Remove legacy HTML templates and API service configuration for contact management system

- Deleted base template (base.html) and all related contact templates (contact_detail.html, contact_form.html, contact_list.html, graph.html, need_list.html).
- Removed partial templates for managing contacts and needs (contact_table.html, manage_needs.html, manage_relationships.html, need_items.html).
- Eliminated contact API service configuration (contact_api.nix) from the NixOS setup.

.vscode/settings.json

@@ -71,7 +71,6 @@
     "ehci",
     "emerg",
     "endlessh",
-    "ents",
     "errorlens",
     "esbenp",
     "esphome",
@@ -173,8 +172,6 @@
     "Networkd",
     "networkmanager",
     "newtabpage",
-    "ngram",
-    "ngrams",
     "nixfmt",
     "nixos",
     "nixpkgs",
@@ -301,9 +298,7 @@
     "uiprotect",
     "uitour",
     "unifi",
-    "unjudged",
     "unrar",
-    "unstorable",
     "unsubmitted",
     "uptimekuma",
     "urlbar",
@@ -331,7 +326,6 @@
     "xcursorgen",
     "xdist",
     "xhci",
-    "yake",
     "yazi",
     "yubikey",
     "yubioath",

pyproject.toml

@@ -25,7 +25,7 @@ dependencies = [
     "pydantic",
     "pydantic-settings",
     "python-multipart",
-    "sqlalchemy[asyncio]",
+    "sqlalchemy",
     "tenacity",
     "tiktoken",
     "tinytuya",
@@ -41,10 +41,8 @@ whisper-transcribe = "python.tools.whisper.transcribe:main"
 
 [dependency-groups]
 dev = [
-    "aiosqlite",
     "mypy",
     "pyfakefs",
-    "pytest-asyncio",
     "pytest-cov",
     "pytest-mock",
     "pytest-xdist",
@@ -120,6 +118,5 @@ exclude_lines = [
 
 [tool.pytest.ini_options]
 addopts = "-n auto -ra --ignore=tests/ebook_search"
-asyncio_mode = "auto"
 testpaths = ["tests"]
 # --cov=system_tools --cov-report=term-missing --cov-report=xml --cov-report=html --cov-branch

python/alembic/richie/versions/2026_06_29-adding_phrase_metadata_tables_dddee09eddcc.py

@@ -1,206 +0,0 @@
-"""adding Phrase metadata tables.
-
-Revision ID: dddee09eddcc
-Revises: 96d72c748c24
-Create Date: 2026-06-29 00:49:07.344159
-
-"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-import sqlalchemy as sa
-from alembic import op
-from sqlalchemy.dialects import postgresql
-
-from python.orm import RichieBase
-
-if TYPE_CHECKING:
-    from collections.abc import Sequence
-
-# revision identifiers, used by Alembic.
-revision: str = "dddee09eddcc"
-down_revision: str | None = "96d72c748c24"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-schema = RichieBase.schema_name
-
-
-def upgrade() -> None:
-    """Upgrade."""
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.create_table(
-        "candidate_phrases",
-        sa.Column("book_id", sa.Integer(), nullable=False),
-        sa.Column("series_id", sa.Integer(), nullable=True),
-        sa.Column("phrase_text", sa.Text(), nullable=False),
-        sa.Column("phrase_norm", sa.Text(), nullable=False),
-        sa.Column("token_count", sa.Integer(), nullable=False),
-        sa.Column("source_raw_ngram", sa.Boolean(), nullable=False),
-        sa.Column("source_yake", sa.Boolean(), nullable=False),
-        sa.Column("source_spacy_ner", sa.Boolean(), nullable=False),
-        sa.Column("source_spacy_noun_chunk", sa.Boolean(), nullable=False),
-        sa.Column("source_capitalized", sa.Boolean(), nullable=False),
-        sa.Column("source_metadata", sa.Boolean(), nullable=False),
-        sa.Column("spacy_label", sa.String(), nullable=True),
-        sa.Column("raw_count", sa.Integer(), nullable=False),
-        sa.Column("chapter_count", sa.Integer(), nullable=False),
-        sa.Column("yake_score", sa.Float(), nullable=True),
-        sa.Column("candidate_score", sa.Float(), nullable=False),
-        sa.Column(
-            "sample_contexts",
-            sa.JSON().with_variant(postgresql.JSONB(astext_type=sa.Text()), "postgresql"),
-            nullable=True,
-        ),
-        sa.Column("llm_judged", sa.Boolean(), nullable=False),
-        sa.Column("llm_keep", sa.Boolean(), nullable=True),
-        sa.Column("llm_confidence", sa.Float(), nullable=True),
-        sa.Column("llm_category", sa.String(), nullable=True),
-        sa.Column("llm_reason", sa.Text(), nullable=True),
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column("created", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.Column("updated", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["book_id"],
-            [f"{schema}.ebook_source.id"],
-            name=op.f("fk_candidate_phrases_book_id_ebook_source"),
-            ondelete="CASCADE",
-        ),
-        sa.PrimaryKeyConstraint("id", name=op.f("pk_candidate_phrases")),
-        sa.UniqueConstraint("book_id", "phrase_norm", name="uq_candidate_phrases_book_id_phrase_norm"),
-        schema=schema,
-    )
-    op.create_index(
-        "candidate_phrases_book_norm_idx", "candidate_phrases", ["book_id", "phrase_norm"], unique=False, schema=schema
-    )
-    op.create_index(
-        "candidate_phrases_book_score_idx",
-        "candidate_phrases",
-        ["book_id", "candidate_score"],
-        unique=False,
-        schema=schema,
-    )
-    op.create_table(
-        "protected_phrases",
-        sa.Column("book_id", sa.Integer(), nullable=True),
-        sa.Column("series_id", sa.Integer(), nullable=True),
-        sa.Column("phrase_text", sa.Text(), nullable=False),
-        sa.Column("phrase_norm", sa.Text(), nullable=False),
-        sa.Column("canonical_id", sa.String(), nullable=False),
-        sa.Column("phrase_type", sa.String(), nullable=True),
-        sa.Column("token_count", sa.Integer(), nullable=False),
-        sa.Column("confidence", sa.Float(), nullable=False),
-        sa.Column("importance", sa.Float(), nullable=False),
-        sa.Column("allow_nested", sa.Boolean(), nullable=False),
-        sa.Column("suppress_children", sa.Boolean(), nullable=False),
-        sa.Column("source_candidate_id", sa.Integer(), nullable=True),
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column("created", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.Column("updated", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["book_id"],
-            [f"{schema}.ebook_source.id"],
-            name=op.f("fk_protected_phrases_book_id_ebook_source"),
-            ondelete="CASCADE",
-        ),
-        sa.ForeignKeyConstraint(
-            ["source_candidate_id"],
-            [f"{schema}.candidate_phrases.id"],
-            name=op.f("fk_protected_phrases_source_candidate_id_candidate_phrases"),
-            ondelete="SET NULL",
-        ),
-        sa.PrimaryKeyConstraint("id", name=op.f("pk_protected_phrases")),
-        sa.UniqueConstraint("book_id", "phrase_norm", name="uq_protected_phrases_book_id_phrase_norm"),
-        schema=schema,
-    )
-    op.create_index(
-        "protected_phrases_book_norm_idx", "protected_phrases", ["book_id", "phrase_norm"], unique=False, schema=schema
-    )
-    op.create_index("protected_phrases_norm_idx", "protected_phrases", ["phrase_norm"], unique=False, schema=schema)
-    op.create_index(
-        "protected_phrases_series_norm_idx",
-        "protected_phrases",
-        ["series_id", "phrase_norm"],
-        unique=False,
-        schema=schema,
-    )
-    op.create_table(
-        "chunk_phrase_mentions",
-        sa.Column("chunk_id", sa.BigInteger(), nullable=False),
-        sa.Column("phrase_id", sa.Integer(), nullable=False),
-        sa.Column("book_id", sa.Integer(), nullable=True),
-        sa.Column("series_id", sa.Integer(), nullable=True),
-        sa.Column("start_char", sa.Integer(), nullable=False),
-        sa.Column("end_char", sa.Integer(), nullable=True),
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column("created", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.Column("updated", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["book_id"],
-            [f"{schema}.ebook_source.id"],
-            name=op.f("fk_chunk_phrase_mentions_book_id_ebook_source"),
-            ondelete="CASCADE",
-        ),
-        sa.ForeignKeyConstraint(
-            ["chunk_id"],
-            [f"{schema}.ebook_chunk.id"],
-            name=op.f("fk_chunk_phrase_mentions_chunk_id_ebook_chunk"),
-            ondelete="CASCADE",
-        ),
-        sa.ForeignKeyConstraint(
-            ["phrase_id"],
-            [f"{schema}.protected_phrases.id"],
-            name=op.f("fk_chunk_phrase_mentions_phrase_id_protected_phrases"),
-            ondelete="CASCADE",
-        ),
-        sa.PrimaryKeyConstraint("id", name=op.f("pk_chunk_phrase_mentions")),
-        sa.UniqueConstraint("chunk_id", "phrase_id", "start_char", name="uq_chunk_phrase_mentions_chunk_phrase_start"),
-        schema=schema,
-    )
-    op.create_index(
-        "chunk_phrase_mentions_chunk_idx", "chunk_phrase_mentions", ["chunk_id"], unique=False, schema=schema
-    )
-    op.create_index(
-        "chunk_phrase_mentions_phrase_idx", "chunk_phrase_mentions", ["phrase_id"], unique=False, schema=schema
-    )
-    op.create_table(
-        "phrase_aliases",
-        sa.Column("phrase_id", sa.Integer(), nullable=False),
-        sa.Column("alias_text", sa.Text(), nullable=False),
-        sa.Column("alias_norm", sa.Text(), nullable=False),
-        sa.Column("confidence", sa.Float(), nullable=False),
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column("created", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.Column("updated", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["phrase_id"],
-            [f"{schema}.protected_phrases.id"],
-            name=op.f("fk_phrase_aliases_phrase_id_protected_phrases"),
-            ondelete="CASCADE",
-        ),
-        sa.PrimaryKeyConstraint("id", name=op.f("pk_phrase_aliases")),
-        sa.UniqueConstraint("phrase_id", "alias_norm", name="uq_phrase_aliases_phrase_id_alias_norm"),
-        schema=schema,
-    )
-    op.create_index("phrase_aliases_norm_idx", "phrase_aliases", ["alias_norm"], unique=False, schema=schema)
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    """Downgrade."""
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_index("phrase_aliases_norm_idx", table_name="phrase_aliases", schema=schema)
-    op.drop_table("phrase_aliases", schema=schema)
-    op.drop_index("chunk_phrase_mentions_phrase_idx", table_name="chunk_phrase_mentions", schema=schema)
-    op.drop_index("chunk_phrase_mentions_chunk_idx", table_name="chunk_phrase_mentions", schema=schema)
-    op.drop_table("chunk_phrase_mentions", schema=schema)
-    op.drop_index("protected_phrases_series_norm_idx", table_name="protected_phrases", schema=schema)
-    op.drop_index("protected_phrases_norm_idx", table_name="protected_phrases", schema=schema)
-    op.drop_index("protected_phrases_book_norm_idx", table_name="protected_phrases", schema=schema)
-    op.drop_table("protected_phrases", schema=schema)
-    op.drop_index("candidate_phrases_book_score_idx", table_name="candidate_phrases", schema=schema)
-    op.drop_index("candidate_phrases_book_norm_idx", table_name="candidate_phrases", schema=schema)
-    op.drop_table("candidate_phrases", schema=schema)
-    # ### end Alembic commands ###

python/alembic/richie/versions/2026_07_09-remove_spacy_ner_751260fc3228.py

@@ -1,55 +0,0 @@
-"""remove spaCy-ner.
-
-Revision ID: 751260fc3228
-Revises: dddee09eddcc
-Create Date: 2026-07-09 23:03:39.554083
-
-"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-import sqlalchemy as sa
-from alembic import op
-
-from python.orm import RichieBase
-
-if TYPE_CHECKING:
-    from collections.abc import Sequence
-
-# revision identifiers, used by Alembic.
-revision: str = "751260fc3228"
-down_revision: str | None = "dddee09eddcc"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-schema = RichieBase.schema_name
-
-
-def upgrade() -> None:
-    """Upgrade."""
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_column("candidate_phrases", "source_spacy_noun_chunk", schema=schema)
-    op.drop_column("candidate_phrases", "source_spacy_ner", schema=schema)
-    op.drop_column("candidate_phrases", "spacy_label", schema=schema)
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    """Downgrade."""
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.add_column(
-        "candidate_phrases", sa.Column("spacy_label", sa.VARCHAR(), autoincrement=False, nullable=True), schema=schema
-    )
-    op.add_column(
-        "candidate_phrases",
-        sa.Column("source_spacy_ner", sa.BOOLEAN(), autoincrement=False, nullable=False),
-        schema=schema,
-    )
-    op.add_column(
-        "candidate_phrases",
-        sa.Column("source_spacy_noun_chunk", sa.BOOLEAN(), autoincrement=False, nullable=False),
-        schema=schema,
-    )
-    # ### end Alembic commands ###

python/ebook_search/answer.py

@@ -8,20 +8,13 @@ from typing import TYPE_CHECKING
 from python.ebook_search.llm_interface import request_chat_completion
 
 if TYPE_CHECKING:
-    import httpx
-
     from python.ebook_search.config import EbookSearchConfig
     from python.ebook_search.search import SearchResult
 
 logger = logging.getLogger(__name__)
 
 
-async def answer_query(
-    client: httpx.AsyncClient,
-    query: str,
-    results: list[SearchResult],
-    config: EbookSearchConfig,
-) -> str:
+def answer_query(query: str, results: list[SearchResult], config: EbookSearchConfig) -> str:
     """Answer a question using only retrieved chunks."""
     if not config.answer_enabled:
         logger.info("ebook_answer_skipped_disabled")
@@ -32,15 +25,17 @@ async def answer_query(
         return "No relevant sources were found."
 
     logger.info(
-        f"ebook_answer_request_start {config.vllm_base_url=} {config.chat_model=} sources={len(results)} "
-        f"query_length={len(query)}"
+        "ebook_answer_request_start base_url=%s model=%s sources=%s query_length=%s",
+        config.vllm_base_url,
+        config.chat_model,
+        len(results),
+        len(query),
     )
     context = "\n\n".join(
         f"[{index}] {result.source_title}{' - ' + result.chapter_title if result.chapter_title else ''}\n{result.text}"
         for index, result in enumerate(results, start=1)
     )
-    content = await request_chat_completion(
-        client,
+    content = request_chat_completion(
         config,
         [
             {
@@ -54,5 +49,9 @@ async def answer_query(
         ],
     )
 
-    logger.info(f"ebook_answer_request_complete {config.chat_model=} answer_length={len(content)}")
+    logger.info(
+        "ebook_answer_request_complete model=%s answer_length=%s",
+        config.chat_model,
+        len(content),
+    )
     return content or "The model returned an empty answer."

python/ebook_search/api/bm25_tasks.py

@@ -1,22 +1,18 @@
-"""Background BM25 refresh tasks for the web app.
-
-The refresh is scheduled on the event loop instead of a thread because the async psycopg
-driver only works from the loop; a bare thread cannot open a session on the async engine.
-"""
+"""Background BM25 refresh tasks for the web app."""
 
 from __future__ import annotations
 
-import asyncio
 import logging
+from threading import Timer
 from typing import TYPE_CHECKING
 
-from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.orm import Session
 
 from python.ebook_search.bm25_corpus import load_bm25_corpus, refresh_bm25_corpus
 
 if TYPE_CHECKING:
     from fastapi import FastAPI
-    from sqlalchemy.ext.asyncio import AsyncEngine
+    from sqlalchemy.engine import Engine
 
     from python.ebook_search.config import EbookSearchConfig
 
@@ -24,47 +20,41 @@ logger = logging.getLogger(__name__)
 
 
 def schedule_bm25_refresh(app: FastAPI) -> None:
-    """Schedule a delayed BM25 corpus refresh, replacing any pending refresh.
+    """Schedule a delayed BM25 corpus refresh, replacing any pending refresh."""
+    existing_timer = getattr(app.state, "bm25_refresh_timer", None)
+    if existing_timer is not None:
+        existing_timer.cancel()
 
-    Only called from route handlers, so a running event loop is guaranteed.
-    """
-    cancel_bm25_refresh(app)
-
-    loop = asyncio.get_running_loop()
-
-    def start_refresh() -> None:
-        app.state.bm25_refresh_task = loop.create_task(refresh_bm25_for_app(app))
-
-    app.state.bm25_refresh_timer = loop.call_later(app.state.config.bm25_refresh_delay_seconds, start_refresh)
-    logger.info(f"ebook_bm25_refresh_scheduled {app.state.config.bm25_refresh_delay_seconds=}")
+    timer = Timer(app.state.config.bm25_refresh_delay_seconds, refresh_bm25_for_app, args=(app,))
+    timer.daemon = True
+    timer.start()
+    app.state.bm25_refresh_timer = timer
+    logger.info(
+        "ebook_bm25_refresh_scheduled delay_seconds=%s",
+        app.state.config.bm25_refresh_delay_seconds,
+    )
 
 
 def cancel_bm25_refresh(app: FastAPI) -> None:
-    """Cancel any pending BM25 corpus refresh timer and in-flight refresh task."""
+    """Cancel any pending BM25 corpus refresh."""
     existing_timer = getattr(app.state, "bm25_refresh_timer", None)
     if existing_timer is not None:
         existing_timer.cancel()
         app.state.bm25_refresh_timer = None
         logger.info("ebook_bm25_refresh_cancelled")
 
-    existing_task = getattr(app.state, "bm25_refresh_task", None)
-    if existing_task is not None:
-        if not existing_task.done():
-            existing_task.cancel()
-        app.state.bm25_refresh_task = None
 
-
-async def refresh_bm25_for_app(app: FastAPI) -> None:
+def refresh_bm25_for_app(app: FastAPI) -> None:
     """Refresh the BM25 corpus using the app engine and config."""
     try:
-        await refresh_bm25_for_engine(app.state.engine, app.state.config)
+        refresh_bm25_for_engine(app.state.engine, app.state.config)
     except Exception:
         logger.exception("ebook_bm25_refresh_failed")
 
 
-async def refresh_bm25_for_engine(engine: AsyncEngine, config: EbookSearchConfig) -> None:
-    """Refresh the BM25 corpus using an async SQLAlchemy engine."""
-    async with AsyncSession(engine) as session:
-        await refresh_bm25_corpus(session, config)
+def refresh_bm25_for_engine(engine: Engine, config: EbookSearchConfig) -> None:
+    """Refresh the BM25 corpus using a SQLAlchemy engine."""
+    with Session(engine) as session:
+        refresh_bm25_corpus(session, config)
     load_bm25_corpus.cache_clear()
     logger.info("ebook_bm25_corpus_cache_cleared_after_refresh")

python/ebook_search/api/dependencies.py

@@ -4,9 +4,8 @@ from __future__ import annotations
 
 from typing import Annotated
 
-import httpx
 from fastapi import Depends, Request
-from sqlalchemy.ext.asyncio import AsyncEngine
+from sqlalchemy.engine import Engine
 
 from python.ebook_search.config import EbookSearchConfig
 
@@ -16,16 +15,10 @@ def get_config(request: Request) -> EbookSearchConfig:
     return request.app.state.config
 
 
-def get_engine(request: Request) -> AsyncEngine:
+def get_engine(request: Request) -> Engine:
     """Get the database engine from app state."""
     return request.app.state.engine
 
 
-def get_http_client(request: Request) -> httpx.AsyncClient:
-    """Get the shared LLM HTTP client from app state."""
-    return request.app.state.http_client
-
-
 AppConfig = Annotated[EbookSearchConfig, Depends(get_config)]
-AppEngine = Annotated[AsyncEngine, Depends(get_engine)]
-AppHttpClient = Annotated[httpx.AsyncClient, Depends(get_http_client)]
+AppEngine = Annotated[Engine, Depends(get_engine)]

python/ebook_search/api/judge_tasks.py

@@ -1,127 +0,0 @@
-"""Background phrase-judging tasks for the web app.
-
-Judging a book sends one LLM request per candidate phrase, which can take minutes, so it must
-not run inside the request where it would block the UI. Judgments run as async FastAPI
-background tasks, awaited on the event loop after the response is sent, and are tracked per
-book in app state so a second judge request for a book that is already being judged is
-rejected instead of doubling the work.
-
-State is loop-confined: every read and mutation happens on the event loop (async route
-handlers and async background tasks) and no critical section contains an ``await``, so each
-mutation is atomic per loop iteration and no locking is needed.
-"""
-
-from __future__ import annotations
-
-import logging
-from dataclasses import dataclass, field
-from typing import TYPE_CHECKING
-
-from python.ebook_search.protected_phrases.judge_ngrams import judge_candidate_phrases_for_books
-
-if TYPE_CHECKING:
-    from fastapi import BackgroundTasks, FastAPI
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class JudgeTaskState:
-    """Running book judgments and last outcome messages, keyed by book id."""
-
-    running_book_ids: set[int] = field(default_factory=set)
-    outcome_messages: dict[int, str] = field(default_factory=dict)
-
-
-def get_judge_task_state(app: FastAPI) -> JudgeTaskState:
-    """Return the app's judge task state, creating it on first use.
-
-    Args:
-        app (FastAPI): App whose state holds the judge task registry.
-
-    Returns:
-        JudgeTaskState: The shared judge task state for this app.
-    """
-    state = getattr(app.state, "judge_tasks", None)
-    if state is None:
-        state = JudgeTaskState()
-        app.state.judge_tasks = state
-    return state
-
-
-def start_book_phrase_judgment(app: FastAPI, background_tasks: BackgroundTasks, source_id: int) -> bool:
-    """Queue judging of one book's candidate phrases as a FastAPI background task.
-
-    The book is claimed before the response returns, so a repeated judge request cannot queue
-    a second run while one is pending or running.
-
-    Args:
-        app (FastAPI): App supplying the engine, config, and judge task state.
-        background_tasks (BackgroundTasks): Request's background tasks to queue the judgment on.
-        source_id (int): Book to judge candidates for.
-
-    Returns:
-        bool: True when a judgment was queued, False when one is already running for this book.
-    """
-    state = get_judge_task_state(app)
-    if source_id in state.running_book_ids:
-        logger.info(f"ebook_book_phrase_judgment_already_running {source_id=}")
-        return False
-    state.running_book_ids.add(source_id)
-    state.outcome_messages.pop(source_id, None)
-    background_tasks.add_task(judge_book_phrases_for_app, app, source_id)
-    logger.info(f"ebook_book_phrase_judgment_queued {source_id=}")
-    return True
-
-
-async def judge_book_phrases_for_app(app: FastAPI, source_id: int) -> None:
-    """Judge one book using the app engine and config, recording the outcome message.
-
-    Args:
-        app (FastAPI): App supplying the engine, config, and judge task state.
-        source_id (int): Book to judge candidates for.
-    """
-    state = get_judge_task_state(app)
-    try:
-        result = await judge_candidate_phrases_for_books(app.state.engine, app.state.config, source_ids=[source_id])
-        logger.info(
-            f"ebook_book_phrase_judgment_complete {source_id=} {result.candidates_judged=} {result.protected_phrases=} "
-            f"{result.phrase_mentions=} {result.books_failed=}"
-        )
-        if result.books_failed:
-            message = "Judging failed; see server logs for details"
-        else:
-            message = (
-                f"Judged {result.candidates_judged} candidates; {result.protected_phrases} protected phrases promoted"
-            )
-    except Exception:
-        logger.exception(f"ebook_book_phrase_judgment_task_failed {source_id=}")
-        message = "Judging failed; see server logs for details"
-    state.running_book_ids.discard(source_id)
-    state.outcome_messages[source_id] = message
-
-
-def is_judging_book(app: FastAPI, source_id: int) -> bool:
-    """Report whether a judgment is currently queued or running for one book.
-
-    Args:
-        app (FastAPI): App supplying the judge task state.
-        source_id (int): Book to check.
-
-    Returns:
-        bool: True while the book's judgment is pending or running.
-    """
-    return source_id in get_judge_task_state(app).running_book_ids
-
-
-def pop_book_judgment_outcome(app: FastAPI, source_id: int) -> str | None:
-    """Return and clear the outcome message from one book's last finished judgment.
-
-    Args:
-        app (FastAPI): App supplying the judge task state.
-        source_id (int): Book to fetch the outcome for.
-
-    Returns:
-        str | None: The outcome message, or None when there is nothing new to report.
-    """
-    return get_judge_task_state(app).outcome_messages.pop(source_id, None)

python/ebook_search/api/main.py

@@ -6,12 +6,11 @@ import logging
 from contextlib import asynccontextmanager
 from typing import TYPE_CHECKING, Annotated
 
-import httpx
 import typer
 import uvicorn
 from fastapi import FastAPI
 from fastapi.staticfiles import StaticFiles
-from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.orm import Session
 
 from python.common import configure_logger
 from python.ebook_search.api.bm25_tasks import cancel_bm25_refresh
@@ -19,9 +18,8 @@ from python.ebook_search.api.routes import admin_router, health_router, page_rou
 from python.ebook_search.api.web import STATIC_DIR
 from python.ebook_search.bm25_corpus import ensure_bm25_corpus
 from python.ebook_search.config import load_config
-from python.ebook_search.protected_phrases.pool import shutdown_extraction_pool
 from python.fastapi_tools import ZstdMiddleware
-from python.orm.common import get_async_postgres_engine
+from python.orm.common import get_postgres_engine
 
 if TYPE_CHECKING:
     from collections.abc import AsyncIterator
@@ -37,30 +35,27 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
     config = load_config()
     app.state.config = config
     logger.info(
-        f"ebook_search_config_loaded {config.top_k=} {config.embedding_model=} {config.embedding_base_url=} "
-        f"{config.vllm_base_url=} {config.rerank.enabled=} {config.phrase_matching_enabled=} {config.answer_enabled=} "
-        f"library_paths={len(config.library_paths)}"
+        "ebook_search_config_loaded top_k=%s embedding_model=%s embedding_base_url=%s vllm_base_url=%s "
+        "rerank_enabled=%s answer_enabled=%s library_paths=%s",
+        config.top_k,
+        config.embedding_model,
+        config.embedding_base_url,
+        config.vllm_base_url,
+        config.rerank.enabled,
+        config.answer_enabled,
+        len(config.library_paths),
     )
     if not config.library_paths:
         logger.warning("ebook_search_no_library_paths_configured")
-    # Concurrent phrase judging opens one session per book worker on this engine, so size the pool
-    # to cover those plus headroom for ordinary web requests.
-    app.state.engine = get_async_postgres_engine(
-        name="RICHIE",
-        vector_engine=True,
-        pool_size=config.phrase_judge_book_workers + 10,
-    )
-    app.state.http_client = httpx.AsyncClient()
-    async with AsyncSession(app.state.engine, expire_on_commit=False) as session:
-        await ensure_bm25_corpus(session, config)
+    app.state.engine = get_postgres_engine(name="RICHIE", vector_engine=True)
+    with Session(app.state.engine) as session:
+        ensure_bm25_corpus(session, config)
     try:
         yield
     finally:
         logger.info("ebook_search_shutdown")
         cancel_bm25_refresh(app)
-        shutdown_extraction_pool()
-        await app.state.http_client.aclose()
-        await app.state.engine.dispose()
+        app.state.engine.dispose()
 
 
 def create_app() -> FastAPI:

python/ebook_search/api/routes/admin.py

@@ -8,18 +8,13 @@ from fastapi import APIRouter, Request
 from fastapi.responses import HTMLResponse
 
 from python.ebook_search.api.bm25_tasks import schedule_bm25_refresh
-from python.ebook_search.api.dependencies import (  # noqa: TC001 FastAPI resolves these annotated dependencies at runtime
-    AppConfig,
-    AppEngine,
-    AppHttpClient,
+from python.ebook_search.api.dependencies import (
+    AppConfig,  # noqa: TC001 FastAPI resolves this annotated dependency at runtime
 )
-from python.ebook_search.api.web import error_response, templates
+from python.ebook_search.api.web import templates
 from python.ebook_search.embeddings import embed_missing_chunks, embedding_model_stats
 from python.ebook_search.ingest import ingest_configured_paths
-from python.ebook_search.protected_phrases.generate_ngrams import generate_candidate_phrases_for_books
-from python.ebook_search.protected_phrases.judge_ngrams import judge_candidate_phrases_for_books
-from python.ebook_search.protected_phrases.store import book_ids_pending_first_judgment, corpus_phrase_stats
-from python.fastapi_tools import AsyncDbSession  # noqa: TC001 FastAPI resolves this annotated dependency at runtime
+from python.fastapi_tools import DbSession  # noqa: TC001 FastAPI resolves this annotated dependency at runtime
 
 logger = logging.getLogger(__name__)
 
@@ -27,143 +22,40 @@ router = APIRouter(prefix="/admin")
 
 
 @router.get("", response_class=HTMLResponse)
-async def admin(request: Request, config: AppConfig, session: AsyncDbSession) -> HTMLResponse:
+def admin(request: Request, config: AppConfig, session: DbSession) -> HTMLResponse:
     """Render the admin page."""
-    stats = await embedding_model_stats(session)
-    phrase_stats = await corpus_phrase_stats(session)
-    logger.info(
-        f"ebook_admin_page_loaded models={len(stats)} {phrase_stats.candidate_phrases=} "
-        f"{phrase_stats.protected_phrases=}"
-    )
-    return templates.TemplateResponse(
-        request,
-        "admin.html",
-        {"config": config, "stats": stats, "phrase_stats": phrase_stats},
-    )
+    stats = embedding_model_stats(session)
+    logger.info("ebook_admin_page_loaded models=%s", len(stats))
+    return templates.TemplateResponse(request, "admin.html", {"config": config, "stats": stats})
 
 
 @router.post("/scan", response_class=HTMLResponse)
-async def scan_library(request: Request, config: AppConfig, session: AsyncDbSession) -> HTMLResponse:
+def scan_library(request: Request, config: AppConfig, session: DbSession) -> HTMLResponse:
     """Scan configured library paths for EPUB changes."""
     try:
-        count = await ingest_configured_paths(session, config)
-        await session.commit()
+        count = ingest_configured_paths(session, config)
+        session.commit()
     except Exception as error:
         logger.exception("ebook_admin_scan_failed")
-        return error_response(request, error)
+        return templates.TemplateResponse(request, "partials/error.html", {"message": str(error)}, status_code=500)
 
-    logger.info(f"ebook_admin_scan_complete {count=}")
+    logger.info("ebook_admin_scan_complete changed_files=%s", count)
     if count > 0:
         schedule_bm25_refresh(request.app)
     return templates.TemplateResponse(request, "partials/admin_status.html", {"message": f"Indexed {count} EPUBs"})
 
 
-@router.post("/phrases/generate-all", response_class=HTMLResponse)
-async def generate_all_phrases(request: Request, config: AppConfig, engine: AppEngine) -> HTMLResponse:
-    """Regenerate candidate phrases for every indexed book without LLM judging."""
-    try:
-        result = await generate_candidate_phrases_for_books(engine, config)
-    except Exception as error:
-        logger.exception("ebook_admin_generate_phrases_failed")
-        return error_response(request, error)
-
-    logger.info(
-        f"ebook_admin_generate_phrases_complete {result.books_seen=} {result.books_built=} {result.candidate_phrases=}"
-    )
-    return templates.TemplateResponse(
-        request,
-        "partials/admin_status.html",
-        {
-            "message": (
-                f"Generated phrases for {result.books_built} of {result.books_seen} books; "
-                f"{result.candidate_phrases} candidates stored"
-            )
-        },
-    )
-
-
-@router.post("/phrases/judge-all", response_class=HTMLResponse)
-async def judge_all_phrases(request: Request, engine: AppEngine, config: AppConfig) -> HTMLResponse:
-    """Judge unjudged candidate phrases across every indexed book."""
-    return await run_phrase_judgment(request, engine, config, source_ids=None)
-
-
-@router.post("/phrases/judge-missing", response_class=HTMLResponse)
-async def judge_missing_phrases(
-    request: Request,
-    engine: AppEngine,
-    config: AppConfig,
-    session: AsyncDbSession,
-) -> HTMLResponse:
-    """Judge candidate phrases only for books where judging has never run."""
-    source_ids = await book_ids_pending_first_judgment(session)
-    if not source_ids:
-        return templates.TemplateResponse(
-            request,
-            "partials/admin_status.html",
-            {"message": "All books with candidate phrases have been judged"},
-        )
-    return await run_phrase_judgment(request, engine, config, source_ids=source_ids)
-
-
-async def run_phrase_judgment(
-    request: Request,
-    engine: AppEngine,
-    config: AppConfig,
-    *,
-    source_ids: list[int] | None,
-) -> HTMLResponse:
-    """Run LLM judging for candidate phrases and render the outcome as an admin status partial.
-
-    Args:
-        request (Request): Current request, for template rendering.
-        engine (AppEngine): Engine used to open per-book judging sessions.
-        config (AppConfig): Runtime phrase-tuning settings.
-        source_ids (list[int] | None): Books to judge; ``None`` judges every indexed book.
-
-    Returns:
-        HTMLResponse: Status partial describing the judging outcome.
-    """
-    try:
-        result = await judge_candidate_phrases_for_books(engine, config, source_ids=source_ids)
-    except Exception as error:
-        logger.exception("ebook_admin_judge_phrases_failed")
-        return error_response(request, error)
-
-    logger.info(
-        f"ebook_admin_judge_phrases_complete {result.books_seen=} {result.books_judged=} {result.books_failed=} "
-        f"{result.candidates_judged=} {result.protected_phrases=} {result.phrase_mentions=}"
-    )
-    return templates.TemplateResponse(
-        request,
-        "partials/admin_status.html",
-        {
-            "message": (
-                f"Judged {result.candidates_judged} candidates across {result.books_judged} of "
-                f"{result.books_seen} books; {result.protected_phrases} protected phrases, "
-                f"{result.phrase_mentions} mentions"
-                + (f"; {result.books_failed} books failed" if result.books_failed else "")
-            )
-        },
-    )
-
-
 @router.post("/embed-missing", response_class=HTMLResponse)
-async def embed_missing(
-    request: Request,
-    config: AppConfig,
-    session: AsyncDbSession,
-    client: AppHttpClient,
-) -> HTMLResponse:
+def embed_missing(request: Request, config: AppConfig, session: DbSession) -> HTMLResponse:
     """Embed chunks missing vectors for the configured model."""
     try:
-        count = await embed_missing_chunks(session, client, config)
-        await session.commit()
+        count = embed_missing_chunks(session, config)
+        session.commit()
     except Exception as error:
         logger.exception("ebook_admin_embed_missing_failed")
-        return error_response(request, error)
+        return templates.TemplateResponse(request, "partials/error.html", {"message": str(error)}, status_code=500)
 
-    logger.info(f"ebook_admin_embed_missing_complete {count=}")
+    logger.info("ebook_admin_embed_missing_complete chunks=%s", count)
     return templates.TemplateResponse(
         request,
         "partials/admin_status.html",
@@ -172,29 +64,38 @@ async def embed_missing(
 
 
 @router.post("/embed-all", response_class=HTMLResponse)
-async def embed_all(
-    request: Request,
-    config: AppConfig,
-    session: AsyncDbSession,
-    client: AppHttpClient,
-) -> HTMLResponse:
+def embed_all(request: Request, config: AppConfig, session: DbSession) -> HTMLResponse:
     """Embed all chunks missing vectors in fixed-size batches."""
     total = 0
     batches = 0
     try:
         while True:
-            count = await embed_missing_chunks(session, client, config)
+            count = embed_missing_chunks(session, config)
             if count == 0:
                 break
-            await session.commit()
+            session.commit()
             total += count
             batches += 1
-            logger.info(f"ebook_admin_embed_all_batch_complete {batches=} {count=} {total=}")
+            logger.info(
+                "ebook_admin_embed_all_batch_complete batch=%s chunks=%s total_chunks=%s",
+                batches,
+                count,
+                total,
+            )
     except Exception as error:
-        logger.exception(f"ebook_admin_embed_all_failed {batches=} {total=}")
-        return error_response(request, f"Embed all failed after {total} chunks in {batches} batches: {error}")
+        logger.exception(
+            "ebook_admin_embed_all_failed batches=%s chunks=%s",
+            batches,
+            total,
+        )
+        return templates.TemplateResponse(
+            request,
+            "partials/error.html",
+            {"message": f"Embed all failed after {total} chunks in {batches} batches: {error}"},
+            status_code=500,
+        )
 
-    logger.info(f"ebook_admin_embed_all_complete {batches=} {total=}")
+    logger.info("ebook_admin_embed_all_complete batches=%s chunks=%s", batches, total)
     return templates.TemplateResponse(
         request,
         "partials/admin_status.html",

python/ebook_search/api/routes/health.py

@@ -11,17 +11,15 @@ from fastapi.responses import JSONResponse
 from sqlalchemy import literal, select
 from sqlalchemy.exc import SQLAlchemyError
 
-from python.ebook_search.api.dependencies import (  # noqa: TC001 FastAPI resolves these annotated dependencies at runtime
-    AppConfig,
-    AppHttpClient,
+from python.ebook_search.api.dependencies import (
+    AppConfig,  # noqa: TC001 FastAPI resolves this annotated dependency at runtime
 )
 from python.ebook_search.bm25_corpus import bm25_index_exists, bm25_index_path, read_bm25_manifest
 from python.ebook_search.llm_interface import check_chat_endpoint, check_embedding_endpoint
-from python.fastapi_tools import AsyncDbSession  # noqa: TC001 FastAPI resolves this annotated dependency at runtime
+from python.fastapi_tools import DbSession  # noqa: TC001 FastAPI resolves this annotated dependency at runtime
 
 if TYPE_CHECKING:
-    import httpx
-    from sqlalchemy.ext.asyncio import AsyncSession
+    from sqlalchemy.orm import Session
 
     from python.ebook_search.config import EbookSearchConfig
 
@@ -31,17 +29,17 @@ router = APIRouter()
 
 
 @router.get("/health")
-async def health() -> dict[str, str]:
+def health() -> dict[str, str]:
     """Liveness probe that returns ok without touching dependencies."""
     return {"status": "ok"}
 
 
 @router.get("/ready")
-async def ready(config: AppConfig, session: AsyncDbSession, client: AppHttpClient) -> JSONResponse:
+def ready(config: AppConfig, session: DbSession) -> JSONResponse:
     """Readiness probe reporting database, embedding endpoint, and BM25 index status."""
-    database_ok = await check_database(session)
-    embedding_ok = await check_embedding_endpoint(client, config)
-    chat_status = await chat_endpoint_status(client, config)
+    database_ok = check_database(session)
+    embedding_ok = check_embedding_endpoint(config)
+    chat_status = chat_endpoint_status(config)
     bm25_status = check_bm25_status(config)
 
     checks = {
@@ -60,23 +58,30 @@ async def ready(config: AppConfig, session: AsyncDbSession, client: AppHttpClien
         status = "ready"
         status_code = HTTPStatus.OK
 
-    logger.info(f"ebook_ready_check {status=} {database_ok=} {embedding_ok=} {chat_status=} {bm25_status=}")
+    logger.info(
+        "ebook_ready_check status=%s database=%s embedding=%s chat=%s bm25=%s",
+        status,
+        database_ok,
+        embedding_ok,
+        chat_status,
+        bm25_status,
+    )
     return JSONResponse(content={"status": status, "checks": checks}, status_code=status_code)
 
 
-async def chat_endpoint_status(client: httpx.AsyncClient, config: EbookSearchConfig) -> str:
+def chat_endpoint_status(config: EbookSearchConfig) -> str:
     """Return the answering chat endpoint status, or disabled when answers are off."""
     if not config.answer_enabled:
         return "disabled"
-    return "ok" if await check_chat_endpoint(client, config) else "fail"
+    return "ok" if check_chat_endpoint(config) else "fail"
 
 
-async def check_database(session: AsyncSession) -> bool:
+def check_database(session: Session) -> bool:
     """Return whether the database answers a trivial query."""
     try:
-        await session.execute(select(literal(1)))
+        session.execute(select(literal(1)))
     except SQLAlchemyError as error:
-        logger.warning(f"ebook_ready_database_unavailable {error=}")
+        logger.warning("ebook_ready_database_unavailable error=%s", error)
         return False
     return True
 

python/ebook_search/api/routes/page.py

@@ -3,24 +3,17 @@
 from __future__ import annotations
 
 import logging
-from typing import TYPE_CHECKING
 
-from fastapi import APIRouter, BackgroundTasks, HTTPException, Request
-from fastapi.responses import HTMLResponse, RedirectResponse
-from sqlalchemy import func, select
+from fastapi import APIRouter, Request
+from fastapi.responses import HTMLResponse
+from sqlalchemy import select
 
 from python.ebook_search.api.dependencies import (
     AppConfig,  # noqa: TC001 FastAPI resolves this annotated dependency at runtime
 )
-from python.ebook_search.api.judge_tasks import is_judging_book, pop_book_judgment_outcome, start_book_phrase_judgment
 from python.ebook_search.api.web import templates
-from python.ebook_search.protected_phrases.generate_ngrams import recalculate_candidate_phrases_for_book
-from python.ebook_search.protected_phrases.store import count_protected_phrases
-from python.fastapi_tools import AsyncDbSession  # noqa: TC001 FastAPI resolves this annotated dependency at runtime
-from python.orm.richie import EbookCandidatePhrase, EbookChapter, EbookChunk, EbookProtectedPhrase, EbookSource
-
-if TYPE_CHECKING:
-    from sqlalchemy.ext.asyncio import AsyncSession
+from python.fastapi_tools import DbSession  # noqa: TC001 FastAPI resolves this annotated dependency at runtime
+from python.orm.richie import EbookSource
 
 logger = logging.getLogger(__name__)
 
@@ -28,160 +21,38 @@ router = APIRouter()
 
 
 @router.get("/", response_class=HTMLResponse)
-async def index(request: Request, config: AppConfig) -> HTMLResponse:
+def index(request: Request, config: AppConfig) -> HTMLResponse:
     """Render the search page."""
     return templates.TemplateResponse(request, "search.html", {"config": config})
 
 
 @router.get("/books", response_class=HTMLResponse)
-async def books(request: Request, session: AsyncDbSession) -> HTMLResponse:
+def books(request: Request, session: DbSession) -> HTMLResponse:
     """Render the indexed books page."""
-    sources = list((await session.scalars(select(EbookSource).order_by(EbookSource.title))).all())
-    logger.info(f"ebook_books_page_loaded count={len(sources)}")
+    sources = list(session.scalars(select(EbookSource).order_by(EbookSource.title)).all())
+    logger.info("ebook_books_page_loaded count=%s", len(sources))
     return templates.TemplateResponse(request, "books.html", {"sources": sources})
 
 
-async def get_chapter_count(session: AsyncSession, book_id: int) -> int:
-    """Return the number of indexed chapters for one book."""
-    return await session.scalar(select(func.count(EbookChapter.id)).where(EbookChapter.source_id == book_id)) or 0
-
-
-async def get_chunk_count(session: AsyncSession, book_id: int) -> int:
-    """Return the number of indexed chunks for one book."""
-    return await session.scalar(select(func.count(EbookChunk.id)).where(EbookChunk.source_id == book_id)) or 0
-
-
-async def get_candidate_count(session: AsyncSession, book_id: int) -> int:
-    """Return the number of indexed candidates for one book."""
-    return (
-        await session.scalar(select(func.count(EbookCandidatePhrase.id)).where(EbookCandidatePhrase.book_id == book_id))
-        or 0
-    )
-
-
-async def get_judged_candidate_count(session: AsyncSession, book_id: int) -> int:
-    """Return the number of judged candidates for one book."""
-    return (
-        await session.scalar(
-            select(func.count(EbookCandidatePhrase.id)).where(
-                EbookCandidatePhrase.book_id == book_id,
-                EbookCandidatePhrase.llm_judged.is_(True),
-            )
-        )
-        or 0
-    )
-
-
-async def get_candidates(session: AsyncSession, book_id: int) -> list[EbookCandidatePhrase]:
-    """Return the indexed candidates for one book."""
-    return list(
-        await session.scalars(
-            select(EbookCandidatePhrase)
-            .where(EbookCandidatePhrase.book_id == book_id)
-            .order_by(EbookCandidatePhrase.candidate_score.desc())
-            .limit(100)
-        )
-    )
-
-
-async def get_protected_phrases(session: AsyncSession, book_id: int) -> list[EbookProtectedPhrase]:
-    """Return the protected phrases for one book."""
-    return list(
-        await session.scalars(
-            select(EbookProtectedPhrase)
-            .where(EbookProtectedPhrase.book_id == book_id)
-            .order_by(EbookProtectedPhrase.importance.desc())
-            .limit(100)
-        )
-    )
-
-
 @router.get("/books/{source_id}", response_class=HTMLResponse)
-async def book_detail(source_id: int, request: Request, session: AsyncDbSession) -> HTMLResponse:
+def book_detail(source_id: int, request: Request, session: DbSession) -> HTMLResponse:
     """Render details for one indexed book."""
-    source = await session.get(EbookSource, source_id)
-    phrase_status_message = None
-    recalculated = request.query_params.get("phrases_recalculated")
-    if recalculated is not None:
-        phrase_status_message = f"Recalculated phrases; {recalculated} candidates generated"
-    judgment_outcome = pop_book_judgment_outcome(request.app, source_id)
-    if judgment_outcome is not None:
-        phrase_status_message = judgment_outcome
-    judging_in_progress = is_judging_book(request.app, source_id)
-    if judging_in_progress:
-        phrase_status_message = "Judging candidate phrases in the background; refresh to see progress"
+    source = session.get(EbookSource, source_id)
     if source is not None:
-        chapter_count = await get_chapter_count(session, source.id)
-        chunk_count = await get_chunk_count(session, source.id)
-        candidate_count = await get_candidate_count(session, source.id)
-        judged_candidate_count = await get_judged_candidate_count(session, source.id)
-        protected_count = await count_protected_phrases(session, source.id)
-        candidates = await get_candidates(session, source.id)
-        protected_phrases = await get_protected_phrases(session, source.id)
+        chapter_count = len(source.chapters)
+        chunk_count = len(source.chunks)
     else:
         chapter_count = 0
         chunk_count = 0
-        candidate_count = 0
-        judged_candidate_count = 0
-        protected_count = 0
-        candidates = []
-        protected_phrases = []
     logger.info(
-        f"ebook_book_detail_loaded {source_id=} found={source is not None} {chapter_count=} {chunk_count=} "
-        f"{candidate_count=} {judged_candidate_count=} {protected_count=}"
+        "ebook_book_detail_loaded source_id=%s found=%s chapters=%s chunks=%s",
+        source_id,
+        source is not None,
+        chapter_count,
+        chunk_count,
     )
     return templates.TemplateResponse(
         request,
         "book_detail.html",
-        {
-            "candidate_count": candidate_count,
-            "candidates": candidates,
-            "chapter_count": chapter_count,
-            "chunk_count": chunk_count,
-            "judged_candidate_count": judged_candidate_count,
-            "judging_in_progress": judging_in_progress,
-            "protected_count": protected_count,
-            "protected_phrases": protected_phrases,
-            "phrase_status_message": phrase_status_message,
-            "source": source,
-        },
+        {"chapter_count": chapter_count, "chunk_count": chunk_count, "source": source},
     )
-
-
-@router.post("/books/{source_id}/recalculate-phrases")
-async def recalculate_book_phrases(source_id: int, config: AppConfig, session: AsyncDbSession) -> RedirectResponse:
-    """Clear and regenerate candidate phrases for one indexed book."""
-    source = await session.get(EbookSource, source_id)
-    if source is None:
-        raise HTTPException(status_code=404, detail="Book not found")
-
-    try:
-        result = await recalculate_candidate_phrases_for_book(session, source, config)
-    except ValueError as error:
-        raise HTTPException(status_code=409, detail=str(error)) from error
-    logger.info(
-        f"ebook_book_phrase_recalculation_complete {source_id=} {result.candidate_phrases=} "
-        f"{result.deleted_candidates=} {result.deleted_protected_phrases=} {result.deleted_aliases=} "
-        f"{result.deleted_mentions=}"
-    )
-    return RedirectResponse(
-        url=f"/books/{source_id}?phrases_recalculated={result.candidate_phrases}",
-        status_code=303,
-    )
-
-
-@router.post("/books/{source_id}/judge-phrases")
-async def judge_book_phrases(
-    source_id: int,
-    request: Request,
-    background_tasks: BackgroundTasks,
-    session: AsyncDbSession,
-) -> RedirectResponse:
-    """Queue background judging of one book's candidate phrases and return immediately."""
-    source = await session.get(EbookSource, source_id)
-    if source is None:
-        raise HTTPException(status_code=404, detail="Book not found")
-
-    started = start_book_phrase_judgment(request.app, background_tasks, source.id)
-    logger.info(f"ebook_book_phrase_judgment_requested {source_id=} {started=}")
-    return RedirectResponse(url=f"/books/{source_id}", status_code=303)

python/ebook_search/api/routes/search.py

@@ -14,9 +14,8 @@ from python.ebook_search.answer import answer_query
 from python.ebook_search.api.dependencies import (  # noqa: TC001 FastAPI resolves these annotated dependencies at runtime
     AppConfig,
     AppEngine,
-    AppHttpClient,
 )
-from python.ebook_search.api.web import error_response, templates
+from python.ebook_search.api.web import templates
 from python.ebook_search.guardrails import (
     CitationReport,
     is_confident,
@@ -27,8 +26,6 @@ from python.ebook_search.search import SearchResponse, search_ebooks
 from python.ebook_search.timing import runtime_step_from_start
 
 if TYPE_CHECKING:
-    import httpx
-
     from python.ebook_search.config import EbookSearchConfig
 
 logger = logging.getLogger(__name__)
@@ -36,8 +33,7 @@ logger = logging.getLogger(__name__)
 router = APIRouter()
 
 
-async def build_answer(
-    client: httpx.AsyncClient,
+def build_answer(
     query: str,
     response: SearchResponse,
     config: EbookSearchConfig,
@@ -49,8 +45,9 @@ async def build_answer(
 
     if not is_confident(response.results, config):
         logger.info(
-            f"ebook_answer_low_confidence confidence={retrieval_confidence(response.results):.4f} "
-            f"{config.min_retrieval_confidence=:.4f}"
+            "ebook_answer_low_confidence confidence=%.4f threshold=%.4f",
+            retrieval_confidence(response.results),
+            config.min_retrieval_confidence,
         )
         answer = (
             "Retrieval confidence is low for this query, so answer generation was skipped. "
@@ -59,46 +56,40 @@ async def build_answer(
         return answer, True, None
 
     try:
-        answer = await answer_query(client, query, response.results, config)
+        answer = answer_query(query, response.results, config)
     except RuntimeError as error:
-        logger.warning(f"ebook_answer_request_failed_falling_back {error=}")
+        logger.warning("ebook_answer_request_failed_falling_back error=%s", error)
         return "Answer generation failed. Source chunks are still shown below.", False, None
 
     citation_report = None
     if config.validate_citations_enabled and response.results:
         citation_report = validate_citations(answer, len(response.results))
         if citation_report.invalid or not citation_report.grounded:
-            logger.warning(f"ebook_answer_citation_issue {citation_report.invalid=} {citation_report.grounded=}")
+            logger.warning(
+                "ebook_answer_citation_issue invalid=%s grounded=%s",
+                citation_report.invalid,
+                citation_report.grounded,
+            )
     return answer, False, citation_report
 
 
 @router.post("/search", response_class=HTMLResponse)
-async def search(
+def search(
     request: Request,
     config: AppConfig,
     engine: AppEngine,
-    client: AppHttpClient,
     query: Annotated[str, Form()],
-    *,
-    rerank: Annotated[bool, Form()] = False,
-    phrase_matching: Annotated[bool, Form()] = False,
+    rerank: Annotated[str | None, Form()] = None,
 ) -> HTMLResponse:
     """Run a search and render HTMX results."""
     try:
-        response = await search_ebooks(
-            engine,
-            client,
-            query,
-            config,
-            rerank=rerank,
-            phrase_matching=phrase_matching,
-        )
+        response = search_ebooks(engine, query, config, rerank=rerank == "true")
     except Exception as error:
         logger.exception("ebook_search_request_failed")
-        return error_response(request, error)
+        return templates.TemplateResponse(request, "partials/error.html", {"message": str(error)}, status_code=500)
 
     answer_start = perf_counter()
-    answer, low_confidence, citation_report = await build_answer(client, query, response, config)
+    answer, low_confidence, citation_report = build_answer(query, response, config)
     answer_step_name = "Answer generation" if config.answer_enabled else "Answer skipped"
     response = replace(
         response,
@@ -106,10 +97,12 @@ async def search(
     )
 
     for step in response.timings:
-        logger.info(f"ebook_search_timing {step.name=} {step.duration_ms=:.1f}")
+        logger.info("ebook_search_timing step=%r runtime_ms=%.1f", step.name, step.duration_ms)
     logger.info(
-        f"ebook_search_request_complete results={len(response.results)} {response.rank_label=} "
-        f"{response.total_runtime_ms=:.1f}"
+        "ebook_search_request_complete results=%s rank_label=%s runtime_ms=%.1f",
+        len(response.results),
+        response.rank_label,
+        response.total_runtime_ms,
     )
     return templates.TemplateResponse(
         request,

python/ebook_search/api/static/style.css

@@ -181,12 +181,6 @@ textarea:focus {
     flex-wrap: wrap;
 }
 
-.search-toggles {
-    display: flex;
-    flex-wrap: wrap;
-    gap: 14px;
-}
-
 button {
     padding: 10px 20px;
     font: inherit;
@@ -218,11 +212,6 @@ button:hover {
     margin-bottom: 24px;
 }
 
-.actions-grid {
-    display: grid;
-    grid-template-columns: repeat(2, max-content);
-}
-
 /* Answer + results */
 #results {
     display: block;
@@ -314,28 +303,6 @@ button:hover {
     font-variant-numeric: tabular-nums;
 }
 
-.phrase-matches {
-    display: flex;
-    flex-wrap: wrap;
-    gap: 8px;
-    align-items: baseline;
-    margin: 10px 0 0;
-    font-size: 0.78rem;
-}
-
-.phrase-matches-label {
-    color: var(--muted);
-    font-weight: 600;
-}
-
-.phrase-match {
-    padding: 3px 10px;
-    background: var(--bg);
-    border: 1px solid var(--border);
-    border-radius: 999px;
-    color: var(--accent);
-}
-
 /* Runtime — developer diagnostics, hidden unless dev mode is on */
 .runtime {
     display: none;

python/ebook_search/api/templates/admin.html

@@ -1,103 +1,45 @@
-{% extends "base.html" %} {% block title %}EPUB Admin{% endblock %} {% block
-head %}
-<script src="https://unpkg.com/htmx.org@2.0.4"></script>
-{% endblock %} {% block content %}
-<h1>Admin</h1>
-<section id="admin-status"></section>
-<section class="actions">
-  <form hx-post="/admin/scan" hx-target="#admin-status" hx-swap="innerHTML">
-    <button type="submit">Scan</button>
-  </form>
-</section>
-<section>
-  <h2>Embeddings</h2>
-  <section class="actions">
-    <form
-      hx-post="/admin/embed-missing"
-      hx-target="#admin-status"
-      hx-swap="innerHTML"
-    >
-      <button type="submit">Embed</button>
-    </form>
-    <form
-      hx-post="/admin/embed-all"
-      hx-target="#admin-status"
-      hx-swap="innerHTML"
-    >
-      <button type="submit">Embed all</button>
-    </form>
-  </section>
-  <table>
-    <thead>
-      <tr>
-        <th>Model</th>
-        <th>Dimensions</th>
-        <th>Embedded</th>
-        <th>Missing</th>
-        <th>Total chunks</th>
-      </tr>
-    </thead>
-    <tbody>
-      {% for item in stats %}
-      <tr>
-        <td>{{ item.model_name }}</td>
-        <td>{{ item.dimension }}</td>
-        <td>{{ item.embedded_chunks }}</td>
-        <td>{{ item.missing_chunks }}</td>
-        <td>{{ item.total_chunks }}</td>
-      </tr>
-      {% endfor %}
-    </tbody>
-  </table>
-</section>
-<section>
-  <h2>Protected phrases</h2>
-  <section class="actions actions-grid">
-    <form
-      hx-post="/admin/phrases/generate-all"
-      hx-target="#admin-status"
-      hx-swap="innerHTML"
-    >
-      <button type="submit">Regenerate all phrases</button>
-    </form>
-    <form
-      hx-post="/admin/phrases/judge-all"
-      hx-target="#admin-status"
-      hx-swap="innerHTML"
-    >
-      <button type="submit">Judge all phrases</button>
-    </form>
-    <form
-      hx-post="/admin/phrases/judge-missing"
-      hx-target="#admin-status"
-      hx-swap="innerHTML"
-    >
-      <button type="submit">Judge missing phrases</button>
-    </form>
-  </section>
-  <table>
-    <thead>
-      <tr>
-        <th>Candidates</th>
-        <th>Judged</th>
-        <th>Unjudged</th>
-        <th>Protected</th>
-        <th>Books indexed</th>
-        <th>Books generated</th>
-        <th>Books fully judged</th>
-      </tr>
-    </thead>
-    <tbody>
-      <tr>
-        <td>{{ phrase_stats.candidate_phrases }}</td>
-        <td>{{ phrase_stats.judged_candidates }}</td>
-        <td>{{ phrase_stats.unjudged_candidates }}</td>
-        <td>{{ phrase_stats.protected_phrases }}</td>
-        <td>{{ phrase_stats.total_books }}</td>
-        <td>{{ phrase_stats.books_with_candidates }}</td>
-        <td>{{ phrase_stats.books_fully_judged }}</td>
-      </tr>
-    </tbody>
-  </table>
-</section>
+{% extends "base.html" %}
+
+{% block title %}EPUB Admin{% endblock %}
+{% block head %}<script src="https://unpkg.com/htmx.org@2.0.4"></script>{% endblock %}
+
+{% block content %}
+    <h1>Admin</h1>
+    <section id="admin-status"></section>
+    <section class="actions">
+        <form hx-post="/admin/scan" hx-target="#admin-status" hx-swap="innerHTML">
+            <button type="submit">Scan</button>
+        </form>
+        <form hx-post="/admin/embed-missing" hx-target="#admin-status" hx-swap="innerHTML">
+            <button type="submit">Embed</button>
+        </form>
+        <form hx-post="/admin/embed-all" hx-target="#admin-status" hx-swap="innerHTML">
+            <button type="submit">Embed all</button>
+        </form>
+    </section>
+    <section>
+        <h2>Embeddings</h2>
+        <table>
+            <thead>
+                <tr>
+                    <th>Model</th>
+                    <th>Dimensions</th>
+                    <th>Embedded</th>
+                    <th>Missing</th>
+                    <th>Total chunks</th>
+                </tr>
+            </thead>
+            <tbody>
+                {% for item in stats %}
+                    <tr>
+                        <td>{{ item.model_name }}</td>
+                        <td>{{ item.dimension }}</td>
+                        <td>{{ item.embedded_chunks }}</td>
+                        <td>{{ item.missing_chunks }}</td>
+                        <td>{{ item.total_chunks }}</td>
+                    </tr>
+                {% endfor %}
+            </tbody>
+        </table>
+    </section>
 {% endblock %}

python/ebook_search/api/templates/book_detail.html

@@ -6,9 +6,6 @@
     {% if source %}
         <h1>{{ source.title }}</h1>
         <p class="meta">{{ source.author or "Unknown author" }}</p>
-        {% if phrase_status_message %}
-            <p class="status">{{ phrase_status_message }}</p>
-        {% endif %}
         <dl class="card">
             <dt>File</dt>
             <dd>{{ source.file_path }}</dd>
@@ -16,93 +13,7 @@
             <dd>{{ chapter_count }}</dd>
             <dt>Chunks</dt>
             <dd>{{ chunk_count }}</dd>
-            <dt>Candidates</dt>
-            <dd>{{ candidate_count }}</dd>
-            <dt>Judged</dt>
-            <dd>{{ judged_candidate_count }}</dd>
-            <dt>Protected</dt>
-            <dd>{{ protected_count }}</dd>
         </dl>
-        <form
-            method="post"
-            action="/books/{{ source.id }}/recalculate-phrases"
-            onsubmit="return confirm('Remove old phrases for this book and generate new candidates?');"
-        >
-            <button type="submit">Recalculate phrases</button>
-        </form>
-        <form
-            method="post"
-            action="/books/{{ source.id }}/judge-phrases"
-            onsubmit="return confirm('Judge candidate phrases for this book with the LLM?');"
-        >
-            <button type="submit"{% if judging_in_progress %} disabled{% endif %}>
-                {% if judging_in_progress %}Judging&hellip;{% else %}Judge phrases{% endif %}
-            </button>
-        </form>
-
-        <section>
-            <h2>Candidate n-grams</h2>
-            {% if candidates %}
-                <table>
-                    <thead>
-                        <tr>
-                            <th>Phrase</th>
-                            <th>Status</th>
-                            <th>Score</th>
-                            <th>Count</th>
-                            <th>Chapters</th>
-                        </tr>
-                    </thead>
-                    <tbody>
-                        {% for candidate in candidates %}
-                            <tr>
-                                <td>{{ candidate.phrase_text }}</td>
-                                <td>
-                                    {% if candidate.llm_judged %}
-                                        {% if candidate.llm_keep %}Kept{% else %}Rejected{% endif %}
-                                    {% else %}
-                                        Candidate
-                                    {% endif %}
-                                </td>
-                                <td>{{ "%.2f"|format(candidate.candidate_score) }}</td>
-                                <td>{{ candidate.raw_count }}</td>
-                                <td>{{ candidate.chapter_count }}</td>
-                            </tr>
-                        {% endfor %}
-                    </tbody>
-                </table>
-            {% else %}
-                <p>No candidate n-grams.</p>
-            {% endif %}
-        </section>
-
-        <section>
-            <h2>Protected phrases</h2>
-            {% if protected_phrases %}
-                <table>
-                    <thead>
-                        <tr>
-                            <th>Phrase</th>
-                            <th>Type</th>
-                            <th>Confidence</th>
-                            <th>Importance</th>
-                        </tr>
-                    </thead>
-                    <tbody>
-                        {% for phrase in protected_phrases %}
-                            <tr>
-                                <td>{{ phrase.phrase_text }}</td>
-                                <td>{{ phrase.phrase_type or "phrase" }}</td>
-                                <td>{{ "%.2f"|format(phrase.confidence) }}</td>
-                                <td>{{ "%.2f"|format(phrase.importance) }}</td>
-                            </tr>
-                        {% endfor %}
-                    </tbody>
-                </table>
-            {% else %}
-                <p>No protected phrases.</p>
-            {% endif %}
-        </section>
     {% else %}
         <h1>Book not found</h1>
     {% endif %}

python/ebook_search/api/templates/partials/results.html

@@ -82,14 +82,6 @@
                         </div>
                     {% endif %}
                 </dl>
-                {% if result.matched_phrases %}
-                    <p class="phrase-matches">
-                        <span class="phrase-matches-label">boosted by</span>
-                        {% for phrase in result.matched_phrases %}
-                            <span class="phrase-match">{{ phrase }}</span>
-                        {% endfor %}
-                    </p>
-                {% endif %}
             </li>
         {% endfor %}
     </ol>

python/ebook_search/api/templates/search.html

@@ -7,24 +7,12 @@
     <h1>Search</h1>
     <form class="card" hx-post="/search" hx-target="#results" hx-swap="innerHTML">
         <label for="query">What are you looking for?</label>
-        <textarea id="query" name="query" rows="4" placeholder="Ask a question or paste a passage…" required
-            onkeydown="if (event.key === 'Enter' && !event.shiftKey) { event.preventDefault(); this.form.requestSubmit(); }"></textarea>
+        <textarea id="query" name="query" rows="4" placeholder="Ask a question or paste a passage…" required></textarea>
         <div class="form-row">
-            <div class="search-toggles">
-                <label class="check">
-                    <input type="checkbox" name="rerank" value="true" {% if config.rerank.enabled %}checked{% endif %}>
-                    Rerank
-                </label>
-                <label class="check">
-                    <input
-                        type="checkbox"
-                        name="phrase_matching"
-                        value="true"
-                        {% if config.phrase_matching_enabled %}checked{% endif %}
-                    >
-                    Phrase matching
-                </label>
-            </div>
+            <label class="check">
+                <input type="checkbox" name="rerank" value="true" {% if config.rerank.enabled %}checked{% endif %}>
+                Rerank
+            </label>
             <button type="submit">Search</button>
         </div>
     </form>

python/ebook_search/api/web.py

@@ -3,14 +3,9 @@
 from __future__ import annotations
 
 from pathlib import Path
-from typing import TYPE_CHECKING
 
 from fastapi.templating import Jinja2Templates
 
-if TYPE_CHECKING:
-    from fastapi import Request
-    from fastapi.responses import HTMLResponse
-
 PACKAGE_DIR = Path(__file__).resolve().parent
 TEMPLATE_DIR = PACKAGE_DIR / "templates"
 STATIC_DIR = PACKAGE_DIR / "static"
@@ -26,8 +21,3 @@ def static_version(filename: str) -> int:
 
 templates = Jinja2Templates(directory=TEMPLATE_DIR)
 templates.env.globals["static_version"] = static_version
-
-
-def error_response(request: Request, message: object) -> HTMLResponse:
-    """Render the shared error partial for a failed UI request."""
-    return templates.TemplateResponse(request, "partials/error.html", {"message": str(message)}, status_code=500)

python/ebook_search/bm25_corpus.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import asyncio
 import json
 import logging
 import shutil
@@ -15,11 +14,10 @@ from typing import TYPE_CHECKING
 import bm25s
 from sqlalchemy import func, select, union_all
 
-from python.ebook_search.chunk_records import CHUNK_RECORD_COLUMNS
 from python.orm.richie import EbookChapter, EbookChunk, EbookSource
 
 if TYPE_CHECKING:
-    from sqlalchemy.ext.asyncio import AsyncSession
+    from sqlalchemy.orm import Session
 
     from python.ebook_search.config import EbookSearchConfig
 
@@ -75,48 +73,52 @@ def get_current_bm25_index(index_path: Path) -> Path:
     return index_path
 
 
-async def ensure_bm25_corpus(session: AsyncSession, config: EbookSearchConfig) -> None:
+def ensure_bm25_corpus(session: Session, config: EbookSearchConfig) -> None:
     """Create or refresh the persisted BM25 corpus when it is missing or stale."""
     index_path = bm25_index_path(config)
     manifest = read_bm25_manifest(index_path)
-    db_updated_at = await corpus_last_updated_at(session)
+    db_updated_at = corpus_last_updated_at(session)
     if not bm25_index_exists(index_path, manifest):
-        logger.info(f"ebook_bm25_index_missing {index_path=}")
-        await refresh_bm25_corpus(session, config, db_updated_at=db_updated_at)
+        logger.info("ebook_bm25_index_missing path=%s", index_path)
+        refresh_bm25_corpus(session, config, db_updated_at=db_updated_at)
         return
     if db_updated_at is not None and manifest is not None and manifest.created_at < db_updated_at:
         logger.info(
-            f"ebook_bm25_index_stale {index_path=} created_at={manifest.created_at.isoformat()} "
-            f"db_updated_at={db_updated_at.isoformat()}"
+            "ebook_bm25_index_stale path=%s created_at=%s db_updated_at=%s",
+            index_path,
+            manifest.created_at.isoformat(),
+            db_updated_at.isoformat(),
         )
-        await refresh_bm25_corpus(session, config, db_updated_at=db_updated_at)
+        refresh_bm25_corpus(session, config, db_updated_at=db_updated_at)
         return
     logger.info(
-        f"ebook_bm25_index_current {index_path=} chunks={manifest.chunk_count if manifest else 0} "
-        f"created_at={manifest.created_at.isoformat() if manifest else None}"
+        "ebook_bm25_index_current path=%s chunks=%s created_at=%s",
+        index_path,
+        manifest.chunk_count if manifest else 0,
+        manifest.created_at.isoformat() if manifest else None,
     )
 
 
-async def refresh_bm25_corpus(
-    session: AsyncSession,
+def refresh_bm25_corpus(
+    session: Session,
     config: EbookSearchConfig,
     *,
     db_updated_at: datetime | None = None,
 ) -> BM25Manifest:
-    """Rebuild and persist the BM25 corpus from the current database chunks.
-
-    The index build is CPU and disk work, so it runs in a worker thread.
-    """
+    """Rebuild and persist the BM25 corpus from the current database chunks."""
     index_path = bm25_index_path(config)
-    records, texts = await fetch_bm25_corpus_records(session)
+    records, texts = fetch_bm25_corpus_records(session)
     manifest = BM25Manifest(
         created_at=datetime.now(tz=UTC),
-        db_updated_at=db_updated_at if db_updated_at is not None else await corpus_last_updated_at(session),
+        db_updated_at=db_updated_at if db_updated_at is not None else corpus_last_updated_at(session),
         chunk_count=len(records),
     )
-    await asyncio.to_thread(write_bm25_corpus, index_path, records, texts, manifest)
+    write_bm25_corpus(index_path, records, texts, manifest)
     logger.info(
-        f"ebook_bm25_index_refreshed {index_path=} {manifest.chunk_count=} created_at={manifest.created_at.isoformat()}"
+        "ebook_bm25_index_refreshed path=%s chunks=%s created_at=%s",
+        index_path,
+        manifest.chunk_count,
+        manifest.created_at.isoformat(),
     )
     return manifest
 
@@ -129,7 +131,7 @@ def load_bm25_corpus(config: EbookSearchConfig) -> BM25Corpus:
     """
     index_path = bm25_index_path(config)
     active_index_path = get_current_bm25_index(index_path)
-    logger.info(f"ebook_bm25_corpus_cache_load {index_path=} {active_index_path=}")
+    logger.info("ebook_bm25_corpus_cache_load path=%s active_path=%s", index_path, active_index_path)
     manifest = read_bm25_manifest(index_path)
     if manifest is None or not bm25_index_exists(index_path, manifest):
         msg = f"BM25 corpus is not available: {index_path}"
@@ -162,7 +164,7 @@ def score_bm25_corpus(query: str, corpus: BM25Corpus, *, limit: int) -> list[tup
     return results
 
 
-async def fetch_bm25_corpus_records(session: AsyncSession) -> tuple[list[dict[str, object]], list[str]]:
+def fetch_bm25_corpus_records(session: Session) -> tuple[list[dict[str, object]], list[str]]:
     """Fetch persistable BM25 corpus records and their matching index texts from the database.
 
     search_text is only needed to build the index, so it is returned separately instead of
@@ -170,7 +172,12 @@ async def fetch_bm25_corpus_records(session: AsyncSession) -> tuple[list[dict[st
     """
     statement = (
         select(
-            *CHUNK_RECORD_COLUMNS,
+            EbookChunk.id.label("chunk_id"),
+            EbookChunk.text.label("text"),
+            EbookSource.title.label("source_title"),
+            EbookSource.author.label("source_author"),
+            EbookChapter.title.label("chapter_title"),
+            EbookChunk.page_label.label("page_label"),
             EbookChunk.search_text.label("bm25_text"),
         )
         .select_from(EbookChunk)
@@ -180,21 +187,21 @@ async def fetch_bm25_corpus_records(session: AsyncSession) -> tuple[list[dict[st
     )
     records: list[dict[str, object]] = []
     texts: list[str] = []
-    for row in (await session.execute(statement)).mappings():
+    for row in session.execute(statement).mappings():
         record = dict(row)
         texts.append(str(record.pop("bm25_text")))
         records.append(record)
     return records, texts
 
 
-async def corpus_last_updated_at(session: AsyncSession) -> datetime | None:
+def corpus_last_updated_at(session: Session) -> datetime | None:
     """Return the latest source/chapter/chunk update timestamp relevant to BM25 text."""
     update_times = union_all(
         select(func.max(EbookSource.updated).label("updated")),
         select(func.max(EbookChapter.updated).label("updated")),
         select(func.max(EbookChunk.updated).label("updated")),
     ).subquery()
-    return await session.scalar(select(func.max(update_times.c.updated)))
+    return session.scalar(select(func.max(update_times.c.updated)))
 
 
 def write_bm25_corpus(

python/ebook_search/chunk_records.py

@@ -1,13 +0,0 @@
-"""Shared database columns used to build search-result records."""
-
-from python.orm.richie import EbookChapter, EbookChunk, EbookSource
-
-CHUNK_RECORD_COLUMNS = (
-    EbookChunk.id.label("chunk_id"),
-    EbookChunk.text.label("text"),
-    EbookSource.id.label("source_id"),
-    EbookSource.title.label("source_title"),
-    EbookSource.author.label("source_author"),
-    EbookChapter.title.label("chapter_title"),
-    EbookChunk.page_label.label("page_label"),
-)

python/ebook_search/config.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+from os import getenv
 from typing import Annotated, Self
 
 from pydantic import AliasChoices, Field, field_validator, model_validator
@@ -31,6 +32,11 @@ def normalize_embedding_alias(model: str) -> str:
     return standard_model
 
 
+def normalize_embedding_model(default: str = "qwen3-embedding-0.6b") -> str:
+    """Normalize the configured embedding alias to its provider model name."""
+    return normalize_embedding_alias(getenv("EBOOK_SEARCH_EMBEDDING_MODEL", default))
+
+
 class RerankConfig(BaseSettings):
     """vLLM reranker settings."""
 
@@ -80,25 +86,6 @@ class EbookSearchConfig(BaseSettings):
     validate_citations_enabled: bool = True
     bm25_index_dir: str = ".ebook_search_bm25"
     bm25_refresh_delay_seconds: int = 60
-    protected_phrase_max_candidates_per_book: int = 5000
-    protected_phrase_llm_candidates_per_book: int = 500
-    protected_phrase_extraction_workers: int = 16
-    phrase_judge_book_workers: int = 20
-    phrase_judge_phrase_workers: int = 100
-    protected_phrase_confidence_threshold: float = 0.80
-    phrase_matching_enabled: bool = True
-    phrase_hit_boost: float = 0.25
-    phrase_min_tokens: int = 2
-    phrase_max_tokens: int = 5
-    phrase_max_entity_tokens: int = 8
-    phrase_raw_ngram_min_count: int = 2
-    phrase_raw_count_score_threshold: int = 3
-    phrase_raw_count_high_score_threshold: int = 10
-    phrase_chapter_count_score_threshold: int = 2
-    phrase_chapter_count_high_score_threshold: int = 5
-    phrase_target_protected_per_book: int = 100
-    phrase_default_allow_nested: bool = False
-    phrase_default_suppress_children: bool = True
 
     @field_validator("library_paths", mode="before")
     @classmethod

python/ebook_search/docker/containers.py

@@ -32,7 +32,7 @@ def docker_run(
     capture_output: bool = False,
 ) -> subprocess.CompletedProcess[str]:
     """Run docker with repo-root cwd and consistent error handling."""
-    logger.info(f"docker {' '.join(arguments)}")
+    logger.info("docker %s", " ".join(arguments))
     return subprocess.run(
         ["docker", *arguments],
         cwd=get_repo_dir(),

python/ebook_search/docker/pyproject.toml

@@ -16,7 +16,7 @@ dependencies = [
     "pydantic",
     "pydantic-settings",
     "python-multipart",
-    "sqlalchemy[asyncio]",
+    "sqlalchemy",
     "tiktoken",
     "typer",
     "uvicorn[standard]",
@@ -25,9 +25,7 @@ dependencies = [
 
 [dependency-groups]
 dev = [
-    "aiosqlite",
     "pytest",
-    "pytest-asyncio",
     "pytest-mock",
     "pytest-xdist",
 ]
@@ -37,5 +35,4 @@ package = false
 
 [tool.pytest.ini_options]
 addopts = "-n auto -ra"
-asyncio_mode = "auto"
 testpaths = ["tests/ebook_search"]

python/ebook_search/docker/uv.lock

@@ -2,15 +2,6 @@ version = 1
 revision = 3
 requires-python = "==3.14.*"
 
-[[package]]
-name = "aiosqlite"
-version = "0.22.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/4e/8a/64761f4005f17809769d23e518d915db74e6310474e733e3593cfc854ef1/aiosqlite-0.22.1.tar.gz", hash = "sha256:043e0bd78d32888c0a9ca90fc788b38796843360c855a7262a532813133a0650", size = 14821, upload-time = "2025-12-23T19:25:43.997Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/00/b7/e3bf5133d697a08128598c8d0abc5e16377b51465a33756de24fa7dee953/aiosqlite-0.22.1-py3-none-any.whl", hash = "sha256:21c002eb13823fad740196c5a2e9d8e62f6243bd9e7e4a1f87fb5e44ecb4fceb", size = 17405, upload-time = "2025-12-23T19:25:42.139Z" },
-]
-
 [[package]]
 name = "alembic"
 version = "1.18.5"
@@ -162,7 +153,7 @@ dependencies = [
     { name = "pydantic" },
     { name = "pydantic-settings" },
     { name = "python-multipart" },
-    { name = "sqlalchemy", extra = ["asyncio"] },
+    { name = "sqlalchemy" },
     { name = "tiktoken" },
     { name = "typer" },
     { name = "uvicorn", extra = ["standard"] },
@@ -171,9 +162,7 @@ dependencies = [
 
 [package.dev-dependencies]
 dev = [
-    { name = "aiosqlite" },
     { name = "pytest" },
-    { name = "pytest-asyncio" },
     { name = "pytest-mock" },
     { name = "pytest-xdist" },
 ]
@@ -192,7 +181,7 @@ requires-dist = [
     { name = "pydantic" },
     { name = "pydantic-settings" },
     { name = "python-multipart" },
-    { name = "sqlalchemy", extras = ["asyncio"] },
+    { name = "sqlalchemy" },
     { name = "tiktoken" },
     { name = "typer" },
     { name = "uvicorn", extras = ["standard"] },
@@ -201,9 +190,7 @@ requires-dist = [
 
 [package.metadata.requires-dev]
 dev = [
-    { name = "aiosqlite" },
     { name = "pytest" },
-    { name = "pytest-asyncio" },
     { name = "pytest-mock" },
     { name = "pytest-xdist" },
 ]
@@ -255,9 +242,7 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c3/93/43e116ee114b28737ba7e12952a0d4e2f55944d0f84e42bc91ba7192a3c9/greenlet-3.5.3-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:fd2e02fa07485778536a036222d616ab957b1d533f36b3ed98ce725d9c9d3117", size = 288202, upload-time = "2026-06-26T18:23:49.604Z" },
     { url = "https://files.pythonhosted.org/packages/82/2f/146d218299046a43d1f029fd544b3d110d0f175a09c715c7e8da4a4a345d/greenlet-3.5.3-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:df0a0628d1597eb0897b62f55d1343f772405fd25f3b2a796c76874b0c2e22e8", size = 654096, upload-time = "2026-06-26T19:07:12.71Z" },
     { url = "https://files.pythonhosted.org/packages/a0/cc/04738cafb3f45fa991ea44f9de94c47dcec964f5a972300988a6751f49d9/greenlet-3.5.3-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ebd933a6adabc298bab47731a130fe6bfb888bd934eee37810f151159544540d", size = 666304, upload-time = "2026-06-26T19:10:09.503Z" },
-    { url = "https://files.pythonhosted.org/packages/86/a9/73fa62893d5b84b4205544e6b673c654cc43aa5b9899bac00f04d64af73d/greenlet-3.5.3-cp314-cp314-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:8d19fe6c39ebff9259f07bcc685d3290f8fa4ea2278e51dd0008e4d6b0f2d814", size = 670657, upload-time = "2026-06-26T19:24:19.967Z" },
     { url = "https://files.pythonhosted.org/packages/ce/aa/4e0dad5e605c270c784ab911c43da6adb136ccd4d81180f763ca429a723d/greenlet-3.5.3-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4b9d501b40e80b70e32323c799dd9b420a5577a9601469d362ae1ffb690f3a7c", size = 663635, upload-time = "2026-06-26T18:32:20.802Z" },
-    { url = "https://files.pythonhosted.org/packages/29/7e/2ffce64929fb3cab7b65d5a0b20aaf9764e227681d731b041077fc9a525a/greenlet-3.5.3-cp314-cp314-manylinux_2_39_riscv64.whl", hash = "sha256:962c5df2db8cb446da51edf1ca5296c389d93b99c9d8aa2ee4c7d0d8f1218260", size = 473497, upload-time = "2026-06-26T19:25:39.421Z" },
     { url = "https://files.pythonhosted.org/packages/d1/50/13efdbea246fe3d3b735e191fec08fb50809f53cd2383ebe123d0809e44b/greenlet-3.5.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:a1fad1d11e7d6aab184107baa8e4ece11ccba3ec9599cd7efa5ff4d70d43256a", size = 1621252, upload-time = "2026-06-26T19:09:05.647Z" },
     { url = "https://files.pythonhosted.org/packages/f7/22/c0a336ae4a1410fd5f5121098e5bfbf1865f64c5ef80b4b5412886c4a332/greenlet-3.5.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:fad5aec764399f1b5cc347ad250a59660f20c8f8888ea6bae1f93b769cce1154", size = 1684824, upload-time = "2026-06-26T18:31:47.738Z" },
     { url = "https://files.pythonhosted.org/packages/7a/94/91aec0030bea75c4b3244251d0de60a1f3432d1ecb53ab6c437fb5c3ba61/greenlet-3.5.3-cp314-cp314-win_amd64.whl", hash = "sha256:7669aa24cf2a1041d6f7899575b494a3ab4cf68bfcc8609b1dc0be7272db835e", size = 240754, upload-time = "2026-06-26T18:22:15.669Z" },
@@ -265,9 +250,7 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/91/95/3e161213d7f1d378d15aa9e792093e9bfe01844680d04b7fd6e0107c9098/greenlet-3.5.3-cp314-cp314t-macosx_11_0_universal2.whl", hash = "sha256:271a8ea7c1024e8a0d7dd2be66dd66dda8a07193f41a17b9e924f7600f5b62be", size = 296389, upload-time = "2026-06-26T18:22:20.657Z" },
     { url = "https://files.pythonhosted.org/packages/00/92/715c44721abe2b4d1ae9abde4179411868a5bff312479f54e105d372f131/greenlet-3.5.3-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:19131729ae0ddc3c2e1ef85e650169b5e37ee32e400f215f78b94d7b0d567310", size = 653382, upload-time = "2026-06-26T19:07:14.209Z" },
     { url = "https://files.pythonhosted.org/packages/a0/83/37a10372a1090a6624cca8e74c12df1a36c2dc36429ed0255b7fb1aeee23/greenlet-3.5.3-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1540dd8e5fc2a5aec40fbb98ef8e149fa47c89a4b4a1cf2575a14d3d1869d7a8", size = 659401, upload-time = "2026-06-26T19:10:10.876Z" },
-    { url = "https://files.pythonhosted.org/packages/cb/73/8faec206b851c22b1733545fda900829a1f3f5b1c78ae7e0fb3dba57d9f4/greenlet-3.5.3-cp314-cp314t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b897d97759425953f69a9c0fac67f8fe333ec0ce7377ef186fb2b0c3ad5e354d", size = 659582, upload-time = "2026-06-26T19:24:21.357Z" },
     { url = "https://files.pythonhosted.org/packages/db/e2/d1509cad4207da559cc42986ecdd8fc67ad0d1bba2bf03023c467fd5e0f3/greenlet-3.5.3-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e81fa194a1d20967877bdf9c7794db2bc99063e5be36aee710c08f04c5bb087f", size = 656969, upload-time = "2026-06-26T18:32:22.272Z" },
-    { url = "https://files.pythonhosted.org/packages/b4/55/50c19e49f8045834ada71ef12f8ad048eba8517c6aa41161bed676328fae/greenlet-3.5.3-cp314-cp314t-manylinux_2_39_riscv64.whl", hash = "sha256:3236754d423955ea08e9bb5f6c04a7895f9e22c290b66aa7653fcb922d839eb0", size = 491037, upload-time = "2026-06-26T19:25:40.672Z" },
     { url = "https://files.pythonhosted.org/packages/86/7d/eaf70de20aadca3a5884aec58362861c64ce45e7b277f47ed026926a3b89/greenlet-3.5.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:55cf4d777485d43110e47133cbba6d74a8885a87ec1227ef0267f9ee80c5aa21", size = 1617822, upload-time = "2026-06-26T19:09:06.893Z" },
     { url = "https://files.pythonhosted.org/packages/8a/f9/414d38fc400ae4350d4185eaad1827676f7cf5287b9136e0ed1cbbe20a7f/greenlet-3.5.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:12a248ba75f6a9a236375f52296c498c89ff1d8badf32deb9eca7abd5853f7da", size = 1677983, upload-time = "2026-06-26T18:31:49.396Z" },
     { url = "https://files.pythonhosted.org/packages/e4/15/7edb977e08f9bff702fe42d6c902702786ff6b9694058b4e6a2a6ac90e57/greenlet-3.5.3-cp314-cp314t-win_amd64.whl", hash = "sha256:efc6bd60ea02e085862c74a3ef64b147ffc6f1a5ea7d9f26e7a939943f68c1e3", size = 243626, upload-time = "2026-06-26T18:24:41.485Z" },
@@ -682,18 +665,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/24/25/1de2678b631f5a49215c6c96fff41ba892b0a34df68d6d80292b1b48aa7f/pytest-9.1.1-py3-none-any.whl", hash = "sha256:37a86b45efb9a47a61a36449063e8e18d0cab3161329fc099eb21783169c4f0c", size = 386536, upload-time = "2026-06-19T10:58:31.347Z" },
 ]
 
-[[package]]
-name = "pytest-asyncio"
-version = "1.4.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "pytest" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/43/7c/d36d04db312ecf4298932ef77e6e4a9e8ad017906e24e34f0b0c361a2473/pytest_asyncio-1.4.0.tar.gz", hash = "sha256:c6c0d2259945122819f171a32ecea2c349ead889ee28176caaf492143424be42", size = 58514, upload-time = "2026-05-26T09:56:04.083Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/03/e2/08a497ef684b88559c9cc5f4ad53a37e7b99e727094a86d6ea32536d5d3c/pytest_asyncio-1.4.0-py3-none-any.whl", hash = "sha256:933ca923a23075a87fb7070c0ec272a6848489824d887c85c812670932835aa1", size = 16930, upload-time = "2026-05-26T09:56:02.576Z" },
-]
-
 [[package]]
 name = "pytest-mock"
 version = "3.15.1"
@@ -897,11 +868,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e2/22/dbf013a12ec759e54a34a119e9e217435b3f71b2dd5c61a7ade0a25dae87/sqlalchemy-2.0.51-py3-none-any.whl", hash = "sha256:bb024d8b621d0be75f4f44ecc7c950450026e76d66dc8f791bb5331d7fed59d5", size = 1944334, upload-time = "2026-06-15T16:09:22.418Z" },
 ]
 
-[package.optional-dependencies]
-asyncio = [
-    { name = "greenlet" },
-]
-
 [[package]]
 name = "starlette"
 version = "1.3.1"

python/ebook_search/embeddings.py

@@ -23,8 +23,7 @@ logger = logging.getLogger(__name__)
 if TYPE_CHECKING:
     from collections.abc import Sequence
 
-    import httpx
-    from sqlalchemy.ext.asyncio import AsyncSession
+    from sqlalchemy.orm import Session
 
     from python.ebook_search.config import EbookSearchConfig
 
@@ -66,45 +65,51 @@ class EmbeddingModelStats:
         return max(self.total_chunks - self.embedded_chunks, 0)
 
 
-async def embed_texts(
-    client: httpx.AsyncClient,
-    texts: Sequence[str],
-    config: EbookSearchConfig,
-) -> list[list[float]]:
+def embed_texts(texts: Sequence[str], config: EbookSearchConfig) -> list[list[float]]:
     """Embed text with the configured vLLM embedding model."""
-    logger.info(f"ebook_embed_request_start {config.embedding_base_url=} {config.embedding_model=} count={len(texts)}")
-    vectors = await request_embeddings(client, texts, config)
+    logger.info(
+        "ebook_embed_request_start base_url=%s model=%s count=%s",
+        config.embedding_base_url,
+        config.embedding_model,
+        len(texts),
+    )
+    vectors = request_embeddings(texts, config)
     expected_dimension = MODEL_DIMENSIONS[config.embedding_model]
     for vector in vectors:
         if len(vector) != expected_dimension:
             msg = f"Expected {expected_dimension} dimensions, got {len(vector)}"
             raise ValueError(msg)
-    logger.info(f"ebook_embed_request_complete {config.embedding_model=} count={len(vectors)} {expected_dimension=}")
+    logger.info(
+        "ebook_embed_request_complete model=%s count=%s dimension=%s",
+        config.embedding_model,
+        len(vectors),
+        expected_dimension,
+    )
     return vectors
 
 
-async def embed_query(client: httpx.AsyncClient, query: str, config: EbookSearchConfig) -> list[float]:
+def embed_query(query: str, config: EbookSearchConfig) -> list[float]:
     """Embed a search query with the Qwen retrieval instruction."""
     instructed_query = f"Instruct: Retrieve relevant passages for the query.\nQuery: {query}"
-    return (await embed_texts(client, [instructed_query], config))[0]
+    return embed_texts([instructed_query], config)[0]
 
 
-async def ensure_embedding_models(session: AsyncSession) -> None:
+def ensure_embedding_models(session: Session) -> None:
     """Ensure supported embedding model rows exist."""
     for name, dimension in MODEL_DIMENSIONS.items():
-        existing = await session.scalar(select(EbookEmbeddingModel).where(EbookEmbeddingModel.name == name))
+        existing = session.scalar(select(EbookEmbeddingModel).where(EbookEmbeddingModel.name == name))
         if existing is None:
             session.add(EbookEmbeddingModel(name=name, dimension=dimension, is_default=name == "qwen3-embedding-0.6b"))
-            logger.info(f"ebook_embedding_model_created {name=} {dimension=}")
-    await session.flush()
+            logger.info("ebook_embedding_model_created model=%s dimension=%s", name, dimension)
+    session.flush()
 
 
-async def embedding_model_stats(session: AsyncSession) -> list[EmbeddingModelStats]:
+def embedding_model_stats(session: Session) -> list[EmbeddingModelStats]:
     """Return embedding coverage counts for every supported model."""
-    total_chunks = await session.scalar(select(func.count(EbookChunk.id))) or 0
+    total_chunks = session.scalar(select(func.count(EbookChunk.id))) or 0
     models = {
         model.name: model
-        for model in await session.scalars(
+        for model in session.scalars(
             select(EbookEmbeddingModel)
             .where(EbookEmbeddingModel.name.in_(MODEL_DIMENSIONS))
             .order_by(EbookEmbeddingModel.name)
@@ -117,7 +122,7 @@ async def embedding_model_stats(session: AsyncSession) -> list[EmbeddingModelSta
         embedded_chunks = 0
         if model is not None:
             table = get_embedding_table(dimension)
-            embedded_chunks = await session.scalar(select(func.count(table.id)).where(table.model_id == model.id)) or 0
+            embedded_chunks = session.scalar(select(func.count(table.id)).where(table.model_id == model.id)) or 0
         stats.append(
             EmbeddingModelStats(
                 model_name=model_name,
@@ -129,10 +134,10 @@ async def embedding_model_stats(session: AsyncSession) -> list[EmbeddingModelSta
     return stats
 
 
-async def embed_missing_chunks(session: AsyncSession, client: httpx.AsyncClient, config: EbookSearchConfig) -> int:
+def embed_missing_chunks(session: Session, config: EbookSearchConfig) -> int:
     """Embed chunks missing embeddings for the configured model."""
-    await ensure_embedding_models(session)
-    model = await session.scalar(select(EbookEmbeddingModel).where(EbookEmbeddingModel.name == config.embedding_model))
+    ensure_embedding_models(session)
+    model = session.scalar(select(EbookEmbeddingModel).where(EbookEmbeddingModel.name == config.embedding_model))
     if model is None:
         supported_models = ", ".join(MODEL_DIMENSIONS)
         msg = f"Unknown embedding model: {config.embedding_model}. Supported models: {supported_models}"
@@ -140,7 +145,7 @@ async def embed_missing_chunks(session: AsyncSession, client: httpx.AsyncClient,
 
     table = get_embedding_table(model.dimension)
     chunks = list(
-        await session.scalars(
+        session.scalars(
             select(EbookChunk)
             .outerjoin(table, (table.chunk_id == EbookChunk.id) & (table.model_id == model.id))
             .where(table.id.is_(None))
@@ -149,17 +154,17 @@ async def embed_missing_chunks(session: AsyncSession, client: httpx.AsyncClient,
         )
     )
     if not chunks:
-        logger.info(f"ebook_embed_missing_none {config.embedding_model=}")
+        logger.info("ebook_embed_missing_none model=%s", config.embedding_model)
         return 0
 
-    logger.info(f"ebook_embed_missing_batch_start {config.embedding_model=} count={len(chunks)}")
-    vectors = await embed_texts(client, [chunk.text for chunk in chunks], config)
+    logger.info("ebook_embed_missing_batch_start model=%s count=%s", config.embedding_model, len(chunks))
+    vectors = embed_texts([chunk.text for chunk in chunks], config)
     rows = [
         {"chunk_id": chunk.id, "model_id": model.id, "embedding": vector}
         for chunk, vector in zip(chunks, vectors, strict=True)
     ]
     statement = insert(table).values(rows).on_conflict_do_nothing(index_elements=["chunk_id", "model_id"])
-    await session.execute(statement)
-    await session.flush()
-    logger.info(f"ebook_embed_missing_batch_complete {config.embedding_model=} count={len(rows)}")
+    session.execute(statement)
+    session.flush()
+    logger.info("ebook_embed_missing_batch_complete model=%s count=%s", config.embedding_model, len(rows))
     return len(rows)

python/ebook_search/ingest.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import asyncio
 import hashlib
 import logging
 from dataclasses import dataclass
@@ -14,7 +13,6 @@ import tiktoken
 from sqlalchemy import or_, select
 
 from python.ebook_search.epub_parse import parse_epub
-from python.ebook_search.protected_phrases.matching import index_chunk_phrase_mentions_for_book
 from python.orm.richie import EbookChapter, EbookChunk, EbookSource
 
 logger = logging.getLogger(__name__)
@@ -22,7 +20,7 @@ DEFAULT_CHUNK_TOKENS = 700
 DEFAULT_CHUNK_OVERLAP = 100
 
 if TYPE_CHECKING:
-    from sqlalchemy.ext.asyncio import AsyncSession
+    from sqlalchemy.orm import Session
 
     from python.ebook_search.config import EbookSearchConfig
     from python.ebook_search.epub_parse import ParsedChapter
@@ -74,111 +72,90 @@ def chunk_text(
     return [chunk for chunk in chunks if chunk.text]
 
 
-def find_library_epubs(library_path: str) -> tuple[Path, list[Path] | None]:
-    """Resolve one configured library path and collect its EPUB files (blocking filesystem walk).
-
-    Returns:
-        tuple[Path, list[Path] | None]: The expanded path and its EPUB files, or ``None`` when
-        the path is neither an EPUB file nor a directory.
-    """
-    path = Path(library_path).expanduser()
-    if path.is_file() and path.suffix.lower() == ".epub":
-        return path, [path]
-    if path.is_dir():
-        return path, sorted(path.rglob("*.epub"))
-    return path, None
-
-
-async def ingest_configured_paths(session: AsyncSession, config: EbookSearchConfig) -> int:
+def ingest_configured_paths(session: Session, config: EbookSearchConfig) -> int:
     """Ingest every EPUB found under configured library paths."""
     count = 0
     for library_path in config.library_paths:
-        path, epub_paths = await asyncio.to_thread(find_library_epubs, library_path)
-        logger.info(f"ebook_ingest_path_start {path=}")
-        if epub_paths is None:
-            logger.warning(f"ebook_ingest_path_missing {path=}")
-            continue
-        for epub_path in epub_paths:
-            count += int(await ingest_file(session, epub_path, config))
-    logger.info(f"ebook_ingest_paths_complete {count=} configured_paths={len(config.library_paths)}")
+        path = Path(library_path).expanduser()
+        logger.info("ebook_ingest_path_start path=%s", path)
+        if path.is_file() and path.suffix.lower() == ".epub":
+            count += int(ingest_file(session, path, config))
+        elif path.is_dir():
+            for epub_path in sorted(path.rglob("*.epub")):
+                count += int(ingest_file(session, epub_path, config))
+        else:
+            logger.warning("ebook_ingest_path_missing path=%s", path)
+    logger.info("ebook_ingest_paths_complete changed_files=%s configured_paths=%s", count, len(config.library_paths))
     return count
 
 
-def resolve_ingest_path(path: Path) -> Path:
-    """Expand and resolve an ingest path (blocking filesystem call)."""
-    return path.expanduser().resolve()
-
-
-async def ingest_file(session: AsyncSession, path: Path, config: EbookSearchConfig) -> bool:
+def ingest_file(session: Session, path: Path, config: EbookSearchConfig) -> bool:
     """Ingest one EPUB file. Return True when the database changed."""
-    try:
-        resolved_path = await asyncio.to_thread(resolve_ingest_path, path)
-        logger.info(f"ebook_ingest_file_start {resolved_path=}")
-        file_hash = await asyncio.to_thread(sha256_file, resolved_path)
-        existing = await find_existing_source(session, resolved_path, file_hash)
-        if existing is not None and existing.file_sha256 == file_hash:
-            stat = resolved_path.stat()
-            existing.file_path = str(resolved_path)
-            existing.file_mtime = datetime.fromtimestamp(stat.st_mtime, tz=UTC)
-            existing.file_size = stat.st_size
-            await session.flush()
-            logger.info(f"ebook_ingest_file_unchanged {existing.id=} {resolved_path=}")
-            return False
-        if existing is not None:
-            logger.info(f"ebook_ingest_file_replacing {existing.id=} {resolved_path=}")
-            await session.delete(existing)
-            await session.flush()
-
+    resolved_path = path.expanduser().resolve()
+    logger.info("ebook_ingest_file_start path=%s", resolved_path)
+    file_hash = sha256_file(resolved_path)
+    existing = find_existing_source(session, resolved_path, file_hash)
+    if existing is not None and existing.file_sha256 == file_hash:
         stat = resolved_path.stat()
-        parsed = await asyncio.to_thread(parse_epub, resolved_path)
-        source = EbookSource(
-            title=parsed.title,
-            author=parsed.author,
-            language=parsed.language,
-            publisher=parsed.publisher,
-            identifier=parsed.identifier,
-            file_path=str(resolved_path),
-            file_sha256=file_hash,
-            file_mtime=datetime.fromtimestamp(stat.st_mtime, tz=UTC),
-            file_size=stat.st_size,
-        )
-        session.add(source)
-        await session.flush()
-
-        chunk_index = 0
-        for spine_index, parsed_chapter in enumerate(parsed.chapters):
-            chapter = EbookChapter(
-                source_id=source.id,
-                spine_index=spine_index,
-                title=parsed_chapter.title,
-                href=parsed_chapter.href,
-            )
-            session.add(chapter)
-            await session.flush()
-            chunk_index = add_chapter_chunks(session, source, chapter, parsed_chapter, chunk_index, config)
-
-        await session.commit()
-        mention_count = await index_chunk_phrase_mentions_for_book(session, source.id, config)
-        logger.info(
-            f"ebook_ingest_file_complete {source.id=} {resolved_path=} chapters={len(parsed.chapters)} {chunk_index=} "
-            f"{mention_count=}"
-        )
-    except Exception:
-        logger.exception(f"ebook_ingest_file_error {path=}")
+        existing.file_path = str(resolved_path)
+        existing.file_mtime = datetime.fromtimestamp(stat.st_mtime, tz=UTC)
+        existing.file_size = stat.st_size
+        session.flush()
+        logger.info("ebook_ingest_file_unchanged source_id=%s path=%s", existing.id, resolved_path)
         return False
-    else:
-        return True
+    if existing is not None:
+        logger.info("ebook_ingest_file_replacing source_id=%s path=%s", existing.id, resolved_path)
+        session.delete(existing)
+        session.flush()
+
+    stat = resolved_path.stat()
+    parsed = parse_epub(resolved_path)
+    source = EbookSource(
+        title=parsed.title,
+        author=parsed.author,
+        language=parsed.language,
+        publisher=parsed.publisher,
+        identifier=parsed.identifier,
+        file_path=str(resolved_path),
+        file_sha256=file_hash,
+        file_mtime=datetime.fromtimestamp(stat.st_mtime, tz=UTC),
+        file_size=stat.st_size,
+    )
+    session.add(source)
+    session.flush()
+
+    chunk_index = 0
+    for spine_index, parsed_chapter in enumerate(parsed.chapters):
+        chapter = EbookChapter(
+            source_id=source.id,
+            spine_index=spine_index,
+            title=parsed_chapter.title,
+            href=parsed_chapter.href,
+        )
+        session.add(chapter)
+        session.flush()
+        chunk_index = add_chapter_chunks(session, source, chapter, parsed_chapter, chunk_index, config)
+
+    session.flush()
+    logger.info(
+        "ebook_ingest_file_complete source_id=%s path=%s chapters=%s chunks=%s",
+        source.id,
+        resolved_path,
+        len(parsed.chapters),
+        chunk_index,
+    )
+    return True
 
 
-async def find_existing_source(session: AsyncSession, path: Path, file_hash: str) -> EbookSource | None:
+def find_existing_source(session: Session, path: Path, file_hash: str) -> EbookSource | None:
     """Find an existing source by canonical path or file hash."""
-    return await session.scalar(
+    return session.scalar(
         select(EbookSource).where(or_(EbookSource.file_path == str(path), EbookSource.file_sha256 == file_hash))
     )
 
 
 def add_chapter_chunks(
-    session: AsyncSession,
+    session: Session,
     source: EbookSource,
     chapter: EbookChapter,
     parsed_chapter: ParsedChapter,

python/ebook_search/llm_interface.py

@@ -22,26 +22,10 @@ def auth_headers(api_key: str) -> dict[str, str]:
     return {"Authorization": f"Bearer {api_key}"}
 
 
-async def request_embeddings(
-    client: httpx.AsyncClient,
-    texts: Sequence[str],
-    config: EbookSearchConfig,
-) -> list[list[float]]:
-    """Request embeddings from the configured OpenAI-compatible endpoint.
-
-    Args:
-        client (httpx.AsyncClient): Shared async client for LLM calls.
-        texts (Sequence[str]): Texts to embed.
-        config (EbookSearchConfig): Runtime settings supplying the endpoint, model, and auth.
-
-    Returns:
-        list[list[float]]: One embedding vector per input text.
-
-    Raises:
-        RuntimeError: If the request fails or the response cannot be parsed.
-    """
+def request_embeddings(texts: Sequence[str], config: EbookSearchConfig) -> list[list[float]]:
+    """Request embeddings from the configured OpenAI-compatible endpoint."""
     try:
-        response = await client.post(
+        response = httpx.post(
             f"{config.embedding_base_url.rstrip('/')}/embeddings",
             headers=auth_headers(config.embedding_api_key),
             json={"model": config.embedding_model, "input": list(texts)},
@@ -51,62 +35,41 @@ async def request_embeddings(
         return embedding_vectors_from_response(response.json())
     except (httpx.HTTPError, ValueError, KeyError, TypeError) as error:
         logger.exception(
-            f"ebook_embed_request_failed {config.embedding_base_url=} {config.embedding_model=} count={len(texts)}"
+            "ebook_embed_request_failed base_url=%s model=%s count=%s",
+            config.embedding_base_url,
+            config.embedding_model,
+            len(texts),
         )
         msg = f"Embedding request failed. base_url={config.embedding_base_url} model={config.embedding_model}"
         raise RuntimeError(msg) from error
 
 
-async def check_embedding_endpoint(
-    client: httpx.AsyncClient,
-    config: EbookSearchConfig,
-    *,
-    timeout_seconds: float = 5.0,
-) -> bool:
+def check_embedding_endpoint(config: EbookSearchConfig, *, timeout_seconds: float = 5.0) -> bool:
     """Return whether the configured embedding endpoint answers a model listing."""
-    return await _check_endpoint(
-        client,
-        base_url=config.embedding_base_url,
-        api_key=config.embedding_api_key,
-        timeout_seconds=timeout_seconds,
-        unavailable_log=f"ebook_embedding_endpoint_unreachable {config.embedding_base_url=}",
-    )
-
-
-async def check_chat_endpoint(
-    client: httpx.AsyncClient,
-    config: EbookSearchConfig,
-    *,
-    timeout_seconds: float = 5.0,
-) -> bool:
-    """Return whether the configured chat (answering) endpoint answers a model listing."""
-    return await _check_endpoint(
-        client,
-        base_url=config.vllm_base_url,
-        api_key=config.vllm_api_key,
-        timeout_seconds=timeout_seconds,
-        unavailable_log=f"ebook_chat_endpoint_unreachable {config.vllm_base_url=}",
-    )
-
-
-async def _check_endpoint(
-    client: httpx.AsyncClient,
-    *,
-    base_url: str,
-    api_key: str,
-    timeout_seconds: float,
-    unavailable_log: str,
-) -> bool:
-    """Return whether an OpenAI-compatible endpoint answers a model listing."""
     try:
-        response = await client.get(
-            f"{base_url.rstrip('/')}/models",
-            headers=auth_headers(api_key),
+        response = httpx.get(
+            f"{config.embedding_base_url.rstrip('/')}/models",
+            headers=auth_headers(config.embedding_api_key),
             timeout=timeout_seconds,
         )
         response.raise_for_status()
     except httpx.HTTPError as error:
-        logger.warning(f"{unavailable_log} {error=}")
+        logger.warning("ebook_embedding_endpoint_unreachable base_url=%s error=%s", config.embedding_base_url, error)
+        return False
+    return True
+
+
+def check_chat_endpoint(config: EbookSearchConfig, *, timeout_seconds: float = 5.0) -> bool:
+    """Return whether the configured chat (answering) endpoint answers a model listing."""
+    try:
+        response = httpx.get(
+            f"{config.vllm_base_url.rstrip('/')}/models",
+            headers=auth_headers(config.vllm_api_key),
+            timeout=timeout_seconds,
+        )
+        response.raise_for_status()
+    except httpx.HTTPError as error:
+        logger.warning("ebook_chat_endpoint_unreachable base_url=%s error=%s", config.vllm_base_url, error)
         return False
     return True
 
@@ -135,29 +98,18 @@ def embedding_vectors_from_response(body: object) -> list[list[float]]:
     return vectors
 
 
-async def request_rerank(
-    client: httpx.AsyncClient,
+def request_rerank(
     query: str,
     documents: Sequence[str],
     config: RerankConfig,
 ) -> object | None:
-    """Request rerank scores from the configured vLLM endpoint.
-
-    Args:
-        client (httpx.AsyncClient): Shared async client for LLM calls.
-        query (str): Query the documents are scored against.
-        documents (Sequence[str]): Candidate documents to score.
-        config (RerankConfig): Rerank endpoint settings.
-
-    Returns:
-        object | None: The decoded response body, or ``None`` when it is not valid JSON.
-    """
+    """Request rerank scores from the configured vLLM endpoint."""
     payload = {
         "model": config.model,
         "query": query,
         "documents": list(documents),
     }
-    response = await client.post(
+    response = httpx.post(
         f"{config.base_url.rstrip('/')}/rerank",
         json=payload,
         timeout=config.timeout_seconds,
@@ -170,26 +122,13 @@ async def request_rerank(
         return None
 
 
-async def request_chat_completion(
-    client: httpx.AsyncClient,
+def request_chat_completion(
     config: EbookSearchConfig,
     messages: Sequence[dict[str, str]],
 ) -> str:
-    """Request a chat completion over a shared async client.
-
-    Args:
-        client (httpx.AsyncClient): Shared async client whose connection pool bounds concurrency.
-        config (EbookSearchConfig): Runtime settings supplying the endpoint, model, and auth.
-        messages (Sequence[dict[str, str]]): OpenAI-style chat messages.
-
-    Returns:
-        str: The assistant message text.
-
-    Raises:
-        RuntimeError: If the request fails or the response cannot be parsed.
-    """
+    """Request a chat completion from the configured OpenAI-compatible endpoint."""
     try:
-        response = await client.post(
+        response = httpx.post(
             f"{config.vllm_base_url.rstrip('/')}/chat/completions",
             headers=auth_headers(config.vllm_api_key),
             json={

python/ebook_search/loadtest.py

@@ -112,7 +112,7 @@ async def send_search(client: httpx.AsyncClient, query: str, *, rerank: bool) ->
     try:
         response = await client.post("/search", data=data)
     except httpx.HTTPError as error:
-        logger.warning(f"ebook_loadtest_request_failed {error=}")
+        logger.warning("ebook_loadtest_request_failed error=%s", error)
         return RequestResult(status_code=0, latency_ms=(time.perf_counter() - start) * 1000, ok=False)
     return RequestResult(
         status_code=response.status_code,
@@ -192,7 +192,14 @@ def main(
     """Load test the search endpoint and report latency and throughput."""
     configure_logger(log_level)
     queries = load_queries(queries_file)
-    logger.info(f"ebook_loadtest_start {base_url=} {request_count=} {concurrency=} {rerank=} queries={len(queries)}")
+    logger.info(
+        "ebook_loadtest_start base_url=%s requests=%s concurrency=%s rerank=%s queries=%s",
+        base_url,
+        request_count,
+        concurrency,
+        rerank,
+        len(queries),
+    )
     summary = asyncio.run(
         run_load(
             base_url=base_url,

python/ebook_search/protected_phrases/__init__.py

@@ -1 +0,0 @@
-"""Protected phrase extraction and matching for ebook search."""

python/ebook_search/protected_phrases/config/__init__.py

@@ -1,17 +0,0 @@
-"""Protected phrase extraction, storage, and runtime matching."""
-
-from python.ebook_search.protected_phrases.config.lib import (
-    get_bad_ends,
-    get_bad_starts,
-    get_ignored_phrases,
-    get_junk_tokens,
-    get_most_common_words,
-)
-
-__all__ = [
-    "get_bad_ends",
-    "get_bad_starts",
-    "get_ignored_phrases",
-    "get_junk_tokens",
-    "get_most_common_words",
-]

python/ebook_search/protected_phrases/config/bad_ends.toml

@@ -1,31 +0,0 @@
-tokens = [
-  "a",
-  "an",
-  "and",
-  "any",
-  "as",
-  "at",
-  "be",
-  "because",
-  "but",
-  "by",
-  "can",
-  "could",
-  "do",
-  "for",
-  "from",
-  "have",
-  "if",
-  "of",
-  "or",
-  "some",
-  "than",
-  "the",
-  "these",
-  "this",
-  "to",
-  "will",
-  "with",
-  "would",
-  "did",
-]

python/ebook_search/protected_phrases/config/bad_starts.toml

@@ -1,27 +0,0 @@
-tokens = [
-    "a",
-    "an",
-    "did",
-    "didn't",
-    "he",
-    "here",
-    "how",
-    "i",
-    "it",
-    "she",
-    "that",
-    "the",
-    "there",
-    "they",
-    "this",
-    "we",
-    "what",
-    "when",
-    "where",
-    "which",
-    "who",
-    "whom",
-    "whose",
-    "why",
-    "you",
-]

python/ebook_search/protected_phrases/config/ignored_phrases.toml

@@ -1,212 +0,0 @@
-phrases = [
-  "a little",
-  "across the",
-  "and she",
-  "anyone in",
-  "are you",
-  "around him",
-  "around the",
-  "as much",
-  "as soon",
-  "at all",
-  "at least",
-  "before the",
-  "behind him",
-  "between the",
-  "but she",
-  "could not",
-  "did he",
-  "did i",
-  "did it",
-  "did not believe",
-  "did not care",
-  "did not even",
-  "did not know what",
-  "did not know",
-  "did not like",
-  "did not look",
-  "did not mean",
-  "did not move",
-  "did not need",
-  "did not see",
-  "did not seem",
-  "did not think",
-  "did not understand",
-  "did not want",
-  "did not",
-  "did she",
-  "did so",
-  "did that",
-  "did the",
-  "did they",
-  "did what",
-  "did you",
-  "didn't answer",
-  "didn't care",
-  "didn't even",
-  "didn't expect",
-  "didn't feel",
-  "didn't get",
-  "didn't i",
-  "didn't know",
-  "didn't like",
-  "didn't look",
-  "didn't make",
-  "didn't mean",
-  "didn't need",
-  "didn't really",
-  "didn't say",
-  "didn't see",
-  "didn't seem",
-  "didn't think",
-  "didn't want",
-  "didn't you",
-  "end up",
-  "ended up",
-  "had a",
-  "had been",
-  "have been",
-  "he asked",
-  "he concluded",
-  "he continued",
-  "he couldn't",
-  "he did",
-  "he didn't",
-  "he felt",
-  "he had",
-  "he hadn't",
-  "he knew",
-  "he noted",
-  "he pointed",
-  "he realized",
-  "he replied",
-  "he said",
-  "he saw",
-  "he tapped",
-  "he told",
-  "he was",
-  "he wasn't",
-  "his body",
-  "his chair",
-  "his feet",
-  "his hands",
-  "his head",
-  "his office",
-  "his own",
-  "his pc",
-  "his power",
-  "his shield",
-  "his sight",
-  "his voice",
-  "his wrist",
-  "how many",
-  "i am",
-  "i don't",
-  "i said",
-  "i was",
-  "i wouldn't",
-  "i'm not",
-  "if he",
-  "if they",
-  "is in",
-  "is not",
-  "is that",
-  "is the",
-  "it had",
-  "it had",
-  "it is",
-  "it was",
-  "it wasn't",
-  "it wasn't",
-  "no one",
-  "of course",
-  "of force",
-  "of it",
-  "of magic",
-  "of marines",
-  "of power",
-  "of those",
-  "old man",
-  "older man",
-  "one of",
-  "out of",
-  "set up",
-  "she admitted",
-  "she asked",
-  "she had",
-  "she replied",
-  "she said",
-  "she snapped",
-  "she told",
-  "she was",
-  "she'd been",
-  "shook his",
-  "sure he",
-  "tell you",
-  "that had",
-  "that is",
-  "that she",
-  "that was",
-  "the dark",
-  "the door",
-  "the first",
-  "the last",
-  "the man",
-  "the one",
-  "the only",
-  "the other",
-  "the rest",
-  "the room",
-  "the same",
-  "the two",
-  "the way",
-  "the world",
-  "there are",
-  "there was",
-  "there were",
-  "they are",
-  "they had",
-  "they were",
-  "they weren't",
-  "this is",
-  "this place",
-  "though he",
-  "through his",
-  "through the",
-  "to find",
-  "to get",
-  "to keep",
-  "to stay",
-  "to stop",
-  "to tell",
-  "to try",
-  "told her",
-  "told him",
-  "under his",
-  "was a",
-  "was enough",
-  "was going",
-  "was in",
-  "was no",
-  "was not",
-  "was now",
-  "was on",
-  "was one",
-  "was only",
-  "was still",
-  "was that",
-  "was the",
-  "was there",
-  "were in",
-  "what had",
-  "what happened",
-  "what was",
-  "where the",
-  "while i",
-  "you are",
-  "you can't",
-  "you don't",
-  "you know",
-  "you need",
-  "you were",
-]

python/ebook_search/protected_phrases/config/junk_tokens.toml

@@ -1,71 +0,0 @@
-tokens = [
-    "said",
-    "asked",
-    "replied",
-    "answered",
-    "looked",
-    "nodded",
-    "turned",
-    "shook",
-    "smiled",
-    "shrugged",
-    "pointed",
-    "continued",
-    "repeated",
-    "stared",
-    "agreed",
-    "glanced",
-    "walked",
-    "told",
-    "thought",
-    "knew",
-    "wanted",
-    "muttered",
-    "whispered",
-    "laughed",
-    "sighed",
-    "paused",
-    "gestured",
-    "waved",
-    "frowned",
-    "grinned",
-    "admitted",
-    "found",
-    "noted",
-    "murmured",
-    "ordered",
-    "i'm",
-    "i've",
-    "i'd",
-    "i'll",
-    "it's",
-    "that's",
-    "don't",
-    "didn't",
-    "doesn't",
-    "can't",
-    "won't",
-    "wouldn't",
-    "couldn't",
-    "shouldn't",
-    "isn't",
-    "wasn't",
-    "aren't",
-    "weren't",
-    "you're",
-    "you've",
-    "you'll",
-    "we're",
-    "we've",
-    "we'll",
-    "they're",
-    "they've",
-    "he's",
-    "she's",
-    "there's",
-    "what's",
-    "let's",
-    "who's",
-    "he'd",
-    "she'd",
-]

python/ebook_search/protected_phrases/config/lib.py

@@ -1,60 +0,0 @@
-"""Protected phrase extraction, storage, and runtime matching."""
-
-from __future__ import annotations
-
-import logging
-import tomllib
-from functools import cache
-from pathlib import Path
-
-from python.ebook_search.protected_phrases.text_normalization import normalize_text
-
-logger = logging.getLogger(__name__)
-
-
-def _load_toml_string_set(path: Path, key: str) -> frozenset[str]:
-    """Load and validate a TOML string list as a normalized immutable set."""
-    with path.open("rb") as file:
-        body = tomllib.load(file)
-
-    values = body.get(key)
-    if not isinstance(values, list) or not all(isinstance(item, str) for item in values):
-        msg = f"{path} must contain a {key!r} string list"
-        raise ValueError(msg)
-    return frozenset(normalize_text(value) for value in values if normalize_text(value))
-
-
-@cache
-def _get_phrase_config_dir() -> Path:
-    """Return the directory containing phrase configuration files."""
-    return Path(__file__).resolve().parent
-
-
-@cache
-def get_ignored_phrases() -> frozenset[str]:
-    """Return ignored phrase strings loaded from TOML."""
-    return _load_toml_string_set(_get_phrase_config_dir() / "ignored_phrases.toml", "phrases")
-
-
-@cache
-def get_bad_ends() -> frozenset[str]:
-    """Return bad phrase-ending tokens loaded from TOML."""
-    return _load_toml_string_set(_get_phrase_config_dir() / "bad_ends.toml", "tokens")
-
-
-@cache
-def get_bad_starts() -> frozenset[str]:
-    """Return bad phrase-starting tokens loaded from TOML."""
-    return _load_toml_string_set(_get_phrase_config_dir() / "bad_starts.toml", "tokens")
-
-
-@cache
-def get_most_common_words() -> frozenset[str]:
-    """Return the most common English words loaded from TOML."""
-    return _load_toml_string_set(_get_phrase_config_dir() / "most_common_words.toml", "words")
-
-
-@cache
-def get_junk_tokens() -> frozenset[str]:
-    """Return junk tokens (dialogue verbs and pronoun contractions) loaded from TOML."""
-    return _load_toml_string_set(_get_phrase_config_dir() / "junk_tokens.toml", "tokens")

python/ebook_search/protected_phrases/config/most_common_words.toml

@@ -1,102 +0,0 @@
-words = [
-    "the",
-    "be",
-    "to",
-    "of",
-    "and",
-    "a",
-    "in",
-    "that",
-    "have",
-    "I",
-    "it",
-    "for",
-    "not",
-    "on",
-    "with",
-    "he",
-    "as",
-    "you",
-    "do",
-    "at",
-    "this",
-    "but",
-    "his",
-    "by",
-    "from",
-    "they",
-    "we",
-    "say",
-    "her",
-    "she",
-    "or",
-    "an",
-    "will",
-    "my",
-    "one",
-    "all",
-    "would",
-    "there",
-    "their",
-    "what",
-    "so",
-    "up",
-    "out",
-    "if",
-    "about",
-    "who",
-    "get",
-    "which",
-    "go",
-    "me",
-    "when",
-    "make",
-    "can",
-    "like",
-    "time",
-    "no",
-    "just",
-    "him",
-    "know",
-    "take",
-    "people",
-    "into",
-    "year",
-    "your",
-    "good",
-    "some",
-    "could",
-    "them",
-    "see",
-    "other",
-    "than",
-    "then",
-    "now",
-    "look",
-    "only",
-    "come",
-    "its",
-    "over",
-    "think",
-    "also",
-    "back",
-    "after",
-    "use",
-    "two",
-    "how",
-    "our",
-    "work",
-    "first",
-    "well",
-    "way",
-    "even",
-    "new",
-    "want",
-    "because",
-    "any",
-    "these",
-    "give",
-    "day",
-    "most",
-    "us",
-]

python/ebook_search/protected_phrases/extraction.py

@@ -1,725 +0,0 @@
-"""Candidate phrase extraction and scoring for protected phrases."""
-
-from __future__ import annotations
-
-import logging
-import re
-from collections import Counter, defaultdict
-from functools import lru_cache
-from time import perf_counter
-from typing import TYPE_CHECKING, Protocol
-
-from yake import KeywordExtractor
-
-from python.ebook_search.protected_phrases.config import (
-    get_bad_ends,
-    get_bad_starts,
-    get_ignored_phrases,
-    get_junk_tokens,
-    get_most_common_words,
-)
-from python.ebook_search.protected_phrases.models import PhraseCandidate
-from python.ebook_search.protected_phrases.text_normalization import tokenize, tokenize_with_offsets
-
-if TYPE_CHECKING:
-    from collections.abc import Iterable, Mapping, Sequence
-
-    from python.ebook_search.config import EbookSearchConfig
-
-logger = logging.getLogger(__name__)
-
-
-BAD_START_SCORE_PENALTY = 10.0
-BAD_END_SCORE_PENALTY = 10.0
-MULTI_SOURCE_SCORE_BONUS = 2.0
-MULTI_SOURCE_MIN_SOURCES = 2
-CAPITALIZED_PHRASE_RE = re.compile(r"\b(?:[A-Z][a-zA-Z']+)(?:\s+(?:of|the|and|in|on|for|[A-Z][a-zA-Z']+)){0,6}")
-
-
-class YakeExtractor(Protocol):
-    """Small protocol for the YAKE extractor used by this module."""
-
-    def extract_keywords(self, text: str) -> Iterable[tuple[str, float]]:
-        """Return YAKE keyword tuples."""
-
-
-class YakeExtractorFactory(Protocol):
-    """Callable constructor protocol for YAKE keyword extractors."""
-
-    def __call__(self, *, lan: str, n: int, dedupLim: float, top: int) -> YakeExtractor:  # noqa: N803
-        """Create a YAKE keyword extractor.
-
-        Args:
-            lan (str): Language code passed to YAKE.
-            n (int): Maximum n-gram size to extract.
-            dedupLim (float): Deduplication similarity threshold.
-            top (int): Maximum number of keyphrases to return.
-
-        Returns:
-            YakeExtractor: The constructed keyword extractor.
-        """
-
-
-def normalize_candidate_phrase(
-    phrase_text: str,
-    config: EbookSearchConfig,
-    *,
-    max_tokens: int | None = None,
-) -> tuple[str, str, int] | None:
-    """Normalize a candidate phrase and validate token bounds.
-
-    Args:
-        phrase_text (str): Raw phrase text to normalize.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        max_tokens (int | None): Maximum token count override; defaults to ``config.phrase_max_tokens``.
-
-    Returns:
-        tuple[str, str, int] | None: Display text, normalized phrase, and token count, or ``None``
-        when the phrase falls outside the token bounds or is ignored.
-    """
-    normalized_tokens = tokenize_with_offsets(phrase_text)
-    max_count = config.phrase_max_tokens if max_tokens is None else max_tokens
-    if len(normalized_tokens) < config.phrase_min_tokens or len(normalized_tokens) > max_count:
-        return None
-
-    phrase_norm = " ".join(token.text for token in normalized_tokens)
-    if phrase_norm in get_ignored_phrases():
-        return None
-
-    display_text = phrase_text[normalized_tokens[0].start_char : normalized_tokens[-1].end_char].strip()
-    return display_text or phrase_norm, phrase_norm, len(normalized_tokens)
-
-
-def count_raw_ngrams(tokens: Sequence[str], config: EbookSearchConfig) -> Counter[str]:
-    """Count every n-gram window in one normalized token block.
-
-    ``tokens`` are already normalized (see :func:`tokenize`), so each window's normalized form
-    is the joined tokens directly. Counting into a plain :class:`Counter` rather than
-    :class:`PhraseCandidate` objects keeps this hot loop cheap; callers filter ignored phrases
-    and materialize candidates per unique phrase afterwards, which is far fewer operations than
-    doing either per window.
-
-    Args:
-        tokens (Sequence[str]): Normalized tokens for one text block.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        Counter[str]: Raw occurrence counts keyed by normalized phrase.
-    """
-    return Counter(
-        " ".join(tokens[start : start + ngram_size])
-        for ngram_size in range(config.phrase_min_tokens, config.phrase_max_tokens + 1)
-        for start in range(len(tokens) - ngram_size + 1)
-    )
-
-
-def extract_raw_ngrams_by_chapter(
-    chapters: Sequence[str],
-    config: EbookSearchConfig,
-) -> dict[str, PhraseCandidate]:
-    """Extract raw n-grams across chapters, tracking both raw counts and chapter spread.
-
-    Counting each chapter separately makes chapter spread fall out of dict membership: a phrase's
-    ``chapter_count`` is simply how many per-chapter count maps contain it, so no per-window seen
-    tracking is needed. This also lets the enrichment step skip re-sliding the same n-gram sizes.
-
-    Phrases below the minimum raw count are dropped here rather than materialized: most unique
-    n-grams occur once, and :func:`filter_storable_candidates` would discard them as too rare
-    anyway, so building ``PhraseCandidate`` objects for them is wasted work.
-
-    Args:
-        chapters (Sequence[str]): Chapter-like text blocks to slide n-gram windows over.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        dict[str, PhraseCandidate]: Candidates meeting the minimum raw count, keyed by normalized
-        phrase, with raw and chapter counts.
-    """
-    chapter_count_maps = [count_raw_ngrams(tokenize(chapter), config) for chapter in chapters]
-    total_counts: Counter[str] = Counter()
-    chapter_spread: Counter[str] = Counter()
-    for chapter_counts in chapter_count_maps:
-        total_counts.update(chapter_counts)
-        chapter_spread.update(chapter_counts.keys())
-    min_raw_count = minimum_candidate_raw_count(config)
-    ignored = get_ignored_phrases()
-    return {
-        phrase_norm: PhraseCandidate(
-            phrase_text=phrase_norm,
-            phrase_norm=phrase_norm,
-            token_count=phrase_norm.count(" ") + 1,
-            source_raw_ngram=True,
-            raw_count=raw_count,
-            chapter_count=chapter_spread[phrase_norm],
-        )
-        for phrase_norm, raw_count in total_counts.items()
-        if raw_count >= min_raw_count and phrase_norm not in ignored
-    }
-
-
-@lru_cache(maxsize=2)
-def get_yake_extractor(max_ngram: int, top_k: int) -> KeywordExtractor:
-    """Return a cached YAKE extractor for the given settings.
-
-    Constructing a ``KeywordExtractor`` loads the language's stopword list from disk, so it is
-    cached and reused across books rather than rebuilt on every call.
-
-    Args:
-        max_ngram (int): Maximum n-gram size to extract.
-        top_k (int): Maximum number of keyphrases to request.
-
-    Returns:
-        KeywordExtractor: A shared extractor instance for the given settings.
-    """
-    return KeywordExtractor(lan="en", n=max_ngram, dedupLim=0.85, top=top_k)
-
-
-def extract_yake_candidates(
-    book_text: str,
-    config: EbookSearchConfig,
-    top_k: int = 1000,
-) -> dict[str, PhraseCandidate]:
-    """Extract YAKE keyphrases when the optional YAKE package is installed.
-
-    Args:
-        book_text (str): Full book text to extract keyphrases from.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        top_k (int): Maximum number of YAKE keyphrases to request.
-
-    Returns:
-        dict[str, PhraseCandidate]: Candidates keyed by normalized phrase, with YAKE scores.
-    """
-    extractor = get_yake_extractor(config.phrase_max_tokens, top_k)
-    out: dict[str, PhraseCandidate] = {}
-    for phrase_text, yake_score in extractor.extract_keywords(book_text):
-        normalized = normalize_candidate_phrase(phrase_text, config)
-        if normalized is None:
-            continue
-        display_text, phrase_norm, token_count = normalized
-        out[phrase_norm] = PhraseCandidate(
-            phrase_text=display_text,
-            phrase_norm=phrase_norm,
-            token_count=token_count,
-            source_yake=True,
-            yake_score=float(yake_score),
-        )
-    return out
-
-
-def extract_capitalized_phrases(original_text: str, config: EbookSearchConfig) -> dict[str, PhraseCandidate]:
-    """Extract capitalized phrase runs that often carry fictional terms.
-
-    Args:
-        original_text (str): Original-case book text to scan for capitalized runs.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        dict[str, PhraseCandidate]: Candidates keyed by normalized phrase from capitalized runs.
-    """
-    out: dict[str, PhraseCandidate] = {}
-    for match in CAPITALIZED_PHRASE_RE.finditer(original_text):
-        phrase_text = match.group(0).strip()
-        normalized = normalize_candidate_phrase(
-            phrase_text,
-            config,
-            max_tokens=config.phrase_max_entity_tokens,
-        )
-        if normalized is None:
-            continue
-        display_text, phrase_norm, token_count = normalized
-        out[phrase_norm] = PhraseCandidate(
-            phrase_text=display_text,
-            phrase_norm=phrase_norm,
-            token_count=token_count,
-            source_capitalized=True,
-        )
-    return out
-
-
-def extract_metadata_candidates(
-    metadata: Mapping[str, object] | None,
-    config: EbookSearchConfig,
-) -> dict[str, PhraseCandidate]:
-    """Extract phrases from book metadata values such as title, author, and series.
-
-    Args:
-        metadata (Mapping[str, object] | None): Book metadata values, or ``None`` when unavailable.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        dict[str, PhraseCandidate]: Candidates keyed by normalized phrase from metadata values.
-    """
-    if metadata is None:
-        return {}
-
-    out: dict[str, PhraseCandidate] = {}
-    for value in metadata.values():
-        if value is None:
-            continue
-        phrase_text = str(value).strip()
-        normalized = normalize_candidate_phrase(
-            phrase_text,
-            config,
-            max_tokens=config.phrase_max_entity_tokens,
-        )
-        if normalized is None:
-            continue
-        display_text, phrase_norm, token_count = normalized
-        out[phrase_norm] = PhraseCandidate(
-            phrase_text=display_text,
-            phrase_norm=phrase_norm,
-            token_count=token_count,
-            source_metadata=True,
-        )
-    return out
-
-
-def merge_candidate_sources(*sources: Mapping[str, PhraseCandidate]) -> dict[str, PhraseCandidate]:
-    """Merge candidate dictionaries by normalized phrase.
-
-    Args:
-        *sources (Mapping[str, PhraseCandidate]): Candidate maps to combine, keyed by normalized phrase.
-
-    Returns:
-        dict[str, PhraseCandidate]: One merged candidate per normalized phrase.
-    """
-    merged: dict[str, PhraseCandidate] = {}
-    for source in sources:
-        for phrase_norm, item in source.items():
-            existing = merged.setdefault(
-                phrase_norm,
-                PhraseCandidate(
-                    phrase_text=item.phrase_text,
-                    phrase_norm=phrase_norm,
-                    token_count=item.token_count,
-                ),
-            )
-            merge_candidate(existing, item)
-    return merged
-
-
-def merge_candidate(existing: PhraseCandidate, item: PhraseCandidate) -> None:
-    """Merge one candidate into an existing candidate object.
-
-    Args:
-        existing (PhraseCandidate): Candidate mutated in place to absorb ``item``.
-        item (PhraseCandidate): Candidate whose sources, counts, and scores are merged in.
-    """
-    existing.source_raw_ngram = existing.source_raw_ngram or item.source_raw_ngram
-    existing.source_yake = existing.source_yake or item.source_yake
-    existing.source_capitalized = existing.source_capitalized or item.source_capitalized
-    existing.source_metadata = existing.source_metadata or item.source_metadata
-    existing.raw_count += item.raw_count
-    existing.chapter_count = max(existing.chapter_count, item.chapter_count)
-    if item.yake_score is not None:
-        existing.yake_score = item.yake_score
-
-
-def enrich_with_frequency_and_chapter_counts(
-    candidates: Mapping[str, PhraseCandidate],
-    chapters: Sequence[str],
-    *,
-    counted_sizes: Iterable[int] = (),
-) -> dict[str, PhraseCandidate]:
-    """Add raw occurrence and chapter-spread counts to candidates.
-
-    Candidates whose ``token_count`` is in ``counted_sizes`` are left untouched: those counts
-    were already computed while sliding the chapters in :func:`extract_raw_ngrams_by_chapter`,
-    so re-sliding those n-gram sizes here would just duplicate that work.
-
-    Args:
-        candidates (Mapping[str, PhraseCandidate]): Candidates to enrich, keyed by normalized phrase.
-        chapters (Sequence[str]): Chapter-like text blocks used to count occurrences and spread.
-        counted_sizes (Iterable[int]): Token counts whose counts are already populated and should be skipped.
-
-    Returns:
-        dict[str, PhraseCandidate]: Candidates with updated ``raw_count`` and ``chapter_count`` values.
-    """
-    if not candidates:
-        return {}
-
-    already_counted = set(counted_sizes)
-    candidate_sets_by_size: dict[int, set[str]] = defaultdict(set)
-    for phrase_norm, candidate in candidates.items():
-        if candidate.token_count in already_counted:
-            continue
-        candidate_sets_by_size[candidate.token_count].add(phrase_norm)
-
-    enriched = dict(candidates)
-    if not candidate_sets_by_size:
-        return enriched
-
-    total_counts, chapter_counts = count_candidate_occurrences(candidate_sets_by_size, chapters)
-    for phrase_norm, candidate in enriched.items():
-        if candidate.token_count in already_counted:
-            continue
-        candidate.raw_count = max(candidate.raw_count, total_counts[phrase_norm])
-        candidate.chapter_count = chapter_counts[phrase_norm]
-    return enriched
-
-
-def count_candidate_occurrences(
-    candidate_sets_by_size: Mapping[int, set[str]],
-    chapters: Sequence[str],
-) -> tuple[dict[str, int], dict[str, int]]:
-    """Count total occurrences and chapter spread for candidate phrases across chapters.
-
-    Args:
-        candidate_sets_by_size (Mapping[int, set[str]]): Candidate normalized phrases grouped by token count.
-        chapters (Sequence[str]): Chapter-like text blocks to slide n-gram windows over.
-
-    Returns:
-        tuple[dict[str, int], dict[str, int]]: Total occurrence counts and chapter-spread counts,
-        each keyed by normalized phrase.
-    """
-    total_counts: defaultdict[str, int] = defaultdict(int)
-    chapter_counts: defaultdict[str, int] = defaultdict(int)
-    for chapter in chapters:
-        seen_in_chapter: set[str] = set()
-        chapter_tokens = tokenize(chapter)
-        for ngram_size, candidate_norms in candidate_sets_by_size.items():
-            for start in range(len(chapter_tokens) - ngram_size + 1):
-                phrase_norm = " ".join(chapter_tokens[start : start + ngram_size])
-                if phrase_norm not in candidate_norms:
-                    continue
-                total_counts[phrase_norm] += 1
-                seen_in_chapter.add(phrase_norm)
-        for phrase_norm in seen_in_chapter:
-            chapter_counts[phrase_norm] += 1
-    return total_counts, chapter_counts
-
-
-def filter_storable_candidates(
-    candidates: Mapping[str, PhraseCandidate],
-    config: EbookSearchConfig,
-) -> tuple[dict[str, PhraseCandidate], int, int, int, int]:
-    """Remove candidates that should not be persisted.
-
-    Args:
-        candidates (Mapping[str, PhraseCandidate]): Candidates to filter, keyed by normalized phrase.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        tuple[dict[str, PhraseCandidate], int, int, int, int]: The storable candidates followed by the
-        counts dropped for being too short, too rare, too common, and junk.
-    """
-    min_raw_count = minimum_candidate_raw_count(config)
-    filtered: dict[str, PhraseCandidate] = {}
-    too_short = 0
-    too_rare = 0
-    too_common = 0
-    junk = 0
-    for phrase_norm, candidate in candidates.items():
-        if candidate.token_count < config.phrase_min_tokens:
-            too_short += 1
-            continue
-        if candidate.raw_count < min_raw_count:
-            too_rare += 1
-            continue
-        phrase_tokens = phrase_norm.split()
-        if is_most_common_word_phrase(phrase_tokens):
-            too_common += 1
-            continue
-        if is_junk_phrase(phrase_tokens):
-            junk += 1
-            continue
-        filtered[phrase_norm] = candidate
-    return filtered, too_short, too_rare, too_common, junk
-
-
-def minimum_candidate_raw_count(config: EbookSearchConfig) -> int:
-    """Return the minimum occurrence count required before storing a candidate.
-
-    Args:
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        int: The minimum raw occurrence count, never less than 1.
-    """
-    return max(config.phrase_raw_ngram_min_count, 1)
-
-
-def is_most_common_word_phrase(phrase_tokens: list[str]) -> bool:
-    """Return whether every token in a normalized phrase is a common word.
-
-    Args:
-        phrase_tokens (list[str]): Normalized phrase tokens to inspect.
-
-    Returns:
-        bool: True when the phrase is non-empty and every token is a common word.
-    """
-    common_words = get_most_common_words()
-    return bool(phrase_tokens) and all(token in common_words for token in phrase_tokens)
-
-
-def is_junk_phrase(phrase_tokens: list[str]) -> bool:
-    """Return whether a normalized phrase is lexical junk not worth LLM judging.
-
-    Judged data shows phrases containing a dialogue/action verb or a pronoun contraction are
-    never kept, and phrases whose tokens are mostly common words almost never are. Possessives
-    of proper nouns (``chapman's death``) pass because matching is by exact token, and
-    exactly-half-common bigrams (``data feed``) pass because the common-word rule is strict.
-
-    Args:
-        phrase_tokens (list[str]): Normalized phrase tokens to inspect.
-
-    Returns:
-        bool: True when the phrase contains a junk token or is majority common words.
-    """
-    if not phrase_tokens:
-        return False
-    junk_tokens = get_junk_tokens()
-    if any(token in junk_tokens for token in phrase_tokens):
-        return True
-    common_words = get_most_common_words()
-    half_phrase_len = len(phrase_tokens) // 2
-    return sum(token in common_words for token in phrase_tokens) > half_phrase_len
-
-
-def score_candidate(candidate: PhraseCandidate, config: EbookSearchConfig) -> float:
-    """Score a phrase candidate before LLM judging.
-
-    Args:
-        candidate (PhraseCandidate): Candidate to score.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        float: Combined score from sources, frequency, and length, less any penalties.
-    """
-    score = source_score(candidate) + frequency_score(candidate, config) + token_count_score(candidate, config)
-    if non_raw_source_count(candidate) >= MULTI_SOURCE_MIN_SOURCES:
-        score += MULTI_SOURCE_SCORE_BONUS
-    if candidate.phrase_norm in get_ignored_phrases():
-        score -= 100.0
-    if has_bad_start(candidate.phrase_norm):
-        score -= BAD_START_SCORE_PENALTY
-    if has_bad_end(candidate.phrase_norm):
-        score -= BAD_END_SCORE_PENALTY
-    return score
-
-
-def non_raw_source_count(candidate: PhraseCandidate) -> int:
-    """Count the non-raw-ngram extraction sources that produced a candidate.
-
-    Args:
-        candidate (PhraseCandidate): Candidate whose enabled sources are counted.
-
-    Returns:
-        int: Number of enabled sources other than the raw n-gram slide.
-    """
-    return sum(
-        (
-            candidate.source_yake,
-            candidate.source_capitalized,
-            candidate.source_metadata,
-        )
-    )
-
-
-def has_bad_start(phrase_norm: str) -> bool:
-    """Return whether a normalized phrase starts with a bad starting token.
-
-    Args:
-        phrase_norm (str): Normalized phrase text to inspect.
-
-    Returns:
-        bool: True when the first token is a known bad starting token.
-    """
-    phrase_tokens = phrase_norm.split()
-    return bool(phrase_tokens and phrase_tokens[0] in get_bad_starts())
-
-
-def has_bad_end(phrase_norm: str) -> bool:
-    """Return whether a normalized phrase ends with a bad ending token.
-
-    Args:
-        phrase_norm (str): Normalized phrase text to inspect.
-
-    Returns:
-        bool: True when the last token is a known bad ending token.
-    """
-    phrase_tokens = phrase_norm.split()
-    return bool(phrase_tokens and phrase_tokens[-1] in get_bad_ends())
-
-
-def source_score(candidate: PhraseCandidate) -> float:
-    """Return the score contribution from extraction sources.
-
-    Args:
-        candidate (PhraseCandidate): Candidate whose enabled sources are weighted.
-
-    Returns:
-        float: Summed weight of the candidate's enabled extraction sources.
-    """
-    return sum(
-        weight
-        for enabled, weight in (
-            (candidate.source_yake, 2.0),
-            (candidate.source_capitalized, 2.0),
-            (candidate.source_metadata, 2.0),
-            (candidate.source_raw_ngram, 0.5),
-        )
-        if enabled
-    )
-
-
-def frequency_score(candidate: PhraseCandidate, config: EbookSearchConfig) -> float:
-    """Return the score contribution from frequency and chapter spread.
-
-    Args:
-        candidate (PhraseCandidate): Candidate whose counts are scored.
-        config (EbookSearchConfig): Runtime phrase-tuning settings holding score thresholds.
-
-    Returns:
-        float: Summed weight for each frequency and chapter-spread threshold the candidate meets.
-    """
-    return sum(
-        weight
-        for count, threshold, weight in (
-            (candidate.raw_count, config.phrase_raw_count_score_threshold, 0.5),
-            (candidate.raw_count, config.phrase_raw_count_high_score_threshold, 0.5),
-            (candidate.chapter_count, config.phrase_chapter_count_score_threshold, 0.5),
-            (candidate.chapter_count, config.phrase_chapter_count_high_score_threshold, 0.5),
-        )
-        if count >= threshold
-    )
-
-
-def token_count_score(candidate: PhraseCandidate, config: EbookSearchConfig) -> float:
-    """Return the score contribution from phrase length.
-
-    Args:
-        candidate (PhraseCandidate): Candidate whose token count is scored.
-        config (EbookSearchConfig): Runtime phrase-tuning settings holding the max token bound.
-
-    Returns:
-        float: Length-based score contribution, which may be negative for over- or under-length phrases.
-    """
-    if candidate.token_count == 1:
-        return -0.5
-    if candidate.token_count in {2, 3, 4}:
-        return 0.5
-    if candidate.token_count > config.phrase_max_tokens:
-        return -1.0
-    return 0.0
-
-
-def get_sample_contexts(normalized_book_text: str, phrase_norm: str, max_contexts: int = 5) -> list[str]:
-    """Return normalized context snippets containing a candidate phrase.
-
-    ``normalized_book_text`` is expected to already be ``normalize_text``-ed by the caller
-    so the whole book is not re-normalized for every phrase.
-
-    Args:
-        normalized_book_text (str): Whole book text, already normalized, to search.
-        phrase_norm (str): Normalized phrase to find contexts around.
-        max_contexts (int): Maximum number of context snippets to return.
-
-    Returns:
-        list[str]: Up to ``max_contexts`` normalized snippets surrounding the phrase.
-    """
-    contexts: list[str] = []
-    start = 0
-    while len(contexts) < max_contexts:
-        index = normalized_book_text.find(phrase_norm, start)
-        if index == -1:
-            break
-        left = max(0, index - 300)
-        right = min(len(normalized_book_text), index + len(phrase_norm) + 300)
-        contexts.append(normalized_book_text[left:right])
-        start = index + len(phrase_norm)
-    return contexts
-
-
-def candidate_source_names(candidate: PhraseCandidate) -> list[str]:
-    """Return enabled source names for an extracted candidate.
-
-    Args:
-        candidate (PhraseCandidate): Candidate whose enabled sources are listed.
-
-    Returns:
-        list[str]: Names of the extraction sources that produced the candidate.
-    """
-    names: list[str] = []
-    if candidate.source_raw_ngram:
-        names.append("raw_ngram")
-    if candidate.source_yake:
-        names.append("yake")
-    if candidate.source_capitalized:
-        names.append("capitalized")
-    if candidate.source_metadata:
-        names.append("metadata")
-    return names
-
-
-def extract_phrase_candidates_for_book(
-    book_text: str,
-    chapters: Sequence[str],
-    config: EbookSearchConfig,
-    *,
-    metadata: Mapping[str, object] | None = None,
-) -> list[PhraseCandidate]:
-    """Extract, score, and limit phrase candidates for one book.
-
-    Args:
-        book_text (str): Full book text used for most extraction sources.
-        chapters (Sequence[str]): Chapter-like text blocks used for frequency counts.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        metadata (Mapping[str, object] | None): Optional book metadata used as a candidate source.
-
-    Returns:
-        list[PhraseCandidate]: Scored candidates sorted best-first and capped per book.
-    """
-    started_at = perf_counter()
-    logger.info(
-        f"ebook_phrase_candidate_extract_start chapters={len(chapters)} chars={len(book_text)} "
-        f"{config.phrase_min_tokens=} {config.phrase_max_tokens=} {config.protected_phrase_max_candidates_per_book=}"
-    )
-    raw_started_at = perf_counter()
-    raw = extract_raw_ngrams_by_chapter(chapters, config)
-    logger.info(
-        f"ebook_phrase_candidate_extract_raw_complete candidates={len(raw)} "
-        f"duration_ms={(perf_counter() - raw_started_at) * 1000:.1f}"
-    )
-    yake_started_at = perf_counter()
-    yake_candidates = extract_yake_candidates(book_text, config)
-    logger.info(
-        f"ebook_phrase_candidate_extract_yake_complete candidates={len(yake_candidates)} "
-        f"duration_ms={(perf_counter() - yake_started_at) * 1000:.1f}"
-    )
-    capitalized_started_at = perf_counter()
-    capitalized = extract_capitalized_phrases(book_text, config)
-    logger.info(
-        f"ebook_phrase_candidate_extract_capitalized_complete candidates={len(capitalized)} "
-        f"duration_ms={(perf_counter() - capitalized_started_at) * 1000:.1f}"
-    )
-    metadata_candidates = extract_metadata_candidates(metadata, config)
-
-    candidates = merge_candidate_sources(raw, yake_candidates, capitalized, metadata_candidates)
-    enriched_started_at = perf_counter()
-    # Raw n-gram sizes were already counted per chapter above, so only enrich the remaining
-    # (entity-length) sizes here instead of re-sliding every size over the whole book.
-    candidates = enrich_with_frequency_and_chapter_counts(
-        candidates,
-        chapters,
-        counted_sizes=range(config.phrase_min_tokens, config.phrase_max_tokens + 1),
-    )
-    pre_filter_count = len(candidates)
-    candidates, filtered_too_short, filtered_too_rare, filtered_too_common, filtered_junk = filter_storable_candidates(
-        candidates, config
-    )
-    for candidate in candidates.values():
-        candidate.candidate_score = score_candidate(candidate, config)
-
-    limited = sorted(candidates.values(), key=lambda item: item.candidate_score, reverse=True)[
-        : config.protected_phrase_max_candidates_per_book
-    ]
-    logger.info(
-        f"ebook_phrase_candidate_extract_complete raw={len(raw)} yake={len(yake_candidates)} "
-        f"capitalized={len(capitalized)} metadata={len(metadata_candidates)} {pre_filter_count=} {filtered_too_short=} "
-        f"{filtered_too_rare=} {filtered_too_common=} {filtered_junk=} min_uses={minimum_candidate_raw_count(config)} "
-        f"storable={len(candidates)} limited={len(limited)} "
-        f"enrich_score_ms={(perf_counter() - enriched_started_at) * 1000:.1f} "
-        f"duration_ms={(perf_counter() - started_at) * 1000:.1f}"
-    )
-    return limited

python/ebook_search/protected_phrases/generate_ngrams.py

@@ -1,295 +0,0 @@
-"""Book-level orchestration for candidate n-gram generation and recalculation."""
-
-from __future__ import annotations
-
-import asyncio
-import logging
-from time import perf_counter
-from typing import TYPE_CHECKING
-
-from sqlalchemy import select
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from python.ebook_search.protected_phrases.extraction import extract_phrase_candidates_for_book
-from python.ebook_search.protected_phrases.models import (
-    BookCandidateResult,
-    PhraseCandidateGenerationResult,
-    PhraseRecalculationResult,
-)
-from python.ebook_search.protected_phrases.pool import get_extraction_pool
-from python.ebook_search.protected_phrases.store import (
-    bulk_upsert_unjudged_candidates,
-    delete_phrase_data_for_book,
-    load_book_chapter_texts,
-    metadata_for_source_id,
-    new_candidate_row,
-    prune_unstorable_unjudged_candidate_phrases,
-)
-from python.orm.common import get_async_postgres_engine
-from python.orm.richie import EbookSource
-
-if TYPE_CHECKING:
-    from sqlalchemy.ext.asyncio import AsyncEngine
-
-    from python.ebook_search.config import EbookSearchConfig
-    from python.ebook_search.protected_phrases.models import PhraseCandidate
-
-logger = logging.getLogger(__name__)
-
-
-class BookHasNoChaptersError(ValueError):
-    """Raised when a book has no indexed chapter text to generate phrases from."""
-
-
-async def generate_candidate_phrases_for_books(
-    engine: AsyncEngine,
-    config: EbookSearchConfig,
-) -> PhraseCandidateGenerationResult:
-    """Create or refresh candidate phrases for indexed books without calling the LLM judge.
-
-    Every book is submitted to the shared process pool up front and runs in parallel across the
-    pool's workers; the call blocks until all books have finished. Each worker opens its own
-    database engine from environment variables, loads the book's chapters, and commits the
-    book's candidates independently.
-
-    Args:
-        engine (AsyncEngine): Engine used to read the book list in this process.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        PhraseCandidateGenerationResult: Per-corpus counts of books seen, built, and candidates stored.
-
-    Results are collected in book order while the pool keeps working. A book failure (including
-    a book with no indexed chapters) is logged and counted as not built; the remaining books
-    are unaffected.
-    """
-    async with AsyncSession(engine, expire_on_commit=False) as session:
-        source_query = select(EbookSource.id).order_by(EbookSource.id)
-        source_ids = (await session.scalars(source_query)).all()
-    books_seen = len(source_ids)
-    logger.info(
-        f"ebook_candidate_phrase_generation_start {books_seen=} {config.phrase_min_tokens=} "
-        f"{config.phrase_max_tokens=} {config.protected_phrase_max_candidates_per_book=}"
-    )
-
-    pool = get_extraction_pool(config.protected_phrase_extraction_workers)
-    wrapped_futures = [
-        (
-            source_id,
-            asyncio.wrap_future(pool.submit(generate_candidate_phrases_for_book_in_worker, source_id, None, config)),
-        )
-        for source_id in source_ids
-    ]
-    outcomes: list[BookCandidateResult] = []
-    for source_id, wrapped_future in wrapped_futures:
-        await asyncio.wait([wrapped_future])
-        exception = wrapped_future.exception()
-        if exception is not None:
-            logger.error(f"ebook_candidate_phrase_generation_book_failed {source_id=}")
-            outcomes.append(BookCandidateResult())
-            continue
-        saved_count = wrapped_future.result()
-        logger.info(f"ebook_candidate_phrase_generation_book_committed {source_id=} {saved_count=}")
-        outcomes.append(BookCandidateResult(candidates=saved_count, built=True))
-
-    result = PhraseCandidateGenerationResult(
-        books_seen=books_seen,
-        books_built=sum(1 for outcome in outcomes if outcome.built),
-        candidate_phrases=sum(outcome.candidates for outcome in outcomes),
-    )
-    logger.info(
-        f"ebook_candidate_phrase_generation_complete {result.books_seen=} {result.books_built=} "
-        f"{result.candidate_phrases=}"
-    )
-    return result
-
-
-async def recalculate_candidate_phrases_for_book(
-    session: AsyncSession,
-    source: EbookSource,
-    config: EbookSearchConfig,
-) -> PhraseRecalculationResult:
-    """Remove all book phrase data, regenerate candidates, and commit the completed book.
-
-    Args:
-        session (AsyncSession): Active database session; deletion and regeneration commit on it.
-        source (EbookSource): Indexed book to recalculate.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        PhraseRecalculationResult: Deleted-row counts and the number of candidates regenerated.
-
-    Raises:
-        BookHasNoChaptersError: If the book has no indexed chapters. The deletion is rolled
-            back, so the book's existing phrases stay intact.
-
-    The deletion and regeneration share the caller's session, so they commit together; a
-    regeneration failure rolls the deletion back.
-    """
-    started_at = perf_counter()
-    logger.info(f"ebook_candidate_phrase_recalculation_start {source.id=} {source.title=}")
-    deleted = await delete_phrase_data_for_book(session, source.id)
-    candidate_count = await generate_candidate_phrases_for_book(
-        session,
-        source.id,
-        series_id=None,
-        config=config,
-        replace_all=True,
-    )
-
-    result = PhraseRecalculationResult(
-        book_id=source.id,
-        deleted_candidates=deleted.deleted_candidates,
-        deleted_protected_phrases=deleted.deleted_protected_phrases,
-        deleted_aliases=deleted.deleted_aliases,
-        deleted_mentions=deleted.deleted_mentions,
-        candidate_phrases=candidate_count,
-    )
-    logger.info(
-        f"ebook_candidate_phrase_recalculation_complete {source.id=} {result.deleted_candidates=} "
-        f"{result.deleted_protected_phrases=} {result.deleted_aliases=} {result.deleted_mentions=} "
-        f"{result.candidate_phrases=} duration_ms={(perf_counter() - started_at) * 1000:.1f}"
-    )
-    return result
-
-
-def generate_candidate_phrases_for_book_in_worker(
-    book_id: int,
-    series_id: int | None,
-    config: EbookSearchConfig,
-) -> int:
-    """Run one book's candidate generation in a pooled worker process.
-
-    The worker has no engine or session to inherit (neither can cross process boundaries), so
-    it creates its own engine from environment variables, opens the book's session on it, and
-    disposes the engine once the book is stored.
-
-    Args:
-        book_id (int): Book the candidates belong to.
-        series_id (int | None): Series scope for the stored candidates.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        int: Number of candidate phrase rows stored.
-    """
-
-    async def generate_with_worker_engine() -> int:
-        engine = get_async_postgres_engine(name="RICHIE", vector_engine=True, pool_size=1)
-        try:
-            async with AsyncSession(engine, expire_on_commit=False) as session:
-                return await generate_candidate_phrases_for_book(
-                    session,
-                    book_id,
-                    series_id,
-                    config,
-                )
-        finally:
-            await engine.dispose()
-
-    return asyncio.run(generate_with_worker_engine())
-
-
-async def generate_candidate_phrases_for_book(
-    session: AsyncSession,
-    book_id: int,
-    series_id: int | None,
-    config: EbookSearchConfig,
-    *,
-    replace_all: bool = False,
-) -> int:
-    """Load a book's chapters and metadata, extract candidate phrases, and store them without LLM judging.
-
-    The session commits only when the whole book succeeds; any failure rolls the session back,
-    which also restores rows the caller deleted in the same transaction (e.g. a recalculation).
-
-    Args:
-        session (AsyncSession): Active database session; committed on success, rolled back on failure.
-        book_id (int): Book the candidates belong to.
-        series_id (int | None): Series scope for the stored candidates.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        replace_all (bool): When the caller has already cleared this book's candidates (e.g. a
-            recalculation), skip the per-candidate existence lookup and bulk-insert new rows.
-
-    Returns:
-        int: Number of candidate phrase rows stored.
-
-    Raises:
-        BookHasNoChaptersError: If the book has no indexed chapter text.
-    """
-    started_at = perf_counter()
-    chapters = await load_book_chapter_texts(session, book_id)
-    if not chapters:
-        await session.rollback()
-        message = f"book {book_id} has no indexed chapters"
-        raise BookHasNoChaptersError(message)
-    metadata = await metadata_for_source_id(session, book_id)
-    try:
-        book_text = "\n\n".join(chapters)
-        candidates = extract_phrase_candidates_for_book(
-            book_text,
-            chapters,
-            config,
-            metadata=metadata,
-        )
-        saved_count = await store_candidate_phrases_for_book(
-            session,
-            book_id,
-            series_id,
-            candidates,
-            config,
-            replace_all=replace_all,
-        )
-        await session.commit()
-    except Exception:
-        await session.rollback()
-        raise
-    logger.info(
-        f"ebook_candidate_phrase_generation_book_duration {book_id=} {saved_count=} "
-        f"duration_ms={(perf_counter() - started_at) * 1000:.1f}"
-    )
-    return saved_count
-
-
-async def store_candidate_phrases_for_book(
-    session: AsyncSession,
-    book_id: int,
-    series_id: int | None,
-    limited_candidates: list[PhraseCandidate],
-    config: EbookSearchConfig,
-    *,
-    replace_all: bool = False,
-) -> int:
-    """Persist already-extracted candidate phrase rows for one book without committing.
-
-    Args:
-        session (Session): Active database session.
-        book_id (int): Book the candidates belong to.
-        series_id (int | None): Series scope for the stored candidates.
-        limited_candidates (list[PhraseCandidate]): Scored candidates to persist.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        replace_all (bool): When the caller has already cleared this book's candidates, skip the
-            per-candidate existence lookup and bulk-insert new rows.
-
-    Returns:
-        int: Number of candidate phrase rows stored.
-    """
-    save_started_at = perf_counter()
-    if replace_all:
-        rows = [new_candidate_row(book_id, series_id, candidate) for candidate in limited_candidates]
-        session.add_all(rows)
-        await session.flush()
-        saved_count = len(rows)
-        logger.info(
-            f"ebook_candidate_phrase_save_start {book_id=} candidates={len(limited_candidates)} mode=bulk_insert"
-        )
-    else:
-        pruned_count = await prune_unstorable_unjudged_candidate_phrases(session, book_id, config)
-        logger.info(
-            f"ebook_candidate_phrase_save_start {book_id=} candidates={len(limited_candidates)} {pruned_count=}"
-        )
-        saved_count = await bulk_upsert_unjudged_candidates(session, book_id, series_id, limited_candidates)
-    logger.info(
-        f"ebook_candidate_phrase_save_complete {book_id=} {saved_count=} "
-        f"save_ms={(perf_counter() - save_started_at) * 1000:.1f}"
-    )
-    return saved_count

python/ebook_search/protected_phrases/judge_ngrams.py

@@ -1,471 +0,0 @@
-"""Book-level orchestration for LLM judging and promotion of candidate phrases."""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import logging
-import re
-from time import perf_counter
-from typing import TYPE_CHECKING
-
-import httpx
-from sqlalchemy import select
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from python.ebook_search.llm_interface import request_chat_completion
-from python.ebook_search.protected_phrases.extraction import (
-    candidate_source_names,
-    get_sample_contexts,
-    is_junk_phrase,
-    is_most_common_word_phrase,
-    score_candidate,
-)
-from python.ebook_search.protected_phrases.matching import index_chunk_phrase_mentions_for_book
-from python.ebook_search.protected_phrases.models import BookJudgmentResult, LLMJudgment, PhraseJudgmentBackfillResult
-from python.ebook_search.protected_phrases.store import (
-    count_protected_phrases,
-    count_unjudged_candidates,
-    load_book_text,
-    load_candidates_for_judgment,
-    phrase_candidate_from_row,
-    save_candidate_to_db,
-    upsert_protected_phrase,
-)
-from python.ebook_search.protected_phrases.text_normalization import normalize_text
-from python.orm.richie import EbookSource
-
-if TYPE_CHECKING:
-    from collections.abc import Sequence
-
-    from sqlalchemy.ext.asyncio import AsyncEngine
-
-    from python.ebook_search.config import EbookSearchConfig
-    from python.ebook_search.protected_phrases.models import PhraseCandidate
-    from python.orm.richie import EbookProtectedPhrase
-
-JSON_OBJECT_RE = re.compile(r"\{.*\}", re.DOTALL)
-
-
-logger = logging.getLogger(__name__)
-
-
-async def judge_candidate_phrases_for_books(
-    engine: AsyncEngine,
-    config: EbookSearchConfig,
-    *,
-    source_ids: Sequence[int] | None = None,
-) -> PhraseJudgmentBackfillResult:
-    """Judge candidate phrases for books, fanning LLM calls out across books and phrases.
-
-    Up to ``phrase_judge_book_workers`` books are judged at once, and within each book candidates
-    are judged in concurrent chunks of ``phrase_judge_phrase_workers``. Each book uses its own
-    short-lived sessions for reads and writes; no database connection is held while LLM calls are
-    in flight. For a pseudo-single-threaded run (solo testing, debugging), set both worker
-    settings to 1.
-
-    Args:
-        engine (AsyncEngine): Engine used to open one session per book.
-        config (EbookSearchConfig): Runtime phrase-tuning settings and chat configuration.
-        source_ids (Sequence[int] | None): Books to judge; ``None`` judges every indexed book.
-
-    Returns:
-        PhraseJudgmentBackfillResult: Per-corpus counts of books judged, failures, candidates,
-        protected phrases, and mentions.
-    """
-    if source_ids is None:
-        async with AsyncSession(engine) as session:
-            source_ids = list((await session.scalars(select(EbookSource.id).order_by(EbookSource.id))).all())
-    books_seen = len(source_ids)
-    book_workers = max(1, config.phrase_judge_book_workers)
-    phrase_workers = max(1, config.phrase_judge_phrase_workers)
-    logger.info(
-        f"ebook_candidate_phrase_judgment_start {books_seen=} {book_workers=} {phrase_workers=} "
-        f"{config.protected_phrase_confidence_threshold=:.2f}"
-    )
-
-    book_semaphore = asyncio.Semaphore(book_workers)
-    max_connections = book_workers * phrase_workers
-    limits = httpx.Limits(max_connections=max_connections, max_keepalive_connections=max_connections)
-    async with httpx.AsyncClient(limits=limits) as client:
-        outcomes = await asyncio.gather(
-            *(judge_one_book_async(engine, source_id, config, client, book_semaphore) for source_id in source_ids)
-        )
-
-    result = PhraseJudgmentBackfillResult(
-        books_seen=books_seen,
-        books_judged=sum(1 for outcome in outcomes if outcome.committed),
-        books_failed=sum(1 for outcome in outcomes if outcome.failed),
-        candidates_judged=sum(outcome.judged for outcome in outcomes),
-        protected_phrases=sum(outcome.protected for outcome in outcomes),
-        phrase_mentions=sum(outcome.mentions for outcome in outcomes),
-    )
-    logger.info(
-        f"ebook_candidate_phrase_judgment_complete {result.books_seen=} {result.books_judged=} {result.books_failed=} "
-        f"{result.candidates_judged=} {result.protected_phrases=} {result.phrase_mentions=}"
-    )
-    return result
-
-
-async def judge_one_book_async(
-    engine: AsyncEngine,
-    source_id: int,
-    config: EbookSearchConfig,
-    client: httpx.AsyncClient,
-    book_semaphore: asyncio.Semaphore,
-) -> BookJudgmentResult:
-    """Judge one book concurrently and persist the outcome, honoring the book-level limit.
-
-    Args:
-        engine (AsyncEngine): Engine used to open the book's read and write sessions.
-        source_id (int): Book to judge candidates for.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        client (httpx.AsyncClient): Shared async client for LLM calls.
-        book_semaphore (asyncio.Semaphore): Caps how many books judge at once.
-
-    Returns:
-        BookJudgmentResult: The book's judgment outcome.
-    """
-    async with book_semaphore:
-        try:
-            prepared = await prepare_book_judgment(engine, source_id, config)
-            if prepared is None:
-                return BookJudgmentResult()
-            work_items, target_remaining = prepared
-            judged = await judge_book_candidates_async(client, config, source_id, work_items, target_remaining)
-            if not judged:
-                return BookJudgmentResult()
-            return await persist_book_judgments(engine, source_id, config, judged)
-        except Exception:
-            logger.exception(f"ebook_candidate_phrase_judgment_book_failed {source_id=}")
-            return BookJudgmentResult(failed=True)
-
-
-async def prepare_book_judgment(
-    engine: AsyncEngine,
-    source_id: int,
-    config: EbookSearchConfig,
-) -> tuple[list[tuple[int, PhraseCandidate]], int | None] | None:
-    """Load one book's candidates to judge, with sample contexts, on a short-lived read session.
-
-    Args:
-        engine (AsyncEngine): Engine used to open the read session.
-        source_id (int): Book to load candidates for.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        tuple[list[tuple[int, PhraseCandidate]], int | None] | None: Candidate rows paired with
-        in-memory candidates and the remaining protected-phrase target, or ``None`` when the book
-        has nothing to judge.
-    """
-    judgment_limit = config.protected_phrase_llm_candidates_per_book
-    if judgment_limit <= 0:
-        return None
-    async with AsyncSession(engine) as session:
-        if not await count_unjudged_candidates(session, source_id, config):
-            logger.info(f"ebook_candidate_phrase_judgment_book_skip_no_unjudged {source_id=}")
-            return None
-        existing_protected = await count_protected_phrases(session, source_id)
-        target_remaining: int | None = None
-        if config.phrase_target_protected_per_book > 0:
-            target_remaining = max(config.phrase_target_protected_per_book - existing_protected, 0)
-            if target_remaining == 0:
-                logger.info(
-                    f"ebook_candidate_phrase_judgment_skipped_target_met {source_id=} {existing_protected=} "
-                    f"{config.phrase_target_protected_per_book=}"
-                )
-                return None
-        book_text = await load_book_text(session, source_id)
-        if not book_text:
-            logger.warning(f"ebook_candidate_phrase_judgment_book_empty {source_id=}")
-            return None
-        normalized_book_text = normalize_text(book_text)
-        # Stored rows may predate the current junk filters and score weights, so re-filter and
-        # rescore every unjudged row here instead of trusting the persisted candidate_score.
-        rows = await load_candidates_for_judgment(session, source_id, config)
-        scored_items: list[tuple[int, PhraseCandidate]] = []
-        skipped_junk = 0
-        for row in rows:
-            candidate = phrase_candidate_from_row(row)
-            if is_junk_phrase(candidate.phrase_norm.split()):
-                skipped_junk += 1
-                continue
-            candidate.candidate_score = score_candidate(candidate, config)
-            scored_items.append((row.id, candidate))
-        scored_items.sort(key=lambda item: item[1].candidate_score, reverse=True)
-        work_items = scored_items[:judgment_limit]
-        for _, candidate in work_items:
-            candidate.sample_contexts = candidate.sample_contexts or get_sample_contexts(
-                normalized_book_text, candidate.phrase_norm
-            )
-    logger.info(
-        f"ebook_candidate_phrase_judgment_candidates_loaded {source_id=} candidates={len(work_items)} {skipped_junk=} "
-        f"unjudged_rows={len(rows)} {existing_protected=} {target_remaining=} {judgment_limit=}"
-    )
-    return work_items, target_remaining
-
-
-async def judge_book_candidates_async(
-    client: httpx.AsyncClient,
-    config: EbookSearchConfig,
-    source_id: int,
-    work_items: list[tuple[int, PhraseCandidate]],
-    target_remaining: int | None,
-) -> list[tuple[int, PhraseCandidate, LLMJudgment, bool]]:
-    """Judge a book's candidates in concurrent chunks, stopping once the target is reached.
-
-    Promotion decisions are made in memory so judging can stop early without any database writes.
-
-    Args:
-        client (httpx.AsyncClient): Shared async client for LLM calls.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        source_id (int): Book being judged, for logging.
-        work_items (list[tuple[int, PhraseCandidate]]): Candidate row ids paired with candidates,
-            in best-first score order.
-        target_remaining (int | None): Remaining protected-phrase target, or ``None`` for no cap.
-
-    Returns:
-        list[tuple[int, PhraseCandidate, LLMJudgment, bool]]: Judged rows with their judgment and
-        whether each should be promoted.
-    """
-    chunk_size = max(1, config.phrase_judge_phrase_workers)
-    judged: list[tuple[int, PhraseCandidate, LLMJudgment, bool]] = []
-    promoted = 0
-    for start in range(0, len(work_items), chunk_size):
-        chunk = work_items[start : start + chunk_size]
-        judgments = await asyncio.gather(*(judge_candidate_async(client, config, candidate) for _, candidate in chunk))
-        for (candidate_id, candidate), judgment in zip(chunk, judgments, strict=True):
-            promote = (target_remaining is None or promoted < target_remaining) and should_protect_judged_candidate(
-                candidate, judgment, source_id, config, candidate_id=candidate_id
-            )
-            if promote:
-                promoted += 1
-            judged.append((candidate_id, candidate, judgment, promote))
-        if target_remaining is not None and promoted >= target_remaining:
-            break
-    return judged
-
-
-async def judge_candidate_async(
-    client: httpx.AsyncClient,
-    config: EbookSearchConfig,
-    candidate: PhraseCandidate,
-) -> LLMJudgment:
-    """Judge one candidate with the LLM over the shared async client.
-
-    Args:
-        client (httpx.AsyncClient): Shared async client for LLM calls.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        candidate (PhraseCandidate): Candidate to judge.
-
-    Returns:
-        LLMJudgment: The parsed judgment.
-    """
-    content = await request_chat_completion(client, config, build_judge_messages(candidate))
-    return parse_llm_judgment(content, config)
-
-
-async def persist_book_judgments(
-    engine: AsyncEngine,
-    source_id: int,
-    config: EbookSearchConfig,
-    judged: list[tuple[int, PhraseCandidate, LLMJudgment, bool]],
-) -> BookJudgmentResult:
-    """Persist one book's judgments and promotions in a single committed transaction.
-
-    Args:
-        engine (AsyncEngine): Engine used to open the write session.
-        source_id (int): Book being persisted.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        judged (list[tuple[int, PhraseCandidate, LLMJudgment, bool]]): Judged candidates with their
-            judgment and promotion flag.
-
-    Returns:
-        BookJudgmentResult: The book's committed counts, or a failed result on error.
-    """
-    book_started_at = perf_counter()
-    async with AsyncSession(engine, expire_on_commit=False) as session:
-        try:
-            protected: list[EbookProtectedPhrase] = []
-            for candidate_id, candidate, judgment, promote in judged:
-                candidate_row = await save_candidate_to_db(session, source_id, None, candidate, judgment=judgment)
-                if promote:
-                    protected.append(
-                        await upsert_protected_phrase(session, source_id, None, candidate, judgment, candidate_row)
-                    )
-                logger.info(
-                    f"ebook_candidate_phrase_judgment_candidate_complete {source_id=} {candidate_id=} "
-                    f"{candidate.phrase_norm=} {judgment.keep=} {judgment.confidence=:.3f} {judgment.category=} "
-                    f"{promote=}"
-                )
-            await session.flush()
-            mentions = await index_chunk_phrase_mentions_for_book(session, source_id, config) if protected else 0
-            await session.commit()
-        except Exception:
-            await session.rollback()
-            logger.exception(f"ebook_candidate_phrase_judgment_book_persist_failed {source_id=}")
-            return BookJudgmentResult(failed=True)
-    logger.info(
-        f"ebook_candidate_phrase_judgment_book_committed {source_id=} judged={len(judged)} protected={len(protected)} "
-        f"{mentions=} duration_ms={(perf_counter() - book_started_at) * 1000:.1f}"
-    )
-    return BookJudgmentResult(judged=len(judged), protected=len(protected), mentions=mentions, committed=True)
-
-
-def should_protect_judged_candidate(
-    candidate: PhraseCandidate,
-    judgment: LLMJudgment,
-    book_id: int,
-    config: EbookSearchConfig,
-    *,
-    candidate_id: int,
-) -> bool:
-    """Report whether a judged candidate qualifies to become a protected phrase.
-
-    Args:
-        candidate (PhraseCandidate): In-memory candidate that was judged.
-        judgment (LLMJudgment): Judge decision for the candidate.
-        book_id (int): Book the candidate belongs to, for logging.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        candidate_id (int): Stored candidate row id the judgment came from, for logging.
-
-    Returns:
-        bool: True when the judged candidate should be promoted to a protected phrase.
-    """
-    if not judgment.keep or judgment.confidence < config.protected_phrase_confidence_threshold:
-        return False
-    accepted_norm = normalize_text(judgment.canonical or candidate.phrase_text)
-    accepted_tokens = accepted_norm.split()
-    accepted_token_count = len(accepted_tokens)
-    if accepted_token_count < config.phrase_min_tokens:
-        logger.info(
-            f"ebook_candidate_phrase_judgment_candidate_skip_short_canonical {book_id=} {candidate_id=} "
-            f"{candidate.phrase_norm=} {accepted_norm=} {accepted_token_count=} {config.phrase_min_tokens=}"
-        )
-        return False
-    if is_most_common_word_phrase(accepted_tokens):
-        logger.info(
-            f"ebook_candidate_phrase_judgment_candidate_skip_common_canonical {book_id=} {candidate_id=} "
-            f"{candidate.phrase_norm=} {accepted_norm=}"
-        )
-        return False
-    return True
-
-
-def build_judge_messages(candidate: PhraseCandidate) -> list[dict[str, str]]:
-    """Build the chat messages used to judge one candidate phrase.
-
-    Args:
-        candidate (PhraseCandidate): Candidate to describe for the judge.
-
-    Returns:
-        list[dict[str, str]]: OpenAI-style system and user messages.
-    """
-    payload = {
-        "phrase": candidate.phrase_norm,
-        "token_count": candidate.token_count,
-        "sources": candidate_source_names(candidate),
-        "raw_count": candidate.raw_count,
-        "chapter_count": candidate.chapter_count,
-        "contexts": candidate.sample_contexts,
-    }
-    return [
-        {
-            "role": "system",
-            "content": (
-                "Judge whether a candidate phrase from a book should be protected for RAG retrieval. "
-                "Do not extract new phrases. Reject common grammar fragments, ordinary nonspecific phrases, "
-                "unstable fragments, and phrases kept only because they are frequent. Keep people, places, "
-                "organizations, factions, events, technologies, fictional conditions, magic systems, formal titles, "
-                "named concepts, and recurring world-specific terms. Return only a JSON object with keys: keep, "
-                "canonical, category, aliases, confidence, importance, allow_nested, suppress_children, reason."
-            ),
-        },
-        {"role": "user", "content": json.dumps(payload, ensure_ascii=True)},
-    ]
-
-
-def parse_llm_judgment(content: str, config: EbookSearchConfig) -> LLMJudgment:
-    """Parse and validate an LLM phrase-judge response.
-
-    Args:
-        content (str): Raw model response text.
-        config (EbookSearchConfig): Runtime phrase-tuning settings supplying nesting defaults.
-
-    Returns:
-        LLMJudgment: The parsed and validated judgment.
-
-    Raises:
-        TypeError: If the decoded JSON body is not an object.
-    """
-    body = json.loads(extract_json_object(content))
-    if not isinstance(body, dict):
-        msg = "LLM phrase judge response is not a JSON object"
-        raise TypeError(msg)
-
-    aliases = body.get("aliases", ())
-    if not isinstance(aliases, list | tuple):
-        aliases = ()
-    return LLMJudgment(
-        keep=bool(body.get("keep", False)),
-        canonical=optional_text(body.get("canonical")),
-        category=optional_text(body.get("category")),
-        aliases=tuple(str(alias) for alias in aliases if isinstance(alias, str) and alias.strip()),
-        confidence=clamped_float(body.get("confidence"), default=0.0),
-        importance=clamped_float(body.get("importance"), default=0.5),
-        allow_nested=bool(body.get("allow_nested", config.phrase_default_allow_nested)),
-        suppress_children=bool(body.get("suppress_children", config.phrase_default_suppress_children)),
-        reason=optional_text(body.get("reason")),
-    )
-
-
-def extract_json_object(content: str) -> str:
-    """Extract a JSON object from plain or fenced model output.
-
-    Args:
-        content (str): Raw model response text.
-
-    Returns:
-        str: The substring spanning the first JSON object.
-
-    Raises:
-        ValueError: If no JSON object is found in the response.
-    """
-    stripped = content.strip()
-    if stripped.startswith("{") and stripped.endswith("}"):
-        return stripped
-    match = JSON_OBJECT_RE.search(stripped)
-    if match is None:
-        msg = "LLM phrase judge response did not contain a JSON object"
-        raise ValueError(msg)
-    return match.group(0)
-
-
-def optional_text(value: object) -> str | None:
-    """Return stripped text for a nullable JSON value.
-
-    Args:
-        value (object): Decoded JSON value that may or may not be a string.
-
-    Returns:
-        str | None: The stripped string, or ``None`` when it is not a non-empty string.
-    """
-    if not isinstance(value, str):
-        return None
-    stripped = value.strip()
-    return stripped or None
-
-
-def clamped_float(value: object, *, default: float) -> float:
-    """Coerce a JSON number into the 0.0 to 1.0 range.
-
-    Args:
-        value (object): Decoded JSON value that may or may not be a number.
-        default (float): Fallback returned when ``value`` is not numeric.
-
-    Returns:
-        float: The value clamped to ``[0.0, 1.0]``, or ``default`` when non-numeric.
-    """
-    if not isinstance(value, int | float):
-        return default
-    return min(max(float(value), 0.0), 1.0)

python/ebook_search/protected_phrases/matching.py

@@ -1,357 +0,0 @@
-"""Runtime protected-phrase matching and chunk mention indexing."""
-
-from __future__ import annotations
-
-import logging
-from collections import defaultdict
-from typing import TYPE_CHECKING
-
-from sqlalchemy import and_, delete, or_, select, union
-
-from python.ebook_search.protected_phrases.config import get_ignored_phrases
-from python.ebook_search.protected_phrases.models import (
-    PhraseLookup,
-    PhraseMatch,
-)
-from python.ebook_search.protected_phrases.text_normalization import tokenize_with_offsets
-from python.orm.richie import (
-    EbookChunk,
-    EbookChunkPhraseMention,
-    EbookPhraseAlias,
-    EbookProtectedPhrase,
-)
-
-if TYPE_CHECKING:
-    from collections.abc import Iterator, Sequence
-
-    from sqlalchemy.ext.asyncio import AsyncSession
-
-    from python.ebook_search.config import EbookSearchConfig
-
-logger = logging.getLogger(__name__)
-
-
-async def detect_protected_phrases_for_query(
-    session: AsyncSession,
-    query_text: str,
-    config: EbookSearchConfig,
-) -> list[PhraseMatch]:
-    """Find query phrases with indexed exact matches on canonical and alias norms.
-
-    Args:
-        session (AsyncSession): Active database session.
-        query_text (str): User query text to detect phrases in.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        list[PhraseMatch]: Metadata-backed, overlap-resolved phrase matches for the query.
-    """
-    tokens_ = tokenize_with_offsets(query_text)
-    windows_by_norm: defaultdict[str, list[tuple[int, int]]] = defaultdict(list)
-    token_texts = [token.text for token in tokens_]
-    max_tokens = max(config.phrase_max_tokens, config.phrase_max_entity_tokens)
-    for phrase_norm, start, end in generate_query_ngrams(
-        token_texts,
-        min_n=config.phrase_min_tokens,
-        max_n=max_tokens,
-    ):
-        windows_by_norm[phrase_norm].append((start, end))
-    if not windows_by_norm:
-        return []
-
-    query_norms = tuple(windows_by_norm)
-    matched_norms = union(
-        select(
-            EbookProtectedPhrase.id.label("phrase_id"),
-            EbookProtectedPhrase.phrase_norm.label("matched_norm"),
-        ).where(EbookProtectedPhrase.phrase_norm.in_(query_norms)),
-        select(
-            EbookPhraseAlias.phrase_id.label("phrase_id"),
-            EbookPhraseAlias.alias_norm.label("matched_norm"),
-        ).where(EbookPhraseAlias.alias_norm.in_(query_norms)),
-    ).subquery()
-    statement = select(EbookProtectedPhrase, matched_norms.c.matched_norm).join(
-        matched_norms,
-        matched_norms.c.phrase_id == EbookProtectedPhrase.id,
-    )
-
-    matches: list[PhraseMatch] = []
-    for phrase, matched_norm in await session.execute(statement):
-        for start, end in windows_by_norm[matched_norm]:
-            matches.append(
-                PhraseMatch(
-                    phrase_id=phrase.id,
-                    matched_norm=matched_norm,
-                    phrase_text=phrase.phrase_text,
-                    phrase_norm=phrase.phrase_norm,
-                    canonical_id=phrase.canonical_id,
-                    phrase_type=phrase.phrase_type,
-                    token_count=end - start,
-                    confidence=phrase.confidence,
-                    importance=phrase.importance,
-                    allow_nested=phrase.allow_nested,
-                    suppress_children=phrase.suppress_children,
-                    start_token=start,
-                    end_token=end,
-                    start_char=tokens_[start].start_char,
-                    end_char=tokens_[end - 1].end_char,
-                    book_id=phrase.book_id,
-                    series_id=phrase.series_id,
-                )
-            )
-    return resolve_overlaps(matches)
-
-
-async def index_chunk_phrase_mentions_for_book(
-    session: AsyncSession,
-    book_id: int,
-    config: EbookSearchConfig,
-) -> int:
-    """Rebuild chunk phrase mentions for all chunks in one book.
-
-    Args:
-        session (AsyncSession): Active database session.
-        book_id (int): Book whose chunk mentions are rebuilt.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        int: Total number of chunk phrase mentions indexed for the book.
-    """
-    lookup = await load_phrase_lookup(session, config, book_id=book_id)
-    await session.execute(delete(EbookChunkPhraseMention).where(EbookChunkPhraseMention.book_id == book_id))
-    chunks = await session.scalars(select(EbookChunk).where(EbookChunk.source_id == book_id).order_by(EbookChunk.id))
-    count = 0
-    for chunk in chunks:
-        count += index_chunk_phrase_mentions(session, chunk, lookup=lookup)
-    await session.flush()
-    logger.info(f"ebook_chunk_phrase_mentions_indexed {book_id=} {count=}")
-    return count
-
-
-async def load_phrase_lookup(
-    session: AsyncSession,
-    config: EbookSearchConfig,
-    *,
-    book_id: int | None = None,
-    series_id: int | None = None,
-) -> PhraseLookup:
-    """Load protected phrases and aliases into RAM lookup maps.
-
-    Args:
-        session (AsyncSession): Active database session.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        book_id (int | None): Optional book scope to restrict loaded phrases.
-        series_id (int | None): Optional series scope to restrict loaded phrases.
-
-    Returns:
-        PhraseLookup: Normalized phrase and alias maps with the token-window bounds to test.
-    """
-    phrase_ids_by_norm: defaultdict[str, set[int]] = defaultdict(set)
-    phrases_by_id: dict[int, EbookProtectedPhrase] = {}
-    max_tokens = config.phrase_max_tokens
-
-    statement = select(
-        EbookProtectedPhrase,
-        EbookPhraseAlias.alias_norm,
-    ).outerjoin(EbookPhraseAlias, EbookPhraseAlias.phrase_id == EbookProtectedPhrase.id)
-    scope_filter = protected_phrase_scope_filter(book_id=book_id, series_id=series_id)
-    if scope_filter is not None:
-        statement = statement.where(scope_filter)
-
-    for phrase, alias_norm in await session.execute(statement):
-        phrases_by_id[phrase.id] = phrase
-        phrase_ids_by_norm[phrase.phrase_norm].add(phrase.id)
-        max_tokens = max(max_tokens, phrase.token_count)
-        if alias_norm is not None:
-            phrase_ids_by_norm[alias_norm].add(phrase.id)
-            max_tokens = max(max_tokens, len(alias_norm.split()))
-
-    return PhraseLookup(
-        phrase_ids_by_norm={key: tuple(sorted(values)) for key, values in phrase_ids_by_norm.items()},
-        phrases_by_id=phrases_by_id,
-        min_tokens=config.phrase_min_tokens,
-        max_tokens=max_tokens,
-    )
-
-
-def protected_phrase_scope_filter(*, book_id: int | None, series_id: int | None) -> object | None:
-    """Build a SQLAlchemy filter for optional phrase book and series scope.
-
-    Args:
-        book_id (int | None): Optional book scope to include alongside global phrases.
-        series_id (int | None): Optional series scope to include alongside global phrases.
-
-    Returns:
-        object | None: A combined SQLAlchemy filter clause, or ``None`` when no scope is given.
-    """
-    conditions = []
-    if book_id is not None:
-        conditions.append(or_(EbookProtectedPhrase.book_id.is_(None), EbookProtectedPhrase.book_id == book_id))
-    if series_id is not None:
-        conditions.append(or_(EbookProtectedPhrase.series_id.is_(None), EbookProtectedPhrase.series_id == series_id))
-    if not conditions:
-        return None
-    return and_(*conditions)
-
-
-def is_inside(child: PhraseMatch, parent: PhraseMatch) -> bool:
-    """Return whether one token span is strictly inside another.
-
-    Args:
-        child (PhraseMatch): Candidate nested match.
-        parent (PhraseMatch): Candidate enclosing match.
-
-    Returns:
-        bool: True when ``child`` lies within ``parent`` and is not the same span.
-    """
-    return (
-        child.start_token >= parent.start_token
-        and child.end_token <= parent.end_token
-        and (child.start_token, child.end_token, child.phrase_id)
-        != (parent.start_token, parent.end_token, parent.phrase_id)
-    )
-
-
-def index_chunk_phrase_mentions(session: AsyncSession, chunk: EbookChunk, *, lookup: PhraseLookup) -> int:
-    """Store protected phrase mentions for one chunk.
-
-    Args:
-        session (AsyncSession): Active database session.
-        chunk (EbookChunk): Chunk whose text is scanned for phrase mentions.
-        lookup (PhraseLookup): In-memory phrase and alias lookup maps.
-
-    Returns:
-        int: Number of phrase mentions stored for the chunk.
-    """
-    tokens_ = tokenize_with_offsets(chunk.text)
-    token_texts = [token.text for token in tokens_]
-    raw_matches: list[PhraseMatch] = []
-    phrase_windows = generate_query_ngrams(token_texts, min_n=lookup.min_tokens, max_n=lookup.max_tokens)
-    for matched_norm, start, end in phrase_windows:
-        for phrase_id in lookup.phrase_ids_by_norm.get(matched_norm, ()):
-            phrase = lookup.phrases_by_id[phrase_id]
-            raw_matches.append(
-                PhraseMatch(
-                    phrase_id=phrase_id,
-                    matched_norm=matched_norm,
-                    phrase_text=phrase.phrase_text,
-                    phrase_norm=phrase.phrase_norm,
-                    canonical_id=phrase.canonical_id,
-                    phrase_type=phrase.phrase_type,
-                    confidence=phrase.confidence,
-                    importance=phrase.importance,
-                    allow_nested=phrase.allow_nested,
-                    suppress_children=phrase.suppress_children,
-                    start_token=start,
-                    end_token=end,
-                    token_count=end - start,
-                    start_char=tokens_[start].start_char,
-                    end_char=tokens_[end - 1].end_char,
-                    book_id=phrase.book_id,
-                    series_id=phrase.series_id,
-                )
-            )
-
-    matches = resolve_overlaps(raw_matches)
-    for match in matches:
-        session.add(
-            EbookChunkPhraseMention(
-                chunk_id=chunk.id,
-                phrase_id=match.phrase_id,
-                book_id=match.book_id if match.book_id is not None else chunk.source_id,
-                series_id=match.series_id,
-                start_char=match.start_char if match.start_char is not None else 0,
-                end_char=match.end_char,
-            )
-        )
-    return len(matches)
-
-
-def resolve_overlaps(matches: Sequence[PhraseMatch]) -> list[PhraseMatch]:
-    """Resolve overlapping phrase matches without relying only on longest match.
-
-    Args:
-        matches (Sequence[PhraseMatch]): Metadata-backed matches that may overlap.
-
-    Returns:
-        list[PhraseMatch]: The kept, non-suppressed matches.
-    """
-    sorted_matches = sorted(
-        matches,
-        key=lambda match: (match.start_token, -match.token_count, -match.importance, -match.confidence),
-    )
-    kept: list[PhraseMatch] = []
-    for candidate in sorted_matches:
-        if any(should_suppress(candidate, existing) for existing in kept):
-            continue
-        kept.append(candidate)
-    return kept
-
-
-def should_suppress(candidate: PhraseMatch, kept: PhraseMatch) -> bool:
-    """Return whether an already-kept match should suppress a candidate.
-
-    Args:
-        candidate (PhraseMatch): Match being considered for keeping.
-        kept (PhraseMatch): Match already kept that may suppress the candidate.
-
-    Returns:
-        bool: True when the candidate should be dropped in favor of the kept match.
-    """
-    if not overlaps(candidate, kept):
-        return False
-    if candidate.canonical_id == kept.canonical_id:
-        return rank_match(kept) >= rank_match(candidate)
-    if is_inside(candidate, kept) and kept.suppress_children and not candidate.allow_nested:
-        return True
-    return not candidate.allow_nested and rank_match(kept) > rank_match(candidate)
-
-
-def overlaps(first: PhraseMatch, second: PhraseMatch) -> bool:
-    """Return whether two token spans overlap.
-
-    Args:
-        first (PhraseMatch): First match to compare.
-        second (PhraseMatch): Second match to compare.
-
-    Returns:
-        bool: True when the two token spans share at least one token position.
-    """
-    return not (first.end_token <= second.start_token or first.start_token >= second.end_token)
-
-
-def rank_match(match: PhraseMatch) -> tuple[float, float, int]:
-    """Rank phrase matches by importance, confidence, then token count.
-
-    Args:
-        match (PhraseMatch): Match to build a sort key for.
-
-    Returns:
-        tuple[float, float, int]: A comparable key of importance, confidence, and token count.
-    """
-    return (match.importance, match.confidence, match.token_count)
-
-
-def generate_query_ngrams(
-    tokens_: Sequence[str],
-    min_n: int,
-    max_n: int,
-) -> Iterator[tuple[str, int, int]]:
-    """Generate normalized query windows from longest to shortest.
-
-    Args:
-        tokens_ (Sequence[str]): Normalized query tokens.
-        min_n (int): Smallest window size to yield.
-        max_n (int): Largest window size to yield, capped at the token count.
-
-    Yields:
-        tuple[str, int, int]: Normalized window text with its start and end token indices.
-    """
-    capped_max_n = min(max_n, len(tokens_))
-    for ngram_size in range(capped_max_n, min_n - 1, -1):
-        for start in range(len(tokens_) - ngram_size + 1):
-            end = start + ngram_size
-            phrase_norm = " ".join(tokens_[start:end])
-            if phrase_norm in get_ignored_phrases():
-                continue
-            yield phrase_norm, start, end

python/ebook_search/protected_phrases/models.py

@@ -1,243 +0,0 @@
-"""Dataclasses shared by protected phrase extraction, judging, matching, and backfills."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from collections.abc import Mapping
-
-    from python.orm.richie import EbookProtectedPhrase
-
-
-@dataclass(slots=True)
-class PhraseCandidate:
-    """A phrase candidate with merged extraction-source metadata.
-
-    Attributes:
-        phrase_text (str): Display text for the phrase.
-        phrase_norm (str): Normalized phrase used as the merge key.
-        token_count (int): Number of normalized tokens in the phrase.
-        source_raw_ngram (bool): Whether the raw n-gram extractor produced the phrase.
-        source_yake (bool): Whether YAKE keyword extraction produced the phrase.
-        source_capitalized (bool): Whether the capitalized-run extractor produced the phrase.
-        source_metadata (bool): Whether book metadata produced the phrase.
-        raw_count (int): Occurrences counted across the book text.
-        chapter_count (int): Number of chapters containing the phrase.
-        yake_score (float | None): Raw YAKE score when available; lower is better.
-        candidate_score (float): Combined pre-judging score.
-        sample_contexts (list[str]): Normalized context snippets around occurrences.
-    """
-
-    phrase_text: str
-    phrase_norm: str
-    token_count: int
-    source_raw_ngram: bool = False
-    source_yake: bool = False
-    source_capitalized: bool = False
-    source_metadata: bool = False
-    raw_count: int = 0
-    chapter_count: int = 0
-    yake_score: float | None = None
-    candidate_score: float = 0.0
-    sample_contexts: list[str] = field(default_factory=list)
-
-
-@dataclass(frozen=True, slots=True)
-class LLMJudgment:
-    """A structured phrase judgment returned by the LLM judge.
-
-    Attributes:
-        keep (bool): Whether the judge accepted the phrase for protection.
-        canonical (str | None): Canonical phrase text chosen by the judge.
-        category (str | None): Phrase category such as person, place, or event.
-        aliases (tuple[str, ...]): Alternate surface forms for the phrase.
-        confidence (float): Judge confidence between 0.0 and 1.0.
-        importance (float): Judge importance between 0.0 and 1.0.
-        allow_nested (bool): Whether the phrase may match inside a larger kept match.
-        suppress_children (bool): Whether the phrase suppresses matches nested inside it.
-        reason (str | None): Free-text explanation from the judge.
-    """
-
-    keep: bool
-    canonical: str | None
-    category: str | None
-    aliases: tuple[str, ...]
-    confidence: float
-    importance: float = 0.5
-    allow_nested: bool = False
-    suppress_children: bool = True
-    reason: str | None = None
-
-
-@dataclass(frozen=True, slots=True)
-class PhraseLookup:
-    """In-memory phrase metadata used for constant-time text-window checks.
-
-    Attributes:
-        phrase_ids_by_norm (Mapping[str, tuple[int, ...]]): Canonical and alias norms to phrase ids.
-        phrases_by_id (Mapping[int, EbookProtectedPhrase]): Protected phrase metadata by id.
-        min_tokens (int): Smallest token-window size to test.
-        max_tokens (int): Largest token-window size to test.
-    """
-
-    phrase_ids_by_norm: Mapping[str, tuple[int, ...]]
-    phrases_by_id: Mapping[int, EbookProtectedPhrase]
-    min_tokens: int
-    max_tokens: int
-
-
-@dataclass(frozen=True, slots=True)
-class PhraseMatch:
-    """A detected phrase match with protected-phrase metadata attached.
-
-    Attributes:
-        phrase_id (int): Protected phrase id.
-        matched_norm (str): Normalized window text that matched.
-        phrase_text (str): Display text of the protected phrase.
-        phrase_norm (str): Normalized text of the protected phrase.
-        canonical_id (str): Deterministic ``category:slug`` identifier.
-        phrase_type (str | None): Phrase category.
-        token_count (int): Number of tokens in the match.
-        confidence (float): Stored judge confidence.
-        importance (float): Stored judge importance.
-        allow_nested (bool): Whether the phrase may match inside a larger kept match.
-        suppress_children (bool): Whether the phrase suppresses matches nested inside it.
-        start_token (int): Index of the first matched token.
-        end_token (int): Index one past the last matched token.
-        start_char (int | None): Start character offset in the source text.
-        end_char (int | None): End character offset in the source text.
-        book_id (int | None): Book scope of the phrase.
-        series_id (int | None): Series scope of the phrase.
-    """
-
-    phrase_id: int
-    matched_norm: str
-    phrase_text: str
-    phrase_norm: str
-    canonical_id: str
-    phrase_type: str | None
-    token_count: int
-    confidence: float
-    importance: float
-    allow_nested: bool
-    suppress_children: bool
-    start_token: int
-    end_token: int
-    start_char: int | None = None
-    end_char: int | None = None
-    book_id: int | None = None
-    series_id: int | None = None
-
-
-@dataclass(frozen=True, slots=True)
-class PhraseCandidateGenerationResult:
-    """Summary of candidate phrase extraction for indexed books.
-
-    Attributes:
-        books_seen (int): Indexed books examined.
-        books_built (int): Books that had candidates generated and committed.
-        candidate_phrases (int): Candidate phrases stored across all books.
-    """
-
-    books_seen: int
-    books_built: int
-    candidate_phrases: int
-
-
-@dataclass(frozen=True, slots=True)
-class CorpusPhraseStats:
-    """Corpus-wide candidate and protected phrase counts for the admin page.
-
-    Attributes:
-        total_books (int): Indexed books in the corpus.
-        books_with_candidates (int): Books that have candidate phrases generated.
-        books_fully_judged (int): Books with candidates where every candidate has been judged.
-        candidate_phrases (int): Candidate phrases stored across all books.
-        judged_candidates (int): Candidate phrases that have been LLM judged.
-        unjudged_candidates (int): Candidate phrases still waiting for judgment.
-        protected_phrases (int): Protected phrases promoted across all books.
-    """
-
-    total_books: int
-    books_with_candidates: int
-    books_fully_judged: int
-    candidate_phrases: int
-    judged_candidates: int
-    unjudged_candidates: int
-    protected_phrases: int
-
-
-@dataclass(frozen=True, slots=True)
-class PhraseJudgmentBackfillResult:
-    """Summary of LLM judging for stored candidate phrases.
-
-    Attributes:
-        books_seen (int): Indexed books examined.
-        books_judged (int): Books with judgments committed.
-        books_failed (int): Books rolled back after an error.
-        candidates_judged (int): Candidate phrases sent to the LLM judge.
-        protected_phrases (int): Protected phrases promoted from candidates.
-        phrase_mentions (int): Chunk phrase mentions indexed across all books.
-    """
-
-    books_seen: int
-    books_judged: int
-    books_failed: int
-    candidates_judged: int
-    protected_phrases: int
-    phrase_mentions: int
-
-
-@dataclass(frozen=True, slots=True)
-class BookJudgmentResult:
-    """Outcome of judging one book's candidate phrases.
-
-    Attributes:
-        judged (int): Candidate phrases sent to the LLM judge.
-        protected (int): Protected phrases promoted from candidates.
-        mentions (int): Chunk phrase mentions indexed for the book.
-        committed (bool): Whether the book's judgments were committed.
-        failed (bool): Whether the book was rolled back after an error.
-    """
-
-    judged: int = 0
-    protected: int = 0
-    mentions: int = 0
-    committed: bool = False
-    failed: bool = False
-
-
-@dataclass(frozen=True, slots=True)
-class BookCandidateResult:
-    """Outcome of generating one book's candidate phrases.
-
-    Attributes:
-        candidates (int): Candidate phrases stored for the book.
-        built (bool): Whether candidate generation was committed.
-    """
-
-    candidates: int = 0
-    built: bool = False
-
-
-@dataclass(frozen=True, slots=True)
-class PhraseRecalculationResult:
-    """Summary of phrase cleanup and candidate regeneration for one book.
-
-    Attributes:
-        book_id (int): Book the recalculation ran against.
-        deleted_candidates (int): Candidate phrase rows deleted.
-        deleted_protected_phrases (int): Protected phrase rows deleted.
-        deleted_aliases (int): Phrase alias rows deleted.
-        deleted_mentions (int): Chunk phrase mention rows deleted.
-        candidate_phrases (int): Candidate phrases regenerated after cleanup.
-    """
-
-    book_id: int
-    deleted_candidates: int
-    deleted_protected_phrases: int
-    deleted_aliases: int
-    deleted_mentions: int
-    candidate_phrases: int

python/ebook_search/protected_phrases/pool.py

@@ -1,58 +0,0 @@
-"""Process pool for offloading CPU-bound phrase extraction off the request thread.
-
-Phrase extraction is pure-Python CPU work (n-gram sliding, YAKE), so running it inline in a
-sync request handler serializes concurrent recalculations behind the GIL. Submitting it to a
-``ProcessPoolExecutor`` lets concurrent extractions run in parallel across cores instead. A
-``spawn`` context is used so workers do not inherit the parent's database engine, connections,
-or server threads.
-"""
-
-from __future__ import annotations
-
-import logging
-import multiprocessing
-import os
-from concurrent.futures import ProcessPoolExecutor
-from threading import Lock
-
-logger = logging.getLogger(__name__)
-
-
-class _ExtractionPool:
-    """Lazily created process-wide extraction pool and the lock guarding it."""
-
-    def __init__(self) -> None:
-        self.lock = Lock()
-        self.pool: ProcessPoolExecutor | None = None
-
-
-_extraction_pool = _ExtractionPool()
-
-
-def get_extraction_pool(max_workers: int) -> ProcessPoolExecutor:
-    """Return the shared extraction process pool, creating it on first use.
-
-    Args:
-        max_workers (int): Desired worker count; values below 1 fall back to the CPU count.
-
-    Returns:
-        ProcessPoolExecutor: The shared pool for phrase extraction.
-    """
-    with _extraction_pool.lock:
-        if _extraction_pool.pool is None:
-            workers = max_workers if max_workers > 0 else (os.cpu_count() or 1)
-            _extraction_pool.pool = ProcessPoolExecutor(
-                max_workers=workers,
-                mp_context=multiprocessing.get_context("spawn"),
-            )
-            logger.info(f"ebook_phrase_extraction_pool_started {workers=}")
-        return _extraction_pool.pool
-
-
-def shutdown_extraction_pool() -> None:
-    """Shut down the shared extraction pool if it was started."""
-    with _extraction_pool.lock:
-        if _extraction_pool.pool is not None:
-            _extraction_pool.pool.shutdown(wait=False, cancel_futures=True)
-            _extraction_pool.pool = None
-            logger.info("ebook_phrase_extraction_pool_shutdown")

python/ebook_search/protected_phrases/store.py

@@ -1,684 +0,0 @@
-"""Database persistence for candidate and protected phrase rows."""
-
-from __future__ import annotations
-
-import logging
-import re
-from typing import TYPE_CHECKING
-
-from sqlalchemy import delete, func, or_, select
-from sqlalchemy.dialects.postgresql import insert as pg_insert
-from sqlalchemy.dialects.sqlite import insert as sqlite_insert
-
-from python.ebook_search.protected_phrases.extraction import minimum_candidate_raw_count
-from python.ebook_search.protected_phrases.models import (
-    CorpusPhraseStats,
-    PhraseCandidate,
-    PhraseRecalculationResult,
-)
-from python.ebook_search.protected_phrases.text_normalization import normalize_text
-from python.orm.richie import (
-    EbookCandidatePhrase,
-    EbookChunk,
-    EbookChunkPhraseMention,
-    EbookPhraseAlias,
-    EbookProtectedPhrase,
-    EbookSource,
-)
-
-if TYPE_CHECKING:
-    from collections.abc import Sequence
-
-    from sqlalchemy.dialects.postgresql.dml import Insert as PostgresInsert
-    from sqlalchemy.dialects.sqlite.dml import Insert as SqliteInsert
-    from sqlalchemy.ext.asyncio import AsyncSession
-
-    from python.ebook_search.config import EbookSearchConfig
-    from python.ebook_search.protected_phrases.models import LLMJudgment
-    from python.orm.richie.base import TableBase
-
-logger = logging.getLogger(__name__)
-
-
-def dialect_insert(session: AsyncSession, table: type[TableBase]) -> PostgresInsert | SqliteInsert:
-    """Return a dialect-specific INSERT construct that supports ``ON CONFLICT DO UPDATE``.
-
-    Production runs on PostgreSQL while tests run on SQLite; both support upserts with
-    compatible SQLAlchemy constructs, so the correct one is chosen from the bound dialect.
-
-    Args:
-        session (AsyncSession): Active database session whose bind selects the dialect.
-        table (type[TableBase]): Mapped table to insert into.
-
-    Returns:
-        PostgresInsert | SqliteInsert: A dialect insert exposing ``on_conflict_do_update``.
-    """
-    if session.get_bind().dialect.name == "sqlite":
-        return sqlite_insert(table)
-    return pg_insert(table)
-
-
-async def load_book_text(session: AsyncSession, book_id: int) -> str:
-    """Load a book's indexed chunk text as one string for phrase extraction.
-
-    Args:
-        session (AsyncSession): Active database session.
-        book_id (int): Book whose chunk text is loaded.
-
-    Returns:
-        str: The book's chunk text joined into a single string.
-    """
-    texts = await session.scalars(
-        select(EbookChunk.text).where(EbookChunk.source_id == book_id).order_by(EbookChunk.chunk_index)
-    )
-    return "\n\n".join(stripped for text in texts if (stripped := text.strip()))
-
-
-async def load_book_chapter_texts(session: AsyncSession, book_id: int) -> list[str]:
-    """Reconstruct chapter-like text blocks from indexed chunks for phrase extraction.
-
-    Args:
-        session (AsyncSession): Active database session.
-        book_id (int): Book whose chunks are grouped into chapters.
-
-    Returns:
-        list[str]: Non-empty chapter-like text blocks in chunk order.
-    """
-    rows = await session.execute(
-        select(EbookChunk.chapter_id, EbookChunk.text)
-        .where(EbookChunk.source_id == book_id)
-        .order_by(EbookChunk.chunk_index)
-    )
-    chapters: list[str] = []
-    current_chapter_id: int | None = None
-    current_parts: list[str] = []
-    have_current = False
-
-    for chapter_id, text in rows:
-        if have_current and chapter_id != current_chapter_id:
-            chapter_text = "\n\n".join(current_parts).strip()
-            if chapter_text:
-                chapters.append(chapter_text)
-            current_parts = []
-        current_chapter_id = chapter_id
-        current_parts.append(str(text))
-        have_current = True
-
-    if current_parts:
-        chapter_text = "\n\n".join(current_parts).strip()
-        if chapter_text:
-            chapters.append(chapter_text)
-    return chapters
-
-
-def metadata_for_source(source: EbookSource) -> dict[str, object | None]:
-    """Return phrase extraction metadata for one indexed source.
-
-    Args:
-        source (EbookSource): Indexed source to read metadata from.
-
-    Returns:
-        dict[str, object | None]: Title, author, language, publisher, and identifier values.
-    """
-    return {
-        "title": source.title,
-        "author": source.author,
-        "language": source.language,
-        "publisher": source.publisher,
-        "identifier": source.identifier,
-    }
-
-
-async def metadata_for_source_id(session: AsyncSession, source_id: int) -> dict[str, object | None]:
-    """Return phrase extraction metadata for one indexed source by id.
-
-    Args:
-        session (AsyncSession): Active database session.
-        source_id (int): Id of the indexed source to read metadata from.
-
-    Returns:
-        dict[str, object | None]: Title, author, language, publisher, and identifier values.
-
-    Raises:
-        ValueError: If no source exists with the given id.
-    """
-    source = await session.get(EbookSource, source_id)
-    if source is None:
-        msg = f"No indexed source with id {source_id}"
-        raise ValueError(msg)
-    return metadata_for_source(source)
-
-
-async def count_protected_phrases(session: AsyncSession, book_id: int) -> int:
-    """Count stored protected phrases for one book.
-
-    Args:
-        session (AsyncSession): Active database session.
-        book_id (int): Book whose protected phrases are counted.
-
-    Returns:
-        int: Number of protected phrases stored for the book.
-    """
-    return (
-        await session.scalars(
-            select(func.count(EbookProtectedPhrase.id)).where(EbookProtectedPhrase.book_id == book_id)
-        )
-    ).one()
-
-
-async def count_unjudged_candidates(session: AsyncSession, book_id: int, config: EbookSearchConfig) -> int:
-    """Count storable candidate rows for a book that have not yet been judged.
-
-    Args:
-        session (AsyncSession): Active database session.
-        book_id (int): Book whose unjudged candidates are counted.
-        config (EbookSearchConfig): Runtime phrase-tuning settings supplying storage thresholds.
-
-    Returns:
-        int: Number of storable, unjudged candidate rows for the book.
-    """
-    return (
-        await session.scalars(
-            select(func.count(EbookCandidatePhrase.id)).where(
-                EbookCandidatePhrase.book_id == book_id,
-                EbookCandidatePhrase.llm_judged.is_(False),
-                EbookCandidatePhrase.token_count >= config.phrase_min_tokens,
-                EbookCandidatePhrase.raw_count >= minimum_candidate_raw_count(config),
-            )
-        )
-    ).one()
-
-
-async def corpus_phrase_stats(session: AsyncSession) -> CorpusPhraseStats:
-    """Summarize candidate and protected phrase coverage across the whole corpus.
-
-    Args:
-        session (AsyncSession): Active database session.
-
-    Returns:
-        CorpusPhraseStats: Corpus-wide phrase counts and per-book coverage counts.
-    """
-    total_books = (await session.scalars(select(func.count(EbookSource.id)))).one()
-    candidate_phrases, judged_candidates, books_with_candidates, books_with_unjudged = (
-        await session.execute(
-            select(
-                func.count(EbookCandidatePhrase.id),
-                func.count(EbookCandidatePhrase.id).filter(EbookCandidatePhrase.llm_judged.is_(True)),
-                func.count(func.distinct(EbookCandidatePhrase.book_id)),
-                func.count(func.distinct(EbookCandidatePhrase.book_id)).filter(
-                    EbookCandidatePhrase.llm_judged.is_(False)
-                ),
-            )
-        )
-    ).one()
-    protected_phrases = (await session.scalars(select(func.count(EbookProtectedPhrase.id)))).one()
-    return CorpusPhraseStats(
-        total_books=total_books,
-        books_with_candidates=books_with_candidates,
-        books_fully_judged=books_with_candidates - books_with_unjudged,
-        candidate_phrases=candidate_phrases,
-        judged_candidates=judged_candidates,
-        unjudged_candidates=candidate_phrases - judged_candidates,
-        protected_phrases=protected_phrases,
-    )
-
-
-async def book_ids_pending_first_judgment(session: AsyncSession) -> list[int]:
-    """Return books that have candidate phrases but no judged candidates yet.
-
-    Args:
-        session (AsyncSession): Active database session.
-
-    Returns:
-        list[int]: Book ids with candidates where judging has never run, ordered by id.
-    """
-    judged_books = select(EbookCandidatePhrase.book_id).where(EbookCandidatePhrase.llm_judged.is_(True)).distinct()
-    return list(
-        (
-            await session.scalars(
-                select(EbookCandidatePhrase.book_id)
-                .where(EbookCandidatePhrase.book_id.not_in(judged_books))
-                .distinct()
-                .order_by(EbookCandidatePhrase.book_id)
-            )
-        ).all()
-    )
-
-
-async def load_candidates_for_judgment(
-    session: AsyncSession,
-    book_id: int,
-    config: EbookSearchConfig,
-) -> Sequence[EbookCandidatePhrase]:
-    """Load every storable unjudged candidate row for a book.
-
-    Rows may have been stored before the current junk filters and score weights existed, so
-    callers re-check :func:`is_junk_phrase` and rescore before selecting what to judge.
-
-    Args:
-        session (AsyncSession): Active database session.
-        book_id (int): Book whose candidates are loaded.
-        config (EbookSearchConfig): Runtime phrase-tuning settings supplying storage thresholds.
-
-    Returns:
-        Sequence[EbookCandidatePhrase]: Storable, unjudged candidate rows ordered by stored score.
-    """
-    query = (
-        select(EbookCandidatePhrase)
-        .where(
-            EbookCandidatePhrase.book_id == book_id,
-            EbookCandidatePhrase.llm_judged.is_(False),
-            EbookCandidatePhrase.token_count >= config.phrase_min_tokens,
-            EbookCandidatePhrase.raw_count >= minimum_candidate_raw_count(config),
-        )
-        .order_by(
-            EbookCandidatePhrase.candidate_score.desc(),
-            EbookCandidatePhrase.raw_count.desc(),
-            EbookCandidatePhrase.id,
-        )
-    )
-    return (await session.scalars(query)).all()
-
-
-def phrase_candidate_from_row(row: EbookCandidatePhrase) -> PhraseCandidate:
-    """Recreate an in-memory candidate from a persisted candidate row.
-
-    Args:
-        row (EbookCandidatePhrase): Stored candidate row to convert.
-
-    Returns:
-        PhraseCandidate: An in-memory candidate mirroring the row's fields.
-    """
-    return PhraseCandidate(
-        phrase_text=row.phrase_text,
-        phrase_norm=row.phrase_norm,
-        token_count=row.token_count,
-        source_raw_ngram=row.source_raw_ngram,
-        source_yake=row.source_yake,
-        source_capitalized=row.source_capitalized,
-        source_metadata=row.source_metadata,
-        raw_count=row.raw_count,
-        chapter_count=row.chapter_count,
-        yake_score=row.yake_score,
-        candidate_score=row.candidate_score,
-        sample_contexts=row.sample_contexts or [],
-    )
-
-
-def candidate_row_values(
-    book_id: int,
-    series_id: int | None,
-    candidate: PhraseCandidate,
-    *,
-    judgment: LLMJudgment | None,
-) -> dict[str, object]:
-    """Build the column values for one candidate phrase upsert.
-
-    Args:
-        book_id (int): Book the candidate belongs to.
-        series_id (int | None): Series scope stored on the row.
-        candidate (PhraseCandidate): Candidate whose fields are written to the row.
-        judgment (LLMJudgment | None): Judgment to record, or ``None`` to leave the row unjudged.
-
-    Returns:
-        dict[str, object]: Column values keyed by column name.
-    """
-    values: dict[str, object] = {
-        "book_id": book_id,
-        "phrase_norm": candidate.phrase_norm,
-        "series_id": series_id,
-        "phrase_text": candidate.phrase_text,
-        "token_count": candidate.token_count,
-        "source_raw_ngram": candidate.source_raw_ngram,
-        "source_yake": candidate.source_yake,
-        "source_capitalized": candidate.source_capitalized,
-        "source_metadata": candidate.source_metadata,
-        "raw_count": candidate.raw_count,
-        "chapter_count": candidate.chapter_count,
-        "yake_score": candidate.yake_score,
-        "candidate_score": candidate.candidate_score,
-        "llm_judged": judgment is not None,
-    }
-    if candidate.sample_contexts:
-        values["sample_contexts"] = list(candidate.sample_contexts)
-    if judgment is not None:
-        values.update(
-            llm_keep=judgment.keep,
-            llm_confidence=judgment.confidence,
-            llm_category=judgment.category,
-            llm_reason=judgment.reason,
-        )
-    return values
-
-
-async def save_candidate_to_db(
-    session: AsyncSession,
-    book_id: int,
-    series_id: int | None,
-    candidate: PhraseCandidate,
-    *,
-    judgment: LLMJudgment | None,
-) -> EbookCandidatePhrase:
-    """Insert or update one candidate phrase row.
-
-    Args:
-        session (AsyncSession): Active database session.
-        book_id (int): Book the candidate belongs to.
-        series_id (int | None): Series scope stored on the row.
-        candidate (PhraseCandidate): Candidate whose fields are written to the row.
-        judgment (LLMJudgment | None): Judgment to record, or ``None`` to leave the row unjudged.
-
-    Returns:
-        EbookCandidatePhrase: The inserted or updated candidate row.
-    """
-    values = candidate_row_values(book_id, series_id, candidate, judgment=judgment)
-
-    # Preserve an existing judgment when this call is only refreshing candidate fields.
-    skip_update = {"book_id", "phrase_norm"}
-    if judgment is None:
-        skip_update.add("llm_judged")
-    insert_statement = dialect_insert(session, EbookCandidatePhrase).values(**values)
-    statement = insert_statement.on_conflict_do_update(
-        index_elements=["book_id", "phrase_norm"],
-        set_={column: insert_statement.excluded[column] for column in values if column not in skip_update},
-    ).returning(EbookCandidatePhrase)
-    return (await session.scalars(statement, execution_options={"populate_existing": True})).one()
-
-
-BULK_CANDIDATE_UPSERT_CHUNK = 1000
-
-
-async def bulk_upsert_unjudged_candidates(
-    session: AsyncSession,
-    book_id: int,
-    series_id: int | None,
-    candidates: Sequence[PhraseCandidate],
-) -> int:
-    """Insert or update many freshly extracted candidate rows in chunked multi-row upserts.
-
-    Saving one row per statement costs one database round trip per candidate, which dominated
-    generation time for full books, so candidates are written ``BULK_CANDIDATE_UPSERT_CHUNK``
-    rows per statement instead. Existing judgments and sample contexts are never overwritten:
-    fresh extractions carry no contexts, and ``llm_judged`` plus the ``llm_*`` columns are left
-    out of the conflict update. Candidates must have unique ``phrase_norm`` values, as produced
-    by extraction, since one multi-row upsert cannot touch the same row twice.
-
-    Args:
-        session (Session): Active database session.
-        book_id (int): Book the candidates belong to.
-        series_id (int | None): Series scope stored on the rows.
-        candidates (Sequence[PhraseCandidate]): Freshly extracted candidates to persist.
-
-    Returns:
-        int: Number of candidate rows written.
-    """
-    values = [
-        candidate_row_values(book_id, series_id, candidate, judgment=None)
-        for candidate in candidates
-        if not candidate.sample_contexts
-    ]
-    if len(values) != len(candidates):
-        msg = "bulk_upsert_unjudged_candidates only accepts freshly extracted candidates without sample contexts"
-        raise ValueError(msg)
-    skip_update = {"book_id", "phrase_norm", "llm_judged"}
-    for chunk_start in range(0, len(values), BULK_CANDIDATE_UPSERT_CHUNK):
-        chunk = values[chunk_start : chunk_start + BULK_CANDIDATE_UPSERT_CHUNK]
-        insert_statement = dialect_insert(session, EbookCandidatePhrase).values(chunk)
-        statement = insert_statement.on_conflict_do_update(
-            index_elements=["book_id", "phrase_norm"],
-            set_={column: insert_statement.excluded[column] for column in chunk[0] if column not in skip_update},
-        )
-        await session.execute(statement)
-    return len(values)
-
-
-def new_candidate_row(book_id: int, series_id: int | None, candidate: PhraseCandidate) -> EbookCandidatePhrase:
-    """Build a fresh unjudged candidate row without checking for an existing one.
-
-    Unlike :func:`save_candidate_to_db`, this does no lookup, so it is only safe when the caller
-    guarantees there is no existing row for ``(book_id, candidate.phrase_norm)`` — for example
-    right after :func:`delete_phrase_data_for_book` has cleared the book.
-
-    Args:
-        book_id (int): Book the candidate belongs to.
-        series_id (int | None): Series scope stored on the row.
-        candidate (PhraseCandidate): Candidate whose fields are written to the row.
-
-    Returns:
-        EbookCandidatePhrase: A new, unattached candidate row.
-    """
-    row = EbookCandidatePhrase(book_id=book_id, phrase_norm=candidate.phrase_norm)
-    row.llm_judged = False
-    row.series_id = series_id
-    row.phrase_text = candidate.phrase_text
-    row.token_count = candidate.token_count
-    row.source_raw_ngram = candidate.source_raw_ngram
-    row.source_yake = candidate.source_yake
-    row.source_capitalized = candidate.source_capitalized
-    row.source_metadata = candidate.source_metadata
-    row.raw_count = candidate.raw_count
-    row.chapter_count = candidate.chapter_count
-    row.yake_score = candidate.yake_score
-    row.candidate_score = candidate.candidate_score
-    if candidate.sample_contexts:
-        row.sample_contexts = list(candidate.sample_contexts)
-    return row
-
-
-async def upsert_protected_phrase(
-    session: AsyncSession,
-    book_id: int,
-    series_id: int | None,
-    candidate: PhraseCandidate,
-    judgment: LLMJudgment,
-    source_candidate: EbookCandidatePhrase,
-) -> EbookProtectedPhrase:
-    """Insert or update one accepted protected phrase and its aliases.
-
-    Args:
-        session (AsyncSession): Active database session.
-        book_id (int): Book the protected phrase belongs to.
-        series_id (int | None): Series scope stored on the phrase.
-        candidate (PhraseCandidate): Candidate the phrase was promoted from.
-        judgment (LLMJudgment): Accepted judgment supplying canonical text, category, and aliases.
-        source_candidate (EbookCandidatePhrase): Candidate row the phrase was promoted from.
-
-    Returns:
-        EbookProtectedPhrase: The inserted or updated protected phrase row.
-
-    Raises:
-        ValueError: If the chosen phrase text normalizes to empty.
-    """
-    phrase_text = judgment.canonical or candidate.phrase_text
-    phrase_norm = normalize_text(phrase_text)
-    if not phrase_norm:
-        msg = f"Protected phrase normalized to empty text: {phrase_text!r}"
-        raise ValueError(msg)
-
-    values = {
-        "book_id": book_id,
-        "phrase_norm": phrase_norm,
-        "series_id": series_id,
-        "phrase_text": phrase_text,
-        "canonical_id": make_canonical_id(judgment, phrase_norm),
-        "phrase_type": judgment.category,
-        "token_count": len(phrase_norm.split()),
-        "confidence": judgment.confidence,
-        "importance": judgment.importance,
-        "allow_nested": judgment.allow_nested,
-        "suppress_children": judgment.suppress_children,
-        "source_candidate_id": source_candidate.id,
-    }
-    insert_statement = dialect_insert(session, EbookProtectedPhrase).values(**values)
-    statement = insert_statement.on_conflict_do_update(
-        index_elements=["book_id", "phrase_norm"],
-        set_={
-            column: insert_statement.excluded[column] for column in values if column not in {"book_id", "phrase_norm"}
-        },
-    ).returning(EbookProtectedPhrase)
-    row = (await session.scalars(statement, execution_options={"populate_existing": True})).one()
-
-    for alias_text in judgment.aliases:
-        await upsert_phrase_alias(session, row, alias_text)
-    return row
-
-
-async def upsert_phrase_alias(
-    session: AsyncSession,
-    phrase: EbookProtectedPhrase,
-    alias_text: str,
-) -> EbookPhraseAlias | None:
-    """Insert or update one protected phrase alias.
-
-    Args:
-        session (AsyncSession): Active database session.
-        phrase (EbookProtectedPhrase): Protected phrase the alias points to.
-        alias_text (str): Alias surface form to store.
-
-    Returns:
-        EbookPhraseAlias | None: The alias row, or ``None`` when the alias is empty or equals the phrase.
-    """
-    alias_norm = normalize_text(alias_text)
-    if not alias_norm or alias_norm == phrase.phrase_norm:
-        return None
-
-    insert_statement = dialect_insert(session, EbookPhraseAlias).values(
-        phrase_id=phrase.id,
-        alias_norm=alias_norm,
-        alias_text=alias_text,
-        confidence=1.0,
-    )
-    statement = insert_statement.on_conflict_do_update(
-        index_elements=["phrase_id", "alias_norm"],
-        set_={
-            "alias_text": insert_statement.excluded.alias_text,
-            "confidence": insert_statement.excluded.confidence,
-        },
-    ).returning(EbookPhraseAlias)
-    return (await session.scalars(statement, execution_options={"populate_existing": True})).one()
-
-
-def make_canonical_id(judgment: LLMJudgment, phrase_norm: str) -> str:
-    """Create a deterministic canonical id from a judgment category and phrase.
-
-    Args:
-        judgment (LLMJudgment): Judgment supplying the phrase category.
-        phrase_norm (str): Normalized phrase text to slugify.
-
-    Returns:
-        str: A ``category:slug`` canonical identifier.
-    """
-    category = slugify_identifier(judgment.category or "phrase")
-    phrase_slug = slugify_identifier(phrase_norm)
-    return f"{category}:{phrase_slug}"
-
-
-def slugify_identifier(value: str) -> str:
-    """Normalize text for use inside a canonical id.
-
-    Args:
-        value (str): Text to slugify.
-
-    Returns:
-        str: A lowercase underscore slug, or ``"unknown"`` when empty.
-    """
-    slug = re.sub(r"[^a-z0-9]+", "_", normalize_text(value).replace("'", ""))
-    return slug.strip("_") or "unknown"
-
-
-async def prune_unstorable_unjudged_candidate_phrases(
-    session: AsyncSession,
-    book_id: int,
-    config: EbookSearchConfig,
-) -> int:
-    """Delete old unjudged candidate rows that no longer satisfy storage filters.
-
-    Args:
-        session (AsyncSession): Active database session.
-        book_id (int): Book whose stale candidates are pruned.
-        config (EbookSearchConfig): Runtime phrase-tuning settings supplying storage thresholds.
-
-    Returns:
-        int: Number of candidate rows deleted.
-    """
-    deleted = rowcount(
-        await session.execute(
-            delete(EbookCandidatePhrase).where(
-                EbookCandidatePhrase.book_id == book_id,
-                EbookCandidatePhrase.llm_judged.is_(False),
-                or_(
-                    EbookCandidatePhrase.token_count < config.phrase_min_tokens,
-                    EbookCandidatePhrase.raw_count < minimum_candidate_raw_count(config),
-                ),
-            )
-        )
-    )
-    if deleted:
-        logger.info(
-            f"ebook_candidate_phrase_unstorable_pruned {book_id=} {deleted=} {config.phrase_min_tokens=} "
-            f"min_uses={minimum_candidate_raw_count(config)}"
-        )
-    return deleted
-
-
-async def delete_phrase_data_for_book(session: AsyncSession, book_id: int) -> PhraseRecalculationResult:
-    """Delete all candidate, protected, alias, and mention phrase data for one book.
-
-    Args:
-        session (AsyncSession): Active database session.
-        book_id (int): Book whose phrase data is deleted.
-
-    Returns:
-        PhraseRecalculationResult: Deleted-row counts with ``candidate_phrases`` set to 0.
-    """
-    protected_ids = (
-        await session.scalars(select(EbookProtectedPhrase.id).where(EbookProtectedPhrase.book_id == book_id))
-    ).all()
-    deleted_aliases = 0
-    if protected_ids:
-        deleted_aliases = rowcount(
-            await session.execute(delete(EbookPhraseAlias).where(EbookPhraseAlias.phrase_id.in_(protected_ids)))
-        )
-
-    deleted_mentions = rowcount(
-        await session.execute(delete(EbookChunkPhraseMention).where(EbookChunkPhraseMention.book_id == book_id))
-    )
-    if protected_ids:
-        deleted_mentions += rowcount(
-            await session.execute(
-                delete(EbookChunkPhraseMention).where(EbookChunkPhraseMention.phrase_id.in_(protected_ids))
-            )
-        )
-
-    deleted_protected = rowcount(
-        await session.execute(delete(EbookProtectedPhrase).where(EbookProtectedPhrase.book_id == book_id))
-    )
-    deleted_candidates = rowcount(
-        await session.execute(delete(EbookCandidatePhrase).where(EbookCandidatePhrase.book_id == book_id))
-    )
-    await session.flush()
-    logger.info(
-        f"ebook_candidate_phrase_data_deleted {book_id=} {deleted_candidates=} {deleted_protected=} {deleted_aliases=} "
-        f"{deleted_mentions=}"
-    )
-    return PhraseRecalculationResult(
-        book_id=book_id,
-        deleted_candidates=deleted_candidates,
-        deleted_protected_phrases=deleted_protected,
-        deleted_aliases=deleted_aliases,
-        deleted_mentions=deleted_mentions,
-        candidate_phrases=0,
-    )
-
-
-def rowcount(result: object) -> int:
-    """Return a safe integer rowcount from a SQLAlchemy execution result.
-
-    Args:
-        result (object): SQLAlchemy execution result that may expose ``rowcount``.
-
-    Returns:
-        int: The result's rowcount, or 0 when it is missing or negative.
-    """
-    count = getattr(result, "rowcount", 0)
-    return int(count if count is not None and count >= 0 else 0)

python/ebook_search/protected_phrases/text_normalization.py

@@ -1,91 +0,0 @@
-"""Protected phrase extraction, storage, and runtime matching."""
-
-from __future__ import annotations
-
-import re
-from dataclasses import dataclass
-
-JSON_OBJECT_RE = re.compile(r"\{.*\}", re.DOTALL)
-
-
-@dataclass(frozen=True, slots=True)
-class NormalizedToken:
-    """A normalized token plus its source character span."""
-
-    text: str
-    start_char: int
-    end_char: int
-
-
-def normalize_text(text: str) -> str:
-    """Normalize text for phrase storage and lookup.
-
-    Args:
-        text (str): Raw text to normalize.
-
-    Returns:
-        str: Normalized tokens joined by single spaces.
-    """
-    return " ".join(token.text for token in tokenize_with_offsets(text))
-
-
-def tokenize(text: str) -> list[str]:
-    """Normalize and split text into phrase-detection tokens.
-
-    Args:
-        text (str): Raw text to tokenize.
-
-    Returns:
-        list[str]: Normalized token strings.
-    """
-    return [token.text for token in tokenize_with_offsets(text)]
-
-
-def tokenize_with_offsets(text: str) -> list[NormalizedToken]:
-    """Normalize text into tokens while preserving original character offsets.
-
-    Args:
-        text (str): Raw text to tokenize.
-
-    Returns:
-        list[NormalizedToken]: Normalized tokens with their source character spans.
-    """
-    tokens: list[NormalizedToken] = []
-    current: list[str] = []
-    start_char: int | None = None
-
-    for index, char in enumerate(text):
-        normalized = normalize_char(char)
-        if normalized == " ":
-            if current and start_char is not None:
-                tokens.append(NormalizedToken(text="".join(current), start_char=start_char, end_char=index))
-                current = []
-                start_char = None
-            continue
-        if start_char is None:
-            start_char = index
-        current.append(normalized)
-
-    if current and start_char is not None:
-        tokens.append(NormalizedToken(text="".join(current), start_char=start_char, end_char=len(text)))
-    return tokens
-
-
-def normalize_char(char: str) -> str:
-    """Normalize one character into a token character or a separator.
-
-    Args:
-        char (str): Single source character to normalize.
-
-    Returns:
-        str: The normalized token character, or a space acting as a separator.
-    """
-    if char in {"\u2019", "\u2018"}:
-        return "'"
-    if char in {"-", "\u2013", "\u2014"}:
-        return " "
-
-    lowered = char.lower()
-    if lowered in "abcdefghijklmnopqrstuvwxyz0123456789'":
-        return lowered
-    return " "

python/ebook_search/rerank.py

@@ -9,8 +9,6 @@ from typing import TYPE_CHECKING
 from python.ebook_search.llm_interface import request_rerank
 
 if TYPE_CHECKING:
-    import httpx
-
     from python.ebook_search.config import RerankConfig
     from python.ebook_search.search import SearchResult
 
@@ -25,18 +23,18 @@ class RerankResult:
     score: float
 
 
-async def rerank_chunks(
-    client: httpx.AsyncClient,
-    query: str,
-    candidates: list[SearchResult],
-    config: RerankConfig,
-) -> list[SearchResult]:
+def rerank_chunks(query: str, candidates: list[SearchResult], config: RerankConfig) -> list[SearchResult]:
     """Rerank candidates with a vLLM rerank endpoint."""
     if not candidates:
         return []
 
-    logger.info(f"ebook_rerank_request_start {config.base_url=} {config.model=} candidates={len(candidates)}")
-    scores = await score_candidates(client, query, candidates, config)
+    logger.info(
+        "ebook_rerank_request_start base_url=%s model=%s candidates=%s",
+        config.base_url,
+        config.model,
+        len(candidates),
+    )
+    scores = score_candidates(query, candidates, config)
     results = sorted(
         (
             replace(
@@ -49,24 +47,28 @@ async def rerank_chunks(
         key=lambda result: result.score,
         reverse=True,
     )
-    logger.info(f"ebook_rerank_request_complete {config.base_url=} {config.model=} candidates={len(results)}")
+    logger.info(
+        "ebook_rerank_request_complete base_url=%s model=%s candidates=%s",
+        config.base_url,
+        config.model,
+        len(results),
+    )
     return results
 
 
-async def score_candidates(
-    client: httpx.AsyncClient,
+def score_candidates(
     query: str,
     candidates: list[SearchResult],
     config: RerankConfig,
 ) -> dict[int, RerankResult]:
     """Score candidate chunks with the configured rerank API."""
-    body = await request_rerank(client, query, [candidate.text for candidate in candidates], config)
+    body = request_rerank(query, [candidate.text for candidate in candidates], config)
     if body is None:
         return zero_rerank_scores(candidates)
 
     scores = parse_vllm_scores(body, candidates)
     for result in scores.values():
-        logger.debug(f"ebook_rerank_candidate_scored {result.chunk_id=} {result.score=}")
+        logger.debug("ebook_rerank_candidate_scored chunk_id=%s score=%s", result.chunk_id, result.score)
     return scores
 
 

python/ebook_search/search.py

@@ -2,31 +2,25 @@
 
 from __future__ import annotations
 
-import asyncio
 import logging
 import re
 from collections import defaultdict
+from concurrent.futures import ThreadPoolExecutor
 from dataclasses import dataclass, replace
 from typing import TYPE_CHECKING
 
 from pgvector.sqlalchemy import Vector
 from sqlalchemy import literal, select
-from sqlalchemy.exc import SQLAlchemyError
-from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.orm import Session
 
 from python.ebook_search.bm25_corpus import (
     BM25CorpusUnavailableError,
     load_bm25_corpus,
     score_bm25_corpus,
 )
-from python.ebook_search.chunk_records import CHUNK_RECORD_COLUMNS
 from python.ebook_search.embeddings import MODEL_DIMENSIONS, embed_query, get_embedding_table
-from python.ebook_search.protected_phrases.matching import (
-    detect_protected_phrases_for_query,
-    phrase_hits_for_chunks,
-)
 from python.ebook_search.rerank import rerank_chunks
-from python.ebook_search.timing import RuntimeStep, async_timed_result, timed_result
+from python.ebook_search.timing import RuntimeStep, timed_result
 from python.orm.richie import (
     EbookChapter,
     EbookChunk,
@@ -35,13 +29,11 @@ from python.orm.richie import (
 )
 
 if TYPE_CHECKING:
-    from collections.abc import Mapping, Sequence
+    from collections.abc import Mapping
 
-    import httpx
-    from sqlalchemy.ext.asyncio import AsyncEngine
+    from sqlalchemy.engine import Engine
 
     from python.ebook_search.config import EbookSearchConfig
-    from python.ebook_search.protected_phrases.models import PhraseMatch
 
 logger = logging.getLogger(__name__)
 
@@ -53,14 +45,11 @@ class SearchResult:
     chunk_id: int
     text: str
     source_title: str
-    source_id: int | None = None
     score: float = 0.0
     vector_score: float | None = None
     bm25_score: float | None = None
     fused_score: float | None = None
     rerank_score: float | None = None
-    phrase_hit_count: int = 0
-    matched_phrases: tuple[str, ...] = ()
     source_author: str | None = None
     chapter_title: str | None = None
     page_label: str | None = None
@@ -75,7 +64,6 @@ class SearchResponse:
     results: list[SearchResult]
     rank_label: str
     timings: tuple[RuntimeStep, ...] = ()
-    phrase_matches: tuple[PhraseMatch, ...] = ()
 
     @property
     def total_runtime_ms(self) -> float:
@@ -85,40 +73,34 @@ class SearchResponse:
 
 @dataclass(frozen=True)
 class RetrievalResponse:
-    """Parallel retrieval output for vector, BM25, and protected phrase candidates."""
+    """Parallel retrieval output for vector and BM25 candidates."""
 
     vector_results: list[SearchResult]
     lexical_results: list[SearchResult]
-    phrase_matches: list[PhraseMatch]
     timings: tuple[RuntimeStep, ...]
 
 
-async def search_ebooks(
-    engine: AsyncEngine,
-    client: httpx.AsyncClient,
+def search_ebooks(
+    engine: Engine,
     query: str,
     config: EbookSearchConfig,
     *,
-    rerank: bool,
-    phrase_matching: bool,
+    rerank: bool = False,
 ) -> SearchResponse:
-    """Run hybrid vector/BM25 search and optional reranking.
-
-    Phrase matching only runs when both the request asks for it and
-    ``config.phrase_matching_enabled`` allows it.
-    """
+    """Run hybrid vector/BM25 search and optional reranking."""
     if not query.strip():
         logger.info("ebook_search_empty_query")
         return SearchResponse(query=query, results=[], rank_label="Hybrid")
 
-    phrase_matching = phrase_matching and config.phrase_matching_enabled
-    logger.info(f"ebook_search_start query_length={len(query)} {rerank=} {phrase_matching=}")
+    logger.info("ebook_search_start query_length=%s rerank=%s", len(query), rerank)
     timings: list[RuntimeStep] = []
-    retrieval, timing = await async_timed_result(
+    retrieval, timing = timed_result(
         "Hybrid retrieval",
-        parallel_retrieval(engine, client, query, config, phrase_matching=phrase_matching),
+        parallel_retrieval,
+        engine,
+        query,
+        config,
     )
-    phrase_matches = retrieval.phrase_matches
     timings.extend(retrieval.timings)
     timings.append(timing)
     fused, timing = timed_result(
@@ -129,147 +111,61 @@ async def search_ebooks(
         rank_constant=config.rrf_rank_constant,
     )
     timings.append(timing)
-    if phrase_matching:
-        fused, timing = await async_timed_result(
-            "Phrase mention boost",
-            apply_phrase_mention_boosts(engine, fused, phrase_matches, config.phrase_hit_boost),
-        )
-    else:
-        fused, timing = timed_result("Phrase mention boost skipped", skip_phrase_mention_boosts, fused)
-    timings.append(timing)
     if config.rerank.enabled and rerank:
-        response, timing = await async_timed_result("Rerank", apply_rerank(client, query, fused, config))
+        response, timing = timed_result("Rerank", apply_rerank, query, fused, config)
     else:
         response, timing = timed_result("Rerank skipped", skip_rerank, query, fused, config)
     timings.append(timing)
-    response = replace(response, timings=tuple(timings), phrase_matches=tuple(phrase_matches))
+    response = replace(response, timings=tuple(timings))
     logger.info(
-        f"ebook_search_complete vector_candidates={len(retrieval.vector_results)} "
-        f"lexical_candidates={len(retrieval.lexical_results)} fused_candidates={len(fused)} {phrase_matching=} "
-        f"phrase_matches={len(phrase_matches)} returned={len(response.results)} {response.rank_label=} "
-        f"{response.total_runtime_ms=:.1f}"
+        "ebook_search_complete vector_candidates=%s lexical_candidates=%s "
+        "fused_candidates=%s returned=%s rank_label=%s runtime_ms=%.1f",
+        len(retrieval.vector_results),
+        len(retrieval.lexical_results),
+        len(fused),
+        len(response.results),
+        response.rank_label,
+        response.total_runtime_ms,
     )
     return response
 
 
-async def query_phrase_matches(
-    engine: AsyncEngine,
+def parallel_retrieval(
+    engine: Engine,
     query: str,
     config: EbookSearchConfig,
-    *,
-    phrase_matching: bool,
-) -> list[PhraseMatch]:
-    """Detect protected phrases in a query without making search fail when phrase tables are unavailable."""
-    if not phrase_matching:
-        logger.info("ebook_protected_phrase_detection_skipped")
-        return []
-    try:
-        async with AsyncSession(engine) as session:
-            return await detect_protected_phrases_for_query(session, query, config)
-    except SQLAlchemyError as error:
-        logger.warning(f"ebook_protected_phrase_detection_unavailable {error=}")
-        return []
-
-
-def skip_phrase_mention_boosts(candidates: list[SearchResult]) -> list[SearchResult]:
-    """Return candidates unchanged when phrase matching is disabled."""
-    logger.info(f"ebook_phrase_boost_skipped candidates={len(candidates)}")
-    return candidates
-
-
-async def apply_phrase_mention_boosts(
-    engine: AsyncEngine,
-    candidates: list[SearchResult],
-    phrase_matches: Sequence[PhraseMatch],
-    phrase_hit_boost: float,
-) -> list[SearchResult]:
-    """Boost retrieved chunks that have indexed mentions for detected protected phrases."""
-    phrase_ids = sorted({match.phrase_id for match in phrase_matches})
-    if not candidates or not phrase_ids or phrase_hit_boost <= 0:
-        return candidates
-
-    chunk_ids = [candidate.chunk_id for candidate in candidates]
-    try:
-        async with AsyncSession(engine) as session:
-            phrase_hits = await phrase_hits_for_chunks(session, chunk_ids=chunk_ids, phrase_ids=phrase_ids)
-    except SQLAlchemyError as error:
-        logger.warning(f"ebook_phrase_boost_unavailable {error=}")
-        return candidates
-
-    if not phrase_hits:
-        return candidates
-
-    hit_counts = {
-        chunk_id: sum(hit.mention_count for hit in chunk_hits) for chunk_id, chunk_hits in phrase_hits.items()
-    }
-    boosted = [
-        replace(
-            candidate,
-            score=candidate.score + (hit_counts.get(candidate.chunk_id, 0) * phrase_hit_boost),
-            fused_score=boosted_fused_score(candidate, hit_counts.get(candidate.chunk_id, 0), phrase_hit_boost),
-            phrase_hit_count=hit_counts.get(candidate.chunk_id, 0),
-            matched_phrases=tuple(hit.phrase_text for hit in phrase_hits.get(candidate.chunk_id, ())),
-            rank_source=phrase_rank_source(candidate.rank_source, hit_counts.get(candidate.chunk_id, 0)),
-        )
-        for candidate in candidates
-    ]
-    return sorted(boosted, key=lambda candidate: candidate.score, reverse=True)
-
-
-def boosted_fused_score(candidate: SearchResult, phrase_hit_count: int, phrase_hit_boost: float) -> float | None:
-    """Return a fused score adjusted by phrase hits when a fused score exists."""
-    if candidate.fused_score is None:
-        return None
-    return candidate.fused_score + (phrase_hit_count * phrase_hit_boost)
-
-
-def phrase_rank_source(rank_source: str, phrase_hit_count: int) -> str:
-    """Append phrase evidence to a rank-source label when a chunk was boosted."""
-    if phrase_hit_count <= 0 or "phrases" in rank_source:
-        return rank_source
-    return f"{rank_source} + phrases"
-
-
-async def parallel_retrieval(
-    engine: AsyncEngine,
-    client: httpx.AsyncClient,
-    query: str,
-    config: EbookSearchConfig,
-    *,
-    phrase_matching: bool,
 ) -> RetrievalResponse:
-    """Run vector, BM25, and protected phrase retrieval concurrently with separate database sessions.
-
-    BM25 scoring is pure CPU work over the cached corpus, so it runs in a worker thread
-    instead of on the event loop. Protected phrase detection only depends on the query, so
-    it joins the gather as a third task and returns immediately when phrase matching is disabled.
-    """
-    phrase_timing_name = "Protected phrase detection" if phrase_matching else "Protected phrase detection skipped"
-    (
-        (vector_results, vector_timing),
-        (lexical_results, lexical_timing),
-        (phrase_matches, phrase_timing),
-    ) = await asyncio.gather(
-        async_timed_result("Embedding + vector search", vector_candidates(engine, client, query, config)),
-        async_timed_result("BM25 search", asyncio.to_thread(bm25_candidates, query, config)),
-        async_timed_result(
-            phrase_timing_name,
-            query_phrase_matches(engine, query, config, phrase_matching=phrase_matching),
-        ),
-    )
+    """Run vector and BM25 candidate retrieval concurrently with separate database sessions."""
+    with ThreadPoolExecutor(max_workers=2, thread_name_prefix="ebook-search") as executor:
+        vector_future = executor.submit(
+            timed_result,
+            "Embedding + vector search",
+            vector_candidates,
+            engine,
+            query,
+            config,
+        )
+        bm25_future = executor.submit(
+            timed_result,
+            "BM25 search",
+            bm25_candidates,
+            query,
+            config,
+        )
+        vector_results, vector_timing = vector_future.result()
+        lexical_results, lexical_timing = bm25_future.result()
 
     logger.info(
-        f"ebook_parallel_retrieval_complete vector_candidates={len(vector_results)} "
-        f"lexical_candidates={len(lexical_results)} phrase_matches={len(phrase_matches)}"
+        "ebook_parallel_retrieval_complete vector_candidates=%s lexical_candidates=%s",
+        len(vector_results),
+        len(lexical_results),
     )
     return RetrievalResponse(
         vector_results=vector_results,
         lexical_results=lexical_results,
-        phrase_matches=phrase_matches,
         timings=(
             replace(vector_timing, counts_toward_total=False),
             replace(lexical_timing, counts_toward_total=False),
-            replace(phrase_timing, counts_toward_total=False),
         ),
     )
 
@@ -280,21 +176,21 @@ def skip_rerank(
     config: EbookSearchConfig,
 ) -> SearchResponse:
     """Return fused hybrid results without reranking."""
-    logger.info(f"ebook_rerank_skipped candidates={len(candidates)}")
+    logger.info("ebook_rerank_skipped candidates=%s", len(candidates))
     return SearchResponse(query=query, results=candidates[: config.top_k], rank_label="Hybrid")
 
 
-async def apply_rerank(
-    client: httpx.AsyncClient,
+def apply_rerank(
     query: str,
     candidates: list[SearchResult],
     config: EbookSearchConfig,
 ) -> SearchResponse:
     """Rerank already-fused hybrid candidates."""
-    reranked = await rerank_chunks(client, query, candidates[: config.rerank.candidates], config.rerank)
+    reranked = rerank_chunks(query, candidates[: config.rerank.candidates], config.rerank)
     logger.info(
-        f"ebook_rerank_complete input_candidates={min(len(candidates), config.rerank.candidates)} "
-        f"returned={len(reranked)}"
+        "ebook_rerank_complete input_candidates=%s returned=%s",
+        min(len(candidates), config.rerank.candidates),
+        len(reranked),
     )
     return SearchResponse(
         query=query,
@@ -303,17 +199,10 @@ async def apply_rerank(
     )
 
 
-async def vector_candidates(
-    engine: AsyncEngine,
-    client: httpx.AsyncClient,
-    query: str,
-    config: EbookSearchConfig,
-) -> list[SearchResult]:
+def vector_candidates(engine: Engine, query: str, config: EbookSearchConfig) -> list[SearchResult]:
     """Return pgvector cosine candidates for a natural-language query."""
-    async with AsyncSession(engine) as session:
-        model = await session.scalar(
-            select(EbookEmbeddingModel).where(EbookEmbeddingModel.name == config.embedding_model)
-        )
+    with Session(engine) as session:
+        model = session.scalar(select(EbookEmbeddingModel).where(EbookEmbeddingModel.name == config.embedding_model))
         if model is None:
             msg = f"Embedding model is not registered: {config.embedding_model}"
             raise ValueError(msg)
@@ -323,7 +212,7 @@ async def vector_candidates(
             msg = f"Model row dimension {model.dimension} does not match configured dimension {expected_dimension}"
             raise ValueError(msg)
 
-        embedding = await embed_query(client, query, config)
+        embedding = embed_query(query, config)
         limit = max(config.rerank.candidates, config.top_k) * config.vector_candidate_multiplier
         embedding_table = get_embedding_table(model.dimension)
 
@@ -332,7 +221,12 @@ async def vector_candidates(
         score = (literal(1.0) - distance).label("score")
         statement = (
             select(
-                *CHUNK_RECORD_COLUMNS,
+                EbookChunk.id.label("chunk_id"),
+                EbookChunk.text.label("text"),
+                EbookSource.title.label("source_title"),
+                EbookSource.author.label("source_author"),
+                EbookChapter.title.label("chapter_title"),
+                EbookChunk.page_label.label("page_label"),
                 score,
             )
             .select_from(embedding_table)
@@ -343,10 +237,13 @@ async def vector_candidates(
             .order_by(distance)
             .limit(limit)
         )
-        rows = (await session.execute(statement)).mappings()
+        rows = session.execute(statement).mappings()
         results = [search_result_from_row(row) for row in rows]
         logger.info(
-            f"ebook_vector_search_complete {config.embedding_model=} {model.dimension=} candidates={len(results)}"
+            "ebook_vector_search_complete model=%s dimension=%s candidates=%s",
+            config.embedding_model,
+            model.dimension,
+            len(results),
         )
         return results
 
@@ -356,7 +253,7 @@ def bm25_candidates(query: str, config: EbookSearchConfig) -> list[SearchResult]
     try:
         corpus = load_bm25_corpus(config)
     except BM25CorpusUnavailableError as error:
-        logger.warning(f"ebook_bm25_index_unavailable_skipping {error=}")
+        logger.warning("ebook_bm25_index_unavailable_skipping error=%s", error)
         return []
 
     if not corpus.records:
@@ -371,7 +268,12 @@ def bm25_candidates(query: str, config: EbookSearchConfig) -> list[SearchResult]
     ]
 
     max_score = results[0].bm25_score if results else 0.0
-    logger.info(f"ebook_bm25_search_complete corpus={len(corpus.records)} candidates={len(results)} {max_score=:.6f}")
+    logger.info(
+        "ebook_bm25_search_complete corpus=%s candidates=%s max_score=%.6f",
+        len(corpus.records),
+        len(results),
+        max_score,
+    )
     return results
 
 
@@ -415,11 +317,9 @@ def reciprocal_rank_fusion(
 
 def search_result_from_row(row: Mapping[str, object]) -> SearchResult:
     """Convert a database row mapping into a search result."""
-    source_id = row.get("source_id")
     return SearchResult(
         chunk_id=int(row["chunk_id"]),
         text=str(row["text"]),
-        source_id=int(source_id) if source_id is not None else None,
         source_title=str(row["source_title"]),
         source_author=optional_str(row["source_author"]),
         chapter_title=optional_str(row["chapter_title"]),

python/ebook_search/timing.py

@@ -7,7 +7,7 @@ from time import perf_counter
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from collections.abc import Awaitable, Callable
+    from collections.abc import Callable
 
 
 @dataclass(frozen=True)
@@ -34,10 +34,3 @@ def timed_result[T, **P](
     start_seconds = perf_counter()
     result = operation(*args, **kwargs)
     return result, runtime_step_from_start(name, start_seconds)
-
-
-async def async_timed_result[T](name: str, awaitable: Awaitable[T]) -> tuple[T, RuntimeStep]:
-    """Await an operation and return its result plus elapsed runtime."""
-    start_seconds = perf_counter()
-    result = await awaitable
-    return result, runtime_step_from_start(name, start_seconds)

python/fastapi_tools/__init__.py

@@ -1,6 +1,6 @@
 """Reusable FastAPI tools."""
 
-from python.fastapi_tools.db import AsyncDbSession, DbSession, get_async_db, get_db
+from python.fastapi_tools.db import DbSession, get_db
 from python.fastapi_tools.zstd_middleware import ZstdMiddleware
 
-__all__ = ["AsyncDbSession", "DbSession", "ZstdMiddleware", "get_async_db", "get_db"]
+__all__ = ["DbSession", "ZstdMiddleware", "get_db"]

python/fastapi_tools/db.py

@@ -5,11 +5,10 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, Annotated
 
 from fastapi import Depends, Request
-from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy.orm import Session
 
 if TYPE_CHECKING:
-    from collections.abc import AsyncIterator, Iterator
+    from collections.abc import Iterator
 
 
 def get_db(request: Request) -> Iterator[Session]:
@@ -18,15 +17,4 @@ def get_db(request: Request) -> Iterator[Session]:
         yield session
 
 
-async def get_async_db(request: Request) -> AsyncIterator[AsyncSession]:
-    """Get an async database session from app state.
-
-    expire_on_commit=False keeps ORM attributes readable after commit without
-    triggering implicit IO, which would raise under asyncio.
-    """
-    async with AsyncSession(request.app.state.engine, expire_on_commit=False) as session:
-        yield session
-
-
 DbSession = Annotated[Session, Depends(get_db)]
-AsyncDbSession = Annotated[AsyncSession, Depends(get_async_db)]

python/orm/common.py

@@ -7,7 +7,6 @@ from typing import cast
 
 from sqlalchemy import create_engine
 from sqlalchemy.engine import URL, Engine
-from sqlalchemy.ext.asyncio import AsyncEngine, create_async_engine
 
 NAMING_CONVENTION = {
     "ix": "ix_%(table_name)s_%(column_0_name)s",
@@ -32,16 +31,23 @@ def get_connection_info(name: str) -> tuple[str, str, str, str, str | None]:
     return cast("tuple[str, str, str, str, str | None]", (database, host, port, username, password))
 
 
-def build_postgres_url(name: str, *, vector_engine: bool = False) -> tuple[URL, dict[str, str]]:
-    """Build the Postgres connection URL and connect_args from environment variables.
+def get_postgres_engine(
+    *,
+    name: str = "POSTGRES",
+    pool_pre_ping: bool = True,
+    vector_engine: bool = False,
+) -> Engine:
+    """Create a SQLAlchemy engine from environment variables.
 
     Args:
-        name (str): The name of the environment variable prefix.
+        name (str, optional): The name of the environment variable prefix. Defaults to "POSTGRES".
+        pool_pre_ping (bool, optional): Whether to ping the database before each connection. Defaults to True.
+        This fixes the issue of trying to use a conection that has timed out on the database side.
         vector_engine (bool, optional): Whether to use the vector search schema. Defaults to False.
-        This updates the search path to include the vector types and operators.
+        This updates the search path the incldued the vecore types and operators.
 
     Returns:
-        tuple[URL, dict[str, str]]: The SQLAlchemy URL and connect_args for create_engine.
+        Engine: The SQLAlchemy engine.
     """
     database, host, port, username, password = get_connection_info(name)
 
@@ -59,66 +65,9 @@ def build_postgres_url(name: str, *, vector_engine: bool = False) -> tuple[URL,
     if vector_engine:
         connect_args["options"] = "-csearch_path=main,public"
 
-    return url, connect_args
-
-
-def get_postgres_engine(
-    *,
-    name: str = "POSTGRES",
-    pool_pre_ping: bool = True,
-    vector_engine: bool = False,
-    pool_size: int = 8,
-) -> Engine:
-    """Create a SQLAlchemy engine from environment variables.
-
-    Args:
-        name (str, optional): The name of the environment variable prefix. Defaults to "POSTGRES".
-        pool_pre_ping (bool, optional): Whether to ping the database before each connection. Defaults to True.
-        This fixes the issue of trying to use a conection that has timed out on the database side.
-        vector_engine (bool, optional): Whether to use the vector search schema. Defaults to False.
-        This updates the search path the incldued the vecore types and operators.
-        pool_size (int, optional): Number of connections to keep in the pool. Defaults to 8.
-
-    Returns:
-        Engine: The SQLAlchemy engine.
-    """
-    url, connect_args = build_postgres_url(name, vector_engine=vector_engine)
-
     return create_engine(
         url=url,
         pool_pre_ping=pool_pre_ping,
         pool_recycle=1800,
         connect_args=connect_args,
-        pool_size=pool_size,
-    )
-
-
-def get_async_postgres_engine(
-    *,
-    name: str = "POSTGRES",
-    pool_pre_ping: bool = True,
-    vector_engine: bool = False,
-    pool_size: int = 8,
-) -> AsyncEngine:
-    """Create an async SQLAlchemy engine from environment variables.
-
-    Args:
-        name (str, optional): The name of the environment variable prefix. Defaults to "POSTGRES".
-        pool_pre_ping (bool, optional): Whether to ping the database before each connection. Defaults to True.
-        This fixes the issue of trying to use a conection that has timed out on the database side.
-        vector_engine (bool, optional): Whether to use the vector search schema. Defaults to False.
-        This updates the search path the incldued the vecore types and operators.
-        pool_size (int, optional): Number of connections to keep in the pool. Defaults to 8.
-
-    Returns:
-        AsyncEngine: The async SQLAlchemy engine.
-    """
-    url, connect_args = build_postgres_url(name, vector_engine=vector_engine)
-
-    return create_async_engine(
-        url=url,
-        pool_pre_ping=pool_pre_ping,
-        pool_recycle=1800,
-        connect_args=connect_args,
-        pool_size=pool_size,
     )

python/orm/richie/__init__.py

@@ -12,16 +12,12 @@ from python.orm.richie.contact import (
     RelationshipType,
 )
 from python.orm.richie.ebook import (
-    EbookCandidatePhrase,
     EbookChapter,
     EbookChunk,
     EbookChunkEmbedding1024,
     EbookChunkEmbedding2560,
     EbookChunkEmbedding4096,
-    EbookChunkPhraseMention,
     EbookEmbeddingModel,
-    EbookPhraseAlias,
-    EbookProtectedPhrase,
     EbookSource,
 )
 
@@ -32,16 +28,12 @@ __all__ = [
     "Contact",
     "ContactNeed",
     "ContactRelationship",
-    "EbookCandidatePhrase",
     "EbookChapter",
     "EbookChunk",
     "EbookChunkEmbedding1024",
     "EbookChunkEmbedding2560",
     "EbookChunkEmbedding4096",
-    "EbookChunkPhraseMention",
     "EbookEmbeddingModel",
-    "EbookPhraseAlias",
-    "EbookProtectedPhrase",
     "EbookSource",
     "Need",
     "RelationshipType",

python/orm/richie/ebook.py

@@ -5,23 +5,11 @@ from __future__ import annotations
 from datetime import datetime
 
 from pgvector.sqlalchemy import Vector
-from sqlalchemy import (
-    JSON,
-    BigInteger,
-    DateTime,
-    ForeignKey,
-    Index,
-    String,
-    Text,
-    UniqueConstraint,
-)
-from sqlalchemy.dialects.postgresql import JSONB
+from sqlalchemy import BigInteger, Boolean, DateTime, ForeignKey, Index, String, UniqueConstraint
 from sqlalchemy.orm import Mapped, mapped_column, relationship
 
 from python.orm.richie.base import TableBase, TableBaseBig
 
-JSON_DOCUMENT = JSON().with_variant(JSONB, "postgresql")
-
 
 class EbookSource(TableBase):
     """One indexed EPUB file."""
@@ -106,7 +94,7 @@ class EbookEmbeddingModel(TableBase):
 
     name: Mapped[str] = mapped_column(String, unique=True)
     dimension: Mapped[int]
-    is_default: Mapped[bool] = mapped_column(default=False)
+    is_default: Mapped[bool] = mapped_column(Boolean, default=False)
 
 
 class EbookChunkEmbedding1024(TableBaseBig):
@@ -148,94 +136,3 @@ class EbookChunkEmbedding4096(TableBaseBig):
     chunk_id: Mapped[int] = mapped_column(ForeignKey("main.ebook_chunk.id", ondelete="CASCADE"))
     model_id: Mapped[int] = mapped_column(ForeignKey("main.ebook_embedding_model.id", ondelete="CASCADE"))
     embedding: Mapped[list[float]] = mapped_column(Vector(4096))
-
-
-class EbookCandidatePhrase(TableBase):
-    """A high-recall phrase candidate extracted from one book."""
-
-    __tablename__ = "candidate_phrases"
-    __table_args__ = (
-        UniqueConstraint("book_id", "phrase_norm", name="uq_candidate_phrases_book_id_phrase_norm"),
-        Index("candidate_phrases_book_score_idx", "book_id", "candidate_score"),
-        Index("candidate_phrases_book_norm_idx", "book_id", "phrase_norm"),
-    )
-
-    book_id: Mapped[int] = mapped_column(ForeignKey("main.ebook_source.id", ondelete="CASCADE"))
-    series_id: Mapped[int | None]
-    phrase_text: Mapped[str] = mapped_column(Text)
-    phrase_norm: Mapped[str] = mapped_column(Text)
-    token_count: Mapped[int]
-    source_raw_ngram: Mapped[bool] = mapped_column(default=False)
-    source_yake: Mapped[bool] = mapped_column(default=False)
-    source_capitalized: Mapped[bool] = mapped_column(default=False)
-    source_metadata: Mapped[bool] = mapped_column(default=False)
-    raw_count: Mapped[int] = mapped_column(default=0)
-    chapter_count: Mapped[int] = mapped_column(default=0)
-    yake_score: Mapped[float | None]
-    candidate_score: Mapped[float] = mapped_column(default=0.0)
-    sample_contexts: Mapped[list[str] | None] = mapped_column(JSON_DOCUMENT)
-    llm_judged: Mapped[bool] = mapped_column(default=False)
-    llm_keep: Mapped[bool | None]
-    llm_confidence: Mapped[float | None]
-    llm_category: Mapped[str | None]
-    llm_reason: Mapped[str | None] = mapped_column(Text)
-
-
-class EbookProtectedPhrase(TableBase):
-    """A phrase accepted by the LLM judge for protected query matching."""
-
-    __tablename__ = "protected_phrases"
-    __table_args__ = (
-        UniqueConstraint("book_id", "phrase_norm", name="uq_protected_phrases_book_id_phrase_norm"),
-        Index("protected_phrases_norm_idx", "phrase_norm"),
-        Index("protected_phrases_book_norm_idx", "book_id", "phrase_norm"),
-        Index("protected_phrases_series_norm_idx", "series_id", "phrase_norm"),
-    )
-
-    book_id: Mapped[int | None] = mapped_column(ForeignKey("main.ebook_source.id", ondelete="CASCADE"))
-    series_id: Mapped[int | None]
-    phrase_text: Mapped[str] = mapped_column(Text)
-    phrase_norm: Mapped[str] = mapped_column(Text)
-    canonical_id: Mapped[str]
-    phrase_type: Mapped[str | None]
-    token_count: Mapped[int]
-    confidence: Mapped[float]
-    importance: Mapped[float] = mapped_column(default=0.5)
-    allow_nested: Mapped[bool] = mapped_column(default=False)
-    suppress_children: Mapped[bool] = mapped_column(default=True)
-    source_candidate_id: Mapped[int | None] = mapped_column(
-        ForeignKey("main.candidate_phrases.id", ondelete="SET NULL")
-    )
-
-
-class EbookPhraseAlias(TableBase):
-    """A normalized alias that maps to a protected phrase."""
-
-    __tablename__ = "phrase_aliases"
-    __table_args__ = (
-        UniqueConstraint("phrase_id", "alias_norm", name="uq_phrase_aliases_phrase_id_alias_norm"),
-        Index("phrase_aliases_norm_idx", "alias_norm"),
-    )
-
-    phrase_id: Mapped[int] = mapped_column(ForeignKey("main.protected_phrases.id", ondelete="CASCADE"))
-    alias_text: Mapped[str] = mapped_column(Text)
-    alias_norm: Mapped[str] = mapped_column(Text)
-    confidence: Mapped[float] = mapped_column(default=1.0)
-
-
-class EbookChunkPhraseMention(TableBase):
-    """A precomputed occurrence of a protected phrase inside one chunk."""
-
-    __tablename__ = "chunk_phrase_mentions"
-    __table_args__ = (
-        UniqueConstraint("chunk_id", "phrase_id", "start_char", name="uq_chunk_phrase_mentions_chunk_phrase_start"),
-        Index("chunk_phrase_mentions_phrase_idx", "phrase_id"),
-        Index("chunk_phrase_mentions_chunk_idx", "chunk_id"),
-    )
-
-    chunk_id: Mapped[int] = mapped_column(ForeignKey("main.ebook_chunk.id", ondelete="CASCADE"))
-    phrase_id: Mapped[int] = mapped_column(ForeignKey("main.protected_phrases.id", ondelete="CASCADE"))
-    book_id: Mapped[int | None] = mapped_column(ForeignKey("main.ebook_source.id", ondelete="CASCADE"))
-    series_id: Mapped[int | None]
-    start_char: Mapped[int]
-    end_char: Mapped[int | None]

tests/ebook_search/test_core.py

@@ -10,8 +10,8 @@ from types import ModuleType
 from typing import TYPE_CHECKING
 
 import pytest
-from sqlalchemy import select
-from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession, create_async_engine
+from sqlalchemy import create_engine, select
+from sqlalchemy.orm import sessionmaker
 
 from python.ebook_search.answer import answer_query
 from python.ebook_search.bm25_corpus import (
@@ -25,7 +25,7 @@ from python.ebook_search.bm25_corpus import (
     score_bm25_corpus,
     write_bm25_corpus,
 )
-from python.ebook_search.config import EbookSearchConfig, RerankConfig, load_config
+from python.ebook_search.config import EbookSearchConfig, RerankConfig, load_config, normalize_embedding_model
 from python.ebook_search.embeddings import MODEL_DIMENSIONS, ensure_embedding_models
 from python.ebook_search.ingest import chunk_text, find_existing_source
 from python.ebook_search.search import (
@@ -77,17 +77,10 @@ def test_reciprocal_rank_fusion_combines_vector_and_bm25_rankings() -> None:
     assert fused[0].fused_score == fused[0].score
 
 
-async def build_async_engine() -> AsyncEngine:
-    """Create an in-memory async engine with the Richie schema."""
-    engine = create_async_engine("sqlite+aiosqlite:///:memory:")
-    async with engine.begin() as connection:
-        await connection.run_sync(RichieBase.metadata.create_all)
-    return engine
-
-
-async def test_find_existing_source_matches_path_or_hash() -> None:
-    engine = await build_async_engine()
-    async with AsyncSession(engine, expire_on_commit=False) as session:
+def test_find_existing_source_matches_path_or_hash() -> None:
+    engine = create_engine("sqlite+pysqlite:///:memory:", future=True)
+    RichieBase.metadata.create_all(engine)
+    with sessionmaker(bind=engine, expire_on_commit=False, future=True)() as session:
         source = EbookSource(
             title="Book",
             author=None,
@@ -100,15 +93,16 @@ async def test_find_existing_source_matches_path_or_hash() -> None:
             file_size=10,
         )
         session.add(source)
-        await session.commit()
+        session.commit()
 
-        assert await find_existing_source(session, Path("/old/book.epub"), "b" * 64) == source
-        assert await find_existing_source(session, Path("/new/book.epub"), "a" * 64) == source
+        assert find_existing_source(session, Path("/old/book.epub"), "b" * 64) == source
+        assert find_existing_source(session, Path("/new/book.epub"), "a" * 64) == source
 
 
-async def test_bm25_corpus_uses_existing_search_text_without_duplicate_metadata() -> None:
-    engine = await build_async_engine()
-    async with AsyncSession(engine, expire_on_commit=False) as session:
+def test_bm25_corpus_uses_existing_search_text_without_duplicate_metadata() -> None:
+    engine = create_engine("sqlite+pysqlite:///:memory:", future=True)
+    RichieBase.metadata.create_all(engine)
+    with sessionmaker(bind=engine, expire_on_commit=False, future=True)() as session:
         source = EbookSource(
             title="Book",
             author="Author",
@@ -121,10 +115,10 @@ async def test_bm25_corpus_uses_existing_search_text_without_duplicate_metadata(
             file_size=10,
         )
         session.add(source)
-        await session.flush()
+        session.flush()
         chapter = EbookChapter(source_id=source.id, spine_index=0, title="Chapter", href=None)
         session.add(chapter)
-        await session.flush()
+        session.flush()
         session.add(
             EbookChunk(
                 id=1,
@@ -139,9 +133,9 @@ async def test_bm25_corpus_uses_existing_search_text_without_duplicate_metadata(
                 search_text="Book Author Chapter content",
             )
         )
-        await session.commit()
+        session.commit()
 
-        records, texts = await fetch_bm25_corpus_records(session)
+        records, texts = fetch_bm25_corpus_records(session)
 
     assert texts == ["Book Author Chapter content"]
     assert records[0]["chunk_id"] == 1
@@ -376,7 +370,7 @@ def test_load_bm25_corpus_raises_when_index_is_missing(mocker: MockerFixture, tm
         load_bm25_corpus.cache_clear()
 
 
-async def test_ensure_bm25_corpus_refreshes_missing_index(mocker: MockerFixture) -> None:
+def test_ensure_bm25_corpus_refreshes_missing_index(mocker: MockerFixture) -> None:
     refreshed: list[object] = []
     db_updated_at = datetime.now(tz=UTC)
 
@@ -391,12 +385,12 @@ async def test_ensure_bm25_corpus_refreshes_missing_index(mocker: MockerFixture)
     config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
     session = object()
 
-    await ensure_bm25_corpus(session, config)
+    ensure_bm25_corpus(session, config)
 
     assert refreshed == [(session, config, db_updated_at)]
 
 
-async def test_ensure_bm25_corpus_refreshes_stale_index(mocker: MockerFixture) -> None:
+def test_ensure_bm25_corpus_refreshes_stale_index(mocker: MockerFixture) -> None:
     refreshed: list[object] = []
     created_at = datetime(2026, 1, 1, tzinfo=UTC)
     db_updated_at = datetime(2026, 1, 2, tzinfo=UTC)
@@ -413,7 +407,7 @@ async def test_ensure_bm25_corpus_refreshes_stale_index(mocker: MockerFixture) -
     config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
     session = object()
 
-    await ensure_bm25_corpus(session, config)
+    ensure_bm25_corpus(session, config)
 
     assert refreshed == [(session, config, db_updated_at)]
 
@@ -426,13 +420,14 @@ def test_supported_embedding_models_match_service_names() -> None:
     }
 
 
-async def test_ensure_embedding_models_registers_service_names() -> None:
-    engine = await build_async_engine()
-    async with AsyncSession(engine, expire_on_commit=False) as session:
-        await ensure_embedding_models(session)
-        await session.commit()
+def test_ensure_embedding_models_registers_service_names() -> None:
+    engine = create_engine("sqlite+pysqlite:///:memory:", future=True)
+    RichieBase.metadata.create_all(engine)
+    with sessionmaker(bind=engine, expire_on_commit=False, future=True)() as session:
+        ensure_embedding_models(session)
+        session.commit()
 
-        models = list(await session.scalars(select(EbookEmbeddingModel).order_by(EbookEmbeddingModel.name)))
+        models = list(session.scalars(select(EbookEmbeddingModel).order_by(EbookEmbeddingModel.name)))
 
     assert [(model.name, model.dimension) for model in models] == [
         ("qwen3-embedding-0.6b", 1024),
@@ -452,25 +447,24 @@ def test_1024_embedding_table_has_cosine_hnsw_index() -> None:
 
 def test_embedding_model_aliases_normalize_to_provider_names(mocker: MockerFixture) -> None:
     mocker.patch.dict(environ, {}, clear=False)
-    environ.pop("EBOOK_SEARCH_EMBEDDING_MODEL", None)
 
-    assert load_config().embedding_model == "qwen3-embedding-0.6b"
+    assert normalize_embedding_model() == "qwen3-embedding-0.6b"
 
     environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "qwen3-embedding-0.6b"
-    assert load_config().embedding_model == "qwen3-embedding-0.6b"
+    assert normalize_embedding_model() == "qwen3-embedding-0.6b"
 
     environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "Qwen3-Embedding-0.6B"
-    assert load_config().embedding_model == "qwen3-embedding-0.6b"
+    assert normalize_embedding_model() == "qwen3-embedding-0.6b"
 
     environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "Qwen/Qwen3-Embedding-4B"
 
-    assert load_config().embedding_model == "qwen3-embedding-4b"
+    assert normalize_embedding_model() == "qwen3-embedding-4b"
 
     environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "qwen3-embedding:8b"
-    assert load_config().embedding_model == "qwen3-embedding-8b"
+    assert normalize_embedding_model() == "qwen3-embedding-8b"
 
     environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "qwen3-embedding-8b"
-    assert load_config().embedding_model == "qwen3-embedding-8b"
+    assert normalize_embedding_model() == "qwen3-embedding-8b"
 
 
 def test_answer_generation_is_enabled_by_default(mocker: MockerFixture) -> None:
@@ -502,10 +496,10 @@ def test_chat_api_key_falls_back_to_ollama_api_key(mocker: MockerFixture) -> Non
     assert config.vllm_api_key == "ollama-key"
 
 
-async def test_answer_query_does_not_call_model_when_disabled(mocker: MockerFixture) -> None:
+def test_answer_query_does_not_call_model_when_disabled() -> None:
     config = load_config().model_copy(update={"answer_enabled": False})
     result = SearchResult(chunk_id=1, text="source text", source_title="Book")
 
-    answer = await answer_query(mocker.Mock(), "question", [result], config)
+    answer = answer_query("question", [result], config)
 
     assert "Answer generation is disabled" in answer

tests/ebook_search/test_guardrails.py

@@ -5,7 +5,7 @@ from __future__ import annotations
 from typing import TYPE_CHECKING
 
 from fastapi.testclient import TestClient
-from sqlalchemy.ext.asyncio import create_async_engine
+from sqlalchemy import create_engine
 
 from python.ebook_search.api.main import create_app
 from python.ebook_search.config import EbookSearchConfig, RerankConfig
@@ -66,8 +66,8 @@ def test_is_confident_against_threshold() -> None:
 
 def patch_app_runtime(mocker: MockerFixture):
     mocker.patch(
-        "python.ebook_search.api.main.get_async_postgres_engine",
-        side_effect=lambda **_kwargs: create_async_engine("sqlite+aiosqlite:///:memory:"),
+        "python.ebook_search.api.main.get_postgres_engine",
+        side_effect=lambda **_kwargs: create_engine("sqlite+pysqlite:///:memory:", future=True),
     )
     mocker.patch("python.ebook_search.api.main.ensure_bm25_corpus", side_effect=lambda _session, _config: None)
 
@@ -75,12 +75,11 @@ def patch_app_runtime(mocker: MockerFixture):
 def test_low_confidence_skips_answer_generation(mocker: MockerFixture) -> None:
     called = False
 
-    def fake_search_ebooks(_engine, _client, query, _config, *, rerank=False, phrase_matching=False):
+    def fake_search_ebooks(_engine, query, _config, *, rerank=False):
         del rerank
-        del phrase_matching
         return SearchResponse(query=query, rank_label="Hybrid", results=make_results(1, vector_score=0.05))
 
-    def fake_answer_query(_client, _query, _results, _config):
+    def fake_answer_query(_query, _results, _config):
         nonlocal called
         called = True
         return "answer"
@@ -105,15 +104,14 @@ def test_low_confidence_skips_answer_generation(mocker: MockerFixture) -> None:
 
 
 def test_invalid_citation_is_flagged(mocker: MockerFixture) -> None:
-    def fake_search_ebooks(_engine, _client, query, _config, *, rerank=False, phrase_matching=False):
+    def fake_search_ebooks(_engine, query, _config, *, rerank=False):
         del rerank
-        del phrase_matching
         return SearchResponse(query=query, rank_label="Hybrid", results=make_results(2, vector_score=0.9))
 
     mocker.patch("python.ebook_search.api.routes.search.search_ebooks", side_effect=fake_search_ebooks)
     mocker.patch(
         "python.ebook_search.api.routes.search.answer_query",
-        side_effect=lambda _client, _query, _results, _config: "Per the text [9].",
+        side_effect=lambda _query, _results, _config: "Per the text [9].",
     )
     patch_app_runtime(mocker)
     app = create_app()
@@ -128,15 +126,14 @@ def test_invalid_citation_is_flagged(mocker: MockerFixture) -> None:
 
 
 def test_grounded_answer_has_no_warning_badge(mocker: MockerFixture) -> None:
-    def fake_search_ebooks(_engine, _client, query, _config, *, rerank=False, phrase_matching=False):
+    def fake_search_ebooks(_engine, query, _config, *, rerank=False):
         del rerank
-        del phrase_matching
         return SearchResponse(query=query, rank_label="Hybrid", results=make_results(2, vector_score=0.9))
 
     mocker.patch("python.ebook_search.api.routes.search.search_ebooks", side_effect=fake_search_ebooks)
     mocker.patch(
         "python.ebook_search.api.routes.search.answer_query",
-        side_effect=lambda _client, _query, _results, _config: "Grounded in [1] and [2].",
+        side_effect=lambda _query, _results, _config: "Grounded in [1] and [2].",
     )
     patch_app_runtime(mocker)
     app = create_app()

tests/ebook_search/test_health.py

@@ -5,7 +5,7 @@ from __future__ import annotations
 from typing import TYPE_CHECKING
 
 from fastapi.testclient import TestClient
-from sqlalchemy.ext.asyncio import create_async_engine
+from sqlalchemy import create_engine
 
 from python.ebook_search.api.main import create_app
 from python.ebook_search.config import EbookSearchConfig, RerankConfig
@@ -18,18 +18,18 @@ if TYPE_CHECKING:
 
 def fake_get_postgres_engine(**_kwargs):
     """Return an in-memory engine for route tests."""
-    return create_async_engine("sqlite+aiosqlite:///:memory:")
+    return create_engine("sqlite+pysqlite:///:memory:", future=True)
 
 
 def patch_app_runtime(mocker: MockerFixture):
-    mocker.patch("python.ebook_search.api.main.get_async_postgres_engine", side_effect=fake_get_postgres_engine)
+    mocker.patch("python.ebook_search.api.main.get_postgres_engine", side_effect=fake_get_postgres_engine)
     mocker.patch("python.ebook_search.api.main.ensure_bm25_corpus", side_effect=lambda _session, _config: None)
 
 
 def patch_dependencies(mocker: MockerFixture, *, database=True, embedding=True, chat=True, bm25="ok"):
     mocker.patch(f"{HEALTH_MODULE}.check_database", side_effect=lambda _session: database)
-    mocker.patch(f"{HEALTH_MODULE}.check_embedding_endpoint", side_effect=lambda _client, _config: embedding)
-    mocker.patch(f"{HEALTH_MODULE}.check_chat_endpoint", side_effect=lambda _client, _config: chat)
+    mocker.patch(f"{HEALTH_MODULE}.check_embedding_endpoint", side_effect=lambda _config: embedding)
+    mocker.patch(f"{HEALTH_MODULE}.check_chat_endpoint", side_effect=lambda _config: chat)
     mocker.patch(f"{HEALTH_MODULE}.check_bm25_status", side_effect=lambda _config: bm25)
 
 

tests/ebook_search/test_http.py

@@ -10,59 +10,13 @@ import pytest
 from python.ebook_search.answer import answer_query
 from python.ebook_search.config import EbookSearchConfig, RerankConfig
 from python.ebook_search.embeddings import embed_texts
-from python.ebook_search.llm_interface import check_chat_endpoint, check_embedding_endpoint
 from python.ebook_search.search import SearchResult
 
 if TYPE_CHECKING:
     from pytest_mock import MockerFixture
 
 
-def make_async_client(mocker: MockerFixture, fake_post) -> httpx.AsyncClient:
-    """Build a mock async client whose post call is served by fake_post."""
-    client = mocker.MagicMock(spec=httpx.AsyncClient)
-    client.post = mocker.AsyncMock(side_effect=fake_post)
-    return client
-
-
-async def test_model_endpoint_checks_share_http_probe(mocker: MockerFixture) -> None:
-    client = mocker.MagicMock(spec=httpx.AsyncClient)
-    response = mocker.MagicMock(spec=httpx.Response)
-    client.get = mocker.AsyncMock(return_value=response)
-    config = EbookSearchConfig(
-        rerank=RerankConfig(enabled=False),
-        embedding_base_url="https://embedding.example/v1/",
-        vllm_base_url="https://chat.example/v1/",
-        vllm_api_key="secret",
-    )
-
-    assert await check_embedding_endpoint(client, config, timeout_seconds=2.0)
-    assert await check_chat_endpoint(client, config, timeout_seconds=3.0)
-    assert client.get.await_args_list == [
-        mocker.call("https://embedding.example/v1/models", headers={}, timeout=2.0),
-        mocker.call(
-            "https://chat.example/v1/models",
-            headers={"Authorization": "Bearer secret"},
-            timeout=3.0,
-        ),
-    ]
-    assert response.raise_for_status.call_count == 2
-
-
-async def test_model_endpoint_checks_report_http_failures(mocker: MockerFixture) -> None:
-    client = mocker.MagicMock(spec=httpx.AsyncClient)
-    client.get = mocker.AsyncMock(
-        side_effect=[
-            httpx.ConnectError("embedding offline"),
-            httpx.ConnectError("chat offline"),
-        ]
-    )
-    config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
-
-    assert not await check_embedding_endpoint(client, config)
-    assert not await check_chat_endpoint(client, config)
-
-
-async def test_answer_query_uses_httpx_chat_completions(mocker: MockerFixture) -> None:
+def test_answer_query_uses_httpx_chat_completions(mocker: MockerFixture) -> None:
     captured: dict[str, object] = {}
 
     def fake_post(url: str, **kwargs: object) -> httpx.Response:
@@ -74,7 +28,7 @@ async def test_answer_query_uses_httpx_chat_completions(mocker: MockerFixture) -
             request=httpx.Request("POST", url),
         )
 
-    client = make_async_client(mocker, fake_post)
+    mocker.patch.object(httpx, "post", side_effect=fake_post)
     config = EbookSearchConfig(
         rerank=RerankConfig(enabled=False),
         vllm_base_url="https://ollama.com/v1",
@@ -82,8 +36,7 @@ async def test_answer_query_uses_httpx_chat_completions(mocker: MockerFixture) -
         chat_model="deepseek-v4-flash",
     )
 
-    results = [SearchResult(chunk_id=1, text="source", source_title="Book")]
-    answer = await answer_query(client, "question", results, config)
+    answer = answer_query("question", [SearchResult(chunk_id=1, text="source", source_title="Book")], config)
 
     assert answer == "grounded answer"
     assert captured["url"] == "https://ollama.com/v1/chat/completions"
@@ -95,7 +48,7 @@ async def test_answer_query_uses_httpx_chat_completions(mocker: MockerFixture) -
     assert payload["model"] == "deepseek-v4-flash"
 
 
-async def test_embed_texts_uses_httpx_embeddings(mocker: MockerFixture) -> None:
+def test_embed_texts_uses_httpx_embeddings(mocker: MockerFixture) -> None:
     captured: dict[str, object] = {}
     vector = [0.0] * 1024
 
@@ -108,14 +61,14 @@ async def test_embed_texts_uses_httpx_embeddings(mocker: MockerFixture) -> None:
             request=httpx.Request("POST", url),
         )
 
-    client = make_async_client(mocker, fake_post)
+    mocker.patch.object(httpx, "post", side_effect=fake_post)
     config = EbookSearchConfig(
         rerank=RerankConfig(enabled=False),
         embedding_base_url="http://bob:8000/v1",
         embedding_model="qwen3-embedding-0.6b",
     )
 
-    embeddings = await embed_texts(client, ["hello"], config)
+    embeddings = embed_texts(["hello"], config)
 
     assert embeddings == [vector]
     assert captured["url"] == "http://bob:8000/v1/embeddings"
@@ -125,12 +78,12 @@ async def test_embed_texts_uses_httpx_embeddings(mocker: MockerFixture) -> None:
     assert kwargs["json"] == {"model": "qwen3-embedding-0.6b", "input": ["hello"]}
 
 
-async def test_embed_texts_rejects_bad_response_shape(mocker: MockerFixture) -> None:
+def test_embed_texts_rejects_bad_response_shape(mocker: MockerFixture) -> None:
     def fake_post(url: str, **_kwargs: object) -> httpx.Response:
         return httpx.Response(200, json={"data": [{}]}, request=httpx.Request("POST", url))
 
-    client = make_async_client(mocker, fake_post)
+    mocker.patch.object(httpx, "post", side_effect=fake_post)
     config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
 
     with pytest.raises(RuntimeError, match="Embedding request failed"):
-        await embed_texts(client, ["hello"], config)
+        embed_texts(["hello"], config)

tests/ebook_search/test_rag_pipeline.py

@@ -2,11 +2,10 @@
 
 from __future__ import annotations
 
-import asyncio
 from threading import Event
 from typing import TYPE_CHECKING
 
-from sqlalchemy.ext.asyncio import create_async_engine
+from sqlalchemy import create_engine
 
 from python.ebook_search.config import EbookSearchConfig, RerankConfig
 from python.ebook_search.search import SearchResult, search_ebooks
@@ -15,18 +14,18 @@ if TYPE_CHECKING:
     from pytest_mock import MockerFixture
 
 
-async def test_search_ebooks_runs_vector_and_bm25_in_parallel(mocker: MockerFixture) -> None:
-    engine = create_async_engine("sqlite+aiosqlite:///:memory:")
+def test_search_ebooks_runs_vector_and_bm25_in_parallel(mocker: MockerFixture) -> None:
+    engine = create_engine("sqlite+pysqlite:///:memory:", future=True)
     vector_started = Event()
     bm25_started = Event()
     received_engines: list[object] = []
 
-    async def fake_vector_candidates(received_engine, _client, query, _config):
+    def fake_vector_candidates(received_engine, query, _config):
         """Return vector candidates after confirming BM25 has started."""
         received_engines.append(received_engine)
         assert query == "what is parallel"
         vector_started.set()
-        assert await asyncio.to_thread(bm25_started.wait, 2)
+        assert bm25_started.wait(timeout=2)
         return [SearchResult(chunk_id=1, text="vector", source_title="Vector", vector_score=0.9)]
 
     def fake_bm25_candidates(query, _config):
@@ -40,9 +39,7 @@ async def test_search_ebooks_runs_vector_and_bm25_in_parallel(mocker: MockerFixt
     mocker.patch("python.ebook_search.search.bm25_candidates", side_effect=fake_bm25_candidates)
     config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
 
-    response = await search_ebooks(
-        engine, mocker.Mock(), "what is parallel", config, rerank=False, phrase_matching=False
-    )
+    response = search_ebooks(engine, "what is parallel", config)
 
     timings = {step.name: step for step in response.timings}
     assert [result.chunk_id for result in response.results] == [1, 2]
@@ -50,84 +47,3 @@ async def test_search_ebooks_runs_vector_and_bm25_in_parallel(mocker: MockerFixt
     assert timings["BM25 search"].counts_toward_total is False
     assert timings["Hybrid retrieval"].counts_toward_total is True
     assert received_engines == [engine]
-
-
-async def test_search_ebooks_runs_phrase_detection_in_parallel_with_retrieval(mocker: MockerFixture) -> None:
-    """Phrase detection joins the retrieval gather instead of running before it."""
-    engine = create_async_engine("sqlite+aiosqlite:///:memory:")
-    phrase_started = Event()
-
-    async def fake_vector_candidates(_engine, _client, _query, _config):
-        """Return vector candidates only once phrase detection has started."""
-        assert await asyncio.to_thread(phrase_started.wait, 2)
-        return [SearchResult(chunk_id=1, text="vector", source_title="Vector", vector_score=0.9)]
-
-    async def fake_query_phrase_matches(_engine, _query, _config, *, phrase_matching):
-        """Record that phrase detection started and return no matches."""
-        assert phrase_matching is True
-        phrase_started.set()
-        return []
-
-    mocker.patch("python.ebook_search.search.vector_candidates", side_effect=fake_vector_candidates)
-    mocker.patch("python.ebook_search.search.bm25_candidates", return_value=[])
-    mocker.patch("python.ebook_search.search.query_phrase_matches", side_effect=fake_query_phrase_matches)
-    config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
-
-    response = await search_ebooks(
-        engine, mocker.Mock(), "what is parallel", config, rerank=False, phrase_matching=True
-    )
-
-    timings = {step.name: step for step in response.timings}
-    assert [result.chunk_id for result in response.results] == [1]
-    assert timings["Protected phrase detection"].counts_toward_total is False
-    assert timings["Hybrid retrieval"].counts_toward_total is True
-
-
-async def test_search_ebooks_skips_phrase_matching_when_disabled(mocker: MockerFixture) -> None:
-    """Phrase matching can be disabled for one search request."""
-    engine = create_async_engine("sqlite+aiosqlite:///:memory:")
-    mocker.patch(
-        "python.ebook_search.search.vector_candidates",
-        return_value=[SearchResult(chunk_id=1, text="vector", source_title="Vector", vector_score=0.9)],
-    )
-    mocker.patch("python.ebook_search.search.bm25_candidates", return_value=[])
-    detect_mock = mocker.patch("python.ebook_search.search.detect_protected_phrases_for_query")
-    boost_mock = mocker.patch("python.ebook_search.search.apply_phrase_mention_boosts")
-    config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
-
-    response = await search_ebooks(
-        engine, mocker.Mock(), "what is parallel", config, rerank=False, phrase_matching=False
-    )
-
-    timing_names = {step.name for step in response.timings}
-    assert [result.chunk_id for result in response.results] == [1]
-    assert response.phrase_matches == ()
-    assert "Protected phrase detection skipped" in timing_names
-    assert "Phrase mention boost skipped" in timing_names
-    detect_mock.assert_not_called()
-    boost_mock.assert_not_called()
-
-
-async def test_search_ebooks_ignores_phrase_matching_when_config_disabled(mocker: MockerFixture) -> None:
-    """The config kill switch overrides a request that asks for phrase matching."""
-    engine = create_async_engine("sqlite+aiosqlite:///:memory:")
-    mocker.patch(
-        "python.ebook_search.search.vector_candidates",
-        return_value=[SearchResult(chunk_id=1, text="vector", source_title="Vector", vector_score=0.9)],
-    )
-    mocker.patch("python.ebook_search.search.bm25_candidates", return_value=[])
-    detect_mock = mocker.patch("python.ebook_search.search.detect_protected_phrases_for_query")
-    boost_mock = mocker.patch("python.ebook_search.search.apply_phrase_mention_boosts")
-    config = EbookSearchConfig(rerank=RerankConfig(enabled=False), phrase_matching_enabled=False)
-
-    response = await search_ebooks(
-        engine, mocker.Mock(), "what is parallel", config, rerank=False, phrase_matching=True
-    )
-
-    timing_names = {step.name for step in response.timings}
-    assert [result.chunk_id for result in response.results] == [1]
-    assert response.phrase_matches == ()
-    assert "Protected phrase detection skipped" in timing_names
-    assert "Phrase mention boost skipped" in timing_names
-    detect_mock.assert_not_called()
-    boost_mock.assert_not_called()

tests/ebook_search/test_rerank.py

@@ -24,13 +24,6 @@ def candidates() -> list[SearchResult]:
     ]
 
 
-def make_async_client(mocker: MockerFixture, fake_post) -> httpx.AsyncClient:
-    """Build a mock async client whose post call is served by fake_post."""
-    client = mocker.MagicMock(spec=httpx.AsyncClient)
-    client.post = mocker.AsyncMock(side_effect=fake_post)
-    return client
-
-
 def rerank_response(payload: dict[str, object] | None = None, *, content: bytes | None = None) -> httpx.Response:
     return httpx.Response(
         200,
@@ -66,7 +59,7 @@ def test_reranking_disabled_returns_original_fused_order() -> None:
     assert [result.chunk_id for result in response.results] == [1, 2]
 
 
-async def test_reranking_enabled_reorders_candidates(mocker: MockerFixture) -> None:
+def test_reranking_enabled_reorders_candidates(mocker: MockerFixture) -> None:
     def fake_post(_url: str, *, json: dict[str, object], timeout: float) -> httpx.Response:
         assert timeout == 30
         assert json == {
@@ -84,16 +77,16 @@ async def test_reranking_enabled_reorders_candidates(mocker: MockerFixture) -> N
             }
         )
 
-    client = make_async_client(mocker, fake_post)
+    mocker.patch.object(httpx, "post", side_effect=fake_post)
 
-    results = await rerank_chunks(client, "query", candidates(), RerankConfig())
+    results = rerank_chunks("query", candidates(), RerankConfig())
 
     assert [result.chunk_id for result in results] == [2, 1, 3]
     assert [round(result.score, 3) for result in results] == [0.78, 0.37, 0.28]
     assert [result.rerank_score for result in results] == [0.9, 0.1, 0.4]
 
 
-async def test_reranking_cannot_ignore_hybrid_score(mocker: MockerFixture) -> None:
+def test_reranking_cannot_ignore_hybrid_score(mocker: MockerFixture) -> None:
     candidates = [
         SearchResult(chunk_id=1, text="strong hybrid", source_title="A", score=1.0),
         SearchResult(chunk_id=2, text="weak hybrid", source_title="B", score=0.1),
@@ -109,9 +102,9 @@ async def test_reranking_cannot_ignore_hybrid_score(mocker: MockerFixture) -> No
             }
         )
 
-    client = make_async_client(mocker, fake_post)
+    mocker.patch.object(httpx, "post", side_effect=fake_post)
 
-    results = await rerank_chunks(client, "query", candidates, RerankConfig())
+    results = rerank_chunks("query", candidates, RerankConfig())
 
     assert [result.chunk_id for result in results] == [1, 2]
     assert results[0].score == pytest.approx(0.79)
@@ -119,9 +112,8 @@ async def test_reranking_cannot_ignore_hybrid_score(mocker: MockerFixture) -> No
     assert results[1].rerank_score == 1.0
 
 
-async def test_vllm_rerank_timeout_raises(mocker: MockerFixture) -> None:
+def test_vllm_rerank_timeout_raises(mocker: MockerFixture) -> None:
     def fake_rerank_chunks(
-        _client: httpx.AsyncClient,
         _query: str,
         _candidates: list[SearchResult],
         _config: RerankConfig,
@@ -133,21 +125,21 @@ async def test_vllm_rerank_timeout_raises(mocker: MockerFixture) -> None:
     config = EbookSearchConfig(rerank=RerankConfig(enabled=True), top_k=2)
 
     with pytest.raises(httpx.TimeoutException, match="timeout"):
-        await apply_rerank(mocker.Mock(), "query", candidates(), config)
+        apply_rerank("query", candidates(), config)
 
 
-async def test_malformed_vllm_rerank_json_does_not_crash_search(mocker: MockerFixture) -> None:
+def test_malformed_vllm_rerank_json_does_not_crash_search(mocker: MockerFixture) -> None:
     def fake_post(_url: str, **_kwargs: object) -> httpx.Response:
         return rerank_response(content=b"not-json")
 
-    client = make_async_client(mocker, fake_post)
+    mocker.patch.object(httpx, "post", side_effect=fake_post)
 
-    results = await rerank_chunks(client, "query", candidates()[:1], RerankConfig())
+    results = rerank_chunks("query", candidates()[:1], RerankConfig())
 
     assert results[0].score == 0.3
 
 
-async def test_vllm_rerank_scores_are_clamped(mocker: MockerFixture) -> None:
+def test_vllm_rerank_scores_are_clamped(mocker: MockerFixture) -> None:
     def fake_post(_url: str, **_kwargs: object) -> httpx.Response:
         return rerank_response(
             {
@@ -158,8 +150,8 @@ async def test_vllm_rerank_scores_are_clamped(mocker: MockerFixture) -> None:
             }
         )
 
-    client = make_async_client(mocker, fake_post)
+    mocker.patch.object(httpx, "post", side_effect=fake_post)
 
-    results = await rerank_chunks(client, "query", candidates()[:2], RerankConfig())
+    results = rerank_chunks("query", candidates()[:2], RerankConfig())
 
     assert {result.chunk_id: result.rerank_score for result in results} == {1: 0.0, 2: 1.0}

tests/ebook_search/test_ui.py

@@ -2,49 +2,32 @@
 
 from __future__ import annotations
 
-import asyncio
 from compression import zstd
-from datetime import UTC, datetime
 from typing import TYPE_CHECKING
 
-from fastapi import BackgroundTasks
 from fastapi.testclient import TestClient
-from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
-from sqlalchemy.pool import StaticPool
+from sqlalchemy import create_engine
 
 from python.ebook_search.api.bm25_tasks import refresh_bm25_for_engine
-from python.ebook_search.api.judge_tasks import (
-    is_judging_book,
-    judge_book_phrases_for_app,
-    pop_book_judgment_outcome,
-    start_book_phrase_judgment,
-)
 from python.ebook_search.api.main import create_app
 from python.ebook_search.config import EbookSearchConfig, RerankConfig
 from python.ebook_search.embeddings import EmbeddingModelStats
-from python.ebook_search.protected_phrases.models import (
-    CorpusPhraseStats,
-    PhraseCandidateGenerationResult,
-    PhraseJudgmentBackfillResult,
-)
 from python.ebook_search.search import SearchResponse, SearchResult
 from python.ebook_search.timing import RuntimeStep
-from python.orm.richie import EbookSource, RichieBase
 
 if TYPE_CHECKING:
     from pytest_mock import MockerFixture
-    from sqlalchemy.ext.asyncio import AsyncEngine
 
 
 def patch_app_runtime(mocker: MockerFixture):
     """Patch app startup dependencies used by UI route tests."""
-    mocker.patch("python.ebook_search.api.main.get_async_postgres_engine", side_effect=fake_get_postgres_engine)
+    mocker.patch("python.ebook_search.api.main.get_postgres_engine", side_effect=fake_get_postgres_engine)
     mocker.patch("python.ebook_search.api.main.ensure_bm25_corpus", side_effect=lambda _session, _config: None)
 
 
 def fake_get_postgres_engine(**_kwargs):
     """Return an in-memory engine for route tests."""
-    return create_async_engine("sqlite+aiosqlite:///:memory:")
+    return create_engine("sqlite+pysqlite:///:memory:", future=True)
 
 
 def test_search_page_uses_zstd_when_requested(mocker: MockerFixture) -> None:
@@ -60,68 +43,36 @@ def test_search_page_uses_zstd_when_requested(mocker: MockerFixture) -> None:
     assert b"EPUB Search" in zstd.decompress(response.content)
 
 
-def test_ui_form_passes_search_toggles_to_search_handler(mocker: MockerFixture) -> None:
+def test_ui_form_passes_rerank_flag_to_search_handler(mocker: MockerFixture) -> None:
     captured: dict[str, object] = {}
 
-    def fake_search_ebooks(_engine, _client, query, config, *, rerank=False, phrase_matching=False):
+    def fake_search_ebooks(_engine, query, config, *, rerank=False):
         captured["query"] = query
         captured["rerank"] = rerank
-        captured["phrase_matching"] = phrase_matching
         captured["config"] = config
         return SearchResponse(query=query, results=[], rank_label="Hybrid + rerank")
 
     mocker.patch("python.ebook_search.api.routes.search.search_ebooks", side_effect=fake_search_ebooks)
     mocker.patch(
         "python.ebook_search.api.routes.search.answer_query",
-        side_effect=lambda _client, _query, _results, _config: "answer",
+        side_effect=lambda _query, _results, _config: "answer",
     )
     patch_app_runtime(mocker)
     app = create_app()
     app.state.config = EbookSearchConfig(rerank=RerankConfig(enabled=False), top_k=12, answer_enabled=True)
 
     with TestClient(app) as client:
-        response = client.post(
-            "/search",
-            data={"query": "where is the quote?", "rerank": "true", "phrase_matching": "true"},
-        )
+        response = client.post("/search", data={"query": "where is the quote?", "rerank": "true"})
 
     assert response.status_code == 200
     assert "Hybrid + rerank" in response.text
     assert captured["query"] == "where is the quote?"
     assert captured["rerank"] is True
-    assert captured["phrase_matching"] is True
-
-
-def test_ui_form_can_disable_phrase_matching(mocker: MockerFixture) -> None:
-    captured: dict[str, object] = {}
-
-    def fake_search_ebooks(_engine, _client, query, _config, *, rerank=False, phrase_matching=False):
-        del rerank
-        captured["query"] = query
-        captured["phrase_matching"] = phrase_matching
-        return SearchResponse(query=query, results=[], rank_label="Hybrid")
-
-    mocker.patch("python.ebook_search.api.routes.search.search_ebooks", side_effect=fake_search_ebooks)
-    mocker.patch(
-        "python.ebook_search.api.routes.search.answer_query",
-        side_effect=lambda _client, _query, _results, _config: "answer",
-    )
-    patch_app_runtime(mocker)
-    app = create_app()
-    app.state.config = EbookSearchConfig(rerank=RerankConfig(enabled=False), top_k=12, answer_enabled=True)
-
-    with TestClient(app) as client:
-        response = client.post("/search", data={"query": "where is the quote?"})
-
-    assert response.status_code == 200
-    assert captured["query"] == "where is the quote?"
-    assert captured["phrase_matching"] is False
 
 
 def test_ui_search_failure_returns_visible_error(mocker: MockerFixture) -> None:
-    def fake_search_ebooks(_engine, _client, _query, _config, *, rerank=False, phrase_matching=False):
+    def fake_search_ebooks(_engine, _query, _config, *, rerank=False):
         del rerank
-        del phrase_matching
         msg = "search exploded"
         raise RuntimeError(msg)
 
@@ -138,12 +89,11 @@ def test_ui_search_failure_returns_visible_error(mocker: MockerFixture) -> None:
 
 
 def test_ui_answer_failure_still_returns_sources(mocker: MockerFixture) -> None:
-    def fake_search_ebooks(_engine, _client, query, _config, *, rerank=False, phrase_matching=False):
+    def fake_search_ebooks(_engine, query, _config, *, rerank=False):
         del rerank
-        del phrase_matching
         return SearchResponse(query=query, results=[], rank_label="Hybrid")
 
-    def fake_answer_query(_client, _query, _results, _config):
+    def fake_answer_query(_query, _results, _config):
         msg = "answer exploded"
         raise RuntimeError(msg)
 
@@ -163,12 +113,11 @@ def test_ui_answer_failure_still_returns_sources(mocker: MockerFixture) -> None:
 def test_ui_skips_answer_when_disabled(mocker: MockerFixture) -> None:
     called = False
 
-    def fake_search_ebooks(_engine, _client, query, _config, *, rerank=False, phrase_matching=False):
+    def fake_search_ebooks(_engine, query, _config, *, rerank=False):
         del rerank
-        del phrase_matching
         return SearchResponse(query=query, results=[], rank_label="Hybrid")
 
-    def fake_answer_query(_client, _query, _results, _config):
+    def fake_answer_query(_query, _results, _config):
         nonlocal called
         called = True
         return "answer"
@@ -189,9 +138,8 @@ def test_ui_skips_answer_when_disabled(mocker: MockerFixture) -> None:
 
 
 def test_ui_shows_component_scores(mocker: MockerFixture) -> None:
-    def fake_search_ebooks(_engine, _client, query, _config, *, rerank=False, phrase_matching=False):
+    def fake_search_ebooks(_engine, query, _config, *, rerank=False):
         del rerank
-        del phrase_matching
         return SearchResponse(
             query=query,
             rank_label="Hybrid + rerank",
@@ -212,7 +160,7 @@ def test_ui_shows_component_scores(mocker: MockerFixture) -> None:
     mocker.patch("python.ebook_search.api.routes.search.search_ebooks", side_effect=fake_search_ebooks)
     mocker.patch(
         "python.ebook_search.api.routes.search.answer_query",
-        side_effect=lambda _client, _query, _results, _config: "answer",
+        side_effect=lambda _query, _results, _config: "answer",
     )
     patch_app_runtime(mocker)
     app = create_app()
@@ -228,47 +176,9 @@ def test_ui_shows_component_scores(mocker: MockerFixture) -> None:
     assert "RRF" in response.text
 
 
-def test_ui_shows_matched_phrases_that_boosted_a_result(mocker: MockerFixture) -> None:
-    def fake_search_ebooks(_engine, _client, query, _config, *, rerank=False, phrase_matching=False):
-        del rerank
-        del phrase_matching
-        return SearchResponse(
-            query=query,
-            rank_label="Hybrid",
-            results=[
-                SearchResult(
-                    chunk_id=1,
-                    text="source text",
-                    source_title="Book",
-                    score=0.9,
-                    phrase_hit_count=3,
-                    matched_phrases=("lock in", "haden's syndrome"),
-                )
-            ],
-        )
-
-    mocker.patch("python.ebook_search.api.routes.search.search_ebooks", side_effect=fake_search_ebooks)
-    mocker.patch(
-        "python.ebook_search.api.routes.search.answer_query",
-        side_effect=lambda _client, _query, _results, _config: "answer",
-    )
-    patch_app_runtime(mocker)
-    app = create_app()
-    app.state.config = EbookSearchConfig(rerank=RerankConfig(enabled=False), answer_enabled=True)
-
-    with TestClient(app) as client:
-        response = client.post("/search", data={"query": "what is lock in?"})
-
-    assert response.status_code == 200
-    assert "boosted by" in response.text
-    assert "lock in" in response.text
-    assert "haden's syndrome" in response.text
-
-
 def test_ui_shows_search_runtime_chart(mocker: MockerFixture) -> None:
-    def fake_search_ebooks(_engine, _client, query, _config, *, rerank=False, phrase_matching=False):
+    def fake_search_ebooks(_engine, query, _config, *, rerank=False):
         del rerank
-        del phrase_matching
         return SearchResponse(
             query=query,
             rank_label="Hybrid",
@@ -282,7 +192,7 @@ def test_ui_shows_search_runtime_chart(mocker: MockerFixture) -> None:
     mocker.patch("python.ebook_search.api.routes.search.search_ebooks", side_effect=fake_search_ebooks)
     mocker.patch(
         "python.ebook_search.api.routes.search.answer_query",
-        side_effect=lambda _client, _query, _results, _config: "answer",
+        side_effect=lambda _query, _results, _config: "answer",
     )
     patch_app_runtime(mocker)
     app = create_app()
@@ -304,7 +214,7 @@ def test_ui_embed_all_batches_until_complete(mocker: MockerFixture) -> None:
     counts = iter([32, 32, 5, 0])
     batch_sizes: list[int] = []
 
-    def fake_embed_missing_chunks(_session, _client, config):
+    def fake_embed_missing_chunks(_session, config):
         batch_sizes.append(config.embedding_batch_size)
         return next(counts)
 
@@ -346,7 +256,7 @@ def test_ui_scan_schedules_bm25_refresh_after_database_change(mocker: MockerFixt
     assert scheduled is True
 
 
-async def test_bm25_refresh_clears_loaded_corpus_cache(mocker: MockerFixture) -> None:
+def test_bm25_refresh_clears_loaded_corpus_cache(mocker: MockerFixture) -> None:
     refreshed: list[object] = []
     cache_cleared = False
 
@@ -359,146 +269,16 @@ async def test_bm25_refresh_clears_loaded_corpus_cache(mocker: MockerFixture) ->
 
     mocker.patch("python.ebook_search.api.bm25_tasks.refresh_bm25_corpus", side_effect=fake_refresh_bm25_corpus)
     mocker.patch("python.ebook_search.api.bm25_tasks.load_bm25_corpus.cache_clear", side_effect=fake_cache_clear)
-    engine = create_async_engine("sqlite+aiosqlite:///:memory:")
+    engine = create_engine("sqlite+pysqlite:///:memory:", future=True)
     config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
 
-    await refresh_bm25_for_engine(engine, config)
+    refresh_bm25_for_engine(engine, config)
 
     assert len(refreshed) == 1
     assert refreshed[0][1] == config
     assert cache_cleared is True
 
 
-def build_engine_with_book() -> AsyncEngine:
-    """Create a shareable in-memory async engine holding one indexed book."""
-    engine = create_async_engine(
-        "sqlite+aiosqlite:///:memory:",
-        connect_args={"check_same_thread": False},
-        poolclass=StaticPool,
-    )
-
-    async def seed() -> None:
-        async with engine.begin() as connection:
-            await connection.run_sync(RichieBase.metadata.create_all)
-        async with AsyncSession(engine) as session:
-            session.add(
-                EbookSource(
-                    title="Book",
-                    author="Author",
-                    language=None,
-                    publisher=None,
-                    identifier=None,
-                    file_path="/library/book.epub",
-                    file_sha256="a" * 64,
-                    file_mtime=datetime.now(tz=UTC),
-                    file_size=10,
-                )
-            )
-            await session.commit()
-
-    asyncio.run(seed())
-    return engine
-
-
-def test_ui_judge_phrases_redirects_and_judges_in_background(mocker: MockerFixture) -> None:
-    mocker.patch("python.ebook_search.api.main.get_async_postgres_engine", return_value=build_engine_with_book())
-    mocker.patch("python.ebook_search.api.main.ensure_bm25_corpus", side_effect=lambda _session, _config: None)
-    judged_source_ids: list[list[int]] = []
-
-    def fake_judge(_engine: object, _config: object, *, source_ids: list[int]) -> PhraseJudgmentBackfillResult:
-        judged_source_ids.append(source_ids)
-        return PhraseJudgmentBackfillResult(
-            books_seen=1,
-            books_judged=1,
-            books_failed=0,
-            candidates_judged=3,
-            protected_phrases=2,
-            phrase_mentions=4,
-        )
-
-    mocker.patch(
-        "python.ebook_search.api.judge_tasks.judge_candidate_phrases_for_books",
-        side_effect=fake_judge,
-    )
-    app = create_app()
-
-    with TestClient(app) as client:
-        response = client.post("/books/1/judge-phrases", follow_redirects=False)
-        detail_after = client.get("/books/1")
-        detail_again = client.get("/books/1")
-
-    assert response.status_code == 303
-    assert response.headers["location"] == "/books/1"
-    assert judged_source_ids == [[1]]
-    assert "Judged 3 candidates; 2 protected phrases promoted" in detail_after.text
-    assert "Judged 3 candidates" not in detail_again.text
-
-
-def test_ui_book_detail_shows_judging_in_progress(mocker: MockerFixture) -> None:
-    mocker.patch("python.ebook_search.api.main.get_async_postgres_engine", return_value=build_engine_with_book())
-    mocker.patch("python.ebook_search.api.main.ensure_bm25_corpus", side_effect=lambda _session, _config: None)
-    mocker.patch("python.ebook_search.api.routes.page.is_judging_book", return_value=True)
-    app = create_app()
-
-    with TestClient(app) as client:
-        response = client.get("/books/1")
-
-    assert response.status_code == 200
-    assert "Judging candidate phrases in the background" in response.text
-    assert "disabled" in response.text
-
-
-def test_book_phrase_judgment_rejects_duplicate_while_queued(mocker: MockerFixture) -> None:
-    mocker.patch(
-        "python.ebook_search.api.judge_tasks.judge_candidate_phrases_for_books",
-        return_value=PhraseJudgmentBackfillResult(
-            books_seen=1,
-            books_judged=1,
-            books_failed=0,
-            candidates_judged=3,
-            protected_phrases=2,
-            phrase_mentions=4,
-        ),
-    )
-    app = create_app()
-    app.state.engine = None
-    app.state.config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
-    background_tasks = BackgroundTasks()
-
-    assert start_book_phrase_judgment(app, background_tasks, 1) is True
-    assert is_judging_book(app, 1) is True
-    assert start_book_phrase_judgment(app, background_tasks, 1) is False
-    assert len(background_tasks.tasks) == 1
-
-    asyncio.run(judge_book_phrases_for_app(app, 1))
-
-    assert is_judging_book(app, 1) is False
-    assert pop_book_judgment_outcome(app, 1) == "Judged 3 candidates; 2 protected phrases promoted"
-    assert pop_book_judgment_outcome(app, 1) is None
-    assert start_book_phrase_judgment(app, background_tasks, 1) is True
-
-
-def test_book_phrase_judgment_records_failure_outcome(mocker: MockerFixture) -> None:
-    def fake_judge(_engine: object, _config: object, *, source_ids: list[int]) -> PhraseJudgmentBackfillResult:
-        del source_ids
-        message = "llm judge unavailable"
-        raise RuntimeError(message)
-
-    mocker.patch(
-        "python.ebook_search.api.judge_tasks.judge_candidate_phrases_for_books",
-        side_effect=fake_judge,
-    )
-    app = create_app()
-    app.state.engine = None
-    app.state.config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
-
-    start_book_phrase_judgment(app, BackgroundTasks(), 7)
-    asyncio.run(judge_book_phrases_for_app(app, 7))
-
-    assert is_judging_book(app, 7) is False
-    assert pop_book_judgment_outcome(app, 7) == "Judging failed; see server logs for details"
-
-
 def test_admin_page_shows_embedding_counts_by_model(mocker: MockerFixture) -> None:
     def fake_embedding_model_stats(_session):
         return [
@@ -517,10 +297,6 @@ def test_admin_page_shows_embedding_counts_by_model(mocker: MockerFixture) -> No
         ]
 
     mocker.patch("python.ebook_search.api.routes.admin.embedding_model_stats", side_effect=fake_embedding_model_stats)
-    mocker.patch(
-        "python.ebook_search.api.routes.admin.corpus_phrase_stats",
-        return_value=fake_corpus_phrase_stats(),
-    )
     patch_app_runtime(mocker)
     app = create_app()
 
@@ -534,102 +310,3 @@ def test_admin_page_shows_embedding_counts_by_model(mocker: MockerFixture) -> No
     assert "24" in response.text
     assert "qwen3-embedding-4b" in response.text
     assert "2560" in response.text
-
-
-def fake_corpus_phrase_stats() -> CorpusPhraseStats:
-    """Build distinctive corpus phrase stats for admin page assertions."""
-    return CorpusPhraseStats(
-        total_books=17,
-        books_with_candidates=13,
-        books_fully_judged=11,
-        candidate_phrases=901,
-        judged_candidates=703,
-        unjudged_candidates=198,
-        protected_phrases=157,
-    )
-
-
-def test_admin_page_shows_protected_phrase_stats(mocker: MockerFixture) -> None:
-    mocker.patch("python.ebook_search.api.routes.admin.embedding_model_stats", return_value=[])
-    mocker.patch(
-        "python.ebook_search.api.routes.admin.corpus_phrase_stats",
-        return_value=fake_corpus_phrase_stats(),
-    )
-    patch_app_runtime(mocker)
-    app = create_app()
-
-    with TestClient(app) as client:
-        response = client.get("/admin")
-
-    assert response.status_code == 200
-    assert "Protected phrases" in response.text
-    for value in ("17", "13", "11", "901", "703", "198", "157"):
-        assert value in response.text
-
-
-def test_ui_regenerate_all_phrases_generates_every_book(mocker: MockerFixture) -> None:
-    def fake_generate(_session, _config):
-        return PhraseCandidateGenerationResult(books_seen=5, books_built=5, candidate_phrases=99)
-
-    mocker.patch(
-        "python.ebook_search.api.routes.admin.generate_candidate_phrases_for_books",
-        side_effect=fake_generate,
-    )
-    patch_app_runtime(mocker)
-    app = create_app()
-
-    with TestClient(app) as client:
-        response = client.post("/admin/phrases/generate-all")
-
-    assert response.status_code == 200
-    assert "5 of 5 books" in response.text
-
-
-def test_ui_judge_missing_phrases_judges_only_pending_books(mocker: MockerFixture) -> None:
-    captured: dict[str, object] = {}
-
-    async def fake_judge(_engine, _config, *, source_ids=None):
-        captured["source_ids"] = source_ids
-        return PhraseJudgmentBackfillResult(
-            books_seen=2,
-            books_judged=2,
-            books_failed=0,
-            candidates_judged=10,
-            protected_phrases=4,
-            phrase_mentions=9,
-        )
-
-    mocker.patch(
-        "python.ebook_search.api.routes.admin.judge_candidate_phrases_for_books",
-        side_effect=fake_judge,
-    )
-    mocker.patch(
-        "python.ebook_search.api.routes.admin.book_ids_pending_first_judgment",
-        return_value=[3, 5],
-    )
-    patch_app_runtime(mocker)
-    app = create_app()
-
-    with TestClient(app) as client:
-        response = client.post("/admin/phrases/judge-missing")
-
-    assert response.status_code == 200
-    assert captured["source_ids"] == [3, 5]
-    assert "4 protected phrases" in response.text
-
-
-def test_ui_judge_missing_phrases_reports_when_nothing_is_pending(mocker: MockerFixture) -> None:
-    judge = mocker.patch("python.ebook_search.api.routes.admin.judge_candidate_phrases_for_books")
-    mocker.patch(
-        "python.ebook_search.api.routes.admin.book_ids_pending_first_judgment",
-        return_value=[],
-    )
-    patch_app_runtime(mocker)
-    app = create_app()
-
-    with TestClient(app) as client:
-        response = client.post("/admin/phrases/judge-missing")
-
-    assert response.status_code == 200
-    assert "have been judged" in response.text
-    judge.assert_not_called()

users/richie/home/gui/vscode/settings.json

@@ -76,7 +76,6 @@
   "cSpell.userWords": [
     "Cahill",
     "Corvidae",
-    "dedup",
     "drivername",
     "fastapi",
     "Michal",