chore: update flake.lock

.gitignore

@@ -172,4 +172,3 @@ frontend/node_modules/
 
 # data from testing llms
 data/*
-.ebook_search_bm25

AGENTS.md

@@ -0,0 +1,12 @@
+## Dev environment tips
+
+- use treefmt to format all files
+- make python code ruff compliant
+- use pytest to test python code
+- always use the minimum amount of complexity
+- if judgment calls are easy to reverse make them. if not ask me first
+- Match existing code style.
+- Use builtin helpers getenv() over os.environ.get.
+- Prefer single-purpose functions over “do everything” helpers.
+- Avoid compatibility branches like PG_USER and POSTGRESQL_URL unless requested.
+- Keep helpers only if reused or they simplify the code otherwise inline.

flake.lock

@@ -8,11 +8,11 @@
       },
       "locked": {
         "dir": "pkgs/firefox-addons",
-        "lastModified": 1781150628,
+        "lastModified": 1781270820,
-        "narHash": "sha256-b4mp8l3qWuSCyYYo9HSngDtcB3PpecYiOXjULrjwwlw=",
+        "narHash": "sha256-KuDdN/p4UtqUb1wnjo8a/YougmRXEaKvUoGHkYnuAq0=",
         "owner": "rycee",
         "repo": "nur-expressions",
-        "rev": "753319310f4673a2dabbfab87482187b40bf9bac",
+        "rev": "5f4259c0c832a93e13c5ec481d3318ce1394a8f9",
         "type": "gitlab"
       },
       "original": {
@@ -29,11 +29,11 @@
         ]
       },
       "locked": {
-        "lastModified": 1781189114,
+        "lastModified": 1781305496,
-        "narHash": "sha256-5inaamLgUMWy+MOBE9ChF9QAF1o/74LFuHkI0W/9rqc=",
+        "narHash": "sha256-g8Vv4Qfc7n+lgov97REu3X6BeJtvYY0hlSUZR1GrGQQ=",
         "owner": "nix-community",
         "repo": "home-manager",
-        "rev": "486595d2cf49cfcd649b58a284fa11ac0e34da22",
+        "rev": "c87a39aa979acc4848016d2220c6238390d84779",
         "type": "github"
       },
       "original": {
@@ -76,11 +76,11 @@
     },
     "nixpkgs-master": {
       "locked": {
-        "lastModified": 1781229721,
+        "lastModified": 1781306963,
-        "narHash": "sha256-ORvqDbb/LYxiJljGIejapjkc/kJbVote2N1WSb9W45I=",
+        "narHash": "sha256-qWZ+XEwsf8pN4DnYyyLbmAyj1a74gOO/VNCff44MuB4=",
         "owner": "nixos",
         "repo": "nixpkgs",
-        "rev": "173d0ad7a974f8543a9ab01d2271b2e290341b33",
+        "rev": "1eee1a273e2e3fdc3db5b1a4a66f06cd5749db69",
         "type": "github"
       },
       "original": {

frontend/.gitignore

@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?

overlays/default.nix

@@ -17,41 +17,15 @@
 
   python-env = final: _prev: {
     my_python = final.python314.withPackages (
-      ps:
+      ps: with ps; [
-      let
-        bm25s = ps.buildPythonPackage rec {
-          pname = "bm25s";
-          version = "0.3.9";
-          pyproject = true;
-
-          src = final.fetchPypi {
-            inherit pname version;
-            hash = "sha256-iVxnnZUrfeg1XttfPhpiCh4vKU0dQrkZvwghzOLi9Zc=";
-          };
-
-          build-system = [ ps.setuptools ];
-          dependencies = with ps; [
-            numpy
-            scipy
-          ];
-
-          pythonImportsCheck = [ "bm25s" ];
-        };
-      in
-      with ps;
-      [
         alembic
         apprise
         apscheduler
-        beautifulsoup4
-        ebooklib
         fastapi
         fastapi-cli
         httpx
         mypy
-        numpy
         orjson
-        pgvector
         polars
         psycopg
         pydantic
@@ -65,7 +39,6 @@
         scalene
         sqlalchemy
         sqlalchemy
-        bm25s
         tenacity
         textual
         tiktoken

pyproject.toml

@@ -84,6 +84,9 @@ lint.ignore = [
 "python/alembic/**" = [
     "INP001", # (perm) this creates LSP issues for alembic
 ]
+"python/signal_bot/**" = [
+    "D107", # (perm) class docstrings cover __init__
+]
 
 [tool.ruff.lint.pydocstyle]
 convention = "google"

python/alembic/data_science_dev/versions/2026_03_24-adding_failedingestion_2f43120e3ffc.py

@@ -0,0 +1,50 @@
+"""adding FailedIngestion.
+
+Revision ID: 2f43120e3ffc
+Revises: f99be864fe69
+Create Date: 2026-03-24 23:46:17.277897
+
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import sqlalchemy as sa
+from alembic import op
+
+from python.orm import DataScienceDevBase
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+# revision identifiers, used by Alembic.
+revision: str = "2f43120e3ffc"
+down_revision: str | None = "f99be864fe69"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+schema = DataScienceDevBase.schema_name
+
+
+def upgrade() -> None:
+    """Upgrade."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table(
+        "failed_ingestion",
+        sa.Column("raw_line", sa.Text(), nullable=False),
+        sa.Column("error", sa.Text(), 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.PrimaryKeyConstraint("id", name=op.f("pk_failed_ingestion")),
+        schema=schema,
+    )
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_table("failed_ingestion", schema=schema)
+    # ### end Alembic commands ###

python/alembic/data_science_dev/versions/2026_03_25-attach_partitions_to_posts.py

@@ -0,0 +1,72 @@
+"""Attach all partition tables to the posts parent table.
+
+Alembic autogenerate creates partition tables as standalone tables but does not
+emit the ALTER TABLE ... ATTACH PARTITION statements needed for PostgreSQL to
+route inserts to the correct partition.
+
+Revision ID: a1b2c3d4e5f6
+Revises: 605b1794838f
+Create Date: 2026-03-25 10:00:00.000000
+
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from alembic import op
+from sqlalchemy import text
+
+from python.orm import DataScienceDevBase
+from python.orm.data_science_dev.posts.partitions import (
+    PARTITION_END_YEAR,
+    PARTITION_START_YEAR,
+    iso_weeks_in_year,
+    week_bounds,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+# revision identifiers, used by Alembic.
+revision: str = "a1b2c3d4e5f6"
+down_revision: str | None = "605b1794838f"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+schema = DataScienceDevBase.schema_name
+
+ALREADY_ATTACHED_QUERY = text("""
+    SELECT inhrelid::regclass::text
+    FROM pg_inherits
+    WHERE inhparent = :parent::regclass
+""")
+
+
+def upgrade() -> None:
+    """Attach all weekly partition tables to the posts parent table."""
+    connection = op.get_bind()
+    already_attached = {row[0] for row in connection.execute(ALREADY_ATTACHED_QUERY, {"parent": f"{schema}.posts"})}
+
+    for year in range(PARTITION_START_YEAR, PARTITION_END_YEAR + 1):
+        for week in range(1, iso_weeks_in_year(year) + 1):
+            table_name = f"posts_{year}_{week:02d}"
+            qualified_name = f"{schema}.{table_name}"
+            if qualified_name in already_attached:
+                continue
+            start, end = week_bounds(year, week)
+            start_str = start.strftime("%Y-%m-%d %H:%M:%S")
+            end_str = end.strftime("%Y-%m-%d %H:%M:%S")
+            op.execute(
+                f"ALTER TABLE {schema}.posts "
+                f"ATTACH PARTITION {qualified_name} "
+                f"FOR VALUES FROM ('{start_str}') TO ('{end_str}')"
+            )
+
+
+def downgrade() -> None:
+    """Detach all weekly partition tables from the posts parent table."""
+    for year in range(PARTITION_START_YEAR, PARTITION_END_YEAR + 1):
+        for week in range(1, iso_weeks_in_year(year) + 1):
+            table_name = f"posts_{year}_{week:02d}"
+            op.execute(f"ALTER TABLE {schema}.posts DETACH PARTITION {schema}.{table_name}")

python/alembic/data_science_dev/versions/2026_03_27-adding_congress_data_83bfc8af92d8.py

@@ -0,0 +1,153 @@
+"""adding congress data.
+
+Revision ID: 83bfc8af92d8
+Revises: a1b2c3d4e5f6
+Create Date: 2026-03-27 10:43:02.324510
+
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import sqlalchemy as sa
+from alembic import op
+
+from python.orm import DataScienceDevBase
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+# revision identifiers, used by Alembic.
+revision: str = "83bfc8af92d8"
+down_revision: str | None = "a1b2c3d4e5f6"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+schema = DataScienceDevBase.schema_name
+
+
+def upgrade() -> None:
+    """Upgrade."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table(
+        "bill",
+        sa.Column("congress", sa.Integer(), nullable=False),
+        sa.Column("bill_type", sa.String(), nullable=False),
+        sa.Column("number", sa.Integer(), nullable=False),
+        sa.Column("title", sa.String(), nullable=True),
+        sa.Column("title_short", sa.String(), nullable=True),
+        sa.Column("official_title", sa.String(), nullable=True),
+        sa.Column("status", sa.String(), nullable=True),
+        sa.Column("status_at", sa.Date(), nullable=True),
+        sa.Column("sponsor_bioguide_id", sa.String(), nullable=True),
+        sa.Column("subjects_top_term", sa.String(), 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.PrimaryKeyConstraint("id", name=op.f("pk_bill")),
+        sa.UniqueConstraint("congress", "bill_type", "number", name="uq_bill_congress_type_number"),
+        schema=schema,
+    )
+    op.create_index("ix_bill_congress", "bill", ["congress"], unique=False, schema=schema)
+    op.create_table(
+        "legislator",
+        sa.Column("bioguide_id", sa.Text(), nullable=False),
+        sa.Column("thomas_id", sa.String(), nullable=True),
+        sa.Column("lis_id", sa.String(), nullable=True),
+        sa.Column("govtrack_id", sa.Integer(), nullable=True),
+        sa.Column("opensecrets_id", sa.String(), nullable=True),
+        sa.Column("fec_ids", sa.String(), nullable=True),
+        sa.Column("first_name", sa.String(), nullable=False),
+        sa.Column("last_name", sa.String(), nullable=False),
+        sa.Column("official_full_name", sa.String(), nullable=True),
+        sa.Column("nickname", sa.String(), nullable=True),
+        sa.Column("birthday", sa.Date(), nullable=True),
+        sa.Column("gender", sa.String(), nullable=True),
+        sa.Column("current_party", sa.String(), nullable=True),
+        sa.Column("current_state", sa.String(), nullable=True),
+        sa.Column("current_district", sa.Integer(), nullable=True),
+        sa.Column("current_chamber", sa.String(), 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.PrimaryKeyConstraint("id", name=op.f("pk_legislator")),
+        schema=schema,
+    )
+    op.create_index(op.f("ix_legislator_bioguide_id"), "legislator", ["bioguide_id"], unique=True, schema=schema)
+    op.create_table(
+        "bill_text",
+        sa.Column("bill_id", sa.Integer(), nullable=False),
+        sa.Column("version_code", sa.String(), nullable=False),
+        sa.Column("version_name", sa.String(), nullable=True),
+        sa.Column("text_content", sa.String(), nullable=True),
+        sa.Column("date", sa.Date(), 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(
+            ["bill_id"], [f"{schema}.bill.id"], name=op.f("fk_bill_text_bill_id_bill"), ondelete="CASCADE"
+        ),
+        sa.PrimaryKeyConstraint("id", name=op.f("pk_bill_text")),
+        sa.UniqueConstraint("bill_id", "version_code", name="uq_bill_text_bill_id_version_code"),
+        schema=schema,
+    )
+    op.create_table(
+        "vote",
+        sa.Column("congress", sa.Integer(), nullable=False),
+        sa.Column("chamber", sa.String(), nullable=False),
+        sa.Column("session", sa.Integer(), nullable=False),
+        sa.Column("number", sa.Integer(), nullable=False),
+        sa.Column("vote_type", sa.String(), nullable=True),
+        sa.Column("question", sa.String(), nullable=True),
+        sa.Column("result", sa.String(), nullable=True),
+        sa.Column("result_text", sa.String(), nullable=True),
+        sa.Column("vote_date", sa.Date(), nullable=False),
+        sa.Column("yea_count", sa.Integer(), nullable=True),
+        sa.Column("nay_count", sa.Integer(), nullable=True),
+        sa.Column("not_voting_count", sa.Integer(), nullable=True),
+        sa.Column("present_count", sa.Integer(), nullable=True),
+        sa.Column("bill_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(["bill_id"], [f"{schema}.bill.id"], name=op.f("fk_vote_bill_id_bill")),
+        sa.PrimaryKeyConstraint("id", name=op.f("pk_vote")),
+        sa.UniqueConstraint("congress", "chamber", "session", "number", name="uq_vote_congress_chamber_session_number"),
+        schema=schema,
+    )
+    op.create_index("ix_vote_congress_chamber", "vote", ["congress", "chamber"], unique=False, schema=schema)
+    op.create_index("ix_vote_date", "vote", ["vote_date"], unique=False, schema=schema)
+    op.create_table(
+        "vote_record",
+        sa.Column("vote_id", sa.Integer(), nullable=False),
+        sa.Column("legislator_id", sa.Integer(), nullable=False),
+        sa.Column("position", sa.String(), nullable=False),
+        sa.ForeignKeyConstraint(
+            ["legislator_id"],
+            [f"{schema}.legislator.id"],
+            name=op.f("fk_vote_record_legislator_id_legislator"),
+            ondelete="CASCADE",
+        ),
+        sa.ForeignKeyConstraint(
+            ["vote_id"], [f"{schema}.vote.id"], name=op.f("fk_vote_record_vote_id_vote"), ondelete="CASCADE"
+        ),
+        sa.PrimaryKeyConstraint("vote_id", "legislator_id", name=op.f("pk_vote_record")),
+        schema=schema,
+    )
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_table("vote_record", schema=schema)
+    op.drop_index("ix_vote_date", table_name="vote", schema=schema)
+    op.drop_index("ix_vote_congress_chamber", table_name="vote", schema=schema)
+    op.drop_table("vote", schema=schema)
+    op.drop_table("bill_text", schema=schema)
+    op.drop_index(op.f("ix_legislator_bioguide_id"), table_name="legislator", schema=schema)
+    op.drop_table("legislator", schema=schema)
+    op.drop_index("ix_bill_congress", table_name="bill", schema=schema)
+    op.drop_table("bill", schema=schema)
+    # ### end Alembic commands ###

python/alembic/data_science_dev/versions/2026_03_29-adding_legislatorsocialmedia_5cd7eee3549d.py

@@ -0,0 +1,58 @@
+"""adding LegislatorSocialMedia.
+
+Revision ID: 5cd7eee3549d
+Revises: 83bfc8af92d8
+Create Date: 2026-03-29 11:53:44.224799
+
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import sqlalchemy as sa
+from alembic import op
+
+from python.orm import DataScienceDevBase
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+# revision identifiers, used by Alembic.
+revision: str = "5cd7eee3549d"
+down_revision: str | None = "83bfc8af92d8"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+schema = DataScienceDevBase.schema_name
+
+
+def upgrade() -> None:
+    """Upgrade."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table(
+        "legislator_social_media",
+        sa.Column("legislator_id", sa.Integer(), nullable=False),
+        sa.Column("platform", sa.String(), nullable=False),
+        sa.Column("account_name", sa.String(), nullable=False),
+        sa.Column("url", sa.String(), nullable=True),
+        sa.Column("source", sa.String(), 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(
+            ["legislator_id"],
+            [f"{schema}.legislator.id"],
+            name=op.f("fk_legislator_social_media_legislator_id_legislator"),
+        ),
+        sa.PrimaryKeyConstraint("id", name=op.f("pk_legislator_social_media")),
+        schema=schema,
+    )
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_table("legislator_social_media", schema=schema)
+    # ### end Alembic commands ###

python/alembic/richie/versions/2026_06_03-adding_audiobook_libreary_metadata_d7864d1ffc17.py

@@ -1,93 +0,0 @@
-"""adding audiobook libreary metadata.
-
-Revision ID: d7864d1ffc17
-Revises: c8a794340928
-Create Date: 2026-06-03 20:24:09.200837
-
-"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-import sqlalchemy as sa
-from alembic import op
-
-from python.orm import RichieBase
-
-if TYPE_CHECKING:
-    from collections.abc import Sequence
-
-# revision identifiers, used by Alembic.
-revision: str = "d7864d1ffc17"
-down_revision: str | None = "c8a794340928"
-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(
-        "audiobook_author",
-        sa.Column("name", sa.String(), 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.PrimaryKeyConstraint("id", name=op.f("pk_audiobook_author")),
-        sa.UniqueConstraint("name", name=op.f("uq_audiobook_author_name")),
-        schema=schema,
-    )
-    op.create_table(
-        "audiobook_series",
-        sa.Column("name", sa.String(), nullable=False),
-        sa.Column("author_id", sa.Integer(), 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(
-            ["author_id"],
-            [f"{schema}.audiobook_author.id"],
-            name=op.f("fk_audiobook_series_author_id_audiobook_author"),
-            ondelete="CASCADE",
-        ),
-        sa.PrimaryKeyConstraint("id", name=op.f("pk_audiobook_series")),
-        sa.UniqueConstraint("author_id", "name", name=op.f("uq_audiobook_series_author_id")),
-        schema=schema,
-    )
-    op.create_table(
-        "audiobook",
-        sa.Column("title", sa.String(), nullable=False),
-        sa.Column("author_id", sa.Integer(), nullable=False),
-        sa.Column("series_id", sa.Integer(), nullable=True),
-        sa.Column("series_index", sa.Integer(), 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(
-            ["author_id"],
-            [f"{schema}.audiobook_author.id"],
-            name=op.f("fk_audiobook_author_id_audiobook_author"),
-            ondelete="CASCADE",
-        ),
-        sa.ForeignKeyConstraint(
-            ["series_id"],
-            [f"{schema}.audiobook_series.id"],
-            name=op.f("fk_audiobook_series_id_audiobook_series"),
-            ondelete="SET NULL",
-        ),
-        sa.PrimaryKeyConstraint("id", name=op.f("pk_audiobook")),
-        schema=schema,
-    )
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    """Downgrade."""
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_table("audiobook", schema=schema)
-    op.drop_table("audiobook_series", schema=schema)
-    op.drop_table("audiobook_author", schema=schema)
-    # ### end Alembic commands ###

python/alembic/richie/versions/2026_06_10-add_ebook_search_tables_2db132cace1a.py

@@ -1,200 +0,0 @@
-"""add ebook search tables.
-
-Revision ID: 2db132cace1a
-Revises: b3c60cc5beb5
-Create Date: 2026-06-10 22:10:54.379159
-
-"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-import pgvector
-import sqlalchemy as sa
-from alembic import op
-
-from python.orm import RichieBase
-
-if TYPE_CHECKING:
-    from collections.abc import Sequence
-
-# revision identifiers, used by Alembic.
-revision: str = "2db132cace1a"
-down_revision: str | None = "b3c60cc5beb5"
-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(
-        "ebook_embedding_model",
-        sa.Column("name", sa.String(), nullable=False),
-        sa.Column("dimension", sa.Integer(), nullable=False),
-        sa.Column("is_default", sa.Boolean(), 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.PrimaryKeyConstraint("id", name=op.f("pk_ebook_embedding_model")),
-        sa.UniqueConstraint("name", name=op.f("uq_ebook_embedding_model_name")),
-        schema=schema,
-    )
-    op.create_table(
-        "ebook_source",
-        sa.Column("title", sa.String(), nullable=False),
-        sa.Column("author", sa.String(), nullable=True),
-        sa.Column("language", sa.String(), nullable=True),
-        sa.Column("publisher", sa.String(), nullable=True),
-        sa.Column("identifier", sa.String(), nullable=True),
-        sa.Column("file_path", sa.String(), nullable=False),
-        sa.Column("file_sha256", sa.String(length=64), nullable=False),
-        sa.Column("file_mtime", sa.DateTime(timezone=True), nullable=False),
-        sa.Column("file_size", sa.BigInteger(), 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.PrimaryKeyConstraint("id", name=op.f("pk_ebook_source")),
-        sa.UniqueConstraint("file_path", name=op.f("uq_ebook_source_file_path")),
-        sa.UniqueConstraint("file_sha256", name=op.f("uq_ebook_source_file_sha256")),
-        schema=schema,
-    )
-    op.create_table(
-        "ebook_chapter",
-        sa.Column("source_id", sa.Integer(), nullable=False),
-        sa.Column("spine_index", sa.Integer(), nullable=False),
-        sa.Column("title", sa.String(), nullable=True),
-        sa.Column("href", sa.String(), 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(
-            ["source_id"],
-            [f"{schema}.ebook_source.id"],
-            name=op.f("fk_ebook_chapter_source_id_ebook_source"),
-            ondelete="CASCADE",
-        ),
-        sa.PrimaryKeyConstraint("id", name=op.f("pk_ebook_chapter")),
-        sa.UniqueConstraint("source_id", "spine_index", name=op.f("uq_ebook_chapter_source_id")),
-        schema=schema,
-    )
-    op.create_table(
-        "ebook_chunk",
-        sa.Column("source_id", sa.Integer(), nullable=False),
-        sa.Column("chapter_id", sa.Integer(), nullable=True),
-        sa.Column("chunk_index", sa.Integer(), nullable=False),
-        sa.Column("text", sa.String(), nullable=False),
-        sa.Column("token_start", sa.Integer(), nullable=False),
-        sa.Column("token_count", sa.Integer(), nullable=False),
-        sa.Column("page_label", sa.String(), nullable=True),
-        sa.Column("content_sha256", sa.String(length=64), nullable=False),
-        sa.Column("search_text", sa.String(), nullable=False),
-        sa.Column("id", sa.BigInteger(), 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(
-            ["chapter_id"],
-            [f"{schema}.ebook_chapter.id"],
-            name=op.f("fk_ebook_chunk_chapter_id_ebook_chapter"),
-            ondelete="SET NULL",
-        ),
-        sa.ForeignKeyConstraint(
-            ["source_id"],
-            [f"{schema}.ebook_source.id"],
-            name=op.f("fk_ebook_chunk_source_id_ebook_source"),
-            ondelete="CASCADE",
-        ),
-        sa.PrimaryKeyConstraint("id", name=op.f("pk_ebook_chunk")),
-        sa.UniqueConstraint("source_id", "chunk_index", name="uq_ebook_chunk_source_id_chunk_index"),
-        sa.UniqueConstraint("source_id", "content_sha256", name="uq_ebook_chunk_source_id_content_sha256"),
-        schema=schema,
-    )
-    op.create_table(
-        "ebook_chunk_embedding_1024",
-        sa.Column("chunk_id", sa.BigInteger(), nullable=False),
-        sa.Column("model_id", sa.Integer(), nullable=False),
-        sa.Column("embedding", pgvector.sqlalchemy.vector.VECTOR(dim=1024), nullable=False),
-        sa.Column("id", sa.BigInteger(), 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(
-            ["chunk_id"],
-            [f"{schema}.ebook_chunk.id"],
-            name=op.f("fk_ebook_chunk_embedding_1024_chunk_id_ebook_chunk"),
-            ondelete="CASCADE",
-        ),
-        sa.ForeignKeyConstraint(
-            ["model_id"],
-            [f"{schema}.ebook_embedding_model.id"],
-            name=op.f("fk_ebook_chunk_embedding_1024_model_id_ebook_embedding_model"),
-            ondelete="CASCADE",
-        ),
-        sa.PrimaryKeyConstraint("id", name=op.f("pk_ebook_chunk_embedding_1024")),
-        sa.UniqueConstraint("chunk_id", "model_id", name=op.f("uq_ebook_chunk_embedding_1024_chunk_id")),
-        schema=schema,
-    )
-    op.create_table(
-        "ebook_chunk_embedding_2560",
-        sa.Column("chunk_id", sa.BigInteger(), nullable=False),
-        sa.Column("model_id", sa.Integer(), nullable=False),
-        sa.Column("embedding", pgvector.sqlalchemy.vector.VECTOR(dim=2560), nullable=False),
-        sa.Column("id", sa.BigInteger(), 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(
-            ["chunk_id"],
-            [f"{schema}.ebook_chunk.id"],
-            name=op.f("fk_ebook_chunk_embedding_2560_chunk_id_ebook_chunk"),
-            ondelete="CASCADE",
-        ),
-        sa.ForeignKeyConstraint(
-            ["model_id"],
-            [f"{schema}.ebook_embedding_model.id"],
-            name=op.f("fk_ebook_chunk_embedding_2560_model_id_ebook_embedding_model"),
-            ondelete="CASCADE",
-        ),
-        sa.PrimaryKeyConstraint("id", name=op.f("pk_ebook_chunk_embedding_2560")),
-        sa.UniqueConstraint("chunk_id", "model_id", name=op.f("uq_ebook_chunk_embedding_2560_chunk_id")),
-        schema=schema,
-    )
-    op.create_table(
-        "ebook_chunk_embedding_4096",
-        sa.Column("chunk_id", sa.BigInteger(), nullable=False),
-        sa.Column("model_id", sa.Integer(), nullable=False),
-        sa.Column("embedding", pgvector.sqlalchemy.vector.VECTOR(dim=4096), nullable=False),
-        sa.Column("id", sa.BigInteger(), 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(
-            ["chunk_id"],
-            [f"{schema}.ebook_chunk.id"],
-            name=op.f("fk_ebook_chunk_embedding_4096_chunk_id_ebook_chunk"),
-            ondelete="CASCADE",
-        ),
-        sa.ForeignKeyConstraint(
-            ["model_id"],
-            [f"{schema}.ebook_embedding_model.id"],
-            name=op.f("fk_ebook_chunk_embedding_4096_model_id_ebook_embedding_model"),
-            ondelete="CASCADE",
-        ),
-        sa.PrimaryKeyConstraint("id", name=op.f("pk_ebook_chunk_embedding_4096")),
-        sa.UniqueConstraint("chunk_id", "model_id", name=op.f("uq_ebook_chunk_embedding_4096_chunk_id")),
-        schema=schema,
-    )
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    """Downgrade."""
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_table("ebook_chunk_embedding_4096", schema=schema)
-    op.drop_table("ebook_chunk_embedding_2560", schema=schema)
-    op.drop_table("ebook_chunk_embedding_1024", schema=schema)
-    op.drop_table("ebook_chunk", schema=schema)
-    op.drop_table("ebook_chapter", schema=schema)
-    op.drop_table("ebook_source", schema=schema)
-    op.drop_table("ebook_embedding_model", schema=schema)
-    # ### end Alembic commands ###

python/alembic/richie/versions/2026_06_10-updated_series_index_to_float_and_added__b3c60cc5beb5.py

@@ -1,63 +0,0 @@
-"""updated series_index to float and added UniqueConstraint to audiobook and audiobook_author.
-
-Revision ID: b3c60cc5beb5
-Revises: d7864d1ffc17
-Create Date: 2026-06-10 20:02:43.073725
-
-"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-import sqlalchemy as sa
-from alembic import op
-
-from python.orm import RichieBase
-
-if TYPE_CHECKING:
-    from collections.abc import Sequence
-
-# revision identifiers, used by Alembic.
-revision: str = "b3c60cc5beb5"
-down_revision: str | None = "d7864d1ffc17"
-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.alter_column(
-        "audiobook",
-        "series_index",
-        existing_type=sa.INTEGER(),
-        type_=sa.Float(),
-        existing_nullable=False,
-        schema=schema,
-    )
-    op.create_unique_constraint(
-        op.f("uq_audiobook_author_id"),
-        "audiobook",
-        ["author_id", "series_id", "title"],
-        schema=schema,
-        postgresql_nulls_not_distinct=True,
-    )
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    """Downgrade."""
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_constraint(op.f("uq_audiobook_author_id"), "audiobook", schema=schema, type_="unique")
-    op.alter_column(
-        "audiobook",
-        "series_index",
-        existing_type=sa.Float(),
-        type_=sa.INTEGER(),
-        existing_nullable=False,
-        schema=schema,
-    )
-    # ### end Alembic commands ###

python/alembic/richie/versions/2026_06_13-add_1024_ebook_embedding_cosine_index_c460105682d2.py

@@ -1,54 +0,0 @@
-"""add 1024 ebook embedding cosine index.
-
-Revision ID: c460105682d2
-Revises: 2db132cace1a
-Create Date: 2026-06-13 19:53:45.680289
-
-"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from alembic import op
-
-from python.orm import RichieBase
-
-if TYPE_CHECKING:
-    from collections.abc import Sequence
-
-# revision identifiers, used by Alembic.
-revision: str = "c460105682d2"
-down_revision: str | None = "2db132cace1a"
-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_index(
-        "ix_ebook_chunk_embedding_1024_embedding_cosine",
-        "ebook_chunk_embedding_1024",
-        ["embedding"],
-        unique=False,
-        schema=schema,
-        postgresql_using="hnsw",
-        postgresql_ops={"embedding": "vector_cosine_ops"},
-    )
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    """Downgrade."""
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_index(
-        "ix_ebook_chunk_embedding_1024_embedding_cosine",
-        table_name="ebook_chunk_embedding_1024",
-        schema=schema,
-        postgresql_using="hnsw",
-        postgresql_ops={"embedding": "vector_cosine_ops"},
-    )
-    # ### end Alembic commands ###

python/alembic/signal_bot/versions/2026_03_17-seprating_signal_bot_database_6eaf696e07a5.py

@@ -0,0 +1,100 @@
+"""seprating signal_bot database.
+
+Revision ID: 6eaf696e07a5
+Revises:
+Create Date: 2026-03-17 21:35:37.612672
+
+"""
+
+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 SignalBotBase
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+# revision identifiers, used by Alembic.
+revision: str = "6eaf696e07a5"
+down_revision: str | None = None
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+schema = SignalBotBase.schema_name
+
+
+def upgrade() -> None:
+    """Upgrade."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table(
+        "dead_letter_message",
+        sa.Column("source", sa.String(), nullable=False),
+        sa.Column("message", sa.Text(), nullable=False),
+        sa.Column("received_at", sa.DateTime(timezone=True), nullable=False),
+        sa.Column(
+            "status", postgresql.ENUM("UNPROCESSED", "PROCESSED", name="message_status", schema=schema), 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.PrimaryKeyConstraint("id", name=op.f("pk_dead_letter_message")),
+        schema=schema,
+    )
+    op.create_table(
+        "role",
+        sa.Column("name", sa.String(length=50), nullable=False),
+        sa.Column("id", sa.SmallInteger(), 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.PrimaryKeyConstraint("id", name=op.f("pk_role")),
+        sa.UniqueConstraint("name", name=op.f("uq_role_name")),
+        schema=schema,
+    )
+    op.create_table(
+        "signal_device",
+        sa.Column("phone_number", sa.String(length=50), nullable=False),
+        sa.Column("safety_number", sa.String(), nullable=True),
+        sa.Column(
+            "trust_level",
+            postgresql.ENUM("VERIFIED", "UNVERIFIED", "BLOCKED", name="trust_level", schema=schema),
+            nullable=False,
+        ),
+        sa.Column("last_seen", sa.DateTime(timezone=True), 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.PrimaryKeyConstraint("id", name=op.f("pk_signal_device")),
+        sa.UniqueConstraint("phone_number", name=op.f("uq_signal_device_phone_number")),
+        schema=schema,
+    )
+    op.create_table(
+        "device_role",
+        sa.Column("device_id", sa.Integer(), nullable=False),
+        sa.Column("role_id", sa.SmallInteger(), 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(
+            ["device_id"], [f"{schema}.signal_device.id"], name=op.f("fk_device_role_device_id_signal_device")
+        ),
+        sa.ForeignKeyConstraint(["role_id"], [f"{schema}.role.id"], name=op.f("fk_device_role_role_id_role")),
+        sa.PrimaryKeyConstraint("id", name=op.f("pk_device_role")),
+        sa.UniqueConstraint("device_id", "role_id", name="uq_device_role_device_role"),
+        schema=schema,
+    )
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_table("device_role", schema=schema)
+    op.drop_table("signal_device", schema=schema)
+    op.drop_table("role", schema=schema)
+    op.drop_table("dead_letter_message", schema=schema)
+    # ### end Alembic commands ###

python/alembic/signal_bot/versions/2026_03_18-test_66bdd532bcab.py

@@ -0,0 +1,72 @@
+"""test.
+
+Revision ID: 66bdd532bcab
+Revises: 6eaf696e07a5
+Create Date: 2026-03-18 19:21:14.561568
+
+"""
+
+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 SignalBotBase
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+# revision identifiers, used by Alembic.
+revision: str = "66bdd532bcab"
+down_revision: str | None = "6eaf696e07a5"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+schema = SignalBotBase.schema_name
+
+
+def upgrade() -> None:
+    """Upgrade."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.alter_column(
+        "dead_letter_message",
+        "status",
+        existing_type=postgresql.ENUM("UNPROCESSED", "PROCESSED", name="message_status", schema=schema),
+        type_=sa.Enum("UNPROCESSED", "PROCESSED", name="message_status", native_enum=False),
+        existing_nullable=False,
+        schema=schema,
+    )
+    op.alter_column(
+        "signal_device",
+        "trust_level",
+        existing_type=postgresql.ENUM("VERIFIED", "UNVERIFIED", "BLOCKED", name="trust_level", schema=schema),
+        type_=sa.Enum("VERIFIED", "UNVERIFIED", "BLOCKED", name="trust_level", native_enum=False),
+        existing_nullable=False,
+        schema=schema,
+    )
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.alter_column(
+        "signal_device",
+        "trust_level",
+        existing_type=sa.Enum("VERIFIED", "UNVERIFIED", "BLOCKED", name="trust_level", native_enum=False),
+        type_=postgresql.ENUM("VERIFIED", "UNVERIFIED", "BLOCKED", name="trust_level", schema=schema),
+        existing_nullable=False,
+        schema=schema,
+    )
+    op.alter_column(
+        "dead_letter_message",
+        "status",
+        existing_type=sa.Enum("UNPROCESSED", "PROCESSED", name="message_status", native_enum=False),
+        type_=postgresql.ENUM("UNPROCESSED", "PROCESSED", name="message_status", schema=schema),
+        existing_nullable=False,
+        schema=schema,
+    )
+    # ### end Alembic commands ###

python/api/main.py

@@ -9,9 +9,9 @@ import typer
 import uvicorn
 from fastapi import FastAPI
 
+from python.api.middleware import ZstdMiddleware
 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
 
 logger = logging.getLogger(__name__)

python/api/middleware.py

@@ -1,4 +1,4 @@
-"""Zstd response compression middleware."""
+"""Middleware for the FastAPI application."""
 
 from compression import zstd
 from starlette.middleware.base import BaseHTTPMiddleware, RequestResponseEndpoint

python/api/routers/contact.py

@@ -9,7 +9,7 @@ from pydantic import BaseModel
 from sqlalchemy import select
 from sqlalchemy.orm import selectinload
 
-from python.fastapi_tools.db import DbSession
+from python.api.dependencies import DbSession
 from python.orm.richie.contact import Contact, ContactRelationship, Need, RelationshipType
 
 TEMPLATES_DIR = Path(__file__).parent.parent / "templates"

python/api/routers/views.py

@@ -9,7 +9,7 @@ from fastapi.templating import Jinja2Templates
 from sqlalchemy import select
 from sqlalchemy.orm import Session, selectinload
 
-from python.fastapi_tools.db import DbSession
+from python.api.dependencies import DbSession
 from python.orm.richie.contact import Contact, ContactRelationship, Need, RelationshipType
 
 TEMPLATES_DIR = Path(__file__).parent.parent / "templates"

python/data_science/__init__.py

@@ -0,0 +1,3 @@
+"""Data science CLI tools."""
+
+from __future__ import annotations

python/data_science/ingest_congress.py

@@ -0,0 +1,613 @@
+"""Ingestion pipeline for loading congress data from unitedstates/congress JSON files.
+
+Loads legislators, bills, votes, vote records, and bill text into the data_science_dev database.
+Expects the parent directory to contain congress-tracker/ and congress-legislators/ as siblings.
+
+Usage:
+    ingest-congress /path/to/parent/
+    ingest-congress /path/to/parent/ --congress 118
+    ingest-congress /path/to/parent/ --congress 118 --only bills
+"""
+
+from __future__ import annotations
+
+import logging
+from pathlib import Path  # noqa: TC003 needed at runtime for typer CLI argument
+from typing import TYPE_CHECKING, Annotated
+
+import orjson
+import typer
+import yaml
+from sqlalchemy import select
+from sqlalchemy.orm import Session
+
+from python.common import configure_logger
+from python.orm.common import get_postgres_engine
+from python.orm.data_science_dev.congress import Bill, BillText, Legislator, LegislatorSocialMedia, Vote, VoteRecord
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
+    from sqlalchemy.engine import Engine
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 10_000
+
+app = typer.Typer(help="Ingest unitedstates/congress data into data_science_dev.")
+
+
+@app.command()
+def main(
+    parent_dir: Annotated[
+        Path,
+        typer.Argument(help="Parent directory containing congress-tracker/ and congress-legislators/"),
+    ],
+    congress: Annotated[int | None, typer.Option(help="Only ingest a specific congress number")] = None,
+    only: Annotated[
+        str | None,
+        typer.Option(help="Only run a specific step: legislators, social-media, bills, votes, bill-text"),
+    ] = None,
+) -> None:
+    """Ingest congress data from unitedstates/congress JSON files."""
+    configure_logger(level="INFO")
+
+    data_dir = parent_dir / "congress-tracker/congress/data/"
+    legislators_dir = parent_dir / "congress-legislators"
+
+    if not data_dir.is_dir():
+        typer.echo(f"Expected congress-tracker/ directory: {data_dir}", err=True)
+        raise typer.Exit(code=1)
+
+    if not legislators_dir.is_dir():
+        typer.echo(f"Expected congress-legislators/ directory: {legislators_dir}", err=True)
+        raise typer.Exit(code=1)
+
+    engine = get_postgres_engine(name="DATA_SCIENCE_DEV")
+
+    congress_dirs = _resolve_congress_dirs(data_dir, congress)
+    if not congress_dirs:
+        typer.echo("No congress directories found.", err=True)
+        raise typer.Exit(code=1)
+
+    logger.info("Found %d congress directories to process", len(congress_dirs))
+
+    steps: dict[str, tuple] = {
+        "legislators": (ingest_legislators, (engine, legislators_dir)),
+        "legislators-social-media": (ingest_social_media, (engine, legislators_dir)),
+        "bills": (ingest_bills, (engine, congress_dirs)),
+        "votes": (ingest_votes, (engine, congress_dirs)),
+        "bill-text": (ingest_bill_text, (engine, congress_dirs)),
+    }
+
+    if only:
+        if only not in steps:
+            typer.echo(f"Unknown step: {only}. Choose from: {', '.join(steps)}", err=True)
+            raise typer.Exit(code=1)
+        steps = {only: steps[only]}
+
+    for step_name, (step_func, step_args) in steps.items():
+        logger.info("=== Starting step: %s ===", step_name)
+        step_func(*step_args)
+        logger.info("=== Finished step: %s ===", step_name)
+
+    logger.info("ingest-congress done")
+
+
+def _resolve_congress_dirs(data_dir: Path, congress: int | None) -> list[Path]:
+    """Find congress number directories under data_dir."""
+    if congress is not None:
+        target = data_dir / str(congress)
+        return [target] if target.is_dir() else []
+    return sorted(path for path in data_dir.iterdir() if path.is_dir() and path.name.isdigit())
+
+
+def _flush_batch(session: Session, batch: list[object], label: str) -> int:
+    """Add a batch of ORM objects to the session and commit. Returns count added."""
+    if not batch:
+        return 0
+    session.add_all(batch)
+    session.commit()
+    count = len(batch)
+    logger.info("Committed %d %s", count, label)
+    batch.clear()
+    return count
+
+
+# ---------------------------------------------------------------------------
+# Legislators — loaded from congress-legislators YAML files
+# ---------------------------------------------------------------------------
+
+
+def ingest_legislators(engine: Engine, legislators_dir: Path) -> None:
+    """Load legislators from congress-legislators YAML files."""
+    legislators_data = _load_legislators_yaml(legislators_dir)
+    logger.info("Loaded %d legislators from YAML files", len(legislators_data))
+
+    with Session(engine) as session:
+        existing_legislators = {
+            legislator.bioguide_id: legislator for legislator in session.scalars(select(Legislator)).all()
+        }
+        logger.info("Found %d existing legislators in DB", len(existing_legislators))
+
+        total_inserted = 0
+        total_updated = 0
+        for entry in legislators_data:
+            bioguide_id = entry.get("id", {}).get("bioguide")
+            if not bioguide_id:
+                continue
+
+            fields = _parse_legislator(entry)
+            if existing := existing_legislators.get(bioguide_id):
+                changed = False
+                for field, value in fields.items():
+                    if value is not None and getattr(existing, field) != value:
+                        setattr(existing, field, value)
+                        changed = True
+                if changed:
+                    total_updated += 1
+            else:
+                session.add(Legislator(bioguide_id=bioguide_id, **fields))
+                total_inserted += 1
+
+        session.commit()
+    logger.info("Inserted %d new legislators, updated %d existing", total_inserted, total_updated)
+
+
+def _load_legislators_yaml(legislators_dir: Path) -> list[dict]:
+    """Load and combine legislators-current.yaml and legislators-historical.yaml."""
+    legislators: list[dict] = []
+    for filename in ("legislators-current.yaml", "legislators-historical.yaml"):
+        path = legislators_dir / filename
+        if not path.exists():
+            logger.warning("Legislators file not found: %s", path)
+            continue
+        with path.open() as file:
+            data = yaml.safe_load(file)
+            if isinstance(data, list):
+                legislators.extend(data)
+    return legislators
+
+
+def _parse_legislator(entry: dict) -> dict:
+    """Extract Legislator fields from a congress-legislators YAML entry."""
+    ids = entry.get("id", {})
+    name = entry.get("name", {})
+    bio = entry.get("bio", {})
+    terms = entry.get("terms", [])
+    latest_term = terms[-1] if terms else {}
+
+    fec_ids = ids.get("fec")
+    fec_ids_joined = ",".join(fec_ids) if isinstance(fec_ids, list) else fec_ids
+
+    chamber = latest_term.get("type")
+    chamber_normalized = {"rep": "House", "sen": "Senate"}.get(chamber, chamber)
+
+    return {
+        "thomas_id": ids.get("thomas"),
+        "lis_id": ids.get("lis"),
+        "govtrack_id": ids.get("govtrack"),
+        "opensecrets_id": ids.get("opensecrets"),
+        "fec_ids": fec_ids_joined,
+        "first_name": name.get("first"),
+        "last_name": name.get("last"),
+        "official_full_name": name.get("official_full"),
+        "nickname": name.get("nickname"),
+        "birthday": bio.get("birthday"),
+        "gender": bio.get("gender"),
+        "current_party": latest_term.get("party"),
+        "current_state": latest_term.get("state"),
+        "current_district": latest_term.get("district"),
+        "current_chamber": chamber_normalized,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Social Media — loaded from legislators-social-media.yaml
+# ---------------------------------------------------------------------------
+
+SOCIAL_MEDIA_PLATFORMS = {
+    "twitter": "https://twitter.com/{account}",
+    "facebook": "https://facebook.com/{account}",
+    "youtube": "https://youtube.com/{account}",
+    "instagram": "https://instagram.com/{account}",
+    "mastodon": None,
+}
+
+
+def ingest_social_media(engine: Engine, legislators_dir: Path) -> None:
+    """Load social media accounts from legislators-social-media.yaml."""
+    social_media_path = legislators_dir / "legislators-social-media.yaml"
+    if not social_media_path.exists():
+        logger.warning("Social media file not found: %s", social_media_path)
+        return
+
+    with social_media_path.open() as file:
+        social_media_data = yaml.safe_load(file)
+
+    if not isinstance(social_media_data, list):
+        logger.warning("Unexpected format in %s", social_media_path)
+        return
+
+    logger.info("Loaded %d entries from legislators-social-media.yaml", len(social_media_data))
+
+    with Session(engine) as session:
+        legislator_map = _build_legislator_map(session)
+        existing_accounts = {
+            (account.legislator_id, account.platform)
+            for account in session.scalars(select(LegislatorSocialMedia)).all()
+        }
+        logger.info("Found %d existing social media accounts in DB", len(existing_accounts))
+
+        total_inserted = 0
+        total_updated = 0
+        for entry in social_media_data:
+            bioguide_id = entry.get("id", {}).get("bioguide")
+            if not bioguide_id:
+                continue
+
+            legislator_id = legislator_map.get(bioguide_id)
+            if legislator_id is None:
+                continue
+
+            social = entry.get("social", {})
+            for platform, url_template in SOCIAL_MEDIA_PLATFORMS.items():
+                account_name = social.get(platform)
+                if not account_name:
+                    continue
+
+                url = url_template.format(account=account_name) if url_template else None
+
+                if (legislator_id, platform) in existing_accounts:
+                    total_updated += 1
+                else:
+                    session.add(
+                        LegislatorSocialMedia(
+                            legislator_id=legislator_id,
+                            platform=platform,
+                            account_name=str(account_name),
+                            url=url,
+                            source="https://github.com/unitedstates/congress-legislators",
+                        )
+                    )
+                    existing_accounts.add((legislator_id, platform))
+                    total_inserted += 1
+
+        session.commit()
+    logger.info("Inserted %d new social media accounts, updated %d existing", total_inserted, total_updated)
+
+
+def _iter_voters(position_group: object) -> Iterator[dict]:
+    """Yield voter dicts from a vote position group (handles list, single dict, or string)."""
+    if isinstance(position_group, dict):
+        yield position_group
+    elif isinstance(position_group, list):
+        for voter in position_group:
+            if isinstance(voter, dict):
+                yield voter
+
+
+# ---------------------------------------------------------------------------
+# Bills
+# ---------------------------------------------------------------------------
+
+
+def ingest_bills(engine: Engine, congress_dirs: list[Path]) -> None:
+    """Load bill data.json files."""
+    with Session(engine) as session:
+        existing_bills = {(bill.congress, bill.bill_type, bill.number) for bill in session.scalars(select(Bill)).all()}
+        logger.info("Found %d existing bills in DB", len(existing_bills))
+
+        total_inserted = 0
+        batch: list[Bill] = []
+        for congress_dir in congress_dirs:
+            bills_dir = congress_dir / "bills"
+            if not bills_dir.is_dir():
+                continue
+            logger.info("Scanning bills from %s", congress_dir.name)
+            for bill_file in bills_dir.rglob("data.json"):
+                data = _read_json(bill_file)
+                if data is None:
+                    continue
+                bill = _parse_bill(data, existing_bills)
+                if bill is not None:
+                    batch.append(bill)
+                    if len(batch) >= BATCH_SIZE:
+                        total_inserted += _flush_batch(session, batch, "bills")
+
+        total_inserted += _flush_batch(session, batch, "bills")
+    logger.info("Inserted %d new bills total", total_inserted)
+
+
+def _parse_bill(data: dict, existing_bills: set[tuple[int, str, int]]) -> Bill | None:
+    """Parse a bill data.json dict into a Bill ORM object, skipping existing."""
+    raw_congress = data.get("congress")
+    bill_type = data.get("bill_type")
+    raw_number = data.get("number")
+    if raw_congress is None or bill_type is None or raw_number is None:
+        return None
+    congress = int(raw_congress)
+    number = int(raw_number)
+    if (congress, bill_type, number) in existing_bills:
+        return None
+
+    sponsor_bioguide = None
+    sponsor = data.get("sponsor")
+    if sponsor:
+        sponsor_bioguide = sponsor.get("bioguide_id")
+
+    return Bill(
+        congress=congress,
+        bill_type=bill_type,
+        number=number,
+        title=data.get("short_title") or data.get("official_title"),
+        title_short=data.get("short_title"),
+        official_title=data.get("official_title"),
+        status=data.get("status"),
+        status_at=data.get("status_at"),
+        sponsor_bioguide_id=sponsor_bioguide,
+        subjects_top_term=data.get("subjects_top_term"),
+    )
+
+
+# ---------------------------------------------------------------------------
+# Votes (and vote records)
+# ---------------------------------------------------------------------------
+
+
+def ingest_votes(engine: Engine, congress_dirs: list[Path]) -> None:
+    """Load vote data.json files with their vote records."""
+    with Session(engine) as session:
+        legislator_map = _build_legislator_map(session)
+        logger.info("Loaded %d legislators into lookup map", len(legislator_map))
+        bill_map = _build_bill_map(session)
+        logger.info("Loaded %d bills into lookup map", len(bill_map))
+        existing_votes = {
+            (vote.congress, vote.chamber, vote.session, vote.number) for vote in session.scalars(select(Vote)).all()
+        }
+        logger.info("Found %d existing votes in DB", len(existing_votes))
+
+        total_inserted = 0
+        batch: list[Vote] = []
+        for congress_dir in congress_dirs:
+            votes_dir = congress_dir / "votes"
+            if not votes_dir.is_dir():
+                continue
+            logger.info("Scanning votes from %s", congress_dir.name)
+            for vote_file in votes_dir.rglob("data.json"):
+                data = _read_json(vote_file)
+                if data is None:
+                    continue
+                vote = _parse_vote(data, legislator_map, bill_map, existing_votes)
+                if vote is not None:
+                    batch.append(vote)
+                    if len(batch) >= BATCH_SIZE:
+                        total_inserted += _flush_batch(session, batch, "votes")
+
+        total_inserted += _flush_batch(session, batch, "votes")
+    logger.info("Inserted %d new votes total", total_inserted)
+
+
+def _build_legislator_map(session: Session) -> dict[str, int]:
+    """Build a mapping of bioguide_id -> legislator.id."""
+    return {legislator.bioguide_id: legislator.id for legislator in session.scalars(select(Legislator)).all()}
+
+
+def _build_bill_map(session: Session) -> dict[tuple[int, str, int], int]:
+    """Build a mapping of (congress, bill_type, number) -> bill.id."""
+    return {(bill.congress, bill.bill_type, bill.number): bill.id for bill in session.scalars(select(Bill)).all()}
+
+
+def _parse_vote(
+    data: dict,
+    legislator_map: dict[str, int],
+    bill_map: dict[tuple[int, str, int], int],
+    existing_votes: set[tuple[int, str, int, int]],
+) -> Vote | None:
+    """Parse a vote data.json dict into a Vote ORM object with records."""
+    raw_congress = data.get("congress")
+    chamber = data.get("chamber")
+    raw_number = data.get("number")
+    vote_date = data.get("date")
+    if raw_congress is None or chamber is None or raw_number is None or vote_date is None:
+        return None
+
+    raw_session = data.get("session")
+    if raw_session is None:
+        return None
+
+    congress = int(raw_congress)
+    number = int(raw_number)
+    session_number = int(raw_session)
+
+    # Normalize chamber from "h"/"s" to "House"/"Senate"
+    chamber_normalized = {"h": "House", "s": "Senate"}.get(chamber, chamber)
+
+    if (congress, chamber_normalized, session_number, number) in existing_votes:
+        return None
+
+    # Resolve linked bill
+    bill_id = None
+    bill_ref = data.get("bill")
+    if bill_ref:
+        bill_key = (
+            int(bill_ref.get("congress", congress)),
+            bill_ref.get("type"),
+            int(bill_ref.get("number", 0)),
+        )
+        bill_id = bill_map.get(bill_key)
+
+    raw_votes = data.get("votes", {})
+    vote_counts = _count_votes(raw_votes)
+    vote_records = _build_vote_records(raw_votes, legislator_map)
+
+    return Vote(
+        congress=congress,
+        chamber=chamber_normalized,
+        session=session_number,
+        number=number,
+        vote_type=data.get("type"),
+        question=data.get("question"),
+        result=data.get("result"),
+        result_text=data.get("result_text"),
+        vote_date=vote_date[:10] if isinstance(vote_date, str) else vote_date,
+        bill_id=bill_id,
+        vote_records=vote_records,
+        **vote_counts,
+    )
+
+
+def _count_votes(raw_votes: dict) -> dict[str, int]:
+    """Count voters per position category, correctly handling dict and list formats."""
+    yea_count = 0
+    nay_count = 0
+    not_voting_count = 0
+    present_count = 0
+
+    for position, position_group in raw_votes.items():
+        voter_count = sum(1 for _ in _iter_voters(position_group))
+        if position in ("Yea", "Aye"):
+            yea_count += voter_count
+        elif position in ("Nay", "No"):
+            nay_count += voter_count
+        elif position == "Not Voting":
+            not_voting_count += voter_count
+        elif position == "Present":
+            present_count += voter_count
+
+    return {
+        "yea_count": yea_count,
+        "nay_count": nay_count,
+        "not_voting_count": not_voting_count,
+        "present_count": present_count,
+    }
+
+
+def _build_vote_records(raw_votes: dict, legislator_map: dict[str, int]) -> list[VoteRecord]:
+    """Build VoteRecord objects from raw vote data."""
+    records: list[VoteRecord] = []
+    for position, position_group in raw_votes.items():
+        for voter in _iter_voters(position_group):
+            bioguide_id = voter.get("id")
+            if not bioguide_id:
+                continue
+            legislator_id = legislator_map.get(bioguide_id)
+            if legislator_id is None:
+                continue
+            records.append(
+                VoteRecord(
+                    legislator_id=legislator_id,
+                    position=position,
+                )
+            )
+    return records
+
+
+# ---------------------------------------------------------------------------
+# Bill Text
+# ---------------------------------------------------------------------------
+
+
+def ingest_bill_text(engine: Engine, congress_dirs: list[Path]) -> None:
+    """Load bill text from text-versions directories."""
+    with Session(engine) as session:
+        bill_map = _build_bill_map(session)
+        logger.info("Loaded %d bills into lookup map", len(bill_map))
+        existing_bill_texts = {
+            (bill_text.bill_id, bill_text.version_code) for bill_text in session.scalars(select(BillText)).all()
+        }
+        logger.info("Found %d existing bill text versions in DB", len(existing_bill_texts))
+
+        total_inserted = 0
+        batch: list[BillText] = []
+        for congress_dir in congress_dirs:
+            logger.info("Scanning bill texts from %s", congress_dir.name)
+            for bill_text in _iter_bill_texts(congress_dir, bill_map, existing_bill_texts):
+                batch.append(bill_text)
+                if len(batch) >= BATCH_SIZE:
+                    total_inserted += _flush_batch(session, batch, "bill texts")
+
+        total_inserted += _flush_batch(session, batch, "bill texts")
+    logger.info("Inserted %d new bill text versions total", total_inserted)
+
+
+def _iter_bill_texts(
+    congress_dir: Path,
+    bill_map: dict[tuple[int, str, int], int],
+    existing_bill_texts: set[tuple[int, str]],
+) -> Iterator[BillText]:
+    """Yield BillText objects for a single congress directory, skipping existing."""
+    bills_dir = congress_dir / "bills"
+    if not bills_dir.is_dir():
+        return
+
+    for bill_dir in bills_dir.rglob("text-versions"):
+        if not bill_dir.is_dir():
+            continue
+        bill_key = _bill_key_from_dir(bill_dir.parent, congress_dir)
+        if bill_key is None:
+            continue
+        bill_id = bill_map.get(bill_key)
+        if bill_id is None:
+            continue
+
+        for version_dir in sorted(bill_dir.iterdir()):
+            if not version_dir.is_dir():
+                continue
+            if (bill_id, version_dir.name) in existing_bill_texts:
+                continue
+            text_content = _read_bill_text(version_dir)
+            version_data = _read_json(version_dir / "data.json")
+            yield BillText(
+                bill_id=bill_id,
+                version_code=version_dir.name,
+                version_name=version_data.get("version_name") if version_data else None,
+                date=version_data.get("issued_on") if version_data else None,
+                text_content=text_content,
+            )
+
+
+def _bill_key_from_dir(bill_dir: Path, congress_dir: Path) -> tuple[int, str, int] | None:
+    """Extract (congress, bill_type, number) from directory structure."""
+    congress = int(congress_dir.name)
+    bill_type = bill_dir.parent.name
+    name = bill_dir.name
+    # Directory name is like "hr3590" — strip the type prefix to get the number
+    number_str = name[len(bill_type) :]
+    if not number_str.isdigit():
+        return None
+    return (congress, bill_type, int(number_str))
+
+
+def _read_bill_text(version_dir: Path) -> str | None:
+    """Read bill text from a version directory, preferring .txt over .xml."""
+    for extension in ("txt", "htm", "html", "xml"):
+        candidates = list(version_dir.glob(f"document.{extension}"))
+        if not candidates:
+            candidates = list(version_dir.glob(f"*.{extension}"))
+        if candidates:
+            try:
+                return candidates[0].read_text(encoding="utf-8")
+            except Exception:
+                logger.exception("Failed to read %s", candidates[0])
+    return None
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _read_json(path: Path) -> dict | None:
+    """Read and parse a JSON file, returning None on failure."""
+    try:
+        return orjson.loads(path.read_bytes())
+    except FileNotFoundError:
+        return None
+    except Exception:
+        logger.exception("Failed to parse %s", path)
+        return None
+
+
+if __name__ == "__main__":
+    app()

python/data_science/ingest_posts.py

@@ -0,0 +1,247 @@
+"""Ingestion pipeline for loading JSONL post files into the weekly-partitioned posts table.
+
+Usage:
+    ingest-posts /path/to/files/
+    ingest-posts /path/to/single_file.jsonl
+    ingest-posts /data/dir/ --workers 4 --batch-size 5000
+"""
+
+from __future__ import annotations
+
+import logging
+from datetime import UTC, datetime
+from pathlib import Path  # noqa: TC003 this is needed for typer
+from typing import TYPE_CHECKING, Annotated
+
+import orjson
+import psycopg
+import typer
+
+from python.common import configure_logger
+from python.orm.common import get_connection_info
+from python.parallelize import parallelize_process
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
+logger = logging.getLogger(__name__)
+
+
+app = typer.Typer(help="Ingest JSONL post files into the partitioned posts table.")
+
+
+@app.command()
+def main(
+    path: Annotated[Path, typer.Argument(help="Directory containing JSONL files, or a single JSONL file")],
+    batch_size: Annotated[int, typer.Option(help="Rows per INSERT batch")] = 10000,
+    workers: Annotated[int, typer.Option(help="Parallel workers for multi-file ingestion")] = 4,
+    pattern: Annotated[str, typer.Option(help="Glob pattern for JSONL files")] = "*.jsonl",
+) -> None:
+    """Ingest JSONL post files into the weekly-partitioned posts table."""
+    configure_logger(level="INFO")
+
+    logger.info("starting ingest-posts")
+    logger.info("path=%s batch_size=%d workers=%d pattern=%s", path, batch_size, workers, pattern)
+    if path.is_file():
+        ingest_file(path, batch_size=batch_size)
+    elif path.is_dir():
+        ingest_directory(path, batch_size=batch_size, max_workers=workers, pattern=pattern)
+    else:
+        typer.echo(f"Path does not exist: {path}", err=True)
+        raise typer.Exit(code=1)
+
+    logger.info("ingest-posts done")
+
+
+def ingest_directory(
+    directory: Path,
+    *,
+    batch_size: int,
+    max_workers: int,
+    pattern: str = "*.jsonl",
+) -> None:
+    """Ingest all JSONL files in a directory using parallel workers."""
+    files = sorted(directory.glob(pattern))
+    if not files:
+        logger.warning("No JSONL files found in %s", directory)
+        return
+
+    logger.info("Found %d JSONL files to ingest", len(files))
+
+    kwargs_list = [{"path": fp, "batch_size": batch_size} for fp in files]
+    parallelize_process(ingest_file, kwargs_list, max_workers=max_workers)
+
+
+SCHEMA = "main"
+
+COLUMNS = (
+    "post_id",
+    "user_id",
+    "instance",
+    "date",
+    "text",
+    "langs",
+    "like_count",
+    "reply_count",
+    "repost_count",
+    "reply_to",
+    "replied_author",
+    "thread_root",
+    "thread_root_author",
+    "repost_from",
+    "reposted_author",
+    "quotes",
+    "quoted_author",
+    "labels",
+    "sent_label",
+    "sent_score",
+)
+
+INSERT_FROM_STAGING = f"""
+    INSERT INTO {SCHEMA}.posts ({", ".join(COLUMNS)})
+    SELECT {", ".join(COLUMNS)} FROM pg_temp.staging
+    ON CONFLICT (post_id, date) DO NOTHING
+"""  # noqa: S608
+
+FAILED_INSERT = f"""
+    INSERT INTO {SCHEMA}.failed_ingestion (raw_line, error)
+    VALUES (%(raw_line)s, %(error)s)
+"""  # noqa: S608
+
+
+def get_psycopg_connection() -> psycopg.Connection:
+    """Create a raw psycopg3 connection from environment variables."""
+    database, host, port, username, password = get_connection_info("DATA_SCIENCE_DEV")
+    return psycopg.connect(
+        dbname=database,
+        host=host,
+        port=int(port),
+        user=username,
+        password=password,
+        autocommit=False,
+    )
+
+
+def ingest_file(path: Path, *, batch_size: int) -> None:
+    """Ingest a single JSONL file into the posts table."""
+    log_trigger = max(100_000 // batch_size, 1)
+    failed_lines: list[dict] = []
+    try:
+        with get_psycopg_connection() as connection:
+            for index, batch in enumerate(read_jsonl_batches(path, batch_size, failed_lines), 1):
+                ingest_batch(connection, batch)
+                if index % log_trigger == 0:
+                    logger.info("Ingested %d batches (%d rows) from %s", index, index * batch_size, path)
+
+            if failed_lines:
+                logger.warning("Recording %d malformed lines from %s", len(failed_lines), path.name)
+                with connection.cursor() as cursor:
+                    cursor.executemany(FAILED_INSERT, failed_lines)
+                connection.commit()
+    except Exception:
+        logger.exception("Failed to ingest file: %s", path)
+        raise
+
+
+def ingest_batch(connection: psycopg.Connection, batch: list[dict]) -> None:
+    """COPY batch into a temp staging table, then INSERT ... ON CONFLICT into posts."""
+    if not batch:
+        return
+
+    try:
+        with connection.cursor() as cursor:
+            cursor.execute(f"""
+                CREATE TEMP TABLE IF NOT EXISTS staging
+                (LIKE {SCHEMA}.posts INCLUDING DEFAULTS)
+                ON COMMIT DELETE ROWS
+            """)
+            cursor.execute("TRUNCATE pg_temp.staging")
+
+            with cursor.copy(f"COPY pg_temp.staging ({', '.join(COLUMNS)}) FROM STDIN") as copy:
+                for row in batch:
+                    copy.write_row(tuple(row.get(column) for column in COLUMNS))
+
+            cursor.execute(INSERT_FROM_STAGING)
+        connection.commit()
+    except Exception as error:
+        connection.rollback()
+
+        if len(batch) == 1:
+            logger.exception("Skipping bad row post_id=%s", batch[0].get("post_id"))
+            with connection.cursor() as cursor:
+                cursor.execute(
+                    FAILED_INSERT,
+                    {
+                        "raw_line": orjson.dumps(batch[0], default=str).decode(),
+                        "error": str(error),
+                    },
+                )
+            connection.commit()
+            return
+
+        midpoint = len(batch) // 2
+        ingest_batch(connection, batch[:midpoint])
+        ingest_batch(connection, batch[midpoint:])
+
+
+def read_jsonl_batches(file_path: Path, batch_size: int, failed_lines: list[dict]) -> Iterator[list[dict]]:
+    """Stream a JSONL file and yield batches of transformed rows."""
+    batch: list[dict] = []
+    with file_path.open("r", encoding="utf-8") as handle:
+        for raw_line in handle:
+            line = raw_line.strip()
+            if not line:
+                continue
+            batch.extend(parse_line(line, file_path, failed_lines))
+            if len(batch) >= batch_size:
+                yield batch
+                batch = []
+    if batch:
+        yield batch
+
+
+def parse_line(line: str, file_path: Path, failed_lines: list[dict]) -> Iterator[dict]:
+    """Parse a JSONL line, handling concatenated JSON objects."""
+    try:
+        yield transform_row(orjson.loads(line))
+    except orjson.JSONDecodeError:
+        if "}{" not in line:
+            logger.warning("Skipping malformed line in %s: %s", file_path.name, line[:120])
+            failed_lines.append({"raw_line": line, "error": "malformed JSON"})
+            return
+        fragments = line.replace("}{", "}\n{").split("\n")
+        for fragment in fragments:
+            try:
+                yield transform_row(orjson.loads(fragment))
+            except (orjson.JSONDecodeError, KeyError, ValueError) as error:
+                logger.warning("Skipping malformed fragment in %s: %s", file_path.name, fragment[:120])
+                failed_lines.append({"raw_line": fragment, "error": str(error)})
+    except Exception as error:
+        logger.exception("Skipping bad row in %s: %s", file_path.name, line[:120])
+        failed_lines.append({"raw_line": line, "error": str(error)})
+
+
+def transform_row(raw: dict) -> dict:
+    """Transform a raw JSONL row into a dict matching the Posts table columns."""
+    raw["date"] = parse_date(raw["date"])
+    if raw.get("langs") is not None:
+        raw["langs"] = orjson.dumps(raw["langs"])
+    if raw.get("text") is not None:
+        raw["text"] = raw["text"].replace("\x00", "")
+    return raw
+
+
+def parse_date(raw_date: int) -> datetime:
+    """Parse compact YYYYMMDDHHmm integer into a naive datetime (input is UTC by spec)."""
+    return datetime(
+        raw_date // 100000000,
+        (raw_date // 1000000) % 100,
+        (raw_date // 10000) % 100,
+        (raw_date // 100) % 100,
+        raw_date % 100,
+        tzinfo=UTC,
+    )
+
+
+if __name__ == "__main__":
+    app()

python/database_cli.py

@@ -83,6 +83,20 @@ DATABASES: dict[str, DatabaseConfig] = {
         base_class_name="VanInventoryBase",
         models_module="python.orm.van_inventory.models",
     ),
+    "signal_bot": DatabaseConfig(
+        env_prefix="SIGNALBOT",
+        version_location="python/alembic/signal_bot/versions",
+        base_module="python.orm.signal_bot.base",
+        base_class_name="SignalBotBase",
+        models_module="python.orm.signal_bot.models",
+    ),
+    "data_science_dev": DatabaseConfig(
+        env_prefix="DATA_SCIENCE_DEV",
+        version_location="python/alembic/data_science_dev/versions",
+        base_module="python.orm.data_science_dev.base",
+        base_class_name="DataScienceDevBase",
+        models_module="python.orm.data_science_dev.models",
+    ),
 }
 
 

python/ebook_search/__init__.py

@@ -1 +0,0 @@
-"""EPUB search package."""

python/ebook_search/answer.py

@@ -1,57 +0,0 @@
-"""Grounded answer generation."""
-
-from __future__ import annotations
-
-import logging
-from typing import TYPE_CHECKING
-
-from python.ebook_search.llm_interface import request_chat_completion
-
-if TYPE_CHECKING:
-    from python.ebook_search.config import EbookSearchConfig
-    from python.ebook_search.search import SearchResult
-
-logger = logging.getLogger(__name__)
-
-
-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")
-        return "Answer generation is disabled. Source chunks are shown below."
-
-    if not results:
-        logger.info("ebook_answer_skipped_no_results")
-        return "No relevant sources were found."
-
-    logger.info(
-        "ebook_answer_request_start base_url=%s model=%s sources=%s query_length=%s",
-        config.vllm_base_url,
-        config.chat_model,
-        len(results),
-        len(query),
-    )
-    context = "\n\n".join(
-        f"[{index}] {result.source_title}{' - ' + result.chapter_title if result.chapter_title else ''}\n{result.text}"
-        for index, result in enumerate(results, start=1)
-    )
-    content = request_chat_completion(
-        config,
-        [
-            {
-                "role": "system",
-                "content": (
-                    "Answer only from the provided context. Cite sources with bracketed numbers like [1]. "
-                    "If the context is insufficient, say so."
-                ),
-            },
-            {"role": "user", "content": f"Question:\n{query}\n\nContext:\n{context}"},
-        ],
-    )
-
-    logger.info(
-        "ebook_answer_request_complete model=%s answer_length=%s",
-        config.chat_model,
-        len(content),
-    )
-    return content or "The model returned an empty answer."

python/ebook_search/api/__init__.py

@@ -1 +0,0 @@
-"""Web and external API adapters for EPUB search."""

python/ebook_search/api/bm25_tasks.py

@@ -1,60 +0,0 @@
-"""Background BM25 refresh tasks for the web app."""
-
-from __future__ import annotations
-
-import logging
-from threading import Timer
-from typing import TYPE_CHECKING
-
-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.engine import Engine
-
-    from python.ebook_search.config import EbookSearchConfig
-
-logger = logging.getLogger(__name__)
-
-
-def schedule_bm25_refresh(app: FastAPI) -> None:
-    """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()
-
-    timer = Timer(app.state.config.bm25_refresh_delay_seconds, refresh_bm25_for_app, args=(app,))
-    timer.daemon = True
-    timer.start()
-    app.state.bm25_refresh_timer = timer
-    logger.info(
-        "ebook_bm25_refresh_scheduled delay_seconds=%s",
-        app.state.config.bm25_refresh_delay_seconds,
-    )
-
-
-def cancel_bm25_refresh(app: FastAPI) -> None:
-    """Cancel any pending BM25 corpus refresh."""
-    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")
-
-
-def refresh_bm25_for_app(app: FastAPI) -> None:
-    """Refresh the BM25 corpus using the app engine and config."""
-    try:
-        refresh_bm25_for_engine(app.state.engine, app.state.config)
-    except Exception:
-        logger.exception("ebook_bm25_refresh_failed")
-
-
-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/main.py

@@ -1,79 +0,0 @@
-"""FastAPI HTMX app for EPUB search."""
-
-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 fastapi.staticfiles import StaticFiles
-from sqlalchemy.orm import Session
-
-from python.common import configure_logger
-from python.ebook_search.api.bm25_tasks import cancel_bm25_refresh
-from python.ebook_search.api.routes import admin_router, page_router, search_router
-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.fastapi_tools import ZstdMiddleware
-from python.orm.common import get_postgres_engine
-
-if TYPE_CHECKING:
-    from collections.abc import AsyncIterator
-
-
-logger = logging.getLogger(__name__)
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI) -> AsyncIterator[None]:
-    """Manage application startup and shutdown resources."""
-    logger.info("ebook_search_startup")
-    app.state.engine = get_postgres_engine(name="RICHIE", vector_engine=True)
-    with Session(app.state.engine) as session:
-        ensure_bm25_corpus(session, app.state.config)
-    try:
-        yield
-    finally:
-        logger.info("ebook_search_shutdown")
-        cancel_bm25_refresh(app)
-        app.state.engine.dispose()
-
-
-def create_app() -> FastAPI:
-    """Create the EPUB search web app."""
-    app = FastAPI(title="EPUB Search", lifespan=lifespan)
-    app.add_middleware(ZstdMiddleware)
-    app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
-    app.state.config = load_config()
-    logger.info(
-        "ebook_search_config_loaded top_k=%s embedding_model=%s rerank_enabled=%s answer_enabled=%s library_paths=%s",
-        app.state.config.top_k,
-        app.state.config.embedding_model,
-        app.state.config.rerank.enabled,
-        app.state.config.answer_enabled,
-        len(app.state.config.library_paths),
-    )
-
-    app.include_router(admin_router)
-    app.include_router(page_router)
-    app.include_router(search_router)
-
-    return app
-
-
-def serve(
-    host: Annotated[str, typer.Option("--host", "-h", help="Host to bind to")] = "127.0.0.1",
-    port: Annotated[int, typer.Option("--port", "-p", help="Port to bind to")] = 8070,
-    log_level: Annotated[str, typer.Option("--log-level", "-l", help="Log level")] = "INFO",
-) -> None:
-    """Start the EPUB search server."""
-    configure_logger(log_level)
-    uvicorn.run(create_app(), host=host, port=port)
-
-
-if __name__ == "__main__":
-    typer.run(serve)

python/ebook_search/api/routes/__init__.py

@@ -1,11 +0,0 @@
-"""EPUB search web route modules."""
-
-from python.ebook_search.api.routes.admin import router as admin_router
-from python.ebook_search.api.routes.page import router as page_router
-from python.ebook_search.api.routes.search import router as search_router
-
-__all__ = [
-    "admin_router",
-    "page_router",
-    "search_router",
-]

python/ebook_search/api/routes/admin.py

@@ -1,107 +0,0 @@
-"""Admin routes for the EPUB search web UI."""
-
-from __future__ import annotations
-
-import logging
-from dataclasses import replace
-
-from fastapi import APIRouter, Request
-from fastapi.responses import HTMLResponse
-from sqlalchemy.orm import Session
-
-from python.ebook_search.api.bm25_tasks import schedule_bm25_refresh
-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
-
-logger = logging.getLogger(__name__)
-
-router = APIRouter(prefix="/admin")
-EMBED_ALL_BATCH_SIZE = 32
-
-
-@router.get("", response_class=HTMLResponse)
-def admin(request: Request) -> HTMLResponse:
-    """Render the admin page."""
-    with Session(request.app.state.engine) as session:
-        stats = embedding_model_stats(session)
-    logger.info("ebook_admin_page_loaded models=%s", len(stats))
-    return templates.TemplateResponse(request, "admin.html", {"config": request.app.state.config, "stats": stats})
-
-
-@router.post("/scan", response_class=HTMLResponse)
-def scan_library(request: Request) -> HTMLResponse:
-    """Scan configured library paths for EPUB changes."""
-    try:
-        with Session(request.app.state.engine) as session:
-            count = ingest_configured_paths(session, request.app.state.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)
-
-    logger.info("ebook_admin_scan_complete changed_files=%s", count)
-    if count > 0:
-        schedule_bm25_refresh(request.app)
-    return templates.TemplateResponse(request, "partials/admin_status.html", {"message": f"Indexed {count} EPUBs"})
-
-
-@router.post("/embed-missing", response_class=HTMLResponse)
-def embed_missing(request: Request) -> HTMLResponse:
-    """Embed chunks missing vectors for the configured model."""
-    try:
-        with Session(request.app.state.engine) as session:
-            count = embed_missing_chunks(session, request.app.state.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)
-
-    logger.info("ebook_admin_embed_missing_complete chunks=%s", count)
-    return templates.TemplateResponse(
-        request,
-        "partials/admin_status.html",
-        {"message": f"Embedded {count} chunks"},
-    )
-
-
-@router.post("/embed-all", response_class=HTMLResponse)
-def embed_all(request: Request) -> HTMLResponse:
-    """Embed all chunks missing vectors in fixed-size batches."""
-    total = 0
-    batches = 0
-    config = replace(request.app.state.config, embedding_batch_size=EMBED_ALL_BATCH_SIZE)
-    try:
-        with Session(request.app.state.engine) as session:
-            while True:
-                count = embed_missing_chunks(session, config)
-                if count == 0:
-                    break
-                session.commit()
-                total += count
-                batches += 1
-                logger.info(
-                    "ebook_admin_embed_all_batch_complete batch=%s chunks=%s total_chunks=%s",
-                    batches,
-                    count,
-                    total,
-                )
-    except Exception as error:
-        logger.exception(
-            "ebook_admin_embed_all_failed batches=%s chunks=%s",
-            batches,
-            total,
-        )
-        return templates.TemplateResponse(
-            request,
-            "partials/error.html",
-            {"message": f"Embed all failed after {total} chunks in {batches} batches: {error}"},
-            status_code=500,
-        )
-
-    logger.info("ebook_admin_embed_all_complete batches=%s chunks=%s", batches, total)
-    return templates.TemplateResponse(
-        request,
-        "partials/admin_status.html",
-        {"message": f"Embedded {total} chunks in {batches} batches of {EMBED_ALL_BATCH_SIZE}"},
-    )

python/ebook_search/api/routes/page.py

@@ -1,57 +0,0 @@
-"""Page routes for the EPUB search web UI."""
-
-from __future__ import annotations
-
-import logging
-
-from fastapi import APIRouter, Request
-from fastapi.responses import HTMLResponse
-from sqlalchemy import select
-from sqlalchemy.orm import Session
-
-from python.ebook_search.api.web import templates
-from python.orm.richie import EbookSource
-
-logger = logging.getLogger(__name__)
-
-router = APIRouter()
-
-
-@router.get("/", response_class=HTMLResponse)
-def index(request: Request) -> HTMLResponse:
-    """Render the search page."""
-    return templates.TemplateResponse(request, "search.html", {"config": request.app.state.config})
-
-
-@router.get("/books", response_class=HTMLResponse)
-def books(request: Request) -> HTMLResponse:
-    """Render the indexed books page."""
-    with Session(request.app.state.engine) as session:
-        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})
-
-
-@router.get("/books/{source_id}", response_class=HTMLResponse)
-def book_detail(source_id: int, request: Request) -> HTMLResponse:
-    """Render details for one indexed book."""
-    with Session(request.app.state.engine) as session:
-        source = session.get(EbookSource, source_id)
-        if source is not None:
-            chapter_count = len(source.chapters)
-            chunk_count = len(source.chunks)
-        else:
-            chapter_count = 0
-            chunk_count = 0
-    logger.info(
-        "ebook_book_detail_loaded source_id=%s found=%s chapters=%s chunks=%s",
-        source_id,
-        source is not None,
-        chapter_count,
-        chunk_count,
-    )
-    return templates.TemplateResponse(
-        request,
-        "book_detail.html",
-        {"chapter_count": chapter_count, "chunk_count": chunk_count, "source": source},
-    )

python/ebook_search/api/routes/search.py

@@ -1,58 +0,0 @@
-"""Search routes for the EPUB search web UI."""
-
-from __future__ import annotations
-
-import logging
-from dataclasses import replace
-from time import perf_counter
-from typing import Annotated
-
-from fastapi import APIRouter, Form, Request
-from fastapi.responses import HTMLResponse
-
-from python.ebook_search.answer import answer_query
-from python.ebook_search.api.web import templates
-from python.ebook_search.search import search_ebooks
-from python.ebook_search.timing import runtime_step_from_start
-
-logger = logging.getLogger(__name__)
-
-router = APIRouter()
-
-
-@router.post("/search", response_class=HTMLResponse)
-def search(
-    request: Request,
-    query: Annotated[str, Form()],
-    rerank: Annotated[str | None, Form()] = None,
-) -> HTMLResponse:
-    """Run a search and render HTMX results."""
-    try:
-        response = search_ebooks(request.app.state.engine, query, request.app.state.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()
-    if request.app.state.config.answer_enabled:
-        try:
-            answer = answer_query(query, response.results, request.app.state.config)
-        except RuntimeError as error:
-            logger.warning("ebook_answer_request_failed_falling_back error=%s", error)
-            answer = "Answer generation failed. Source chunks are still shown below."
-    else:
-        logger.info("ebook_answer_skipped_disabled")
-        answer = "Answer generation is disabled. Source chunks are shown below."
-    answer_step_name = "Answer generation" if request.app.state.config.answer_enabled else "Answer skipped"
-    response = replace(
-        response,
-        timings=(*response.timings, runtime_step_from_start(answer_step_name, answer_start)),
-    )
-
-    logger.info(
-        "ebook_search_request_complete results=%s rank_label=%s runtime_ms=%.1f",
-        len(response.results),
-        response.rank_label,
-        response.total_runtime_ms,
-    )
-    return templates.TemplateResponse(request, "partials/results.html", {"answer": answer, "response": response})

python/ebook_search/api/static/style.css

@@ -1,140 +0,0 @@
-body {
-    margin: 0;
-    background: #f7f7f4;
-    color: #202124;
-    font-family: system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
-}
-
-main {
-    max-width: 960px;
-    margin: 0 auto;
-    padding: 24px;
-}
-
-nav {
-    display: flex;
-    gap: 12px;
-    align-items: center;
-    margin-bottom: 20px;
-}
-
-nav form {
-    margin: 0;
-}
-
-.actions {
-    display: flex;
-    flex-wrap: wrap;
-    gap: 12px;
-    margin-bottom: 24px;
-}
-
-textarea {
-    display: block;
-    width: 100%;
-    margin: 8px 0 12px;
-}
-
-button {
-    padding: 8px 14px;
-}
-
-.check {
-    display: inline-flex;
-    gap: 8px;
-    align-items: center;
-    margin-right: 12px;
-}
-
-.rank-label {
-    margin-top: 24px;
-    font-weight: 700;
-}
-
-.results {
-    padding-left: 24px;
-}
-
-.meta,
-.scores,
-.status {
-    color: #626a73;
-}
-
-.scores {
-    display: flex;
-    flex-wrap: wrap;
-    gap: 8px;
-    margin: 12px 0;
-}
-
-.scores div {
-    display: inline-flex;
-    gap: 4px;
-    align-items: baseline;
-}
-
-.scores dt {
-    font-weight: 700;
-}
-
-.scores dd {
-    margin: 0;
-}
-
-.runtime {
-    margin-top: 16px;
-}
-
-.timing-chart {
-    display: grid;
-    gap: 8px;
-    padding: 0;
-    list-style: none;
-}
-
-.timing-chart li {
-    display: grid;
-    grid-template-columns: minmax(150px, 1fr) minmax(160px, 2fr) auto auto;
-    gap: 8px;
-    align-items: center;
-}
-
-.timing-bar {
-    height: 10px;
-    overflow: hidden;
-    background: #e5e5df;
-}
-
-.timing-bar span {
-    display: block;
-    height: 100%;
-    background: #3767c8;
-}
-
-.timing-value,
-.timing-remaining {
-    color: #626a73;
-    font-variant-numeric: tabular-nums;
-}
-
-table {
-    width: 100%;
-    border-collapse: collapse;
-}
-
-th,
-td {
-    padding: 8px;
-    border-bottom: 1px solid #d8d8d2;
-    text-align: left;
-}
-
-th {
-    font-weight: 700;
-}
-
-.error {
-    color: #9f1d20;
-    font-weight: 700;
-}

python/ebook_search/api/templates/admin.html

@@ -1,57 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>EPUB Admin</title>
-    <script src="https://unpkg.com/htmx.org@2.0.4"></script>
-    <link rel="stylesheet" href="/static/style.css">
-</head>
-<body>
-    <main>
-        <nav>
-            <a href="/">Search</a>
-            <a href="/books">Books</a>
-            <a href="/admin">Admin</a>
-        </nav>
-        <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>
-    </main>
-</body>
-</html>

python/ebook_search/api/templates/book_detail.html

@@ -1,32 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>{% if source %}{{ source.title }}{% else %}Book not found{% endif %}</title>
-    <link rel="stylesheet" href="/static/style.css">
-</head>
-<body>
-    <main>
-        <nav>
-            <a href="/">Search</a>
-            <a href="/books">Books</a>
-            <a href="/admin">Admin</a>
-        </nav>
-        {% if source %}
-            <h1>{{ source.title }}</h1>
-            <p class="meta">{{ source.author or "Unknown author" }}</p>
-            <dl>
-                <dt>File</dt>
-                <dd>{{ source.file_path }}</dd>
-                <dt>Chapters</dt>
-                <dd>{{ chapter_count }}</dd>
-                <dt>Chunks</dt>
-                <dd>{{ chunk_count }}</dd>
-            </dl>
-        {% else %}
-            <h1>Book not found</h1>
-        {% endif %}
-    </main>
-</body>
-</html>

python/ebook_search/api/templates/books.html

@@ -1,31 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>EPUB Books</title>
-    <link rel="stylesheet" href="/static/style.css">
-</head>
-<body>
-    <main>
-        <nav>
-            <a href="/">Search</a>
-            <a href="/books">Books</a>
-            <a href="/admin">Admin</a>
-        </nav>
-        <h1>Books</h1>
-        {% if sources %}
-            <ol class="results">
-                {% for source in sources %}
-                    <li>
-                        <h2><a href="/books/{{ source.id }}">{{ source.title }}</a></h2>
-                        <p class="meta">{{ source.author or "Unknown author" }}</p>
-                    </li>
-                {% endfor %}
-            </ol>
-        {% else %}
-            <p>No EPUBs indexed.</p>
-        {% endif %}
-    </main>
-</body>
-</html>

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

@@ -1 +0,0 @@
-<p class="status">{{ message }}</p>

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

@@ -1 +0,0 @@
-<p class="error">{{ message }}</p>

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

@@ -1,74 +0,0 @@
-<div class="rank-label">{{ response.rank_label }}</div>
-{% if response.timings %}
-    <section class="runtime">
-        <h2>Runtime</h2>
-        <p class="meta">Total {{ "%.1f"|format(response.total_runtime_ms) }} ms</p>
-        <ol class="timing-chart">
-            {% set total = response.total_runtime_ms %}
-            {% set ns = namespace(remaining=total) %}
-            {% for step in response.timings %}
-                {% set width = (step.duration_ms / total * 100) if total else 0 %}
-                {% if step.counts_toward_total %}
-                    {% set ns.remaining = ns.remaining - step.duration_ms %}
-                {% endif %}
-                <li>
-                    <span class="timing-label">{{ step.name }}</span>
-                    <span class="timing-bar"><span style="width: {{ "%.2f"|format(width) }}%"></span></span>
-                    <span class="timing-value">{{ "%.1f"|format(step.duration_ms) }} ms</span>
-                    <span class="timing-remaining">{{ "%.1f"|format([ns.remaining, 0]|max) }} ms left</span>
-                </li>
-            {% endfor %}
-        </ol>
-    </section>
-{% endif %}
-<section class="answer">
-    <h2>Answer</h2>
-    <p>{{ answer }}</p>
-</section>
-{% if response.results %}
-    <ol class="results">
-        {% for result in response.results %}
-            <li>
-                <h2>{{ result.source_title }}</h2>
-                <p class="meta">
-                    {% if result.source_author %}{{ result.source_author }}{% endif %}
-                    {% if result.chapter_title %} · {{ result.chapter_title }}{% endif %}
-                    {% if result.page_label %} · page {{ result.page_label }}{% endif %}
-                </p>
-                <p>{{ result.text }}</p>
-                <dl class="scores">
-                    <div>
-                        <dt>final</dt>
-                        <dd>{{ "%.3f"|format(result.score) }}</dd>
-                    </div>
-                    {% if result.rerank_score is not none %}
-                        <div>
-                            <dt>rerank</dt>
-                            <dd>{{ "%.3f"|format(result.rerank_score) }}</dd>
-                        </div>
-                    {% endif %}
-                    {% if result.vector_score is not none %}
-                        <div>
-                            <dt>vector cosine</dt>
-                            <dd>{{ "%.3f"|format(result.vector_score) }}</dd>
-                        </div>
-                    {% endif %}
-                    {% if result.bm25_score is not none %}
-                        <div>
-                            <dt>BM25</dt>
-                            <dd>{{ "%.6f"|format(result.bm25_score) }}</dd>
-                        </div>
-                    {% endif %}
-                    {% if result.fused_score is not none %}
-                        <div>
-                            <dt>RRF</dt>
-                            <dd>{{ "%.3f"|format(result.fused_score) }}</dd>
-                        </div>
-                    {% endif %}
-                </dl>
-            </li>
-        {% endfor %}
-    </ol>
-{% else %}
-    <p>No results.</p>
-{% endif %}

python/ebook_search/api/templates/search.html

@@ -1,30 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>EPUB Search</title>
-    <script src="https://unpkg.com/htmx.org@2.0.4"></script>
-    <link rel="stylesheet" href="/static/style.css">
-</head>
-<body>
-    <main>
-        <nav>
-            <a href="/">Search</a>
-            <a href="/books">Books</a>
-            <a href="/admin">Admin</a>
-        </nav>
-        <h1>EPUB Search</h1>
-        <form hx-post="/search" hx-target="#results" hx-swap="innerHTML">
-            <label for="query">Search</label>
-            <textarea id="query" name="query" rows="4" required></textarea>
-            <label class="check">
-                <input type="checkbox" name="rerank" value="true" {% if config.rerank.enabled %}checked{% endif %}>
-                Rerank
-            </label>
-            <button type="submit">Search</button>
-        </form>
-        <section id="results"></section>
-    </main>
-</body>
-</html>

python/ebook_search/api/web.py

@@ -1,13 +0,0 @@
-"""Shared web UI resources for EPUB search."""
-
-from __future__ import annotations
-
-from pathlib import Path
-
-from fastapi.templating import Jinja2Templates
-
-PACKAGE_DIR = Path(__file__).resolve().parent
-TEMPLATE_DIR = PACKAGE_DIR / "templates"
-STATIC_DIR = PACKAGE_DIR / "static"
-
-templates = Jinja2Templates(directory=TEMPLATE_DIR)

python/ebook_search/bm25_corpus.py

@@ -1,281 +0,0 @@
-"""Persisted BM25 corpus management."""
-
-from __future__ import annotations
-
-import json
-import logging
-import shutil
-from dataclasses import dataclass
-from datetime import UTC, datetime
-from functools import cache
-from pathlib import Path
-from typing import TYPE_CHECKING
-
-import bm25s
-from sqlalchemy import func, select, union_all
-
-from python.orm.richie import EbookChapter, EbookChunk, EbookSource
-
-if TYPE_CHECKING:
-    from sqlalchemy.orm import Session
-
-    from python.ebook_search.config import EbookSearchConfig
-
-logger = logging.getLogger(__name__)
-MANIFEST_NAME = "manifest.json"
-REQUIRED_INDEX_FILES = frozenset(
-    {
-        "data.csc.index.npy",
-        "indices.csc.index.npy",
-        "indptr.csc.index.npy",
-        "params.index.json",
-        "vocab.index.json",
-        "corpus.jsonl",
-    }
-)
-
-
-@dataclass(frozen=True)
-class BM25Manifest:
-    """Metadata describing a persisted BM25 corpus."""
-
-    created_at: datetime
-    db_updated_at: datetime | None
-    chunk_count: int
-
-
-@dataclass(frozen=True)
-class BM25Corpus:
-    """Loaded persisted BM25 corpus and retriever."""
-
-    retriever: object | None
-    records: tuple[dict[str, object], ...]
-    manifest: BM25Manifest
-
-
-class BM25CorpusUnavailableError(RuntimeError):
-    """Raised when the persisted BM25 corpus cannot be loaded."""
-
-
-def bm25_index_path(config: EbookSearchConfig) -> Path:
-    """Return the configured BM25 index root path relative to the current working directory."""
-    path = Path(config.bm25_index_dir).expanduser()
-    if path.is_absolute():
-        return path
-    return Path.cwd() / path
-
-
-def get_current_bm25_index(index_path: Path) -> Path:
-    """Return the live BM25 index directory."""
-    current_path = index_path / "current"
-    if current_path.exists() or current_path.is_symlink():
-        return current_path
-    return index_path
-
-
-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 = corpus_last_updated_at(session)
-    if not bm25_index_exists(index_path, manifest):
-        logger.info("ebook_bm25_index_missing path=%s", index_path)
-        refresh_bm25_corpus(session, config, db_updated_at=db_updated_at)
-        return
-    if db_updated_at is not None and manifest is not None and manifest.created_at < db_updated_at:
-        logger.info(
-            "ebook_bm25_index_stale path=%s created_at=%s db_updated_at=%s",
-            index_path,
-            manifest.created_at.isoformat(),
-            db_updated_at.isoformat(),
-        )
-        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",
-        index_path,
-        manifest.chunk_count if manifest else 0,
-        manifest.created_at.isoformat() if manifest else None,
-    )
-
-
-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."""
-    index_path = bm25_index_path(config)
-    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 corpus_last_updated_at(session),
-        chunk_count=len(records),
-    )
-    write_bm25_corpus(index_path, records, texts, manifest)
-    logger.info(
-        "ebook_bm25_index_refreshed path=%s chunks=%s created_at=%s",
-        index_path,
-        manifest.chunk_count,
-        manifest.created_at.isoformat(),
-    )
-    return manifest
-
-
-@cache
-def load_bm25_corpus(config: EbookSearchConfig) -> BM25Corpus:
-    """Load the BM25 corpus into memory once per process.
-
-    Background refresh tasks clear this cache after rebuilding the on-disk corpus.
-    """
-    index_path = bm25_index_path(config)
-    active_index_path = get_current_bm25_index(index_path)
-    logger.info("ebook_bm25_corpus_cache_load path=%s active_path=%s", index_path, active_index_path)
-    manifest = read_bm25_manifest(index_path)
-    if manifest is None or not bm25_index_exists(index_path, manifest):
-        msg = f"BM25 corpus is not available: {index_path}"
-        raise BM25CorpusUnavailableError(msg)
-    if manifest.chunk_count == 0:
-        return BM25Corpus(retriever=None, records=(), manifest=manifest)
-
-    retriever = bm25s.BM25.load(active_index_path, load_corpus=True, mmap=True)
-    records = tuple(dict(record) for record in retriever.corpus)
-    return BM25Corpus(retriever=retriever, records=records, manifest=manifest)
-
-
-def score_bm25_corpus(query: str, corpus: BM25Corpus, *, limit: int) -> list[tuple[dict[str, object], float]]:
-    """Score a query against a loaded BM25 corpus."""
-    if corpus.retriever is None or not corpus.records:
-        return []
-    k = min(limit, len(corpus.records))
-    documents, scores = corpus.retriever.retrieve(
-        bm25s.tokenize(query, show_progress=False),
-        corpus=list(corpus.records),
-        k=k,
-        show_progress=False,
-    )
-    results: list[tuple[dict[str, object], float]] = []
-    for document, score in zip(documents[0], scores[0], strict=True):
-        score_value = float(score)
-        if score_value <= 0:
-            continue
-        results.append((dict(document), score_value))
-    return results
-
-
-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
-    being persisted into the corpus records, which would double the corpus size.
-    """
-    statement = (
-        select(
-            EbookChunk.id.label("chunk_id"),
-            EbookChunk.text.label("text"),
-            EbookSource.title.label("source_title"),
-            EbookSource.author.label("source_author"),
-            EbookChapter.title.label("chapter_title"),
-            EbookChunk.page_label.label("page_label"),
-            EbookChunk.search_text.label("bm25_text"),
-        )
-        .select_from(EbookChunk)
-        .join(EbookSource, EbookSource.id == EbookChunk.source_id)
-        .outerjoin(EbookChapter, EbookChapter.id == EbookChunk.chapter_id)
-        .order_by(EbookChunk.id)
-    )
-    records: list[dict[str, object]] = []
-    texts: list[str] = []
-    for row in session.execute(statement).mappings():
-        record = dict(row)
-        texts.append(str(record.pop("bm25_text")))
-        records.append(record)
-    return records, texts
-
-
-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 session.scalar(select(func.max(update_times.c.updated)))
-
-
-def write_bm25_corpus(
-    index_path: Path,
-    records: list[dict[str, object]],
-    texts: list[str],
-    manifest: BM25Manifest,
-) -> None:
-    """Write a BM25 corpus generation and publish it through the current symlink."""
-    index_path.mkdir(parents=True, exist_ok=True)
-
-    generations_path = index_path / "generations"
-    generations_path.mkdir(exist_ok=True)
-
-    generation_path = next_bm25_generation_path(generations_path, manifest.created_at)
-    current_path = index_path / "current"
-    next_current_path = index_path / f".current.{generation_path.name}.tmp"
-    try:
-        generation_path.mkdir()
-
-        # Empty corpora publish a manifest-only generation so startup succeeds before any chunks exist.
-        if records:
-            retriever = bm25s.BM25()
-            retriever.index(bm25s.tokenize(texts, show_progress=False), show_progress=False)
-            retriever.save(generation_path, corpus=records, show_progress=False)
-        write_bm25_manifest(generation_path, manifest)
-        next_current_path.unlink(missing_ok=True)
-        next_current_path.symlink_to(generation_path, target_is_directory=True)
-        next_current_path.replace(current_path)
-    except Exception:
-        next_current_path.unlink(missing_ok=True)
-        shutil.rmtree(generation_path, ignore_errors=True)
-        raise
-
-
-def read_bm25_manifest(index_path: Path) -> BM25Manifest | None:
-    """Read the BM25 manifest if it exists and is valid."""
-    manifest_path = get_current_bm25_index(index_path) / MANIFEST_NAME
-    if not manifest_path.exists():
-        return None
-    body = json.loads(manifest_path.read_text(encoding="utf-8"))
-    return BM25Manifest(
-        created_at=datetime.fromisoformat(str(body["created_at"])),
-        db_updated_at=datetime.fromisoformat(str(body["db_updated_at"])) if body.get("db_updated_at") else None,
-        chunk_count=int(body["chunk_count"]),
-    )
-
-
-def write_bm25_manifest(index_path: Path, manifest: BM25Manifest) -> None:
-    """Write the BM25 manifest to an index directory."""
-    body = {
-        "created_at": manifest.created_at.isoformat(),
-        "db_updated_at": manifest.db_updated_at.isoformat() if manifest.db_updated_at else None,
-        "chunk_count": manifest.chunk_count,
-    }
-    (index_path / MANIFEST_NAME).write_text(json.dumps(body, indent=2, sort_keys=True), encoding="utf-8")
-
-
-def bm25_index_exists(index_path: Path, manifest: BM25Manifest | None) -> bool:
-    """Return whether a usable persisted BM25 index exists."""
-    active_index_path = get_current_bm25_index(index_path)
-    if manifest is None or not active_index_path.is_dir():
-        return False
-    if manifest.chunk_count == 0:
-        return True
-    return all((active_index_path / file_name).exists() for file_name in REQUIRED_INDEX_FILES)
-
-
-def next_bm25_generation_path(generations_path: Path, created_at: datetime) -> Path:
-    """Return an unused dated BM25 generation path."""
-    base_name = created_at.astimezone(UTC).strftime("%Y%m%dT%H%M%S.%fZ")
-    generation_path = generations_path / base_name
-    suffix = 1
-    while generation_path.exists():
-        generation_path = generations_path / f"{base_name}.{suffix}"
-        suffix += 1
-    return generation_path

python/ebook_search/config.py

@@ -1,117 +0,0 @@
-"""Configuration for the EPUB search app."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from os import getenv
-
-
-def getenv_bool(name: str, *, default: bool) -> bool:
-    """Read a boolean environment variable with a default fallback."""
-    value = getenv(name)
-    if value is None:
-        return default
-    return value.strip().lower() in {"1", "true", "yes", "on"}
-
-
-def getenv_int(name: str, *, default: int) -> int:
-    """Read an integer environment variable with a default fallback."""
-    value = getenv(name)
-    if value is None or not value.strip():
-        return default
-    return int(value)
-
-
-@dataclass(frozen=True)
-class RerankConfig:
-    """vLLM reranker settings."""
-
-    enabled: bool = False
-    base_url: str = "http://192.168.90.25:8001"
-    model: str = "qwen3-reranker-06b"
-    candidates: int = 24
-    timeout_seconds: float = 30.0
-
-
-@dataclass(frozen=True)
-class EbookSearchConfig:
-    """Runtime settings for EPUB search."""
-
-    rerank: RerankConfig
-    top_k: int = 12
-    library_paths: tuple[str, ...] = ()
-    vllm_base_url: str = "https://ollama.com/v1"
-    vllm_api_key: str = "not-needed"
-    chat_model: str = "deepseek-v4-flash"
-    answer_enabled: bool = True
-    embedding_base_url: str = "http://192.168.90.25:8000/v1"
-    embedding_api_key: str = "not-needed"
-    embedding_model: str = "qwen3-embedding-0.6b"
-    embedding_batch_size: int = 32
-    bm25_index_dir: str = ".ebook_search_bm25"
-    bm25_refresh_delay_seconds: int = 60
-
-
-def load_rerank_config() -> RerankConfig:
-    """Load reranker config from environment variables."""
-    return RerankConfig(
-        enabled=getenv_bool("EBOOK_SEARCH_RERANK_ENABLED", default=False),
-        base_url=getenv("EBOOK_SEARCH_RERANK_BASE_URL", "http://192.168.90.25:8001"),
-        model=getenv("EBOOK_SEARCH_RERANK_MODEL", "qwen3-reranker-06b"),
-        candidates=getenv_int("EBOOK_SEARCH_RERANK_CANDIDATES", default=24),
-        timeout_seconds=float(getenv_int("EBOOK_SEARCH_RERANK_TIMEOUT_SECONDS", default=30)),
-    )
-
-
-def load_config() -> EbookSearchConfig:
-    """Load EPUB search config from environment variables."""
-    return EbookSearchConfig(
-        rerank=load_rerank_config(),
-        top_k=getenv_int("EBOOK_SEARCH_TOP_K", default=12),
-        library_paths=library_paths_from_env(),
-        vllm_base_url=getenv("EBOOK_SEARCH_VLLM_BASE_URL", "https://ollama.com/v1"),
-        vllm_api_key=getenv("EBOOK_SEARCH_VLLM_API_KEY") or getenv("OLLAMA_API_KEY") or "not-needed",
-        chat_model=getenv("EBOOK_SEARCH_CHAT_MODEL", "deepseek-v4-flash"),
-        answer_enabled=getenv_bool("EBOOK_SEARCH_ANSWER_ENABLED", default=True),
-        embedding_base_url=getenv("EBOOK_SEARCH_EMBEDDING_BASE_URL", "http://192.168.90.25:8000/v1"),
-        embedding_api_key=getenv("EBOOK_SEARCH_EMBEDDING_API_KEY", "not-needed"),
-        embedding_model=normalize_embedding_model(),
-        embedding_batch_size=getenv_int("EBOOK_SEARCH_EMBEDDING_BATCH_SIZE", default=32),
-        bm25_index_dir=getenv("EBOOK_SEARCH_BM25_INDEX_DIR", ".ebook_search_bm25"),
-        bm25_refresh_delay_seconds=getenv_int("EBOOK_SEARCH_BM25_REFRESH_DELAY_SECONDS", default=60),
-    )
-
-
-def normalize_embedding_model(default: str = "qwen3-embedding-0.6b") -> str:
-    """Normalize supported embedding aliases to provider model names."""
-    aliases = {
-        "Qwen3-Embedding-0.6B": "qwen3-embedding-0.6b",
-        "Qwen3-Embedding-4B": "qwen3-embedding-4b",
-        "Qwen3-Embedding-8B": "qwen3-embedding-8b",
-        "Qwen/Qwen3-Embedding-0.6B": "qwen3-embedding-0.6b",
-        "Qwen/Qwen3-Embedding-4B": "qwen3-embedding-4b",
-        "Qwen/Qwen3-Embedding-8B": "qwen3-embedding-8b",
-        "qwen3-embedding:0.6b": "qwen3-embedding-0.6b",
-        "qwen3-embedding:4b": "qwen3-embedding-4b",
-        "qwen3-embedding:8b": "qwen3-embedding-8b",
-        "qwen3-embedding-0.6b": "qwen3-embedding-0.6b",
-        "qwen3-embedding-4b": "qwen3-embedding-4b",
-        "qwen3-embedding-8b": "qwen3-embedding-8b",
-    }
-
-    model = getenv("EBOOK_SEARCH_EMBEDDING_MODEL", default)
-    standard_model = aliases.get(model)
-
-    if standard_model is None:
-        error = f"Embedding model {model} is not supported. Supported models are {aliases.keys()}"
-        raise ValueError(error)
-
-    return standard_model
-
-
-def library_paths_from_env() -> tuple[str, ...]:
-    """Read configured EPUB library paths from the environment."""
-    value = getenv("EBOOK_SEARCH_LIBRARY_PATHS")
-    if value is None:
-        return ()
-    return tuple(path for path in value.split(":") if path)

python/ebook_search/embeddings.py

@@ -1,170 +0,0 @@
-"""Embedding model helpers."""
-
-from __future__ import annotations
-
-import logging
-from dataclasses import dataclass
-from typing import TYPE_CHECKING
-
-from sqlalchemy import func, select
-from sqlalchemy.dialects.postgresql import insert
-
-from python.ebook_search.llm_interface import request_embeddings
-from python.orm.richie import (
-    EbookChunk,
-    EbookChunkEmbedding1024,
-    EbookChunkEmbedding2560,
-    EbookChunkEmbedding4096,
-    EbookEmbeddingModel,
-)
-
-logger = logging.getLogger(__name__)
-
-if TYPE_CHECKING:
-    from collections.abc import Sequence
-
-    from sqlalchemy.orm import Session
-
-    from python.ebook_search.config import EbookSearchConfig
-
-MODEL_DIMENSIONS = {
-    "qwen3-embedding-0.6b": 1024,
-    "qwen3-embedding-4b": 2560,
-    "qwen3-embedding-8b": 4096,
-}
-
-
-def get_embedding_table(
-    dimension: int,
-) -> type[EbookChunkEmbedding1024 | EbookChunkEmbedding2560 | EbookChunkEmbedding4096]:
-    """Return the embedding table mapped to an embedding dimension."""
-    embedding_tables = {
-        1024: EbookChunkEmbedding1024,
-        2560: EbookChunkEmbedding2560,
-        4096: EbookChunkEmbedding4096,
-    }
-    table = embedding_tables.get(dimension)
-    if not table:
-        msg = f"Embedding dimension {dimension} is not supported"
-        raise ValueError(msg)
-    return table
-
-
-@dataclass(frozen=True)
-class EmbeddingModelStats:
-    """Embedding coverage for one model."""
-
-    model_name: str
-    dimension: int
-    embedded_chunks: int
-    total_chunks: int
-
-    @property
-    def missing_chunks(self) -> int:
-        """Return chunks missing this embedding model."""
-        return max(self.total_chunks - self.embedded_chunks, 0)
-
-
-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",
-        config.embedding_base_url,
-        config.embedding_model,
-        len(texts),
-    )
-    vectors = request_embeddings(texts, config)
-    expected_dimension = MODEL_DIMENSIONS[config.embedding_model]
-    for vector in vectors:
-        if len(vector) != expected_dimension:
-            msg = f"Expected {expected_dimension} dimensions, got {len(vector)}"
-            raise ValueError(msg)
-    logger.info(
-        "ebook_embed_request_complete model=%s count=%s dimension=%s",
-        config.embedding_model,
-        len(vectors),
-        expected_dimension,
-    )
-    return vectors
-
-
-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 embed_texts([instructed_query], config)[0]
-
-
-def ensure_embedding_models(session: Session) -> None:
-    """Ensure supported embedding model rows exist."""
-    for name, dimension in MODEL_DIMENSIONS.items():
-        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)
-    session.flush()
-
-
-def embedding_model_stats(session: Session) -> list[EmbeddingModelStats]:
-    """Return embedding coverage counts for every supported model."""
-    total_chunks = session.scalar(select(func.count(EbookChunk.id))) or 0
-    models = {
-        model.name: model
-        for model in session.scalars(
-            select(EbookEmbeddingModel)
-            .where(EbookEmbeddingModel.name.in_(MODEL_DIMENSIONS))
-            .order_by(EbookEmbeddingModel.name)
-        )
-    }
-
-    stats: list[EmbeddingModelStats] = []
-    for model_name, dimension in MODEL_DIMENSIONS.items():
-        model = models.get(model_name)
-        embedded_chunks = 0
-        if model is not None:
-            table = get_embedding_table(dimension)
-            embedded_chunks = session.scalar(select(func.count(table.id)).where(table.model_id == model.id)) or 0
-        stats.append(
-            EmbeddingModelStats(
-                model_name=model_name,
-                dimension=dimension,
-                embedded_chunks=embedded_chunks,
-                total_chunks=total_chunks,
-            )
-        )
-    return stats
-
-
-def embed_missing_chunks(session: Session, config: EbookSearchConfig) -> int:
-    """Embed chunks missing embeddings for the configured 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}"
-        raise ValueError(msg)
-
-    table = get_embedding_table(model.dimension)
-    chunks = list(
-        session.scalars(
-            select(EbookChunk)
-            .outerjoin(table, (table.chunk_id == EbookChunk.id) & (table.model_id == model.id))
-            .where(table.id.is_(None))
-            .order_by(EbookChunk.id)
-            .limit(config.embedding_batch_size)
-        )
-    )
-    if not chunks:
-        logger.info("ebook_embed_missing_none model=%s", config.embedding_model)
-        return 0
-
-    logger.info("ebook_embed_missing_batch_start model=%s count=%s", config.embedding_model, len(chunks))
-    vectors = embed_texts([chunk.text for chunk in chunks], config)
-    rows = [
-        {"chunk_id": chunk.id, "model_id": model.id, "embedding": vector}
-        for chunk, vector in zip(chunks, vectors, strict=True)
-    ]
-    statement = insert(table).values(rows).on_conflict_do_nothing(index_elements=["chunk_id", "model_id"])
-    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/epub_parse.py

@@ -1,95 +0,0 @@
-"""EPUB parsing helpers."""
-
-from __future__ import annotations
-
-import re
-from dataclasses import dataclass
-from typing import TYPE_CHECKING
-
-from bs4 import BeautifulSoup
-from ebooklib import ITEM_DOCUMENT, epub
-
-if TYPE_CHECKING:
-    from pathlib import Path
-
-WHITESPACE_RE = re.compile(r"\s+")
-
-
-@dataclass(frozen=True)
-class ParsedChapter:
-    """Text extracted from one EPUB spine document."""
-
-    title: str | None
-    href: str | None
-    text: str
-    page_labels: tuple[str, ...]
-
-
-@dataclass(frozen=True)
-class ParsedEpub:
-    """Parsed EPUB metadata and text."""
-
-    title: str
-    author: str | None
-    language: str | None
-    publisher: str | None
-    identifier: str | None
-    chapters: tuple[ParsedChapter, ...]
-
-
-def parse_epub(path: Path) -> ParsedEpub:
-    """Parse EPUB metadata and spine text."""
-    book = epub.read_epub(path)
-    chapters = []
-    for item in book.get_items_of_type(ITEM_DOCUMENT):
-        soup = BeautifulSoup(item.get_content(), "html.parser")
-        title = chapter_title(soup)
-        page_labels = tuple(extract_page_labels(soup))
-        text = clean_text(soup.get_text(" "))
-        if text:
-            chapters.append(ParsedChapter(title=title, href=item.get_name(), text=text, page_labels=page_labels))
-
-    return ParsedEpub(
-        title=metadata_value(book, "title") or path.stem,
-        author=metadata_value(book, "creator"),
-        language=metadata_value(book, "language"),
-        publisher=metadata_value(book, "publisher"),
-        identifier=metadata_value(book, "identifier"),
-        chapters=tuple(chapters),
-    )
-
-
-def metadata_value(book: epub.EpubBook, name: str) -> str | None:
-    """Return the first non-empty Dublin Core metadata value for a name."""
-    values = book.get_metadata("DC", name)
-    if not values:
-        return None
-    value = values[0][0]
-    return str(value).strip() or None
-
-
-def chapter_title(soup: BeautifulSoup) -> str | None:
-    """Extract the best available title from an EPUB document soup."""
-    heading = soup.find(["h1", "h2", "h3"])
-    if heading is None:
-        title = soup.find("title")
-        if title is None:
-            return None
-        return clean_text(title.get_text(" ")) or None
-    return clean_text(heading.get_text(" ")) or None
-
-
-def extract_page_labels(soup: BeautifulSoup) -> list[str]:
-    """Extract EPUB page-break labels from a document soup."""
-    labels: list[str] = []
-    for tag in soup.find_all(attrs={"epub:type": "pagebreak"}):
-        label = tag.get("title") or tag.get("aria-label") or tag.get_text(" ")
-        clean = clean_text(str(label))
-        if clean:
-            labels.append(clean)
-    return labels
-
-
-def clean_text(text: str) -> str:
-    """Normalize whitespace in extracted EPUB text."""
-    return WHITESPACE_RE.sub(" ", text).strip()

python/ebook_search/ingest.py

@@ -1,190 +0,0 @@
-"""EPUB ingestion into Richie DB."""
-
-from __future__ import annotations
-
-import hashlib
-import logging
-from dataclasses import dataclass
-from datetime import UTC, datetime
-from pathlib import Path
-from typing import TYPE_CHECKING
-
-import tiktoken
-from sqlalchemy import or_, select
-
-from python.ebook_search.epub_parse import parse_epub
-from python.orm.richie import EbookChapter, EbookChunk, EbookSource
-
-logger = logging.getLogger(__name__)
-DEFAULT_CHUNK_TOKENS = 700
-DEFAULT_CHUNK_OVERLAP = 100
-
-if TYPE_CHECKING:
-    from sqlalchemy.orm import Session
-
-    from python.ebook_search.config import EbookSearchConfig
-    from python.ebook_search.epub_parse import ParsedChapter
-
-
-@dataclass(frozen=True)
-class TextChunk:
-    """A token-bounded chunk of text."""
-
-    text: str
-    token_start: int
-    token_count: int
-
-
-def chunk_text(
-    text: str,
-    *,
-    chunk_tokens: int = DEFAULT_CHUNK_TOKENS,
-    overlap_tokens: int = DEFAULT_CHUNK_OVERLAP,
-) -> list[TextChunk]:
-    """Split text into overlapping token chunks."""
-    if chunk_tokens <= 0:
-        msg = "chunk_tokens must be positive"
-        raise ValueError(msg)
-    if overlap_tokens < 0 or overlap_tokens >= chunk_tokens:
-        msg = "overlap_tokens must be non-negative and smaller than chunk_tokens"
-        raise ValueError(msg)
-
-    encoding = tiktoken.get_encoding("cl100k_base")
-    tokens = encoding.encode(text)
-    if not tokens:
-        return []
-
-    chunks: list[TextChunk] = []
-    step = chunk_tokens - overlap_tokens
-    for start in range(0, len(tokens), step):
-        chunk = tokens[start : start + chunk_tokens]
-        if not chunk:
-            continue
-        chunks.append(
-            TextChunk(
-                text=encoding.decode(chunk).strip(),
-                token_start=start,
-                token_count=len(chunk),
-            )
-        )
-        if start + chunk_tokens >= len(tokens):
-            break
-    return [chunk for chunk in chunks if chunk.text]
-
-
-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 = Path(library_path).expanduser()
-        logger.info("ebook_ingest_path_start path=%s", path)
-        if path.is_file() and path.suffix.lower() == ".epub":
-            count += int(ingest_file(session, path))
-        elif path.is_dir():
-            for epub_path in sorted(path.rglob("*.epub")):
-                count += int(ingest_file(session, epub_path))
-        else:
-            logger.warning("ebook_ingest_path_missing path=%s", path)
-    logger.info("ebook_ingest_paths_complete changed_files=%s configured_paths=%s", count, len(config.library_paths))
-    return count
-
-
-def ingest_file(session: Session, path: Path) -> bool:
-    """Ingest one EPUB file. Return True when the database changed."""
-    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()
-        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
-    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)
-
-    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
-
-
-def find_existing_source(session: Session, path: Path, file_hash: str) -> EbookSource | None:
-    """Find an existing source by canonical path or file hash."""
-    return session.scalar(
-        select(EbookSource).where(or_(EbookSource.file_path == str(path), EbookSource.file_sha256 == file_hash))
-    )
-
-
-def add_chapter_chunks(
-    session: Session,
-    source: EbookSource,
-    chapter: EbookChapter,
-    parsed_chapter: ParsedChapter,
-    chunk_index: int,
-) -> int:
-    """Add chunk rows for one parsed chapter and return the next chunk index."""
-    page_label = parsed_chapter.page_labels[0] if parsed_chapter.page_labels else None
-    for text_chunk in chunk_text(parsed_chapter.text):
-        session.add(
-            EbookChunk(
-                source_id=source.id,
-                chapter_id=chapter.id,
-                chunk_index=chunk_index,
-                text=text_chunk.text,
-                token_start=text_chunk.token_start,
-                token_count=text_chunk.token_count,
-                page_label=page_label,
-                content_sha256=hashlib.sha256(text_chunk.text.encode()).hexdigest(),
-                search_text=f"{source.title} {source.author or ''} {chapter.title or ''} {text_chunk.text}",
-            )
-        )
-        chunk_index += 1
-    return chunk_index
-
-
-def sha256_file(path: Path) -> str:
-    """Calculate the SHA-256 digest for a file."""
-    digest = hashlib.sha256()
-    with path.open("rb") as file:
-        for block in iter(lambda: file.read(1024 * 1024), b""):
-            digest.update(block)
-    return digest.hexdigest()

python/ebook_search/llm_interface.py

@@ -1,143 +0,0 @@
-"""LLM provider HTTP adapters."""
-
-from __future__ import annotations
-
-import logging
-from typing import TYPE_CHECKING
-
-import httpx
-
-if TYPE_CHECKING:
-    from collections.abc import Sequence
-
-    from python.ebook_search.config import EbookSearchConfig, RerankConfig
-
-logger = logging.getLogger(__name__)
-
-
-def auth_headers(api_key: str) -> dict[str, str]:
-    """Build authorization headers when an API key is configured."""
-    if api_key == "not-needed":
-        return {}
-    return {"Authorization": f"Bearer {api_key}"}
-
-
-def request_embeddings(texts: Sequence[str], config: EbookSearchConfig) -> list[list[float]]:
-    """Request embeddings from the configured OpenAI-compatible endpoint."""
-    try:
-        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)},
-            timeout=60,
-        )
-        response.raise_for_status()
-        return embedding_vectors_from_response(response.json())
-    except (httpx.HTTPError, ValueError, KeyError, TypeError) as error:
-        logger.exception(
-            "ebook_embed_request_failed base_url=%s model=%s count=%s",
-            config.embedding_base_url,
-            config.embedding_model,
-            len(texts),
-        )
-        msg = f"Embedding request failed. base_url={config.embedding_base_url} model={config.embedding_model}"
-        raise RuntimeError(msg) from error
-
-
-def embedding_vectors_from_response(body: object) -> list[list[float]]:
-    """Extract embedding vectors from an OpenAI-compatible embedding response."""
-    if not isinstance(body, dict):
-        msg = "Embedding response is not an object"
-        raise TypeError(msg)
-
-    data = body["data"]
-    if not isinstance(data, list):
-        msg = "Embedding response data is not a list"
-        raise TypeError(msg)
-
-    vectors: list[list[float]] = []
-    for item in data:
-        if not isinstance(item, dict):
-            msg = "Embedding item is not an object"
-            raise TypeError(msg)
-        embedding = item["embedding"]
-        if not isinstance(embedding, list):
-            msg = "Embedding value is not a list"
-            raise TypeError(msg)
-        vectors.append([float(value) for value in embedding])
-    return vectors
-
-
-def request_rerank(
-    query: str,
-    documents: Sequence[str],
-    config: RerankConfig,
-) -> object | None:
-    """Request rerank scores from the configured vLLM endpoint."""
-    payload = {
-        "model": config.model,
-        "query": query,
-        "documents": list(documents),
-    }
-    response = httpx.post(
-        f"{config.base_url.rstrip('/')}/rerank",
-        json=payload,
-        timeout=config.timeout_seconds,
-    )
-    response.raise_for_status()
-    try:
-        return response.json()
-    except ValueError:
-        logger.debug("ebook_rerank_response_invalid_json", extra={"response": response.text})
-        return None
-
-
-def request_chat_completion(
-    config: EbookSearchConfig,
-    messages: Sequence[dict[str, str]],
-) -> str:
-    """Request a chat completion from the configured OpenAI-compatible endpoint."""
-    try:
-        response = httpx.post(
-            f"{config.vllm_base_url.rstrip('/')}/chat/completions",
-            headers=auth_headers(config.vllm_api_key),
-            json={
-                "model": config.chat_model,
-                "messages": list(messages),
-                "temperature": 0,
-            },
-            timeout=60,
-        )
-        response.raise_for_status()
-        return chat_content_from_response(response.json())
-    except (httpx.HTTPError, ValueError, KeyError, TypeError) as error:
-        msg = f"Chat request failed. base_url={config.vllm_base_url} model={config.chat_model}"
-        raise RuntimeError(msg) from error
-
-
-def chat_content_from_response(body: object) -> str:
-    """Extract text content from an OpenAI-compatible chat response."""
-    if not isinstance(body, dict):
-        msg = "Chat response is not an object"
-        raise TypeError(msg)
-
-    choices = body["choices"]
-    if not isinstance(choices, list) or not choices:
-        msg = "Chat response has no choices"
-        raise ValueError(msg)
-
-    first = choices[0]
-    if not isinstance(first, dict):
-        msg = "Chat choice is not an object"
-        raise TypeError(msg)
-
-    message = first["message"]
-    if not isinstance(message, dict):
-        msg = "Chat message is not an object"
-        raise TypeError(msg)
-
-    content = message.get("content") or ""
-    if not isinstance(content, str):
-        msg = "Chat content is not text"
-        raise TypeError(msg)
-    return content

python/ebook_search/rerank.py

@@ -1,129 +0,0 @@
-"""vLLM-backed optional reranking."""
-
-from __future__ import annotations
-
-import logging
-from dataclasses import dataclass, replace
-from typing import TYPE_CHECKING
-
-from python.ebook_search.llm_interface import request_rerank
-
-if TYPE_CHECKING:
-    from python.ebook_search.config import RerankConfig
-    from python.ebook_search.search import SearchResult
-
-logger = logging.getLogger(__name__)
-RERANK_SCORE_WEIGHT = 0.7
-HYBRID_SCORE_WEIGHT = 0.3
-
-
-@dataclass(frozen=True)
-class RerankResult:
-    """A relevance score for one candidate chunk."""
-
-    chunk_id: int
-    score: float
-
-
-def rerank_chunks(query: str, candidates: list[SearchResult], config: RerankConfig) -> list[SearchResult]:
-    """Rerank candidates with a vLLM rerank endpoint."""
-    if not candidates:
-        return []
-
-    logger.info(
-        "ebook_rerank_request_start base_url=%s model=%s candidates=%s",
-        config.base_url,
-        config.model,
-        len(candidates),
-    )
-    scores = score_candidates(query, candidates, config)
-    results = sorted(
-        (
-            replace(
-                result,
-                score=final_rerank_score(result, scores[result.chunk_id].score, candidates),
-                rerank_score=scores[result.chunk_id].score,
-            )
-            for result in candidates
-        ),
-        key=lambda result: result.score,
-        reverse=True,
-    )
-    logger.info(
-        "ebook_rerank_request_complete base_url=%s model=%s candidates=%s",
-        config.base_url,
-        config.model,
-        len(results),
-    )
-    return results
-
-
-def score_candidates(
-    query: str,
-    candidates: list[SearchResult],
-    config: RerankConfig,
-) -> dict[int, RerankResult]:
-    """Score candidate chunks with the configured rerank API."""
-    body = request_rerank(query, [candidate.text for candidate in candidates], config)
-    if body is None:
-        return zero_rerank_scores(candidates)
-
-    scores = parse_vllm_scores(body, candidates)
-    for result in scores.values():
-        logger.debug("ebook_rerank_candidate_scored chunk_id=%s score=%s", result.chunk_id, result.score)
-    return scores
-
-
-def parse_vllm_scores(body: object, candidates: list[SearchResult]) -> dict[int, RerankResult]:
-    """Parse vLLM rerank scores into chunk-id keyed results."""
-    if not isinstance(body, dict):
-        logger.debug("ebook_rerank_response_not_object", extra={"response": body})
-        return zero_rerank_scores(candidates)
-
-    results = body.get("results") or body.get("data")
-    if not isinstance(results, list):
-        logger.debug("ebook_rerank_response_missing_results", extra={"response": body})
-        return zero_rerank_scores(candidates)
-
-    scores = zero_rerank_scores(candidates)
-    for item in results:
-        if not isinstance(item, dict):
-            continue
-        index = item.get("index")
-        score = item.get("relevance_score", item.get("score"))
-        if not isinstance(index, int) or index < 0 or index >= len(candidates):
-            continue
-        if not isinstance(score, int | float):
-            continue
-        chunk_id = candidates[index].chunk_id
-        scores[chunk_id] = RerankResult(chunk_id=chunk_id, score=clamp_score(float(score)))
-    return scores
-
-
-def zero_rerank_scores(candidates: list[SearchResult]) -> dict[int, RerankResult]:
-    """Return zero relevance scores for all candidate chunks."""
-    return {candidate.chunk_id: RerankResult(chunk_id=candidate.chunk_id, score=0.0) for candidate in candidates}
-
-
-def clamp_score(score: float) -> float:
-    """Clamp a rerank score into the supported 0.0 to 1.0 range."""
-    return min(max(score, 0.0), 1.0)
-
-
-def final_rerank_score(result: SearchResult, rerank_score: float, candidates: list[SearchResult]) -> float:
-    """Combine rerank relevance with normalized hybrid retrieval evidence."""
-    return (RERANK_SCORE_WEIGHT * rerank_score) + (HYBRID_SCORE_WEIGHT * normalized_hybrid_score(result, candidates))
-
-
-def normalized_hybrid_score(result: SearchResult, candidates: list[SearchResult]) -> float:
-    """Normalize a candidate hybrid score against the rerank candidate set."""
-    hybrid_scores = [
-        candidate.fused_score if candidate.fused_score is not None else candidate.score for candidate in candidates
-    ]
-    low = min(hybrid_scores)
-    high = max(hybrid_scores)
-    if high == low:
-        return 1.0
-
-    score = result.fused_score if result.fused_score is not None else result.score
-    return (score - low) / (high - low)

python/ebook_search/search.py

@@ -1,383 +0,0 @@
-"""Hybrid search orchestration."""
-
-from __future__ import annotations
-
-import logging
-import re
-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.orm import Session
-
-from python.ebook_search.bm25_corpus import (
-    BM25CorpusUnavailableError,
-    load_bm25_corpus,
-    score_bm25_corpus,
-)
-from python.ebook_search.embeddings import MODEL_DIMENSIONS, embed_query, get_embedding_table
-from python.ebook_search.rerank import rerank_chunks
-from python.ebook_search.timing import RuntimeStep, timed_result
-from python.orm.richie import (
-    EbookChapter,
-    EbookChunk,
-    EbookEmbeddingModel,
-    EbookSource,
-)
-
-if TYPE_CHECKING:
-    from collections.abc import Mapping
-
-    from sqlalchemy.engine import Engine
-
-    from python.ebook_search.config import EbookSearchConfig
-
-logger = logging.getLogger(__name__)
-BM25_CANDIDATE_LIMIT = 120
-
-
-@dataclass(frozen=True)
-class SearchResult:
-    """One source chunk returned by search."""
-
-    chunk_id: int
-    text: str
-    source_title: str
-    score: float = 0.0
-    vector_score: float | None = None
-    bm25_score: float | None = None
-    fused_score: float | None = None
-    rerank_score: float | None = None
-    source_author: str | None = None
-    chapter_title: str | None = None
-    page_label: str | None = None
-    rank_source: str = "Hybrid"
-
-
-@dataclass(frozen=True)
-class SearchResponse:
-    """Search output for the UI."""
-
-    query: str
-    results: list[SearchResult]
-    rank_label: str
-    timings: tuple[RuntimeStep, ...] = ()
-
-    @property
-    def total_runtime_ms(self) -> float:
-        """Return total measured runtime for the response."""
-        return sum(step.duration_ms for step in self.timings if step.counts_toward_total)
-
-
-@dataclass(frozen=True)
-class RetrievalResponse:
-    """Parallel retrieval output for vector and BM25 candidates."""
-
-    vector_results: list[SearchResult]
-    lexical_results: list[SearchResult]
-    timings: tuple[RuntimeStep, ...]
-
-
-def search_ebooks(
-    engine: Engine,
-    query: str,
-    config: EbookSearchConfig,
-    *,
-    rerank: bool = False,
-) -> 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")
-
-    logger.info("ebook_search_start query_length=%s rerank=%s", len(query), rerank)
-    timings: list[RuntimeStep] = []
-    bm25_query, timing = timed_result("BM25 query preparation", retrieval_query_from_text, query)
-    timings.append(timing)
-    retrieval, timing = timed_result(
-        "Hybrid retrieval",
-        parallel_retrieval,
-        engine,
-        query,
-        bm25_query,
-        config,
-    )
-    timings.extend(retrieval.timings)
-    timings.append(timing)
-    fused, timing = timed_result(
-        "Reciprocal rank fusion",
-        reciprocal_rank_fusion,
-        retrieval.vector_results,
-        retrieval.lexical_results,
-    )
-    timings.append(timing)
-    if config.rerank.enabled and rerank:
-        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))
-    logger.info(
-        "ebook_search_complete vector_candidates=%s lexical_candidates=%s "
-        "fused_candidates=%s returned=%s rank_label=%s runtime_ms=%.1f",
-        len(retrieval.vector_results),
-        len(retrieval.lexical_results),
-        len(fused),
-        len(response.results),
-        response.rank_label,
-        response.total_runtime_ms,
-    )
-    return response
-
-
-def parallel_retrieval(
-    engine: Engine,
-    vector_query: str,
-    bm25_query: str,
-    config: EbookSearchConfig,
-) -> RetrievalResponse:
-    """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,
-            vector_query,
-            config,
-        )
-        bm25_future = executor.submit(
-            timed_result,
-            "BM25 search",
-            bm25_candidates,
-            bm25_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",
-        len(vector_results),
-        len(lexical_results),
-    )
-    return RetrievalResponse(
-        vector_results=vector_results,
-        lexical_results=lexical_results,
-        timings=(
-            replace(vector_timing, counts_toward_total=False),
-            replace(lexical_timing, counts_toward_total=False),
-        ),
-    )
-
-
-def skip_rerank(
-    query: str,
-    candidates: list[SearchResult],
-    config: EbookSearchConfig,
-) -> SearchResponse:
-    """Return fused hybrid results without reranking."""
-    logger.info("ebook_rerank_skipped candidates=%s", len(candidates))
-    return SearchResponse(query=query, results=candidates[: config.top_k], rank_label="Hybrid")
-
-
-def apply_rerank(
-    query: str,
-    candidates: list[SearchResult],
-    config: EbookSearchConfig,
-) -> SearchResponse:
-    """Rerank already-fused hybrid candidates."""
-    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),
-        len(reranked),
-    )
-    return SearchResponse(
-        query=query,
-        results=[replace(result, rank_source="Hybrid + rerank") for result in reranked[: config.top_k]],
-        rank_label="Hybrid + rerank",
-    )
-
-
-def vector_candidates(engine: Engine, query: str, config: EbookSearchConfig) -> list[SearchResult]:
-    """Return pgvector cosine candidates for a natural-language query."""
-    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)
-
-        expected_dimension = MODEL_DIMENSIONS[config.embedding_model]
-        if model.dimension != expected_dimension:
-            msg = f"Model row dimension {model.dimension} does not match configured dimension {expected_dimension}"
-            raise ValueError(msg)
-
-        embedding = embed_query(query, config)
-        limit = max(config.rerank.candidates, config.top_k) * 4
-        embedding_table = get_embedding_table(model.dimension)
-
-        embedding_param = literal(embedding, type_=Vector(model.dimension))
-        distance = embedding_table.embedding.op("<=>")(embedding_param)
-        score = (literal(1.0) - distance).label("score")
-        statement = (
-            select(
-                EbookChunk.id.label("chunk_id"),
-                EbookChunk.text.label("text"),
-                EbookSource.title.label("source_title"),
-                EbookSource.author.label("source_author"),
-                EbookChapter.title.label("chapter_title"),
-                EbookChunk.page_label.label("page_label"),
-                score,
-            )
-            .select_from(embedding_table)
-            .join(EbookChunk, EbookChunk.id == embedding_table.chunk_id)
-            .join(EbookSource, EbookSource.id == EbookChunk.source_id)
-            .outerjoin(EbookChapter, EbookChapter.id == EbookChunk.chapter_id)
-            .where(embedding_table.model_id == model.id)
-            .order_by(distance)
-            .limit(limit)
-        )
-        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",
-            config.embedding_model,
-            model.dimension,
-            len(results),
-        )
-        return results
-
-
-def bm25_candidates(query: str, config: EbookSearchConfig) -> list[SearchResult]:
-    """Return BM25-ranked lexical candidates using the persisted corpus."""
-    try:
-        corpus = load_bm25_corpus(config)
-    except BM25CorpusUnavailableError as error:
-        logger.warning("ebook_bm25_index_unavailable_skipping error=%s", error)
-        return []
-
-    if not corpus.records:
-        logger.info("ebook_bm25_search_complete corpus=0 candidates=0")
-        return []
-
-    scored_records = score_bm25_corpus(query, corpus, limit=BM25_CANDIDATE_LIMIT)
-    results = [
-        replace(search_result_from_row(record), score=score, vector_score=None, bm25_score=score)
-        for record, score in scored_records
-    ]
-
-    max_score = results[0].bm25_score if results else 0.0
-    logger.info(
-        "ebook_bm25_search_complete corpus=%s candidates=%s max_score=%.6f",
-        len(corpus.records),
-        len(results),
-        max_score,
-    )
-    return results
-
-
-def reciprocal_rank_fusion(
-    vector_results: list[SearchResult],
-    lexical_results: list[SearchResult],
-    *,
-    rank_constant: int = 60,
-) -> list[SearchResult]:
-    """Fuse vector and lexical rankings with Reciprocal Rank Fusion."""
-    by_chunk: dict[int, SearchResult] = {}
-    scores: dict[int, float] = {}
-    vector_scores: dict[int, float] = {}
-    bm25_scores: dict[int, float] = {}
-
-    for rank, result in enumerate(vector_results, start=1):
-        by_chunk.setdefault(result.chunk_id, result)
-        vector_scores[result.chunk_id] = result.vector_score if result.vector_score is not None else result.score
-        scores[result.chunk_id] = scores.get(result.chunk_id, 0.0) + (1 / (rank_constant + rank))
-
-    for rank, result in enumerate(lexical_results, start=1):
-        by_chunk.setdefault(result.chunk_id, result)
-        bm25_scores[result.chunk_id] = result.bm25_score if result.bm25_score is not None else result.score
-        scores[result.chunk_id] = scores.get(result.chunk_id, 0.0) + (1 / (rank_constant + rank))
-
-    return sorted(
-        (
-            replace(
-                result,
-                score=scores[result.chunk_id],
-                vector_score=vector_scores.get(result.chunk_id),
-                bm25_score=bm25_scores.get(result.chunk_id),
-                fused_score=scores[result.chunk_id],
-                rank_source="Hybrid",
-            )
-            for result in by_chunk.values()
-        ),
-        key=lambda result: result.score,
-        reverse=True,
-    )
-
-
-def search_result_from_row(row: Mapping[str, object]) -> SearchResult:
-    """Convert a database row mapping into a search result."""
-    return SearchResult(
-        chunk_id=int(row["chunk_id"]),
-        text=str(row["text"]),
-        source_title=str(row["source_title"]),
-        source_author=optional_str(row["source_author"]),
-        chapter_title=optional_str(row["chapter_title"]),
-        page_label=optional_str(row["page_label"]),
-        score=float(row["score"]) if "score" in row else 0.0,
-        vector_score=float(row["score"]) if "score" in row else None,
-    )
-
-
-def optional_str(value: object) -> str | None:
-    """Convert nullable database values to optional strings."""
-    if value is None:
-        return None
-    return str(value)
-
-
-TOKEN_RE = re.compile(r"[A-Za-z0-9_]+")
-
-
-def tokens(text_value: str) -> list[str]:
-    """Extract tokens from a text value.
-
-    This is a simple approximation of the tokenization used by PostgreSQL's full-text search,
-    which is sufficient for BM25 candidate retrieval. It lowercases tokens and includes alphanumeric characters and
-    underscores.
-    """
-    return [match.group(0).lower() for match in TOKEN_RE.finditer(text_value)]
-
-
-QUERY_STOP_WORDS = {
-    "a",
-    "an",
-    "and",
-    "are",
-    "as",
-    "at",
-    "does",
-    "for",
-    "in",
-    "is",
-    "of",
-    "the",
-    "to",
-    "what",
-    "when",
-    "where",
-    "which",
-    "who",
-    "why",
-}
-
-
-def retrieval_query_from_text(query: str) -> str:
-    """Remove generic question words while preserving entity and series terms."""
-    keywords = [token for token in tokens(query) if token not in QUERY_STOP_WORDS]
-    if not keywords:
-        return query
-    return " ".join(keywords)

python/ebook_search/timing.py

@@ -1,36 +0,0 @@
-"""Runtime timing helpers for EPUB search."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from time import perf_counter
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from collections.abc import Callable
-
-
-@dataclass(frozen=True)
-class RuntimeStep:
-    """Elapsed runtime for one named search step."""
-
-    name: str
-    duration_ms: float
-    counts_toward_total: bool = True
-
-
-def runtime_step_from_start(name: str, start_seconds: float) -> RuntimeStep:
-    """Create a runtime step from a prior perf_counter timestamp."""
-    return RuntimeStep(name=name, duration_ms=(perf_counter() - start_seconds) * 1000)
-
-
-def timed_result[T, **P](
-    name: str,
-    operation: Callable[P, T],
-    *args: P.args,
-    **kwargs: P.kwargs,
-) -> tuple[T, RuntimeStep]:
-    """Run an operation and return its result plus elapsed runtime."""
-    start_seconds = perf_counter()
-    result = operation(*args, **kwargs)
-    return result, runtime_step_from_start(name, start_seconds)

python/fastapi_tools/__init__.py

@@ -1,6 +0,0 @@
-"""Reusable FastAPI tools."""
-
-from python.fastapi_tools.db import DbSession, get_db
-from python.fastapi_tools.zstd_middleware import ZstdMiddleware
-
-__all__ = ["DbSession", "ZstdMiddleware", "get_db"]

python/orm/__init__.py

@@ -1,9 +1,13 @@
 """ORM package exports."""
 
+from python.orm.data_science_dev.base import DataScienceDevBase
 from python.orm.richie.base import RichieBase
+from python.orm.signal_bot.base import SignalBotBase
 from python.orm.van_inventory.base import VanInventoryBase
 
 __all__ = [
+    "DataScienceDevBase",
     "RichieBase",
+    "SignalBotBase",
     "VanInventoryBase",
 ]

python/orm/common.py

@@ -31,24 +31,8 @@ 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 get_postgres_engine(
+def get_postgres_engine(*, name: str = "POSTGRES", pool_pre_ping: bool = True) -> Engine:
-    *,
+    """Create a SQLAlchemy engine from environment variables."""
-    name: str = "POSTGRES",
-    pool_pre_ping: bool = True,
-    vector_engine: bool = False,
-) -> 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.
-
-    Returns:
-        Engine: The SQLAlchemy engine.
-    """
     database, host, port, username, password = get_connection_info(name)
 
     url = URL.create(
@@ -60,14 +44,8 @@ def get_postgres_engine(
         database=database,
     )
 
-    connect_args = {}
-    # There more better way to do this is with separate PG account and a dedicated vector schema for the vector types
-    if vector_engine:
-        connect_args["options"] = "-csearch_path=main,public"
-
     return create_engine(
         url=url,
         pool_pre_ping=pool_pre_ping,
         pool_recycle=1800,
-        connect_args=connect_args,
     )

python/orm/data_science_dev/__init__.py

@@ -0,0 +1,11 @@
+"""Data science dev database ORM exports."""
+
+from __future__ import annotations
+
+from python.orm.data_science_dev.base import DataScienceDevBase, DataScienceDevTableBase, DataScienceDevTableBaseBig
+
+__all__ = [
+    "DataScienceDevBase",
+    "DataScienceDevTableBase",
+    "DataScienceDevTableBaseBig",
+]

python/orm/data_science_dev/base.py

@@ -0,0 +1,52 @@
+"""Data science dev database ORM base."""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+from sqlalchemy import BigInteger, DateTime, MetaData, func
+from sqlalchemy.ext.declarative import AbstractConcreteBase
+from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
+
+from python.orm.common import NAMING_CONVENTION
+
+
+class DataScienceDevBase(DeclarativeBase):
+    """Base class for data_science_dev database ORM models."""
+
+    schema_name = "main"
+
+    metadata = MetaData(
+        schema=schema_name,
+        naming_convention=NAMING_CONVENTION,
+    )
+
+
+class _TableMixin:
+    """Shared timestamp columns for all table bases."""
+
+    created: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True),
+        server_default=func.now(),
+    )
+    updated: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True),
+        server_default=func.now(),
+        onupdate=func.now(),
+    )
+
+
+class DataScienceDevTableBase(_TableMixin, AbstractConcreteBase, DataScienceDevBase):
+    """Table with Integer primary key."""
+
+    __abstract__ = True
+
+    id: Mapped[int] = mapped_column(primary_key=True)
+
+
+class DataScienceDevTableBaseBig(_TableMixin, AbstractConcreteBase, DataScienceDevBase):
+    """Table with BigInteger primary key."""
+
+    __abstract__ = True
+
+    id: Mapped[int] = mapped_column(BigInteger, primary_key=True)

python/orm/data_science_dev/congress/__init__.py

@@ -0,0 +1,14 @@
+"""init."""
+
+from python.orm.data_science_dev.congress.bill import Bill, BillText
+from python.orm.data_science_dev.congress.legislator import Legislator, LegislatorSocialMedia
+from python.orm.data_science_dev.congress.vote import Vote, VoteRecord
+
+__all__ = [
+    "Bill",
+    "BillText",
+    "Legislator",
+    "LegislatorSocialMedia",
+    "Vote",
+    "VoteRecord",
+]

python/orm/data_science_dev/congress/bill.py

@@ -0,0 +1,66 @@
+"""Bill model - legislation introduced in Congress."""
+
+from __future__ import annotations
+
+from datetime import date
+from typing import TYPE_CHECKING
+
+from sqlalchemy import ForeignKey, Index, UniqueConstraint
+from sqlalchemy.orm import Mapped, mapped_column, relationship
+
+from python.orm.data_science_dev.base import DataScienceDevTableBase
+
+if TYPE_CHECKING:
+    from python.orm.data_science_dev.congress.vote import Vote
+
+
+class Bill(DataScienceDevTableBase):
+    """Legislation with congress number, type, titles, status, and sponsor."""
+
+    __tablename__ = "bill"
+
+    congress: Mapped[int]
+    bill_type: Mapped[str]
+    number: Mapped[int]
+
+    title: Mapped[str | None]
+    title_short: Mapped[str | None]
+    official_title: Mapped[str | None]
+
+    status: Mapped[str | None]
+    status_at: Mapped[date | None]
+
+    sponsor_bioguide_id: Mapped[str | None]
+
+    subjects_top_term: Mapped[str | None]
+
+    votes: Mapped[list[Vote]] = relationship(
+        "Vote",
+        back_populates="bill",
+    )
+    bill_texts: Mapped[list[BillText]] = relationship(
+        "BillText",
+        back_populates="bill",
+        cascade="all, delete-orphan",
+    )
+
+    __table_args__ = (
+        UniqueConstraint("congress", "bill_type", "number", name="uq_bill_congress_type_number"),
+        Index("ix_bill_congress", "congress"),
+    )
+
+
+class BillText(DataScienceDevTableBase):
+    """Stores different text versions of a bill (introduced, enrolled, etc.)."""
+
+    __tablename__ = "bill_text"
+
+    bill_id: Mapped[int] = mapped_column(ForeignKey("main.bill.id", ondelete="CASCADE"))
+    version_code: Mapped[str]
+    version_name: Mapped[str | None]
+    text_content: Mapped[str | None]
+    date: Mapped[date | None]
+
+    bill: Mapped[Bill] = relationship("Bill", back_populates="bill_texts")
+
+    __table_args__ = (UniqueConstraint("bill_id", "version_code", name="uq_bill_text_bill_id_version_code"),)

python/orm/data_science_dev/congress/legislator.py

@@ -0,0 +1,66 @@
+"""Legislator model - members of Congress."""
+
+from __future__ import annotations
+
+from datetime import date
+from typing import TYPE_CHECKING
+
+from sqlalchemy import ForeignKey, Text
+from sqlalchemy.orm import Mapped, mapped_column, relationship
+
+from python.orm.data_science_dev.base import DataScienceDevTableBase
+
+if TYPE_CHECKING:
+    from python.orm.data_science_dev.congress.vote import VoteRecord
+
+
+class Legislator(DataScienceDevTableBase):
+    """Members of Congress with identification and current term info."""
+
+    __tablename__ = "legislator"
+
+    bioguide_id: Mapped[str] = mapped_column(Text, unique=True, index=True)
+
+    thomas_id: Mapped[str | None]
+    lis_id: Mapped[str | None]
+    govtrack_id: Mapped[int | None]
+    opensecrets_id: Mapped[str | None]
+    fec_ids: Mapped[str | None]
+
+    first_name: Mapped[str]
+    last_name: Mapped[str]
+    official_full_name: Mapped[str | None]
+    nickname: Mapped[str | None]
+
+    birthday: Mapped[date | None]
+    gender: Mapped[str | None]
+
+    current_party: Mapped[str | None]
+    current_state: Mapped[str | None]
+    current_district: Mapped[int | None]
+    current_chamber: Mapped[str | None]
+
+    social_media_accounts: Mapped[list[LegislatorSocialMedia]] = relationship(
+        "LegislatorSocialMedia",
+        back_populates="legislator",
+        cascade="all, delete-orphan",
+    )
+    vote_records: Mapped[list[VoteRecord]] = relationship(
+        "VoteRecord",
+        back_populates="legislator",
+        cascade="all, delete-orphan",
+    )
+
+
+class LegislatorSocialMedia(DataScienceDevTableBase):
+    """Social media account linked to a legislator."""
+
+    __tablename__ = "legislator_social_media"
+
+    legislator_id: Mapped[int] = mapped_column(ForeignKey("main.legislator.id"))
+    platform: Mapped[str]
+    account_name: Mapped[str]
+    url: Mapped[str | None]
+    source: Mapped[str]
+
+    legislator: Mapped[Legislator] = relationship(back_populates="social_media_accounts")

python/orm/data_science_dev/congress/vote.py

@@ -0,0 +1,79 @@
+"""Vote model - roll call votes in Congress."""
+
+from __future__ import annotations
+
+from datetime import date
+from typing import TYPE_CHECKING
+
+from sqlalchemy import ForeignKey, Index, UniqueConstraint
+from sqlalchemy.orm import Mapped, mapped_column, relationship
+
+from python.orm.data_science_dev.base import DataScienceDevBase, DataScienceDevTableBase
+
+if TYPE_CHECKING:
+    from python.orm.data_science_dev.congress.bill import Bill
+    from python.orm.data_science_dev.congress.legislator import Legislator
+    from python.orm.data_science_dev.congress.vote import Vote
+
+
+class VoteRecord(DataScienceDevBase):
+    """Links a vote to a legislator with their position (Yea, Nay, etc.)."""
+
+    __tablename__ = "vote_record"
+
+    vote_id: Mapped[int] = mapped_column(
+        ForeignKey("main.vote.id", ondelete="CASCADE"),
+        primary_key=True,
+    )
+    legislator_id: Mapped[int] = mapped_column(
+        ForeignKey("main.legislator.id", ondelete="CASCADE"),
+        primary_key=True,
+    )
+    position: Mapped[str]
+
+    vote: Mapped[Vote] = relationship("Vote", back_populates="vote_records")
+    legislator: Mapped[Legislator] = relationship("Legislator", back_populates="vote_records")
+
+
+class Vote(DataScienceDevTableBase):
+    """Roll call votes with counts and optional bill linkage."""
+
+    __tablename__ = "vote"
+
+    congress: Mapped[int]
+    chamber: Mapped[str]
+    session: Mapped[int]
+    number: Mapped[int]
+
+    vote_type: Mapped[str | None]
+    question: Mapped[str | None]
+    result: Mapped[str | None]
+    result_text: Mapped[str | None]
+
+    vote_date: Mapped[date]
+
+    yea_count: Mapped[int | None]
+    nay_count: Mapped[int | None]
+    not_voting_count: Mapped[int | None]
+    present_count: Mapped[int | None]
+
+    bill_id: Mapped[int | None] = mapped_column(ForeignKey("main.bill.id"))
+
+    bill: Mapped[Bill | None] = relationship("Bill", back_populates="votes")
+    vote_records: Mapped[list[VoteRecord]] = relationship(
+        "VoteRecord",
+        back_populates="vote",
+        cascade="all, delete-orphan",
+    )
+
+    __table_args__ = (
+        UniqueConstraint(
+            "congress",
+            "chamber",
+            "session",
+            "number",
+            name="uq_vote_congress_chamber_session_number",
+        ),
+        Index("ix_vote_date", "vote_date"),
+        Index("ix_vote_congress_chamber", "congress", "chamber"),
+    )

python/orm/data_science_dev/models.py

@@ -0,0 +1,16 @@
+"""Data science dev database ORM models."""
+
+from __future__ import annotations
+
+from python.orm.data_science_dev.congress import Bill, BillText, Legislator, Vote, VoteRecord
+from python.orm.data_science_dev.posts import partitions  # noqa: F401 — registers partition classes in metadata
+from python.orm.data_science_dev.posts.tables import Posts
+
+__all__ = [
+    "Bill",
+    "BillText",
+    "Legislator",
+    "Posts",
+    "Vote",
+    "VoteRecord",
+]

python/orm/data_science_dev/posts/__init__.py

@@ -0,0 +1,11 @@
+"""Posts module — weekly-partitioned posts table and partition ORM models."""
+
+from __future__ import annotations
+
+from python.orm.data_science_dev.posts.failed_ingestion import FailedIngestion
+from python.orm.data_science_dev.posts.tables import Posts
+
+__all__ = [
+    "FailedIngestion",
+    "Posts",
+]

python/orm/data_science_dev/posts/columns.py

@@ -0,0 +1,33 @@
+"""Shared column definitions for the posts partitioned table family."""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+from sqlalchemy import BigInteger, SmallInteger, Text
+from sqlalchemy.orm import Mapped, mapped_column
+
+
+class PostsColumns:
+    """Mixin providing all posts columns. Used by both the parent table and partitions."""
+
+    post_id: Mapped[int] = mapped_column(BigInteger, primary_key=True)
+    user_id: Mapped[int] = mapped_column(BigInteger)
+    instance: Mapped[str]
+    date: Mapped[datetime] = mapped_column(primary_key=True)
+    text: Mapped[str] = mapped_column(Text)
+    langs: Mapped[str | None]
+    like_count: Mapped[int]
+    reply_count: Mapped[int]
+    repost_count: Mapped[int]
+    reply_to: Mapped[int | None] = mapped_column(BigInteger)
+    replied_author: Mapped[int | None] = mapped_column(BigInteger)
+    thread_root: Mapped[int | None] = mapped_column(BigInteger)
+    thread_root_author: Mapped[int | None] = mapped_column(BigInteger)
+    repost_from: Mapped[int | None] = mapped_column(BigInteger)
+    reposted_author: Mapped[int | None] = mapped_column(BigInteger)
+    quotes: Mapped[int | None] = mapped_column(BigInteger)
+    quoted_author: Mapped[int | None] = mapped_column(BigInteger)
+    labels: Mapped[str | None]
+    sent_label: Mapped[int | None] = mapped_column(SmallInteger)
+    sent_score: Mapped[float | None]

python/orm/data_science_dev/posts/failed_ingestion.py

@@ -0,0 +1,17 @@
+"""Table for storing JSONL lines that failed during post ingestion."""
+
+from __future__ import annotations
+
+from sqlalchemy import Text
+from sqlalchemy.orm import Mapped, mapped_column
+
+from python.orm.data_science_dev.base import DataScienceDevTableBase
+
+
+class FailedIngestion(DataScienceDevTableBase):
+    """Stores raw JSONL lines and their error messages when ingestion fails."""
+
+    __tablename__ = "failed_ingestion"
+
+    raw_line: Mapped[str] = mapped_column(Text)
+    error: Mapped[str] = mapped_column(Text)

python/orm/data_science_dev/posts/partitions.py

@@ -0,0 +1,71 @@
+"""Dynamically generated ORM classes for each weekly partition of the posts table.
+
+Each class maps to a PostgreSQL partition table (e.g. posts_2024_01).
+These are real ORM models tracked by Alembic autogenerate.
+
+Uses ISO week numbering (datetime.isocalendar().week). ISO years can have
+52 or 53 weeks, and week boundaries are always Monday to Monday.
+"""
+
+from __future__ import annotations
+
+import sys
+from datetime import UTC, datetime
+
+from python.orm.data_science_dev.base import DataScienceDevBase
+from python.orm.data_science_dev.posts.columns import PostsColumns
+
+PARTITION_START_YEAR = 2023
+PARTITION_END_YEAR = 2026
+
+_current_module = sys.modules[__name__]
+
+
+def iso_weeks_in_year(year: int) -> int:
+    """Return the number of ISO weeks in a given year (52 or 53)."""
+    dec_28 = datetime(year, 12, 28, tzinfo=UTC)
+    return dec_28.isocalendar().week
+
+
+def week_bounds(year: int, week: int) -> tuple[datetime, datetime]:
+    """Return (start, end) datetimes for an ISO week.
+
+    Start = Monday 00:00:00 UTC of the given ISO week.
+    End   = Monday 00:00:00 UTC of the following ISO week.
+    """
+    start = datetime.fromisocalendar(year, week, 1).replace(tzinfo=UTC)
+    if week < iso_weeks_in_year(year):
+        end = datetime.fromisocalendar(year, week + 1, 1).replace(tzinfo=UTC)
+    else:
+        end = datetime.fromisocalendar(year + 1, 1, 1).replace(tzinfo=UTC)
+    return start, end
+
+
+def _build_partition_classes() -> dict[str, type]:
+    """Generate one ORM class per ISO week partition."""
+    classes: dict[str, type] = {}
+
+    for year in range(PARTITION_START_YEAR, PARTITION_END_YEAR + 1):
+        for week in range(1, iso_weeks_in_year(year) + 1):
+            class_name = f"PostsWeek{year}W{week:02d}"
+            table_name = f"posts_{year}_{week:02d}"
+
+            partition_class = type(
+                class_name,
+                (PostsColumns, DataScienceDevBase),
+                {
+                    "__tablename__": table_name,
+                    "__table_args__": ({"implicit_returning": False},),
+                },
+            )
+
+            classes[class_name] = partition_class
+
+    return classes
+
+
+# Generate all partition classes and register them on this module
+_partition_classes = _build_partition_classes()
+for _name, _cls in _partition_classes.items():
+    setattr(_current_module, _name, _cls)
+__all__ = list(_partition_classes.keys())

python/orm/data_science_dev/posts/tables.py

@@ -0,0 +1,13 @@
+"""Posts parent table with PostgreSQL weekly range partitioning on date column."""
+
+from __future__ import annotations
+
+from python.orm.data_science_dev.base import DataScienceDevBase
+from python.orm.data_science_dev.posts.columns import PostsColumns
+
+
+class Posts(PostsColumns, DataScienceDevBase):
+    """Parent partitioned table for posts, partitioned by week on `date`."""
+
+    __tablename__ = "posts"
+    __table_args__ = ({"postgresql_partition_by": "RANGE (date)"},)

python/orm/richie/__init__.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-from python.orm.richie.audiobook import Audiobook, AudiobookAuthor, AudiobookSeries
 from python.orm.richie.base import RichieBase, TableBase, TableBaseBig, TableBaseSmall
 from python.orm.richie.contact import (
     Contact,
@@ -11,30 +10,11 @@ from python.orm.richie.contact import (
     Need,
     RelationshipType,
 )
-from python.orm.richie.ebook import (
-    EbookChapter,
-    EbookChunk,
-    EbookChunkEmbedding1024,
-    EbookChunkEmbedding2560,
-    EbookChunkEmbedding4096,
-    EbookEmbeddingModel,
-    EbookSource,
-)
 
 __all__ = [
-    "Audiobook",
-    "AudiobookAuthor",
-    "AudiobookSeries",
     "Contact",
     "ContactNeed",
     "ContactRelationship",
-    "EbookChapter",
-    "EbookChunk",
-    "EbookChunkEmbedding1024",
-    "EbookChunkEmbedding2560",
-    "EbookChunkEmbedding4096",
-    "EbookEmbeddingModel",
-    "EbookSource",
     "Need",
     "RelationshipType",
     "RichieBase",

python/orm/richie/audiobook.py

@@ -1,55 +0,0 @@
-"""Audiobook catalog models."""
-
-from __future__ import annotations
-
-from sqlalchemy import ForeignKey, String, UniqueConstraint
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from python.orm.richie.base import TableBase
-
-
-class AudiobookAuthor(TableBase):
-    """Canonical audiobook author."""
-
-    __tablename__ = "audiobook_author"
-    __table_args__ = (UniqueConstraint("name"),)
-
-    name: Mapped[str] = mapped_column(String, unique=True)
-
-    books: Mapped[list[Audiobook]] = relationship("Audiobook", back_populates="author")
-    series: Mapped[list[AudiobookSeries]] = relationship("AudiobookSeries", back_populates="author")
-
-
-class AudiobookSeries(TableBase):
-    """Canonical audiobook series."""
-
-    __tablename__ = "audiobook_series"
-    __table_args__ = (UniqueConstraint("author_id", "name"),)
-
-    name: Mapped[str] = mapped_column(String)
-    author_id: Mapped[int] = mapped_column(ForeignKey("main.audiobook_author.id", ondelete="CASCADE"))
-
-    author: Mapped[AudiobookAuthor] = relationship("AudiobookAuthor", back_populates="series")
-    books: Mapped[list[Audiobook]] = relationship("Audiobook", back_populates="series")
-
-
-class Audiobook(TableBase):
-    """Canonical audiobook title."""
-
-    __tablename__ = "audiobook"
-    __table_args__ = (
-        UniqueConstraint(
-            "author_id",
-            "series_id",
-            "title",
-            postgresql_nulls_not_distinct=True,
-        ),
-    )
-
-    title: Mapped[str] = mapped_column(String)
-    author_id: Mapped[int] = mapped_column(ForeignKey("main.audiobook_author.id", ondelete="CASCADE"))
-    series_id: Mapped[int | None] = mapped_column(ForeignKey("main.audiobook_series.id", ondelete="SET NULL"))
-    series_index: Mapped[float] = mapped_column(default=0.0)
-
-    author: Mapped[AudiobookAuthor] = relationship("AudiobookAuthor", back_populates="books")
-    series: Mapped[AudiobookSeries | None] = relationship("AudiobookSeries", back_populates="books")

python/orm/richie/ebook.py

@@ -1,138 +0,0 @@
-"""EPUB search models."""
-
-from __future__ import annotations
-
-from datetime import datetime
-
-from pgvector.sqlalchemy import Vector
-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
-
-
-class EbookSource(TableBase):
-    """One indexed EPUB file."""
-
-    __tablename__ = "ebook_source"
-    __table_args__ = (
-        UniqueConstraint("file_path"),
-        UniqueConstraint("file_sha256"),
-    )
-
-    title: Mapped[str]
-    author: Mapped[str | None]
-    language: Mapped[str | None]
-    publisher: Mapped[str | None]
-    identifier: Mapped[str | None]
-    file_path: Mapped[str]
-    file_sha256: Mapped[str] = mapped_column(String(64))
-    file_mtime: Mapped[datetime] = mapped_column(DateTime(timezone=True))
-    file_size: Mapped[int] = mapped_column(BigInteger)
-
-    chapters: Mapped[list[EbookChapter]] = relationship(
-        "EbookChapter",
-        back_populates="source",
-        cascade="all, delete-orphan",
-        passive_deletes=True,
-    )
-    chunks: Mapped[list[EbookChunk]] = relationship(
-        "EbookChunk",
-        back_populates="source",
-        cascade="all, delete-orphan",
-        passive_deletes=True,
-    )
-
-
-class EbookChapter(TableBase):
-    """A chapter or spine document inside an EPUB."""
-
-    __tablename__ = "ebook_chapter"
-    __table_args__ = (UniqueConstraint("source_id", "spine_index"),)
-
-    source_id: Mapped[int] = mapped_column(ForeignKey("main.ebook_source.id", ondelete="CASCADE"))
-    spine_index: Mapped[int]
-    title: Mapped[str | None]
-    href: Mapped[str | None]
-
-    source: Mapped[EbookSource] = relationship("EbookSource", back_populates="chapters")
-    chunks: Mapped[list[EbookChunk]] = relationship(
-        "EbookChunk",
-        back_populates="chapter",
-        cascade="all, delete-orphan",
-        passive_deletes=True,
-    )
-
-
-class EbookChunk(TableBaseBig):
-    """A searchable text chunk."""
-
-    __tablename__ = "ebook_chunk"
-    __table_args__ = (
-        UniqueConstraint("source_id", "chunk_index", name="uq_ebook_chunk_source_id_chunk_index"),
-        UniqueConstraint("source_id", "content_sha256", name="uq_ebook_chunk_source_id_content_sha256"),
-    )
-
-    source_id: Mapped[int] = mapped_column(ForeignKey("main.ebook_source.id", ondelete="CASCADE"))
-    chapter_id: Mapped[int | None] = mapped_column(ForeignKey("main.ebook_chapter.id", ondelete="SET NULL"))
-    chunk_index: Mapped[int]
-    text: Mapped[str]
-    token_start: Mapped[int]
-    token_count: Mapped[int]
-    page_label: Mapped[str | None]
-    content_sha256: Mapped[str] = mapped_column(String(64))
-    search_text: Mapped[str]
-
-    source: Mapped[EbookSource] = relationship("EbookSource", back_populates="chunks")
-    chapter: Mapped[EbookChapter | None] = relationship("EbookChapter", back_populates="chunks")
-
-
-class EbookEmbeddingModel(TableBase):
-    """A supported embedding model."""
-
-    __tablename__ = "ebook_embedding_model"
-
-    name: Mapped[str] = mapped_column(String, unique=True)
-    dimension: Mapped[int]
-    is_default: Mapped[bool] = mapped_column(Boolean, default=False)
-
-
-class EbookChunkEmbedding1024(TableBaseBig):
-    """1024-dimensional chunk embedding."""
-
-    __tablename__ = "ebook_chunk_embedding_1024"
-    __table_args__ = (
-        UniqueConstraint("chunk_id", "model_id"),
-        Index(
-            "ix_ebook_chunk_embedding_1024_embedding_cosine",
-            "embedding",
-            postgresql_using="hnsw",
-            postgresql_ops={"embedding": "vector_cosine_ops"},
-        ),
-    )
-
-    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(1024))
-
-
-class EbookChunkEmbedding2560(TableBaseBig):
-    """2560-dimensional chunk embedding."""
-
-    __tablename__ = "ebook_chunk_embedding_2560"
-    __table_args__ = (UniqueConstraint("chunk_id", "model_id"),)
-
-    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(2560))
-
-
-class EbookChunkEmbedding4096(TableBaseBig):
-    """4096-dimensional chunk embedding."""
-
-    __tablename__ = "ebook_chunk_embedding_4096"
-    __table_args__ = (UniqueConstraint("chunk_id", "model_id"),)
-
-    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))

python/orm/signal_bot/__init__.py

@@ -0,0 +1,16 @@
+"""Signal bot database ORM exports."""
+
+from __future__ import annotations
+
+from python.orm.signal_bot.base import SignalBotBase, SignalBotTableBase, SignalBotTableBaseSmall
+from python.orm.signal_bot.models import DeadLetterMessage, DeviceRole, RoleRecord, SignalDevice
+
+__all__ = [
+    "DeadLetterMessage",
+    "DeviceRole",
+    "RoleRecord",
+    "SignalBotBase",
+    "SignalBotTableBase",
+    "SignalBotTableBaseSmall",
+    "SignalDevice",
+]

python/orm/signal_bot/base.py

@@ -0,0 +1,52 @@
+"""Signal bot database ORM base."""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+from sqlalchemy import DateTime, MetaData, SmallInteger, func
+from sqlalchemy.ext.declarative import AbstractConcreteBase
+from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
+
+from python.orm.common import NAMING_CONVENTION
+
+
+class SignalBotBase(DeclarativeBase):
+    """Base class for signal_bot database ORM models."""
+
+    schema_name = "main"
+
+    metadata = MetaData(
+        schema=schema_name,
+        naming_convention=NAMING_CONVENTION,
+    )
+
+
+class _TableMixin:
+    """Shared timestamp columns for all table bases."""
+
+    created: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True),
+        server_default=func.now(),
+    )
+    updated: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True),
+        server_default=func.now(),
+        onupdate=func.now(),
+    )
+
+
+class SignalBotTableBaseSmall(_TableMixin, AbstractConcreteBase, SignalBotBase):
+    """Table with SmallInteger primary key."""
+
+    __abstract__ = True
+
+    id: Mapped[int] = mapped_column(SmallInteger, primary_key=True)
+
+
+class SignalBotTableBase(_TableMixin, AbstractConcreteBase, SignalBotBase):
+    """Table with Integer primary key."""
+
+    __abstract__ = True
+
+    id: Mapped[int] = mapped_column(primary_key=True)

python/orm/signal_bot/models.py

@@ -0,0 +1,62 @@
+"""Signal bot device, role, and dead letter ORM models."""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+from sqlalchemy import DateTime, Enum, ForeignKey, SmallInteger, String, Text, UniqueConstraint
+from sqlalchemy.orm import Mapped, mapped_column, relationship
+
+from python.orm.signal_bot.base import SignalBotTableBase, SignalBotTableBaseSmall
+from python.signal_bot.models import MessageStatus, TrustLevel
+
+
+class RoleRecord(SignalBotTableBaseSmall):
+    """Lookup table for RBAC roles, keyed by smallint."""
+
+    __tablename__ = "role"
+
+    name: Mapped[str] = mapped_column(String(50), unique=True)
+
+
+class DeviceRole(SignalBotTableBase):
+    """Association between a device and a role."""
+
+    __tablename__ = "device_role"
+    __table_args__ = (
+        UniqueConstraint("device_id", "role_id", name="uq_device_role_device_role"),
+        {"schema": "main"},
+    )
+
+    device_id: Mapped[int] = mapped_column(ForeignKey("main.signal_device.id"))
+    role_id: Mapped[int] = mapped_column(SmallInteger, ForeignKey("main.role.id"))
+
+
+class SignalDevice(SignalBotTableBase):
+    """A Signal device tracked by phone number and safety number."""
+
+    __tablename__ = "signal_device"
+
+    phone_number: Mapped[str] = mapped_column(String(50), unique=True)
+    safety_number: Mapped[str | None]
+    trust_level: Mapped[TrustLevel] = mapped_column(
+        Enum(TrustLevel, name="trust_level", create_constraint=False, native_enum=False),
+        default=TrustLevel.UNVERIFIED,
+    )
+    last_seen: Mapped[datetime] = mapped_column(DateTime(timezone=True))
+
+    roles: Mapped[list[RoleRecord]] = relationship(secondary=DeviceRole.__table__)
+
+
+class DeadLetterMessage(SignalBotTableBase):
+    """A Signal message that failed processing and was sent to the dead letter queue."""
+
+    __tablename__ = "dead_letter_message"
+
+    source: Mapped[str]
+    message: Mapped[str] = mapped_column(Text)
+    received_at: Mapped[datetime] = mapped_column(DateTime(timezone=True))
+    status: Mapped[MessageStatus] = mapped_column(
+        Enum(MessageStatus, name="message_status", create_constraint=False, native_enum=False),
+        default=MessageStatus.UNPROCESSED,
+    )

python/signal_bot/__init__.py

@@ -0,0 +1 @@
+"""Signal command and control bot."""

python/signal_bot/commands/__init__.py

@@ -0,0 +1 @@
+"""Signal bot commands."""

python/signal_bot/commands/inventory.py

@@ -0,0 +1,137 @@
+"""Van inventory command — parse receipts and item lists via LLM, push to API."""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import TYPE_CHECKING, Any
+
+import httpx
+
+from python.signal_bot.models import InventoryItem, InventoryUpdate
+
+if TYPE_CHECKING:
+    from python.signal_bot.llm_client import LLMClient
+    from python.signal_bot.models import SignalMessage
+    from python.signal_bot.signal_client import SignalClient
+
+logger = logging.getLogger(__name__)
+
+SYSTEM_PROMPT = """\
+You are an inventory assistant. Extract items from the input and return ONLY
+a JSON array. Each element must have these fields:
+  - "name": item name (string)
+  - "quantity": numeric count or amount (default 1)
+  - "unit": unit of measure (e.g. "each", "lb", "oz", "gallon", "bag", "box")
+  - "category": category like "food", "tools", "supplies", etc.
+  - "notes": any extra detail (empty string if none)
+
+Example output:
+[{"name": "water bottles", "quantity": 6, "unit": "gallon", "category": "supplies", "notes": "1 gallon each"}]
+
+Return ONLY the JSON array, no other text.\
+"""
+
+IMAGE_PROMPT = "Extract all items from this receipt or inventory photo."
+TEXT_PROMPT = "Extract all items from this inventory list."
+
+
+def parse_llm_response(raw: str) -> list[InventoryItem]:
+    """Parse the LLM JSON response into InventoryItem list."""
+    text = raw.strip()
+    # Strip markdown code fences if present
+    if text.startswith("```"):
+        lines = text.split("\n")
+        lines = [line for line in lines if not line.startswith("```")]
+        text = "\n".join(lines)
+
+    items_data: list[dict[str, Any]] = json.loads(text)
+    return [InventoryItem.model_validate(item) for item in items_data]
+
+
+def _upsert_item(api_url: str, item: InventoryItem) -> None:
+    """Create or update an item via the van_inventory API.
+
+    Fetches existing items, and if one with the same name exists,
+    patches its quantity (summing). Otherwise creates a new item.
+    """
+    base = api_url.rstrip("/")
+    response = httpx.get(f"{base}/api/items", timeout=10)
+    response.raise_for_status()
+    existing: list[dict[str, Any]] = response.json()
+
+    match = next((e for e in existing if e["name"].lower() == item.name.lower()), None)
+
+    if match:
+        new_qty = match["quantity"] + item.quantity
+        patch = {"quantity": new_qty}
+        if item.category:
+            patch["category"] = item.category
+        response = httpx.patch(f"{base}/api/items/{match['id']}", json=patch, timeout=10)
+        response.raise_for_status()
+        return
+    payload = {
+        "name": item.name,
+        "quantity": item.quantity,
+        "unit": item.unit,
+        "category": item.category or None,
+    }
+    response = httpx.post(f"{base}/api/items", json=payload, timeout=10)
+    response.raise_for_status()
+
+
+def handle_inventory_update(
+    message: SignalMessage,
+    signal: SignalClient,
+    llm: LLMClient,
+    api_url: str,
+) -> InventoryUpdate:
+    """Process an inventory update from a Signal message.
+
+    Accepts either an image (receipt photo) or text list.
+    Uses the LLM to extract structured items, then pushes to the van_inventory API.
+    """
+    try:
+        logger.info(f"Processing inventory update from {message.source}")
+        if message.attachments:
+            image_data = signal.get_attachment(message.attachments[0])
+            raw_response = llm.chat(
+                IMAGE_PROMPT,
+                image_data=image_data,
+                system=SYSTEM_PROMPT,
+            )
+            source_type = "receipt_photo"
+        elif message.message.strip():
+            raw_response = llm.chat(
+                f"{TEXT_PROMPT}\n\n{message.message}",
+                system=SYSTEM_PROMPT,
+            )
+            source_type = "text_list"
+        else:
+            signal.reply(message, "Send a photo of a receipt or a text list of items to update inventory.")
+            return InventoryUpdate()
+
+        logger.info(f"{raw_response=}")
+
+        new_items = parse_llm_response(raw_response)
+
+        logger.info(f"{new_items=}")
+
+        for item in new_items:
+            _upsert_item(api_url, item)
+
+        summary = _format_summary(new_items)
+        signal.reply(message, f"Inventory updated with {len(new_items)} item(s):\n{summary}")
+
+        return InventoryUpdate(items=new_items, raw_response=raw_response, source_type=source_type)
+
+    except Exception:
+        logger.exception("Failed to process inventory update")
+        signal.reply(message, "Failed to process inventory update. Check logs for details.")
+        return InventoryUpdate()
+
+
+def _format_summary(items: list[InventoryItem]) -> str:
+    """Format items into a readable summary."""
+    lines = [f"  - {item.name} x{item.quantity} {item.unit} [{item.category}]" for item in items]
+    return "\n".join(lines)

python/signal_bot/commands/location.py

@@ -0,0 +1,64 @@
+"""Location command for the Signal bot."""
+
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING, Any
+
+import httpx
+
+if TYPE_CHECKING:
+    from python.signal_bot.models import SignalMessage
+    from python.signal_bot.signal_client import SignalClient
+
+logger = logging.getLogger(__name__)
+
+
+def _get_entity_state(ha_url: str, ha_token: str, entity_id: str) -> dict[str, Any]:
+    """Fetch an entity's state from Home Assistant."""
+    entity_url = f"{ha_url}/api/states/{entity_id}"
+    logger.debug(f"Fetching {entity_url=}")
+    response = httpx.get(
+        entity_url,
+        headers={"Authorization": f"Bearer {ha_token}"},
+        timeout=30,
+    )
+    response.raise_for_status()
+    return response.json()
+
+
+def _format_location(latitude: str, longitude: str) -> str:
+    """Render a friendly location response."""
+    return f"Van location: {latitude}, {longitude}\nhttps://maps.google.com/?q={latitude},{longitude}"
+
+
+def handle_location_request(
+    message: SignalMessage,
+    signal: SignalClient,
+    ha_url: str | None,
+    ha_token: str | None,
+) -> None:
+    """Reply with van location from Home Assistant."""
+    if ha_url is None or ha_token is None:
+        signal.reply(message, "Location command is not configured (missing HA_URL or HA_TOKEN).")
+        return
+
+    lat_payload = None
+    lon_payload = None
+    try:
+        lat_payload = _get_entity_state(ha_url, ha_token, "sensor.van_last_known_latitude")
+        lon_payload = _get_entity_state(ha_url, ha_token, "sensor.van_last_known_longitude")
+    except httpx.HTTPError:
+        logger.exception("Couldn't fetch van location from Home Assistant right now.")
+        logger.debug(f"{ha_url=} {lat_payload=} {lon_payload=}")
+        signal.reply(message, "Couldn't fetch van location from Home Assistant right now.")
+        return
+
+    latitude = lat_payload.get("state", "")
+    longitude = lon_payload.get("state", "")
+
+    if not latitude or not longitude or latitude == "unavailable" or longitude == "unavailable":
+        signal.reply(message, "Van location is unavailable in Home Assistant right now.")
+        return
+
+    signal.reply(message, _format_location(latitude, longitude))

python/signal_bot/device_registry.py

@@ -0,0 +1,284 @@
+"""Device registry — tracks verified/unverified devices by safety number."""
+
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timedelta
+from typing import TYPE_CHECKING, NamedTuple
+
+from sqlalchemy import delete, select
+from sqlalchemy.orm import Session
+
+from python.common import utcnow
+from python.orm.signal_bot.models import RoleRecord, SignalDevice
+from python.signal_bot.models import Role, TrustLevel
+
+if TYPE_CHECKING:
+    from sqlalchemy.engine import Engine
+
+    from python.signal_bot.signal_client import SignalClient
+
+logger = logging.getLogger(__name__)
+
+_BLOCKED_TTL = timedelta(minutes=60)
+_DEFAULT_TTL = timedelta(minutes=5)
+
+
+class _CacheEntry(NamedTuple):
+    expires: datetime
+    trust_level: TrustLevel
+    has_safety_number: bool
+    safety_number: str | None
+    roles: list[Role]
+
+
+class DeviceRegistry:
+    """Manage device trust based on Signal safety numbers.
+
+    Devices start as UNVERIFIED. An admin verifies them over SSH by calling
+    ``verify(phone_number)`` which marks the device VERIFIED and also tells
+    signal-cli to trust the identity.
+
+    Only VERIFIED devices may execute commands.
+    """
+
+    def __init__(self, signal_client: SignalClient, engine: Engine) -> None:
+        self.signal_client = signal_client
+        self.engine = engine
+        self._contact_cache: dict[str, _CacheEntry] = {}
+
+    def is_verified(self, phone_number: str) -> bool:
+        """Check if a phone number is verified."""
+        if entry := self._cached(phone_number):
+            return entry.trust_level == TrustLevel.VERIFIED
+        device = self._load_device(phone_number)
+        return device is not None and device.trust_level == TrustLevel.VERIFIED
+
+    def record_contact(self, phone_number: str, safety_number: str | None = None) -> None:
+        """Record seeing a device. Creates entry if new, updates last_seen."""
+        now = utcnow()
+
+        entry = self._cached(phone_number)
+        if entry and entry.safety_number == safety_number:
+            return
+
+        with Session(self.engine) as session:
+            device = session.scalars(
+                select(SignalDevice).where(SignalDevice.phone_number == phone_number)
+            ).one_or_none()
+
+            if device:
+                if device.safety_number != safety_number and device.trust_level != TrustLevel.BLOCKED:
+                    logger.warning(f"Safety number changed for {phone_number}, resetting to UNVERIFIED")
+                    device.safety_number = safety_number
+                    device.trust_level = TrustLevel.UNVERIFIED
+                device.last_seen = now
+            else:
+                device = SignalDevice(
+                    phone_number=phone_number,
+                    safety_number=safety_number,
+                    trust_level=TrustLevel.UNVERIFIED,
+                    last_seen=now,
+                )
+                session.add(device)
+                logger.info(f"New device registered: {phone_number}")
+
+            session.commit()
+            self._update_cache(phone_number, device)
+
+    def has_safety_number(self, phone_number: str) -> bool:
+        """Check if a device has a safety number on file."""
+        if entry := self._cached(phone_number):
+            return entry.has_safety_number
+        device = self._load_device(phone_number)
+        return device is not None and device.safety_number is not None
+
+    def verify(self, phone_number: str) -> bool:
+        """Mark a device as verified. Called by admin over SSH.
+
+        Returns True if the device was found and verified.
+        """
+        with Session(self.engine) as session:
+            device = session.scalars(
+                select(SignalDevice).where(SignalDevice.phone_number == phone_number)
+            ).one_or_none()
+
+            if not device:
+                logger.warning(f"Cannot verify unknown device: {phone_number}")
+                return False
+
+            device.trust_level = TrustLevel.VERIFIED
+            self.signal_client.trust_identity(phone_number, trust_all_known_keys=True)
+            session.commit()
+            self._update_cache(phone_number, device)
+            logger.info(f"Device verified: {phone_number}")
+            return True
+
+    def block(self, phone_number: str) -> bool:
+        """Block a device."""
+        return self._set_trust(phone_number, TrustLevel.BLOCKED, "Device blocked")
+
+    def unverify(self, phone_number: str) -> bool:
+        """Reset a device to unverified."""
+        return self._set_trust(phone_number, TrustLevel.UNVERIFIED)
+
+    # -- role management ------------------------------------------------------
+
+    def get_roles(self, phone_number: str) -> list[Role]:
+        """Return the roles for a device, defaulting to empty."""
+        if entry := self._cached(phone_number):
+            return entry.roles
+        device = self._load_device(phone_number)
+        return _extract_roles(device) if device else []
+
+    def has_role(self, phone_number: str, role: Role) -> bool:
+        """Check if a device has a specific role or is admin."""
+        roles = self.get_roles(phone_number)
+        return Role.ADMIN in roles or role in roles
+
+    def grant_role(self, phone_number: str, role: Role) -> bool:
+        """Add a role to a device. Called by admin over SSH."""
+        with Session(self.engine) as session:
+            device = session.scalars(
+                select(SignalDevice).where(SignalDevice.phone_number == phone_number)
+            ).one_or_none()
+
+            if not device:
+                logger.warning(f"Cannot grant role for unknown device: {phone_number}")
+                return False
+
+            if any(record.name == role for record in device.roles):
+                return True
+
+            role_record = session.scalars(select(RoleRecord).where(RoleRecord.name == role)).one_or_none()
+
+            if not role_record:
+                logger.warning(f"Unknown role: {role}")
+                return False
+
+            device.roles.append(role_record)
+            session.commit()
+            self._update_cache(phone_number, device)
+            logger.info(f"Device {phone_number} granted role {role}")
+            return True
+
+    def revoke_role(self, phone_number: str, role: Role) -> bool:
+        """Remove a role from a device. Called by admin over SSH."""
+        with Session(self.engine) as session:
+            device = session.scalars(
+                select(SignalDevice).where(SignalDevice.phone_number == phone_number)
+            ).one_or_none()
+
+            if not device:
+                logger.warning(f"Cannot revoke role for unknown device: {phone_number}")
+                return False
+
+            device.roles = [record for record in device.roles if record.name != role]
+            session.commit()
+            self._update_cache(phone_number, device)
+            logger.info(f"Device {phone_number} revoked role {role}")
+            return True
+
+    def set_roles(self, phone_number: str, roles: list[Role]) -> bool:
+        """Replace all roles for a device. Called by admin over SSH."""
+        with Session(self.engine) as session:
+            device = session.scalars(
+                select(SignalDevice).where(SignalDevice.phone_number == phone_number)
+            ).one_or_none()
+
+            if not device:
+                logger.warning(f"Cannot set roles for unknown device: {phone_number}")
+                return False
+
+            role_names = [str(role) for role in roles]
+            records = session.scalars(select(RoleRecord).where(RoleRecord.name.in_(role_names))).all()
+            device.roles = records
+            session.commit()
+            self._update_cache(phone_number, device)
+            logger.info(f"Device {phone_number} roles set to {role_names}")
+            return True
+
+    # -- queries --------------------------------------------------------------
+
+    def list_devices(self) -> list[SignalDevice]:
+        """Return all known devices."""
+        with Session(self.engine) as session:
+            return list(session.scalars(select(SignalDevice)).all())
+
+    def sync_identities(self) -> None:
+        """Pull identity list from signal-cli and record any new ones."""
+        identities = self.signal_client.get_identities()
+        for identity in identities:
+            number = identity.get("number", "")
+            safety = identity.get("safety_number", identity.get("fingerprint", ""))
+            if number:
+                self.record_contact(number, safety)
+
+    # -- internals ------------------------------------------------------------
+
+    def _cached(self, phone_number: str) -> _CacheEntry | None:
+        """Return the cache entry if it exists and hasn't expired."""
+        entry = self._contact_cache.get(phone_number)
+        if entry and utcnow() < entry.expires:
+            return entry
+        return None
+
+    def _load_device(self, phone_number: str) -> SignalDevice | None:
+        """Fetch a device by phone number (with joined roles)."""
+        with Session(self.engine) as session:
+            return session.scalars(select(SignalDevice).where(SignalDevice.phone_number == phone_number)).one_or_none()
+
+    def _update_cache(self, phone_number: str, device: SignalDevice) -> None:
+        """Refresh the cache entry for a device."""
+        ttl = _BLOCKED_TTL if device.trust_level == TrustLevel.BLOCKED else _DEFAULT_TTL
+        self._contact_cache[phone_number] = _CacheEntry(
+            expires=utcnow() + ttl,
+            trust_level=device.trust_level,
+            has_safety_number=device.safety_number is not None,
+            safety_number=device.safety_number,
+            roles=_extract_roles(device),
+        )
+
+    def _set_trust(self, phone_number: str, level: str, log_msg: str | None = None) -> bool:
+        """Update the trust level for a device."""
+        with Session(self.engine) as session:
+            device = session.scalars(
+                select(SignalDevice).where(SignalDevice.phone_number == phone_number)
+            ).one_or_none()
+
+            if not device:
+                return False
+
+            device.trust_level = level
+            session.commit()
+            self._update_cache(phone_number, device)
+            if log_msg:
+                logger.info(f"{log_msg}: {phone_number}")
+            return True
+
+
+def _extract_roles(device: SignalDevice) -> list[Role]:
+    """Convert a device's RoleRecord objects to a list of Role enums."""
+    return [Role(record.name) for record in device.roles]
+
+
+def sync_roles(engine: Engine) -> None:
+    """Sync the Role enum to the role table, adding new and removing stale entries."""
+    expected = {role.value for role in Role}
+
+    with Session(engine) as session:
+        existing = set(session.scalars(select(RoleRecord.name)).all())
+
+        to_add = expected - existing
+        to_remove = existing - expected
+
+        for name in to_add:
+            session.add(RoleRecord(name=name))
+            logger.info(f"Role added: {name}")
+
+        if to_remove:
+            session.execute(delete(RoleRecord).where(RoleRecord.name.in_(to_remove)))
+            for name in to_remove:
+                logger.info(f"Role removed: {name}")
+
+        session.commit()

python/signal_bot/llm_client.py

@@ -0,0 +1,80 @@
+"""Flexible LLM client for ollama backends."""
+
+from __future__ import annotations
+
+import base64
+import logging
+from typing import Any, Self
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+
+class LLMClient:
+    """Talk to an ollama instance.
+
+    Args:
+        model: Ollama model name.
+        host: Ollama host.
+        port: Ollama port.
+        temperature: Sampling temperature.
+    """
+
+    def __init__(
+        self,
+        *,
+        model: str,
+        host: str,
+        port: int = 11434,
+        temperature: float = 0.1,
+        timeout: int = 300,
+    ) -> None:
+        self.model = model
+        self.temperature = temperature
+        self._client = httpx.Client(base_url=f"http://{host}:{port}", timeout=timeout)
+
+    def chat(self, prompt: str, image_data: bytes | None = None, system: str | None = None) -> str:
+        """Send a text prompt and return the response."""
+        messages: list[dict[str, Any]] = []
+        if system:
+            messages.append({"role": "system", "content": system})
+
+        user_msg = {"role": "user", "content": prompt}
+        if image_data:
+            user_msg["images"] = [base64.b64encode(image_data).decode()]
+
+        messages.append(user_msg)
+        return self._generate(messages)
+
+    def _generate(self, messages: list[dict[str, Any]]) -> str:
+        """Call the ollama chat API."""
+        payload = {
+            "model": self.model,
+            "messages": messages,
+            "stream": False,
+            "options": {"temperature": self.temperature},
+        }
+        logger.info(f"LLM request to {self.model}")
+        response = self._client.post("/api/chat", json=payload)
+        response.raise_for_status()
+        data = response.json()
+        return data["message"]["content"]
+
+    def list_models(self) -> list[str]:
+        """List available models on the ollama instance."""
+        response = self._client.get("/api/tags")
+        response.raise_for_status()
+        return [m["name"] for m in response.json().get("models", [])]
+
+    def __enter__(self) -> Self:
+        """Enter the context manager."""
+        return self
+
+    def __exit__(self, *args: object) -> None:
+        """Close the HTTP client on exit."""
+        self.close()
+
+    def close(self) -> None:
+        """Close the HTTP client."""
+        self._client.close()

python/signal_bot/main.py

@@ -0,0 +1,239 @@
+"""Signal command and control bot — main entry point."""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from os import getenv
+from typing import TYPE_CHECKING, Annotated
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+import typer
+from alembic.command import upgrade
+from sqlalchemy.orm import Session
+from tenacity import before_sleep_log, retry, stop_after_attempt, wait_exponential
+
+from python.common import configure_logger, utcnow
+from python.database_cli import DATABASES
+from python.orm.common import get_postgres_engine
+from python.orm.signal_bot.models import DeadLetterMessage
+from python.signal_bot.commands.inventory import handle_inventory_update
+from python.signal_bot.commands.location import handle_location_request
+from python.signal_bot.device_registry import DeviceRegistry, sync_roles
+from python.signal_bot.llm_client import LLMClient
+from python.signal_bot.models import BotConfig, MessageStatus, Role, SignalMessage
+from python.signal_bot.signal_client import SignalClient
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass(frozen=True, slots=True)
+class Command:
+    """A registered bot command."""
+
+    action: Callable[[SignalMessage, str], None]
+    help_text: str
+    role: Role | None  # None = no role required (always allowed)
+
+
+class Bot:
+    """Holds shared resources and dispatches incoming messages to command handlers."""
+
+    def __init__(
+        self,
+        signal: SignalClient,
+        llm: LLMClient,
+        registry: DeviceRegistry,
+        config: BotConfig,
+    ) -> None:
+        self.signal = signal
+        self.llm = llm
+        self.registry = registry
+        self.config = config
+        self.commands: dict[str, Command] = {
+            "help": Command(action=self._help, help_text="show this help message", role=None),
+            "status": Command(action=self._status, help_text="show bot status", role=Role.STATUS),
+            "inventory": Command(
+                action=self._inventory,
+                help_text="update van inventory from a text list or receipt photo",
+                role=Role.INVENTORY,
+            ),
+            "location": Command(
+                action=self._location,
+                help_text="get current van location",
+                role=Role.LOCATION,
+            ),
+        }
+
+    # -- actions --------------------------------------------------------------
+
+    def _help(self, message: SignalMessage, _cmd: str) -> None:
+        """Return help text filtered to the sender's roles."""
+        self.signal.reply(message, self._build_help(self.registry.get_roles(message.source)))
+
+    def _status(self, message: SignalMessage, _cmd: str) -> None:
+        """Return the status of the bot."""
+        models = self.llm.list_models()
+        model_list = ", ".join(models[:10])
+        device_count = len(self.registry.list_devices())
+        self.signal.reply(
+            message,
+            f"Bot online.\nLLM: {self.llm.model}\nAvailable models: {model_list}\nKnown devices: {device_count}",
+        )
+
+    def _inventory(self, message: SignalMessage, _cmd: str) -> None:
+        """Process an inventory update."""
+        handle_inventory_update(message, self.signal, self.llm, self.config.inventory_api_url)
+
+    def _location(self, message: SignalMessage, _cmd: str) -> None:
+        """Reply with current van location."""
+        handle_location_request(message, self.signal, self.config.ha_url, self.config.ha_token)
+
+    # -- dispatch -------------------------------------------------------------
+
+    def _build_help(self, roles: list[Role]) -> str:
+        """Build help text showing only the commands the user can access."""
+        is_admin = Role.ADMIN in roles
+        lines = ["Available commands:"]
+        for name, cmd in self.commands.items():
+            if cmd.role is None or is_admin or cmd.role in roles:
+                lines.append(f"  {name:20s} — {cmd.help_text}")
+        return "\n".join(lines)
+
+    def dispatch(self, message: SignalMessage) -> None:
+        """Route an incoming message to the right command handler."""
+        source = message.source
+
+        if not self.registry.is_verified(source):
+            logger.info(f"Device {source} not verified, ignoring message")
+            return
+
+        if not self.registry.has_safety_number(source) and self.registry.has_role(source, Role.ADMIN):
+            logger.warning(f"Admin device {source} missing safety number, ignoring message")
+            return
+
+        text = message.message.strip()
+        parts = text.split()
+
+        if not parts and not message.attachments:
+            return
+
+        cmd = parts[0].lower() if parts else ""
+
+        logger.info(f"f{source=} running {cmd=} with {message=}")
+
+        command = self.commands.get(cmd)
+        if command is None:
+            if message.attachments:
+                command = self.commands["inventory"]
+                cmd = "inventory"
+            else:
+                return
+
+        if command.role is not None and not self.registry.has_role(source, command.role):
+            logger.warning(f"Device {source} denied access to {cmd!r}")
+            self.signal.reply(message, f"Permission denied: you do not have the '{command.role}' role.")
+            return
+
+        command.action(message, cmd)
+
+    def process_message(self, message: SignalMessage) -> None:
+        """Process a single message, sending it to the dead letter queue after repeated failures."""
+        max_attempts = self.config.max_message_attempts
+        for attempt in range(1, max_attempts + 1):
+            try:
+                safety_number = self.signal.get_safety_number(message.source)
+                self.registry.record_contact(message.source, safety_number)
+                self.dispatch(message)
+            except Exception:
+                logger.exception(f"Failed to process message (attempt {attempt}/{max_attempts})")
+            else:
+                return
+
+        logger.error(f"Message from {message.source} failed {max_attempts} times, sending to dead letter queue")
+        with Session(self.config.engine) as session:
+            session.add(
+                DeadLetterMessage(
+                    source=message.source,
+                    message=message.message,
+                    received_at=utcnow(),
+                    status=MessageStatus.UNPROCESSED,
+                )
+            )
+            session.commit()
+
+    def run(self) -> None:
+        """Listen for messages via WebSocket, reconnecting on failure."""
+        logger.info("Bot started — listening via WebSocket")
+
+        @retry(
+            stop=stop_after_attempt(self.config.max_retries),
+            wait=wait_exponential(multiplier=self.config.reconnect_delay, max=self.config.max_reconnect_delay),
+            before_sleep=before_sleep_log(logger, logging.WARNING),
+            reraise=True,
+        )
+        def _listen() -> None:
+            for message in self.signal.listen():
+                logger.info(f"Message from {message.source}: {message.message[:80]}")
+                self.process_message(message)
+
+        try:
+            _listen()
+        except Exception:
+            logger.critical("Max retries exceeded, shutting down")
+            raise
+
+
+def main(
+    log_level: Annotated[str, typer.Option()] = "DEBUG",
+    llm_timeout: Annotated[int, typer.Option()] = 600,
+) -> None:
+    """Run the Signal command and control bot."""
+    configure_logger(log_level)
+    signal_api_url = getenv("SIGNAL_API_URL")
+    phone_number = getenv("SIGNAL_PHONE_NUMBER")
+    inventory_api_url = getenv("INVENTORY_API_URL")
+
+    if signal_api_url is None:
+        error = "SIGNAL_API_URL environment variable not set"
+        raise ValueError(error)
+    if phone_number is None:
+        error = "SIGNAL_PHONE_NUMBER environment variable not set"
+        raise ValueError(error)
+    if inventory_api_url is None:
+        error = "INVENTORY_API_URL environment variable not set"
+        raise ValueError(error)
+
+    signal_bot_config = DATABASES["signal_bot"].alembic_config()
+    upgrade(signal_bot_config, "head")
+    engine = get_postgres_engine(name="SIGNALBOT")
+    sync_roles(engine)
+    config = BotConfig(
+        signal_api_url=signal_api_url,
+        phone_number=phone_number,
+        inventory_api_url=inventory_api_url,
+        ha_url=getenv("HA_URL"),
+        ha_token=getenv("HA_TOKEN"),
+        engine=engine,
+    )
+
+    llm_host = getenv("LLM_HOST")
+    llm_model = getenv("LLM_MODEL", "qwen3-vl:32b")
+    llm_port = int(getenv("LLM_PORT", "11434"))
+    if llm_host is None:
+        error = "LLM_HOST environment variable not set"
+        raise ValueError(error)
+
+    with (
+        SignalClient(config.signal_api_url, config.phone_number) as signal,
+        LLMClient(model=llm_model, host=llm_host, port=llm_port, timeout=llm_timeout) as llm,
+    ):
+        registry = DeviceRegistry(signal, engine)
+        bot = Bot(signal, llm, registry, config)
+        bot.run()
+
+
+if __name__ == "__main__":
+    typer.run(main)

python/signal_bot/models.py

@@ -0,0 +1,97 @@
+"""Models for the Signal command and control bot."""
+
+from __future__ import annotations
+
+from datetime import datetime  # noqa: TC003 - pydantic needs this at runtime
+from enum import StrEnum
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict
+from sqlalchemy.engine import Engine  # noqa: TC002 - pydantic needs this at runtime
+
+
+class TrustLevel(StrEnum):
+    """Device trust level."""
+
+    VERIFIED = "verified"
+    UNVERIFIED = "unverified"
+    BLOCKED = "blocked"
+
+
+class Role(StrEnum):
+    """RBAC roles — one per command, plus admin which grants all."""
+
+    ADMIN = "admin"
+    STATUS = "status"
+    INVENTORY = "inventory"
+    LOCATION = "location"
+
+
+class MessageStatus(StrEnum):
+    """Dead letter queue message status."""
+
+    UNPROCESSED = "unprocessed"
+    PROCESSED = "processed"
+
+
+class Device(BaseModel):
+    """A registered device tracked by safety number."""
+
+    phone_number: str
+    safety_number: str
+    trust_level: TrustLevel = TrustLevel.UNVERIFIED
+    first_seen: datetime
+    last_seen: datetime
+
+
+class SignalMessage(BaseModel):
+    """An incoming Signal message."""
+
+    source: str
+    timestamp: int
+    message: str = ""
+    attachments: list[str] = []
+    group_id: str | None = None
+    is_receipt: bool = False
+
+
+class SignalEnvelope(BaseModel):
+    """Raw envelope from signal-cli-rest-api."""
+
+    envelope: dict[str, Any]
+    account: str | None = None
+
+
+class InventoryItem(BaseModel):
+    """An item in the van inventory."""
+
+    name: str
+    quantity: float = 1
+    unit: str = "each"
+    category: str = ""
+    notes: str = ""
+
+
+class InventoryUpdate(BaseModel):
+    """Result of processing an inventory update."""
+
+    items: list[InventoryItem] = []
+    raw_response: str = ""
+    source_type: str = ""  # "receipt_photo" or "text_list"
+
+
+class BotConfig(BaseModel):
+    """Top-level bot configuration."""
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    signal_api_url: str
+    phone_number: str
+    inventory_api_url: str
+    ha_url: str | None = None
+    ha_token: str | None = None
+    engine: Engine
+    reconnect_delay: int = 5
+    max_reconnect_delay: int = 300
+    max_retries: int = 10
+    max_message_attempts: int = 3

python/signal_bot/signal_client.py

@@ -0,0 +1,141 @@
+"""Client for the signal-cli-rest-api."""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import TYPE_CHECKING, Any, Self
+
+import httpx
+import websockets.sync.client
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+from python.signal_bot.models import SignalMessage
+
+logger = logging.getLogger(__name__)
+
+
+def _parse_envelope(envelope: dict[str, Any]) -> SignalMessage | None:
+    """Parse a signal-cli envelope into a SignalMessage, or None if not a data message."""
+    data_message = envelope.get("dataMessage")
+    if not data_message:
+        return None
+
+    attachment_ids = [att["id"] for att in data_message.get("attachments", []) if "id" in att]
+
+    group_info = data_message.get("groupInfo")
+    group_id = group_info.get("groupId") if group_info else None
+
+    return SignalMessage(
+        source=envelope.get("source", ""),
+        timestamp=envelope.get("timestamp", 0),
+        message=data_message.get("message", "") or "",
+        attachments=attachment_ids,
+        group_id=group_id,
+    )
+
+
+class SignalClient:
+    """Communicate with signal-cli-rest-api.
+
+    Args:
+        base_url: URL of the signal-cli-rest-api (e.g. http://localhost:8989).
+        phone_number: The registered phone number to send/receive as.
+    """
+
+    def __init__(self, base_url: str, phone_number: str) -> None:
+        self.base_url = base_url.rstrip("/")
+        self.phone_number = phone_number
+        self._client = httpx.Client(base_url=self.base_url, timeout=30)
+
+    def _ws_url(self) -> str:
+        """Build the WebSocket URL from the base HTTP URL."""
+        url = self.base_url.replace("http://", "ws://").replace("https://", "wss://")
+        return f"{url}/v1/receive/{self.phone_number}"
+
+    def listen(self) -> Generator[SignalMessage]:
+        """Connect via WebSocket and yield messages as they arrive."""
+        ws_url = self._ws_url()
+        logger.info(f"Connecting to WebSocket: {ws_url}")
+
+        with websockets.sync.client.connect(ws_url) as ws:
+            for raw in ws:
+                try:
+                    data = json.loads(raw)
+                    envelope = data.get("envelope", {})
+                    message = _parse_envelope(envelope)
+                    if message:
+                        yield message
+                except json.JSONDecodeError:
+                    logger.warning(f"Non-JSON WebSocket frame: {raw[:200]}")
+
+    def send(self, recipient: str, message: str) -> None:
+        """Send a text message."""
+        payload = {
+            "message": message,
+            "number": self.phone_number,
+            "recipients": [recipient],
+        }
+        response = self._client.post("/v2/send", json=payload)
+        response.raise_for_status()
+
+    def send_to_group(self, group_id: str, message: str) -> None:
+        """Send a message to a group."""
+        payload = {
+            "message": message,
+            "number": self.phone_number,
+            "recipients": [group_id],
+        }
+        response = self._client.post("/v2/send", json=payload)
+        response.raise_for_status()
+
+    def get_attachment(self, attachment_id: str) -> bytes:
+        """Download an attachment by ID."""
+        response = self._client.get(f"/v1/attachments/{attachment_id}")
+        response.raise_for_status()
+        return response.content
+
+    def get_identities(self) -> list[dict[str, Any]]:
+        """List known identities and their trust levels."""
+        response = self._client.get(f"/v1/identities/{self.phone_number}")
+        response.raise_for_status()
+        return response.json()
+
+    def get_safety_number(self, phone_number: str) -> str | None:
+        """Look up the safety number for a contact from signal-cli's local store."""
+        for identity in self.get_identities():
+            if identity.get("number") == phone_number:
+                return identity.get("safety_number", identity.get("fingerprint", ""))
+        return None
+
+    def trust_identity(self, number_to_trust: str, *, trust_all_known_keys: bool = False) -> None:
+        """Trust an identity (verify safety number)."""
+        payload: dict[str, Any] = {}
+        if trust_all_known_keys:
+            payload["trust_all_known_keys"] = True
+        response = self._client.put(
+            f"/v1/identities/{self.phone_number}/trust/{number_to_trust}",
+            json=payload,
+        )
+        response.raise_for_status()
+
+    def reply(self, message: SignalMessage, text: str) -> None:
+        """Reply to a message, routing to group or individual."""
+        if message.group_id:
+            self.send_to_group(message.group_id, text)
+        else:
+            self.send(message.source, text)
+
+    def __enter__(self) -> Self:
+        """Enter the context manager."""
+        return self
+
+    def __exit__(self, *args: object) -> None:
+        """Close the HTTP client on exit."""
+        self.close()
+
+    def close(self) -> None:
+        """Close the HTTP client."""
+        self._client.close()

python/tools/audiobook/__init__.py

@@ -1 +0,0 @@
-"""Audiobook tools."""

python/tools/audiobook/audible_convert.py

@@ -1,471 +0,0 @@
-"""Convert Audible AAX downloads into Audiobookshelf-friendly M4B files."""
-
-from __future__ import annotations
-
-import json
-import logging
-import re
-import shutil
-import subprocess
-from concurrent.futures import ThreadPoolExecutor
-from dataclasses import asdict, dataclass
-from os import getenv
-from pathlib import Path  # noqa: TC003 This is required for the typer CLI
-from typing import TYPE_CHECKING, Annotated, Any
-from uuid import uuid7
-
-import typer
-
-from python.common import configure_logger
-from python.orm.common import get_postgres_engine
-from python.tools.audiobook.metadata_agent import (
-    AgentConfig,
-    StandardBookMetadata,
-    standard_book_metadata,
-    write_agent_log,
-)
-
-if TYPE_CHECKING:
-    from sqlalchemy.engine import Engine
-
-logger = logging.getLogger(__name__)
-
-SENSITIVE_COMMAND_ARGUMENTS = {"-activation_bytes"}
-BOOK_RANGE_PATTERN = re.compile(r"(?:^|-)books?-(?P<start>[1-9]\d*)-(?P<end>[1-9]\d*)(?:-|$)")
-
-
-@dataclass(frozen=True)
-class ConversionConfig:
-    """Runtime settings for one conversion command."""
-
-    resolved_output: Path
-    ollama_api_key: str
-    agent_config: AgentConfig
-    engine: Engine
-    activation_bytes: str | None
-    dry_run: bool
-    overwrite: bool
-    work_directory_name: str = ".audible_convert"
-    dry_run_directory_name: str = "dry-run"
-    temp_directory_name: str = "tmp"
-    log_directory_name: str = "logs"
-    review_directory_name: str = "review"
-
-
-@dataclass(frozen=True)
-class ConcurrentConversionResult:
-    """Result from running ffmpeg and metadata resolution together."""
-
-    metadata: StandardBookMetadata | None
-    conversion_error: Exception | None
-    metadata_error: Exception | None
-
-
-class CommandExecutionError(RuntimeError):
-    """Command failed without exposing sensitive arguments."""
-
-    def __init__(self, arguments: list[str], returncode: int) -> None:
-        """Create a redacted command failure."""
-        self.arguments = tuple(arguments)
-        self.returncode = returncode
-        command = " ".join(redact_command_arguments(arguments))
-        super().__init__(f"Command failed with exit code {returncode}: {command}")
-
-
-def main(
-    input_directory: Annotated[Path, typer.Argument(help="Directory audible-cli downloads AAX files into.")],
-    output_directory: Annotated[Path, typer.Argument(help="Audiobook output directory.")],
-    *,
-    dry_run: Annotated[
-        bool,
-        typer.Option("--dry-run", help="Print planned output files and write marker files without converting."),
-    ] = False,
-    overwrite: Annotated[bool, typer.Option("--overwrite", help="Overwrite existing M4B files.")] = False,
-) -> None:
-    """Convert AAX files from a download directory into M4B files."""
-    configure_logger()
-    resolved_input = input_directory.resolve(strict=True)
-    resolved_output = output_directory.resolve()
-    if not dry_run:
-        resolved_output.mkdir(parents=True, exist_ok=True)
-
-    ollama_api_key = getenv("OLLAMA_API_KEY")
-    if not ollama_api_key:
-        msg = "OLLAMA_API_KEY is required for audiobook metadata resolution"
-        raise RuntimeError(msg)
-
-    config = ConversionConfig(
-        resolved_output=resolved_output,
-        ollama_api_key=ollama_api_key,
-        agent_config=AgentConfig(),
-        engine=get_postgres_engine(name="RICHIE"),
-        activation_bytes=getenv("AUDIBLE_ACTIVATION_BYTES"),
-        dry_run=dry_run,
-        overwrite=overwrite,
-    )
-
-    aax_files = sorted(resolved_input.glob("*.aax"))
-    if not aax_files:
-        logger.info("No AAX files found in %s", resolved_input)
-        return
-    for aax_file in aax_files:
-        logger.info("Converting %s", aax_file)
-        convert_aax_file_with_agent(aax_file, config)
-
-
-def run_command(arguments: list[str], *, capture: bool = False) -> subprocess.CompletedProcess[str]:
-    """Run a command and return the completed process.
-
-    Args:
-        arguments: Command and arguments to run.
-        capture: Whether to capture stdout and stderr.
-
-    Returns:
-        The completed process.
-    """
-    logger.debug("%s", " ".join(redact_command_arguments(arguments)))
-    try:
-        return subprocess.run(arguments, check=True, capture_output=capture, text=True)
-    except subprocess.CalledProcessError as error:
-        raise CommandExecutionError(arguments, error.returncode) from error
-
-
-def redact_command_arguments(arguments: list[str]) -> list[str]:
-    """Return command arguments with sensitive values redacted."""
-    redacted = []
-    redact_next = False
-    for argument in arguments:
-        if redact_next:
-            redacted.append("<redacted>")
-            redact_next = False
-            continue
-
-        redacted.append(argument)
-        redact_next = argument in SENSITIVE_COMMAND_ARGUMENTS
-    return redacted
-
-
-def read_metadata(aax_file: Path) -> dict[str, str]:
-    """Read ffprobe format tags from an AAX file.
-
-    Args:
-        aax_file: AAX file to inspect.
-
-    Returns:
-        Lower-cased metadata tag names mapped to their values.
-    """
-    completed = run_command(
-        [
-            "ffprobe",
-            "-v",
-            "quiet",
-            "-print_format",
-            "json",
-            "-show_format",
-            str(aax_file),
-        ],
-        capture=True,
-    )
-    ffprobe_data: dict[str, Any] = json.loads(completed.stdout)
-    tags = ffprobe_data.get("format", {}).get("tags", {})
-    return {str(key).lower(): str(value) for key, value in tags.items()}
-
-
-def output_stem(metadata: StandardBookMetadata) -> str:
-    """Build the output stem for a book.
-
-    Args:
-        metadata: Book metadata.
-
-    Returns:
-        Output stem in author-series_01-title form.
-    """
-    index_slug = series_index_slug(metadata.series_index, metadata.title)
-    return f"{metadata.author}-{metadata.series}_{index_slug}-{metadata.title}"
-
-
-def series_index_slug(series_index: float, title: str = "") -> str:
-    """Return a filename-safe series index."""
-    if title_range := title_series_range_slug(series_index, title):
-        return title_range
-    index = float(series_index)
-    if index.is_integer():
-        return f"{int(index):02}"
-    return f"{int(index):02}.5"
-
-
-def title_series_range_slug(series_index: float, title: str) -> str | None:
-    """Return a series range slug found in an omnibus title."""
-    index = float(series_index)
-    if not index.is_integer():
-        return None
-    first_index = int(index)
-    for match in BOOK_RANGE_PATTERN.finditer(title):
-        start = int(match.group("start"))
-        end = int(match.group("end"))
-        if start == first_index and end > start:
-            return f"{start:02}-{end:02}"
-    return None
-
-
-def metadata_output_path(output_directory: Path, metadata: StandardBookMetadata) -> Path:
-    """Build the final M4B path from resolved metadata."""
-    stem = output_stem(metadata)
-    return output_directory / stem / f"{stem}.m4b"
-
-
-def convert_aax_file(
-    aax_file: Path,
-    destination: Path,
-    activation_bytes: str | None,
-    *,
-    overwrite: bool,
-) -> None:
-    """Convert an AAX file into an M4B file.
-
-    Args:
-        aax_file: Source AAX file.
-        destination: Destination M4B file.
-        activation_bytes: Optional Audible activation bytes for ffmpeg.
-        overwrite: Whether to overwrite an existing M4B.
-    """
-    if destination.exists() and not overwrite:
-        logger.info("Skipping existing file %s", destination)
-        return
-
-    destination.parent.mkdir(parents=True, exist_ok=True)
-    arguments = ["ffmpeg", "-hide_banner", "-y" if overwrite else "-n"]
-    if activation_bytes:
-        arguments.extend(["-activation_bytes", activation_bytes])
-    arguments.extend(["-i", str(aax_file), "-map_metadata", "0", "-c", "copy", str(destination)])
-    run_command(arguments)
-
-
-def write_review_file(
-    *,
-    destination: Path | None,
-    ffprobe_metadata: dict[str, str],
-    log_file: Path,
-    metadata: StandardBookMetadata | None,
-    reason: str,
-    review_file: Path,
-    source: Path,
-    temp_file: Path | None,
-) -> None:
-    """Write a manual review file for an unresolved conversion."""
-    review_file.parent.mkdir(parents=True, exist_ok=True)
-    payload = {
-        "destination": str(destination) if destination else None,
-        "ffprobe_metadata": ffprobe_metadata,
-        "metadata": asdict(metadata) if metadata else None,
-        "reason": reason,
-        "source": str(source),
-        "temp_file": str(temp_file) if temp_file else None,
-    }
-    review_file.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8")
-    write_agent_log(log_file, "review_written", path=str(review_file), reason=reason)
-
-
-def cleanup_temp_output(temp_file: Path) -> None:
-    """Remove a run's temporary output directory."""
-    shutil.rmtree(temp_file.parent, ignore_errors=True)
-
-
-def dry_run_aax_file_with_agent(
-    aax_file: Path,
-    ffprobe_metadata: dict[str, str],
-    engine: Engine,
-    config: ConversionConfig,
-    log_file: Path,
-    review_file: Path,
-) -> None:
-    """Resolve and print the planned output path without converting."""
-    metadata = standard_book_metadata(
-        aax_file.name,
-        ffprobe_metadata,
-        engine,
-        log_file,
-        config.ollama_api_key,
-        config.agent_config,
-    )
-    destination = None if metadata.needs_review else metadata_output_path(config.resolved_output, metadata)
-    if metadata.needs_review:
-        write_review_file(
-            destination=destination,
-            ffprobe_metadata=ffprobe_metadata,
-            log_file=log_file,
-            metadata=metadata,
-            reason="metadata_needs_review",
-            review_file=review_file,
-            source=aax_file,
-            temp_file=None,
-        )
-        typer.echo(f"{aax_file} -> REVIEW {review_file}")
-    else:
-        stem = output_stem(metadata)
-        dry_run_file = (
-            config.resolved_output / config.work_directory_name / config.dry_run_directory_name / stem / f"{stem}.m4b"
-        )
-        dry_run_file.parent.mkdir(parents=True, exist_ok=True)
-        dry_run_file.write_text(f"{destination}\n", encoding="utf-8")
-        write_agent_log(
-            log_file,
-            "dry_run_file_written",
-            destination=str(destination),
-            path=str(dry_run_file),
-        )
-        typer.echo(f"{aax_file} -> {destination}")
-
-
-def convert_temp_file_and_resolve_metadata(
-    aax_file: Path,
-    temp_file: Path,
-    ffprobe_metadata: dict[str, str],
-    config: ConversionConfig,
-    log_file: Path,
-) -> ConcurrentConversionResult:
-    """Run ffmpeg and metadata resolution in parallel."""
-    conversion_error: Exception | None = None
-    metadata_error: Exception | None = None
-    metadata: StandardBookMetadata | None = None
-
-    with ThreadPoolExecutor(max_workers=2) as executor:
-        conversion_future = executor.submit(
-            convert_aax_file,
-            aax_file,
-            temp_file,
-            config.activation_bytes,
-            overwrite=True,
-        )
-        metadata_future = executor.submit(
-            standard_book_metadata,
-            aax_file.name,
-            ffprobe_metadata,
-            config.engine,
-            log_file,
-            config.ollama_api_key,
-            config.agent_config,
-        )
-
-        conversion_error = conversion_future.exception()
-        if conversion_error is None:
-            conversion_future.result()
-
-        metadata_error = metadata_future.exception()
-        if metadata_error is None:
-            metadata = metadata_future.result()
-
-    return ConcurrentConversionResult(
-        metadata=metadata,
-        conversion_error=conversion_error,
-        metadata_error=metadata_error,
-    )
-
-
-def convert_aax_file_with_agent(aax_file: Path, config: ConversionConfig) -> None:
-    """Convert one AAX file using the metadata agent for the final path."""
-    run_id = uuid7().hex
-    log_file = config.resolved_output / config.work_directory_name / config.log_directory_name / f"{run_id}.jsonl"
-    review_file = config.resolved_output / config.work_directory_name / config.review_directory_name / f"{run_id}.json"
-    write_agent_log(log_file, "conversion_start", source=str(aax_file), dry_run=config.dry_run)
-    try:
-        ffprobe_metadata = read_metadata(aax_file)
-    except Exception as error:
-        logger.exception("ffprobe failed")
-        write_review_file(
-            destination=None,
-            ffprobe_metadata={},
-            log_file=log_file,
-            metadata=None,
-            reason=f"ffprobe_failed: {error}",
-            review_file=review_file,
-            source=aax_file,
-            temp_file=None,
-        )
-        return
-
-    if config.dry_run:
-        dry_run_aax_file_with_agent(
-            aax_file,
-            ffprobe_metadata,
-            config.engine,
-            config,
-            log_file,
-            review_file,
-        )
-        return
-
-    temp_file = (
-        config.resolved_output / config.work_directory_name / config.temp_directory_name / run_id / "converted.m4b"
-    )
-    temp_file.parent.mkdir(parents=True, exist_ok=True)
-
-    result = convert_temp_file_and_resolve_metadata(aax_file, temp_file, ffprobe_metadata, config, log_file)
-
-    if result.conversion_error:
-        reason = f"ffmpeg_failed: {result.conversion_error}"
-        write_review_file(
-            destination=None,
-            ffprobe_metadata=ffprobe_metadata,
-            log_file=log_file,
-            metadata=result.metadata,
-            reason=reason,
-            review_file=review_file,
-            source=aax_file,
-            temp_file=temp_file if temp_file.exists() else None,
-        )
-        return
-
-    if result.metadata_error:
-        write_review_file(
-            destination=None,
-            ffprobe_metadata=ffprobe_metadata,
-            log_file=log_file,
-            metadata=None,
-            reason=f"metadata_failed: {result.metadata_error}",
-            review_file=review_file,
-            source=aax_file,
-            temp_file=temp_file,
-        )
-        return
-
-    if result.metadata is None or result.metadata.needs_review:
-        write_review_file(
-            destination=None,
-            ffprobe_metadata=ffprobe_metadata,
-            log_file=log_file,
-            metadata=result.metadata,
-            reason="metadata_needs_review",
-            review_file=review_file,
-            source=aax_file,
-            temp_file=temp_file,
-        )
-        return
-
-    destination = metadata_output_path(config.resolved_output, result.metadata)
-    if destination.exists() and not config.overwrite:
-        write_agent_log(log_file, "destination_exists", destination=str(destination))
-        cleanup_temp_output(temp_file)
-        return
-
-    destination.parent.mkdir(parents=True, exist_ok=True)
-    try:
-        temp_file.replace(destination)
-    except Exception as error:  # noqa: BLE001
-        write_review_file(
-            destination=destination,
-            ffprobe_metadata=ffprobe_metadata,
-            log_file=log_file,
-            metadata=result.metadata,
-            reason=f"rename_failed: {error}",
-            review_file=review_file,
-            source=aax_file,
-            temp_file=temp_file if temp_file.exists() else None,
-        )
-    else:
-        cleanup_temp_output(temp_file)
-        write_agent_log(log_file, "conversion_complete", destination=str(destination))
-
-
-if __name__ == "__main__":
-    typer.run(main)

python/tools/audiobook/catalog.py

@@ -1,176 +0,0 @@
-"""Import audiobook catalog authors and series from CSV files."""
-
-from __future__ import annotations
-
-import csv
-import logging
-from pathlib import Path  # noqa: TC003 This is required for the typer CLI
-from typing import Annotated
-
-import typer
-from sqlalchemy import select
-from sqlalchemy.orm import Session
-
-from python.common import configure_logger
-from python.orm.common import get_postgres_engine
-from python.orm.richie import AudiobookAuthor, AudiobookSeries
-
-logger = logging.getLogger(__name__)
-
-AUTHOR_NAME_COLUMN = "author_name"
-ID_COLUMN = "id"
-NAME_COLUMN = "name"
-
-
-class CatalogImportError(ValueError):
-    """CSV catalog import failed validation."""
-
-
-def main(
-    authors_csv: Annotated[Path, typer.Argument(help="CSV with name and optional id.")],
-    series_csv: Annotated[Path, typer.Argument(help="CSV with name, author_name, and optional id.")],
-) -> None:
-    """Upsert audiobook authors and series from CSV files."""
-    configure_logger()
-    try:
-        engine = get_postgres_engine(name="RICHIE")
-        with Session(engine) as session:
-            author_count = upsert_authors_from_csv(session, authors_csv)
-            series_count = upsert_series_from_csv(session, series_csv)
-            session.commit()
-    except CatalogImportError as error:
-        typer.echo(str(error), err=True)
-        raise typer.Exit(code=1) from error
-
-    logger.info("Upserted %s authors and %s series", author_count, series_count)
-
-
-def upsert_authors_from_csv(session: Session, authors_csv: Path) -> int:
-    """Upsert authors from a CSV file."""
-    count = 0
-    for row_number, row in csv_rows(authors_csv):
-        name = required_csv_value(row, authors_csv, row_number, NAME_COLUMN)
-        upsert_author(session, name, csv_id(row, authors_csv, row_number))
-        count += 1
-    return count
-
-
-def upsert_series_from_csv(session: Session, series_csv: Path) -> int:
-    """Upsert series from a CSV file."""
-    count = 0
-    for row_number, row in csv_rows(series_csv):
-        series_name = required_csv_value(row, series_csv, row_number, NAME_COLUMN)
-        author_name = required_csv_value(row, series_csv, row_number, AUTHOR_NAME_COLUMN)
-        author = find_author_by_name(session, author_name)
-        if author is None:
-            msg = f"{series_csv}:{row_number}: author not found: {author_name}"
-            raise CatalogImportError(msg)
-        upsert_series(session, series_name, author, csv_id(row, series_csv, row_number))
-        count += 1
-    return count
-
-
-def upsert_author(session: Session, name: str, author_id: int | None) -> AudiobookAuthor:
-    """Upsert one author by id or exact name."""
-    if author_id is not None:
-        author = session.get(AudiobookAuthor, author_id)
-        if author is None:
-            author = AudiobookAuthor(id=author_id, name=name)
-            session.add(author)
-        else:
-            author.name = name
-        session.flush()
-        return author
-
-    author = find_author_by_name(session, name)
-    if author is None:
-        author = AudiobookAuthor(name=name)
-        session.add(author)
-        session.flush()
-    return author
-
-
-def upsert_series(
-    session: Session,
-    name: str,
-    author: AudiobookAuthor,
-    series_id: int | None,
-) -> AudiobookSeries:
-    """Upsert one series by id or exact author/name match."""
-    if series_id is not None:
-        series = session.get(AudiobookSeries, series_id)
-        if series is None:
-            series = AudiobookSeries(id=series_id, name=name, author=author)
-            session.add(series)
-        else:
-            series.name = name
-            series.author = author
-        session.flush()
-        return series
-
-    series = find_series_by_name_and_author(session, name, author.id)
-    if series is None:
-        series = AudiobookSeries(name=name, author=author)
-        session.add(series)
-        session.flush()
-    return series
-
-
-def find_author_by_name(session: Session, name: str) -> AudiobookAuthor | None:
-    """Find one author by exact name."""
-    return session.scalar(select(AudiobookAuthor).where(AudiobookAuthor.name == name))
-
-
-def find_series_by_name_and_author(
-    session: Session,
-    name: str,
-    author_id: int,
-) -> AudiobookSeries | None:
-    """Find one series by exact name and author."""
-    return session.scalar(
-        select(AudiobookSeries).where(
-            AudiobookSeries.name == name,
-            AudiobookSeries.author_id == author_id,
-        ),
-    )
-
-
-def csv_rows(csv_path: Path) -> list[tuple[int, dict[str, str | None]]]:
-    """Read a CSV file as numbered rows."""
-    with csv_path.open(newline="", encoding="utf-8") as file:
-        reader = csv.DictReader(file)
-        if reader.fieldnames is None:
-            msg = f"{csv_path}: missing CSV header"
-            raise CatalogImportError(msg)
-        return [(row_number, row) for row_number, row in enumerate(reader, start=2)]
-
-
-def required_csv_value(
-    row: dict[str, str | None],
-    csv_path: Path,
-    row_number: int,
-    column: str,
-) -> str:
-    """Read a required CSV value."""
-    value = row.get(column)
-    if value and value.strip():
-        return value.strip()
-    msg = f"{csv_path}:{row_number}: missing required column value: {column}"
-    raise CatalogImportError(msg)
-
-
-def csv_id(row: dict[str, str | None], csv_path: Path, row_number: int) -> int | None:
-    """Read an optional id field from a CSV row."""
-    value = row.get(ID_COLUMN)
-    if value is None or not value.strip():
-        return None
-    try:
-        return int(value)
-    except ValueError as error:
-        msg = f"{csv_path}:{row_number}: id must be an integer: {value}"
-        raise CatalogImportError(msg) from error
-    return None
-
-
-if __name__ == "__main__":
-    typer.run(main)

python/tools/audiobook/llm_tool_calling.py

@@ -1,599 +0,0 @@
-"""LLM tool calling support for audiobook metadata resolution."""
-
-from __future__ import annotations
-
-import json
-import re
-import time
-from collections.abc import Callable
-from dataclasses import dataclass
-from typing import TYPE_CHECKING
-
-from sqlalchemy import or_, select
-
-from python.orm.richie import Audiobook, AudiobookAuthor, AudiobookSeries
-
-if TYPE_CHECKING:
-    from pathlib import Path
-
-    from sqlalchemy.orm import Session
-
-    from python.tools.audiobook.metadata_agent import AgentConfig
-
-CATALOG_SLUG_PATTERN = re.compile(r"^[a-z0-9]+(?:_[a-z0-9]+)*$")
-TITLE_SLUG_PATTERN = re.compile(r"^[a-z0-9]+(?:-[a-z0-9]+)*$")
-
-LogWriter = Callable[..., None]
-
-
-class MetadataResolutionError(ValueError):
-    """Metadata resolution failed validation."""
-
-
-@dataclass(frozen=True)
-class EnsuredBook:
-    """Book row plus whether it was created."""
-
-    book: Audiobook
-    action: str
-
-
-class CatalogToolRegistry:
-    """Controlled catalog tools exposed to the metadata model."""
-
-    def __init__(
-        self,
-        session: Session,
-        log_path: Path,
-        config: AgentConfig,
-        write_log: LogWriter,
-    ) -> None:
-        """Create a registry bound to one database session and audit log."""
-        self.session = session
-        self.log_path = log_path
-        self.config = config
-        self.write_log = write_log
-        self.seen_author_ids: set[int] = set()
-        self.seen_series_ids: set[int] = set()
-        self.seen_book_ids: set[int] = set()
-        self.created_author_ids: set[int] = set()
-        self.created_series_ids: set[int] = set()
-        self.created_book_ids: set[int] = set()
-
-    def tool_schemas(self) -> list[dict[str, object]]:
-        """Return Ollama tool schemas."""
-        schemas = [
-            {
-                "type": "function",
-                "function": {
-                    "name": "search_authors",
-                    "description": "Search canonical audiobook authors by slug or noisy source text.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {"query": {"type": "string"}},
-                        "required": ["query"],
-                    },
-                },
-            },
-            {
-                "type": "function",
-                "function": {
-                    "name": "search_series",
-                    "description": "Search canonical audiobook series by slug or noisy source text.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "query": {"type": "string"},
-                            "author_id": {"type": ["integer", "null"]},
-                        },
-                        "required": ["query"],
-                    },
-                },
-            },
-            {
-                "type": "function",
-                "function": {
-                    "name": "search_books",
-                    "description": "Search canonical audiobook titles with optional author and series filters.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "query": {"type": "string"},
-                            "author_id": {"type": ["integer", "null"]},
-                            "series_id": {"type": ["integer", "null"]},
-                        },
-                        "required": ["query"],
-                    },
-                },
-            },
-            {
-                "type": "function",
-                "function": {
-                    "name": "ensure_author",
-                    "description": "Normalize an author name to a catalog slug, then return or create that author.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {"name": {"type": "string"}},
-                        "required": ["name"],
-                    },
-                },
-            },
-            {
-                "type": "function",
-                "function": {
-                    "name": "ensure_series",
-                    "description": "Normalize a series name to a catalog slug, then return or create it for an author.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "name": {"type": "string"},
-                            "author_id": {"type": "integer"},
-                        },
-                        "required": ["name", "author_id"],
-                    },
-                },
-            },
-            {
-                "type": "function",
-                "function": {
-                    "name": "ensure_book",
-                    "description": "Normalize a title to a book slug, then return or create it for an author/series.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "title": {"type": "string"},
-                            "author_id": {"type": "integer"},
-                            "series_id": {"type": ["integer", "null"]},
-                            "series_index": {"type": "number", "multipleOf": 0.5},
-                        },
-                        "required": ["title", "author_id", "series_id", "series_index"],
-                    },
-                },
-            },
-        ]
-        enabled_tool_names = set(self.config.tool_names)
-        return [schema for schema in schemas if schema["function"]["name"] in enabled_tool_names]
-
-    def run(self, name: str, arguments: dict[str, object]) -> list[dict[str, object]]:
-        """Run one catalog tool and audit the call."""
-        handlers = {
-            "search_authors": self.run_search_authors,
-            "search_series": self.run_search_series,
-            "search_books": self.run_search_books,
-            "ensure_author": self.run_ensure_author,
-            "ensure_series": self.run_ensure_series,
-            "ensure_book": self.run_ensure_book,
-        }
-        handler = handlers.get(name)
-        if handler is None:
-            self.write_log(self.log_path, "tool_error", tool=name, arguments=arguments, error="unknown_tool")
-            msg = f"Unknown audiobook metadata tool: {name}"
-            raise MetadataResolutionError(msg)
-        if name not in self.config.tool_names:
-            self.write_log(self.log_path, "tool_error", tool=name, arguments=arguments, error="tool_not_enabled")
-            msg = f"Audiobook metadata tool is not enabled: {name}"
-            raise MetadataResolutionError(msg)
-
-        started = time.perf_counter()
-        self.write_log(self.log_path, "tool_call", tool=name, arguments=arguments)
-        result = handler(arguments)
-        duration_ms = round((time.perf_counter() - started) * 1000, 3)
-        self.write_log(
-            self.log_path,
-            "tool_result",
-            tool=name,
-            duration_ms=duration_ms,
-            result_count=len(result),
-            preview=result[:3],
-        )
-        return result
-
-    def get_author(self, author_id: int) -> AudiobookAuthor | None:
-        """Return an author by id."""
-        return self.session.get(AudiobookAuthor, author_id)
-
-    def get_book(self, book_id: int) -> Audiobook | None:
-        """Return a book by id."""
-        return self.session.get(Audiobook, book_id)
-
-    def get_series(self, series_id: int) -> AudiobookSeries | None:
-        """Return a series by id."""
-        return self.session.get(AudiobookSeries, series_id)
-
-    def prune_unused_created_rows(self, *, author_id: int, book_id: int | None, series_id: int | None) -> None:
-        """Remove catalog rows created during this run but not used by final metadata."""
-        used_book_ids = {book_id} if book_id is not None else set()
-        for created_book_id in self.created_book_ids - used_book_ids:
-            if book := self.get_book(created_book_id):
-                self.session.delete(book)
-
-        self.session.flush()
-        used_series_ids = {series_id} if series_id is not None else set()
-        for created_series_id in self.created_series_ids - used_series_ids:
-            series = self.get_series(created_series_id)
-            if series and not series.books:
-                self.session.delete(series)
-
-        self.session.flush()
-        for created_author_id in self.created_author_ids - {author_id}:
-            author = self.get_author(created_author_id)
-            if author and not author.books and not author.series:
-                self.session.delete(author)
-
-    def run_search_authors(self, arguments: dict[str, object]) -> list[dict[str, object]]:
-        """Search authors from tool arguments and remember returned ids."""
-        query = required_string(arguments, "query")
-        statement = select(AudiobookAuthor).order_by(AudiobookAuthor.name).limit(self.config.max_tool_results)
-        if terms := query_terms(query):
-            statement = statement.where(or_(*(AudiobookAuthor.name.ilike(f"%{term}%") for term in terms)))
-
-        authors = self.session.scalars(statement).all()
-        self.seen_author_ids.update(author.id for author in authors)
-        return [{"id": author.id, "name": author.name} for author in authors]
-
-    def run_search_series(self, arguments: dict[str, object]) -> list[dict[str, object]]:
-        """Search series from tool arguments and remember returned ids."""
-        query = required_string(arguments, "query")
-        author_id = optional_int(arguments.get("author_id"), "author_id")
-        statement = select(AudiobookSeries).order_by(AudiobookSeries.name).limit(self.config.max_tool_results)
-        if terms := query_terms(query):
-            statement = statement.where(or_(*(AudiobookSeries.name.ilike(f"%{term}%") for term in terms)))
-        if author_id is not None:
-            statement = statement.where(AudiobookSeries.author_id == author_id)
-
-        series_rows = self.session.scalars(statement).all()
-        self.seen_series_ids.update(series.id for series in series_rows)
-        self.seen_author_ids.update(series.author_id for series in series_rows)
-        return [
-            {
-                "id": series.id,
-                "name": series.name,
-                "author_id": series.author_id,
-                "author": series.author.name,
-            }
-            for series in series_rows
-        ]
-
-    def run_search_books(self, arguments: dict[str, object]) -> list[dict[str, object]]:
-        """Search books from tool arguments and remember returned ids."""
-        query = required_string(arguments, "query")
-        author_id = optional_int(arguments.get("author_id"), "author_id")
-        series_id = optional_int(arguments.get("series_id"), "series_id")
-        statement = select(Audiobook).order_by(Audiobook.title).limit(self.config.max_tool_results)
-        if terms := query_terms(query):
-            statement = statement.where(or_(*(Audiobook.title.ilike(f"%{term}%") for term in terms)))
-        if author_id is not None:
-            statement = statement.where(Audiobook.author_id == author_id)
-        if series_id is not None:
-            statement = statement.where(Audiobook.series_id == series_id)
-
-        books = self.session.scalars(statement).all()
-        self.seen_book_ids.update(book.id for book in books)
-        self.seen_author_ids.update(book.author_id for book in books)
-        self.seen_series_ids.update(book.series_id for book in books if book.series_id is not None)
-        return [
-            {
-                "id": book.id,
-                "title": book.title,
-                "author_id": book.author_id,
-                "author": book.author.name,
-                "series_id": book.series_id,
-                "series": book.series.name if book.series else self.config.standalone_series,
-                "series_index": book.series_index,
-            }
-            for book in books
-        ]
-
-    def run_ensure_author(self, arguments: dict[str, object]) -> list[dict[str, object]]:
-        """Ensure an author from tool arguments and return a tool result."""
-        name = normalize_catalog_slug(required_string(arguments, "name"))
-        validate_catalog_slug(name, "author")
-        author = self.session.scalar(select(AudiobookAuthor).where(AudiobookAuthor.name == name))
-        action = "existing"
-        if author is None:
-            author = AudiobookAuthor(name=name)
-            self.session.add(author)
-            self.session.flush()
-            self.created_author_ids.add(author.id)
-            action = "created"
-
-        self.seen_author_ids.add(author.id)
-        return [{"id": author.id, "name": author.name, "action": action}]
-
-    def run_ensure_series(self, arguments: dict[str, object]) -> list[dict[str, object]]:
-        """Ensure a series from tool arguments and return a tool result."""
-        name = normalize_catalog_slug(required_string(arguments, "name"))
-        author_id = required_int(arguments, "author_id")
-        validate_catalog_slug(name, "series")
-        author = self.required_author(author_id)
-        series = self.find_series_by_catalog_slug(name, author.id)
-        action = "existing"
-        if series is None:
-            series = AudiobookSeries(name=name, author=author)
-            self.session.add(series)
-            self.session.flush()
-            self.created_series_ids.add(series.id)
-            action = "created"
-
-        self.seen_author_ids.add(author.id)
-        self.seen_series_ids.add(series.id)
-        return [self.series_result(series, action)]
-
-    def run_ensure_book(self, arguments: dict[str, object]) -> list[dict[str, object]]:
-        """Ensure a book from tool arguments and return a tool result."""
-        title = required_string(arguments, "title")
-        author_id = required_int(arguments, "author_id")
-        series_id = optional_int(arguments.get("series_id"), "series_id")
-        series_index = required_series_index(arguments, "series_index")
-        ensured = self.ensure_book(title, author_id, series_id, series_index)
-        return [self.book_result(ensured.book, ensured.action)]
-
-    def ensure_book(
-        self,
-        title: str,
-        author_id: int,
-        series_id: int | None,
-        series_index: float,
-    ) -> EnsuredBook:
-        """Return an existing book row, or create it after validating ownership."""
-        title = normalize_title_slug(title)
-        validate_title_slug(title)
-        author = self.required_author(author_id)
-        series = None
-        if series_id is None:
-            if series_index != 0:
-                msg = "standalone books must use series_index 0"
-                raise MetadataResolutionError(msg)
-        else:
-            series = self.required_series(series_id)
-            if series.author_id != author.id:
-                msg = f"series_id {series_id} does not belong to author_id {author_id}"
-                raise MetadataResolutionError(msg)
-            if series_index <= 0:
-                msg = "series books must use a positive series_index"
-                raise MetadataResolutionError(msg)
-
-        statement = select(Audiobook).where(
-            Audiobook.title == title,
-            Audiobook.author_id == author.id,
-        )
-        if series is None:
-            statement = statement.where(Audiobook.series_id.is_(None))
-        else:
-            statement = statement.where(Audiobook.series_id == series.id)
-        book = self.session.scalar(statement)
-        if book is None:
-            book = Audiobook(title=title, author=author, series=series, series_index=series_index)
-            self.session.add(book)
-            self.session.flush()
-            self.created_book_ids.add(book.id)
-            action = "created"
-        else:
-            action = "existing"
-
-        self.seen_book_ids.add(book.id)
-        self.seen_author_ids.add(author.id)
-        if book.series_id is not None:
-            self.seen_series_ids.add(book.series_id)
-        return EnsuredBook(book=book, action=action)
-
-    def required_author(self, author_id: int) -> AudiobookAuthor:
-        """Return an author or fail metadata resolution."""
-        author = self.get_author(author_id)
-        if author is None:
-            msg = f"author_id {author_id} does not exist"
-            raise MetadataResolutionError(msg)
-        return author
-
-    def required_series(self, series_id: int) -> AudiobookSeries:
-        """Return a series or fail metadata resolution."""
-        series = self.get_series(series_id)
-        if series is None:
-            msg = f"series_id {series_id} does not exist"
-            raise MetadataResolutionError(msg)
-        return series
-
-    def find_series_by_catalog_slug(self, name: str, author_id: int) -> AudiobookSeries | None:
-        """Return a series by exact slug or underscore-insensitive slug."""
-        exact = self.session.scalar(
-            select(AudiobookSeries).where(
-                AudiobookSeries.name == name,
-                AudiobookSeries.author_id == author_id,
-            ),
-        )
-        if exact is not None:
-            return exact
-
-        compact_name = compact_catalog_slug(name)
-        series_rows = self.session.scalars(
-            select(AudiobookSeries).where(AudiobookSeries.author_id == author_id).order_by(AudiobookSeries.name),
-        ).all()
-        for series in series_rows:
-            if compact_catalog_slug(series.name) == compact_name:
-                return series
-        return None
-
-    def series_result(self, series: AudiobookSeries, action: str) -> dict[str, object]:
-        """Build a normalized series tool result."""
-        return {
-            "id": series.id,
-            "name": series.name,
-            "author_id": series.author_id,
-            "author": series.author.name,
-            "action": action,
-        }
-
-    def book_result(self, book: Audiobook, action: str) -> dict[str, object]:
-        """Build a normalized book tool result."""
-        return {
-            "id": book.id,
-            "title": book.title,
-            "author_id": book.author_id,
-            "author": book.author.name,
-            "series_id": book.series_id,
-            "series": book.series.name if book.series else self.config.standalone_series,
-            "series_index": book.series_index,
-            "action": action,
-        }
-
-
-def run_tool_calls(
-    messages: list[dict[str, object]],
-    message: dict[str, object],
-    tool_calls: list[tuple[str, dict[str, object]]],
-    registry: CatalogToolRegistry,
-    log_path: Path,
-    write_log: LogWriter,
-) -> str | None:
-    """Run tool calls, append tool messages, and return fatal error text when stopped."""
-    messages.append(message)
-    for tool_name, arguments in tool_calls:
-        try:
-            tool_result = registry.run(tool_name, arguments)
-        except MetadataResolutionError as error:
-            if is_fatal_tool_error(error):
-                return str(error)
-            write_log(log_path, "tool_error", tool=tool_name, arguments=arguments, error=str(error))
-            messages.append(
-                {
-                    "role": "tool",
-                    "tool_name": tool_name,
-                    "content": json.dumps({"error": str(error)}, sort_keys=True),
-                },
-            )
-            continue
-        messages.append(
-            {
-                "role": "tool",
-                "tool_name": tool_name,
-                "content": json.dumps(tool_result, sort_keys=True),
-            },
-        )
-    return None
-
-
-def parse_tool_calls(message: dict[str, object]) -> list[tuple[str, dict[str, object]]]:
-    """Parse Ollama tool calls from a response message."""
-    raw_tool_calls = message.get("tool_calls") or []
-    if not isinstance(raw_tool_calls, list):
-        msg = "tool_calls must be a list"
-        raise MetadataResolutionError(msg)
-
-    tool_calls = []
-    for raw_call in raw_tool_calls:
-        if not isinstance(raw_call, dict):
-            msg = "tool call must be an object"
-            raise MetadataResolutionError(msg)
-        function = raw_call.get("function")
-        if not isinstance(function, dict):
-            msg = "tool call is missing function"
-            raise MetadataResolutionError(msg)
-        name = function.get("name")
-        if not isinstance(name, str) or not name:
-            msg = "tool call is missing function name"
-            raise MetadataResolutionError(msg)
-        arguments = parse_tool_arguments(function.get("arguments", {}))
-        tool_calls.append((name, arguments))
-    return tool_calls
-
-
-def parse_tool_arguments(raw_arguments: object) -> dict[str, object]:
-    """Parse tool call arguments returned by Ollama."""
-    if isinstance(raw_arguments, dict):
-        return {str(key): value for key, value in raw_arguments.items()}
-    if isinstance(raw_arguments, str):
-        parsed = json.loads(raw_arguments) if raw_arguments else {}
-        if isinstance(parsed, dict):
-            return {str(key): value for key, value in parsed.items()}
-    msg = "tool arguments must be an object"
-    raise MetadataResolutionError(msg)
-
-
-def validate_title_slug(title: str) -> None:
-    """Validate a canonical book title slug."""
-    if not TITLE_SLUG_PATTERN.fullmatch(title):
-        msg = f"title slug is invalid: {title}"
-        raise MetadataResolutionError(msg)
-
-
-def validate_catalog_slug(value: str, label: str) -> None:
-    """Validate a canonical catalog slug."""
-    if not CATALOG_SLUG_PATTERN.fullmatch(value):
-        msg = f"{label} slug is invalid: {value}"
-        raise MetadataResolutionError(msg)
-
-
-def normalize_catalog_slug(value: str) -> str:
-    """Normalize noisy catalog names into lower snake-case slugs."""
-    return re.sub(r"[^a-z0-9]+", "_", value.strip().casefold()).strip("_")
-
-
-def compact_catalog_slug(value: str) -> str:
-    """Return a catalog slug comparison key that ignores underscores."""
-    return normalize_catalog_slug(value).replace("_", "")
-
-
-def normalize_title_slug(value: str) -> str:
-    """Normalize noisy book titles into lower kebab-case slugs."""
-    return re.sub(r"[^a-z0-9]+", "-", value.strip().casefold()).strip("-")
-
-
-def is_fatal_tool_error(error: MetadataResolutionError) -> bool:
-    """Return whether a tool error should stop the agent immediately."""
-    message = str(error)
-    return message.startswith(
-        (
-            "Unknown audiobook metadata tool",
-            "Audiobook metadata tool is not enabled",
-        ),
-    )
-
-
-def query_terms(query: str) -> tuple[str, ...]:
-    """Return text variants useful for matching noisy audiobook metadata."""
-    normalized = query.strip().casefold()
-    underscore_slug = normalize_catalog_slug(normalized)
-    compact_slug = compact_catalog_slug(normalized)
-    hyphen_slug = normalize_title_slug(normalized)
-    return tuple(dict.fromkeys(term for term in (normalized, underscore_slug, compact_slug, hyphen_slug) if term))
-
-
-def required_string(data: dict[str, object], key: str) -> str:
-    """Read a required string field."""
-    value = data.get(key)
-    if not isinstance(value, str) or not value.strip():
-        msg = f"{key} must be a non-empty string"
-        raise MetadataResolutionError(msg)
-    return value.strip()
-
-
-def required_int(data: dict[str, object], key: str) -> int:
-    """Read a required integer field."""
-    value = data.get(key)
-    if isinstance(value, bool) or not isinstance(value, int):
-        msg = f"{key} must be an integer"
-        raise MetadataResolutionError(msg)
-    return value
-
-
-def required_series_index(data: dict[str, object], key: str) -> float:
-    """Read a required whole-number or half-number series index."""
-    value = data.get(key)
-    if isinstance(value, bool) or not isinstance(value, int | float):
-        msg = f"{key} must be a number"
-        raise MetadataResolutionError(msg)
-    series_index = float(value)
-    if not (series_index * 2).is_integer():
-        msg = f"{key} must be a whole number or .5 increment"
-        raise MetadataResolutionError(msg)
-    return series_index
-
-
-def optional_int(value: object, key: str) -> int | None:
-    """Read an optional integer field."""
-    if value is None:
-        return None
-    if isinstance(value, bool) or not isinstance(value, int):
-        msg = f"{key} must be an integer or null"
-        raise MetadataResolutionError(msg)
-    return value

python/tools/audiobook/metadata_agent.py

@@ -1,575 +0,0 @@
-"""Resolve audiobook metadata with a controlled Ollama tool loop."""
-
-from __future__ import annotations
-
-import json
-import re
-from dataclasses import asdict, dataclass, is_dataclass, replace
-from os import PathLike
-from typing import TYPE_CHECKING
-
-import httpx
-from sqlalchemy.orm import Session
-
-from python.common import utcnow
-from python.tools.audiobook.llm_tool_calling import (
-    CatalogToolRegistry,
-    MetadataResolutionError,
-    normalize_title_slug,
-    optional_int,
-    parse_tool_calls,
-    required_int,
-    required_series_index,
-    required_string,
-    run_tool_calls,
-    validate_catalog_slug,
-    validate_title_slug,
-)
-
-if TYPE_CHECKING:
-    from pathlib import Path
-
-    from sqlalchemy.engine import Engine
-
-    from python.orm.richie import AudiobookAuthor
-
-FENCED_JSON_PATTERN = re.compile(r"^```(?:json)?\s*(?P<json>.*?)\s*```$", re.IGNORECASE | re.DOTALL)
-
-
-@dataclass(frozen=True)
-class AgentConfig:
-    """Runtime settings for the audiobook metadata agent."""
-
-    model: str = "deepseek-v4-flash:cloud"
-    ollama_chat_url: str = "https://ollama.com/api/chat"
-    http_timeout_seconds: int = 300
-    max_agent_turns: int = 8
-    max_tool_results: int = 10
-    min_confidence: float = 0.85
-    invalid_final_retries: int = 1
-    standalone_series: str = "standalone"
-    tool_names: tuple[str, ...] = (
-        "search_authors",
-        "search_series",
-        "search_books",
-        "ensure_author",
-        "ensure_series",
-        "ensure_book",
-    )
-
-
-@dataclass(frozen=True)
-class StandardBookMetadata:
-    """Canonical metadata for the final audiobook path."""
-
-    author_id: int
-    author: str
-    book_id: int | None
-    title: str
-    series_id: int | None
-    series: str
-    series_index: float
-    confidence: float
-    needs_review: bool
-    evidence: list[str]
-
-
-@dataclass(frozen=True)
-class FinalMetadataFields:
-    """Raw model fields after schema validation."""
-
-    author_id: int
-    book_id: int | None
-    title: str
-    series_id: int | None
-    series_index: float
-    confidence: float
-    evidence: list[str]
-
-
-@dataclass(frozen=True)
-class ResolvedBookFields:
-    """Book fields after optional catalog book resolution."""
-
-    book_id: int | None
-    title: str
-    series_id: int | None
-    series_index: float
-
-
-@dataclass(frozen=True)
-class AgentStepResult:
-    """Outcome from one model response."""
-
-    metadata: StandardBookMetadata | None
-    invalid_final_count: int
-    should_continue: bool
-
-
-def standard_book_metadata(
-    aax_file_name: str,
-    aax_metadata_from_ffprobe: dict[str, str],
-    engine: Engine,
-    log_path: Path,
-    ollama_api_key: str,
-    config: AgentConfig,
-) -> StandardBookMetadata:
-    """Resolve canonical audiobook metadata with the configured Ollama Cloud model."""
-    with Session(engine) as session:
-        registry = CatalogToolRegistry(session, log_path, config, write_agent_log)
-        agent = AudiobookMetadataAgent(
-            registry=registry, log_path=log_path, ollama_api_key=ollama_api_key, config=config
-        )
-        metadata = agent.run(aax_file_name, aax_metadata_from_ffprobe)
-        if metadata.needs_review:
-            session.rollback()
-        else:
-            registry.prune_unused_created_rows(
-                author_id=metadata.author_id,
-                book_id=metadata.book_id,
-                series_id=metadata.series_id,
-            )
-            session.commit()
-        return metadata
-
-
-class AudiobookMetadataAgent:
-    """Ollama-backed metadata resolver with a fixed local tool registry."""
-
-    def __init__(
-        self,
-        *,
-        registry: CatalogToolRegistry,
-        log_path: Path,
-        ollama_api_key: str,
-        config: AgentConfig,
-    ) -> None:
-        """Create an Ollama metadata agent."""
-        self._registry = registry
-        self._log_path = log_path
-        self._ollama_api_key = ollama_api_key
-        self._config = config
-
-    def run(self, aax_file_name: str, aax_metadata_from_ffprobe: dict[str, str]) -> StandardBookMetadata:
-        """Resolve metadata for one AAX file."""
-        messages = [
-            {"role": "system", "content": system_prompt()},
-            {"role": "user", "content": user_prompt(aax_file_name, aax_metadata_from_ffprobe)},
-        ]
-        invalid_final_count = 0
-        result: StandardBookMetadata | None = None
-
-        for turn in range(1, self._config.max_agent_turns + 1):
-            step = self.run_step(messages, turn, invalid_final_count)
-            invalid_final_count = step.invalid_final_count
-            if step.should_continue:
-                continue
-            result = step.metadata
-            break
-
-        if result is None:
-            return self.force_final_response(messages)
-        return result
-
-    def run_step(
-        self,
-        messages: list[dict[str, object]],
-        turn: int,
-        invalid_final_count: int,
-    ) -> AgentStepResult:
-        """Run one model turn and return the next agent-loop action."""
-        data = self.chat(messages, turn)
-        message = data.get("message")
-        if not isinstance(message, dict):
-            return AgentStepResult(
-                metadata=review_metadata("Ollama response did not include a message", self._config),
-                invalid_final_count=invalid_final_count,
-                should_continue=False,
-            )
-
-        try:
-            tool_calls = parse_tool_calls(message)
-        except (json.JSONDecodeError, MetadataResolutionError) as error:
-            return AgentStepResult(
-                metadata=review_metadata(str(error), self._config),
-                invalid_final_count=invalid_final_count,
-                should_continue=False,
-            )
-        if tool_calls:
-            fatal_error = run_tool_calls(messages, message, tool_calls, self._registry, self._log_path, write_agent_log)
-            if fatal_error is not None:
-                return AgentStepResult(
-                    metadata=review_metadata(fatal_error, self._config),
-                    invalid_final_count=invalid_final_count,
-                    should_continue=False,
-                )
-            return AgentStepResult(metadata=None, invalid_final_count=invalid_final_count, should_continue=True)
-        return self.handle_final_message(messages, message, invalid_final_count)
-
-    def handle_final_message(
-        self,
-        messages: list[dict[str, object]],
-        message: dict[str, object],
-        invalid_final_count: int,
-    ) -> AgentStepResult:
-        """Validate a final model message or request one retry."""
-        content = message.get("content")
-        if not isinstance(content, str):
-            return AgentStepResult(
-                metadata=review_metadata("Ollama final response did not include string content", self._config),
-                invalid_final_count=invalid_final_count,
-                should_continue=False,
-            )
-
-        try:
-            resolved = self.validate_final(parse_final_json_content(content))
-        except (json.JSONDecodeError, MetadataResolutionError) as error:
-            return self.handle_invalid_final(messages, error, invalid_final_count)
-
-        write_agent_log(self._log_path, "final_metadata", metadata=resolved)
-        return AgentStepResult(metadata=resolved, invalid_final_count=invalid_final_count, should_continue=False)
-
-    def handle_invalid_final(
-        self,
-        messages: list[dict[str, object]],
-        error: json.JSONDecodeError | MetadataResolutionError,
-        invalid_final_count: int,
-    ) -> AgentStepResult:
-        """Log invalid final JSON and either retry or return review metadata."""
-        invalid_final_count += 1
-        write_agent_log(
-            self._log_path,
-            "final_validation_error",
-            error=str(error),
-            invalid_final_count=invalid_final_count,
-        )
-        if invalid_final_count > self._config.invalid_final_retries:
-            return AgentStepResult(
-                metadata=review_metadata(str(error), self._config),
-                invalid_final_count=invalid_final_count,
-                should_continue=False,
-            )
-        messages.append(
-            {
-                "role": "user",
-                "content": (
-                    "Your previous final answer was invalid. Return only valid JSON matching the required "
-                    f"schema. Validation error: {error}"
-                ),
-            },
-        )
-        return AgentStepResult(metadata=None, invalid_final_count=invalid_final_count, should_continue=True)
-
-    def force_final_response(self, messages: list[dict[str, object]]) -> StandardBookMetadata:
-        """Request a no-tool final answer after the normal turn limit."""
-        messages.append({"role": "user", "content": forced_final_prompt()})
-        write_agent_log(self._log_path, "forced_final_request", reason="max_turns")
-        data = self.chat(messages, self._config.max_agent_turns + 1, tools_enabled=False)
-        message = data.get("message")
-        if not isinstance(message, dict):
-            return review_metadata("Ollama forced final response did not include a message", self._config)
-        content = message.get("content")
-        if not isinstance(content, str):
-            return review_metadata("Ollama forced final response did not include string content", self._config)
-        try:
-            resolved = self.validate_final(parse_final_json_content(content))
-        except (json.JSONDecodeError, MetadataResolutionError) as error:
-            return review_metadata(f"Ollama forced final response was invalid: {error}", self._config)
-        write_agent_log(self._log_path, "final_metadata", metadata=resolved)
-        return resolved
-
-    def chat(self, messages: list[dict[str, object]], turn: int, *, tools_enabled: bool = True) -> dict[str, object]:
-        """Send one chat request to Ollama and log the request and response."""
-        payload = {
-            "model": self._config.model,
-            "messages": messages,
-            "stream": False,
-            "options": {"temperature": 0.1},
-        }
-        tool_names = []
-        if tools_enabled:
-            payload["tools"] = self._registry.tool_schemas()
-            tool_names = self._config.tool_names
-        write_agent_log(
-            self._log_path,
-            "model_request",
-            model=self._config.model,
-            turn=turn,
-            message_count=len(messages),
-            tool_names=tool_names,
-            tools_enabled=tools_enabled,
-        )
-        write_agent_log(
-            self._log_path,
-            "llm_messages_sent",
-            model=self._config.model,
-            turn=turn,
-            messages=messages,
-            tools_enabled=tools_enabled,
-        )
-        response = httpx.post(
-            self._config.ollama_chat_url,
-            headers={"Authorization": f"Bearer {self._ollama_api_key}"},
-            json=payload,
-            timeout=self._config.http_timeout_seconds,
-        )
-        response.raise_for_status()
-        raw_data = response.json()
-        if not isinstance(raw_data, dict):
-            return {}
-        data = {str(key): value for key, value in raw_data.items()}
-        message = data.get("message", {})
-        content = message.get("content") if isinstance(message, dict) else ""
-        write_agent_log(
-            self._log_path,
-            "llm_message_received",
-            model=self._config.model,
-            turn=turn,
-            message=message,
-        )
-        write_agent_log(
-            self._log_path,
-            "model_response",
-            model=self._config.model,
-            turn=turn,
-            has_tool_calls=bool(isinstance(message, dict) and message.get("tool_calls")),
-            content_chars=len(content) if isinstance(content, str) else 0,
-        )
-        return data
-
-    def validate_final(self, raw_metadata: object) -> StandardBookMetadata:
-        """Validate final model metadata against catalog rows."""
-        fields = parse_final_metadata_fields(raw_metadata)
-        fields = replace(fields, title=normalize_title_slug(fields.title))
-        author = self.validate_author(fields.author_id)
-        validate_title_slug(fields.title)
-        book_fields = self.resolve_book_fields(fields)
-        series = self.validate_series(fields.author_id, book_fields.series_id, book_fields.series_index)
-
-        return StandardBookMetadata(
-            author_id=fields.author_id,
-            author=author.name,
-            book_id=book_fields.book_id,
-            title=book_fields.title,
-            series_id=book_fields.series_id,
-            series=series,
-            series_index=book_fields.series_index,
-            confidence=fields.confidence,
-            needs_review=fields.confidence < self._config.min_confidence,
-            evidence=fields.evidence,
-        )
-
-    def validate_author(self, author_id: int) -> AudiobookAuthor:
-        """Validate that an author id was seen and exists."""
-        if author_id not in self._registry.seen_author_ids:
-            msg = f"author_id {author_id} was not returned by search_authors"
-            raise MetadataResolutionError(msg)
-        author = self._registry.get_author(author_id)
-        if author is None:
-            msg = f"author_id {author_id} does not exist"
-            raise MetadataResolutionError(msg)
-        validate_catalog_slug(author.name, "author")
-        return author
-
-    def resolve_book_fields(self, fields: FinalMetadataFields) -> ResolvedBookFields:
-        """Resolve final book fields from a seen book id or created book."""
-        if fields.book_id is None:
-            ensured = self._registry.ensure_book(
-                fields.title,
-                fields.author_id,
-                fields.series_id,
-                fields.series_index,
-            )
-            return ResolvedBookFields(
-                book_id=ensured.book.id,
-                title=ensured.book.title,
-                series_id=ensured.book.series_id,
-                series_index=ensured.book.series_index,
-            )
-
-        if fields.book_id not in self._registry.seen_book_ids:
-            msg = f"book_id {fields.book_id} was not returned by search_books"
-            raise MetadataResolutionError(msg)
-        book = self._registry.get_book(fields.book_id)
-        if book is None:
-            msg = f"book_id {fields.book_id} does not exist"
-            raise MetadataResolutionError(msg)
-        if book.author_id != fields.author_id:
-            msg = f"book_id {fields.book_id} does not belong to author_id {fields.author_id}"
-            raise MetadataResolutionError(msg)
-        return ResolvedBookFields(
-            book_id=fields.book_id,
-            title=book.title,
-            series_id=book.series_id,
-            series_index=book.series_index,
-        )
-
-    def validate_series(self, author_id: int, series_id: int | None, series_index: float) -> str:
-        """Validate final series fields and return the canonical series slug."""
-        if series_id is None:
-            if series_index != 0:
-                msg = "standalone books must use series_index 0"
-                raise MetadataResolutionError(msg)
-            return self._config.standalone_series
-
-        if series_id not in self._registry.seen_series_ids:
-            msg = f"series_id {series_id} was not returned by search_series"
-            raise MetadataResolutionError(msg)
-        series = self._registry.get_series(series_id)
-        if series is None:
-            msg = f"series_id {series_id} does not exist"
-            raise MetadataResolutionError(msg)
-        if series.author_id != author_id:
-            msg = f"series_id {series_id} does not belong to author_id {author_id}"
-            raise MetadataResolutionError(msg)
-        if series_index <= 0:
-            msg = "series books must use a positive series_index"
-            raise MetadataResolutionError(msg)
-        validate_catalog_slug(series.name, "series")
-        return series.name
-
-
-def write_agent_log(log_path: Path, event: str, **fields: object) -> None:
-    """Append one JSONL audit event."""
-    log_path.parent.mkdir(parents=True, exist_ok=True)
-    record = {
-        "created": utcnow().isoformat(),
-        "event": event,
-        **{key: json_log_value(value) for key, value in fields.items()},
-    }
-    with log_path.open("a", encoding="utf-8") as file:
-        file.write(json.dumps(record, sort_keys=True))
-        file.write("\n")
-
-
-def json_log_value(value: object) -> object:
-    """Return a JSON-serializable value for audit logs."""
-    if is_dataclass(value) and not isinstance(value, type):
-        return json_log_value(asdict(value))
-    if isinstance(value, dict):
-        return {str(key): json_log_value(item) for key, item in value.items()}
-    if isinstance(value, list | tuple):
-        return [json_log_value(item) for item in value]
-    if isinstance(value, set):
-        return [json_log_value(item) for item in sorted(value, key=str)]
-    if isinstance(value, PathLike):
-        return str(value)
-    return value
-
-
-def system_prompt() -> str:
-    """Return the stable system prompt."""
-    return """You standardize Audible audiobook metadata against a private catalog.
-
-Rules:
-- You must use the provided tools before returning final metadata.
-- Only use author_id, series_id, or book_id values returned by tools.
-- Return final metadata as JSON only. Do not wrap it in Markdown.
-- The final JSON object must contain author_id, book_id, title, series_id, series_index, confidence, and evidence.
-- title must be a canonical title slug using lower-case words separated by hyphens.
-- Use series_id null and series_index 0 for standalone books.
-- If you use a series_id, series_index must be a whole number or .5 value greater than 0.
-- Treat series slugs that differ only by underscores as the same series. Prefer the existing catalog row instead of
-  creating a new series.
-- Detect omnibus or box-set editions that contain multiple numbered novels, books, or novellas.
-- For an omnibus, make a best-effort range from the filename, tags, and catalog rows. Keep series_index as the
-  first covered book number and include the range in the title when the source title includes it, for example
-  books-1-3.
-- Be careful with omnibuses of novels or novellas later published as one book: keep the omnibus as the audiobook's
-  book record unless catalog rows clearly identify a better match.
-- Do not create publisher collections or author collections as series unless the book metadata clearly gives a
-  numbered series.
-- Series belong to authors. Use a series_id only when it belongs to the selected author_id.
-- Always search for the author before creating one. If no exact author slug exists, call ensure_author.
-- Always search for a series with author_id before creating one. If no exact series slug exists, call ensure_series.
-- Always search for a book before creating one. If no exact title slug exists, call ensure_book.
-- If a tool returns an error, correct your tool arguments or final metadata before continuing.
-- confidence must be a number from 0 to 1.
-- evidence must be a short list of strings explaining which filename, tags, and catalog rows support the answer."""
-
-
-def forced_final_prompt() -> str:
-    """Return the no-tools finalization prompt."""
-    return (
-        "Stop calling tools. Return final metadata as JSON only using the tool results already provided. "
-        "If search_books returned no matching rows but author and series are known, use book_id null and resolve "
-        "the title slug from the AAX filename and ffprobe tags. The validator will create the missing book. "
-        "Use only author_id and series_id values returned by earlier tool results."
-    )
-
-
-def user_prompt(aax_file_name: str, metadata: dict[str, str]) -> str:
-    """Build the user prompt from source metadata."""
-    return (
-        "Resolve this Audible audiobook.\n\n"
-        f"AAX file name: {aax_file_name}\n\n"
-        "ffprobe format tags:\n"
-        f"{json.dumps(metadata, indent=2, sort_keys=True)}"
-    )
-
-
-def parse_final_json_content(content: str) -> object:
-    """Parse final model content, accepting bare or fenced JSON."""
-    stripped = content.strip()
-    if match := FENCED_JSON_PATTERN.fullmatch(stripped):
-        stripped = match.group("json").strip()
-    return json.loads(stripped)
-
-
-def parse_final_metadata_fields(raw_metadata: object) -> FinalMetadataFields:
-    """Parse the model's final JSON object into typed fields."""
-    if not isinstance(raw_metadata, dict):
-        msg = "Final metadata must be a JSON object"
-        raise MetadataResolutionError(msg)
-    data = {str(key): value for key, value in raw_metadata.items()}
-    return FinalMetadataFields(
-        author_id=required_int(data, "author_id"),
-        book_id=optional_int(data.get("book_id"), "book_id"),
-        title=required_string(data, "title"),
-        series_id=optional_int(data.get("series_id"), "series_id"),
-        series_index=required_series_index(data, "series_index"),
-        confidence=required_float(data, "confidence"),
-        evidence=required_string_list(data, "evidence"),
-    )
-
-
-def review_metadata(reason: str, config: AgentConfig) -> StandardBookMetadata:
-    """Return a metadata result that must be reviewed manually."""
-    return StandardBookMetadata(
-        author_id=0,
-        author="unknown_author",
-        book_id=None,
-        title="unknown-title",
-        series_id=None,
-        series=config.standalone_series,
-        series_index=0,
-        confidence=0,
-        needs_review=True,
-        evidence=[reason],
-    )
-
-
-def required_float(data: dict[str, object], key: str) -> float:
-    """Read a required float field."""
-    value = data.get(key)
-    if isinstance(value, bool) or not isinstance(value, int | float):
-        msg = f"{key} must be a number"
-        raise MetadataResolutionError(msg)
-    confidence = float(value)
-    if confidence < 0 or confidence > 1:
-        msg = f"{key} must be between 0 and 1"
-        raise MetadataResolutionError(msg)
-    return confidence
-
-
-def required_string_list(data: dict[str, object], key: str) -> list[str]:
-    """Read a required list of strings."""
-    value = data.get(key)
-    if not isinstance(value, list) or not value or not all(isinstance(item, str) for item in value):
-        msg = f"{key} must be a non-empty list of strings"
-        raise MetadataResolutionError(msg)
-    strings = [item.strip() for item in value if item.strip()]
-    if not strings:
-        msg = f"{key} must include at least one non-empty string"
-        raise MetadataResolutionError(msg)
-    return strings

systems/bob/default.nix

@@ -32,8 +32,6 @@
       enable = true;
       allowedTCPPorts = [
         8000
-        8001
-        8002
       ];
     };
     networkmanager.enable = true;

systems/bob/llms.nix

@@ -4,7 +4,7 @@
     host = "0.0.0.0";
     enable = true;
 
-    syncModels = false;
+    syncModels = true;
     loadModels = [
       "codellama:7b"
       "deepscaler:1.5b"

systems/jeeves/networking.nix

@@ -17,9 +17,6 @@
         allowedTCPPorts = [ ];
         allowedUDPPorts = [ ];
       };
-      allowedTCPPorts = [
-        8070
-      ];
     };
     useNetworkd = true;
   };

systems/jeeves/services/llms.nix

@@ -6,7 +6,7 @@ in
     user = "ollama";
     enable = true;
     host = "0.0.0.0";
-    syncModels = false;
+    syncModels = true;
     loadModels = [
       "codellama:7b"
       "deepscaler:1.5b"
@@ -30,9 +30,6 @@ in
       "ministral-3:14b"
       "nemotron-3-nano:30b"
       "qwen3-coder:30b"
-      "qwen3-embedding:0.6b"
-      "qwen3-embedding:4b"
-      "qwen3-embedding:8b"
       "qwen3-vl:32b"
       "qwen3:14b"
       "qwen3.5:35b"

systems/jeeves/services/postgress.nix

@@ -38,6 +38,9 @@ in
       # signalbot
       local signalbot  signalbot   trust
 
+      # hedgedoc
+      local hedgedoc   hedgedoc    trust
+
       # math
       local postgres  math   trust
       host  postgres  math   127.0.0.1/32    trust
@@ -117,11 +120,19 @@ in
           login = true;
         };
       }
+      {
+        name = "hedgedoc";
+        ensureDBOwnership = true;
+        ensureClauses = {
+          login = true;
+        };
+      }
     ];
     ensureDatabases = [
       "data_science_dev"
       "hass"
       "gitea"
+      "hedgedoc"
       "math"
       "n8n"
       "richie"

tests/conftest.py

@@ -0,0 +1,40 @@
+"""Shared test fixtures."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import pytest
+from sqlalchemy import create_engine, event
+
+from python.orm.signal_bot.base import SignalBotBase
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+    from sqlalchemy.engine import Engine
+
+
+@pytest.fixture(scope="session")
+def sqlite_engine() -> Generator[Engine]:
+    """Create an in-memory SQLite engine for testing."""
+    engine = create_engine("sqlite:///:memory:")
+
+    @event.listens_for(engine, "connect")
+    def _set_sqlite_pragma(dbapi_connection, _connection_record):
+        cursor = dbapi_connection.cursor()
+        cursor.execute("PRAGMA foreign_keys=ON")
+        cursor.close()
+
+    SignalBotBase.metadata.create_all(engine)
+    yield engine
+    engine.dispose()
+
+
+@pytest.fixture
+def engine(sqlite_engine: Engine) -> Generator[Engine]:
+    """Yield the shared engine after cleaning all tables between tests."""
+    yield sqlite_engine
+    with sqlite_engine.begin() as connection:
+        for table in reversed(SignalBotBase.metadata.sorted_tables):
+            connection.execute(table.delete())

tests/test_audiobook_catalog.py

@@ -1,126 +0,0 @@
-"""test_audiobook_catalog."""
-
-from __future__ import annotations
-
-import pytest
-from sqlalchemy import create_engine, select
-from sqlalchemy.orm import sessionmaker
-
-from python.orm.richie import AudiobookAuthor, AudiobookSeries, RichieBase
-from python.tools.audiobook import catalog
-
-
-@pytest.fixture
-def audiobook_session():
-    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:
-        yield session
-    engine.dispose()
-
-
-def test_upsert_catalog_csv_inserts_and_updates_authors_and_series(tmp_path, audiobook_session) -> None:
-    audiobook_session.add_all(
-        [
-            AudiobookAuthor(id=10, name="old_author"),
-            AudiobookAuthor(id=11, name="craig_alanson"),
-        ],
-    )
-    audiobook_session.commit()
-    authors_csv = tmp_path / "authors.csv"
-    series_csv = tmp_path / "series.csv"
-    authors_csv.write_text(
-        "name,id\n"
-        "glynn_stewart,\n"
-        "craig_alanson,\n"
-        "updated_author,10\n",
-        encoding="utf-8",
-    )
-    series_csv.write_text(
-        "name,author_name,id\n"
-        "starships_mage,glynn_stewart,\n"
-        "expeditionary_force,craig_alanson,\n",
-        encoding="utf-8",
-    )
-
-    author_count = catalog.upsert_authors_from_csv(audiobook_session, authors_csv)
-    series_count = catalog.upsert_series_from_csv(audiobook_session, series_csv)
-    audiobook_session.commit()
-
-    authors = audiobook_session.scalars(select(AudiobookAuthor).order_by(AudiobookAuthor.id)).all()
-    series = audiobook_session.scalars(select(AudiobookSeries).order_by(AudiobookSeries.name)).all()
-    assert author_count == 3
-    assert series_count == 2
-    assert [(author.id, author.name) for author in authors] == [
-        (10, "updated_author"),
-        (11, "craig_alanson"),
-        (12, "glynn_stewart"),
-    ]
-    assert [(row.name, row.author.name) for row in series] == [
-        ("expeditionary_force", "craig_alanson"),
-        ("starships_mage", "glynn_stewart"),
-    ]
-
-
-def test_upsert_series_csv_updates_series_by_id(tmp_path, audiobook_session) -> None:
-    author = AudiobookAuthor(id=1, name="glynn_stewart")
-    audiobook_session.add_all(
-        [
-            author,
-            AudiobookSeries(id=7, name="old_series", author=author),
-        ],
-    )
-    audiobook_session.commit()
-    series_csv = tmp_path / "series.csv"
-    series_csv.write_text(
-        "name,author_name,id\n"
-        "starships_mage,glynn_stewart,7\n",
-        encoding="utf-8",
-    )
-
-    count = catalog.upsert_series_from_csv(audiobook_session, series_csv)
-    audiobook_session.commit()
-
-    series = audiobook_session.get(AudiobookSeries, 7)
-    assert count == 1
-    assert series.name == "starships_mage"
-    assert series.author.name == "glynn_stewart"
-
-
-def test_upsert_csv_allows_missing_id_column(tmp_path, audiobook_session) -> None:
-    authors_csv = tmp_path / "authors.csv"
-    series_csv = tmp_path / "series.csv"
-    authors_csv.write_text(
-        "name\n"
-        "glynn_stewart\n",
-        encoding="utf-8",
-    )
-    series_csv.write_text(
-        "name,author_name\n"
-        "starships_mage,glynn_stewart\n",
-        encoding="utf-8",
-    )
-
-    author_count = catalog.upsert_authors_from_csv(audiobook_session, authors_csv)
-    series_count = catalog.upsert_series_from_csv(audiobook_session, series_csv)
-    audiobook_session.commit()
-
-    series = audiobook_session.scalar(select(AudiobookSeries))
-    assert author_count == 1
-    assert series_count == 1
-    assert series.name == "starships_mage"
-    assert series.author.name == "glynn_stewart"
-
-
-def test_upsert_series_csv_rejects_unknown_author(tmp_path, audiobook_session) -> None:
-    series_csv = tmp_path / "series.csv"
-    series_csv.write_text(
-        "name,author_name,id\n"
-        "starships_mage,glynn_stewart,\n",
-        encoding="utf-8",
-    )
-
-    with pytest.raises(catalog.CatalogImportError) as error:
-        catalog.upsert_series_from_csv(audiobook_session, series_csv)
-
-    assert "author not found: glynn_stewart" in str(error.value)

tests/test_ebook_search_core.py

@@ -1,536 +0,0 @@
-"""Tests for EPUB search core helpers."""
-
-from __future__ import annotations
-
-import logging
-from dataclasses import replace
-from datetime import UTC, datetime
-from os import environ
-from pathlib import Path
-from threading import Event
-from types import ModuleType
-
-import pytest
-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 (
-    BM25Corpus,
-    BM25CorpusUnavailableError,
-    BM25Manifest,
-    ensure_bm25_corpus,
-    fetch_bm25_corpus_records,
-    load_bm25_corpus,
-    read_bm25_manifest,
-    score_bm25_corpus,
-    write_bm25_corpus,
-)
-from python.ebook_search.config import EbookSearchConfig, RerankConfig, load_config, normalize_embedding_model
-from python.ebook_search.embeddings import MODEL_DIMENSIONS, ensure_embedding_models
-from python.ebook_search.ingest import chunk_text, find_existing_source
-from python.ebook_search.search import (
-    SearchResponse,
-    SearchResult,
-    bm25_candidates,
-    reciprocal_rank_fusion,
-    retrieval_query_from_text,
-    search_ebooks,
-)
-from python.ebook_search.timing import RuntimeStep
-from python.orm.richie import (
-    EbookChapter,
-    EbookChunk,
-    EbookChunkEmbedding1024,
-    EbookEmbeddingModel,
-    EbookSource,
-    RichieBase,
-)
-
-
-def test_chunk_text_uses_overlap() -> None:
-    chunks = chunk_text(" ".join(str(index) for index in range(100)), chunk_tokens=20, overlap_tokens=5)
-
-    assert len(chunks) > 1
-    assert chunks[0].token_start == 0
-    assert chunks[1].token_start == 15
-    assert all(chunk.token_count <= 20 for chunk in chunks)
-
-
-def test_reciprocal_rank_fusion_combines_vector_and_bm25_rankings() -> None:
-    vector_results = [
-        SearchResult(chunk_id=1, text="a", source_title="A", score=0.9, vector_score=0.9),
-        SearchResult(chunk_id=2, text="b", source_title="B", score=0.8, vector_score=0.8),
-    ]
-    lexical_results = [
-        SearchResult(chunk_id=2, text="b", source_title="B", score=4.2, bm25_score=4.2),
-        SearchResult(chunk_id=3, text="c", source_title="C", score=2.1, bm25_score=2.1),
-    ]
-
-    fused = reciprocal_rank_fusion(vector_results, lexical_results)
-
-    assert [result.chunk_id for result in fused] == [2, 1, 3]
-    assert fused[0].rank_source == "Hybrid"
-    assert fused[0].vector_score == 0.8
-    assert fused[0].bm25_score == 4.2
-    assert fused[0].fused_score == fused[0].score
-
-
-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,
-            language=None,
-            publisher=None,
-            identifier=None,
-            file_path="/old/book.epub",
-            file_sha256="a" * 64,
-            file_mtime=datetime.now(tz=UTC),
-            file_size=10,
-        )
-        session.add(source)
-        session.commit()
-
-        assert find_existing_source(session, Path("/old/book.epub"), "b" * 64) == source
-        assert find_existing_source(session, Path("/new/book.epub"), "a" * 64) == source
-
-
-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",
-            language=None,
-            publisher=None,
-            identifier=None,
-            file_path="/book.epub",
-            file_sha256="a" * 64,
-            file_mtime=datetime.now(tz=UTC),
-            file_size=10,
-        )
-        session.add(source)
-        session.flush()
-        chapter = EbookChapter(source_id=source.id, spine_index=0, title="Chapter", href=None)
-        session.add(chapter)
-        session.flush()
-        session.add(
-            EbookChunk(
-                id=1,
-                source_id=source.id,
-                chapter_id=chapter.id,
-                chunk_index=0,
-                text="content",
-                token_start=0,
-                token_count=1,
-                page_label=None,
-                content_sha256="b" * 64,
-                search_text="Book Author Chapter content",
-            )
-        )
-        session.commit()
-
-        records, texts = fetch_bm25_corpus_records(session)
-
-    assert texts == ["Book Author Chapter content"]
-    assert records[0]["chunk_id"] == 1
-    assert "bm25_text" not in records[0]
-
-
-def test_reciprocal_rank_fusion_marks_hybrid_source() -> None:
-    vector_results = [SearchResult(chunk_id=1, text="a", source_title="A")]
-    lexical_results = [SearchResult(chunk_id=2, text="b", source_title="B")]
-
-    fused = reciprocal_rank_fusion(vector_results, lexical_results)
-
-    assert {result.rank_source for result in fused} == {"Hybrid"}
-
-
-def test_search_response_sums_runtime_steps() -> None:
-    response = SearchResponse(
-        query="query",
-        results=[],
-        rank_label="Hybrid",
-        timings=(
-            RuntimeStep(name="A", duration_ms=1.25),
-            RuntimeStep(name="B", duration_ms=2.75),
-            RuntimeStep(name="Parallel detail", duration_ms=10.0, counts_toward_total=False),
-        ),
-    )
-
-    assert response.total_runtime_ms == 4.0
-
-
-def test_search_ebooks_runs_vector_and_bm25_in_parallel(monkeypatch) -> None:
-    engine = create_engine("sqlite+pysqlite:///:memory:", future=True)
-    vector_started = Event()
-    bm25_started = Event()
-    received_engines: list[object] = []
-
-    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 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):
-        """Return BM25 candidates after confirming vector search has started."""
-        assert query == "parallel"
-        bm25_started.set()
-        assert vector_started.wait(timeout=2)
-        return [SearchResult(chunk_id=2, text="bm25", source_title="BM25", bm25_score=2.0)]
-
-    monkeypatch.setattr("python.ebook_search.search.vector_candidates", fake_vector_candidates)
-    monkeypatch.setattr("python.ebook_search.search.bm25_candidates", fake_bm25_candidates)
-    config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
-
-    response = search_ebooks(engine, "what is parallel", config)
-
-    timings = {step.name: step for step in response.timings}
-    assert [result.chunk_id for result in response.results] == [1, 2]
-    assert timings["Embedding + vector search"].counts_toward_total is False
-    assert timings["BM25 search"].counts_toward_total is False
-    assert timings["Hybrid retrieval"].counts_toward_total is True
-    assert timings["BM25 query preparation"].counts_toward_total is True
-    assert received_engines == [engine]
-
-
-def test_retrieval_query_keeps_entity_and_series_terms() -> None:
-    assert retrieval_query_from_text("what does Damien Montgomery stand for in starship mage") == (
-        "damien montgomery stand starship mage"
-    )
-
-
-def test_bm25_candidates_scores_whole_corpus(monkeypatch) -> None:
-    record = {
-        "chunk_id": 2,
-        "text": "high",
-        "source_title": "B",
-        "source_author": None,
-        "chapter_title": None,
-        "page_label": None,
-        "bm25_text": "high",
-    }
-    manifest = BM25Manifest(created_at=datetime.now(tz=UTC), db_updated_at=None, chunk_count=1)
-    corpus = BM25Corpus(retriever=object(), records=(record,), manifest=manifest)
-    captured: dict[str, object] = {}
-
-    def fake_score_bm25_corpus(query, saved_corpus, *, limit):
-        captured["query"] = query
-        captured["corpus"] = saved_corpus
-        captured["limit"] = limit
-        return [(record, 1.5)]
-
-    monkeypatch.setattr("python.ebook_search.search.load_bm25_corpus", lambda _config: corpus)
-    monkeypatch.setattr("python.ebook_search.search.score_bm25_corpus", fake_score_bm25_corpus)
-    config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
-
-    results = bm25_candidates("high", config)
-
-    assert captured["query"] == "high"
-    assert captured["corpus"] == corpus
-    assert captured["limit"] == 120
-    assert [result.chunk_id for result in results] == [2]
-    assert [result.bm25_score for result in results] == [1.5]
-
-
-def test_bm25_candidates_returns_empty_when_corpus_is_unavailable(monkeypatch, caplog) -> None:
-    def fake_load_bm25_corpus(_config):
-        raise BM25CorpusUnavailableError
-
-    monkeypatch.setattr("python.ebook_search.search.load_bm25_corpus", fake_load_bm25_corpus)
-    config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
-
-    with caplog.at_level(logging.WARNING):
-        results = bm25_candidates("high", config)
-
-    assert results == []
-    assert "ebook_bm25_index_unavailable_skipping" in caplog.text
-
-
-def test_write_bm25_corpus_publishes_dated_generation(tmp_path) -> None:
-    index_path = tmp_path / "bm25"
-    index_path.mkdir()
-    generations_path = index_path / "generations"
-    generations_path.mkdir()
-    old_generation = generations_path / "20260101T000000.000000Z"
-    old_generation.mkdir()
-    (old_generation / "sentinel").write_text("old", encoding="utf-8")
-    (index_path / "current").symlink_to(Path("generations") / old_generation.name, target_is_directory=True)
-    manifest = BM25Manifest(
-        created_at=datetime(2026, 6, 12, 1, 2, 3, 456789, tzinfo=UTC),
-        db_updated_at=None,
-        chunk_count=0,
-    )
-
-    write_bm25_corpus(index_path, [], [], manifest)
-
-    current_path = index_path / "current"
-    assert current_path.is_symlink()
-    assert current_path.readlink() == generations_path / "20260612T010203.456789Z"
-    assert old_generation.is_dir()
-    assert (old_generation / "sentinel").read_text(encoding="utf-8") == "old"
-    assert (generations_path / "20260612T010203.456789Z").is_dir()
-    assert read_bm25_manifest(index_path) == manifest
-
-
-def test_write_bm25_corpus_keeps_current_generation_when_publish_fails(monkeypatch, tmp_path) -> None:
-    index_path = tmp_path / "bm25"
-    index_path.mkdir()
-    generations_path = index_path / "generations"
-    generations_path.mkdir()
-    old_generation = generations_path / "20260101T000000.000000Z"
-    old_generation.mkdir()
-    (old_generation / "sentinel").write_text("old", encoding="utf-8")
-    current_path = index_path / "current"
-    current_path.symlink_to(Path("generations") / old_generation.name, target_is_directory=True)
-    original_replace = Path.replace
-
-    def fail_current_replace(self, target):
-        if self.parent == index_path and self.name.startswith(".current.") and target == current_path:
-            msg = "current publish failed"
-            raise OSError(msg)
-        return original_replace(self, target)
-
-    monkeypatch.setattr(Path, "replace", fail_current_replace)
-    manifest = BM25Manifest(
-        created_at=datetime(2026, 6, 12, 1, 2, 3, 456789, tzinfo=UTC),
-        db_updated_at=None,
-        chunk_count=0,
-    )
-
-    with pytest.raises(OSError, match="current publish failed"):
-        write_bm25_corpus(index_path, [], [], manifest)
-
-    assert current_path.readlink() == Path("generations") / old_generation.name
-    assert (old_generation / "sentinel").read_text(encoding="utf-8") == "old"
-    assert not (generations_path / "20260612T010203.456789Z").exists()
-
-
-def test_load_bm25_corpus_uses_current_generation(tmp_path) -> None:
-    load_bm25_corpus.cache_clear()
-    index_path = tmp_path / "bm25"
-    manifest = BM25Manifest(
-        created_at=datetime(2026, 6, 12, 1, 2, 3, 456789, tzinfo=UTC),
-        db_updated_at=None,
-        chunk_count=1,
-    )
-    record = {
-        "chunk_id": 2,
-        "text": "cached",
-        "source_title": "B",
-        "source_author": None,
-        "chapter_title": None,
-        "page_label": None,
-    }
-    write_bm25_corpus(index_path, [record], ["cached phrase"], manifest)
-    config = EbookSearchConfig(rerank=RerankConfig(enabled=False), bm25_index_dir=str(index_path))
-
-    try:
-        corpus = load_bm25_corpus(config)
-    finally:
-        load_bm25_corpus.cache_clear()
-
-    assert corpus.manifest == manifest
-    assert corpus.records[0]["chunk_id"] == 2
-    assert score_bm25_corpus("cached", corpus, limit=10)
-
-
-def test_load_bm25_corpus_caches_disk_load(monkeypatch, tmp_path) -> None:
-    load_bm25_corpus.cache_clear()
-    manifest = BM25Manifest(created_at=datetime.now(tz=UTC), db_updated_at=None, chunk_count=1)
-    record = {
-        "chunk_id": 2,
-        "text": "cached",
-        "source_title": "B",
-        "source_author": None,
-        "chapter_title": None,
-        "page_label": None,
-        "bm25_text": "cached",
-    }
-    load_count = 0
-
-    class FakeRetriever:
-        """Fake persisted BM25 retriever."""
-
-        corpus = (record,)
-
-    class FakeBM25:
-        """Fake BM25 class with observable load count."""
-
-        @staticmethod
-        def load(index_path, *, load_corpus, mmap):
-            nonlocal load_count
-            load_count += 1
-            assert index_path == tmp_path
-            assert load_corpus is True
-            assert mmap is True
-            return FakeRetriever()
-
-    fake_bm25s = ModuleType("bm25s")
-    fake_bm25s.BM25 = FakeBM25
-    monkeypatch.setattr("python.ebook_search.bm25_corpus.read_bm25_manifest", lambda _path: manifest)
-    monkeypatch.setattr("python.ebook_search.bm25_corpus.bm25_index_exists", lambda _path, _manifest: True)
-    monkeypatch.setattr("python.ebook_search.bm25_corpus.bm25s", fake_bm25s)
-    config = EbookSearchConfig(rerank=RerankConfig(enabled=False), bm25_index_dir=str(tmp_path))
-
-    try:
-        first = load_bm25_corpus(config)
-        second = load_bm25_corpus(config)
-    finally:
-        load_bm25_corpus.cache_clear()
-
-    assert first is second
-    assert first is not None
-    assert first.records == (record,)
-    assert load_count == 1
-
-
-def test_load_bm25_corpus_raises_when_index_is_missing(monkeypatch, tmp_path) -> None:
-    load_bm25_corpus.cache_clear()
-    monkeypatch.setattr("python.ebook_search.bm25_corpus.read_bm25_manifest", lambda _path: None)
-    monkeypatch.setattr("python.ebook_search.bm25_corpus.bm25_index_exists", lambda _path, _manifest: False)
-    config = EbookSearchConfig(rerank=RerankConfig(enabled=False), bm25_index_dir=str(tmp_path))
-
-    try:
-        with pytest.raises(BM25CorpusUnavailableError, match="BM25 corpus is not available"):
-            load_bm25_corpus(config)
-    finally:
-        load_bm25_corpus.cache_clear()
-
-
-def test_ensure_bm25_corpus_refreshes_missing_index(monkeypatch) -> None:
-    refreshed: list[object] = []
-    db_updated_at = datetime.now(tz=UTC)
-
-    monkeypatch.setattr("python.ebook_search.bm25_corpus.read_bm25_manifest", lambda _path: None)
-    monkeypatch.setattr("python.ebook_search.bm25_corpus.bm25_index_exists", lambda _path, _manifest: False)
-    monkeypatch.setattr("python.ebook_search.bm25_corpus.corpus_last_updated_at", lambda _session: db_updated_at)
-    monkeypatch.setattr(
-        "python.ebook_search.bm25_corpus.refresh_bm25_corpus",
-        lambda session, config, *, db_updated_at: refreshed.append((session, config, db_updated_at)),
-    )
-
-    config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
-    session = object()
-
-    ensure_bm25_corpus(session, config)
-
-    assert refreshed == [(session, config, db_updated_at)]
-
-
-def test_ensure_bm25_corpus_refreshes_stale_index(monkeypatch) -> None:
-    refreshed: list[object] = []
-    created_at = datetime(2026, 1, 1, tzinfo=UTC)
-    db_updated_at = datetime(2026, 1, 2, tzinfo=UTC)
-    manifest = BM25Manifest(created_at=created_at, db_updated_at=created_at, chunk_count=10)
-
-    monkeypatch.setattr("python.ebook_search.bm25_corpus.read_bm25_manifest", lambda _path: manifest)
-    monkeypatch.setattr("python.ebook_search.bm25_corpus.bm25_index_exists", lambda _path, _manifest: True)
-    monkeypatch.setattr("python.ebook_search.bm25_corpus.corpus_last_updated_at", lambda _session: db_updated_at)
-    monkeypatch.setattr(
-        "python.ebook_search.bm25_corpus.refresh_bm25_corpus",
-        lambda session, config, *, db_updated_at: refreshed.append((session, config, db_updated_at)),
-    )
-
-    config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
-    session = object()
-
-    ensure_bm25_corpus(session, config)
-
-    assert refreshed == [(session, config, db_updated_at)]
-
-
-def test_supported_embedding_models_match_service_names() -> None:
-    assert MODEL_DIMENSIONS == {
-        "qwen3-embedding-0.6b": 1024,
-        "qwen3-embedding-4b": 2560,
-        "qwen3-embedding-8b": 4096,
-    }
-
-
-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(session.scalars(select(EbookEmbeddingModel).order_by(EbookEmbeddingModel.name)))
-
-    assert [(model.name, model.dimension) for model in models] == [
-        ("qwen3-embedding-0.6b", 1024),
-        ("qwen3-embedding-4b", 2560),
-        ("qwen3-embedding-8b", 4096),
-    ]
-
-
-def test_1024_embedding_table_has_cosine_hnsw_index() -> None:
-    indexes = {index.name: index for index in EbookChunkEmbedding1024.__table__.indexes}
-    index = indexes["ix_ebook_chunk_embedding_1024_embedding_cosine"]
-
-    assert [column.name for column in index.columns] == ["embedding"]
-    assert index.dialect_options["postgresql"]["using"] == "hnsw"
-    assert index.dialect_options["postgresql"]["ops"] == {"embedding": "vector_cosine_ops"}
-
-
-def test_embedding_model_aliases_normalize_to_provider_names() -> None:
-    assert normalize_embedding_model() == "qwen3-embedding-0.6b"
-
-    environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "qwen3-embedding-0.6b"
-    assert normalize_embedding_model() == "qwen3-embedding-0.6b"
-
-    environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "Qwen3-Embedding-0.6B"
-    assert normalize_embedding_model() == "qwen3-embedding-0.6b"
-
-    environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "Qwen/Qwen3-Embedding-4B"
-
-    assert normalize_embedding_model() == "qwen3-embedding-4b"
-
-    environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "qwen3-embedding:8b"
-    assert normalize_embedding_model() == "qwen3-embedding-8b"
-
-    environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "qwen3-embedding-8b"
-    assert normalize_embedding_model() == "qwen3-embedding-8b"
-
-
-def test_answer_generation_is_enabled_by_default(monkeypatch) -> None:
-    monkeypatch.delenv("EBOOK_SEARCH_ANSWER_ENABLED", raising=False)
-
-    config = load_config()
-
-    assert config.answer_enabled is True
-
-
-def test_chat_defaults_use_ollama_cloud(monkeypatch) -> None:
-    monkeypatch.delenv("EBOOK_SEARCH_VLLM_BASE_URL", raising=False)
-    monkeypatch.delenv("EBOOK_SEARCH_CHAT_MODEL", raising=False)
-
-    config = load_config()
-
-    assert config.vllm_base_url == "https://ollama.com/v1"
-    assert config.chat_model == "deepseek-v4-flash"
-
-
-def test_chat_api_key_falls_back_to_ollama_api_key(monkeypatch) -> None:
-    monkeypatch.delenv("EBOOK_SEARCH_VLLM_API_KEY", raising=False)
-    monkeypatch.setenv("OLLAMA_API_KEY", "ollama-key")
-
-    config = load_config()
-
-    assert config.vllm_api_key == "ollama-key"
-
-
-def test_answer_query_does_not_call_model_when_disabled() -> None:
-    config = replace(load_config(), answer_enabled=False)
-    result = SearchResult(chunk_id=1, text="source text", source_title="Book")
-
-    answer = answer_query("question", [result], config)
-
-    assert "Answer generation is disabled" in answer