opning ports for testing

this code was moved to https://gitea.tmmworkshop.com/Nornsight/weave

.github/workflows/build_systems.yml

@@ -23,6 +23,6 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Build default package
-        run: "nixos-rebuild build --flake ./#${{ matrix.system }}"
+        run: "nixos-rebuild build --accept-flake-config --flake ./#${{ matrix.system }}"
       - name: copy to nix-cache
         run: nix copy --accept-flake-config --to unix:///host-nix/var/nix/daemon-socket/socket .#nixosConfigurations.${{ matrix.system }}.config.system.build.toplevel

.github/workflows/fix_eval_warnings.yml

@@ -1,30 +0,0 @@
-name: fix_eval_warnings
-on:
-  workflow_run:
-    workflows: ["build_systems"]
-    types: [completed]
-
-jobs:
-  check-warnings:
-    if: >-
-      github.event.workflow_run.conclusion != 'cancelled' &&
-      github.event.workflow_run.head_branch == 'main' &&
-      (github.event.workflow_run.event == 'push' || github.event.workflow_run.event == 'schedule')
-    runs-on: self-hosted
-    permissions:
-      contents: write
-      pull-requests: write
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Fix eval warnings
-        env:
-          GH_TOKEN: ${{ secrets.GH_TOKEN_FOR_UPDATES }}
-        run: >-
-          nix develop .#devShells.x86_64-linux.default -c
-          python -m python.eval_warnings.main
-          --run-id "${{ github.event.workflow_run.id }}"
-          --repo "${{ github.repository }}"
-          --ollama-url "${{ secrets.OLLAMA_URL }}"
-          --run-url "${{ github.event.workflow_run.html_url }}"

.github/workflows/merge_flake_lock_update.yml

@@ -6,24 +6,18 @@ on:
 
 jobs:
   merge:
-    runs-on: ubuntu-latest
+    runs-on: self-hosted
 
     permissions:
       contents: write
       pull-requests: write
 
     steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
       - name: merge_flake_lock_update
-        run: |
-          pr_number=$(gh pr list --state open --author RichieCahill --label flake_lock_update --json number --jq '.[0].number')
-          echo "pr_number=$pr_number" >> $GITHUB_ENV
-          if [ -n "$pr_number" ]; then
-            gh pr merge "$pr_number" --rebase
-          else
-            echo "No open PR found with label flake_lock_update"
-          fi
+        run: >-
+          nix develop .#devShells.x86_64-linux.default -c
+          python -m python.gitea_flake_lock merge
+          --repo "${{ github.repository }}"
         env:
-          GITHUB_TOKEN: ${{ secrets.GH_TOKEN_FOR_UPDATES }}
+          GITEA_TOKEN: ${{ secrets.GITEA_TOKEN }}
+          GITEA_URL: https://gitea.tmmworkshop.com

.github/workflows/pytest.yml

@@ -1,13 +1,13 @@
 name: pytest
 
 on:
+  workflow_dispatch:
   push:
     branches:
       - main
   pull_request:
     branches:
       - main
-  merge_group:
 
 jobs:
   pytest:

.github/workflows/update-flake-lock.yml

@@ -6,18 +6,21 @@ on:
 
 jobs:
   lockfile:
-    runs-on: ubuntu-latest
+    runs-on: self-hosted
+    permissions:
+      actions: write
+      contents: write
+      pull-requests: write
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
-      - name: Install Nix
-        uses: DeterminateSystems/nix-installer-action@main
       - name: Update flake.lock
-        uses: DeterminateSystems/update-flake-lock@main
-        with:
-          token: ${{ secrets.GH_TOKEN_FOR_UPDATES }}
-          pr-title: "Update flake.lock"
-          pr-labels: |
-            dependencies
-            automated
-            flake_lock_update
+        run: nix flake update
+      - name: Create or update flake.lock PR
+        env:
+          GITEA_TOKEN: ${{ secrets.GITEA_TOKEN }}
+          GITEA_URL: https://gitea.tmmworkshop.com
+        run: >-
+          nix develop .#devShells.x86_64-linux.default -c
+          python -m python.gitea_flake_lock update
+          --repo "${{ github.repository }}"

.gitignore

@@ -170,6 +170,6 @@ test.*
 frontend/dist/
 frontend/node_modules/
 
-# data dir for training, validation, and testing
-data/
-config.toml
+# data from testing llms
+data/*
+.ebook_search_bm25

.vscode/settings.json

@@ -40,7 +40,6 @@
     "cgroupdriver",
     "charliermarsh",
     "Checkpointing",
-    "cloudflared",
     "codellama",
     "codezombiech",
     "compactmode",
@@ -204,6 +203,7 @@
     "peerconnection",
     "PESKYFOX",
     "PGID",
+    "pgvector",
     "pipewire",
     "pkgs",
     "plugdev",

AGENTS.md

@@ -1,12 +0,0 @@
-## 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.

common/global/default.nix

@@ -23,7 +23,10 @@
   boot = {
     tmp.useTmpfs = true;
     kernelPackages = lib.mkDefault pkgs.linuxPackages_6_12;
-    zfs.package = lib.mkDefault pkgs.zfs_2_4;
+    zfs = {
+      package = lib.mkDefault pkgs.zfs_2_4;
+      forceImportRoot = lib.mkDefault false;
+    };
   };
 
   hardware.enableRedistributableFirmware = true;
@@ -37,10 +40,17 @@
 
   nixpkgs = {
     overlays = builtins.attrValues outputs.overlays;
-    config.allowUnfree = true;
+    config = {
+      allowUnfree = true;
+      permittedInsecurePackages = [
+        "openssl-1.1.1w" # This is for discord-canary
+      ];
+    };
   };
 
   services = {
+    dbus.implementation = "dbus";
+
     # firmware update
     fwupd.enable = true;
 

common/global/nix.nix

@@ -34,6 +34,7 @@ in
       warn-dirty = false;
       flake-registry = ""; # disable global flake registries
       connect-timeout = 10;
+      download-buffer-size = 536870912;
       fallback = true;
     };
 

common/optional/monitoring-agent.nix

@@ -0,0 +1,256 @@
+{
+  config,
+  lib,
+  pkgs,
+  ...
+}:
+let
+  monitoringInterface = "ztwfunumly";
+  nodeTextfileDir = "/var/lib/prometheus-node-exporter-textfile";
+
+  mkProcessNameTemplate =
+    perPid: template: if perPid then "${template}:{{.PID}}:{{.StartTime}}" else template;
+
+  mkProcessMatchers = perPid: [
+    {
+      name = mkProcessNameTemplate perPid "{{.Username}}:{{.Matches.Module}}";
+      cmdline = [ "^/nix/store[^ ]*/bin/python[^ ]* -m (?P<Module>[^ ]+)" ];
+    }
+    {
+      name = mkProcessNameTemplate perPid "{{.Username}}:{{.Matches.Wrapped}}";
+      cmdline = [
+        "^/nix/store[^ ]*/bin/python[^ ]* /nix/store[^ ]*/bin/\\.?(?P<Wrapped>[^ /]+?)(?:-wrapped)?(?:\\s|$)"
+      ];
+    }
+    {
+      name = mkProcessNameTemplate perPid "{{.Username}}:{{.Matches.Wrapped}}";
+      cmdline = [
+        "^/nix/store[^ ]*/bin/node /nix/store[^ ]*-(?P<Wrapped>[A-Za-z0-9._+-]+)-[0-9][^ /]*/"
+      ];
+    }
+    {
+      name = mkProcessNameTemplate perPid "{{.Username}}:{{.Matches.Wrapped}}";
+      cmdline = [ "^/nix/store[^ ]*/(?:bin/|lib/[^ ]*/)?\\.?(?P<Wrapped>[^ /]+?)(?:-wrapped)?(?:\\s|$)" ];
+    }
+    {
+      name = mkProcessNameTemplate perPid "{{.Username}}:{{.ExeBase}}";
+      cmdline = [ ".+" ];
+    }
+  ];
+
+  perPidConfig = pkgs.writeText "process-exporter-per-pid.yaml" (
+    builtins.toJSON {
+      process_names = mkProcessMatchers true;
+    }
+  );
+
+  zpoolLatencyScript = pkgs.writeShellScript "zpool-latency-exporter" ''
+        set -euo pipefail
+
+        out_dir=${lib.escapeShellArg nodeTextfileDir}
+        host=${lib.escapeShellArg config.networking.hostName}
+        tmp_file="$(mktemp "$out_dir/zpool.prom.XXXXXX")"
+        trap 'rm -f "$tmp_file"' EXIT
+
+        pools="$(zpool list -H -o name | paste -sd, -)"
+
+        cat >"$tmp_file" <<'EOF'
+    # HELP zpool_iostat_total_wait_read_ns Average total read wait time reported by zpool iostat.
+    # TYPE zpool_iostat_total_wait_read_ns gauge
+    # HELP zpool_iostat_total_wait_write_ns Average total write wait time reported by zpool iostat.
+    # TYPE zpool_iostat_total_wait_write_ns gauge
+    # HELP zpool_iostat_disk_wait_read_ns Average disk read wait time reported by zpool iostat.
+    # TYPE zpool_iostat_disk_wait_read_ns gauge
+    # HELP zpool_iostat_disk_wait_write_ns Average disk write wait time reported by zpool iostat.
+    # TYPE zpool_iostat_disk_wait_write_ns gauge
+    # HELP zpool_iostat_syncq_wait_read_ns Average synchronous queue read wait time reported by zpool iostat.
+    # TYPE zpool_iostat_syncq_wait_read_ns gauge
+    # HELP zpool_iostat_syncq_wait_write_ns Average synchronous queue write wait time reported by zpool iostat.
+    # TYPE zpool_iostat_syncq_wait_write_ns gauge
+    # HELP zpool_iostat_asyncq_wait_read_ns Average asynchronous queue read wait time reported by zpool iostat.
+    # TYPE zpool_iostat_asyncq_wait_read_ns gauge
+    # HELP zpool_iostat_asyncq_wait_write_ns Average asynchronous queue write wait time reported by zpool iostat.
+    # TYPE zpool_iostat_asyncq_wait_write_ns gauge
+    EOF
+
+        zpool iostat -Hplvy -y 1 1 | awk -F '\t' -v host="$host" -v pools="$pools" '
+          function esc(str, out) {
+            out = str
+            gsub(/\\/, "\\\\", out)
+            gsub(/"/, "\\\"", out)
+            return out
+          }
+
+          function emit(metric, pool, vdev, value) {
+            if (value == "" || value == "-") {
+              return
+            }
+
+            printf "%s{host=\"%s\",pool=\"%s\",vdev=\"%s\"} %s\n",
+              metric,
+              esc(host),
+              esc(pool),
+              esc(vdev),
+              value
+          }
+
+          BEGIN {
+            split(pools, pool_names, ",")
+            for (idx in pool_names) {
+              if (pool_names[idx] != "") {
+                known_pools[pool_names[idx]] = 1
+              }
+            }
+          }
+
+          NF == 0 {
+            next
+          }
+
+          {
+            row_name = $1
+
+            if (row_name in known_pools) {
+              current_pool = row_name
+              current_vdev = "_pool"
+            } else if (current_pool == "") {
+              next
+            } else {
+              current_vdev = row_name
+            }
+
+            emit("zpool_iostat_total_wait_read_ns", current_pool, current_vdev, $8)
+            emit("zpool_iostat_total_wait_write_ns", current_pool, current_vdev, $9)
+            emit("zpool_iostat_disk_wait_read_ns", current_pool, current_vdev, $10)
+            emit("zpool_iostat_disk_wait_write_ns", current_pool, current_vdev, $11)
+            emit("zpool_iostat_syncq_wait_read_ns", current_pool, current_vdev, $12)
+            emit("zpool_iostat_syncq_wait_write_ns", current_pool, current_vdev, $13)
+            emit("zpool_iostat_asyncq_wait_read_ns", current_pool, current_vdev, $14)
+            emit("zpool_iostat_asyncq_wait_write_ns", current_pool, current_vdev, $15)
+          }
+        ' >>"$tmp_file"
+
+        mv "$tmp_file" "$out_dir/zpool.prom"
+        trap - EXIT
+  '';
+in
+{
+  networking.firewall.interfaces.${monitoringInterface}.allowedTCPPorts = [
+    9100
+    9134
+    9256
+    9257
+    9633
+  ];
+
+  services.prometheus.exporters = {
+    node = {
+      enable = true;
+      enabledCollectors = [
+        "pressure"
+        "processes"
+        "systemd"
+      ];
+      extraFlags = [ "--collector.textfile.directory=${nodeTextfileDir}" ];
+    };
+
+    process = {
+      enable = true;
+      user = "root";
+      group = "root";
+      settings.process_names = mkProcessMatchers false;
+      extraFlags = [
+        "-gather-smaps=false"
+        "-remove-empty-groups=true"
+        "-threads=false"
+      ];
+    };
+
+    smartctl.enable = true;
+    zfs.enable = true;
+  };
+
+  programs.atop = {
+    enable = true;
+    atopService.enable = true;
+    atopRotateTimer.enable = true;
+    atopacctService.enable = true;
+    settings.interval = 30;
+  };
+
+  systemd = {
+    services = {
+      prometheus-process-pid-exporter = {
+        description = "Prometheus process exporter with per-PID naming";
+        wantedBy = [ "multi-user.target" ];
+        after = [ "network.target" ];
+        serviceConfig = {
+          ExecStart = ''
+            ${pkgs.prometheus-process-exporter}/bin/process-exporter \
+              --web.listen-address 0.0.0.0:9257 \
+              --config.path ${perPidConfig} \
+              -children=false \
+              -gather-smaps=false \
+              -remove-empty-groups=true \
+              -threads=false
+          '';
+          User = "root";
+          Group = "root";
+          Restart = "always";
+          WorkingDirectory = "/tmp";
+          CapabilityBoundingSet = [ "" ];
+          DeviceAllow = [ "" ];
+          LockPersonality = true;
+          MemoryDenyWriteExecute = true;
+          NoNewPrivileges = true;
+          PrivateDevices = true;
+          PrivateTmp = true;
+          ProtectClock = true;
+          ProtectControlGroups = true;
+          ProtectHome = true;
+          ProtectHostname = true;
+          ProtectKernelLogs = true;
+          ProtectKernelModules = true;
+          ProtectKernelTunables = true;
+          ProtectSystem = "strict";
+          RemoveIPC = true;
+          RestrictAddressFamilies = [
+            "AF_INET"
+            "AF_INET6"
+          ];
+          RestrictNamespaces = true;
+          RestrictRealtime = true;
+          RestrictSUIDSGID = true;
+          SystemCallArchitectures = "native";
+          UMask = "0077";
+        };
+      };
+
+      zpool-latency-exporter = {
+        description = "Exports ZFS latency metrics for node_exporter textfile collection";
+        after = [ "zfs-import.target" ];
+        requires = [ "zfs-import.target" ];
+        path = [
+          config.boot.zfs.package
+          pkgs.coreutils
+          pkgs.gawk
+        ];
+        serviceConfig = {
+          Type = "oneshot";
+          ExecStart = zpoolLatencyScript;
+        };
+      };
+    };
+
+    timers.zpool-latency-exporter = {
+      wantedBy = [ "timers.target" ];
+      timerConfig = {
+        OnBootSec = "2m";
+        OnUnitActiveSec = "60s";
+        Unit = "zpool-latency-exporter.service";
+      };
+    };
+
+    tmpfiles.rules = [ "d ${nodeTextfileDir} 0755 root root - -" ];
+  };
+}

common/optional/syncthing_base.nix

@@ -12,7 +12,7 @@
       brain.id = "SSCGIPI-IV3VYKB-TRNIJE3-COV4T2H-CDBER7F-I2CGHYA-NWOEUDU-3T5QAAN"; # cspell:disable-line
       ipad.id = "KI76T3X-SFUGV2L-VSNYTKR-TSIUV5L-SHWD3HE-GQRGRCN-GY4UFMD-CW6Z6AX"; # cspell:disable-line
       jeeves.id = "ICRHXZW-ECYJCUZ-I4CZ64R-3XRK7CG-LL2HAAK-FGOHD22-BQA4AI6-5OAL6AG"; # cspell:disable-line
-      phone.id = "TBRULKD-7DZPGGZ-F6LLB7J-MSO54AY-7KLPBIN-QOFK6PX-W2HBEWI-PHM2CQI"; # cspell:disable-line
+      phone.id = "JPVQKQW-CFXOJXT-Q5G5F3H-QIDHDRE-GKHPTQB-GXZUQSP-U7FR7F7-INP3AAH"; # cspell:disable-line
       rhapsody-in-green.id = "ASL3KC4-3XEN6PA-7BQBRKE-A7JXLI6-DJT43BY-Q4WPOER-7UALUAZ-VTPQ6Q4"; # cspell:disable-line
     };
   };

common/optional/update.nix

@@ -4,7 +4,7 @@
     flags = [ "--accept-flake-config" ];
     randomizedDelaySec = "1h";
     persistent = true;
-    flake = "github:RichieCahill/dotfiles";
+    flake = "git+https://gitea.tmmworkshop.com/richie/dotfiles?ref=main";
     allowReboot = true;
     dates = "Sat *-*-* 06:00:00";
   };

docs/zfs_failed_root_import_recovry.md

@@ -0,0 +1,76 @@
+# ZFS failed root import recovery
+
+## Fast path
+
+If the machine fails to boot because ZFS refuses to import `root_pool`:
+
+### GRUB
+
+1. At the bootloader menu, select the normal NixOS entry.
+2. Press `e`.
+3. Find the line that starts with `linux`.
+4. Append this to the end of that line:
+
+```text
+zfs_force=1
+```
+
+5. Boot once with `Ctrl+x` or `F10`.
+
+### systemd-boot
+
+1. At the bootloader menu, highlight the normal NixOS entry.
+2. Press `e`.
+3. Append this to the end of the options line:
+
+```text
+zfs_force=1
+```
+
+4. Press `Enter` to boot once.
+
+## After boot
+
+Run:
+
+```bash
+sudo zpool status
+sudo zpool import
+journalctl -b | rg "ZFS|zfs|import|root_pool"
+```
+
+## Expected result
+
+`sudo zpool status` should show `root_pool` as `ONLINE`.
+
+## Reboot test
+
+Run:
+
+```bash
+sudo reboot
+```
+
+Do not add `zfs_force=1` the second time.
+
+## If it still fails
+
+Boot once more with:
+
+```text
+zfs_force=1
+```
+
+Then run:
+
+```bash
+sudo zpool status -v
+sudo zpool history | tail -n 50
+journalctl -b | rg "ZFS|zfs|import|root_pool"
+```
+
+## Notes
+
+- Root pool name is `root_pool`.
+- This is a one-time recovery path after disk moves, controller changes, dirty exports, or interrupted imports.
+- Some hosts also need the LUKS unlock USB key inserted before boot.

flake.lock

@@ -8,11 +8,11 @@
       },
       "locked": {
         "dir": "pkgs/firefox-addons",
-        "lastModified": 1773979456,
-        "narHash": "sha256-9kBMJ5IvxqNlkkj/swmE8uK1Sc7TL/LIRUI958m7uBM=",
+        "lastModified": 1781150628,
+        "narHash": "sha256-b4mp8l3qWuSCyYYo9HSngDtcB3PpecYiOXjULrjwwlw=",
         "owner": "rycee",
         "repo": "nur-expressions",
-        "rev": "81e28f47ac18d9e89513929c77e711e657b64851",
+        "rev": "753319310f4673a2dabbfab87482187b40bf9bac",
         "type": "gitlab"
       },
       "original": {
@@ -29,11 +29,11 @@
         ]
       },
       "locked": {
-        "lastModified": 1774007980,
-        "narHash": "sha256-FOnZjElEI8pqqCvB6K/1JRHTE8o4rer8driivTpq2uo=",
+        "lastModified": 1781189114,
+        "narHash": "sha256-5inaamLgUMWy+MOBE9ChF9QAF1o/74LFuHkI0W/9rqc=",
         "owner": "nix-community",
         "repo": "home-manager",
-        "rev": "9670de2921812bc4e0452f6e3efd8c859696c183",
+        "rev": "486595d2cf49cfcd649b58a284fa11ac0e34da22",
         "type": "github"
       },
       "original": {
@@ -43,12 +43,15 @@
       }
     },
     "nixos-hardware": {
+      "inputs": {
+        "nixpkgs": "nixpkgs"
+      },
       "locked": {
-        "lastModified": 1774018263,
-        "narHash": "sha256-HHYEwK1A22aSaxv2ibhMMkKvrDGKGlA/qObG4smrSqc=",
+        "lastModified": 1781168557,
+        "narHash": "sha256-LOnLQ2tpYF9gqIDDr3+j3DbpJJr/QCH6zPRT2GzEUOE=",
         "owner": "nixos",
         "repo": "nixos-hardware",
-        "rev": "2d4b4717b2534fad5c715968c1cece04a172b365",
+        "rev": "6358ff76821101c178e3ab4919a62799bfe3652e",
         "type": "github"
       },
       "original": {
@@ -60,27 +63,24 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1773821835,
-        "narHash": "sha256-TJ3lSQtW0E2JrznGVm8hOQGVpXjJyXY2guAxku2O9A4=",
-        "owner": "nixos",
-        "repo": "nixpkgs",
-        "rev": "b40629efe5d6ec48dd1efba650c797ddbd39ace0",
-        "type": "github"
+        "lastModified": 1767892417,
+        "narHash": "sha256-8bW3q88CEg2u4hSP66Vf4lpbLonHz7hqDNBMcCY7E9U=",
+        "rev": "3497aa5c9457a9d88d71fa93a4a8368816fbeeba",
+        "type": "tarball",
+        "url": "https://releases.nixos.org/nixos/unstable/nixos-26.05pre924538.3497aa5c9457/nixexprs.tar.xz"
       },
       "original": {
-        "owner": "nixos",
-        "ref": "nixos-unstable",
-        "repo": "nixpkgs",
-        "type": "github"
+        "type": "tarball",
+        "url": "https://channels.nixos.org/nixos-unstable/nixexprs.tar.xz"
       }
     },
     "nixpkgs-master": {
       "locked": {
-        "lastModified": 1774051532,
-        "narHash": "sha256-d3CGMweyYIcPuTj5BKq+1Lx4zwlgL31nVtN647tOZKo=",
+        "lastModified": 1781229721,
+        "narHash": "sha256-ORvqDbb/LYxiJljGIejapjkc/kJbVote2N1WSb9W45I=",
         "owner": "nixos",
         "repo": "nixpkgs",
-        "rev": "8620c0b5cc8fbe76502442181be1d0514bc3a1b7",
+        "rev": "173d0ad7a974f8543a9ab01d2271b2e290341b33",
         "type": "github"
       },
       "original": {
@@ -106,12 +106,28 @@
         "type": "github"
       }
     },
+    "nixpkgs_2": {
+      "locked": {
+        "lastModified": 1781074563,
+        "narHash": "sha256-md8WlXOlfnIeHeOScMTTHFyf2d6iaTwPl2apR5EQ3P4=",
+        "owner": "nixos",
+        "repo": "nixpkgs",
+        "rev": "9ae611a455b90cf061d8f332b977e387bda8e1ca",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nixos",
+        "ref": "nixos-unstable",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
     "root": {
       "inputs": {
         "firefox-addons": "firefox-addons",
         "home-manager": "home-manager",
         "nixos-hardware": "nixos-hardware",
-        "nixpkgs": "nixpkgs",
+        "nixpkgs": "nixpkgs_2",
         "nixpkgs-master": "nixpkgs-master",
         "nixpkgs-stable": "nixpkgs-stable",
         "sops-nix": "sops-nix",
@@ -125,11 +141,11 @@
         ]
       },
       "locked": {
-        "lastModified": 1773889674,
-        "narHash": "sha256-+ycaiVAk3MEshJTg35cBTUa0MizGiS+bgpYw/f8ohkg=",
+        "lastModified": 1780547341,
+        "narHash": "sha256-Gq8KNx5A7hBB3uGJaj6eQfLDIz5YdLu92gqBcvHvoUo=",
         "owner": "Mic92",
         "repo": "sops-nix",
-        "rev": "29b6519f3e0780452bca0ac0be4584f04ac16cc5",
+        "rev": "9ed65852b6257fbeae4355bc24ecfea307ca759a",
         "type": "github"
       },
       "original": {

frontend/.gitignore

@@ -1,24 +0,0 @@
-# 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

@@ -17,16 +17,41 @@
 
   python-env = final: _prev: {
     my_python = final.python314.withPackages (
-      ps: with ps; [
+      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
-        huggingface-hub
         mypy
+        numpy
         orjson
+        pgvector
         polars
         psycopg
         pydantic
@@ -40,6 +65,7 @@
         scalene
         sqlalchemy
         sqlalchemy
+        bm25s
         tenacity
         textual
         tiktoken

pyproject.toml

@@ -12,7 +12,6 @@ dependencies = [
     "alembic",
     "apprise",
     "apscheduler",
-    "huggingface-hub",
     "httpx",
     "python-multipart",
     "polars",
@@ -27,11 +26,7 @@ dependencies = [
 [project.scripts]
 database = "python.database_cli:app"
 van-inventory = "python.van_inventory.main:serve"
-prompt-bench = "python.prompt_bench.main:cli"
-prompt-bench-download = "python.prompt_bench.downloader:cli"
-finetune = "python.prompt_bench.finetune:cli"
-finetune-container = "python.prompt_bench.finetune_container:cli"
-build-finetune-dataset = "python.prompt_bench.build_finetune_dataset:cli"
+whisper-transcribe = "python.tools.whisper.transcribe:main"
 
 [dependency-groups]
 dev = [
@@ -56,6 +51,7 @@ lint.ignore = [
     "COM812", # (TEMP) conflicts when used with the formatter
     "ISC001", # (TEMP) conflicts when used with the formatter
     "S603",   # (PERM) This is known to cause a false positive
+    "S607",   # (PERM) This is becoming a consistent annoyance
 ]
 
 [tool.ruff.lint.per-file-ignores]
@@ -84,20 +80,10 @@ lint.ignore = [
 "python/congress_tracker/**" = [
     "TC003", # (perm) this creates issues because sqlalchemy uses these at runtime
 ]
-"python/eval_warnings/**" = [
-    "S607", # (perm) gh and git are expected on PATH in the runner environment
-]
-"python/prompt_bench/**" = [
-    "FBT002",  # (perm) typer requires boolean defaults for --flag/--no-flag options
-    "PLR0913", # (perm) typer CLIs naturally have many parameters
-    "S607",    # (perm) docker and nvidia-smi are expected on PATH
-]
+
 "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

@@ -1,50 +0,0 @@
-"""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

@@ -1,72 +0,0 @@
-"""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

@@ -1,153 +0,0 @@
-"""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

@@ -1,58 +0,0 @@
-"""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

@@ -0,0 +1,93 @@
+"""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

@@ -0,0 +1,200 @@
+"""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

@@ -0,0 +1,63 @@
+"""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

@@ -0,0 +1,54 @@
+"""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

@@ -1,100 +0,0 @@
-"""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

@@ -1,72 +0,0 @@
-"""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/routers/contact.py

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

python/data_science/__init__.py

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

python/data_science/ingest_congress.py

@@ -1,613 +0,0 @@
-"""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

@@ -1,247 +0,0 @@
-"""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

@@ -83,20 +83,6 @@ 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

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

python/ebook_search/answer.py

@@ -0,0 +1,57 @@
+"""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

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

python/ebook_search/api/bm25_tasks.py

@@ -0,0 +1,60 @@
+"""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

@@ -0,0 +1,79 @@
+"""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

@@ -0,0 +1,11 @@
+"""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

@@ -0,0 +1,107 @@
+"""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

@@ -0,0 +1,57 @@
+"""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

@@ -0,0 +1,58 @@
+"""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

@@ -0,0 +1,140 @@
+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

@@ -0,0 +1,57 @@
+<!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

@@ -0,0 +1,32 @@
+<!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

@@ -0,0 +1,31 @@
+<!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

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

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

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

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

@@ -0,0 +1,74 @@
+<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

@@ -0,0 +1,30 @@
+<!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

@@ -0,0 +1,13 @@
+"""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

@@ -0,0 +1,281 @@
+"""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

@@ -0,0 +1,117 @@
+"""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

@@ -0,0 +1,170 @@
+"""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

@@ -0,0 +1,95 @@
+"""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

@@ -0,0 +1,190 @@
+"""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

@@ -0,0 +1,143 @@
+"""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

@@ -0,0 +1,129 @@
+"""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

@@ -0,0 +1,377 @@
+"""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 (
+    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."""
+    corpus = load_bm25_corpus(config)
+    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

@@ -0,0 +1,36 @@
+"""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

@@ -0,0 +1,6 @@
+"""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/fastapi_tools/zstd_middleware.py

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

python/gitea.py

@@ -0,0 +1,347 @@
+"""Small Gitea API client for repository automation."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Self
+from urllib.parse import quote
+
+import httpx
+
+DEFAULT_PAGE_SIZE = 100
+EXPECTED_NO_CONTENT = 204
+EXPECTED_CREATED = 201
+EXPECTED_OK = 200
+
+
+@dataclass(frozen=True)
+class CreatedIssue:
+    """Issue data returned by Gitea."""
+
+    number: int | None
+    html_url: str | None
+    title: str
+
+
+@dataclass(frozen=True)
+class PullRequest:
+    """Pull request data returned by Gitea."""
+
+    number: int
+    title: str
+    html_url: str | None
+    labels: tuple[str, ...]
+    head_branch: str | None
+    base_branch: str | None
+
+
+@dataclass(frozen=True)
+class WorkflowJob:
+    """Workflow job data returned by Gitea Actions."""
+
+    id: int
+    name: str
+    run_id: int | None
+    status: str | None
+    conclusion: str | None
+
+
+class GiteaError(RuntimeError):
+    """Raised when Gitea rejects an API request."""
+
+
+def split_repo_name(repo: str) -> tuple[str, str]:
+    """Split an owner/repo string into its parts."""
+    owner, separator, repo_name = repo.partition("/")
+    if not separator or not owner or not repo_name:
+        msg = f"Invalid repository name: {repo}"
+        raise ValueError(msg)
+    return owner, repo_name
+
+
+class GiteaClient:
+    """HTTP client for the subset of Gitea APIs used in this repository."""
+
+    def __init__(
+        self,
+        *,
+        base_url: str,
+        token: str,
+        timeout: int = 30,
+        transport: httpx.BaseTransport | None = None,
+    ) -> None:
+        """Initialize the Gitea client."""
+        self._client = httpx.Client(
+            base_url=base_url.rstrip("/"),
+            timeout=timeout,
+            headers={"Authorization": f"token {token}"},
+            transport=transport,
+        )
+
+    def create_issue(
+        self,
+        *,
+        owner: str,
+        repo: str,
+        title: str,
+        body: str,
+        labels: list[int] | None = None,
+    ) -> CreatedIssue:
+        """Create a Gitea issue."""
+        payload: dict[str, object] = {"title": title, "body": body, "labels": labels or []}
+        response = self._request(
+            "POST",
+            f"/api/v1/repos/{owner}/{repo}/issues",
+            expected_statuses={EXPECTED_CREATED},
+            json=payload,
+        )
+        data = response.json()
+        return CreatedIssue(
+            number=_optional_int(data.get("number")),
+            html_url=_optional_str(data.get("html_url")),
+            title=str(data.get("title", title)),
+        )
+
+    def resolve_label_ids(self, *, owner: str, repo: str, labels: list[str]) -> list[int]:
+        """Resolve label names to Gitea label IDs."""
+        if not labels:
+            return []
+
+        available_labels: dict[str, int] = {}
+        page = 1
+        while True:
+            response = self._request(
+                "GET",
+                f"/api/v1/repos/{owner}/{repo}/labels",
+                params={"page": page, "limit": DEFAULT_PAGE_SIZE},
+            )
+            batch = response.json()
+            if not batch:
+                break
+            for label in batch:
+                label_name = str(label.get("name", ""))
+                label_id = _optional_int(label.get("id"))
+                if label_name and label_id is not None:
+                    available_labels[label_name] = label_id
+            if len(batch) < DEFAULT_PAGE_SIZE:
+                break
+            page += 1
+
+        missing = [label for label in labels if label not in available_labels]
+        if missing:
+            missing_names = ", ".join(sorted(missing))
+            msg = f"Missing Gitea labels: {missing_names}"
+            raise GiteaError(msg)
+
+        return [available_labels[label] for label in labels]
+
+    def list_open_pull_requests(
+        self,
+        *,
+        owner: str,
+        repo: str,
+        labels: list[str] | None = None,
+        head: str | None = None,
+    ) -> list[PullRequest]:
+        """List open pull requests for a repository."""
+        expected_labels = set(labels or [])
+        pull_requests: list[PullRequest] = []
+        page = 1
+        while True:
+            response = self._request(
+                "GET",
+                f"/api/v1/repos/{owner}/{repo}/pulls",
+                params={"state": "open", "page": page, "limit": DEFAULT_PAGE_SIZE},
+            )
+            batch = response.json()
+            if not batch:
+                break
+
+            for item in batch:
+                pull_request = _pull_request_from_api(item)
+                if head and pull_request.head_branch != head:
+                    continue
+                if expected_labels and not expected_labels.issubset(set(pull_request.labels)):
+                    continue
+                pull_requests.append(pull_request)
+
+            if len(batch) < DEFAULT_PAGE_SIZE:
+                break
+            page += 1
+
+        return pull_requests
+
+    def create_pull_request(
+        self,
+        *,
+        owner: str,
+        repo: str,
+        title: str,
+        body: str,
+        head: str,
+        base: str,
+        labels: list[str] | None = None,
+    ) -> PullRequest:
+        """Create a pull request."""
+        payload: dict[str, object] = {
+            "title": title,
+            "body": body,
+            "head": head,
+            "base": base,
+        }
+        if labels:
+            payload["labels"] = self.resolve_label_ids(owner=owner, repo=repo, labels=labels)
+
+        response = self._request(
+            "POST",
+            f"/api/v1/repos/{owner}/{repo}/pulls",
+            expected_statuses={EXPECTED_CREATED},
+            json=payload,
+        )
+        return _pull_request_from_api(response.json())
+
+    def merge_pull_request(
+        self,
+        *,
+        owner: str,
+        repo: str,
+        number: int,
+        merge_method: str = "rebase",
+        head_commit_id: str | None = None,
+        delete_branch_after_merge: bool = False,
+    ) -> None:
+        """Merge a pull request."""
+        payload: dict[str, object] = {
+            "Do": merge_method,
+            "delete_branch_after_merge": delete_branch_after_merge,
+        }
+        if head_commit_id:
+            payload["head_commit_id"] = head_commit_id
+
+        self._request(
+            "POST",
+            f"/api/v1/repos/{owner}/{repo}/pulls/{number}/merge",
+            json=payload,
+        )
+
+    def dispatch_workflow(self, *, owner: str, repo: str, workflow_id: str, ref: str) -> None:
+        """Trigger a workflow_dispatch run."""
+        workflow_path = quote(workflow_id, safe="")
+        self._request(
+            "POST",
+            f"/api/v1/repos/{owner}/{repo}/actions/workflows/{workflow_path}/dispatches",
+            expected_statuses={EXPECTED_OK, EXPECTED_NO_CONTENT},
+            json={"ref": ref},
+        )
+
+    def list_run_jobs(self, *, owner: str, repo: str, run_id: str | int) -> list[WorkflowJob]:
+        """List workflow jobs for a specific run."""
+        jobs: list[WorkflowJob] = []
+        page = 1
+        while True:
+            response = self._request(
+                "GET",
+                f"/api/v1/repos/{owner}/{repo}/actions/jobs",
+                params={"page": page, "limit": DEFAULT_PAGE_SIZE},
+            )
+            payload = response.json()
+            batch = payload.get("jobs", [])
+            if not batch:
+                break
+
+            for item in batch:
+                if str(item.get("run_id")) != str(run_id):
+                    continue
+                jobs.append(_workflow_job_from_api(item))
+
+            if len(batch) < DEFAULT_PAGE_SIZE:
+                break
+            page += 1
+
+        return jobs
+
+    def download_job_logs(self, *, owner: str, repo: str, job_id: int) -> str:
+        """Download logs for a workflow job."""
+        response = self._request(
+            "GET",
+            f"/api/v1/repos/{owner}/{repo}/actions/jobs/{job_id}/logs",
+        )
+        return response.text
+
+    def close(self) -> None:
+        """Close the underlying HTTP client."""
+        self._client.close()
+
+    def __enter__(self) -> Self:
+        """Enter the context manager."""
+        return self
+
+    def __exit__(self, *args: object) -> None:
+        """Close the HTTP client."""
+        self.close()
+
+    def _request(
+        self,
+        method: str,
+        path: str,
+        *,
+        expected_statuses: set[int] | None = None,
+        **kwargs: object,
+    ) -> httpx.Response:
+        """Send an HTTP request and validate the response status."""
+        response = self._client.request(method, path, **kwargs)
+        statuses = expected_statuses or {EXPECTED_OK}
+        if response.status_code not in statuses:
+            msg = f"Gitea request failed ({response.status_code}): {response.text}"
+            raise GiteaError(msg)
+        return response
+
+
+def _pull_request_from_api(data: dict[str, object]) -> PullRequest:
+    """Convert Gitea API pull-request data into a dataclass."""
+    number = _optional_int(data.get("number")) or _optional_int(data.get("index"))
+    if number is None:
+        msg = "Gitea pull request payload is missing a number"
+        raise GiteaError(msg)
+
+    labels = tuple(str(label.get("name", "")) for label in data.get("labels", []))
+    head = data.get("head", {})
+    base = data.get("base", {})
+    return PullRequest(
+        number=number,
+        title=str(data.get("title", "")),
+        html_url=_optional_str(data.get("html_url")),
+        labels=tuple(label for label in labels if label),
+        head_branch=_optional_str(head.get("ref")) or _optional_str(data.get("head_branch")),
+        base_branch=_optional_str(base.get("ref")) or _optional_str(data.get("base_branch")),
+    )
+
+
+def _workflow_job_from_api(data: dict[str, object]) -> WorkflowJob:
+    """Convert Gitea API workflow-job data into a dataclass."""
+    job_id = _optional_int(data.get("id"))
+    if job_id is None:
+        msg = "Gitea workflow job payload is missing an ID"
+        raise GiteaError(msg)
+
+    return WorkflowJob(
+        id=job_id,
+        name=str(data.get("name", "")),
+        run_id=_optional_int(data.get("run_id")),
+        status=_optional_str(data.get("status")),
+        conclusion=_optional_str(data.get("conclusion")),
+    )
+
+
+def _optional_int(value: object) -> int | None:
+    """Convert an API value to an integer when present."""
+    if value is None:
+        return None
+    return int(value)
+
+
+def _optional_str(value: object) -> str | None:
+    """Convert an API value to a string when present."""
+    if value is None:
+        return None
+    return str(value)

python/gitea_flake_lock.py

@@ -0,0 +1,148 @@
+"""Automation helpers for flake.lock pull requests on Gitea."""
+
+from __future__ import annotations
+
+import subprocess
+from os import getenv
+from typing import Annotated
+
+import typer
+
+from python.gitea import GiteaClient, PullRequest, split_repo_name
+
+DEFAULT_BASE_BRANCH = "main"
+DEFAULT_BRANCH = "automation/update-flake-lock"
+DEFAULT_GITEA_URL = "https://gitea.tmmworkshop.com"
+PR_LABELS = ["dependencies", "automated", "flake_lock_update"]
+PR_CHECK_WORKFLOWS = ["build_systems.yml", "treefmt.yml", "pytest.yml"]
+PR_TITLE = "Update flake.lock"
+PR_BODY = "Automated flake.lock update."
+
+app = typer.Typer(add_completion=False)
+
+
+def run_cmd(cmd: list[str], *, check: bool = True) -> subprocess.CompletedProcess[str]:
+    """Run a subprocess command."""
+    return subprocess.run(cmd, capture_output=True, text=True, check=check)
+
+
+def ensure_flake_lock_pull_request(
+    client: GiteaClient,
+    *,
+    owner: str,
+    repo: str,
+    branch: str,
+    base: str,
+) -> PullRequest:
+    """Return an existing flake.lock PR for the branch or create one."""
+    pull_requests = client.list_open_pull_requests(owner=owner, repo=repo, head=branch)
+    if pull_requests:
+        return pull_requests[0]
+
+    return client.create_pull_request(
+        owner=owner,
+        repo=repo,
+        title=PR_TITLE,
+        body=PR_BODY,
+        head=branch,
+        base=base,
+        labels=PR_LABELS,
+    )
+
+
+def find_flake_lock_pull_request(client: GiteaClient, *, owner: str, repo: str) -> PullRequest | None:
+    """Find the first open flake.lock pull request."""
+    pull_requests = client.list_open_pull_requests(owner=owner, repo=repo, labels=["flake_lock_update"])
+    if not pull_requests:
+        return None
+    return pull_requests[0]
+
+
+def dispatch_pull_request_checks(client: GiteaClient, *, owner: str, repo: str, branch: str) -> None:
+    """Dispatch the workflows that normally run for pull requests."""
+    for workflow in PR_CHECK_WORKFLOWS:
+        client.dispatch_workflow(owner=owner, repo=repo, workflow_id=workflow, ref=branch)
+
+
+def has_worktree_changes() -> bool:
+    """Return whether `flake.lock` has worktree changes."""
+    result = run_cmd(["git", "diff", "--quiet", "--", "flake.lock"], check=False)
+    return result.returncode != 0
+
+
+def commit_flake_lock_update(*, branch: str) -> None:
+    """Commit the updated lock file to the automation branch."""
+    run_cmd(["git", "config", "user.name", "gitea-actions[bot]"])
+    run_cmd(["git", "config", "user.email", "gitea-actions@tmmworkshop.com"])
+    run_cmd(["git", "checkout", "-B", branch])
+    run_cmd(["git", "add", "flake.lock"])
+    run_cmd(["git", "commit", "-m", "chore: update flake.lock"])
+
+
+def push_branch(*, branch: str) -> None:
+    """Push the automation branch to origin."""
+    run_cmd(["git", "push", "origin", f"HEAD:{branch}", "--force"])
+
+
+def _required_gitea_token() -> str:
+    """Read the required Gitea token from the environment."""
+    token = getenv("GITEA_TOKEN")
+    if token:
+        return token
+
+    msg = "GITEA_TOKEN environment variable is required"
+    raise RuntimeError(msg)
+
+
+@app.command()
+def update(
+    repo: Annotated[str, typer.Option("--repo", help="Gitea repository in owner/repo form")],
+    base: Annotated[str, typer.Option("--base", help="Base branch")] = DEFAULT_BASE_BRANCH,
+    branch: Annotated[str, typer.Option("--branch", help="Automation branch")] = DEFAULT_BRANCH,
+) -> None:
+    """Commit flake.lock changes and ensure a pull request exists."""
+    if not has_worktree_changes():
+        typer.echo("No flake.lock changes detected")
+        return
+
+    commit_flake_lock_update(branch=branch)
+    push_branch(branch=branch)
+
+    owner, repo_name = split_repo_name(repo)
+    with GiteaClient(
+        base_url=getenv("GITEA_URL", DEFAULT_GITEA_URL),
+        token=_required_gitea_token(),
+    ) as client:
+        pull_request = ensure_flake_lock_pull_request(
+            client,
+            owner=owner,
+            repo=repo_name,
+            branch=branch,
+            base=base,
+        )
+        # We can remove this if Gitea fixes the following issue:
+        # https://github.com/go-gitea/gitea/issues/33963
+        dispatch_pull_request_checks(client, owner=owner, repo=repo_name, branch=branch)
+    typer.echo(pull_request.html_url or f"Pull request #{pull_request.number}")
+
+
+@app.command()
+def merge(
+    repo: Annotated[str, typer.Option("--repo", help="Gitea repository in owner/repo form")],
+) -> None:
+    """Merge the first open flake.lock pull request."""
+    owner, repo_name = split_repo_name(repo)
+    with GiteaClient(
+        base_url=getenv("GITEA_URL", DEFAULT_GITEA_URL),
+        token=_required_gitea_token(),
+    ) as client:
+        pull_request = find_flake_lock_pull_request(client, owner=owner, repo=repo_name)
+        if not pull_request:
+            typer.echo("No open PR found with label flake_lock_update")
+            return
+        client.merge_pull_request(owner=owner, repo=repo_name, number=pull_request.number, merge_method="rebase")
+    typer.echo(f"Merged PR #{pull_request.number}")
+
+
+if __name__ == "__main__":
+    app()

python/orm/__init__.py

@@ -1,13 +1,9 @@
 """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

@@ -31,8 +31,24 @@ 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(*, name: str = "POSTGRES", pool_pre_ping: bool = True) -> Engine:
-    """Create a SQLAlchemy engine from environment variables."""
+def get_postgres_engine(
+    *,
+    name: str = "POSTGRES",
+    pool_pre_ping: bool = True,
+    vector_engine: bool = False,
+) -> Engine:
+    """Create a SQLAlchemy engine from environment variables.
+
+    Args:
+        name (str, 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(
@@ -44,8 +60,14 @@ def get_postgres_engine(*, name: str = "POSTGRES", pool_pre_ping: bool = True) -
         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

@@ -1,11 +0,0 @@
-"""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

@@ -1,52 +0,0 @@
-"""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

@@ -1,14 +0,0 @@
-"""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

@@ -1,66 +0,0 @@
-"""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

@@ -1,66 +0,0 @@
-"""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

@@ -1,79 +0,0 @@
-"""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

@@ -1,16 +0,0 @@
-"""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

@@ -1,11 +0,0 @@
-"""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

@@ -1,33 +0,0 @@
-"""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

@@ -1,17 +0,0 @@
-"""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

@@ -1,71 +0,0 @@
-"""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

@@ -1,13 +0,0 @@
-"""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

@@ -2,6 +2,7 @@
 
 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,
@@ -10,11 +11,30 @@ 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

@@ -0,0 +1,55 @@
+"""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

@@ -0,0 +1,138 @@
+"""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

@@ -1,16 +0,0 @@
-"""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

@@ -1,52 +0,0 @@
-"""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

@@ -1,62 +0,0 @@
-"""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/prompt_bench/Dockerfile.finetune

@@ -1,25 +0,0 @@
-# Unsloth fine-tuning container for Qwen 3.5 4B on RTX 3090.
-#
-# Build:
-#   docker build -f python/prompt_bench/Dockerfile.finetune -t bill-finetune .
-#
-# Run:
-#   docker run --rm --device=nvidia.com/gpu=all --ipc=host \
-#     -v $(pwd)/output:/workspace/output \
-#     -v $(pwd)/output/finetune_dataset.jsonl:/workspace/dataset.jsonl:ro \
-#     -v /zfs/models/hf:/models \
-#     bill-finetune \
-#     --dataset /workspace/dataset.jsonl \
-#     --output-dir /workspace/output/qwen-bill-summarizer
-
-FROM ghcr.io/unslothai/unsloth:latest
-
-RUN pip install --no-cache-dir typer
-
-WORKDIR /workspace
-COPY python/prompt_bench/finetune.py python/prompt_bench/finetune.py
-COPY python/prompt_bench/summarization_prompts.py python/prompt_bench/summarization_prompts.py
-COPY python/prompt_bench/__init__.py python/prompt_bench/__init__.py
-COPY python/__init__.py python/__init__.py
-
-ENTRYPOINT ["python", "-m", "python.prompt_bench.finetune"]

python/prompt_bench/__init__.py

@@ -1 +0,0 @@
-"""Prompt benchmarking system for evaluating LLMs via vLLM."""

python/prompt_bench/batch_bill_summarizer.py

@@ -1,233 +0,0 @@
-"""Submit an OpenAI Batch API bill-summarization job over compressed text.
-
-Reads the first N bills from a CSV with a `text_content` column, compresses
-each via `bill_token_compression.compress_bill_text`, builds a JSONL file of
-summarization requests, and submits it as an asynchronous Batch API job
-against `/v1/chat/completions`. Also writes a CSV of per-bill pre/post-
-compression token counts.
-"""
-
-from __future__ import annotations
-
-import csv
-import json
-import logging
-import re
-import sys
-from os import getenv
-from pathlib import Path
-from typing import Annotated
-
-import httpx
-import typer
-from tiktoken import Encoding, get_encoding
-
-from python.prompt_bench.bill_token_compression import compress_bill_text
-from python.prompt_bench.summarization_prompts import SUMMARIZATION_SYSTEM_PROMPT, SUMMARIZATION_USER_TEMPLATE
-
-logger = logging.getLogger(__name__)
-
-OPENAI_API_BASE = "https://api.openai.com/v1"
-
-
-def load_bills(csv_path: Path, count: int = 0) -> list[tuple[str, str]]:
-    """Return (bill_id, text_content) tuples with non-empty text.
-
-    If `count` is 0 or negative, all rows are returned.
-    """
-    csv.field_size_limit(sys.maxsize)
-    bills: list[tuple[str, str]] = []
-    with csv_path.open(newline="", encoding="utf-8") as handle:
-        reader = csv.DictReader(handle)
-        for row in reader:
-            text_content = (row.get("text_content") or "").strip()
-            if not text_content:
-                continue
-            bill_id = row.get("bill_id") or row.get("id") or f"row-{len(bills)}"
-            version_code = row.get("version_code") or ""
-            unique_id = f"{bill_id}-{version_code}" if version_code else bill_id
-            bills.append((unique_id, text_content))
-            if count > 0 and len(bills) >= count:
-                break
-    return bills
-
-
-def safe_filename(value: str) -> str:
-    """Make a string safe for use as a filename or batch custom_id."""
-    return re.sub(r"[^A-Za-z0-9._-]+", "_", value).strip("_") or "unnamed"
-
-
-def build_request(custom_id: str, model: str, bill_text: str) -> dict:
-    """Build one OpenAI batch request line."""
-    return {
-        "custom_id": custom_id,
-        "method": "POST",
-        "url": "/v1/chat/completions",
-        "body": {
-            "model": model,
-            "messages": [
-                {"role": "system", "content": SUMMARIZATION_SYSTEM_PROMPT},
-                {"role": "user", "content": SUMMARIZATION_USER_TEMPLATE.format(text_content=bill_text)},
-            ],
-        },
-    }
-
-
-def write_jsonl(path: Path, lines: list[dict]) -> None:
-    """Write a list of dicts as JSONL."""
-    with path.open("w", encoding="utf-8") as handle:
-        for line in lines:
-            handle.write(json.dumps(line, ensure_ascii=False))
-            handle.write("\n")
-
-
-def upload_file(client: httpx.Client, path: Path) -> str:
-    """Upload a JSONL file to the OpenAI Files API and return its file id."""
-    with path.open("rb") as handle:
-        response = client.post(
-            f"{OPENAI_API_BASE}/files",
-            files={"file": (path.name, handle, "application/jsonl")},
-            data={"purpose": "batch"},
-        )
-    response.raise_for_status()
-    return response.json()["id"]
-
-
-def prepare_requests(
-    bills: list[tuple[str, str]],
-    *,
-    model: str,
-    encoder: Encoding,
-) -> tuple[list[dict], list[dict]]:
-    """Build (request_lines, token_rows) from bills.
-
-    Each bill is compressed before being turned into a request line.
-    Each `token_rows` entry has chars + token counts for one bill so the caller
-    can write a per-bill CSV.
-    """
-    request_lines: list[dict] = []
-    token_rows: list[dict] = []
-    for bill_id, text_content in bills:
-        raw_token_count = len(encoder.encode(text_content))
-        compressed_text = compress_bill_text(text_content)
-        compressed_token_count = len(encoder.encode(compressed_text))
-        token_rows.append(
-            {
-                "bill_id": bill_id,
-                "raw_chars": len(text_content),
-                "compressed_chars": len(compressed_text),
-                "raw_tokens": raw_token_count,
-                "compressed_tokens": compressed_token_count,
-                "token_ratio": (compressed_token_count / raw_token_count) if raw_token_count else None,
-            },
-        )
-        safe_id = safe_filename(bill_id)
-        request_lines.append(build_request(safe_id, model, compressed_text))
-    return request_lines, token_rows
-
-
-def write_token_csv(path: Path, token_rows: list[dict]) -> tuple[int, int]:
-    """Write per-bill token counts to CSV. Returns (raw_total, compressed_total)."""
-    with path.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(
-            handle,
-            fieldnames=["bill_id", "raw_chars", "compressed_chars", "raw_tokens", "compressed_tokens", "token_ratio"],
-        )
-        writer.writeheader()
-        writer.writerows(token_rows)
-    raw_total = sum(row["raw_tokens"] for row in token_rows)
-    compressed_total = sum(row["compressed_tokens"] for row in token_rows)
-    return raw_total, compressed_total
-
-
-def create_batch(client: httpx.Client, input_file_id: str, description: str) -> dict:
-    """Create a batch job and return its full response payload."""
-    response = client.post(
-        f"{OPENAI_API_BASE}/batches",
-        json={
-            "input_file_id": input_file_id,
-            "endpoint": "/v1/chat/completions",
-            "completion_window": "24h",
-            "metadata": {"description": description},
-        },
-    )
-    response.raise_for_status()
-    return response.json()
-
-
-def main(
-    csv_path: Annotated[Path, typer.Option("--csv", help="Bills CSV path")] = Path("bills.csv"),
-    output_dir: Annotated[Path, typer.Option("--output-dir", help="Where to write JSONL + metadata")] = Path(
-        "output/openai_batch",
-    ),
-    model: Annotated[str, typer.Option(help="OpenAI model id")] = "gpt-5-mini",
-    count: Annotated[int, typer.Option(help="Max bills to process, 0 = all")] = 0,
-    log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
-) -> None:
-    """Submit an OpenAI Batch job of compressed bill summaries."""
-    logging.basicConfig(level=log_level, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
-
-    api_key = getenv("CLOSEDAI_TOKEN") or getenv("OPENAI_API_KEY")
-    if not api_key:
-        message = "Neither CLOSEDAI_TOKEN nor OPENAI_API_KEY is set"
-        raise typer.BadParameter(message)
-    if not csv_path.is_file():
-        message = f"CSV not found: {csv_path}"
-        raise typer.BadParameter(message)
-
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    logger.info("Loading %d bills from %s", count, csv_path)
-    bills = load_bills(csv_path, count)
-    if len(bills) < count:
-        logger.warning("Only %d bills available (requested %d)", len(bills), count)
-
-    encoder = get_encoding("o200k_base")
-    request_lines, token_rows = prepare_requests(bills, model=model, encoder=encoder)
-
-    token_csv_path = output_dir / "token_counts.csv"
-    raw_tokens_total, compressed_tokens_total = write_token_csv(token_csv_path, token_rows)
-    logger.info(
-        "Token counts: raw=%d compressed=%d ratio=%.3f -> %s",
-        raw_tokens_total,
-        compressed_tokens_total,
-        (compressed_tokens_total / raw_tokens_total) if raw_tokens_total else 0.0,
-        token_csv_path,
-    )
-
-    jsonl_path = output_dir / "requests.jsonl"
-    write_jsonl(jsonl_path, request_lines)
-    logger.info("Wrote %s (%d bills)", jsonl_path, len(request_lines))
-
-    headers = {"Authorization": f"Bearer {api_key}"}
-    with httpx.Client(headers=headers, timeout=httpx.Timeout(300.0)) as client:
-        logger.info("Uploading JSONL")
-        file_id = upload_file(client, jsonl_path)
-        logger.info("Uploaded: %s", file_id)
-
-        logger.info("Creating batch")
-        batch = create_batch(client, file_id, f"compressed bill summaries x{len(request_lines)} ({model})")
-        logger.info("Batch created: %s", batch["id"])
-
-    metadata = {
-        "model": model,
-        "count": len(bills),
-        "jsonl": str(jsonl_path),
-        "input_file_id": file_id,
-        "batch_id": batch["id"],
-        "raw_tokens_total": raw_tokens_total,
-        "compressed_tokens_total": compressed_tokens_total,
-        "batch": batch,
-    }
-    metadata_path = output_dir / "batch.json"
-    metadata_path.write_text(json.dumps(metadata, indent=2))
-    logger.info("Wrote metadata to %s", metadata_path)
-
-
-def cli() -> None:
-    """Typer entry point."""
-    typer.run(main)
-
-
-if __name__ == "__main__":
-    cli()

python/prompt_bench/bill_token_compression.py

@@ -1,162 +0,0 @@
-"""Lossless-ish text compression for Congressional bill text."""
-
-from __future__ import annotations
-
-import re
-
-STATES = (
-    "Alabama",
-    "Alaska",
-    "Arizona",
-    "Arkansas",
-    "California",
-    "Colorado",
-    "Connecticut",
-    "Delaware",
-    "Florida",
-    "Georgia",
-    "Hawaii",
-    "Idaho",
-    "Illinois",
-    "Indiana",
-    "Iowa",
-    "Kansas",
-    "Kentucky",
-    "Louisiana",
-    "Maine",
-    "Maryland",
-    "Massachusetts",
-    "Michigan",
-    "Minnesota",
-    "Mississippi",
-    "Missouri",
-    "Montana",
-    "Nebraska",
-    "Nevada",
-    "New Hampshire",
-    "New Jersey",
-    "New Mexico",
-    "New York",
-    "North Carolina",
-    "North Dakota",
-    "Ohio",
-    "Oklahoma",
-    "Oregon",
-    "Pennsylvania",
-    "Rhode Island",
-    "South Carolina",
-    "South Dakota",
-    "Tennessee",
-    "Texas",
-    "Utah",
-    "Vermont",
-    "Virginia",
-    "Washington",
-    "West Virginia",
-    "Wisconsin",
-    "Wyoming",
-    "Puerto Rico",
-    "Guam",
-    "American Samoa",
-    "District of Columbia",
-    "US Virgin Islands",
-)
-STATE_PATTERNS = [(re.compile(re.escape(state), re.IGNORECASE), state) for state in STATES]
-
-
-def normalize_state_names(text: str) -> str:
-    """Replace any casing of state names with title case."""
-    for pattern, replacement in STATE_PATTERNS:
-        text = pattern.sub(replacement, text)
-    return text
-
-
-def strip_number_commas(text: str) -> str:
-    """Remove commas from numeric thousands separators."""
-    return re.sub(r"(\d{1,3}(?:,\d{3})+)", lambda match: match.group().replace(",", ""), text)
-
-
-def strip_horizontal_rules(text: str) -> str:
-    """Remove ASCII horizontal-rule lines built from underscores, dashes, equals, or asterisks."""
-    return re.sub(r"^\s*[_\-=\*]{3,}\s*$", "", text, flags=re.MULTILINE)
-
-
-def collapse_double_dashes(text: str) -> str:
-    """Replace ``--`` em-dash stand-ins with a single space so they don't tokenize oddly."""
-    return text.replace("--", " ")
-
-
-def collapse_inline_whitespace(text: str) -> str:
-    """Collapse runs of horizontal whitespace (spaces, tabs) into a single space, leaving newlines intact."""
-    return re.sub(r"[^\S\n]+", " ", text)
-
-
-def collapse_blank_lines(text: str) -> str:
-    """Collapse three-or-more consecutive newlines down to a blank-line separator."""
-    return re.sub(r"\n{3,}", "\n\n", text)
-
-
-def trim_line_edges(text: str) -> str:
-    """Strip spaces immediately before and after newline characters on every line."""
-    text = re.sub(r" +\n", "\n", text)
-    return re.sub(r"\n +", "\n", text)
-
-
-def shorten_section_markers(text: str) -> str:
-    """Rewrite ``Sec. 12.`` style section headings as the more compact ``SEC 12``."""
-    return re.sub(r"(?i)sec\.\s*(\d+[a-zA-Z]?)\.", r"SEC \1", text)
-
-
-def unwrap_parens(text: str) -> str:
-    """Strip parentheses around short alphanumeric labels like ``(a)`` or ``(12)``."""
-    return re.sub(r"\(([a-zA-Z0-9]+)\)", r"\1", text)
-
-
-def strip_typeset_quotes(text: str) -> str:
-    """Remove the `` and '' typeset quote markers used in the GPO bill format."""
-    return text.replace("``", "").replace("''", "")
-
-
-def normalize_usc_acronym(text: str) -> str:
-    """Collapse ``U.S.C.`` to ``USC`` to save tokens on the common citation."""
-    return text.replace("U.S.C.", "USC")
-
-
-def normalize_us_acronym(text: str) -> str:
-    """Normalize the various ``U.S.``/``U. S.`` spellings to the bare ``US`` form."""
-    for acronym in ("U. S.", "u. s.", "U.S. ", "u.s. "):
-        text = text.replace(acronym, "US ")
-    return text
-
-
-def collapse_ellipses(text: str) -> str:
-    """Collapse runs of two-or-more periods (``...``, ``....``) down to a single period."""
-    return re.sub(r"\.{2,}", ".", text)
-
-
-COMPRESSION_STEPS = (
-    strip_horizontal_rules,
-    collapse_double_dashes,
-    collapse_inline_whitespace,
-    collapse_blank_lines,
-    trim_line_edges,
-    shorten_section_markers,
-    unwrap_parens,
-    strip_typeset_quotes,
-    normalize_usc_acronym,
-    normalize_us_acronym,
-    strip_number_commas,
-    collapse_ellipses,
-    normalize_state_names,
-)
-
-
-def compress_bill_text(text: str) -> str:
-    """Apply lossless-ish whitespace and boilerplate compression to bill text.
-
-    Runs every transform in :data:`COMPRESSION_STEPS` in order, then strips
-    leading/trailing whitespace from the final result.
-    """
-    for step in COMPRESSION_STEPS:
-        text = step(text)
-    return text.strip()

python/prompt_bench/compresion_test.py

@@ -1,236 +0,0 @@
-"""Run two interactive OpenAI chat-completion sweeps over bill text.
-
-Reads the first N bills from a CSV with a `text_content` column and sends two
-sweeps through `/v1/chat/completions` concurrently — one with the raw bill
-text, one with the compressed bill text. Each request's prompt is saved to
-disk alongside the OpenAI response id so the prompts and responses can be
-correlated later.
-"""
-
-from __future__ import annotations
-
-import csv
-import json
-import logging
-import re
-import sys
-import time
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from os import getenv
-from pathlib import Path
-from typing import Annotated
-
-import httpx
-import typer
-
-from python.prompt_bench.bill_token_compression import compress_bill_text
-from python.prompt_bench.summarization_prompts import SUMMARIZATION_SYSTEM_PROMPT, SUMMARIZATION_USER_TEMPLATE
-
-logger = logging.getLogger(__name__)
-
-OPENAI_API_BASE = "https://api.openai.com/v1"
-DEFAULT_MODEL = "gpt-5.4-mini"
-DEFAULT_COUNT = 100
-SEED = 42
-
-
-def load_bills(csv_path: Path, count: int) -> list[tuple[str, str]]:
-    """Return up to `count` (bill_id, text_content) tuples with non-empty text."""
-    csv.field_size_limit(sys.maxsize)
-    bills: list[tuple[str, str]] = []
-    with csv_path.open(newline="", encoding="utf-8") as handle:
-        reader = csv.DictReader(handle)
-        for row in reader:
-            text_content = (row.get("text_content") or "").strip()
-            if not text_content:
-                continue
-            bill_id = row.get("bill_id") or row.get("id") or f"row-{len(bills)}"
-            version_code = row.get("version_code") or ""
-            unique_id = f"{bill_id}-{version_code}" if version_code else bill_id
-            bills.append((unique_id, text_content))
-            if len(bills) >= count:
-                break
-    return bills
-
-
-def build_messages(bill_text: str) -> list[dict]:
-    """Return the system + user message pair for a bill."""
-    return [
-        {"role": "system", "content": SUMMARIZATION_SYSTEM_PROMPT},
-        {"role": "user", "content": SUMMARIZATION_USER_TEMPLATE.format(text_content=bill_text)},
-    ]
-
-
-def safe_filename(value: str) -> str:
-    """Make a string safe for use as a filename."""
-    return re.sub(r"[^A-Za-z0-9._-]+", "_", value).strip("_") or "unnamed"
-
-
-def run_one_request(
-    client: httpx.Client,
-    *,
-    bill_id: str,
-    label: str,
-    bill_text: str,
-    model: str,
-    output_path: Path,
-) -> tuple[bool, float, str | None]:
-    """Send one chat-completion request and persist prompt + response.
-
-    Returns (success, elapsed_seconds, response_id).
-    """
-    messages = build_messages(bill_text)
-    payload = {
-        "model": model,
-        "messages": messages,
-        "seed": SEED,
-    }
-    start = time.monotonic()
-    record: dict = {
-        "bill_id": bill_id,
-        "label": label,
-        "model": model,
-        "seed": SEED,
-        "input_chars": len(bill_text),
-        "messages": messages,
-    }
-    try:
-        response = client.post(f"{OPENAI_API_BASE}/chat/completions", json=payload)
-        response.raise_for_status()
-        body = response.json()
-    except httpx.HTTPStatusError as error:
-        elapsed = time.monotonic() - start
-        record["error"] = {
-            "status_code": error.response.status_code,
-            "body": error.response.text,
-            "elapsed_seconds": elapsed,
-        }
-        output_path.write_text(json.dumps(record, ensure_ascii=False, indent=2))
-        logger.exception("HTTP error for %s/%s after %.2fs", label, bill_id, elapsed)
-        return False, elapsed, None
-    except Exception as error:
-        elapsed = time.monotonic() - start
-        record["error"] = {"message": str(error), "elapsed_seconds": elapsed}
-        output_path.write_text(json.dumps(record, ensure_ascii=False, indent=2))
-        logger.exception("Failed: %s/%s after %.2fs", label, bill_id, elapsed)
-        return False, elapsed, None
-
-    elapsed = time.monotonic() - start
-    response_id = body.get("id")
-    record["response_id"] = response_id
-    record["elapsed_seconds"] = elapsed
-    record["usage"] = body.get("usage")
-    record["response"] = body
-    output_path.write_text(json.dumps(record, ensure_ascii=False, indent=2))
-    logger.info("Done: %s/%s id=%s in %.2fs", label, bill_id, response_id, elapsed)
-    return True, elapsed, response_id
-
-
-def main(
-    csv_path: Annotated[Path, typer.Option("--csv", help="Bills CSV path")] = Path("bills.csv"),
-    output_dir: Annotated[Path, typer.Option("--output-dir", help="Where to write per-request JSON")] = Path(
-        "output/openai_runs",
-    ),
-    model: Annotated[str, typer.Option(help="OpenAI model id")] = DEFAULT_MODEL,
-    count: Annotated[int, typer.Option(help="Number of bills per set")] = DEFAULT_COUNT,
-    concurrency: Annotated[int, typer.Option(help="Concurrent in-flight requests")] = 16,
-    log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
-) -> None:
-    """Run two interactive OpenAI sweeps (compressed + uncompressed) over bill text."""
-    logging.basicConfig(level=log_level, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
-
-    api_key = getenv("CLOSEDAI_TOKEN") or getenv("OPENAI_API_KEY")
-    if not api_key:
-        message = "Neither CLOSEDAI_TOKEN nor OPENAI_API_KEY is set"
-        raise typer.BadParameter(message)
-    if not csv_path.is_file():
-        message = f"CSV not found: {csv_path}"
-        raise typer.BadParameter(message)
-
-    compressed_dir = output_dir / "compressed"
-    uncompressed_dir = output_dir / "uncompressed"
-    compressed_dir.mkdir(parents=True, exist_ok=True)
-    uncompressed_dir.mkdir(parents=True, exist_ok=True)
-
-    logger.info("Loading %d bills from %s", count, csv_path)
-    bills = load_bills(csv_path, count)
-    if len(bills) < count:
-        logger.warning("Only %d bills available (requested %d)", len(bills), count)
-
-    tasks: list[tuple[str, str, str, Path]] = []
-    for bill_id, text_content in bills:
-        filename = f"{safe_filename(bill_id)}.json"
-        tasks.append((bill_id, "compressed", compress_bill_text(text_content), compressed_dir / filename))
-        tasks.append((bill_id, "uncompressed", text_content, uncompressed_dir / filename))
-
-    logger.info("Submitting %d requests at concurrency=%d", len(tasks), concurrency)
-
-    headers = {"Authorization": f"Bearer {api_key}"}
-    completed = 0
-    failed = 0
-    index: list[dict] = []
-    wall_start = time.monotonic()
-    with (
-        httpx.Client(headers=headers, timeout=httpx.Timeout(300.0)) as client,
-        ThreadPoolExecutor(
-            max_workers=concurrency,
-        ) as executor,
-    ):
-        future_to_task = {
-            executor.submit(
-                run_one_request,
-                client,
-                bill_id=bill_id,
-                label=label,
-                bill_text=bill_text,
-                model=model,
-                output_path=output_path,
-            ): (bill_id, label, output_path)
-            for bill_id, label, bill_text, output_path in tasks
-        }
-        for future in as_completed(future_to_task):
-            bill_id, label, output_path = future_to_task[future]
-            success, elapsed, response_id = future.result()
-            if success:
-                completed += 1
-            else:
-                failed += 1
-            index.append(
-                {
-                    "bill_id": bill_id,
-                    "label": label,
-                    "response_id": response_id,
-                    "elapsed_seconds": elapsed,
-                    "success": success,
-                    "path": str(output_path),
-                },
-            )
-    wall_elapsed = time.monotonic() - wall_start
-
-    summary = {
-        "model": model,
-        "count": len(bills),
-        "completed": completed,
-        "failed": failed,
-        "wall_seconds": wall_elapsed,
-        "concurrency": concurrency,
-        "results": index,
-    }
-    summary_path = output_dir / "summary.json"
-    summary_path.write_text(json.dumps(summary, indent=2))
-    logger.info(
-        "Done: completed=%d failed=%d wall=%.1fs summary=%s",
-        completed,
-        failed,
-        wall_elapsed,
-        summary_path,
-    )
-
-
-def cli() -> None:
-    """Typer entry point."""
-    typer.run(main)
-
-
-if __name__ == "__main__":
-    cli()

python/prompt_bench/containers/__init__.py

@@ -1 +0,0 @@
-"""Prompt benchmarking system for evaluating LLMs via vLLM."""

python/prompt_bench/containers/finetune.py

@@ -1,165 +0,0 @@
-"""Docker container lifecycle management for Unsloth fine-tuning."""
-
-from __future__ import annotations
-
-import logging
-import subprocess
-from pathlib import Path
-from typing import Annotated
-
-import typer
-
-from python.prompt_bench.containers.lib import check_gpu_free
-
-logger = logging.getLogger(__name__)
-
-CONTAINER_NAME = "bill-finetune"
-FINETUNE_IMAGE = "bill-finetune:latest"
-DOCKERFILE_PATH = "/home/richie/dotfiles/python/prompt_bench/Dockerfile.finetune"
-DEFAULT_HF_CACHE = Path("/zfs/models/hf")
-
-
-def build_image() -> None:
-    """Build the fine-tuning Docker image."""
-    logger.info("Building fine-tuning image: %s", FINETUNE_IMAGE)
-    result = subprocess.run(
-        ["docker", "build", "-f", DOCKERFILE_PATH, "-t", FINETUNE_IMAGE, "."],
-        text=True,
-        check=False,
-    )
-    if result.returncode != 0:
-        message = "Failed to build fine-tuning image"
-        raise RuntimeError(message)
-    logger.info("Image built: %s", FINETUNE_IMAGE)
-
-
-def start_finetune(
-    *,
-    dataset_path: Path,
-    output_dir: Path,
-    hf_cache: Path = DEFAULT_HF_CACHE,
-) -> None:
-    """Run the fine-tuning container.
-
-    Args:
-        dataset_path: Host path to the fine-tuning JSONL dataset.
-        output_dir: Host path where the trained model will be saved.
-        hf_cache: Host path to HuggingFace model cache (bind-mounted to avoid re-downloading).
-        validation_split: Fraction of data held out for validation.
-    """
-    dataset_path = dataset_path.resolve()
-    output_dir = output_dir.resolve()
-
-    if not dataset_path.is_file():
-        message = f"Dataset not found: {dataset_path}"
-        raise FileNotFoundError(message)
-
-    output_dir.mkdir(parents=True, exist_ok=True)
-    stop_finetune()
-
-    hf_cache = hf_cache.resolve()
-    hf_cache.mkdir(parents=True, exist_ok=True)
-
-    command = [
-        "docker",
-        "run",
-        "--name",
-        CONTAINER_NAME,
-        "--device=nvidia.com/gpu=all",
-        "--ipc=host",
-        "-v",
-        f"{hf_cache}:/root/.cache/huggingface",
-        "-v",
-        f"{output_dir}:/workspace/output/qwen-bill-summarizer",
-        "-v",
-        f"{dataset_path}:/workspace/dataset.jsonl:ro",
-        FINETUNE_IMAGE,
-        "--dataset",
-        "/workspace/dataset.jsonl",
-        "--output-dir",
-        "/workspace/output/qwen-bill-summarizer",
-    ]
-
-    logger.info("Starting fine-tuning container")
-    logger.info("  Dataset:    %s", dataset_path)
-    logger.info("  Output:     %s", output_dir)
-
-    result = subprocess.run(command, text=True, check=False)
-    if result.returncode != 0:
-        message = f"Fine-tuning container exited with code {result.returncode}"
-        raise RuntimeError(message)
-    logger.info("Fine-tuning complete. Model saved to %s", output_dir)
-
-
-def stop_finetune() -> None:
-    """Stop and remove the fine-tuning container."""
-    logger.info("Stopping fine-tuning container")
-    subprocess.run(["docker", "stop", CONTAINER_NAME], capture_output=True, check=False)
-    subprocess.run(["docker", "rm", "-f", CONTAINER_NAME], capture_output=True, check=False)
-
-
-def logs_finetune() -> str | None:
-    """Return recent logs from the fine-tuning container, or None if not running."""
-    result = subprocess.run(
-        ["docker", "logs", "--tail", "50", CONTAINER_NAME],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    if result.returncode != 0:
-        return None
-    return result.stdout + result.stderr
-
-
-app = typer.Typer(help="Fine-tuning container management.")
-
-
-@app.command()
-def build() -> None:
-    """Build the fine-tuning Docker image."""
-    build_image()
-
-
-@app.command()
-def run(
-    dataset: Annotated[Path, typer.Option(help="Fine-tuning JSONL")] = Path(
-        "/home/richie/dotfiles/data/finetune_dataset.jsonl"
-    ),
-    output_dir: Annotated[Path, typer.Option(help="Where to save the trained model")] = Path(
-        "/home/richie/dotfiles/data/output/qwen-bill-summarizer",
-    ),
-    hf_cache: Annotated[Path, typer.Option(help="Host path to HuggingFace model cache")] = DEFAULT_HF_CACHE,
-    log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
-) -> None:
-    """Run fine-tuning inside a Docker container."""
-    logging.basicConfig(level=log_level, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
-    check_gpu_free()
-    start_finetune(
-        dataset_path=dataset,
-        output_dir=output_dir,
-        hf_cache=hf_cache,
-    )
-
-@app.command()
-def stop() -> None:
-    """Stop and remove the fine-tuning container."""
-    stop_finetune()
-
-
-@app.command()
-def logs() -> None:
-    """Show recent logs from the fine-tuning container."""
-    output = logs_finetune()
-    if output is None:
-        typer.echo("No running fine-tuning container found.")
-        raise typer.Exit(code=1)
-    typer.echo(output)
-
-
-def cli() -> None:
-    """Typer entry point."""
-    app()
-
-
-if __name__ == "__main__":
-    cli()

python/prompt_bench/containers/lib.py

@@ -1,23 +0,0 @@
-from __future__ import annotations
-
-import logging
-import subprocess
-
-logger = logging.getLogger(__name__)
-
-
-def check_gpu_free() -> None:
-    """Warn if GPU-heavy processes (e.g. Ollama) are running."""
-    result = subprocess.run(
-        ["nvidia-smi", "--query-compute-apps=pid,process_name", "--format=csv,noheader"],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    if result.returncode != 0:
-        logger.warning("Could not query GPU processes: %s", result.stderr.strip())
-        return
-    processes = result.stdout.strip()
-    if processes:
-        logger.warning("GPU processes detected:\n%s", processes)
-        logger.warning("Consider stopping Ollama (sudo systemctl stop ollama) before benchmarking")

python/prompt_bench/containers/vllm.py

@@ -1,70 +0,0 @@
-"""Docker container lifecycle management for vLLM."""
-
-from __future__ import annotations
-
-import logging
-import subprocess
-
-logger = logging.getLogger(__name__)
-
-CONTAINER_NAME = "vllm-bench"
-VLLM_IMAGE = "vllm/vllm-openai:v0.19.0"
-
-
-def start_vllm(
-    *,
-    model: str,
-    port: int,
-    model_dir: str,
-    gpu_memory_utilization: float,
-) -> None:
-    """Start a vLLM container serving the given model.
-
-    Args:
-        model: HuggingFace model directory name (relative to model_dir).
-        port: Host port to bind.
-        model_dir: Host path containing HuggingFace model directories.
-        gpu_memory_utilization: Fraction of GPU memory to use (0-1).
-    """
-    command = [
-        "docker",
-        "run",
-        "-d",
-        "--name",
-        CONTAINER_NAME,
-        "--device=nvidia.com/gpu=all",
-        "--ipc=host",
-        "-v",
-        f"{model_dir}:/models",
-        "-p",
-        f"{port}:8000",
-        VLLM_IMAGE,
-        "--model",
-        f"/models/{model}",
-        "--served-model-name",
-        model,
-        "--gpu-memory-utilization",
-        str(gpu_memory_utilization),
-        "--max-model-len",
-        "4096",
-    ]
-    logger.info("Starting vLLM container with model: %s", model)
-    stop_vllm()
-    result = subprocess.run(command, capture_output=True, text=True, check=False)
-    if result.returncode != 0:
-        msg = f"Failed to start vLLM container: {result.stderr.strip()}"
-        raise RuntimeError(msg)
-    logger.info("vLLM container started: %s", result.stdout.strip()[:12])
-
-
-def stop_vllm() -> None:
-    """Stop and remove the vLLM benchmark container."""
-    logger.info("Stopping vLLM container")
-    subprocess.run(["docker", "stop", CONTAINER_NAME], capture_output=True, check=False)
-    subprocess.run(["docker", "rm", "-f", CONTAINER_NAME], capture_output=True, check=False)
-    subprocess.run(
-        ["docker", "network", "disconnect", "-f", "bridge", CONTAINER_NAME],
-        capture_output=True,
-        check=False,
-    )
-    logger.info("vLLM container stopped and removed")