProvider
-from nl_sql.llm.providers.mistral import MistralProvider
-from nl_sql.schema_index.indexer import SchemaIndex
-
-# ---------------------------------------------------------- response models
-
-
-class HealthResponse(BaseModel):
-    status: str
-    version: str
-    providers_configured: list[str]
-
-
-class ReadyResponse(BaseModel):
-    status: str
-    chroma_ok: bool
-    registry_ok: bool
-    registered_dbs: int
-    schema_chunks: int
-
-
-class DatabaseInfo(BaseModel):
-    db_id: str
-    dialect: str
-    description: str = ""
-    table_count: int
-
-
-class DatabasesResponse(BaseModel):
-    databases: list[DatabaseInfo]
-
-
-class AskRequest(BaseModel):
-    question: str = Field(min_length=1, max_length=2000)
-    db_id: str = Field(min_length=1)
-
-
-class TraceStep(BaseModel):
-    node: str
-    model: str | None = None
-    tokens_in: int | None = None
-    tokens_out: int | None = None
-    confidence: float | None = None
-
-
-class AskResponse(BaseModel):
-    trace_id: str
-    db_id: str
-    sql: str
-    rationale: str
-    confidence: float
-    confidence_label: str
-    rows: list[list[Any]] | None
-    columns: list[str] | None
-    row_count: int
-    caption: str
-    output_format: str | None
-    error_kind: str | None
-    error_message: str
-    repair_attempted: bool
-    latency_ms: float
-    trace: list[TraceStep]
-
-
-class EvalLatestResponse(BaseModel):
-    configuration: str
-    sql_model: str
-    overall_ea: float | None
-    n: int
-    report_path: str
-
-
-# ---------------------------------------------------------- helpers
-
-
-def _confidence_label(value: float) -> str:
-    if value >= 0.8:
-        return "High"
-    if value >= 0.5:
-        return "Medium"
-    if value > 0.0:
-        return "Low"
-    return "Unknown"
-
-
-def _result_to_response(result: PipelineRunResult, *, latency_ms: float) -> AskResponse:
-    rows: list[list[Any]] | None = None
-    columns: list[str] | None = None
-    row_count = 0
-    if result.outcome is not None and result.outcome.result is not None:
-        rows = [list(r) for r in result.outcome.result.rows]
-        columns = list(result.outcome.result.columns)
-        row_count = result.outcome.result.row_count
-
-    trace_steps: list[TraceStep] = []
-    for step in result.trace:
-        trace_steps.append(
-            TraceStep(
-                node=str(step.get("node", "?")),
-                model=step.get("model"),  # type: ignore[arg-type]
-                tokens_in=step.get("input_tokens"),  # type: ignore[arg-type]
-                tokens_out=step.get("output_tokens"),  # type: ignore[arg-type]
-                confidence=step.get("confidence"),  # type: ignore[arg-type]
-            )
-        )
-
-    fmt_name = None if result.output_format is None else type(result.output_format).__name__
-
-    return AskResponse(
-        trace_id=str(uuid.uuid4()),
-        db_id=result.db_id,
-        sql=result.sql,
-        rationale=result.rationale,
-        confidence=result.confidence,
-        confidence_label=_confidence_label(result.confidence),
-        rows=rows,
-        columns=columns,
-        row_count=row_count,
-        caption=result.caption,
-        output_format=fmt_name,
-        error_kind=result.error_kind.value if result.error_kind else None,
-        error_message=result.error_message,
-        repair_attempted=result.repair_attempted,
-        latency_ms=latency_ms,
-        trace=trace_steps,
-    )
-
-
-# ---------------------------------------------------------- rate limit
-
-
-class _TokenBucket:
-    """Sliding-window token bucket per key.
-
-    Default: 60 requests per 60 seconds. Single-process state — fine for the
-    portfolio demo. Move to Redis if/when running multiple replicas.
-    """
-
-    def __init__(self, *, max_req: int = 60, window_s: int = 60) -> None:
-        self.max_req = max_req
-        self.window_s = window_s
-        self._hits: dict[str, deque[float]] = defaultdict(deque)
-
-    def check(self, key: str) -> tuple[bool, int]:
-        now = time.time()
-        bucket = self._hits[key]
-        cutoff = now - self.window_s
-        while bucket and bucket[0] < cutoff:
-            bucket.popleft()
-        if len(bucket) >= self.max_req:
-            retry_after = int(self.window_s - (now - bucket[0]))
-            return False, max(retry_after, 1)
-        bucket.append(now)
-        return True, 0
-
-
-# ---------------------------------------------------------- bootstrap
-
-
-def _build_pipeline_components(
-    settings: Settings,
-) -> tuple[DatabaseRegistry, SchemaIndex, LLMProvider, LLMProvider]:
-    if not settings.mistral_api_key:
-        raise RuntimeError("MISTRAL_API_KEY is not set — API can't bootstrap embeddings.")
-    raw_sql = build_provider(settings.default_provider, settings=settings)
-    sql_provider: LLMProvider = CachingLLMProvider(raw_sql, cache_dir=settings.llm_cache_dir)
-    explain_provider: LLMProvider = sql_provider
-    raw_embed: EmbeddingProvider = MistralProvider(
-        api_key=settings.mistral_api_key,
-        gen_model=settings.mistral_gen_model,
-        embed_model=settings.mistral_embed_model,
-        base_url=settings.mistral_base_url,
-    )
-    embedder: EmbeddingProvider = CachingEmbeddingProvider(
-        raw_embed, cache_dir=settings.llm_cache_dir
-    )
-    schema_index = SchemaIndex(persist_dir="chroma_data", embedder=embedder)
-    registry = get_default_registry()
-    return registry, schema_index, sql_provider, explain_provider
-
-
-@lru_cache(maxsize=1)
-def _make_singletons() -> tuple[Any, DatabaseRegistry, SchemaIndex, LLMProvider]:
-    """Lazy: build the pipeline only when the first /ask hits — keeps /healthz
-    fast and avoids touching Chroma when the API is used for status probes."""
-    import os
-
-    settings = get_settings()
-    registry, schema_index, sql_provider, explain_provider = _build_pipeline_components(settings)
-    # Eval-script env toggles bootstrap into PipelineConfig once at boot;
-    # individual nodes never read os.environ at runtime (see graph.py docstrings).
-    config = PipelineConfig(
-        sql_provider=sql_provider,
-        explain_provider=explain_provider,
-        schema_index=schema_index,
-        registry=registry,
-        fewshot_top_k=3,
-        sort_schema_block=True,
-        cross_db_fewshot=True,
-        verify_retry_on_empty=True,
-        use_m_schema=os.environ.get("NLSQL_M_SCHEMA") == "1",
-        use_dac_prompt=os.environ.get("NLSQL_DAC") == "1",
-    )
-    pipeline = build_pipeline(config)
-    return pipeline, registry, schema_index, sql_provider
-
-
-def create_app() -> FastAPI:
-    app = FastAPI(
-        title="NL→SQL Assistant",
-        version=__version__,
-        description=(
-            "Portfolio API: natural-language questions → SQL → executed rows. "
-            "BIRD Mini-Dev 57% hybrid, Chinook 100%, $0 budget, AST safety guards."
-        ),
-    )
-    settings = get_settings()
-    rate_limiter = _TokenBucket(max_req=60, window_s=60)
-
-    api_key_env = ""  # `NL_SQL_API_KEY` via env, optional
-    import os
-
-    api_key_env = os.environ.get("NL_SQL_API_KEY", "")
-
-    async def require_api_key(
-        request: Request,
-        x_api_key: str | None = Header(default=None, alias="X-API-Key"),
-    ) -> str:
-        if not api_key_env:
-            # Auth off entirely when no key is configured — useful for local
-            # eval drivers and the Streamlit UI bootstrapping side-by-side.
-            return "anonymous"
-        if x_api_key != api_key_env:
-            raise HTTPException(
-                status_code=status.HTTP_401_UNAUTHORIZED,
-                detail="missing or invalid X-API-Key header",
-            )
-        ok, retry_after = rate_limiter.check(x_api_key)
-        if not ok:
-            raise HTTPException(
-                status_code=status.HTTP_429_TOO_MANY_REQUESTS,
-                detail=f"rate limit exceeded; retry in {retry_after}s",
-                headers={"Retry-After": str(retry_after)},
-            )
-        return x_api_key
-
-    # --------------------------------------------------------- health / ready
-
-    @app.get("/healthz", response_model=HealthResponse, tags=["status"])
-    def healthz() -> HealthResponse:
-        configured: list[str] = []
-        if settings.mistral_api_key:
-            configured.append("mistral")
-        if settings.github_token:
-            configured.append("github_models")
-        if settings.groq_api_key:
-            configured.append("groq")
-        configured.append("ollama")
-        return HealthResponse(
-            status="ok",
-            version=__version__,
-            providers_configured=sorted(configured),
-        )
-
-    @app.get("/readyz", response_model=ReadyResponse, tags=["status"])
-    def readyz() -> ReadyResponse:
-        chroma_ok = False
-        registry_ok = False
-        registered = 0
-        schema_chunks = 0
-        try:
-            _pipeline, registry, schema_index, _sql = _make_singletons()
-            registered = len(registry.ids())
-            registry_ok = registered > 0
-            schema_chunks = schema_index.schema_collection.count()
-            chroma_ok = schema_chunks > 0
-        except Exception:
-            pass
-        all_ok = chroma_ok and registry_ok
-        return ReadyResponse(
-            status="ok" if all_ok else "not_ready",
-            chroma_ok=chroma_ok,
-            registry_ok=registry_ok,
-            registered_dbs=registered,
-            schema_chunks=schema_chunks,
-        )
-
-    # --------------------------------------------------------- product API
-
-    @app.get("/databases", response_model=DatabasesResponse, tags=["catalog"])
-    def databases(_auth: str = Depends(require_api_key)) -> DatabasesResponse:
-        _pipeline, registry, schema_index, _sql = _make_singletons()
-        infos: list[DatabaseInfo] = []
-        for db_id in registry.ids():
-            spec = registry.get(db_id)
-            try:
-                records = schema_index.schema_collection.get(
-                    where={"db_id": db_id}, include=["metadatas"]
-                )
-                table_count = len(records.get("metadatas") or [])
-            except Exception:
-                table_count = 0
-            infos.append(
-                DatabaseInfo(
-                    db_id=db_id,
-                    dialect=str(spec.dialect),
-                    description=str(getattr(spec, "description", "") or ""),
-                    table_count=table_count,
-                )
-            )
-        return DatabasesResponse(databases=infos)
-
-    @app.post("/ask", response_model=AskResponse, tags=["nl-sql"])
-    def ask(req: AskRequest, _auth: str = Depends(require_api_key)) -> AskResponse:
-        pipeline, registry, _schema, _sql = _make_singletons()
-        if req.db_id not in registry.ids():
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail=f"unknown db_id: {req.db_id!r}; see /databases for the list",
-            )
-        spec = registry.get(req.db_id)
-        t0 = time.perf_counter()
-        try:
-            result = run_pipeline(
-                pipeline,
-                question=req.question,
-                db_id=req.db_id,
-                dialect=spec.dialect,
-                verify_retry_on_empty=True,
-            )
-        except Exception as exc:  # pragma: no cover — defensive
-            raise HTTPException(
-                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-                detail=f"pipeline crashed: {type(exc).__name__}: {exc}",
-            ) from exc
-        latency_ms = (time.perf_counter() - t0) * 1000.0
-        return _result_to_response(result, latency_ms=latency_ms)
-
-    @app.get("/eval/latest", response_model=EvalLatestResponse, tags=["transparency"])
-    def eval_latest() -> EvalLatestResponse:
-        """Returns metadata of the latest hybrid eval report committed to repo."""
-        import json
-
-        baseline = Path("eval/baselines/hybrid_n200_v0.json")
-        if not baseline.exists():
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="no committed baseline yet — run scripts/eval_baseline.py",
-            )
-        data = json.loads(baseline.read_text(encoding="utf-8"))
-        overall = data.get("overall") or {}
-        return EvalLatestResponse(
-            configuration=str(data.get("configuration", "unknown")),
-            sql_model=str(data.get("sql_model", "unknown")),
-            overall_ea=overall.get("ea"),
-            n=int(overall.get("n") or 0),
-            report_path=str(baseline),
-        )
-
-    return app
-
-
-app = create_app()
+"""FastAPI surface for the NL→SQL Assistant.
+
+Endpoints:
+    GET  /healthz        — liveness probe + provider configuration snapshot.
+    GET  /readyz         — readiness probe (Chroma + DB registry reachable).
+    GET  /databases      — list registered DBs + table counts.
+    POST /ask            — translate question to SQL, execute, return result.
+    GET  /eval/latest    — metadata of the latest committed eval report.
+
+Auth:
+    Set ``NL_SQL_API_KEY`` (env, .env, or settings). When set, every request
+    to /ask and /databases must include ``X-API-Key`` matching it. /healthz
+    and /readyz are always open for orchestrator probes.
+
+Rate limit:
+    In-process token bucket per API key (60 req/min default). No external
+    Redis — this is a single-replica portfolio demo, not a fleet service.
+"""
+
+from __future__ import annotations
+
+import time
+import uuid
+from collections import defaultdict, deque
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, NamedTuple
+
+from fastapi import Depends, FastAPI, Header, HTTPException, Request, status
+from pydantic import BaseModel, Field
+
+from nl_sql import __version__
+from nl_sql.agent.graph import (
+    PipelineConfig,
+    PipelineRunResult,
+    build_pipeline,
+    run_pipeline,
+)
+from nl_sql.config import Settings, get_settings
+from nl_sql.db.registry import DatabaseRegistry, get_default_registry
+from nl_sql.llm.cache import CachingEmbeddingProvider, CachingLLMProvider
+from nl_sql.llm.providers import build_provider
+from nl_sql.llm.providers.base import EmbeddingProvider, LLMProvider
+from nl_sql.llm.providers.mistral import MistralProvider
+from nl_sql.schema_index.indexer import SchemaIndex
+
+# ---------------------------------------------------------- response models
+
+
+class HealthResponse(BaseModel):
+    status: str
+    version: str
+    providers_configured: list[str]
+
+
+class ReadyResponse(BaseModel):
+    status: str
+    chroma_ok: bool
+    registry_ok: bool
+    registered_dbs: int
+    schema_chunks: int
+
+
+class DatabaseInfo(BaseModel):
+    db_id: str
+    dialect: str
+    description: str = ""
+    table_count: int
+
+
+class DatabasesResponse(BaseModel):
+    databases: list[DatabaseInfo]
+
+
+class AskRequest(BaseModel):
+    question: str = Field(min_length=1, max_length=2000)
+    db_id: str = Field(min_length=1)
+
+
+class TraceStep(BaseModel):
+    node: str
+    model: str | None = None
+    tokens_in: int | None = None
+    tokens_out: int | None = None
+    confidence: float | None = None
+
+
+class AskResponse(BaseModel):
+    trace_id: str
+    db_id: str
+    sql: str
+    rationale: str
+    confidence: float
+    confidence_label: str
+    rows: list[list[Any]] | None
+    columns: list[str] | None
+    row_count: int
+    caption: str
+    output_format: str | None
+    error_kind: str | None
+    error_message: str
+    repair_attempted: bool
+    latency_ms: float
+    trace: list[TraceStep]
+
+
+class EvalLatestResponse(BaseModel):
+    configuration: str
+    sql_model: str
+    overall_ea: float | None
+    n: int
+    report_path: str
+
+
+# ---------------------------------------------------------- helpers
+
+
+def _confidence_label(value: float) -> str:
+    if value >= 0.8:
+        return "High"
+    if value >= 0.5:
+        return "Medium"
+    if value > 0.0:
+        return "Low"
+    return "Unknown"
+
+
+def _result_to_response(result: PipelineRunResult, *, latency_ms: float) -> AskResponse:
+    rows: list[list[Any]] | None = None
+    columns: list[str] | None = None
+    row_count = 0
+    if result.outcome is not None and result.outcome.result is not None:
+        rows = [list(r) for r in result.outcome.result.rows]
+        columns = list(result.outcome.result.columns)
+        row_count = result.outcome.result.row_count
+
+    trace_steps: list[TraceStep] = []
+    for step in result.trace:
+        trace_steps.append(
+            TraceStep(
+                node=str(step.get("node", "?")),
+                model=step.get("model"),  # type: ignore[arg-type]
+                tokens_in=step.get("input_tokens"),  # type: ignore[arg-type]
+                tokens_out=step.get("output_tokens"),  # type: ignore[arg-type]
+                confidence=step.get("confidence"),  # type: ignore[arg-type]
+            )
+        )
+
+    fmt_name = None if result.output_format is None else type(result.output_format).__name__
+
+    return AskResponse(
+        trace_id=str(uuid.uuid4()),
+        db_id=result.db_id,
+        sql=result.sql,
+        rationale=result.rationale,
+        confidence=result.confidence,
+        confidence_label=_confidence_label(result.confidence),
+        rows=rows,
+        columns=columns,
+        row_count=row_count,
+        caption=result.caption,
+        output_format=fmt_name,
+        error_kind=result.error_kind.value if result.error_kind else None,
+        error_message=result.error_message,
+        repair_attempted=result.repair_attempted,
+        latency_ms=latency_ms,
+        trace=trace_steps,
+    )
+
+
+# ---------------------------------------------------------- rate limit
+
+
+class _TokenBucket:
+    """Sliding-window token bucket per key.
+
+    Default: 60 requests per 60 seconds. Single-process state — fine for the
+    portfolio demo. Move to Redis if/when running multiple replicas.
+    """
+
+    def __init__(self, *, max_req: int = 60, window_s: int = 60) -> None:
+        self.max_req = max_req
+        self.window_s = window_s
+        self._hits: dict[str, deque[float]] = defaultdict(deque)
+
+    def check(self, key: str) -> tuple[bool, int]:
+        now = time.time()
+        bucket = self._hits[key]
+        cutoff = now - self.window_s
+        while bucket and bucket[0] < cutoff:
+            bucket.popleft()
+        if len(bucket) >= self.max_req:
+            retry_after = int(self.window_s - (now - bucket[0]))
+            return False, max(retry_after, 1)
+        bucket.append(now)
+        return True, 0
+
+
+# ---------------------------------------------------------- bootstrap
+
+
+def _build_pipeline_components(
+    settings: Settings,
+) -> tuple[DatabaseRegistry, SchemaIndex, LLMProvider, LLMProvider]:
+    if not settings.mistral_api_key:
+        raise RuntimeError("MISTRAL_API_KEY is not set — API can't bootstrap embeddings.")
+    raw_sql = build_provider(settings.default_provider, settings=settings)
+    sql_provider: LLMProvider = CachingLLMProvider(raw_sql, cache_dir=settings.llm_cache_dir)
+    explain_provider: LLMProvider = sql_provider
+    raw_embed: EmbeddingProvider = MistralProvider(
+        api_key=settings.mistral_api_key,
+        gen_model=settings.mistral_gen_model,
+        embed_model=settings.mistral_embed_model,
+        base_url=settings.mistral_base_url,
+    )
+    embedder: EmbeddingProvider = CachingEmbeddingProvider(
+        raw_embed, cache_dir=settings.llm_cache_dir
+    )
+    schema_index = SchemaIndex(persist_dir="chroma_data", embedder=embedder)
+    registry = get_default_registry()
+    return registry, schema_index, sql_provider, explain_provider
+
+
+class Singletons(NamedTuple):
+    """The four runtime objects the API routes share.
+
+    Exposed as a public type so tests can construct mock instances and feed
+    them through ``app.dependency_overrides[get_singletons]``.
+    """
+
+    pipeline: Any
+    registry: DatabaseRegistry
+    schema_index: SchemaIndex
+    sql_provider: LLMProvider
+
+
+@lru_cache(maxsize=1)
+def _make_singletons() -> Singletons:
+    """Lazy: build the pipeline only when the first /ask hits — keeps /healthz
+    fast and avoids touching Chroma when the API is used for status probes."""
+    import os
+
+    settings = get_settings()
+    registry, schema_index, sql_provider, explain_provider = _build_pipeline_components(settings)
+    # Eval-script env toggles bootstrap into PipelineConfig once at boot;
+    # individual nodes never read os.environ at runtime (see graph.py docstrings).
+    config = PipelineConfig(
+        sql_provider=sql_provider,
+        explain_provider=explain_provider,
+        schema_index=schema_index,
+        registry=registry,
+        fewshot_top_k=3,
+        sort_schema_block=True,
+        cross_db_fewshot=True,
+        verify_retry_on_empty=True,
+        use_m_schema=os.environ.get("NLSQL_M_SCHEMA") == "1",
+        use_dac_prompt=os.environ.get("NLSQL_DAC") == "1",
+    )
+    pipeline = build_pipeline(config)
+    return Singletons(pipeline, registry, schema_index, sql_provider)
+
+
+def get_singletons() -> Singletons:
+    """FastAPI Depends-able factory; tests override via ``app.dependency_overrides``."""
+    return _make_singletons()
+
+
+def create_app() -> FastAPI:
+    app = FastAPI(
+        title="NL→SQL Assistant",
+        version=__version__,
+        description=(
+            "Portfolio API: natural-language questions → SQL → executed rows. "
+            "BIRD Mini-Dev 57% hybrid, Chinook 100%, $0 budget, AST safety guards."
+        ),
+    )
+    settings = get_settings()
+    rate_limiter = _TokenBucket(max_req=60, window_s=60)
+
+    api_key_env = ""  # `NL_SQL_API_KEY` via env, optional
+    import os
+
+    api_key_env = os.environ.get("NL_SQL_API_KEY", "")
+
+    async def require_api_key(
+        request: Request,
+        x_api_key: str | None = Header(default=None, alias="X-API-Key"),
+    ) -> str:
+        if not api_key_env:
+            # Auth off entirely when no key is configured — useful for local
+            # eval drivers and the Streamlit UI bootstrapping side-by-side.
+            return "anonymous"
+        if x_api_key != api_key_env:
+            raise HTTPException(
+                status_code=status.HTTP_401_UNAUTHORIZED,
+                detail="missing or invalid X-API-Key header",
+            )
+        ok, retry_after = rate_limiter.check(x_api_key)
+        if not ok:
+            raise HTTPException(
+                status_code=status.HTTP_429_TOO_MANY_REQUESTS,
+                detail=f"rate limit exceeded; retry in {retry_after}s",
+                headers={"Retry-After": str(retry_after)},
+            )
+        return x_api_key
+
+    # --------------------------------------------------------- health / ready
+
+    @app.get("/healthz", response_model=HealthResponse, tags=["status"])
+    def healthz() -> HealthResponse:
+        configured: list[str] = []
+        if settings.mistral_api_key:
+            configured.append("mistral")
+        if settings.github_token:
+            configured.append("github_models")
+        if settings.groq_api_key:
+            configured.append("groq")
+        configured.append("ollama")
+        return HealthResponse(
+            status="ok",
+            version=__version__,
+            providers_configured=sorted(configured),
+        )
+
+    @app.get("/readyz", response_model=ReadyResponse, tags=["status"])
+    def readyz() -> ReadyResponse:
+        chroma_ok = False
+        registry_ok = False
+        registered = 0
+        schema_chunks = 0
+        try:
+            factory: Any = app.dependency_overrides.get(get_singletons, get_singletons)
+            singletons: Singletons = factory()
+            registered = len(singletons.registry.ids())
+            registry_ok = registered > 0
+            schema_chunks = singletons.schema_index.schema_collection.count()
+            chroma_ok = schema_chunks > 0
+        except Exception:
+            pass
+        all_ok = chroma_ok and registry_ok
+        return ReadyResponse(
+            status="ok" if all_ok else "not_ready",
+            chroma_ok=chroma_ok,
+            registry_ok=registry_ok,
+            registered_dbs=registered,
+            schema_chunks=schema_chunks,
+        )
+
+    # --------------------------------------------------------- product API
+
+    @app.get("/databases", response_model=DatabasesResponse, tags=["catalog"])
+    def databases(
+        _auth: str = Depends(require_api_key),
+        singletons: Singletons = Depends(get_singletons),  # noqa: B008
+    ) -> DatabasesResponse:
+        infos: list[DatabaseInfo] = []
+        for db_id in singletons.registry.ids():
+            spec = singletons.registry.get(db_id)
+            try:
+                records = singletons.schema_index.schema_collection.get(
+                    where={"db_id": db_id}, include=["metadatas"]
+                )
+                table_count = len(records.get("metadatas") or [])
+            except Exception:
+                table_count = 0
+            infos.append(
+                DatabaseInfo(
+                    db_id=db_id,
+                    dialect=str(spec.dialect),
+                    description=str(getattr(spec, "description", "") or ""),
+                    table_count=table_count,
+                )
+            )
+        return DatabasesResponse(databases=infos)
+
+    @app.post("/ask", response_model=AskResponse, tags=["nl-sql"])
+    def ask(
+        req: AskRequest,
+        _auth: str = Depends(require_api_key),
+        singletons: Singletons = Depends(get_singletons),  # noqa: B008
+    ) -> AskResponse:
+        if req.db_id not in singletons.registry.ids():
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail=f"unknown db_id: {req.db_id!r}; see /databases for the list",
+            )
+        spec = singletons.registry.get(req.db_id)
+        t0 = time.perf_counter()
+        try:
+            result = run_pipeline(
+                singletons.pipeline,
+                question=req.question,
+                db_id=req.db_id,
+                dialect=spec.dialect,
+                verify_retry_on_empty=True,
+            )
+        except Exception as exc:  # pragma: no cover — defensive
+            raise HTTPException(
+                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+                detail=f"pipeline crashed: {type(exc).__name__}: {exc}",
+            ) from exc
+        latency_ms = (time.perf_counter() - t0) * 1000.0
+        return _result_to_response(result, latency_ms=latency_ms)
+
+    @app.get("/eval/latest", response_model=EvalLatestResponse, tags=["transparency"])
+    def eval_latest() -> EvalLatestResponse:
+        """Returns metadata of the latest hybrid eval report committed to repo."""
+        import json
+
+        baseline = Path("eval/baselines/hybrid_n200_v0.json")
+        if not baseline.exists():
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail="no committed baseline yet — run scripts/eval_baseline.py",
+            )
+        data = json.loads(baseline.read_text(encoding="utf-8"))
+        overall = data.get("overall") or {}
+        return EvalLatestResponse(
+            configuration=str(data.get("configuration", "unknown")),
+            sql_model=str(data.get("sql_model", "unknown")),
+            overall_ea=overall.get("ea"),
+            n=int(overall.get("n") or 0),
+            report_path=str(baseline),
+        )
+
+    return app
+
+
+app = create_app()