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.

.dockerignore

@@ -3,6 +3,19 @@
 .mypy_cache
 .pytest_cache
 .ruff_cache
+.venv
+**/.venv
+.env
+.cache
+.claude
+.coverage
+.vscode
+.stfolder
+.literotica_data
+esphome
+htmlcov
+data
+ebooks
 __pycache__
 **/__pycache__
 *.pyc

.github/workflows/test_ebook_search.yml

@@ -0,0 +1,26 @@
+name: test ebook search
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+env:
+  UV_PYTHON_DOWNLOADS: never
+  UV_CACHE_DIR: /var/cache/uv
+  UV_LINK_MODE: copy
+
+jobs:
+  test-ebook-search:
+    runs-on: self-hosted
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Install dependencies
+        run: nix develop .#devShells.x86_64-linux.ebook-search -c uv sync --locked --project python/ebook_search/docker
+      - name: Run ebook search tests
+        run: nix develop .#devShells.x86_64-linux.ebook-search -c uv run --project python/ebook_search/docker --no-sync pytest tests/ebook_search --override-ini addopts="-n auto -ra"

.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",

flake.lock

@@ -8,11 +8,11 @@
       },
       "locked": {
         "dir": "pkgs/firefox-addons",
-        "lastModified": 1782964936,
-        "narHash": "sha256-wXEBDr7/dFQYhVpDwCKc9fkrYQQE4x0bdirX1bsLBGA=",
+        "lastModified": 1783828963,
+        "narHash": "sha256-eTytzcUJCaDUZ3/9EF0+V3fvlikQMQBwiX1Sx4Gy+No=",
         "owner": "rycee",
         "repo": "nur-expressions",
-        "rev": "64feee871e0373dd6121e412c3fb12e372d1bfb5",
+        "rev": "8d61e9afde605cd6c22dab68b83d7a71f0a6c5b2",
         "type": "gitlab"
       },
       "original": {
@@ -29,11 +29,11 @@
         ]
       },
       "locked": {
-        "lastModified": 1783005591,
-        "narHash": "sha256-NcLHV5uBAeggDUE2wPbKszjfyaSLsoqaYt7izOphkZw=",
+        "lastModified": 1783823409,
+        "narHash": "sha256-OI4IkRjRXa1e7hYmCGJDPDq5H/kPwhsyoS80cNUF9fI=",
         "owner": "nix-community",
         "repo": "home-manager",
-        "rev": "f469c79b955609d6a8fdd9e689be76a93b1621d7",
+        "rev": "7566825d4652a1b885bd4ce65bd9e8def432fec9",
         "type": "github"
       },
       "original": {
@@ -47,11 +47,11 @@
         "nixpkgs": "nixpkgs"
       },
       "locked": {
-        "lastModified": 1782562157,
-        "narHash": "sha256-a7+T6QSeowynwZ1ZJJbP8T8ntAytvrui8kFGJmIZt2c=",
+        "lastModified": 1783792734,
+        "narHash": "sha256-50rvY9GdFvpYDcMLcD/4cWSi0hVxArT5wsGlVsHy8eY=",
         "owner": "nixos",
         "repo": "nixos-hardware",
-        "rev": "a9cf7546a938c737b079e738de73934a13de9784",
+        "rev": "8efb4337e857949f4cfac86d12ef1066f417f31f",
         "type": "github"
       },
       "original": {
@@ -76,11 +76,11 @@
     },
     "nixpkgs-master": {
       "locked": {
-        "lastModified": 1783021952,
-        "narHash": "sha256-8PghAtSGGZ0umfVI8Qbd7ZbFrfZPiH1UwtVbgLeikDA=",
+        "lastModified": 1783874024,
+        "narHash": "sha256-Fd8rPvyBv6JjcO/nZxZiFQan6Fww/jAF4TYj0Th/Yfo=",
         "owner": "nixos",
         "repo": "nixpkgs",
-        "rev": "f136374c679c54171a3ace589d15e9e79a8bd086",
+        "rev": "0b4f03c64b236e4ba4252414274e92796c300124",
         "type": "github"
       },
       "original": {
@@ -108,11 +108,11 @@
     },
     "nixpkgs_2": {
       "locked": {
-        "lastModified": 1782723713,
-        "narHash": "sha256-oPXCU/SSUokcGaJREHibG1CBX3+s/W7orDWQOZDsEeQ=",
+        "lastModified": 1783776592,
+        "narHash": "sha256-UgCQzxeWI75XM8G+hPrPh+MKzEPjG3SpAj7dtqSbksA=",
         "owner": "nixos",
         "repo": "nixpkgs",
-        "rev": "b5aa0fbd538984f6e3d201be0005b4463d8b09f8",
+        "rev": "e7a3ca8092b61ff85b6a45bf863ea2b2d6a661b3",
         "type": "github"
       },
       "original": {
@@ -141,11 +141,11 @@
         ]
       },
       "locked": {
-        "lastModified": 1782165805,
-        "narHash": "sha256-478kKQBvK6SYTOdN2h9jhKJv94nbXRbFMfuL1WshErg=",
+        "lastModified": 1783174389,
+        "narHash": "sha256-aCWC8ngycU7OdJrU2+Je3qf+1a2ykuBvpPhZT/9tXMc=",
         "owner": "Mic92",
         "repo": "sops-nix",
-        "rev": "56b24064fdcaedca53553b1a6d607fd23b613a24",
+        "rev": "f1406619a3884cd5c47992a70b8b35c9c0fcb4c9",
         "type": "github"
       },
       "original": {

overlays/default.nix

@@ -17,9 +17,7 @@
 
   python-env = final: _prev: {
     my_python = final.python314.withPackages (
-      ps:
-      with ps;
-      [
+      ps: with ps; [
         alembic
         apprise
         apscheduler

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",
@@ -119,7 +117,6 @@ exclude_lines = [
 ]
 
 [tool.pytest.ini_options]
-addopts = "-n auto -ra"
-asyncio_mode = "auto"
+addopts = "-n auto -ra --ignore=tests/ebook_search"
 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/api/__init__.py

@@ -1 +0,0 @@
-"""FastAPI applications."""

python/api/main.py

@@ -1,56 +0,0 @@
-"""FastAPI interface for Contact database."""
-
-from __future__ import annotations
-
-import logging
-from contextlib import asynccontextmanager
-from typing import TYPE_CHECKING, Annotated
-
-import typer
-import uvicorn
-from fastapi import FastAPI
-
-from python.api.routers import contact_router, views_router
-from python.common import configure_logger
-from python.fastapi_tools import ZstdMiddleware
-from python.orm.common import get_postgres_engine
-
-if TYPE_CHECKING:
-    from collections.abc import AsyncIterator
-
-logger = logging.getLogger(__name__)
-
-
-def create_app() -> FastAPI:
-    """Create and configure the FastAPI application."""
-
-    @asynccontextmanager
-    async def lifespan(app: FastAPI) -> AsyncIterator[None]:
-        """Manage application lifespan."""
-        app.state.engine = get_postgres_engine()
-        yield
-        app.state.engine.dispose()
-
-    app = FastAPI(title="Contact Database API", lifespan=lifespan)
-    app.add_middleware(ZstdMiddleware)
-
-    app.include_router(contact_router)
-    app.include_router(views_router)
-
-    return app
-
-
-def serve(
-    host: Annotated[str, typer.Option("--host", "-h", help="Host to bind to")],
-    port: Annotated[int, typer.Option("--port", "-p", help="Port to bind to")] = 8000,
-    log_level: Annotated[str, typer.Option("--log-level", "-l", help="Log level")] = "INFO",
-) -> None:
-    """Start the Contact API server."""
-    configure_logger(log_level)
-
-    app = create_app()
-    uvicorn.run(app, host=host, port=port)
-
-
-if __name__ == "__main__":
-    typer.run(serve)

python/api/routers/__init__.py

@@ -1,6 +0,0 @@
-"""API routers."""
-
-from python.api.routers.contact import router as contact_router
-from python.api.routers.views import router as views_router
-
-__all__ = ["contact_router", "views_router"]

python/api/routers/contact.py

@@ -1,481 +0,0 @@
-"""Contact API router."""
-
-from pathlib import Path
-
-from fastapi import APIRouter, HTTPException, Request
-from fastapi.responses import HTMLResponse
-from fastapi.templating import Jinja2Templates
-from pydantic import BaseModel
-from sqlalchemy import select
-from sqlalchemy.orm import selectinload
-
-from python.fastapi_tools.db import DbSession  # noqa: TC001 this is a FastAPI needed at runtime
-from python.orm.richie.contact import Contact, ContactRelationship, Need, RelationshipType
-
-TEMPLATES_DIR = Path(__file__).parent.parent / "templates"
-templates = Jinja2Templates(directory=TEMPLATES_DIR)
-
-
-def _is_htmx(request: Request) -> bool:
-    """Check if the request is from HTMX."""
-    return request.headers.get("HX-Request") == "true"
-
-
-class NeedBase(BaseModel):
-    """Base schema for Need."""
-
-    name: str
-    description: str | None = None
-
-
-class NeedCreate(NeedBase):
-    """Schema for creating a Need."""
-
-
-class NeedResponse(NeedBase):
-    """Schema for Need response."""
-
-    id: int
-
-    model_config = {"from_attributes": True}
-
-
-class ContactRelationshipCreate(BaseModel):
-    """Schema for creating a contact relationship."""
-
-    related_contact_id: int
-    relationship_type: RelationshipType
-    closeness_weight: int | None = None
-
-
-class ContactRelationshipUpdate(BaseModel):
-    """Schema for updating a contact relationship."""
-
-    relationship_type: RelationshipType | None = None
-    closeness_weight: int | None = None
-
-
-class ContactRelationshipResponse(BaseModel):
-    """Schema for contact relationship response."""
-
-    contact_id: int
-    related_contact_id: int
-    relationship_type: str
-    closeness_weight: int
-
-    model_config = {"from_attributes": True}
-
-
-class RelationshipTypeInfo(BaseModel):
-    """Information about a relationship type."""
-
-    value: str
-    display_name: str
-    default_weight: int
-
-
-class GraphNode(BaseModel):
-    """Node in the relationship graph."""
-
-    id: int
-    name: str
-    current_job: str | None = None
-
-
-class GraphEdge(BaseModel):
-    """Edge in the relationship graph."""
-
-    source: int
-    target: int
-    relationship_type: str
-    closeness_weight: int
-
-
-class GraphData(BaseModel):
-    """Complete graph data for visualization."""
-
-    nodes: list[GraphNode]
-    edges: list[GraphEdge]
-
-
-class ContactBase(BaseModel):
-    """Base schema for Contact."""
-
-    name: str
-    age: int | None = None
-    bio: str | None = None
-    current_job: str | None = None
-    gender: str | None = None
-    goals: str | None = None
-    legal_name: str | None = None
-    profile_pic: str | None = None
-    safe_conversation_starters: str | None = None
-    self_sufficiency_score: int | None = None
-    social_structure_style: str | None = None
-    ssn: str | None = None
-    suffix: str | None = None
-    timezone: str | None = None
-    topics_to_avoid: str | None = None
-
-
-class ContactCreate(ContactBase):
-    """Schema for creating a Contact."""
-
-    need_ids: list[int] = []
-
-
-class ContactUpdate(BaseModel):
-    """Schema for updating a Contact."""
-
-    name: str | None = None
-    age: int | None = None
-    bio: str | None = None
-    current_job: str | None = None
-    gender: str | None = None
-    goals: str | None = None
-    legal_name: str | None = None
-    profile_pic: str | None = None
-    safe_conversation_starters: str | None = None
-    self_sufficiency_score: int | None = None
-    social_structure_style: str | None = None
-    ssn: str | None = None
-    suffix: str | None = None
-    timezone: str | None = None
-    topics_to_avoid: str | None = None
-    need_ids: list[int] | None = None
-
-
-class ContactResponse(ContactBase):
-    """Schema for Contact response with relationships."""
-
-    id: int
-    needs: list[NeedResponse] = []
-    related_to: list[ContactRelationshipResponse] = []
-    related_from: list[ContactRelationshipResponse] = []
-
-    model_config = {"from_attributes": True}
-
-
-class ContactListResponse(ContactBase):
-    """Schema for Contact list response."""
-
-    id: int
-
-    model_config = {"from_attributes": True}
-
-
-router = APIRouter(prefix="/api", tags=["contacts"])
-
-
-@router.post("/needs", response_model=NeedResponse)
-def create_need(need: NeedCreate, db: DbSession) -> Need:
-    """Create a new need."""
-    db_need = Need(name=need.name, description=need.description)
-    db.add(db_need)
-    db.commit()
-    db.refresh(db_need)
-    return db_need
-
-
-@router.get("/needs", response_model=list[NeedResponse])
-def list_needs(db: DbSession) -> list[Need]:
-    """List all needs."""
-    return list(db.scalars(select(Need)).all())
-
-
-@router.get("/needs/{need_id}", response_model=NeedResponse)
-def get_need(need_id: int, db: DbSession) -> Need:
-    """Get a need by ID."""
-    need = db.get(Need, need_id)
-    if not need:
-        raise HTTPException(status_code=404, detail="Need not found")
-    return need
-
-
-@router.delete("/needs/{need_id}", response_model=None)
-def delete_need(need_id: int, request: Request, db: DbSession) -> dict[str, bool] | HTMLResponse:
-    """Delete a need by ID."""
-    need = db.get(Need, need_id)
-    if not need:
-        raise HTTPException(status_code=404, detail="Need not found")
-    db.delete(need)
-    db.commit()
-    if _is_htmx(request):
-        return HTMLResponse("")
-    return {"deleted": True}
-
-
-@router.post("/contacts", response_model=ContactResponse)
-def create_contact(contact: ContactCreate, db: DbSession) -> Contact:
-    """Create a new contact."""
-    need_ids = contact.need_ids
-    contact_data = contact.model_dump(exclude={"need_ids"})
-    db_contact = Contact(**contact_data)
-
-    if need_ids:
-        needs = list(db.scalars(select(Need).where(Need.id.in_(need_ids))).all())
-        db_contact.needs = needs
-
-    db.add(db_contact)
-    db.commit()
-    db.refresh(db_contact)
-    return db_contact
-
-
-@router.get("/contacts", response_model=list[ContactListResponse])
-def list_contacts(
-    db: DbSession,
-    skip: int = 0,
-    limit: int = 100,
-) -> list[Contact]:
-    """List all contacts with pagination."""
-    return list(db.scalars(select(Contact).offset(skip).limit(limit)).all())
-
-
-@router.get("/contacts/{contact_id}", response_model=ContactResponse)
-def get_contact(contact_id: int, db: DbSession) -> Contact:
-    """Get a contact by ID with all relationships."""
-    contact = db.scalar(
-        select(Contact)
-        .where(Contact.id == contact_id)
-        .options(
-            selectinload(Contact.needs),
-            selectinload(Contact.related_to),
-            selectinload(Contact.related_from),
-        )
-    )
-    if not contact:
-        raise HTTPException(status_code=404, detail="Contact not found")
-    return contact
-
-
-@router.patch("/contacts/{contact_id}", response_model=ContactResponse)
-def update_contact(
-    contact_id: int,
-    contact: ContactUpdate,
-    db: DbSession,
-) -> Contact:
-    """Update a contact by ID."""
-    db_contact = db.get(Contact, contact_id)
-    if not db_contact:
-        raise HTTPException(status_code=404, detail="Contact not found")
-
-    update_data = contact.model_dump(exclude_unset=True)
-    need_ids = update_data.pop("need_ids", None)
-
-    for key, value in update_data.items():
-        setattr(db_contact, key, value)
-
-    if need_ids is not None:
-        needs = list(db.scalars(select(Need).where(Need.id.in_(need_ids))).all())
-        db_contact.needs = needs
-
-    db.commit()
-    db.refresh(db_contact)
-    return db_contact
-
-
-@router.delete("/contacts/{contact_id}", response_model=None)
-def delete_contact(contact_id: int, request: Request, db: DbSession) -> dict[str, bool] | HTMLResponse:
-    """Delete a contact by ID."""
-    contact = db.get(Contact, contact_id)
-    if not contact:
-        raise HTTPException(status_code=404, detail="Contact not found")
-    db.delete(contact)
-    db.commit()
-    if _is_htmx(request):
-        return HTMLResponse("")
-    return {"deleted": True}
-
-
-@router.post("/contacts/{contact_id}/needs/{need_id}")
-def add_need_to_contact(
-    contact_id: int,
-    need_id: int,
-    db: DbSession,
-) -> dict[str, bool]:
-    """Add a need to a contact."""
-    contact = db.get(Contact, contact_id)
-    if not contact:
-        raise HTTPException(status_code=404, detail="Contact not found")
-
-    need = db.get(Need, need_id)
-    if not need:
-        raise HTTPException(status_code=404, detail="Need not found")
-
-    if need not in contact.needs:
-        contact.needs.append(need)
-        db.commit()
-
-    return {"added": True}
-
-
-@router.delete("/contacts/{contact_id}/needs/{need_id}", response_model=None)
-def remove_need_from_contact(
-    contact_id: int,
-    need_id: int,
-    request: Request,
-    db: DbSession,
-) -> dict[str, bool] | HTMLResponse:
-    """Remove a need from a contact."""
-    contact = db.get(Contact, contact_id)
-    if not contact:
-        raise HTTPException(status_code=404, detail="Contact not found")
-
-    need = db.get(Need, need_id)
-    if not need:
-        raise HTTPException(status_code=404, detail="Need not found")
-
-    if need in contact.needs:
-        contact.needs.remove(need)
-        db.commit()
-
-    if _is_htmx(request):
-        return HTMLResponse("")
-    return {"removed": True}
-
-
-@router.post(
-    "/contacts/{contact_id}/relationships",
-    response_model=ContactRelationshipResponse,
-)
-def add_contact_relationship(
-    contact_id: int,
-    relationship: ContactRelationshipCreate,
-    db: DbSession,
-) -> ContactRelationship:
-    """Add a relationship between two contacts."""
-    contact = db.get(Contact, contact_id)
-    if not contact:
-        raise HTTPException(status_code=404, detail="Contact not found")
-
-    related_contact = db.get(Contact, relationship.related_contact_id)
-    if not related_contact:
-        raise HTTPException(status_code=404, detail="Related contact not found")
-
-    if contact_id == relationship.related_contact_id:
-        raise HTTPException(status_code=400, detail="Cannot relate contact to itself")
-
-    # Use provided weight or default from relationship type
-    weight = relationship.closeness_weight
-    if weight is None:
-        weight = relationship.relationship_type.default_weight
-
-    db_relationship = ContactRelationship(
-        contact_id=contact_id,
-        related_contact_id=relationship.related_contact_id,
-        relationship_type=relationship.relationship_type.value,
-        closeness_weight=weight,
-    )
-    db.add(db_relationship)
-    db.commit()
-    db.refresh(db_relationship)
-    return db_relationship
-
-
-@router.get(
-    "/contacts/{contact_id}/relationships",
-    response_model=list[ContactRelationshipResponse],
-)
-def get_contact_relationships(
-    contact_id: int,
-    db: DbSession,
-) -> list[ContactRelationship]:
-    """Get all relationships for a contact."""
-    contact = db.get(Contact, contact_id)
-    if not contact:
-        raise HTTPException(status_code=404, detail="Contact not found")
-
-    outgoing = list(db.scalars(select(ContactRelationship).where(ContactRelationship.contact_id == contact_id)).all())
-    incoming = list(
-        db.scalars(select(ContactRelationship).where(ContactRelationship.related_contact_id == contact_id)).all()
-    )
-    return outgoing + incoming
-
-
-@router.patch(
-    "/contacts/{contact_id}/relationships/{related_contact_id}",
-    response_model=ContactRelationshipResponse,
-)
-def update_contact_relationship(
-    contact_id: int,
-    related_contact_id: int,
-    update: ContactRelationshipUpdate,
-    db: DbSession,
-) -> ContactRelationship:
-    """Update a relationship between two contacts."""
-    relationship = db.scalar(
-        select(ContactRelationship).where(
-            ContactRelationship.contact_id == contact_id,
-            ContactRelationship.related_contact_id == related_contact_id,
-        )
-    )
-    if not relationship:
-        raise HTTPException(status_code=404, detail="Relationship not found")
-
-    if update.relationship_type is not None:
-        relationship.relationship_type = update.relationship_type.value
-    if update.closeness_weight is not None:
-        relationship.closeness_weight = update.closeness_weight
-
-    db.commit()
-    db.refresh(relationship)
-    return relationship
-
-
-@router.delete("/contacts/{contact_id}/relationships/{related_contact_id}", response_model=None)
-def remove_contact_relationship(
-    contact_id: int,
-    related_contact_id: int,
-    request: Request,
-    db: DbSession,
-) -> dict[str, bool] | HTMLResponse:
-    """Remove a relationship between two contacts."""
-    relationship = db.scalar(
-        select(ContactRelationship).where(
-            ContactRelationship.contact_id == contact_id,
-            ContactRelationship.related_contact_id == related_contact_id,
-        )
-    )
-    if not relationship:
-        raise HTTPException(status_code=404, detail="Relationship not found")
-
-    db.delete(relationship)
-    db.commit()
-    if _is_htmx(request):
-        return HTMLResponse("")
-    return {"deleted": True}
-
-
-@router.get("/relationship-types")
-def list_relationship_types() -> list[RelationshipTypeInfo]:
-    """List all available relationship types with their default weights."""
-    return [
-        RelationshipTypeInfo(
-            value=rt.value,
-            display_name=rt.display_name,
-            default_weight=rt.default_weight,
-        )
-        for rt in RelationshipType
-    ]
-
-
-@router.get("/graph")
-def get_relationship_graph(db: DbSession) -> GraphData:
-    """Get all contacts and relationships as graph data for visualization."""
-    contacts = list(db.scalars(select(Contact)).all())
-    relationships = list(db.scalars(select(ContactRelationship)).all())
-
-    nodes = [GraphNode(id=c.id, name=c.name, current_job=c.current_job) for c in contacts]
-
-    edges = [
-        GraphEdge(
-            source=rel.contact_id,
-            target=rel.related_contact_id,
-            relationship_type=rel.relationship_type,
-            closeness_weight=rel.closeness_weight,
-        )
-        for rel in relationships
-    ]
-
-    return GraphData(nodes=nodes, edges=edges)

python/api/routers/views.py

@@ -1,345 +0,0 @@
-"""HTMX server-rendered view router."""
-
-from pathlib import Path
-from typing import Annotated, Any
-
-from fastapi import APIRouter, Form, HTTPException, Request
-from fastapi.responses import HTMLResponse, RedirectResponse
-from fastapi.templating import Jinja2Templates
-from sqlalchemy import select
-from sqlalchemy.orm import Session, selectinload
-
-from python.fastapi_tools.db import DbSession  # noqa: TC001 this is a FastAPI needed at runtime
-from python.orm.richie.contact import Contact, ContactRelationship, Need, RelationshipType
-
-TEMPLATES_DIR = Path(__file__).parent.parent / "templates"
-templates = Jinja2Templates(directory=TEMPLATES_DIR)
-
-router = APIRouter(tags=["views"])
-
-FAMILIAL_TYPES = {
-    "parent",
-    "child",
-    "sibling",
-    "grandparent",
-    "grandchild",
-    "aunt_uncle",
-    "niece_nephew",
-    "cousin",
-    "in_law",
-}
-FRIEND_TYPES = {"best_friend", "close_friend", "friend", "acquaintance", "neighbor"}
-PARTNER_TYPES = {"spouse", "partner"}
-PROFESSIONAL_TYPES = {"mentor", "mentee", "business_partner", "colleague", "manager", "direct_report", "client"}
-
-CONTACT_STRING_FIELDS = (
-    "name",
-    "legal_name",
-    "suffix",
-    "gender",
-    "current_job",
-    "timezone",
-    "profile_pic",
-    "bio",
-    "goals",
-    "social_structure_style",
-    "safe_conversation_starters",
-    "topics_to_avoid",
-    "ssn",
-)
-
-CONTACT_INT_FIELDS = ("age", "self_sufficiency_score")
-
-
-def _group_relationships(relationships: list[ContactRelationship]) -> dict[str, list[ContactRelationship]]:
-    """Group relationships by category."""
-    groups: dict[str, list[ContactRelationship]] = {
-        "familial": [],
-        "partners": [],
-        "friends": [],
-        "professional": [],
-        "other": [],
-    }
-    for rel in relationships:
-        if rel.relationship_type in FAMILIAL_TYPES:
-            groups["familial"].append(rel)
-        elif rel.relationship_type in PARTNER_TYPES:
-            groups["partners"].append(rel)
-        elif rel.relationship_type in FRIEND_TYPES:
-            groups["friends"].append(rel)
-        elif rel.relationship_type in PROFESSIONAL_TYPES:
-            groups["professional"].append(rel)
-        else:
-            groups["other"].append(rel)
-    return groups
-
-
-def _build_contact_name_map(database: Session, contact: Contact) -> dict[int, str]:
-    """Build a mapping of contact IDs to names for relationship display."""
-    related_ids = {rel.related_contact_id for rel in contact.related_to}
-    related_ids |= {rel.contact_id for rel in contact.related_from}
-    related_ids.discard(contact.id)
-
-    if not related_ids:
-        return {}
-
-    related_contacts = list(database.scalars(select(Contact).where(Contact.id.in_(related_ids))).all())
-    return {related.id: related.name for related in related_contacts}
-
-
-def _get_relationship_type_display() -> dict[str, str]:
-    """Build a mapping of relationship type values to display names."""
-    return {rel_type.value: rel_type.display_name for rel_type in RelationshipType}
-
-
-async def _parse_contact_form(request: Request) -> dict[str, Any]:
-    """Parse contact form data from a multipart/form request."""
-    form_data = await request.form()
-    result: dict[str, Any] = {}
-
-    for field in CONTACT_STRING_FIELDS:
-        value = form_data.get(field, "")
-        result[field] = str(value) if value else None
-
-    for field in CONTACT_INT_FIELDS:
-        value = form_data.get(field, "")
-        result[field] = int(value) if value else None
-
-    result["need_ids"] = [int(value) for value in form_data.getlist("need_ids")]
-    return result
-
-
-def _save_contact_from_form(database: Session, contact: Contact, form_result: dict[str, Any]) -> None:
-    """Apply parsed form data to a Contact and save associated needs."""
-    need_ids = form_result.pop("need_ids")
-
-    for key, value in form_result.items():
-        setattr(contact, key, value)
-
-    if need_ids:
-        contact.needs = list(database.scalars(select(Need).where(Need.id.in_(need_ids))).all())
-    else:
-        contact.needs = []
-
-
-@router.get("/", response_class=HTMLResponse)
-@router.get("/contacts", response_class=HTMLResponse)
-def contact_list_page(request: Request, database: DbSession) -> HTMLResponse:
-    """Render the contacts list page."""
-    contacts = list(database.scalars(select(Contact)).all())
-    return templates.TemplateResponse(request, "contact_list.html", {"contacts": contacts})
-
-
-@router.get("/contacts/new", response_class=HTMLResponse)
-def new_contact_page(request: Request, database: DbSession) -> HTMLResponse:
-    """Render the new contact form page."""
-    all_needs = list(database.scalars(select(Need)).all())
-    return templates.TemplateResponse(request, "contact_form.html", {"contact": None, "all_needs": all_needs})
-
-
-@router.post("/htmx/contacts/new")
-async def create_contact_form(request: Request, database: DbSession) -> RedirectResponse:
-    """Handle the create contact form submission."""
-    form_result = await _parse_contact_form(request)
-    contact = Contact()
-    _save_contact_from_form(database, contact, form_result)
-
-    database.add(contact)
-    database.commit()
-    database.refresh(contact)
-    return RedirectResponse(url=f"/contacts/{contact.id}", status_code=303)
-
-
-@router.get("/contacts/{contact_id}", response_class=HTMLResponse)
-def contact_detail_page(contact_id: int, request: Request, database: DbSession) -> HTMLResponse:
-    """Render the contact detail page."""
-    contact = database.scalar(
-        select(Contact)
-        .where(Contact.id == contact_id)
-        .options(
-            selectinload(Contact.needs),
-            selectinload(Contact.related_to),
-            selectinload(Contact.related_from),
-        )
-    )
-    if not contact:
-        raise HTTPException(status_code=404, detail="Contact not found")
-
-    contact_names = _build_contact_name_map(database, contact)
-    grouped_relationships = _group_relationships(contact.related_to)
-    all_contacts = list(database.scalars(select(Contact)).all())
-    all_needs = list(database.scalars(select(Need)).all())
-    available_needs = [need for need in all_needs if need not in contact.needs]
-
-    return templates.TemplateResponse(
-        request,
-        "contact_detail.html",
-        {
-            "contact": contact,
-            "contact_names": contact_names,
-            "grouped_relationships": grouped_relationships,
-            "all_contacts": all_contacts,
-            "available_needs": available_needs,
-            "relationship_types": list(RelationshipType),
-        },
-    )
-
-
-@router.get("/contacts/{contact_id}/edit", response_class=HTMLResponse)
-def edit_contact_page(contact_id: int, request: Request, database: DbSession) -> HTMLResponse:
-    """Render the edit contact form page."""
-    contact = database.scalar(select(Contact).where(Contact.id == contact_id).options(selectinload(Contact.needs)))
-    if not contact:
-        raise HTTPException(status_code=404, detail="Contact not found")
-
-    all_needs = list(database.scalars(select(Need)).all())
-    return templates.TemplateResponse(request, "contact_form.html", {"contact": contact, "all_needs": all_needs})
-
-
-@router.post("/htmx/contacts/{contact_id}/edit")
-async def update_contact_form(contact_id: int, request: Request, database: DbSession) -> RedirectResponse:
-    """Handle the edit contact form submission."""
-    contact = database.get(Contact, contact_id)
-    if not contact:
-        raise HTTPException(status_code=404, detail="Contact not found")
-
-    form_result = await _parse_contact_form(request)
-    _save_contact_from_form(database, contact, form_result)
-
-    database.commit()
-    return RedirectResponse(url=f"/contacts/{contact_id}", status_code=303)
-
-
-@router.post("/htmx/contacts/{contact_id}/add-need", response_class=HTMLResponse)
-def add_need_to_contact_htmx(
-    contact_id: int,
-    request: Request,
-    database: DbSession,
-    need_id: Annotated[int, Form()],
-) -> HTMLResponse:
-    """Add a need to a contact and return updated manage-needs partial."""
-    contact = database.scalar(select(Contact).where(Contact.id == contact_id).options(selectinload(Contact.needs)))
-    if not contact:
-        raise HTTPException(status_code=404, detail="Contact not found")
-
-    need = database.get(Need, need_id)
-    if not need:
-        raise HTTPException(status_code=404, detail="Need not found")
-
-    if need not in contact.needs:
-        contact.needs.append(need)
-        database.commit()
-        database.refresh(contact)
-
-    return templates.TemplateResponse(request, "partials/manage_needs.html", {"contact": contact})
-
-
-@router.post("/htmx/contacts/{contact_id}/add-relationship", response_class=HTMLResponse)
-def add_relationship_htmx(
-    contact_id: int,
-    request: Request,
-    database: DbSession,
-    related_contact_id: Annotated[int, Form()],
-    relationship_type: Annotated[str, Form()],
-) -> HTMLResponse:
-    """Add a relationship and return updated manage-relationships partial."""
-    contact = database.scalar(select(Contact).where(Contact.id == contact_id).options(selectinload(Contact.related_to)))
-    if not contact:
-        raise HTTPException(status_code=404, detail="Contact not found")
-
-    related_contact = database.get(Contact, related_contact_id)
-    if not related_contact:
-        raise HTTPException(status_code=404, detail="Related contact not found")
-
-    rel_type = RelationshipType(relationship_type)
-    weight = rel_type.default_weight
-
-    relationship = ContactRelationship(
-        contact_id=contact_id,
-        related_contact_id=related_contact_id,
-        relationship_type=relationship_type,
-        closeness_weight=weight,
-    )
-    database.add(relationship)
-    database.commit()
-    database.refresh(contact)
-
-    contact_names = _build_contact_name_map(database, contact)
-    return templates.TemplateResponse(
-        request,
-        "partials/manage_relationships.html",
-        {"contact": contact, "contact_names": contact_names},
-    )
-
-
-@router.post("/htmx/contacts/{contact_id}/relationships/{related_contact_id}/weight")
-def update_relationship_weight_htmx(
-    contact_id: int,
-    related_contact_id: int,
-    database: DbSession,
-    closeness_weight: Annotated[int, Form()],
-) -> HTMLResponse:
-    """Update a relationship's closeness weight from HTMX range input."""
-    relationship = database.scalar(
-        select(ContactRelationship).where(
-            ContactRelationship.contact_id == contact_id,
-            ContactRelationship.related_contact_id == related_contact_id,
-        )
-    )
-    if not relationship:
-        raise HTTPException(status_code=404, detail="Relationship not found")
-
-    relationship.closeness_weight = closeness_weight
-    database.commit()
-    return HTMLResponse("")
-
-
-@router.post("/htmx/needs", response_class=HTMLResponse)
-def create_need_htmx(
-    request: Request,
-    database: DbSession,
-    name: Annotated[str, Form()],
-    description: Annotated[str, Form()] = "",
-) -> HTMLResponse:
-    """Create a need via form data and return updated needs list."""
-    need = Need(name=name, description=description or None)
-    database.add(need)
-    database.commit()
-    needs = list(database.scalars(select(Need)).all())
-    return templates.TemplateResponse(request, "partials/need_items.html", {"needs": needs})
-
-
-@router.get("/needs", response_class=HTMLResponse)
-def needs_page(request: Request, database: DbSession) -> HTMLResponse:
-    """Render the needs list page."""
-    needs = list(database.scalars(select(Need)).all())
-    return templates.TemplateResponse(request, "need_list.html", {"needs": needs})
-
-
-@router.get("/graph", response_class=HTMLResponse)
-def graph_page(request: Request, database: DbSession) -> HTMLResponse:
-    """Render the relationship graph page."""
-    contacts = list(database.scalars(select(Contact)).all())
-    relationships = list(database.scalars(select(ContactRelationship)).all())
-
-    graph_data = {
-        "nodes": [{"id": contact.id, "name": contact.name, "current_job": contact.current_job} for contact in contacts],
-        "edges": [
-            {
-                "source": rel.contact_id,
-                "target": rel.related_contact_id,
-                "relationship_type": rel.relationship_type,
-                "closeness_weight": rel.closeness_weight,
-            }
-            for rel in relationships
-        ],
-    }
-
-    return templates.TemplateResponse(
-        request,
-        "graph.html",
-        {
-            "graph_data": graph_data,
-            "relationship_type_display": _get_relationship_type_display(),
-        },
-    )

python/api/templates/base.html

@@ -1,198 +0,0 @@
-<!DOCTYPE html>
-<html lang="en" data-theme="light">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>{% block title %}Contact Database{% endblock %}</title>
-    <script src="https://unpkg.com/htmx.org@2.0.4"></script>
-    <style>
-        :root {
-            --color-bg: #f5f5f5;
-            --color-bg-card: #ffffff;
-            --color-bg-hover: #f0f0f0;
-            --color-bg-muted: #f9f9f9;
-            --color-bg-error: #ffe0e0;
-            --color-text: #333333;
-            --color-text-muted: #666666;
-            --color-text-error: #cc0000;
-            --color-border: #dddddd;
-            --color-border-light: #eeeeee;
-            --color-border-lighter: #f0f0f0;
-            --color-primary: #0066cc;
-            --color-primary-hover: #0055aa;
-            --color-danger: #cc3333;
-            --color-danger-hover: #aa2222;
-            --color-tag-bg: #e0e0e0;
-            --shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
-            font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
-            line-height: 1.5;
-            color: var(--color-text);
-            background-color: var(--color-bg);
-        }
-        [data-theme="dark"] {
-            --color-bg: #1a1a1a;
-            --color-bg-card: #2d2d2d;
-            --color-bg-hover: #3d3d3d;
-            --color-bg-muted: #252525;
-            --color-bg-error: #4a2020;
-            --color-text: #e0e0e0;
-            --color-text-muted: #a0a0a0;
-            --color-text-error: #ff6b6b;
-            --color-border: #404040;
-            --color-border-light: #353535;
-            --color-border-lighter: #303030;
-            --color-primary: #4da6ff;
-            --color-primary-hover: #7dbfff;
-            --color-danger: #ff6b6b;
-            --color-danger-hover: #ff8a8a;
-            --color-tag-bg: #404040;
-            --shadow: 0 1px 3px rgba(0, 0, 0, 0.3);
-        }
-        * { box-sizing: border-box; }
-        body { margin: 0; background: var(--color-bg); color: var(--color-text); }
-        .app { max-width: 1000px; margin: 0 auto; padding: 20px; }
-        nav { display: flex; align-items: center; gap: 20px; padding: 15px 0; border-bottom: 1px solid var(--color-border); margin-bottom: 20px; }
-        nav a { color: var(--color-primary); text-decoration: none; font-weight: 500; }
-        nav a:hover { text-decoration: underline; }
-        .theme-toggle { margin-left: auto; }
-        main { background: var(--color-bg-card); padding: 20px; border-radius: 8px; box-shadow: var(--shadow); }
-        .header { display: flex; justify-content: space-between; align-items: center; margin-bottom: 20px; }
-        .header h1 { margin: 0; }
-        a { color: var(--color-primary); }
-        a:hover { text-decoration: underline; }
-
-        .btn { display: inline-block; padding: 8px 16px; border: 1px solid var(--color-border); border-radius: 4px; background: var(--color-bg-card); color: var(--color-text); text-decoration: none; cursor: pointer; font-size: 14px; margin-left: 8px; }
-        .btn:hover { background: var(--color-bg-hover); }
-        .btn-primary { background: var(--color-primary); border-color: var(--color-primary); color: white; }
-        .btn-primary:hover { background: var(--color-primary-hover); }
-        .btn-danger { background: var(--color-danger); border-color: var(--color-danger); color: white; }
-        .btn-danger:hover { background: var(--color-danger-hover); }
-        .btn-small { padding: 4px 8px; font-size: 12px; }
-        .btn:disabled { opacity: 0.6; cursor: not-allowed; }
-
-        table { width: 100%; border-collapse: collapse; }
-        th, td { padding: 12px; text-align: left; border-bottom: 1px solid var(--color-border-light); }
-        th { font-weight: 600; background: var(--color-bg-muted); }
-        tr:hover { background: var(--color-bg-muted); }
-
-        .error { background: var(--color-bg-error); color: var(--color-text-error); padding: 10px; border-radius: 4px; margin-bottom: 20px; }
-        .tag { display: inline-block; background: var(--color-tag-bg); padding: 2px 8px; border-radius: 12px; font-size: 12px; color: var(--color-text-muted); }
-
-        .add-form { display: flex; gap: 10px; margin-top: 15px; flex-wrap: wrap; }
-        .add-form select, .add-form input { padding: 8px; border: 1px solid var(--color-border); border-radius: 4px; min-width: 200px; background: var(--color-bg-card); color: var(--color-text); }
-
-        .form-group { margin-bottom: 20px; }
-        .form-group label { display: block; font-weight: 500; margin-bottom: 5px; }
-        .form-group input, .form-group textarea, .form-group select { width: 100%; padding: 10px; border: 1px solid var(--color-border); border-radius: 4px; font-size: 14px; background: var(--color-bg-card); color: var(--color-text); }
-        .form-group textarea { resize: vertical; }
-        .form-row { display: grid; grid-template-columns: 1fr 1fr; gap: 20px; }
-        .checkbox-group { display: flex; flex-wrap: wrap; gap: 15px; }
-        .checkbox-label { display: flex; align-items: center; gap: 5px; cursor: pointer; }
-        .form-actions { display: flex; gap: 10px; margin-top: 30px; padding-top: 20px; border-top: 1px solid var(--color-border-light); }
-
-        .need-form { background: var(--color-bg-muted); padding: 20px; border-radius: 4px; margin-bottom: 20px; }
-        .need-items { list-style: none; padding: 0; }
-        .need-items li { display: flex; justify-content: space-between; align-items: flex-start; padding: 15px; border: 1px solid var(--color-border-light); border-radius: 4px; margin-bottom: 10px; }
-        .need-info p { margin: 5px 0 0; color: var(--color-text-muted); font-size: 14px; }
-
-        .graph-container { width: 100%; }
-        .graph-hint { color: var(--color-text-muted); font-size: 14px; margin-bottom: 15px; }
-        .selected-info { margin-top: 15px; padding: 15px; background: var(--color-bg-muted); border-radius: 8px; }
-        .selected-info h3 { margin: 0 0 10px; }
-        .selected-info p { margin: 5px 0; color: var(--color-text-muted); }
-        .legend { margin-top: 20px; padding: 15px; background: var(--color-bg-muted); border-radius: 8px; }
-        .legend h4 { margin: 0 0 10px; font-size: 14px; }
-        .legend-items { display: flex; flex-wrap: wrap; gap: 15px; }
-        .legend-item { display: flex; align-items: center; gap: 8px; font-size: 12px; color: var(--color-text-muted); }
-        .legend-line { width: 30px; border-radius: 2px; }
-
-        .id-card { width: 100%; }
-        .id-card-inner { background: linear-gradient(135deg, #0a0a0f 0%, #1a1a2e 50%, #0a0a0f 100%); background-image: radial-gradient(white 1px, transparent 1px), linear-gradient(135deg, #0a0a0f 0%, #1a1a2e 50%, #0a0a0f 100%); background-size: 50px 50px, 100% 100%; color: #fff; border-radius: 12px; padding: 25px; min-height: 500px; position: relative; overflow: hidden; }
-        .id-card-header { display: flex; justify-content: space-between; align-items: flex-start; margin-bottom: 15px; }
-        .id-card-header-left { flex: 1; }
-        .id-card-header-right { display: flex; flex-direction: column; align-items: flex-end; gap: 10px; }
-        .id-card-title { font-size: 2.5rem; font-weight: 700; margin: 0; color: #fff; text-shadow: 2px 2px 4px rgba(0,0,0,0.5); }
-        .id-profile-pic { width: 80px; height: 80px; border-radius: 8px; object-fit: cover; border: 2px solid rgba(255,255,255,0.3); }
-        .id-profile-placeholder { width: 80px; height: 80px; border-radius: 8px; background: linear-gradient(135deg, #4ecdc4 0%, #44a8a0 100%); display: flex; align-items: center; justify-content: center; border: 2px solid rgba(255,255,255,0.3); }
-        .id-profile-placeholder span { font-size: 2rem; font-weight: 700; color: #fff; text-shadow: 1px 1px 2px rgba(0,0,0,0.3); }
-        .id-card-actions { display: flex; gap: 8px; }
-        .id-card-actions .btn { background: rgba(255,255,255,0.1); border-color: rgba(255,255,255,0.3); color: #fff; }
-        .id-card-actions .btn:hover { background: rgba(255,255,255,0.2); }
-        .id-card-body { display: grid; grid-template-columns: 1fr 1.5fr; gap: 30px; }
-        .id-card-left { display: flex; flex-direction: column; gap: 8px; }
-        .id-field { font-size: 1rem; line-height: 1.4; }
-        .id-field-block { margin-top: 15px; font-size: 0.95rem; line-height: 1.5; }
-        .id-label { color: #4ecdc4; font-weight: 500; }
-        .id-card-right { display: flex; flex-direction: column; gap: 20px; }
-        .id-bio { font-size: 0.9rem; line-height: 1.6; color: #e0e0e0; }
-        .id-relationships { margin-top: 10px; }
-        .id-section-title { font-size: 1.5rem; margin: 0 0 15px; color: #fff; border-bottom: 1px solid rgba(255,255,255,0.2); padding-bottom: 8px; }
-        .id-rel-group { margin-bottom: 12px; font-size: 0.9rem; line-height: 1.6; }
-        .id-rel-label { color: #a0a0a0; }
-        .id-rel-group a { color: #4ecdc4; text-decoration: none; }
-        .id-rel-group a:hover { text-decoration: underline; }
-        .id-rel-type { color: #888; font-size: 0.85em; }
-        .id-card-warnings { margin-top: 30px; padding-top: 20px; border-top: 1px solid rgba(255,255,255,0.2); display: flex; flex-wrap: wrap; gap: 20px; }
-        .id-warning { display: flex; align-items: center; gap: 8px; font-size: 0.9rem; color: #ff6b6b; }
-        .warning-dot { width: 8px; height: 8px; background: #ff6b6b; border-radius: 50%; flex-shrink: 0; }
-        .warning-desc { color: #ccc; }
-
-        .id-card-manage { margin-top: 20px; background: var(--color-bg-muted); border-radius: 8px; padding: 15px; }
-        .id-card-manage summary { cursor: pointer; font-weight: 600; font-size: 1.1rem; padding: 5px 0; }
-        .id-card-manage[open] summary { margin-bottom: 15px; border-bottom: 1px solid var(--color-border-light); padding-bottom: 10px; }
-        .manage-section { margin-bottom: 25px; }
-        .manage-section h3 { margin: 0 0 15px; font-size: 1rem; }
-        .manage-relationships { display: flex; flex-direction: column; gap: 10px; margin-bottom: 15px; }
-        .manage-rel-item { display: flex; align-items: center; gap: 12px; padding: 10px; background: var(--color-bg-card); border-radius: 6px; flex-wrap: wrap; }
-        .manage-rel-item a { font-weight: 500; min-width: 120px; }
-        .weight-control { display: flex; align-items: center; gap: 8px; font-size: 12px; color: var(--color-text-muted); }
-        .weight-control input[type="range"] { width: 80px; cursor: pointer; }
-        .weight-value { min-width: 20px; text-align: center; font-weight: 600; }
-        .manage-needs-list { list-style: none; padding: 0; margin: 0 0 15px; }
-        .manage-needs-list li { display: flex; align-items: center; gap: 12px; padding: 10px; background: var(--color-bg-card); border-radius: 6px; margin-bottom: 8px; }
-        .manage-needs-list li .btn { margin-left: auto; }
-
-        .htmx-indicator { display: none; }
-        .htmx-request .htmx-indicator { display: inline; }
-        .htmx-request.htmx-indicator { display: inline; }
-
-        @media (max-width: 768px) {
-            .id-card-body { grid-template-columns: 1fr; }
-            .id-card-title { font-size: 1.8rem; }
-            .id-card-header { flex-direction: column; gap: 15px; }
-        }
-    </style>
-</head>
-<body>
-    <div class="app">
-        <nav>
-            <a href="/contacts">Contacts</a>
-            <a href="/graph">Graph</a>
-            <a href="/needs">Needs</a>
-            <button class="btn btn-small theme-toggle" onclick="toggleTheme()">
-                <span id="theme-label">Dark</span>
-            </button>
-        </nav>
-
-        <main id="main-content">
-            {% block content %}{% endblock %}
-        </main>
-    </div>
-
-    <script>
-        function toggleTheme() {
-            const html = document.documentElement;
-            const current = html.getAttribute('data-theme');
-            const next = current === 'light' ? 'dark' : 'light';
-            html.setAttribute('data-theme', next);
-            localStorage.setItem('theme', next);
-            document.getElementById('theme-label').textContent = next === 'light' ? 'Dark' : 'Light';
-        }
-        (function() {
-            const saved = localStorage.getItem('theme') || 'light';
-            document.documentElement.setAttribute('data-theme', saved);
-            document.getElementById('theme-label').textContent = saved === 'light' ? 'Dark' : 'Light';
-        })();
-    </script>
-</body>
-</html>

python/api/templates/contact_detail.html

@@ -1,204 +0,0 @@
-{% extends "base.html" %}
-{% block title %}{{ contact.name }}{% endblock %}
-{% block content %}
-<div class="id-card">
-    <div class="id-card-inner">
-        <div class="id-card-header">
-            <div class="id-card-header-left">
-                <h1 class="id-card-title">I.D.: {{ contact.name }}</h1>
-            </div>
-            <div class="id-card-header-right">
-                {% if contact.profile_pic %}
-                <img src="{{ contact.profile_pic }}" alt="{{ contact.name }}'s profile" class="id-profile-pic">
-                {% else %}
-                <div class="id-profile-placeholder">
-                    <span>{{ contact.name[0]|upper }}</span>
-                </div>
-                {% endif %}
-                <div class="id-card-actions">
-                    <a href="/contacts/{{ contact.id }}/edit" class="btn btn-small">Edit</a>
-                    <a href="/contacts" class="btn btn-small">Back</a>
-                </div>
-            </div>
-        </div>
-
-        <div class="id-card-body">
-            <div class="id-card-left">
-                {% if contact.legal_name %}
-                <div class="id-field">Legal name: {{ contact.legal_name }}</div>
-                {% endif %}
-                {% if contact.suffix %}
-                <div class="id-field">Suffix: {{ contact.suffix }}</div>
-                {% endif %}
-                {% if contact.gender %}
-                <div class="id-field">Gender: {{ contact.gender }}</div>
-                {% endif %}
-                {% if contact.age %}
-                <div class="id-field">Age: {{ contact.age }}</div>
-                {% endif %}
-                {% if contact.current_job %}
-                <div class="id-field">Job: {{ contact.current_job }}</div>
-                {% endif %}
-                {% if contact.social_structure_style %}
-                <div class="id-field">Social style: {{ contact.social_structure_style }}</div>
-                {% endif %}
-                {% if contact.self_sufficiency_score is not none %}
-                <div class="id-field">Self-Sufficiency: {{ contact.self_sufficiency_score }}</div>
-                {% endif %}
-                {% if contact.timezone %}
-                <div class="id-field">Timezone: {{ contact.timezone }}</div>
-                {% endif %}
-                {% if contact.safe_conversation_starters %}
-                <div class="id-field-block">
-                    <span class="id-label">Safe con starters:</span> {{ contact.safe_conversation_starters }}
-                </div>
-                {% endif %}
-                {% if contact.topics_to_avoid %}
-                <div class="id-field-block">
-                    <span class="id-label">Topics to avoid:</span> {{ contact.topics_to_avoid }}
-                </div>
-                {% endif %}
-                {% if contact.goals %}
-                <div class="id-field-block">
-                    <span class="id-label">Goals:</span> {{ contact.goals }}
-                </div>
-                {% endif %}
-            </div>
-
-            <div class="id-card-right">
-                {% if contact.bio %}
-                <div class="id-bio">
-                    <span class="id-label">Bio:</span> {{ contact.bio }}
-                </div>
-                {% endif %}
-
-                <div class="id-relationships">
-                    <h2 class="id-section-title">Relationships</h2>
-
-                    {% if grouped_relationships.familial %}
-                    <div class="id-rel-group">
-                        <span class="id-rel-label">Familial:</span>
-                        {% for rel in grouped_relationships.familial %}
-                        <a href="/contacts/{{ rel.related_contact_id }}">{{ contact_names[rel.related_contact_id] }}</a><span class="id-rel-type">({{ rel.relationship_type|replace("_", " ")|title }})</span>{% if not loop.last %}, {% endif %}
-                        {% endfor %}
-                    </div>
-                    {% endif %}
-
-                    {% if grouped_relationships.partners %}
-                    <div class="id-rel-group">
-                        <span class="id-rel-label">Partners:</span>
-                        {% for rel in grouped_relationships.partners %}
-                        <a href="/contacts/{{ rel.related_contact_id }}">{{ contact_names[rel.related_contact_id] }}</a>{% if not loop.last %}, {% endif %}
-                        {% endfor %}
-                    </div>
-                    {% endif %}
-
-                    {% if grouped_relationships.friends %}
-                    <div class="id-rel-group">
-                        <span class="id-rel-label">Friends:</span>
-                        {% for rel in grouped_relationships.friends %}
-                        <a href="/contacts/{{ rel.related_contact_id }}">{{ contact_names[rel.related_contact_id] }}</a>{% if not loop.last %}, {% endif %}
-                        {% endfor %}
-                    </div>
-                    {% endif %}
-
-                    {% if grouped_relationships.professional %}
-                    <div class="id-rel-group">
-                        <span class="id-rel-label">Professional:</span>
-                        {% for rel in grouped_relationships.professional %}
-                        <a href="/contacts/{{ rel.related_contact_id }}">{{ contact_names[rel.related_contact_id] }}</a><span class="id-rel-type">({{ rel.relationship_type|replace("_", " ")|title }})</span>{% if not loop.last %}, {% endif %}
-                        {% endfor %}
-                    </div>
-                    {% endif %}
-
-                    {% if grouped_relationships.other %}
-                    <div class="id-rel-group">
-                        <span class="id-rel-label">Other:</span>
-                        {% for rel in grouped_relationships.other %}
-                        <a href="/contacts/{{ rel.related_contact_id }}">{{ contact_names[rel.related_contact_id] }}</a><span class="id-rel-type">({{ rel.relationship_type|replace("_", " ")|title }})</span>{% if not loop.last %}, {% endif %}
-                        {% endfor %}
-                    </div>
-                    {% endif %}
-
-                    {% if contact.related_from %}
-                    <div class="id-rel-group">
-                        <span class="id-rel-label">Known by:</span>
-                        {% for rel in contact.related_from %}
-                        <a href="/contacts/{{ rel.contact_id }}">{{ contact_names[rel.contact_id] }}</a>{% if not loop.last %}, {% endif %}
-                        {% endfor %}
-                    </div>
-                    {% endif %}
-                </div>
-            </div>
-        </div>
-
-        {% if contact.needs %}
-        <div class="id-card-warnings">
-            {% for need in contact.needs %}
-            <div class="id-warning">
-                <span class="warning-dot"></span>
-                Warning: {{ need.name }}
-                {% if need.description %}<span class="warning-desc"> - {{ need.description }}</span>{% endif %}
-            </div>
-            {% endfor %}
-        </div>
-        {% endif %}
-    </div>
-
-    <details class="id-card-manage">
-        <summary>Manage Contact</summary>
-
-        <div class="manage-section">
-            <h3>Manage Relationships</h3>
-            <div id="manage-relationships" class="manage-relationships">
-                {% include "partials/manage_relationships.html" %}
-            </div>
-
-            {% if all_contacts %}
-            <form hx-post="/htmx/contacts/{{ contact.id }}/add-relationship"
-                  hx-target="#manage-relationships"
-                  hx-swap="innerHTML"
-                  class="add-form">
-                <select name="related_contact_id" required>
-                    <option value="">Select contact...</option>
-                    {% for other in all_contacts %}
-                    {% if other.id != contact.id %}
-                    <option value="{{ other.id }}">{{ other.name }}</option>
-                    {% endif %}
-                    {% endfor %}
-                </select>
-                <select name="relationship_type" required>
-                    <option value="">Select relationship type...</option>
-                    {% for rel_type in relationship_types %}
-                    <option value="{{ rel_type.value }}">{{ rel_type.display_name }}</option>
-                    {% endfor %}
-                </select>
-                <button type="submit" class="btn btn-primary">Add Relationship</button>
-            </form>
-            {% endif %}
-        </div>
-
-        <div class="manage-section">
-            <h3>Manage Needs/Warnings</h3>
-            <div id="manage-needs">
-                {% include "partials/manage_needs.html" %}
-            </div>
-
-            {% if available_needs %}
-            <form hx-post="/htmx/contacts/{{ contact.id }}/add-need"
-                  hx-target="#manage-needs"
-                  hx-swap="innerHTML"
-                  class="add-form">
-                <select name="need_id" required>
-                    <option value="">Select a need...</option>
-                    {% for need in available_needs %}
-                    <option value="{{ need.id }}">{{ need.name }}</option>
-                    {% endfor %}
-                </select>
-                <button type="submit" class="btn btn-primary">Add Need</button>
-            </form>
-            {% endif %}
-        </div>
-    </details>
-</div>
-{% endblock %}

python/api/templates/contact_form.html

@@ -1,115 +0,0 @@
-{% extends "base.html" %}
-{% block title %}{{ "Edit " + contact.name if contact else "New Contact" }}{% endblock %}
-{% block content %}
-<div class="contact-form">
-    <h1>{{ "Edit Contact" if contact else "New Contact" }}</h1>
-
-    {% if contact %}
-    <form method="post" action="/htmx/contacts/{{ contact.id }}/edit">
-    {% else %}
-    <form method="post" action="/htmx/contacts/new">
-    {% endif %}
-
-        <div class="form-group">
-            <label for="name">Name *</label>
-            <input id="name" name="name" type="text" value="{{ contact.name if contact else '' }}" required>
-        </div>
-
-        <div class="form-row">
-            <div class="form-group">
-                <label for="legal_name">Legal Name</label>
-                <input id="legal_name" name="legal_name" type="text" value="{{ contact.legal_name or '' }}">
-            </div>
-            <div class="form-group">
-                <label for="suffix">Suffix</label>
-                <input id="suffix" name="suffix" type="text" value="{{ contact.suffix or '' }}">
-            </div>
-        </div>
-
-        <div class="form-row">
-            <div class="form-group">
-                <label for="age">Age</label>
-                <input id="age" name="age" type="number" value="{{ contact.age if contact and contact.age is not none else '' }}">
-            </div>
-            <div class="form-group">
-                <label for="gender">Gender</label>
-                <input id="gender" name="gender" type="text" value="{{ contact.gender or '' }}">
-            </div>
-        </div>
-
-        <div class="form-group">
-            <label for="current_job">Current Job</label>
-            <input id="current_job" name="current_job" type="text" value="{{ contact.current_job or '' }}">
-        </div>
-
-        <div class="form-group">
-            <label for="timezone">Timezone</label>
-            <input id="timezone" name="timezone" type="text" value="{{ contact.timezone or '' }}">
-        </div>
-
-        <div class="form-group">
-            <label for="profile_pic">Profile Picture URL</label>
-            <input id="profile_pic" name="profile_pic" type="url" placeholder="https://example.com/photo.jpg" value="{{ contact.profile_pic or '' }}">
-        </div>
-
-        <div class="form-group">
-            <label for="bio">Bio</label>
-            <textarea id="bio" name="bio" rows="3">{{ contact.bio or '' }}</textarea>
-        </div>
-
-        <div class="form-group">
-            <label for="goals">Goals</label>
-            <textarea id="goals" name="goals" rows="3">{{ contact.goals or '' }}</textarea>
-        </div>
-
-        <div class="form-group">
-            <label for="social_structure_style">Social Structure Style</label>
-            <input id="social_structure_style" name="social_structure_style" type="text" value="{{ contact.social_structure_style or '' }}">
-        </div>
-
-        <div class="form-group">
-            <label for="self_sufficiency_score">Self-Sufficiency Score (1-10)</label>
-            <input id="self_sufficiency_score" name="self_sufficiency_score" type="number" min="1" max="10" value="{{ contact.self_sufficiency_score if contact and contact.self_sufficiency_score is not none else '' }}">
-        </div>
-
-        <div class="form-group">
-            <label for="safe_conversation_starters">Safe Conversation Starters</label>
-            <textarea id="safe_conversation_starters" name="safe_conversation_starters" rows="2">{{ contact.safe_conversation_starters or '' }}</textarea>
-        </div>
-
-        <div class="form-group">
-            <label for="topics_to_avoid">Topics to Avoid</label>
-            <textarea id="topics_to_avoid" name="topics_to_avoid" rows="2">{{ contact.topics_to_avoid or '' }}</textarea>
-        </div>
-
-        <div class="form-group">
-            <label for="ssn">SSN</label>
-            <input id="ssn" name="ssn" type="text" value="{{ contact.ssn or '' }}">
-        </div>
-
-        {% if all_needs %}
-        <div class="form-group">
-            <label>Needs/Accommodations</label>
-            <div class="checkbox-group">
-                {% for need in all_needs %}
-                <label class="checkbox-label">
-                    <input type="checkbox" name="need_ids" value="{{ need.id }}"
-                           {% if contact and need in contact.needs %}checked{% endif %}>
-                    {{ need.name }}
-                </label>
-                {% endfor %}
-            </div>
-        </div>
-        {% endif %}
-
-        <div class="form-actions">
-            <button type="submit" class="btn btn-primary">Save</button>
-            {% if contact %}
-            <a href="/contacts/{{ contact.id }}" class="btn">Cancel</a>
-            {% else %}
-            <a href="/contacts" class="btn">Cancel</a>
-            {% endif %}
-        </div>
-    </form>
-</div>
-{% endblock %}

python/api/templates/contact_list.html

@@ -1,14 +0,0 @@
-{% extends "base.html" %}
-{% block title %}Contacts{% endblock %}
-{% block content %}
-<div class="contact-list">
-    <div class="header">
-        <h1>Contacts</h1>
-        <a href="/contacts/new" class="btn btn-primary">Add Contact</a>
-    </div>
-
-    <div id="contact-table">
-        {% include "partials/contact_table.html" %}
-    </div>
-</div>
-{% endblock %}

python/api/templates/graph.html

@@ -1,198 +0,0 @@
-{% extends "base.html" %}
-{% block title %}Relationship Graph{% endblock %}
-{% block content %}
-<div class="graph-container">
-    <div class="header">
-        <h1>Relationship Graph</h1>
-    </div>
-    <p class="graph-hint">Drag nodes to reposition. Closer relationships have shorter, darker edges.</p>
-    <canvas id="graph-canvas" width="900" height="600"
-            style="border: 1px solid var(--color-border); border-radius: 8px; background: var(--color-bg); cursor: grab;">
-    </canvas>
-    <div id="selected-info"></div>
-    <div class="legend">
-        <h4>Relationship Closeness (1-10)</h4>
-        <div class="legend-items">
-            <div class="legend-item">
-                <span class="legend-line" style="background: hsl(220, 70%, 40%); height: 4px; display: inline-block;"></span>
-                <span>10 - Very Close (Spouse, Partner)</span>
-            </div>
-            <div class="legend-item">
-                <span class="legend-line" style="background: hsl(220, 70%, 52%); height: 3px; display: inline-block;"></span>
-                <span>7 - Close (Family, Best Friend)</span>
-            </div>
-            <div class="legend-item">
-                <span class="legend-line" style="background: hsl(220, 70%, 64%); height: 2px; display: inline-block;"></span>
-                <span>4 - Moderate (Friend, Colleague)</span>
-            </div>
-            <div class="legend-item">
-                <span class="legend-line" style="background: hsl(220, 70%, 72%); height: 1px; display: inline-block;"></span>
-                <span>2 - Distant (Acquaintance)</span>
-            </div>
-        </div>
-    </div>
-</div>
-
-<script>
-(function() {
-    const RELATIONSHIP_DISPLAY = {{ relationship_type_display|tojson }};
-    const graphData = {{ graph_data|tojson }};
-
-    const canvas = document.getElementById('graph-canvas');
-    const ctx = canvas.getContext('2d');
-    const width = canvas.width;
-    const height = canvas.height;
-    const centerX = width / 2;
-    const centerY = height / 2;
-
-    const nodes = graphData.nodes.map(function(node) {
-        return Object.assign({}, node, {
-            x: centerX + (Math.random() - 0.5) * 300,
-            y: centerY + (Math.random() - 0.5) * 300,
-            vx: 0,
-            vy: 0
-        });
-    });
-
-    const nodeMap = new Map(nodes.map(function(node) { return [node.id, node]; }));
-
-    const edges = graphData.edges.map(function(edge) {
-        const sourceNode = nodeMap.get(edge.source);
-        const targetNode = nodeMap.get(edge.target);
-        if (!sourceNode || !targetNode) return null;
-        return Object.assign({}, edge, { sourceNode: sourceNode, targetNode: targetNode });
-    }).filter(function(edge) { return edge !== null; });
-
-    let dragNode = null;
-    let selectedNode = null;
-
-    const repulsion = 5000;
-    const springStrength = 0.05;
-    const baseSpringLength = 150;
-    const damping = 0.9;
-    const centerPull = 0.01;
-
-    function simulate() {
-        for (const node of nodes) { node.vx = 0; node.vy = 0; }
-        for (let i = 0; i < nodes.length; i++) {
-            for (let j = i + 1; j < nodes.length; j++) {
-                const dx = nodes[j].x - nodes[i].x;
-                const dy = nodes[j].y - nodes[i].y;
-                const dist = Math.sqrt(dx * dx + dy * dy) || 1;
-                const force = repulsion / (dist * dist);
-                const fx = (dx / dist) * force;
-                const fy = (dy / dist) * force;
-                nodes[i].vx -= fx; nodes[i].vy -= fy;
-                nodes[j].vx += fx; nodes[j].vy += fy;
-            }
-        }
-        for (const edge of edges) {
-            const dx = edge.targetNode.x - edge.sourceNode.x;
-            const dy = edge.targetNode.y - edge.sourceNode.y;
-            const dist = Math.sqrt(dx * dx + dy * dy) || 1;
-            const normalizedWeight = edge.closeness_weight / 10;
-            const idealLength = baseSpringLength * (1.5 - normalizedWeight);
-            const displacement = dist - idealLength;
-            const force = springStrength * displacement;
-            const fx = (dx / dist) * force;
-            const fy = (dy / dist) * force;
-            edge.sourceNode.vx += fx; edge.sourceNode.vy += fy;
-            edge.targetNode.vx -= fx; edge.targetNode.vy -= fy;
-        }
-        for (const node of nodes) {
-            node.vx += (centerX - node.x) * centerPull;
-            node.vy += (centerY - node.y) * centerPull;
-        }
-        for (const node of nodes) {
-            if (node === dragNode) continue;
-            node.x += node.vx * damping;
-            node.y += node.vy * damping;
-            node.x = Math.max(30, Math.min(width - 30, node.x));
-            node.y = Math.max(30, Math.min(height - 30, node.y));
-        }
-    }
-
-    function getEdgeColor(weight) {
-        const normalized = weight / 10;
-        return 'hsl(220, 70%, ' + (80 - normalized * 40) + '%)';
-    }
-
-    function draw() {
-        ctx.clearRect(0, 0, width, height);
-        for (const edge of edges) {
-            const lineWidth = 1 + (edge.closeness_weight / 10) * 3;
-            ctx.strokeStyle = getEdgeColor(edge.closeness_weight);
-            ctx.lineWidth = lineWidth;
-            ctx.beginPath();
-            ctx.moveTo(edge.sourceNode.x, edge.sourceNode.y);
-            ctx.lineTo(edge.targetNode.x, edge.targetNode.y);
-            ctx.stroke();
-            const midX = (edge.sourceNode.x + edge.targetNode.x) / 2;
-            const midY = (edge.sourceNode.y + edge.targetNode.y) / 2;
-            ctx.fillStyle = '#666';
-            ctx.font = '10px sans-serif';
-            ctx.textAlign = 'center';
-            const label = RELATIONSHIP_DISPLAY[edge.relationship_type] || edge.relationship_type;
-            ctx.fillText(label, midX, midY - 5);
-        }
-        for (const node of nodes) {
-            const isSelected = node === selectedNode;
-            const radius = isSelected ? 25 : 20;
-            ctx.beginPath();
-            ctx.arc(node.x, node.y, radius, 0, Math.PI * 2);
-            ctx.fillStyle = isSelected ? '#0066cc' : '#fff';
-            ctx.fill();
-            ctx.strokeStyle = '#0066cc';
-            ctx.lineWidth = 2;
-            ctx.stroke();
-            ctx.fillStyle = isSelected ? '#fff' : '#333';
-            ctx.font = '12px sans-serif';
-            ctx.textAlign = 'center';
-            ctx.textBaseline = 'middle';
-            const name = node.name.length > 10 ? node.name.slice(0, 9) + '\u2026' : node.name;
-            ctx.fillText(name, node.x, node.y);
-        }
-    }
-
-    function animate() {
-        simulate();
-        draw();
-        requestAnimationFrame(animate);
-    }
-    animate();
-
-    function getNodeAt(x, y) {
-        for (const node of nodes) {
-            const dx = x - node.x;
-            const dy = y - node.y;
-            if (dx * dx + dy * dy < 400) return node;
-        }
-        return null;
-    }
-
-    canvas.addEventListener('mousedown', function(event) {
-        const rect = canvas.getBoundingClientRect();
-        const node = getNodeAt(event.clientX - rect.left, event.clientY - rect.top);
-        if (node) {
-            dragNode = node;
-            selectedNode = node;
-            const infoDiv = document.getElementById('selected-info');
-            let html = '<div class="selected-info"><h3>' + node.name + '</h3>';
-            if (node.current_job) html += '<p>Job: ' + node.current_job + '</p>';
-            html += '<a href="/contacts/' + node.id + '">View details</a></div>';
-            infoDiv.innerHTML = html;
-        }
-    });
-
-    canvas.addEventListener('mousemove', function(event) {
-        if (!dragNode) return;
-        const rect = canvas.getBoundingClientRect();
-        dragNode.x = event.clientX - rect.left;
-        dragNode.y = event.clientY - rect.top;
-    });
-
-    canvas.addEventListener('mouseup', function() { dragNode = null; });
-    canvas.addEventListener('mouseleave', function() { dragNode = null; });
-})();
-</script>
-{% endblock %}

python/api/templates/need_list.html

@@ -1,31 +0,0 @@
-{% extends "base.html" %}
-{% block title %}Needs{% endblock %}
-{% block content %}
-<div class="need-list">
-    <div class="header">
-        <h1>Needs / Accommodations</h1>
-        <button class="btn btn-primary" onclick="document.getElementById('need-form').toggleAttribute('hidden')">Add Need</button>
-    </div>
-
-    <form id="need-form" hidden
-          hx-post="/htmx/needs"
-          hx-target="#need-items"
-          hx-swap="innerHTML"
-          hx-on::after-request="if(event.detail.successful) this.reset()"
-          class="need-form">
-        <div class="form-group">
-            <label for="name">Name *</label>
-            <input id="name" name="name" type="text" placeholder="e.g., Light Sensitive, ADHD" required>
-        </div>
-        <div class="form-group">
-            <label for="description">Description</label>
-            <textarea id="description" name="description" placeholder="Optional description..." rows="2"></textarea>
-        </div>
-        <button type="submit" class="btn btn-primary">Create</button>
-    </form>
-
-    <div id="need-items">
-        {% include "partials/need_items.html" %}
-    </div>
-</div>
-{% endblock %}

python/api/templates/partials/contact_table.html

@@ -1,33 +0,0 @@
-{% if contacts %}
-<table>
-    <thead>
-        <tr>
-            <th>Name</th>
-            <th>Job</th>
-            <th>Timezone</th>
-            <th>Actions</th>
-        </tr>
-    </thead>
-    <tbody>
-        {% for contact in contacts %}
-        <tr id="contact-row-{{ contact.id }}">
-            <td><a href="/contacts/{{ contact.id }}">{{ contact.name }}</a></td>
-            <td>{{ contact.current_job or "-" }}</td>
-            <td>{{ contact.timezone or "-" }}</td>
-            <td>
-                <a href="/contacts/{{ contact.id }}/edit" class="btn">Edit</a>
-                <button class="btn btn-danger"
-                        hx-delete="/api/contacts/{{ contact.id }}"
-                        hx-target="#contact-row-{{ contact.id }}"
-                        hx-swap="outerHTML"
-                        hx-confirm="Delete this contact?">
-                    Delete
-                </button>
-            </td>
-        </tr>
-        {% endfor %}
-    </tbody>
-</table>
-{% else %}
-<p>No contacts yet.</p>
-{% endif %}

python/api/templates/partials/manage_needs.html

@@ -1,14 +0,0 @@
-<ul class="manage-needs-list">
-    {% for need in contact.needs %}
-    <li id="contact-need-{{ need.id }}">
-        <strong>{{ need.name }}</strong>
-        {% if need.description %}<span> - {{ need.description }}</span>{% endif %}
-        <button class="btn btn-small btn-danger"
-                hx-delete="/api/contacts/{{ contact.id }}/needs/{{ need.id }}"
-                hx-target="#contact-need-{{ need.id }}"
-                hx-swap="outerHTML">
-            Remove
-        </button>
-    </li>
-    {% endfor %}
-</ul>

python/api/templates/partials/manage_relationships.html

@@ -1,23 +0,0 @@
-{% for rel in contact.related_to %}
-<div class="manage-rel-item" id="rel-{{ contact.id }}-{{ rel.related_contact_id }}">
-    <a href="/contacts/{{ rel.related_contact_id }}">{{ contact_names[rel.related_contact_id] }}</a>
-    <span class="tag">{{ rel.relationship_type|replace("_", " ")|title }}</span>
-    <label class="weight-control">
-        <span>Closeness:</span>
-        <input type="range" min="1" max="10" value="{{ rel.closeness_weight }}"
-               hx-post="/htmx/contacts/{{ contact.id }}/relationships/{{ rel.related_contact_id }}/weight"
-               hx-trigger="change"
-               hx-include="this"
-               name="closeness_weight"
-               hx-swap="none"
-               oninput="this.nextElementSibling.textContent = this.value">
-        <span class="weight-value">{{ rel.closeness_weight }}</span>
-    </label>
-    <button class="btn btn-small btn-danger"
-            hx-delete="/api/contacts/{{ contact.id }}/relationships/{{ rel.related_contact_id }}"
-            hx-target="#rel-{{ contact.id }}-{{ rel.related_contact_id }}"
-            hx-swap="outerHTML">
-        Remove
-    </button>
-</div>
-{% endfor %}

python/api/templates/partials/need_items.html

@@ -1,21 +0,0 @@
-{% if needs %}
-<ul class="need-items">
-    {% for need in needs %}
-    <li id="need-item-{{ need.id }}">
-        <div class="need-info">
-            <strong>{{ need.name }}</strong>
-            {% if need.description %}<p>{{ need.description }}</p>{% endif %}
-        </div>
-        <button class="btn btn-danger"
-                hx-delete="/api/needs/{{ need.id }}"
-                hx-target="#need-item-{{ need.id }}"
-                hx-swap="outerHTML"
-                hx-confirm="Delete this need?">
-            Delete
-        </button>
-    </li>
-    {% endfor %}
-</ul>
-{% else %}
-<p>No needs defined yet.</p>
-{% endif %}

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")
@@ -42,8 +35,7 @@ async def answer_query(
         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,
         [
             {

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
 
@@ -24,18 +20,15 @@ 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)
+    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,
@@ -43,31 +36,25 @@ def schedule_bm25_refresh(app: FastAPI) -> None:
 
 
 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

@@ -1,131 +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("ebook_book_phrase_judgment_already_running source_id=%s", 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("ebook_book_phrase_judgment_queued source_id=%s", 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(
-            "ebook_book_phrase_judgment_complete source_id=%s judged=%s protected=%s mentions=%s failed=%s",
-            source_id,
-            result.candidates_judged,
-            result.protected_phrases,
-            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("ebook_book_phrase_judgment_task_failed source_id=%s", 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
@@ -38,36 +36,26 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
     app.state.config = config
     logger.info(
         "ebook_search_config_loaded top_k=%s embedding_model=%s embedding_base_url=%s vllm_base_url=%s "
-        "rerank_enabled=%s phrase_matching_enabled=%s answer_enabled=%s library_paths=%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.phrase_matching_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 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__)
 
@@ -27,29 +22,19 @@ 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(
-        "ebook_admin_page_loaded models=%s candidate_phrases=%s protected_phrases=%s",
-        len(stats),
-        phrase_stats.candidate_phrases,
-        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 templates.TemplateResponse(request, "partials/error.html", {"message": str(error)}, status_code=500)
@@ -60,152 +45,12 @@ async def scan_library(request: Request, config: AppConfig, session: AsyncDbSess
     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, session: AsyncDbSession) -> HTMLResponse:
-    """Regenerate candidate phrases for every indexed book without LLM judging."""
-    return await run_phrase_generation(request, config, session, only_missing=False)
-
-
-@router.post("/phrases/generate-missing", response_class=HTMLResponse)
-async def generate_missing_phrases(request: Request, config: AppConfig, session: AsyncDbSession) -> HTMLResponse:
-    """Generate candidate phrases only for books that have none yet."""
-    return await run_phrase_generation(request, config, session, only_missing=True)
-
-
-async def run_phrase_generation(
-    request: Request,
-    config: AppConfig,
-    session: AsyncDbSession,
-    *,
-    only_missing: bool,
-) -> HTMLResponse:
-    """Run candidate phrase generation and render the outcome as an admin status partial.
-
-    Args:
-        request (Request): Current request, for template rendering.
-        config (AppConfig): Runtime phrase-tuning settings.
-        session (AsyncDbSession): Active database session.
-        only_missing (bool): Only generate for books without candidates instead of every book.
-
-    Returns:
-        HTMLResponse: Status partial describing the generation outcome.
-    """
-    try:
-        result = await generate_candidate_phrases_for_books(session, config, only_missing=only_missing)
-        await session.commit()
-    except Exception as error:
-        await session.rollback()
-        logger.exception("ebook_admin_generate_phrases_failed only_missing=%s", only_missing)
-        return templates.TemplateResponse(request, "partials/error.html", {"message": str(error)}, status_code=500)
-
-    logger.info(
-        "ebook_admin_generate_phrases_complete only_missing=%s books_seen=%s books_built=%s candidates=%s",
-        only_missing,
-        result.books_seen,
-        result.books_built,
-        result.candidate_phrases,
-    )
-    if only_missing and result.books_seen == 0:
-        return templates.TemplateResponse(
-            request,
-            "partials/admin_status.html",
-            {"message": "All books already have 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 templates.TemplateResponse(request, "partials/error.html", {"message": str(error)}, status_code=500)
-
-    logger.info(
-        "ebook_admin_judge_phrases_complete books_seen=%s books_judged=%s books_failed=%s candidates_judged=%s "
-        "protected=%s mentions=%s",
-        result.books_seen,
-        result.books_judged,
-        result.books_failed,
-        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 templates.TemplateResponse(request, "partials/error.html", {"message": str(error)}, status_code=500)
@@ -219,21 +64,16 @@ 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(

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 = {
@@ -71,17 +69,17 @@ async def ready(config: AppConfig, session: AsyncDbSession, client: AppHttpClien
     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("ebook_ready_database_unavailable error=%s", error)
         return False

python/ebook_search/api/routes/page.py

@@ -3,23 +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.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__)
 
@@ -27,176 +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())
+    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_protected_count(session: AsyncSession, book_id: int) -> int:
-    """Return the number of protected phrases for one book."""
-    return (
-        await session.scalar(select(func.count(EbookProtectedPhrase.id)).where(EbookProtectedPhrase.book_id == book_id))
-        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 get_protected_count(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(
-        "ebook_book_detail_loaded source_id=%s found=%s chapters=%s chunks=%s candidates=%s judged=%s protected=%s",
+        "ebook_book_detail_loaded source_id=%s found=%s chapters=%s chunks=%s",
         source_id,
         source is not None,
         chapter_count,
         chunk_count,
-        candidate_count,
-        judged_candidate_count,
-        protected_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")
-
-    result = await recalculate_candidate_phrases_for_book(session, source, config, use_process_pool=True)
-    logger.info(
-        "ebook_book_phrase_recalculation_complete source_id=%s candidates=%s deleted_candidates=%s "
-        "deleted_protected=%s deleted_aliases=%s deleted_mentions=%s",
-        source_id,
-        result.candidate_phrases,
-        result.deleted_candidates,
-        result.deleted_protected_phrases,
-        result.deleted_aliases,
-        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("ebook_book_phrase_judgment_requested source_id=%s started=%s", source_id, started)
-    return RedirectResponse(url=f"/books/{source_id}", status_code=303)

python/ebook_search/api/routes/search.py

@@ -14,7 +14,6 @@ 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 templates
 from python.ebook_search.guardrails import (
@@ -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,
@@ -60,7 +56,7 @@ 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("ebook_answer_request_failed_falling_back error=%s", error)
         return "Answer generation failed. Source chunks are still shown below.", False, None
@@ -78,31 +74,22 @@ async def build_answer(
 
 
 @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[str | None, Form()] = None,
-    phrase_matching: Annotated[str | None, Form()] = None,
 ) -> HTMLResponse:
     """Run a search and render HTMX results."""
     try:
-        response = await search_ebooks(
-            engine,
-            client,
-            query,
-            config,
-            rerank=rerank == "true",
-            phrase_matching=phrase_matching == "true",
-        )
+        response = search_ebooks(engine, query, config, rerank=rerank == "true")
     except Exception as error:
         logger.exception("ebook_search_request_failed")
         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,

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

@@ -1,110 +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/generate-missing"
-      hx-target="#admin-status"
-      hx-swap="innerHTML"
-    >
-      <button type="submit">Add missing 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/bm25_corpus.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import asyncio
 import json
 import logging
 import shutil
@@ -18,7 +17,7 @@ from sqlalchemy import func, select, union_all
 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
 
@@ -74,14 +73,14 @@ 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("ebook_bm25_index_missing path=%s", index_path)
-        await refresh_bm25_corpus(session, config, db_updated_at=db_updated_at)
+        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(
@@ -90,7 +89,7 @@ async def ensure_bm25_corpus(session: AsyncSession, config: EbookSearchConfig) -
             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(
         "ebook_bm25_index_current path=%s chunks=%s created_at=%s",
@@ -100,24 +99,21 @@ async def ensure_bm25_corpus(session: AsyncSession, config: EbookSearchConfig) -
     )
 
 
-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(
         "ebook_bm25_index_refreshed path=%s chunks=%s created_at=%s",
         index_path,
@@ -168,7 +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
@@ -178,7 +174,6 @@ async def fetch_bm25_corpus_records(session: AsyncSession) -> tuple[list[dict[st
         select(
             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"),
@@ -192,21 +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/config.py

@@ -86,25 +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/Dockerfile

@@ -1,12 +1,17 @@
-FROM python:3.14-slim
+FROM python:3.14-slim AS base
+
+COPY --from=ghcr.io/astral-sh/uv:0.11.26 /uv /uvx /bin/
 
 ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1 \
-    PIP_NO_CACHE_DIR=1 \
     APP_DIR=/home/richie/dotfiles \
-    EBOOK_SEARCH_HOST=0.0.0.0 \
-    EBOOK_SEARCH_PORT=8070 \
-    EBOOK_SEARCH_BM25_INDEX_DIR=/data/bm25
+    UV_PROJECT_ENVIRONMENT=/opt/venv \
+    UV_PYTHON_DOWNLOADS=never \
+    UV_NO_CACHE=1
+
+# Separate ENV instruction so ${APP_DIR} and ${PATH} from above resolve.
+ENV PYTHONPATH=${APP_DIR} \
+    PATH=/opt/venv/bin:${PATH}
 
 WORKDIR ${APP_DIR}
 
@@ -14,29 +19,29 @@ RUN apt-get update \
     && apt-get install -y --no-install-recommends build-essential curl \
     && rm -rf /var/lib/apt/lists/*
 
-COPY pyproject.toml README.md LICENSE ./
-COPY python ./python
+COPY python/ebook_search/docker/pyproject.toml python/ebook_search/docker/uv.lock ./
 
-RUN python -m pip install --upgrade pip \
-    && python -m pip install \
-        "alembic" \
-        "beautifulsoup4" \
-        "bm25s" \
-        "ebooklib" \
-        "fastapi" \
-        "httpx" \
-        "jinja2" \
-        "pgvector" \
-        "psycopg[binary]" \
-        "pydantic" \
-        "pydantic-settings" \
-        "python-multipart" \
-        "sqlalchemy[asyncio]" \
-        "tiktoken" \
-        "typer" \
-        "uvicorn[standard]" \
-        "yake" \
-    && python -m pip install --no-deps --editable "${APP_DIR}"
+RUN uv sync --locked --no-dev
+
+
+FROM base AS test
+
+RUN uv sync --locked
+
+COPY python ./python
+COPY tests/__init__.py ./tests/__init__.py
+COPY tests/ebook_search ./tests/ebook_search
+
+CMD ["pytest"]
+
+
+FROM base AS runtime
+
+ENV EBOOK_SEARCH_HOST=0.0.0.0 \
+    EBOOK_SEARCH_PORT=8070 \
+    EBOOK_SEARCH_BM25_INDEX_DIR=/data/bm25
+
+COPY python ./python
 
 RUN useradd --create-home --uid 10001 app \
     && mkdir -p /data \

python/ebook_search/docker/README.md

@@ -3,26 +3,27 @@
 Run the EPUB search app against the existing Postgres database on `jeeves`:
 
 ```sh
-ebook-search-containers start --library-path /path/to/epubs --build
+python -m python.ebook_search.docker.containers start --library-path /path/to/epubs --build
 ```
 
 All ebook-search Docker files live in this directory:
 
-- `Dockerfile`
+- `Dockerfile` — multi-stage: `test` (runs pytest) and `runtime` (default target, the app image)
 - `docker-compose.yml`
-- `containers.py`
-- `container.py`
+- `containers.py` — Typer lifecycle CLI
+- `pyproject.toml` / `uv.lock` — the container's uv-locked dependencies
 
 The app listens on `http://localhost:8070`.
 
 Useful lifecycle commands:
 
 ```sh
-ebook-search-containers build
-ebook-search-containers start --library-path /path/to/epubs
-ebook-search-containers logs
-ebook-search-containers ps
-ebook-search-containers stop
+python -m python.ebook_search.docker.containers build
+python -m python.ebook_search.docker.containers start --library-path /path/to/epubs
+python -m python.ebook_search.docker.containers test
+python -m python.ebook_search.docker.containers logs
+python -m python.ebook_search.docker.containers ps
+python -m python.ebook_search.docker.containers stop
 ```
 
 Direct compose usage from the repo root:
@@ -31,10 +32,46 @@ Direct compose usage from the repo root:
 docker compose -f python/ebook_search/docker/docker-compose.yml ps
 ```
 
-The compose service also loads the repo root `.env` into the container via `env_file`.
+## Dependencies
+
+The image builds its environment with uv from `pyproject.toml` + `uv.lock` in this
+directory — this is the source of truth for the container's dependencies. To add or
+update a dependency, edit `pyproject.toml` here and regenerate the lock (uv is
+available in the `ebook-search` dev shell):
+
+```sh
+nix develop .#ebook-search -c uv lock --project python/ebook_search/docker
+```
+
+## Tests
+
+The main pytest suite excludes `tests/ebook_search` (its dependencies are no longer
+in the nix dev shell). The `test ebook search` CI workflow runs them in a uv env
+built from the lockfile in this directory — same commands work locally from the
+repo root (the `--override-ini` drops the main suite's ignore):
+
+```sh
+uv sync --locked --project python/ebook_search/docker
+uv run --project python/ebook_search/docker --no-sync pytest tests/ebook_search --override-ini addopts="-n auto -ra"
+```
+
+They can also run inside the Docker `test` image, which validates the image itself:
+
+```sh
+python -m python.ebook_search.docker.containers test
+```
+
+or the raw docker equivalent:
+
+```sh
+docker build --file python/ebook_search/docker/Dockerfile --target test --tag ebook-search:test .
+docker run --rm ebook-search:test
+```
+
+## Configuration
+
+The compose service loads the repo root `.env` into the container via `env_file`.
 
 Mount your EPUB directory by setting `EBOOK_LIBRARY_HOST_PATH` in an env file or on the command line. The container sees it as `/library`, and `EBOOK_SEARCH_LIBRARY_PATHS` is set to `/library` inside the container.
 
 Database connection settings are controlled by `RICHIE_DB`, `RICHIE_HOST`, `RICHIE_PORT`, `RICHIE_USER`, and `RICHIE_PASSWORD`. The default host is `jeeves`.
-
-Startup runs the Richie Alembic migrations automatically after creating the `main` schema and `vector` extension.

python/ebook_search/docker/containers.py

@@ -73,6 +73,23 @@ def build_image() -> None:
         raise RuntimeError(msg)
 
 
+def build_test_image() -> None:
+    """Build the ebook search test Docker image."""
+    dockerfile = Path(__file__).resolve().with_name("Dockerfile")
+    result = docker_run(["build", "--file", str(dockerfile), "--target", "test", "--tag", "ebook-search:test", "."])
+    if result.returncode != 0:
+        msg = "Failed to build ebook search test image"
+        raise RuntimeError(msg)
+
+
+def run_test_image() -> None:
+    """Run the ebook search test suite inside Docker."""
+    result = docker_run(["run", "--rm", "ebook-search:test"])
+    if result.returncode != 0:
+        msg = f"Ebook search tests failed with code {result.returncode}"
+        raise RuntimeError(msg)
+
+
 def start_stack(
     *,
     library_path: Path | None = None,
@@ -210,6 +227,19 @@ def logs(
         typer.echo(output)
 
 
+@app.command("test")
+def run_tests(
+    *,
+    build: Annotated[bool, typer.Option("--build/--no-build", help="Build the test image before running.")] = True,
+    log_level: Annotated[str, typer.Option(help="Log level.")] = "INFO",
+) -> None:
+    """Run ebook search tests inside the Docker test image."""
+    configure_logger(log_level)
+    if build:
+        build_test_image()
+    run_test_image()
+
+
 @app.command("ps")
 def ps() -> None:
     """Show ebook search container status."""

python/ebook_search/docker/pyproject.toml

@@ -0,0 +1,38 @@
+[project]
+name = "ebook-search"
+version = "0.1.0"
+description = "Locked runtime environment for the ebook search container."
+requires-python = "~=3.14.0"
+dependencies = [
+    "alembic",
+    "beautifulsoup4",
+    "bm25s",
+    "ebooklib",
+    "fastapi",
+    "httpx",
+    "jinja2",
+    "pgvector",
+    "psycopg[binary]",
+    "pydantic",
+    "pydantic-settings",
+    "python-multipart",
+    "sqlalchemy",
+    "tiktoken",
+    "typer",
+    "uvicorn[standard]",
+    "yake",
+]
+
+[dependency-groups]
+dev = [
+    "pytest",
+    "pytest-mock",
+    "pytest-xdist",
+]
+
+[tool.uv]
+package = false
+
+[tool.pytest.ini_options]
+addopts = "-n auto -ra"
+testpaths = ["tests/ebook_search"]

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
 
@@ -66,11 +65,7 @@ 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(
         "ebook_embed_request_start base_url=%s model=%s count=%s",
@@ -78,7 +73,7 @@ async def embed_texts(
         config.embedding_model,
         len(texts),
     )
-    vectors = await request_embeddings(client, texts, config)
+    vectors = request_embeddings(texts, config)
     expected_dimension = MODEL_DIMENSIONS[config.embedding_model]
     for vector in vectors:
         if len(vector) != expected_dimension:
@@ -93,28 +88,28 @@ async def embed_texts(
     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("ebook_embedding_model_created model=%s dimension=%s", name, dimension)
-    await session.flush()
+    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)
@@ -127,7 +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,
@@ -139,10 +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}"
@@ -150,7 +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))
@@ -163,13 +158,13 @@ async def embed_missing_chunks(session: AsyncSession, client: httpx.AsyncClient,
         return 0
 
     logger.info("ebook_embed_missing_batch_start model=%s count=%s", config.embedding_model, len(chunks))
-    vectors = await embed_texts(client, [chunk.text for chunk in chunks], config)
+    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()
+    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
@@ -74,115 +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)
+        path = Path(library_path).expanduser()
         logger.info("ebook_ingest_path_start path=%s", path)
-        if epub_paths is None:
+        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)
-            continue
-        for epub_path in epub_paths:
-            count += int(await ingest_file(session, epub_path, config))
     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("ebook_ingest_file_start path=%s", 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("ebook_ingest_file_unchanged source_id=%s path=%s", existing.id, resolved_path)
-            return False
-        if existing is not None:
-            logger.info("ebook_ingest_file_replacing source_id=%s path=%s", 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(
-            "ebook_ingest_file_complete source_id=%s path=%s chapters=%s chunks=%s phrase_mentions=%s",
-            source.id,
-            resolved_path,
-            len(parsed.chapters),
-            chunk_index,
-            mention_count,
-        )
-    except Exception:
-        logger.exception(f"ebook_ingest_file_error path={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)},
@@ -60,15 +44,10 @@ async def request_embeddings(
         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."""
     try:
-        response = await client.get(
+        response = httpx.get(
             f"{config.embedding_base_url.rstrip('/')}/models",
             headers=auth_headers(config.embedding_api_key),
             timeout=timeout_seconds,
@@ -80,15 +59,10 @@ async def check_embedding_endpoint(
     return True
 
 
-async def check_chat_endpoint(
-    client: httpx.AsyncClient,
-    config: EbookSearchConfig,
-    *,
-    timeout_seconds: float = 5.0,
-) -> bool:
+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 = await client.get(
+        response = httpx.get(
             f"{config.vllm_base_url.rstrip('/')}/models",
             headers=auth_headers(config.vllm_api_key),
             timeout=timeout_seconds,
@@ -124,29 +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,
@@ -159,26 +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/protected_phrases/__init__.py

@@ -1 +0,0 @@
-"""Init."""

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

@@ -1,853 +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 SpacySpan(Protocol):
-    """Small protocol for the spaCy span attributes used by this module."""
-
-    text: str
-
-
-class SpacyEntity(SpacySpan, Protocol):
-    """Small protocol for the spaCy entity attributes used by this module."""
-
-    label_: str
-
-
-class SpacyDoc(Protocol):
-    """Small protocol for the spaCy doc attributes used by this module."""
-
-    ents: Iterable[SpacyEntity]
-    noun_chunks: Iterable[SpacySpan]
-
-
-class SpacyLanguage(Protocol):
-    """Small protocol for a callable spaCy language pipeline."""
-
-    def __call__(self, text: str) -> SpacyDoc:
-        """Parse text into a spaCy-like doc."""
-
-
-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,
-    *,
-    min_tokens: int | None = None,
-    max_tokens: int | None = None,
-    strip_leading_article: bool = False,
-) -> 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.
-        min_tokens (int | None): Minimum token count override; defaults to ``config.phrase_min_tokens``.
-        max_tokens (int | None): Maximum token count override; defaults to ``config.phrase_max_tokens``.
-        strip_leading_article (bool): Whether to drop a single leading English article.
-
-    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)
-    start = 0
-    if strip_leading_article and normalized_tokens and normalized_tokens[0].text in {"the", "a", "an"}:
-        start = 1
-
-    selected_tokens = normalized_tokens[start:]
-    min_count = config.phrase_min_tokens if min_tokens is None else min_tokens
-    max_count = config.phrase_max_tokens if max_tokens is None else max_tokens
-    if len(selected_tokens) < min_count or len(selected_tokens) > max_count:
-        return None
-
-    phrase_norm = " ".join(token.text for token in selected_tokens)
-    if phrase_norm in get_ignored_phrases():
-        return None
-
-    display_text = phrase_text[selected_tokens[0].start_char : selected_tokens[-1].end_char].strip()
-    return display_text or phrase_norm, phrase_norm, len(selected_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_spacy_candidates(
-    book_text: str,
-    nlp: SpacyLanguage,
-    config: EbookSearchConfig,
-) -> dict[str, PhraseCandidate]:
-    """Extract spaCy named entities and noun chunks from one text block.
-
-    Args:
-        book_text (str): Text block to parse with spaCy.
-        nlp (SpacyLanguage): Callable spaCy language pipeline.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        dict[str, PhraseCandidate]: Candidates keyed by normalized phrase from entities and noun chunks.
-    """
-    out: dict[str, PhraseCandidate] = {}
-    doc = nlp(book_text)
-
-    for ent in doc.ents:
-        normalized = normalize_candidate_phrase(
-            ent.text,
-            config,
-            max_tokens=config.phrase_max_entity_tokens,
-        )
-        if normalized is None:
-            continue
-        phrase_text, phrase_norm, token_count = normalized
-        out[phrase_norm] = PhraseCandidate(
-            phrase_text=phrase_text,
-            phrase_norm=phrase_norm,
-            token_count=token_count,
-            source_spacy_ner=True,
-            spacy_label=ent.label_,
-        )
-
-    for chunk in doc.noun_chunks:
-        normalized = normalize_candidate_phrase(chunk.text, config, strip_leading_article=True)
-        if normalized is None:
-            continue
-        phrase_text, phrase_norm, token_count = normalized
-        out[phrase_norm] = PhraseCandidate(
-            phrase_text=phrase_text,
-            phrase_norm=phrase_norm,
-            token_count=token_count,
-            source_spacy_noun_chunk=True,
-        )
-    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_spacy_ner = existing.source_spacy_ner or item.source_spacy_ner
-    existing.source_spacy_noun_chunk = existing.source_spacy_noun_chunk or item.source_spacy_noun_chunk
-    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
-    if item.spacy_label:
-        existing.spacy_label = item.spacy_label
-
-
-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_spacy_ner,
-            candidate.source_spacy_noun_chunk,
-            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_spacy_ner, 2.5),
-            (candidate.source_spacy_noun_chunk, 1.5),
-            (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_spacy_ner:
-        names.append("spacy_ner")
-    if candidate.source_spacy_noun_chunk:
-        names.append("spacy_noun_chunk")
-    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,
-    *,
-    nlp: SpacyLanguage | None = None,
-    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 spaCy and frequency counts.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        nlp (SpacyLanguage | None): Optional spaCy pipeline for entity and noun-chunk sources.
-        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(
-        "ebook_phrase_candidate_extract_start chapters=%s chars=%s min_tokens=%s max_tokens=%s max_candidates=%s",
-        len(chapters),
-        len(book_text),
-        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(
-        "ebook_phrase_candidate_extract_raw_complete candidates=%s duration_ms=%.1f",
-        len(raw),
-        (perf_counter() - raw_started_at) * 1000,
-    )
-    yake_started_at = perf_counter()
-    yake_candidates = extract_yake_candidates(book_text, config)
-    logger.info(
-        "ebook_phrase_candidate_extract_yake_complete candidates=%s duration_ms=%.1f",
-        len(yake_candidates),
-        (perf_counter() - yake_started_at) * 1000,
-    )
-    spacy_candidates: dict[str, PhraseCandidate] = {}
-    if nlp is not None:
-        spacy_started_at = perf_counter()
-        for chapter in chapters:
-            spacy_candidates = merge_candidate_sources(spacy_candidates, extract_spacy_candidates(chapter, nlp, config))
-        logger.info(
-            "ebook_phrase_candidate_extract_spacy_complete candidates=%s duration_ms=%.1f",
-            len(spacy_candidates),
-            (perf_counter() - spacy_started_at) * 1000,
-        )
-    capitalized_started_at = perf_counter()
-    capitalized = extract_capitalized_phrases(book_text, config)
-    logger.info(
-        "ebook_phrase_candidate_extract_capitalized_complete candidates=%s duration_ms=%.1f",
-        len(capitalized),
-        (perf_counter() - capitalized_started_at) * 1000,
-    )
-    metadata_candidates = extract_metadata_candidates(metadata, config)
-
-    candidates = merge_candidate_sources(raw, yake_candidates, spacy_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(
-        "ebook_phrase_candidate_extract_complete raw=%s yake=%s spacy=%s capitalized=%s metadata=%s "
-        "merged=%s filtered_too_short=%s filtered_too_rare=%s filtered_too_common=%s filtered_junk=%s "
-        "min_uses=%s storable=%s limited=%s enrich_score_ms=%.1f duration_ms=%.1f",
-        len(raw),
-        len(yake_candidates),
-        len(spacy_candidates),
-        len(capitalized),
-        len(metadata_candidates),
-        pre_filter_count,
-        filtered_too_short,
-        filtered_too_rare,
-        filtered_too_common,
-        filtered_junk,
-        minimum_candidate_raw_count(config),
-        len(candidates),
-        len(limited),
-        (perf_counter() - enriched_started_at) * 1000,
-        (perf_counter() - started_at) * 1000,
-    )
-    return limited

python/ebook_search/protected_phrases/generate_ngrams.py

@@ -1,370 +0,0 @@
-"""Book-level orchestration for candidate n-gram generation and recalculation."""
-
-from __future__ import annotations
-
-import asyncio
-import logging
-from collections import deque
-from time import perf_counter
-from typing import TYPE_CHECKING
-
-from sqlalchemy import select
-
-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 extract_phrase_candidates_in_pool, 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,
-    new_candidate_row,
-    prune_unstorable_unjudged_candidate_phrases,
-)
-from python.orm.richie import EbookCandidatePhrase, EbookSource
-
-if TYPE_CHECKING:
-    from collections.abc import Mapping, Sequence
-    from concurrent.futures import Future
-
-    from sqlalchemy.ext.asyncio import AsyncSession
-
-    from python.ebook_search.config import EbookSearchConfig
-    from python.ebook_search.protected_phrases.extraction import SpacyLanguage
-    from python.ebook_search.protected_phrases.models import PhraseCandidate
-
-logger = logging.getLogger(__name__)
-
-
-async def generate_candidate_phrases_for_books(
-    session: AsyncSession,
-    config: EbookSearchConfig,
-    *,
-    only_missing: bool = False,
-) -> PhraseCandidateGenerationResult:
-    """Create or refresh candidate phrases for indexed books without calling the LLM judge.
-
-    Extraction always runs concurrently in the shared process pool so a full backfill uses
-    multiple cores.
-
-    Args:
-        session (Session): Active database session.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        only_missing (bool): When True, only generate for books that have no candidate phrases
-            yet instead of refreshing every book.
-
-    Returns:
-        PhraseCandidateGenerationResult: Per-corpus counts of books seen, built, and candidates stored.
-    """
-    source_query = select(EbookSource).order_by(EbookSource.id)
-    if only_missing:
-        has_candidates = select(EbookCandidatePhrase.id).where(EbookCandidatePhrase.book_id == EbookSource.id)
-        source_query = source_query.where(~has_candidates.exists())
-    sources = (await session.scalars(source_query)).all()
-    books_seen = len(sources)
-    logger.info(
-        "ebook_candidate_phrase_generation_start books_seen=%s min_tokens=%s max_tokens=%s max_candidates_per_book=%s",
-        books_seen,
-        config.phrase_min_tokens,
-        config.phrase_max_tokens,
-        config.protected_phrase_max_candidates_per_book,
-    )
-
-    outcomes = await generate_candidates_for_sources_pooled(session, sources, config)
-
-    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(
-        "ebook_candidate_phrase_generation_complete books_seen=%s books_built=%s candidate_total=%s",
-        result.books_seen,
-        result.books_built,
-        result.candidate_phrases,
-    )
-    return result
-
-
-async def generate_candidates_for_sources_pooled(
-    session: AsyncSession,
-    sources: Sequence[EbookSource],
-    config: EbookSearchConfig,
-) -> list[BookCandidateResult]:
-    """Generate candidate phrases for many books, extracting them concurrently in worker processes.
-
-    Chapter loading and row persistence stay on the caller's session (serial), while the CPU-bound
-    extraction runs in the shared process pool. A bounded window of in-flight books overlaps
-    extraction across cores without loading every book's candidates into memory at once.
-
-    Args:
-        session (Session): Active database session.
-        sources (Sequence[EbookSource]): Indexed books to generate candidates for.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        list[BookCandidateResult]: One result per book.
-    """
-    pool = get_extraction_pool(config.protected_phrase_extraction_workers)
-    max_in_flight = max(1, config.protected_phrase_extraction_workers) * 2
-    pending: deque[tuple[EbookSource, Future[list[PhraseCandidate]]]] = deque()
-    outcomes: list[BookCandidateResult] = []
-
-    async def drain_one() -> None:
-        source, future = pending.popleft()
-        extracted = await asyncio.wrap_future(future)
-        outcomes.append(await store_source_candidates(session, source, extracted, config))
-
-    try:
-        for source in sources:
-            chapters = await load_book_chapter_texts(session, source.id)
-            if not chapters:
-                logger.warning("ebook_candidate_phrase_generation_book_empty source_id=%s", source.id)
-                outcomes.append(BookCandidateResult())
-                continue
-            future = pool.submit(
-                extract_phrase_candidates_for_book,
-                "\n\n".join(chapters),
-                chapters,
-                config,
-                metadata=metadata_for_source(source),
-            )
-            pending.append((source, future))
-            if len(pending) >= max_in_flight:
-                await drain_one()
-        while pending:
-            await drain_one()
-    except Exception:
-        for _, future in pending:
-            future.cancel()
-        await session.rollback()
-        logger.exception("ebook_candidate_phrase_generation_pooled_failed")
-        raise
-    return outcomes
-
-
-async def store_source_candidates(
-    session: AsyncSession,
-    source: EbookSource,
-    limited_candidates: list[PhraseCandidate],
-    config: EbookSearchConfig,
-) -> BookCandidateResult:
-    """Persist and commit one book's already-extracted candidates.
-
-    Args:
-        session (AsyncSession): Active database session.
-        source (EbookSource): Book the candidates belong to.
-        limited_candidates (list[PhraseCandidate]): Scored candidates to persist.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-
-    Returns:
-        BookCandidateResult: Candidate count and that the book was committed.
-    """
-    book_started_at = perf_counter()
-    saved_count = await store_candidate_phrases_for_book(session, source.id, None, limited_candidates, config)
-    await session.commit()
-    logger.info(
-        "ebook_candidate_phrase_generation_book_committed source_id=%s candidates=%s duration_ms=%.1f",
-        source.id,
-        saved_count,
-        (perf_counter() - book_started_at) * 1000,
-    )
-    return BookCandidateResult(candidates=saved_count, built=True)
-
-
-async def recalculate_candidate_phrases_for_book(
-    session: AsyncSession,
-    source: EbookSource,
-    config: EbookSearchConfig,
-    *,
-    nlp: SpacyLanguage | None = None,
-    use_process_pool: bool = False,
-) -> PhraseRecalculationResult:
-    """Remove all book phrase data, regenerate candidates, and commit the completed book.
-
-    Args:
-        session (Session): Active database session.
-        source (EbookSource): Indexed book to recalculate.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        nlp (SpacyLanguage | None): Optional spaCy pipeline for entity and noun-chunk sources.
-        use_process_pool (bool): Run the CPU-bound extraction in a worker process so concurrent
-            recalculations do not serialize behind the GIL. Defaults to in-process for callers
-            (tests, backfills) that do not need it.
-
-    Returns:
-        PhraseRecalculationResult: Deleted-row counts and the number of candidates regenerated.
-    """
-    started_at = perf_counter()
-    logger.info(
-        "ebook_candidate_phrase_recalculation_start source_id=%s title=%r",
-        source.id,
-        source.title,
-    )
-    try:
-        deleted = await delete_phrase_data_for_book(session, source.id)
-        chapters = await load_book_chapter_texts(session, source.id)
-        if not chapters:
-            logger.warning("ebook_candidate_phrase_recalculation_book_empty source_id=%s", source.id)
-            await session.commit()
-            return 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=0,
-            )
-
-        candidate_count = await generate_candidate_phrases_for_book(
-            session,
-            source.id,
-            series_id=None,
-            chapters=chapters,
-            config=config,
-            nlp=nlp,
-            metadata=metadata_for_source(source),
-            replace_all=True,
-            use_process_pool=use_process_pool,
-        )
-        await session.commit()
-    except Exception:
-        await session.rollback()
-        logger.exception("ebook_candidate_phrase_recalculation_failed source_id=%s", source.id)
-        raise
-
-    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(
-        "ebook_candidate_phrase_recalculation_complete source_id=%s deleted_candidates=%s "
-        "deleted_protected=%s deleted_aliases=%s deleted_mentions=%s candidates=%s duration_ms=%.1f",
-        source.id,
-        result.deleted_candidates,
-        result.deleted_protected_phrases,
-        result.deleted_aliases,
-        result.deleted_mentions,
-        result.candidate_phrases,
-        (perf_counter() - started_at) * 1000,
-    )
-    return result
-
-
-async def generate_candidate_phrases_for_book(
-    session: AsyncSession,
-    book_id: int,
-    series_id: int | None,
-    chapters: Sequence[str],
-    config: EbookSearchConfig,
-    *,
-    nlp: SpacyLanguage | None = None,
-    metadata: Mapping[str, object] | None = None,
-    replace_all: bool = False,
-    use_process_pool: bool = False,
-) -> int:
-    """Extract and store candidate phrases for one book without LLM judging.
-
-    Args:
-        session (Session): Active database session.
-        book_id (int): Book the candidates belong to.
-        series_id (int | None): Series scope for the stored candidates.
-        chapters (Sequence[str]): Chapter-like text blocks used for extraction and frequency counts.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        nlp (SpacyLanguage | None): Optional spaCy pipeline for entity and noun-chunk sources.
-        metadata (Mapping[str, object] | None): Optional book metadata used as a candidate source.
-        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.
-        use_process_pool (bool): Run the CPU-bound extraction in a worker process to avoid
-            serializing concurrent requests behind the GIL. Ignored when ``nlp`` is set, since
-            the spaCy pipeline cannot be sent to a worker process.
-
-    Returns:
-        int: Number of candidate phrase rows stored.
-    """
-    started_at = perf_counter()
-    book_text = "\n\n".join(chapters)
-    if use_process_pool and nlp is None:
-        limited_candidates = await extract_phrase_candidates_in_pool(book_text, chapters, config, metadata=metadata)
-    else:
-        limited_candidates = extract_phrase_candidates_for_book(
-            book_text,
-            chapters,
-            config,
-            nlp=nlp,
-            metadata=metadata,
-        )
-    saved_count = await store_candidate_phrases_for_book(
-        session,
-        book_id,
-        series_id,
-        limited_candidates,
-        config,
-        replace_all=replace_all,
-    )
-    logger.info(
-        "ebook_candidate_phrase_generation_book_duration book_id=%s candidates=%s duration_ms=%.1f",
-        book_id,
-        saved_count,
-        (perf_counter() - started_at) * 1000,
-    )
-    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(
-            "ebook_candidate_phrase_save_start book_id=%s candidates=%s mode=bulk_insert",
-            book_id,
-            len(limited_candidates),
-        )
-    else:
-        pruned_count = await prune_unstorable_unjudged_candidate_phrases(session, book_id, config)
-        logger.info(
-            "ebook_candidate_phrase_save_start book_id=%s candidates=%s pruned_unstorable=%s",
-            book_id,
-            len(limited_candidates),
-            pruned_count,
-        )
-        saved_count = await bulk_upsert_unjudged_candidates(session, book_id, series_id, limited_candidates)
-    logger.info(
-        "ebook_candidate_phrase_save_complete book_id=%s candidates=%s save_ms=%.1f",
-        book_id,
-        saved_count,
-        (perf_counter() - save_started_at) * 1000,
-    )
-    return saved_count

python/ebook_search/protected_phrases/judge_ngrams.py

@@ -1,511 +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(
-        "ebook_candidate_phrase_judgment_start books_seen=%s book_workers=%s phrase_workers=%s "
-        "confidence_threshold=%.2f",
-        books_seen,
-        book_workers,
-        phrase_workers,
-        config.protected_phrase_confidence_threshold,
-    )
-
-    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(
-        "ebook_candidate_phrase_judgment_complete books_seen=%s books_judged=%s books_failed=%s "
-        "candidates_judged=%s protected=%s mentions=%s",
-        result.books_seen,
-        result.books_judged,
-        result.books_failed,
-        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("ebook_candidate_phrase_judgment_book_failed source_id=%s", 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("ebook_candidate_phrase_judgment_book_skip_no_unjudged source_id=%s", 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(
-                    "ebook_candidate_phrase_judgment_skipped_target_met source_id=%s existing_protected=%s target=%s",
-                    source_id,
-                    existing_protected,
-                    config.phrase_target_protected_per_book,
-                )
-                return None
-        book_text = await load_book_text(session, source_id)
-        if not book_text:
-            logger.warning("ebook_candidate_phrase_judgment_book_empty source_id=%s", 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(
-        "ebook_candidate_phrase_judgment_candidates_loaded source_id=%s candidates=%s skipped_junk=%s "
-        "unjudged_rows=%s existing_protected=%s target_remaining=%s judgment_limit=%s",
-        source_id,
-        len(work_items),
-        skipped_junk,
-        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(
-                    "ebook_candidate_phrase_judgment_candidate_complete source_id=%s candidate_id=%s phrase=%r "
-                    "keep=%s confidence=%.3f category=%r promoted=%s",
-                    source_id,
-                    candidate_id,
-                    candidate.phrase_norm,
-                    judgment.keep,
-                    judgment.confidence,
-                    judgment.category,
-                    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("ebook_candidate_phrase_judgment_book_persist_failed source_id=%s", source_id)
-            return BookJudgmentResult(failed=True)
-    logger.info(
-        "ebook_candidate_phrase_judgment_book_committed source_id=%s judged=%s protected=%s mentions=%s "
-        "duration_ms=%.1f",
-        source_id,
-        len(judged),
-        len(protected),
-        mentions,
-        (perf_counter() - book_started_at) * 1000,
-    )
-    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(
-            "ebook_candidate_phrase_judgment_candidate_skip_short_canonical book_id=%s candidate_id=%s "
-            "phrase=%r canonical=%r token_count=%s min_tokens=%s",
-            book_id,
-            candidate_id,
-            candidate.phrase_norm,
-            accepted_norm,
-            accepted_token_count,
-            config.phrase_min_tokens,
-        )
-        return False
-    if is_most_common_word_phrase(accepted_tokens):
-        logger.info(
-            "ebook_candidate_phrase_judgment_candidate_skip_common_canonical book_id=%s candidate_id=%s "
-            "phrase=%r canonical=%r",
-            book_id,
-            candidate_id,
-            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

@@ -1,491 +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, func, or_, select
-
-from python.ebook_search.protected_phrases.config import get_ignored_phrases
-from python.ebook_search.protected_phrases.models import (
-    ChunkPhraseHit,
-    HydratedPhraseMatch,
-    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
-    from python.ebook_search.protected_phrases.text_normalization import NormalizedToken
-
-logger = logging.getLogger(__name__)
-
-
-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.
-    """
-    norm_to_ids: defaultdict[str, list[int]] = defaultdict(list)
-    alias_to_ids: defaultdict[str, list[int]] = defaultdict(list)
-    max_tokens = config.phrase_max_tokens
-
-    phrase_statement = select(
-        EbookProtectedPhrase.id,
-        EbookProtectedPhrase.phrase_norm,
-        EbookProtectedPhrase.token_count,
-    )
-    scope_filter = protected_phrase_scope_filter(book_id=book_id, series_id=series_id)
-    if scope_filter is not None:
-        phrase_statement = phrase_statement.where(scope_filter)
-
-    for row in await session.execute(phrase_statement):
-        phrase_id = int(row.id)
-        phrase_norm = str(row.phrase_norm)
-        norm_to_ids[phrase_norm].append(phrase_id)
-        max_tokens = max(max_tokens, int(row.token_count))
-
-    alias_statement = select(
-        EbookPhraseAlias.alias_norm,
-        EbookPhraseAlias.phrase_id,
-    ).join(EbookProtectedPhrase, EbookProtectedPhrase.id == EbookPhraseAlias.phrase_id)
-    if scope_filter is not None:
-        alias_statement = alias_statement.where(scope_filter)
-
-    for row in await session.execute(alias_statement):
-        alias_norm = str(row.alias_norm)
-        alias_to_ids[alias_norm].append(int(row.phrase_id))
-        max_tokens = max(max_tokens, len(alias_norm.split()))
-
-    return PhraseLookup(
-        norm_to_phrase_ids={key: tuple(values) for key, values in norm_to_ids.items()},
-        alias_to_phrase_ids={key: tuple(values) for key, values in alias_to_ids.items()},
-        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 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
-
-
-def detect_phrase_candidates(query_text: str, lookup: PhraseLookup) -> list[PhraseMatch]:
-    """Detect protected phrase windows in a user query using RAM hash lookups.
-
-    Args:
-        query_text (str): User query text to scan.
-        lookup (PhraseLookup): In-memory phrase and alias lookup maps.
-
-    Returns:
-        list[PhraseMatch]: Unhydrated phrase matches found in the query.
-    """
-    return detect_phrase_candidates_from_tokens(tokenize_with_offsets(query_text), lookup)
-
-
-def detect_phrase_candidates_in_text(text: str, lookup: PhraseLookup) -> list[PhraseMatch]:
-    """Detect protected phrase windows in arbitrary text with character offsets.
-
-    Args:
-        text (str): Arbitrary text, such as a chunk, to scan.
-        lookup (PhraseLookup): In-memory phrase and alias lookup maps.
-
-    Returns:
-        list[PhraseMatch]: Unhydrated phrase matches found in the text.
-    """
-    return detect_phrase_candidates_from_tokens(tokenize_with_offsets(text), lookup)
-
-
-def detect_phrase_candidates_from_tokens(tokens_: Sequence[NormalizedToken], lookup: PhraseLookup) -> list[PhraseMatch]:
-    """Detect protected phrase windows from already-normalized tokens.
-
-    Args:
-        tokens_ (Sequence[NormalizedToken]): Normalized tokens with character offsets.
-        lookup (PhraseLookup): In-memory phrase and alias lookup maps.
-
-    Returns:
-        list[PhraseMatch]: Deduplicated unhydrated phrase matches with token and character spans.
-    """
-    matches: list[PhraseMatch] = []
-    seen: set[tuple[int | None, str, int, int]] = set()
-    token_texts = [token.text for token in tokens_]
-    for phrase_norm, start, end in generate_query_ngrams(token_texts, min_n=lookup.min_tokens, max_n=lookup.max_tokens):
-        phrase_ids = lookup.norm_to_phrase_ids.get(phrase_norm, ())
-        alias_ids = lookup.alias_to_phrase_ids.get(phrase_norm, ())
-        for phrase_id in (*phrase_ids, *alias_ids):
-            key = (phrase_id, phrase_norm, start, end)
-            if key in seen:
-                continue
-            seen.add(key)
-            matches.append(
-                PhraseMatch(
-                    phrase_norm=phrase_norm,
-                    phrase_id=phrase_id,
-                    start_token=start,
-                    end_token=end,
-                    token_count=end - start,
-                    start_char=tokens_[start].start_char,
-                    end_char=tokens_[end - 1].end_char,
-                )
-            )
-    return matches
-
-
-async def hydrate_matches(session: AsyncSession, matches: Sequence[PhraseMatch]) -> list[HydratedPhraseMatch]:
-    """Fetch protected phrase metadata for raw phrase matches.
-
-    Args:
-        session (AsyncSession): Active database session.
-        matches (Sequence[PhraseMatch]): Unhydrated matches to enrich.
-
-    Returns:
-        list[HydratedPhraseMatch]: Matches with protected-phrase metadata attached.
-    """
-    if not matches:
-        return []
-
-    phrase_ids = sorted({match.phrase_id for match in matches if match.phrase_id is not None})
-    if not phrase_ids:
-        return []
-
-    rows = {
-        row.id: row
-        for row in await session.scalars(select(EbookProtectedPhrase).where(EbookProtectedPhrase.id.in_(phrase_ids)))
-    }
-    hydrated: list[HydratedPhraseMatch] = []
-    for match in matches:
-        if match.phrase_id is None:
-            continue
-        phrase = rows.get(match.phrase_id)
-        if phrase is None:
-            continue
-        hydrated.append(
-            HydratedPhraseMatch(
-                phrase_id=phrase.id,
-                matched_norm=match.phrase_norm,
-                phrase_text=phrase.phrase_text,
-                phrase_norm=phrase.phrase_norm,
-                canonical_id=phrase.canonical_id,
-                phrase_type=phrase.phrase_type,
-                token_count=match.token_count,
-                confidence=phrase.confidence,
-                importance=phrase.importance,
-                allow_nested=phrase.allow_nested,
-                suppress_children=phrase.suppress_children,
-                start_token=match.start_token,
-                end_token=match.end_token,
-                start_char=match.start_char,
-                end_char=match.end_char,
-                book_id=phrase.book_id,
-                series_id=phrase.series_id,
-            )
-        )
-    return hydrated
-
-
-def overlaps(first: HydratedPhraseMatch, second: HydratedPhraseMatch) -> bool:
-    """Return whether two token spans overlap.
-
-    Args:
-        first (HydratedPhraseMatch): First match to compare.
-        second (HydratedPhraseMatch): 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 is_inside(child: HydratedPhraseMatch, parent: HydratedPhraseMatch) -> bool:
-    """Return whether one token span is strictly inside another.
-
-    Args:
-        child (HydratedPhraseMatch): Candidate nested match.
-        parent (HydratedPhraseMatch): 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 rank_match(match: HydratedPhraseMatch) -> tuple[float, float, int]:
-    """Rank phrase matches by importance, confidence, then token count.
-
-    Args:
-        match (HydratedPhraseMatch): 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 should_suppress(candidate: HydratedPhraseMatch, kept: HydratedPhraseMatch) -> bool:
-    """Return whether an already-kept match should suppress a candidate.
-
-    Args:
-        candidate (HydratedPhraseMatch): Match being considered for keeping.
-        kept (HydratedPhraseMatch): 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 resolve_overlaps(matches: Sequence[HydratedPhraseMatch]) -> list[HydratedPhraseMatch]:
-    """Resolve overlapping phrase matches without relying only on longest match.
-
-    Args:
-        matches (Sequence[HydratedPhraseMatch]): Hydrated matches that may overlap.
-
-    Returns:
-        list[HydratedPhraseMatch]: The kept, non-suppressed matches.
-    """
-    sorted_matches = sorted(
-        matches,
-        key=lambda match: (match.start_token, -match.token_count, -match.importance, -match.confidence),
-    )
-    kept: list[HydratedPhraseMatch] = []
-    for candidate in sorted_matches:
-        if any(should_suppress(candidate, existing) for existing in kept):
-            continue
-        kept.append(candidate)
-    return kept
-
-
-async def detect_protected_phrases_for_query(
-    session: AsyncSession,
-    query_text: str,
-    config: EbookSearchConfig,
-    *,
-    lookup: PhraseLookup | None = None,
-    book_id: int | None = None,
-    series_id: int | None = None,
-) -> list[HydratedPhraseMatch]:
-    """Run the full online protected-phrase query-detection pipeline.
-
-    Args:
-        session (AsyncSession): Active database session.
-        query_text (str): User query text to detect phrases in.
-        config (EbookSearchConfig): Runtime phrase-tuning settings.
-        lookup (PhraseLookup | None): Optional preloaded lookup; loaded on demand when ``None``.
-        book_id (int | None): Optional book scope for lookup loading.
-        series_id (int | None): Optional series scope for lookup loading.
-
-    Returns:
-        list[HydratedPhraseMatch]: Hydrated, overlap-resolved phrase matches for the query.
-    """
-    active_lookup = (
-        lookup
-        if lookup is not None
-        else await load_phrase_lookup(session, config, book_id=book_id, series_id=series_id)
-    )
-    return resolve_overlaps(await hydrate_matches(session, detect_phrase_candidates(query_text, active_lookup)))
-
-
-async def index_chunk_phrase_mentions_for_book(
-    session: AsyncSession,
-    book_id: int,
-    config: EbookSearchConfig,
-    *,
-    series_id: int | None = None,
-    lookup: PhraseLookup | None = None,
-) -> 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.
-        series_id (int | None): Optional series scope for lookup loading.
-        lookup (PhraseLookup | None): Optional preloaded lookup; loaded on demand when ``None``.
-
-    Returns:
-        int: Total number of chunk phrase mentions indexed for the book.
-    """
-    active_lookup = (
-        lookup
-        if lookup is not None
-        else await load_phrase_lookup(session, config, book_id=book_id, series_id=series_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 += await index_chunk_phrase_mentions(session, chunk, lookup=active_lookup)
-    await session.flush()
-    logger.info("ebook_chunk_phrase_mentions_indexed book_id=%s mentions=%s", book_id, count)
-    return count
-
-
-async 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.
-    """
-    raw_matches = detect_phrase_candidates_in_text(chunk.text, lookup)
-    hydrated = resolve_overlaps(await hydrate_matches(session, raw_matches))
-    for match in hydrated:
-        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(hydrated)
-
-
-async def phrase_hits_for_chunks(
-    session: AsyncSession,
-    *,
-    chunk_ids: Sequence[int],
-    phrase_ids: Sequence[int],
-) -> dict[int, tuple[ChunkPhraseHit, ...]]:
-    """Return matched protected phrases with mention counts by chunk id using indexed chunk mentions.
-
-    Args:
-        session (AsyncSession): Active database session.
-        chunk_ids (Sequence[int]): Chunk ids to look up mentions for.
-        phrase_ids (Sequence[int]): Protected phrase ids to restrict the results to.
-
-    Returns:
-        dict[int, tuple[ChunkPhraseHit, ...]]: Phrase hits per chunk id, ordered by mention count.
-    """
-    if not chunk_ids or not phrase_ids:
-        return {}
-
-    mention_count = func.count(EbookChunkPhraseMention.phrase_id).label("mention_count")
-    statement = (
-        select(
-            EbookChunkPhraseMention.chunk_id,
-            EbookProtectedPhrase.id.label("phrase_id"),
-            EbookProtectedPhrase.phrase_text,
-            mention_count,
-        )
-        .join(EbookProtectedPhrase, EbookProtectedPhrase.id == EbookChunkPhraseMention.phrase_id)
-        .where(
-            EbookChunkPhraseMention.chunk_id.in_(chunk_ids),
-            EbookChunkPhraseMention.phrase_id.in_(phrase_ids),
-        )
-        .group_by(EbookChunkPhraseMention.chunk_id, EbookProtectedPhrase.id, EbookProtectedPhrase.phrase_text)
-        .order_by(EbookChunkPhraseMention.chunk_id, mention_count.desc(), EbookProtectedPhrase.phrase_text)
-    )
-    hits: defaultdict[int, list[ChunkPhraseHit]] = defaultdict(list)
-    for row in await session.execute(statement):
-        hits[int(row.chunk_id)].append(
-            ChunkPhraseHit(
-                phrase_id=int(row.phrase_id),
-                phrase_text=str(row.phrase_text),
-                mention_count=int(row.mention_count),
-            )
-        )
-    return {chunk_id: tuple(chunk_hits) for chunk_id, chunk_hits in hits.items()}
-
-
-async def phrase_hit_counts_for_chunks(
-    session: AsyncSession,
-    *,
-    chunk_ids: Sequence[int],
-    phrase_ids: Sequence[int],
-) -> dict[int, int]:
-    """Return phrase-hit counts by chunk id using indexed chunk mentions.
-
-    Args:
-        session (AsyncSession): Active database session.
-        chunk_ids (Sequence[int]): Chunk ids to count mentions for.
-        phrase_ids (Sequence[int]): Protected phrase ids to restrict the counts to.
-
-    Returns:
-        dict[int, int]: Total mention count per chunk id.
-    """
-    hits = await phrase_hits_for_chunks(session, chunk_ids=chunk_ids, phrase_ids=phrase_ids)
-    return {chunk_id: sum(hit.mention_count for hit in chunk_hits) for chunk_id, chunk_hits in hits.items()}

python/ebook_search/protected_phrases/models.py

@@ -1,285 +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
-
-
-@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_spacy_ner (bool): Whether spaCy named-entity recognition produced the phrase.
-        source_spacy_noun_chunk (bool): Whether spaCy noun chunking produced the phrase.
-        source_capitalized (bool): Whether the capitalized-run extractor produced the phrase.
-        source_metadata (bool): Whether book metadata produced the phrase.
-        spacy_label (str | None): spaCy entity label when NER 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_spacy_ner: bool = False
-    source_spacy_noun_chunk: bool = False
-    source_capitalized: bool = False
-    source_metadata: bool = False
-    spacy_label: str | None = None
-    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 lookup maps used for constant-time phrase-window checks.
-
-    Attributes:
-        norm_to_phrase_ids (Mapping[str, tuple[int, ...]]): Normalized phrase to protected phrase ids.
-        alias_to_phrase_ids (Mapping[str, tuple[int, ...]]): Normalized alias to protected phrase ids.
-        min_tokens (int): Smallest token-window size to test.
-        max_tokens (int): Largest token-window size to test.
-    """
-
-    norm_to_phrase_ids: Mapping[str, tuple[int, ...]]
-    alias_to_phrase_ids: Mapping[str, tuple[int, ...]]
-    min_tokens: int
-    max_tokens: int
-
-
-@dataclass(frozen=True, slots=True)
-class PhraseMatch:
-    """An unhydrated query or chunk phrase match.
-
-    Attributes:
-        phrase_norm (str): Normalized text of the matched window.
-        start_token (int): Index of the first matched token.
-        end_token (int): Index one past the last matched token.
-        token_count (int): Number of tokens in the match.
-        phrase_id (int | None): Matched protected phrase id when known.
-        start_char (int | None): Start character offset in the source text.
-        end_char (int | None): End character offset in the source text.
-    """
-
-    phrase_norm: str
-    start_token: int
-    end_token: int
-    token_count: int
-    phrase_id: int | None = None
-    start_char: int | None = None
-    end_char: int | None = None
-
-
-@dataclass(frozen=True, slots=True)
-class HydratedPhraseMatch:
-    """A 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 ChunkPhraseHit:
-    """One protected phrase with its mention count inside one retrieved chunk.
-
-    Attributes:
-        phrase_id (int): Protected phrase id.
-        phrase_text (str): Display text of the protected phrase.
-        mention_count (int): Indexed mentions of the phrase in the chunk.
-    """
-
-    phrase_id: int
-    phrase_text: str
-    mention_count: int
-
-
-@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

@@ -1,101 +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 asyncio
-import logging
-import multiprocessing
-import os
-from concurrent.futures import ProcessPoolExecutor
-from threading import Lock
-from typing import TYPE_CHECKING
-
-from python.ebook_search.protected_phrases.extraction import extract_phrase_candidates_for_book
-
-if TYPE_CHECKING:
-    from collections.abc import Mapping, Sequence
-
-    from python.ebook_search.config import EbookSearchConfig
-    from python.ebook_search.protected_phrases.models import PhraseCandidate
-
-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("ebook_phrase_extraction_pool_started workers=%s", 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")
-
-
-async def extract_phrase_candidates_in_pool(
-    book_text: str,
-    chapters: Sequence[str],
-    config: EbookSearchConfig,
-    *,
-    metadata: Mapping[str, object] | None,
-) -> list[PhraseCandidate]:
-    """Run book phrase extraction in a worker process and await the result.
-
-    Only the CPU-bound extraction runs in the worker; the caller keeps all database work in the
-    request process. The spaCy pipeline is not supported here because it is not picklable, so
-    this always runs the non-spaCy extraction path.
-
-    Args:
-        book_text (str): Full book text used for extraction.
-        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.
-    """
-    pool = get_extraction_pool(config.protected_phrase_extraction_workers)
-    future = pool.submit(
-        extract_phrase_candidates_for_book,
-        book_text,
-        list(chapters),
-        config,
-        metadata=dict(metadata) if metadata is not None else None,
-    )
-    return await asyncio.wrap_future(future)

python/ebook_search/protected_phrases/store.py

@@ -1,700 +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_spacy_ner=row.source_spacy_ner,
-        source_spacy_noun_chunk=row.source_spacy_noun_chunk,
-        source_capitalized=row.source_capitalized,
-        source_metadata=row.source_metadata,
-        spacy_label=row.spacy_label,
-        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_spacy_ner": candidate.source_spacy_ner,
-        "source_spacy_noun_chunk": candidate.source_spacy_noun_chunk,
-        "source_capitalized": candidate.source_capitalized,
-        "source_metadata": candidate.source_metadata,
-        "spacy_label": candidate.spacy_label,
-        "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_spacy_ner = candidate.source_spacy_ner
-    row.source_spacy_noun_chunk = candidate.source_spacy_noun_chunk
-    row.source_capitalized = candidate.source_capitalized
-    row.source_metadata = candidate.source_metadata
-    row.spacy_label = candidate.spacy_label
-    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(
-            "ebook_candidate_phrase_unstorable_pruned book_id=%s deleted=%s min_tokens=%s min_uses=%s",
-            book_id,
-            deleted,
-            config.phrase_min_tokens,
-            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(
-        "ebook_candidate_phrase_data_deleted book_id=%s candidates=%s protected=%s aliases=%s mentions=%s",
-        book_id,
-        deleted_candidates,
-        deleted_protected,
-        deleted_aliases,
-        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
 
@@ -25,12 +23,7 @@ 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 []
@@ -41,7 +34,7 @@ async def rerank_chunks(
         config.model,
         len(candidates),
     )
-    scores = await score_candidates(client, query, candidates, config)
+    scores = score_candidates(query, candidates, config)
     results = sorted(
         (
             replace(
@@ -63,14 +56,13 @@ async def rerank_chunks(
     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)
 

python/ebook_search/search.py

@@ -2,17 +2,16 @@
 
 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,
@@ -20,12 +19,8 @@ from python.ebook_search.bm25_corpus import (
     score_bm25_corpus,
 )
 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,
@@ -34,13 +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 HydratedPhraseMatch
 
 logger = logging.getLogger(__name__)
 
@@ -52,14 +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
@@ -74,7 +64,6 @@ class SearchResponse:
     results: list[SearchResult]
     rank_label: str
     timings: tuple[RuntimeStep, ...] = ()
-    phrase_matches: tuple[HydratedPhraseMatch, ...] = ()
 
     @property
     def total_runtime_ms(self) -> float:
@@ -91,38 +80,26 @@ class RetrievalResponse:
     timings: tuple[RuntimeStep, ...]
 
 
-async def search_ebooks(
-    engine: AsyncEngine,
-    client: httpx.AsyncClient,
+def search_ebooks(
+    engine: Engine,
     query: str,
     config: EbookSearchConfig,
     *,
     rerank: bool = False,
-    phrase_matching: bool | None = None,
 ) -> SearchResponse:
     """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_enabled = config.phrase_matching_enabled if phrase_matching is None else phrase_matching
-    logger.info(
-        "ebook_search_start query_length=%s rerank=%s phrase_matching=%s",
-        len(query),
-        rerank,
-        phrase_matching_enabled,
-    )
+    logger.info("ebook_search_start query_length=%s rerank=%s", len(query), rerank)
     timings: list[RuntimeStep] = []
-    if phrase_matching_enabled:
-        phrase_matches, timing = await async_timed_result(
-            "Protected phrase detection", query_phrase_matches(engine, query, config)
-        )
-    else:
-        phrase_matches, timing = timed_result("Protected phrase detection skipped", skip_phrase_matches)
-    timings.append(timing)
-    retrieval, timing = await async_timed_result(
+    retrieval, timing = timed_result(
         "Hybrid retrieval",
-        parallel_retrieval(engine, client, query, config),
+        parallel_retrieval,
+        engine,
+        query,
+        config,
     )
     timings.extend(retrieval.timings)
     timings.append(timing)
@@ -134,28 +111,18 @@ async def search_ebooks(
         rank_constant=config.rrf_rank_constant,
     )
     timings.append(timing)
-    if phrase_matching_enabled:
-        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(
         "ebook_search_complete vector_candidates=%s lexical_candidates=%s "
-        "fused_candidates=%s phrase_matching=%s phrase_matches=%s returned=%s rank_label=%s runtime_ms=%.1f",
+        "fused_candidates=%s returned=%s rank_label=%s runtime_ms=%.1f",
         len(retrieval.vector_results),
         len(retrieval.lexical_results),
         len(fused),
-        phrase_matching_enabled,
-        len(phrase_matches),
         len(response.results),
         response.rank_label,
         response.total_runtime_ms,
@@ -163,100 +130,30 @@ async def search_ebooks(
     return response
 
 
-def skip_phrase_matches() -> list[HydratedPhraseMatch]:
-    """Return no protected phrase matches when phrase matching is disabled."""
-    logger.info("ebook_protected_phrase_detection_skipped")
-    return []
-
-
-async def query_phrase_matches(
-    engine: AsyncEngine,
-    query: str,
-    config: EbookSearchConfig,
-) -> list[HydratedPhraseMatch]:
-    """Detect protected phrases in a query without making search fail when phrase tables are unavailable."""
-    try:
-        async with AsyncSession(engine) as session:
-            return await detect_protected_phrases_for_query(session, query, config)
-    except SQLAlchemyError as error:
-        logger.warning("ebook_protected_phrase_detection_unavailable error=%s", error)
-        return []
-
-
-def skip_phrase_mention_boosts(candidates: list[SearchResult]) -> list[SearchResult]:
-    """Return candidates unchanged when phrase matching is disabled."""
-    logger.info("ebook_phrase_boost_skipped candidates=%s", len(candidates))
-    return candidates
-
-
-async def apply_phrase_mention_boosts(
-    engine: AsyncEngine,
-    candidates: list[SearchResult],
-    phrase_matches: Sequence[HydratedPhraseMatch],
-    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("ebook_phrase_boost_unavailable error=%s", 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,
+def parallel_retrieval(
+    engine: Engine,
     query: str,
     config: EbookSearchConfig,
 ) -> RetrievalResponse:
-    """Run vector and BM25 candidate 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.
-    """
-    (vector_results, vector_timing), (lexical_results, lexical_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)),
-    )
+    """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(
         "ebook_parallel_retrieval_complete vector_candidates=%s lexical_candidates=%s",
@@ -283,14 +180,13 @@ def skip_rerank(
     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(
         "ebook_rerank_complete input_candidates=%s returned=%s",
         min(len(candidates), config.rerank.candidates),
@@ -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)
 
@@ -334,7 +223,6 @@ async def vector_candidates(
             select(
                 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"),
@@ -349,7 +237,7 @@ 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(
             "ebook_vector_search_complete model=%s dimension=%s candidates=%s",
@@ -429,11 +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):
@@ -148,97 +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_spacy_ner: Mapped[bool] = mapped_column(default=False)
-    source_spacy_noun_chunk: Mapped[bool] = mapped_column(default=False)
-    source_capitalized: Mapped[bool] = mapped_column(default=False)
-    source_metadata: Mapped[bool] = mapped_column(default=False)
-    spacy_label: Mapped[str | None]
-    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]

shell.nix

@@ -3,6 +3,20 @@
   ...
 }:
 {
+  # For running the uv-managed ebook-search venv outside the container:
+  # PyPI manylinux wheels (numpy via bm25s) expect libstdc++.so.6 on the
+  # loader path, which NixOS does not provide globally.
+  ebook-search = pkgs.mkShell {
+    nativeBuildInputs = with pkgs; [
+      my_python
+      uv
+    ];
+    LD_LIBRARY_PATH = pkgs.lib.makeLibraryPath [
+      pkgs.stdenv.cc.cc.lib
+      pkgs.zlib
+    ];
+  };
+
   default = pkgs.mkShell {
     NIX_CONFIG = "extra-experimental-features = nix-command flakes ca-derivations";
     nativeBuildInputs = with pkgs; [

systems/jeeves/runners/default.nix

@@ -1,6 +1,9 @@
 { ... }:
 {
-  imports = [ ./nix_builder.nix ];
+  imports = [
+    ./nix_builder.nix
+    ./uv_cache_prune.nix
+  ];
 
   services.nix_builder.containers = {
     nix-builder-00.enable = true;

systems/jeeves/runners/nix_builder.nix

@@ -62,6 +62,11 @@ in
             mountPoint = "/run/secrets/gitea-runners";
             isReadOnly = true;
           };
+          uv-cache = {
+            hostPath = vars.uv_cache;
+            mountPoint = "/var/cache/uv";
+            isReadOnly = false;
+          };
         };
         config =
           {
@@ -153,14 +158,20 @@ in
       }
     ) cfg.containers;
 
-    systemd.services = builtins.listToAttrs (
-      map (name: {
-        name = "container@${name}";
-        value = {
-          requires = [ "gitea.service" ];
-          after = [ "gitea.service" ];
-        };
-      }) (builtins.attrNames (filterAttrs (_: c: c.enable) cfg.containers))
-    );
+    systemd = {
+      services = builtins.listToAttrs (
+        map (name: {
+          name = "container@${name}";
+          value = {
+            requires = [ "gitea.service" ];
+            after = [ "gitea.service" ];
+          };
+        }) (builtins.attrNames (filterAttrs (_: c: c.enable) cfg.containers))
+      );
+
+      tmpfiles.rules = [
+        "d ${vars.uv_cache} 0755 ${runnerUsername} ${runnerUsername} - -"
+      ];
+    };
   };
 }

systems/jeeves/runners/uv_cache_prune.nix

@@ -0,0 +1,28 @@
+{ pkgs, ... }:
+let
+  vars = import ../vars.nix;
+  runnerUsername = "gitea-runner";
+in
+{
+  systemd = {
+    services.uv-cache-prune = {
+      description = "Prune the shared gitea runner uv cache";
+      environment.UV_CACHE_DIR = vars.uv_cache;
+      serviceConfig = {
+        Type = "oneshot";
+        User = runnerUsername;
+        Group = runnerUsername;
+        ExecStart = "${pkgs.uv}/bin/uv cache prune";
+      };
+    };
+
+    timers.uv-cache-prune = {
+      description = "Monthly prune of the shared gitea runner uv cache";
+      wantedBy = [ "timers.target" ];
+      timerConfig = {
+        OnCalendar = "*-*-01 04:00:00";
+        Persistent = true;
+      };
+    };
+  };
+}

systems/jeeves/scripts/zfs.sh

@@ -30,6 +30,7 @@ sudo zfs create media/secure/share -o mountpoint=/zfs/media/share -o exec=off
 # scratch datasets
 sudo zfs create scratch/kafka -o mountpoint=/zfs/scratch/kafka -o recordsize=1M
 sudo zfs create scratch/transmission -o mountpoint=/zfs/scratch/transmission -o recordsize=16k -o sync=disabled -o redundant_metadata=none
+sudo zfs create scratch/uv_cache -o mountpoint=/zfs/scratch/uv_cache
 
 # storage datasets
 sudo zfs create storage/ollama -o recordsize=1M -o compression=zstd-19 -o sync=disabled

systems/jeeves/services/contact_api.nix

@@ -1,43 +0,0 @@
-{
-  pkgs,
-  inputs,
-  ...
-}:
-{
-  networking.firewall.allowedTCPPorts = [
-    8069
-  ];
-  systemd.services.contact-api = {
-    description = "Contact Database API";
-    after = [
-      "postgresql.service"
-      "network.target"
-    ];
-    requires = [ "postgresql.service" ];
-    wantedBy = [ "multi-user.target" ];
-
-    environment = {
-      PYTHONPATH = "${inputs.self}";
-      POSTGRES_DB = "richie";
-      POSTGRES_HOST = "/run/postgresql";
-      POSTGRES_USER = "richie";
-      POSTGRES_PORT = "5432";
-    };
-
-    serviceConfig = {
-      Type = "simple";
-      ExecStart = "${pkgs.my_python}/bin/python -m python.api.main --host 192.168.90.40 --port 8069";
-      Restart = "on-failure";
-      RestartSec = "5s";
-      StandardOutput = "journal";
-      StandardError = "journal";
-      NoNewPrivileges = true;
-      ProtectSystem = "strict";
-      ProtectHome = "read-only";
-      PrivateTmp = true;
-      ReadOnlyPaths = [
-        "${inputs.self}"
-      ];
-    };
-  };
-}

systems/jeeves/snapshot_config.toml

@@ -121,3 +121,9 @@ monthly = 2
 hourly = 0
 daily = 0
 monthly = 0
+
+["scratch/uv_cache"]
+15_min = 2
+hourly = 0
+daily = 0
+monthly = 0

systems/jeeves/vars.nix

@@ -17,5 +17,6 @@ in
   transmission = "${zfs_storage}/transmission";
   ollama = "${zfs_storage}/ollama";
   transmission_scratch = "${zfs_scratch}/transmission";
+  uv_cache = "${zfs_scratch}/uv_cache";
   kafka = "${zfs_scratch}/kafka";
 }

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 (
@@ -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),
@@ -501,10 +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

@@ -16,14 +16,7 @@ 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_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:
@@ -35,7 +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",
@@ -43,8 +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"
@@ -56,7 +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
 
@@ -69,14 +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"
@@ -86,12 +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_protected_phrases.py

@@ -1,957 +0,0 @@
-"""Tests for protected phrase extraction and matching."""
-
-from __future__ import annotations
-
-from datetime import UTC, datetime
-from typing import TYPE_CHECKING
-
-import pytest
-from sqlalchemy import select
-from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession, create_async_engine
-from sqlalchemy.pool import StaticPool
-
-from python.ebook_search.config import EbookSearchConfig
-from python.ebook_search.protected_phrases.config import (
-    get_bad_ends,
-    get_most_common_words,
-)
-from python.ebook_search.protected_phrases.extraction import (
-    extract_capitalized_phrases,
-    is_junk_phrase,
-    score_candidate,
-)
-from python.ebook_search.protected_phrases.generate_ngrams import (
-    generate_candidate_phrases_for_books,
-    recalculate_candidate_phrases_for_book,
-)
-from python.ebook_search.protected_phrases.judge_ngrams import (
-    judge_candidate_phrases_for_books,
-    prepare_book_judgment,
-)
-from python.ebook_search.protected_phrases.matching import (
-    detect_protected_phrases_for_query,
-    index_chunk_phrase_mentions,
-    load_phrase_lookup,
-    phrase_hit_counts_for_chunks,
-    phrase_hits_for_chunks,
-    resolve_overlaps,
-)
-from python.ebook_search.protected_phrases.models import (
-    ChunkPhraseHit,
-    HydratedPhraseMatch,
-    LLMJudgment,
-    PhraseCandidate,
-)
-from python.ebook_search.protected_phrases.store import book_ids_pending_first_judgment, corpus_phrase_stats
-from python.ebook_search.protected_phrases.text_normalization import normalize_text, tokenize
-from python.orm.richie import (
-    EbookCandidatePhrase,
-    EbookChunk,
-    EbookChunkPhraseMention,
-    EbookPhraseAlias,
-    EbookProtectedPhrase,
-    EbookSource,
-    RichieBase,
-)
-
-if TYPE_CHECKING:
-    from collections.abc import AsyncGenerator
-
-    from pytest_mock import MockerFixture
-
-
-@pytest.fixture
-async def engine() -> AsyncGenerator[AsyncEngine]:
-    """Create a shared in-memory async database engine for phrase tests."""
-    test_engine = create_async_engine(
-        "sqlite+aiosqlite:///:memory:",
-        connect_args={"check_same_thread": False},
-        poolclass=StaticPool,
-    )
-    async with test_engine.begin() as connection:
-        await connection.run_sync(RichieBase.metadata.create_all)
-    yield test_engine
-    await test_engine.dispose()
-
-
-@pytest.fixture
-async def session(engine: AsyncEngine) -> AsyncGenerator[AsyncSession]:
-    """Provide a session on the shared in-memory database."""
-    async with AsyncSession(engine, expire_on_commit=False) as test_session:
-        yield test_session
-
-
-@pytest.fixture
-def config() -> EbookSearchConfig:
-    """Provide default phrase-tuning settings for tests."""
-    return EbookSearchConfig()
-
-
-def test_normalize_text_preserves_phrase_stopwords_and_word_order() -> None:
-    """Normalization should not collapse protected phrases by removing stopwords."""
-    assert normalize_text("Haden\u2019s syndrome -- lock-in!") == "haden's syndrome lock in"
-    assert tokenize("House of the Dragon") == ["house", "of", "the", "dragon"]
-
-
-def test_capitalized_phrase_keeps_bad_end_tokens_for_downranking(config: EbookSearchConfig) -> None:
-    """Capitalized extraction should keep dangling endings so scoring can downrank them."""
-    candidates = extract_capitalized_phrases("Damion Montgomery and the left.", config)
-
-    assert "damion montgomery and the" in candidates
-    assert candidates["damion montgomery and the"].phrase_text == "Damion Montgomery and the"
-    assert candidates["damion montgomery and the"].phrase_norm.split()[-1] in get_bad_ends()
-
-
-def test_is_junk_phrase_rejects_dialogue_verbs_contractions_and_majority_common() -> None:
-    """Phrases the judge never keeps should be caught lexically before any LLM call."""
-    assert is_junk_phrase(["vann", "said"])
-    assert is_junk_phrase(["shook", "her", "head"])
-    assert is_junk_phrase(["i'm", "going"])
-    assert is_junk_phrase(["don't", "know"])
-    assert is_junk_phrase(["of", "the", "vault"])
-
-
-def test_is_junk_phrase_keeps_possessives_and_half_common_world_terms() -> None:
-    """Proper-noun possessives and phrasal world terms must survive the junk filter."""
-    assert not is_junk_phrase(["chapman's", "death"])
-    assert not is_junk_phrase(["boston", "bays'"])
-    assert not is_junk_phrase(["lock", "in"])
-    assert not is_junk_phrase(["data", "feed"])
-    assert not is_junk_phrase(["haden's", "syndrome"])
-
-
-def test_score_candidate_rewards_multiple_non_raw_sources(config: EbookSearchConfig) -> None:
-    """A second non-raw source should add its weight plus the multi-source bonus."""
-    single_source = PhraseCandidate(
-        phrase_text="lock in",
-        phrase_norm="lock in",
-        token_count=2,
-        source_capitalized=True,
-        raw_count=3,
-        chapter_count=2,
-    )
-    multi_source = PhraseCandidate(
-        phrase_text="lock in",
-        phrase_norm="lock in",
-        token_count=2,
-        source_capitalized=True,
-        source_yake=True,
-        raw_count=3,
-        chapter_count=2,
-    )
-
-    assert score_candidate(multi_source, config) == score_candidate(single_source, config) + 2.0 + 2.0
-
-
-def test_score_candidate_caps_frequency_contribution(config: EbookSearchConfig) -> None:
-    """Very frequent raw-only phrases should no longer out-score sourced entities."""
-    frequent_raw_only = PhraseCandidate(
-        phrase_text="mage king",
-        phrase_norm="mage king",
-        token_count=2,
-        source_raw_ngram=True,
-        raw_count=1000,
-        chapter_count=100,
-    )
-
-    assert score_candidate(frequent_raw_only, config) == 0.5 + 2.0 + 0.5
-
-
-def test_score_candidate_weights_metadata_source(config: EbookSearchConfig) -> None:
-    """Title and series phrases should get credit for the metadata source."""
-    metadata_only = PhraseCandidate(
-        phrase_text="lock in",
-        phrase_norm="lock in",
-        token_count=2,
-        source_metadata=True,
-    )
-
-    assert score_candidate(metadata_only, config) == 2.0 + 0.5
-
-
-async def test_detect_protected_phrases_hydrates_alias_matches(
-    session: AsyncSession,
-    config: EbookSearchConfig,
-) -> None:
-    """Query detection should use RAM aliases and hydrate phrase metadata from the DB."""
-    source = await add_source(session)
-    phrase = await add_phrase(session, source.id, phrase_text="lock in", phrase_norm="lock in")
-    session.add(EbookPhraseAlias(phrase_id=phrase.id, alias_text="locked in", alias_norm="locked in"))
-    await session.commit()
-
-    matches = await detect_protected_phrases_for_query(session, "what is locked-in", config)
-
-    assert [(match.phrase_text, match.canonical_id, match.phrase_type) for match in matches] == [
-        ("lock in", "condition:lock_in", "fictional_condition")
-    ]
-
-
-def test_resolve_overlaps_keeps_independent_nested_phrases() -> None:
-    """Overlap resolution should keep useful nested concepts when metadata permits it."""
-    child = hydrated_match(
-        phrase_id=1,
-        phrase_text="mage king",
-        canonical_id="title:mage_king",
-        start_token=3,
-        end_token=5,
-        allow_nested=True,
-    )
-    parent = hydrated_match(
-        phrase_id=2,
-        phrase_text="mage king of mars",
-        canonical_id="entity:mage_king_of_mars",
-        start_token=3,
-        end_token=7,
-        suppress_children=False,
-    )
-
-    assert [match.phrase_text for match in resolve_overlaps([child, parent])] == [
-        "mage king of mars",
-        "mage king",
-    ]
-
-
-def test_resolve_overlaps_suppresses_weaker_same_canonical_match() -> None:
-    """Same-canonical overlaps should keep the stronger evidence."""
-    weak = hydrated_match(
-        phrase_id=1,
-        phrase_text="lock",
-        canonical_id="condition:lock_in",
-        start_token=2,
-        end_token=3,
-        importance=0.2,
-    )
-    strong = hydrated_match(
-        phrase_id=2,
-        phrase_text="lock in",
-        canonical_id="condition:lock_in",
-        start_token=2,
-        end_token=4,
-        importance=0.9,
-    )
-
-    assert [match.phrase_text for match in resolve_overlaps([weak, strong])] == ["lock in"]
-
-
-async def test_index_chunk_phrase_mentions_uses_normalized_window_lookup(
-    session: AsyncSession,
-    config: EbookSearchConfig,
-) -> None:
-    """Chunk indexing should store mention rows without scanning all phrases at query time."""
-    source = await add_source(session)
-    phrase = await add_phrase(session, source.id, phrase_text="lock in", phrase_norm="lock in")
-    chunk = EbookChunk(
-        id=1,
-        source_id=source.id,
-        chapter_id=None,
-        chunk_index=0,
-        text="Victims experienced lock-in during the crisis.",
-        token_start=0,
-        token_count=7,
-        page_label=None,
-        content_sha256="b" * 64,
-        search_text="Victims experienced lock-in during the crisis.",
-    )
-    session.add(chunk)
-    await session.commit()
-    lookup = await load_phrase_lookup(session, config, book_id=source.id)
-
-    count = await index_chunk_phrase_mentions(session, chunk, lookup=lookup)
-    await session.commit()
-
-    mention = await session.scalar(select(EbookChunkPhraseMention))
-    assert count == 1
-    assert mention is not None
-    assert mention.chunk_id == chunk.id
-    assert mention.phrase_id == phrase.id
-    assert chunk.text[mention.start_char : mention.end_char] == "lock-in"
-    assert await phrase_hit_counts_for_chunks(session, chunk_ids=[chunk.id], phrase_ids=[phrase.id]) == {chunk.id: 1}
-    assert await phrase_hits_for_chunks(session, chunk_ids=[chunk.id], phrase_ids=[phrase.id]) == {
-        chunk.id: (ChunkPhraseHit(phrase_id=phrase.id, phrase_text="lock in", mention_count=1),)
-    }
-
-
-async def test_generate_candidate_phrases_for_books_stores_unjudged_candidates(
-    session: AsyncSession,
-    config: EbookSearchConfig,
-) -> None:
-    """Candidate generation should populate the per-book phrase list without calling the LLM."""
-    source = await add_source(session)
-    session.add(
-        EbookChunk(
-            id=1,
-            source_id=source.id,
-            chapter_id=None,
-            chunk_index=0,
-            text="lock in lock in lock in",
-            token_start=0,
-            token_count=6,
-            page_label=None,
-            content_sha256="d" * 64,
-            search_text="lock in lock in lock in",
-        )
-    )
-    await session.commit()
-    build_config = config.model_copy(
-        update={
-            "protected_phrase_max_candidates_per_book": 1,
-            "phrase_min_tokens": 2,
-            "phrase_max_tokens": 2,
-        }
-    )
-
-    result = await generate_candidate_phrases_for_books(session, build_config)
-    await session.commit()
-
-    candidate = await session.scalar(select(EbookCandidatePhrase))
-    assert result.books_seen == 1
-    assert result.books_built == 1
-    assert result.candidate_phrases == 1
-    assert candidate is not None
-    assert candidate.phrase_norm == "lock in"
-    assert candidate.llm_judged is False
-    assert await session.scalar(select(EbookProtectedPhrase)) is None
-
-
-async def test_generate_candidate_phrases_for_books_filters_one_token_and_one_use_candidates(
-    session: AsyncSession,
-    config: EbookSearchConfig,
-) -> None:
-    """Candidate generation should not persist one-token or single-use phrases."""
-    source = await add_source(session)
-    session.add(
-        EbookChunk(
-            id=1,
-            source_id=source.id,
-            chapter_id=None,
-            chunk_index=0,
-            text=(
-                "Damion walked away. Damion woke up. rare phrase appeared once. "
-                "and then and then. lock in lock in lock in."
-            ),
-            token_start=0,
-            token_count=16,
-            page_label=None,
-            content_sha256="i" * 64,
-            search_text=(
-                "Damion walked away. Damion woke up. rare phrase appeared once. "
-                "and then and then. lock in lock in lock in."
-            ),
-        )
-    )
-    session.add_all(
-        [
-            EbookCandidatePhrase(
-                book_id=source.id,
-                series_id=None,
-                phrase_text="Damion",
-                phrase_norm="damion",
-                token_count=1,
-                source_capitalized=True,
-                raw_count=2,
-                chapter_count=1,
-                candidate_score=10.0,
-                llm_judged=False,
-            ),
-            EbookCandidatePhrase(
-                book_id=source.id,
-                series_id=None,
-                phrase_text="rare phrase",
-                phrase_norm="rare phrase",
-                token_count=2,
-                source_raw_ngram=True,
-                raw_count=1,
-                chapter_count=1,
-                candidate_score=9.0,
-                llm_judged=False,
-            ),
-        ]
-    )
-    await session.commit()
-    build_config = config.model_copy(
-        update={
-            "protected_phrase_max_candidates_per_book": 50,
-            "phrase_min_tokens": 2,
-            "phrase_max_tokens": 2,
-            "phrase_raw_ngram_min_count": 2,
-        }
-    )
-
-    result = await generate_candidate_phrases_for_books(session, build_config)
-    await session.commit()
-
-    candidates = list(await session.scalars(select(EbookCandidatePhrase)))
-    phrase_norms = {candidate.phrase_norm for candidate in candidates}
-    common_words = get_most_common_words()
-    assert result.candidate_phrases == len(candidates)
-    assert "lock in" in phrase_norms
-    assert "damion" not in phrase_norms
-    assert "rare phrase" not in phrase_norms
-    assert "and then" not in phrase_norms
-    assert {"and", "then"}.issubset(common_words)
-    assert all(candidate.token_count >= 2 for candidate in candidates)
-    assert all(candidate.raw_count >= 2 for candidate in candidates)
-    assert all(not all(token in common_words for token in candidate.phrase_norm.split()) for candidate in candidates)
-
-
-async def test_generate_candidate_phrases_for_books_commits_after_each_book(
-    session: AsyncSession,
-    config: EbookSearchConfig,
-    mocker: MockerFixture,
-) -> None:
-    """Candidate generation should persist each completed book independently."""
-    first = await add_source(session)
-    second = await add_source(session, file_path="/library/book-2.epub", file_sha256="z" * 64)
-    session.add_all(
-        [
-            EbookChunk(
-                id=1,
-                source_id=first.id,
-                chapter_id=None,
-                chunk_index=0,
-                text="lock in lock in lock in",
-                token_start=0,
-                token_count=6,
-                page_label=None,
-                content_sha256="f" * 64,
-                search_text="lock in lock in lock in",
-            ),
-            EbookChunk(
-                id=2,
-                source_id=second.id,
-                chapter_id=None,
-                chunk_index=0,
-                text="mage king mage king mage king",
-                token_start=0,
-                token_count=6,
-                page_label=None,
-                content_sha256="g" * 64,
-                search_text="mage king mage king mage king",
-            ),
-        ]
-    )
-    await session.commit()
-    commit_spy = mocker.spy(session, "commit")
-    build_config = config.model_copy(
-        update={
-            "protected_phrase_max_candidates_per_book": 1,
-            "phrase_min_tokens": 2,
-            "phrase_max_tokens": 2,
-        }
-    )
-
-    result = await generate_candidate_phrases_for_books(session, build_config)
-
-    assert result.books_built == 2
-    assert result.candidate_phrases == 2
-    assert commit_spy.call_count == 2
-
-
-async def test_judge_candidate_phrases_for_books_promotes_stored_candidates(
-    engine: AsyncEngine,
-    session: AsyncSession,
-    config: EbookSearchConfig,
-    mocker: MockerFixture,
-) -> None:
-    """Judging should work from existing candidate rows and retain the candidate history."""
-    source = await add_source(session)
-    session.add(
-        EbookChunk(
-            id=1,
-            source_id=source.id,
-            chapter_id=None,
-            chunk_index=0,
-            text="lock in lock in lock in",
-            token_start=0,
-            token_count=6,
-            page_label=None,
-            content_sha256="e" * 64,
-            search_text="lock in lock in lock in",
-        )
-    )
-    await session.commit()
-    build_config = config.model_copy(
-        update={
-            "protected_phrase_max_candidates_per_book": 1,
-            "protected_phrase_llm_candidates_per_book": 1,
-            "phrase_min_tokens": 2,
-            "phrase_max_tokens": 2,
-            "phrase_judge_book_workers": 1,
-            "phrase_judge_phrase_workers": 1,
-        }
-    )
-    await generate_candidate_phrases_for_books(session, build_config)
-    await session.commit()
-    mocker.patch(
-        "python.ebook_search.protected_phrases.judge_ngrams.judge_candidate_async",
-        return_value=LLMJudgment(
-            keep=True,
-            canonical="lock in",
-            category="fictional_condition",
-            aliases=(),
-            confidence=0.95,
-            importance=0.9,
-        ),
-    )
-
-    result = await judge_candidate_phrases_for_books(engine, build_config)
-
-    session.expire_all()
-    candidate = await session.scalar(select(EbookCandidatePhrase))
-    phrase = await session.scalar(select(EbookProtectedPhrase))
-    mention = await session.scalar(select(EbookChunkPhraseMention))
-    assert result.books_seen == 1
-    assert result.books_judged == 1
-    assert result.candidates_judged == 1
-    assert result.protected_phrases == 1
-    assert candidate is not None
-    assert candidate.llm_judged is True
-    assert candidate.llm_keep is True
-    assert phrase is not None
-    assert phrase.source_candidate_id == candidate.id
-    assert mention is not None
-    assert mention.phrase_id == phrase.id
-
-
-async def test_judge_candidate_phrases_for_books_logs_and_continues_after_book_failure(
-    engine: AsyncEngine,
-    session: AsyncSession,
-    config: EbookSearchConfig,
-    mocker: MockerFixture,
-) -> None:
-    """A failing book should be rolled back and logged while later books are still judged and committed."""
-    first = await add_source(session)
-    second = await add_source(session, file_path="/library/book-2.epub", file_sha256="z" * 64)
-    session.add_all(
-        [
-            EbookChunk(
-                id=1,
-                source_id=first.id,
-                chapter_id=None,
-                chunk_index=0,
-                text="lock in lock in lock in",
-                token_start=0,
-                token_count=6,
-                page_label=None,
-                content_sha256="f" * 64,
-                search_text="lock in lock in lock in",
-            ),
-            EbookChunk(
-                id=2,
-                source_id=second.id,
-                chapter_id=None,
-                chunk_index=0,
-                text="mage king mage king mage king",
-                token_start=0,
-                token_count=6,
-                page_label=None,
-                content_sha256="g" * 64,
-                search_text="mage king mage king mage king",
-            ),
-        ]
-    )
-    await session.commit()
-    build_config = config.model_copy(
-        update={
-            "protected_phrase_max_candidates_per_book": 1,
-            "protected_phrase_llm_candidates_per_book": 1,
-            "phrase_min_tokens": 2,
-            "phrase_max_tokens": 2,
-            "phrase_judge_book_workers": 1,
-            "phrase_judge_phrase_workers": 1,
-        }
-    )
-    await generate_candidate_phrases_for_books(session, build_config)
-    await session.commit()
-
-    def judge_or_fail(_client: object, _config: EbookSearchConfig, candidate: PhraseCandidate) -> LLMJudgment:
-        if candidate.phrase_norm == "lock in":
-            message = "llm judge unavailable"
-            raise RuntimeError(message)
-        return LLMJudgment(
-            keep=True,
-            canonical="mage king",
-            category="title",
-            aliases=(),
-            confidence=0.95,
-            importance=0.9,
-        )
-
-    mocker.patch(
-        "python.ebook_search.protected_phrases.judge_ngrams.judge_candidate_async",
-        side_effect=judge_or_fail,
-    )
-
-    second_book_id = second.id
-
-    result = await judge_candidate_phrases_for_books(engine, build_config)
-
-    session.expire_all()
-    assert result.books_seen == 2
-    assert result.books_judged == 1
-    assert result.books_failed == 1
-    assert result.protected_phrases == 1
-    phrase = await session.scalar(select(EbookProtectedPhrase))
-    assert phrase is not None
-    assert phrase.book_id == second_book_id
-
-
-async def test_judge_candidate_phrases_for_books_skips_unstorable_existing_candidates(
-    engine: AsyncEngine,
-    session: AsyncSession,
-    config: EbookSearchConfig,
-    mocker: MockerFixture,
-) -> None:
-    """Old one-token or single-use candidate rows should not be judged or promoted."""
-    source = await add_source(session)
-    session.add(
-        EbookChunk(
-            id=1,
-            source_id=source.id,
-            chapter_id=None,
-            chunk_index=0,
-            text="Damion Damion rare phrase",
-            token_start=0,
-            token_count=4,
-            page_label=None,
-            content_sha256="j" * 64,
-            search_text="Damion Damion rare phrase",
-        )
-    )
-    session.add_all(
-        [
-            EbookCandidatePhrase(
-                book_id=source.id,
-                series_id=None,
-                phrase_text="Damion",
-                phrase_norm="damion",
-                token_count=1,
-                source_capitalized=True,
-                raw_count=2,
-                chapter_count=1,
-                candidate_score=10.0,
-                llm_judged=False,
-            ),
-            EbookCandidatePhrase(
-                book_id=source.id,
-                series_id=None,
-                phrase_text="rare phrase",
-                phrase_norm="rare phrase",
-                token_count=2,
-                source_raw_ngram=True,
-                raw_count=1,
-                chapter_count=1,
-                candidate_score=9.0,
-                llm_judged=False,
-            ),
-        ]
-    )
-    await session.commit()
-    judge_mock = mocker.patch("python.ebook_search.protected_phrases.judge_ngrams.judge_candidate_async")
-    build_config = config.model_copy(
-        update={
-            "phrase_judge_book_workers": 1,
-            "phrase_judge_phrase_workers": 1,
-        }
-    )
-
-    result = await judge_candidate_phrases_for_books(engine, build_config)
-
-    assert result.books_judged == 0
-    assert result.candidates_judged == 0
-    assert result.protected_phrases == 0
-    judge_mock.assert_not_called()
-    assert await session.scalar(select(EbookProtectedPhrase)) is None
-
-
-async def test_prepare_book_judgment_skips_stored_junk_and_rescores_stale_rows(
-    engine: AsyncEngine,
-    session: AsyncSession,
-    config: EbookSearchConfig,
-) -> None:
-    """Judgment selection should drop junk rows and rank by fresh scores, not stored ones."""
-    source = await add_source(session)
-    session.add(
-        EbookChunk(
-            id=1,
-            source_id=source.id,
-            chapter_id=None,
-            chunk_index=0,
-            text="lock in lock in lock in",
-            token_start=0,
-            token_count=6,
-            page_label=None,
-            content_sha256="k" * 64,
-            search_text="lock in lock in lock in",
-        )
-    )
-    session.add_all(
-        [
-            EbookCandidatePhrase(
-                book_id=source.id,
-                series_id=None,
-                phrase_text="vann said",
-                phrase_norm="vann said",
-                token_count=2,
-                source_raw_ngram=True,
-                raw_count=50,
-                chapter_count=10,
-                candidate_score=10.0,
-                llm_judged=False,
-            ),
-            EbookCandidatePhrase(
-                book_id=source.id,
-                series_id=None,
-                phrase_text="boring pair",
-                phrase_norm="boring pair",
-                token_count=2,
-                source_raw_ngram=True,
-                raw_count=50,
-                chapter_count=10,
-                candidate_score=9.0,
-                llm_judged=False,
-            ),
-            EbookCandidatePhrase(
-                book_id=source.id,
-                series_id=None,
-                phrase_text="lock in",
-                phrase_norm="lock in",
-                token_count=2,
-                source_capitalized=True,
-                source_yake=True,
-                raw_count=3,
-                chapter_count=2,
-                candidate_score=1.0,
-                llm_judged=False,
-            ),
-        ]
-    )
-    await session.commit()
-    build_config = config.model_copy(
-        update={
-            "protected_phrase_llm_candidates_per_book": 2,
-            "phrase_min_tokens": 2,
-            "phrase_max_tokens": 2,
-        }
-    )
-
-    prepared = await prepare_book_judgment(engine, source.id, build_config)
-
-    assert prepared is not None
-    work_items, target_remaining = prepared
-    judged_norms = [candidate.phrase_norm for _, candidate in work_items]
-    assert judged_norms == ["lock in", "boring pair"]
-    assert target_remaining == build_config.phrase_target_protected_per_book
-    fresh_scores = [candidate.candidate_score for _, candidate in work_items]
-    assert fresh_scores == sorted(fresh_scores, reverse=True)
-    assert fresh_scores[0] > 1.0
-
-
-async def test_recalculate_candidate_phrases_for_book_removes_old_phrase_data(
-    session: AsyncSession,
-    config: EbookSearchConfig,
-) -> None:
-    """Book-level recalculation should clear stale candidates, protected phrases, aliases, and mentions."""
-    source = await add_source(session)
-    session.add(
-        EbookChunk(
-            id=1,
-            source_id=source.id,
-            chapter_id=None,
-            chunk_index=0,
-            text="new phrase new phrase new phrase",
-            token_start=0,
-            token_count=6,
-            page_label=None,
-            content_sha256="h" * 64,
-            search_text="new phrase new phrase new phrase",
-        )
-    )
-    old_candidate = EbookCandidatePhrase(
-        book_id=source.id,
-        series_id=None,
-        phrase_text="old phrase",
-        phrase_norm="old phrase",
-        token_count=2,
-        source_raw_ngram=True,
-        raw_count=1,
-        chapter_count=1,
-        candidate_score=1.0,
-        llm_judged=False,
-    )
-    session.add(old_candidate)
-    await session.flush()
-    old_phrase = await add_phrase(session, source.id, phrase_text="old phrase", phrase_norm="old phrase")
-    old_phrase.source_candidate_id = old_candidate.id
-    session.add(EbookPhraseAlias(phrase_id=old_phrase.id, alias_text="old alias", alias_norm="old alias"))
-    session.add(
-        EbookChunkPhraseMention(
-            chunk_id=1,
-            phrase_id=old_phrase.id,
-            book_id=source.id,
-            series_id=None,
-            start_char=0,
-            end_char=10,
-        )
-    )
-    await session.commit()
-    build_config = config.model_copy(
-        update={
-            "protected_phrase_max_candidates_per_book": 1,
-            "phrase_min_tokens": 2,
-            "phrase_max_tokens": 2,
-        }
-    )
-
-    result = await recalculate_candidate_phrases_for_book(session, source, build_config)
-
-    candidates = list(await session.scalars(select(EbookCandidatePhrase)))
-    assert result.deleted_candidates == 1
-    assert result.deleted_protected_phrases == 1
-    assert result.deleted_aliases == 1
-    assert result.deleted_mentions == 1
-    assert result.candidate_phrases == 1
-    assert [candidate.phrase_norm for candidate in candidates] == ["new phrase"]
-    assert await session.scalar(select(EbookProtectedPhrase)) is None
-    assert await session.scalar(select(EbookPhraseAlias)) is None
-    assert await session.scalar(select(EbookChunkPhraseMention)) is None
-
-
-async def test_corpus_phrase_stats_counts_phrases_and_book_coverage(session: AsyncSession) -> None:
-    """Corpus stats should count phrases plus how many books are generated and fully judged."""
-    unjudged_book = await add_source(session)
-    judged_book = await add_source(session, file_path="/library/judged.epub", file_sha256="b" * 64)
-    mixed_book = await add_source(session, file_path="/library/mixed.epub", file_sha256="c" * 64)
-    await add_source(session, file_path="/library/empty.epub", file_sha256="d" * 64)
-    await add_candidate(session, unjudged_book.id, phrase_norm="rare phrase", llm_judged=False)
-    await add_candidate(session, judged_book.id, phrase_norm="lock in", llm_judged=True)
-    await add_candidate(session, mixed_book.id, phrase_norm="haden's syndrome", llm_judged=True)
-    await add_candidate(session, mixed_book.id, phrase_norm="boston bays", llm_judged=False)
-    await add_phrase(session, judged_book.id, phrase_text="Lock In", phrase_norm="lock in")
-
-    stats = await corpus_phrase_stats(session)
-
-    assert stats.total_books == 4
-    assert stats.books_with_candidates == 3
-    assert stats.books_fully_judged == 1
-    assert stats.candidate_phrases == 4
-    assert stats.judged_candidates == 2
-    assert stats.unjudged_candidates == 2
-    assert stats.protected_phrases == 1
-
-
-async def test_book_ids_pending_first_judgment_returns_only_never_judged_books(session: AsyncSession) -> None:
-    """Only books whose candidates are all unjudged should be pending a first judgment."""
-    unjudged_book = await add_source(session)
-    judged_book = await add_source(session, file_path="/library/judged.epub", file_sha256="b" * 64)
-    mixed_book = await add_source(session, file_path="/library/mixed.epub", file_sha256="c" * 64)
-    await add_candidate(session, unjudged_book.id, phrase_norm="rare phrase", llm_judged=False)
-    await add_candidate(session, judged_book.id, phrase_norm="lock in", llm_judged=True)
-    await add_candidate(session, mixed_book.id, phrase_norm="haden's syndrome", llm_judged=True)
-    await add_candidate(session, mixed_book.id, phrase_norm="boston bays", llm_judged=False)
-
-    assert await book_ids_pending_first_judgment(session) == [unjudged_book.id]
-
-
-async def add_candidate(
-    session: AsyncSession,
-    book_id: int,
-    *,
-    phrase_norm: str,
-    llm_judged: bool,
-) -> EbookCandidatePhrase:
-    """Add a minimal candidate phrase row."""
-    candidate = EbookCandidatePhrase(
-        book_id=book_id,
-        series_id=None,
-        phrase_text=phrase_norm,
-        phrase_norm=phrase_norm,
-        token_count=len(phrase_norm.split()),
-        source_raw_ngram=True,
-        raw_count=3,
-        chapter_count=2,
-        candidate_score=5.0,
-        llm_judged=llm_judged,
-    )
-    session.add(candidate)
-    await session.flush()
-    return candidate
-
-
-async def add_source(
-    session: AsyncSession,
-    *,
-    file_path: str = "/library/book.epub",
-    file_sha256: str = "a" * 64,
-) -> EbookSource:
-    """Add a minimal ebook source."""
-    source = EbookSource(
-        title="Book",
-        author="Author",
-        language=None,
-        publisher=None,
-        identifier=None,
-        file_path=file_path,
-        file_sha256=file_sha256,
-        file_mtime=datetime.now(tz=UTC),
-        file_size=10,
-    )
-    session.add(source)
-    await session.flush()
-    return source
-
-
-async def add_phrase(
-    session: AsyncSession,
-    book_id: int,
-    *,
-    phrase_text: str,
-    phrase_norm: str,
-) -> EbookProtectedPhrase:
-    """Add a protected phrase row."""
-    phrase = EbookProtectedPhrase(
-        book_id=book_id,
-        series_id=None,
-        phrase_text=phrase_text,
-        phrase_norm=phrase_norm,
-        canonical_id="condition:lock_in",
-        phrase_type="fictional_condition",
-        token_count=len(phrase_norm.split()),
-        confidence=0.91,
-        importance=0.85,
-        allow_nested=False,
-        suppress_children=True,
-        source_candidate_id=None,
-    )
-    session.add(phrase)
-    await session.flush()
-    return phrase
-
-
-def hydrated_match(
-    *,
-    phrase_id: int,
-    phrase_text: str,
-    canonical_id: str,
-    start_token: int,
-    end_token: int,
-    importance: float = 0.8,
-    allow_nested: bool = False,
-    suppress_children: bool = True,
-) -> HydratedPhraseMatch:
-    """Build a hydrated match for overlap tests."""
-    return HydratedPhraseMatch(
-        phrase_id=phrase_id,
-        matched_norm=phrase_text,
-        phrase_text=phrase_text,
-        phrase_norm=phrase_text,
-        canonical_id=canonical_id,
-        phrase_type=None,
-        token_count=end_token - start_token,
-        confidence=0.9,
-        importance=importance,
-        allow_nested=allow_nested,
-        suppress_children=suppress_children,
-        start_token=start_token,
-        end_token=end_token,
-    )

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):
@@ -40,7 +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)
+    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]
@@ -48,26 +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_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.query_phrase_matches")
-    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, 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()

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()
 
@@ -534,128 +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_add_missing_phrases_generates_only_missing_books(mocker: MockerFixture) -> None:
-    captured: dict[str, object] = {}
-
-    def fake_generate(_session, _config, *, only_missing=False):
-        captured["only_missing"] = only_missing
-        return PhraseCandidateGenerationResult(books_seen=3, books_built=2, candidate_phrases=42)
-
-    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-missing")
-
-    assert response.status_code == 200
-    assert captured["only_missing"] is True
-    assert "42 candidates stored" in response.text
-
-
-def test_ui_regenerate_all_phrases_generates_every_book(mocker: MockerFixture) -> None:
-    captured: dict[str, object] = {}
-
-    def fake_generate(_session, _config, *, only_missing=False):
-        captured["only_missing"] = only_missing
-        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 captured["only_missing"] is False
-    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/app_image_path.nix

@@ -1,5 +0,0 @@
-{
-  home.sessionPath = [
-    "/home/richie/app_images/"
-  ];
-}

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

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

users/richie/systems/rhapsody-in-green.nix

@@ -1,6 +1,5 @@
 {
   imports = [
-    ../home/app_image_path.nix
     ../home/global.nix
     ../home/gui
   ];