added bert_topic train.py and infer.py

pipelines/bert_topic/infer.py

@@ -0,0 +1,116 @@
+"""Nornsight — BERTopic POC Inference Script.
+
+Loads the trained model and labels a small batch of posts,
+writing results to main.post_topic for inspection.
+
+POC: processes a single batch of 1k posts to validate the pipeline end-to-end.
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from collections import Counter
+from pathlib import Path
+
+from bertopic import BERTopic
+from sqlalchemy import Engine, func, insert, select
+from sqlalchemy.orm import Session
+
+from pipelines.config import BertTopicInferConfig, get_bertopic_infer_config
+from pipelines.orm.common import get_postgres_engine
+from pipelines.orm.data_science_dev.posts import PostTopic, Posts
+from pipelines.orm.data_science_dev.posts.lang_filters import ENGLISH_LANGS
+from pipelines.pipelines.common import configure_logger
+
+logger = logging.getLogger(__name__)
+
+
+def main() -> None:
+    """Run BERTopic inference against a sample of posts."""
+    configure_logger()
+
+    config = get_bertopic_infer_config()
+    run_inference(config)
+    logger.info(
+        "POC inference complete. Check main.post_topic in DBeaver to inspect results."
+    )
+
+
+def run_inference(config: BertTopicInferConfig) -> None:
+    model_save_path = Path(config.model_save_path)
+
+    logger.info(f"Loading BERTopic model from {model_save_path}")
+    topic_model = BERTopic.load(str(model_save_path))
+
+    topic_info = topic_model.get_topic_info()
+    label_map: dict[int, str] = dict(zip(topic_info["Topic"], topic_info["Name"]))
+    logger.info(f"Model loaded with {len(label_map)} topics")
+
+    engine = get_postgres_engine(name="DATA_SCIENCE_DEV")
+
+    post_ids, texts = get_post_ids_and_test(engine, config)
+
+    logger.info(f"Fetched {len(texts)} posts")
+
+    logger.info("Running BERTopic transform")
+    start = time.perf_counter()
+    topics, _probabilities = topic_model.transform(texts)
+    elapsed = time.perf_counter() - start
+    logger.info(f"Transform complete in {elapsed:.1f}s")
+
+    # Write results to main.post_topic
+    records = [
+        {
+            "post_id": pid,
+            "topic_id": int(topic_id),
+            "topic_label": label_map.get(int(topic_id), "unknown"),
+            "model_version": config.model_version,
+        }
+        for pid, topic_id in zip(post_ids, topics)
+    ]
+    with Session(engine) as session:
+        session.execute(insert(PostTopic), records)
+        session.commit()
+
+    count_topics(records)
+    logger.info(f"Wrote {len(records)} topic labels to main.post_topic")
+
+
+def get_post_ids_and_test(
+    engine: Engine,
+    config: BertTopicInferConfig,
+) -> None | tuple[list[int], list[str]]:
+    with Session(engine) as session:
+        logger.info(f"Fetching {config.poc_batch_size} posts for inference")
+        # Pull a fresh batch for inference — distinct from training sample
+        # using a fixed seed offset so we're not re-labeling training posts
+        stmt = select(Posts).where(
+            Posts.text.is_not(None),
+            Posts.langs.in_(ENGLISH_LANGS),
+            func.length(Posts.text) > config.min_text_length,
+        )
+        if config.poc_batch_size > 0:
+            stmt = stmt.limit(config.poc_batch_size)
+
+        posts = session.scalars(stmt).all()
+        if not posts:
+            logger.warning("No posts were selected for inference")
+            return [], []
+
+        post_ids = [post.post_id for post in posts]
+        texts = [post.text.strip() for post in posts]
+
+    return post_ids, texts
+
+
+def count_topics(records: list[dict]) -> None:
+    topic_counts = Counter(record.get("topic_label", "unknown") for record in records)
+
+    logger.info("Topic distribution in this batch:")
+    for label, count in topic_counts.most_common(10):
+        logger.info("  %s: %d", label, count)
+
+
+if __name__ == "__main__":
+    main()

pipelines/bert_topic/train.py

@@ -0,0 +1,119 @@
+"""Nornsight — BERTopic POC Training Script.
+
+Pulls a small stratified sample (~11.5k posts) from main.posts,
+trains BERTopic with MiniBatchKMeans on Jeeves, and saves the model locally.
+
+POC sample rate: random() < 0.00005 (~0.005% of 230M = ~11.5k posts)
+Full training rate will be: random() < 0.005 (~1.08M posts)
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from pathlib import Path
+
+from bertopic import BERTopic
+from sklearn.cluster import MiniBatchKMeans
+from sqlalchemy import func, select
+from sqlalchemy.orm import Session
+
+from pipelines.config import BertTopicTrainConfig, get_bertopic_train_config
+from pipelines.orm.common import get_postgres_engine
+from pipelines.orm.data_science_dev.posts import Posts
+from pipelines.orm.data_science_dev.posts.lang_filters import ENGLISH_LANGS
+from pipelines.pipelines.common import configure_logger
+
+logger = logging.getLogger(__name__)
+
+
+def main() -> None:
+    """Train and persist the BERTopic model."""
+    configure_logger()
+
+    config = get_bertopic_train_config()
+    docs = load_sample(config)
+    if not docs:
+        logger.warning("No training documents were selected")
+        return
+
+    train(docs, config)
+    logger.info(f"Done. Model saved as version {config.model_version}")
+    logger.info("Next: run infer.py to label a sample of posts in the database")
+
+
+def load_sample(config: BertTopicTrainConfig) -> list[str]:
+    logger.info("Connecting to PostgreSQL via SQLAlchemy")
+    engine = get_postgres_engine(name="DATA_SCIENCE_DEV")
+
+    logger.info(f"Pulling sample from main.posts (sample_rate={config.sample_rate})")
+    start = time.perf_counter()
+
+    with Session(engine) as session:
+        texts = session.scalars(
+            select(Posts.text).where(
+                Posts.text.is_not(None),
+                Posts.langs.in_(ENGLISH_LANGS),
+                func.length(Posts.text) > config.min_text_length,
+                func.random() < config.sample_rate,
+            )
+        ).all()
+
+    elapsed = time.perf_counter() - start
+    logger.info(f"Fetched {len(texts)} rows in {elapsed:.1f}s")
+
+    # Basic cleaning — strip whitespace and deduplicate
+    docs = list({text.strip() for text in texts})
+    logger.info(f"After cleaning and dedup: {len(docs)} posts")
+
+    return docs
+
+
+def train(docs: list[str], config: BertTopicTrainConfig) -> None:
+    logger.info(
+        f"Initialising BERTopic with MiniBatchKMeans (n_topics={config.n_topics})"
+    )
+
+    cluster_model = MiniBatchKMeans(
+        n_clusters=config.n_topics,
+        random_state=42,
+        batch_size=1024,
+        n_init=3,
+        verbose=1,
+    )
+
+    topic_model = BERTopic(
+        hdbscan_model=cluster_model,
+        language="english",
+        calculate_probabilities=False,  # saves memory
+        verbose=True,
+    )
+
+    logger.info(f"Starting fit_transform on {len(docs)} posts (CPU)")
+    start = time.perf_counter()
+
+    topic_model.fit_transform(docs)
+
+    elapsed = time.perf_counter() - start
+    logger.info(f"Training complete in {elapsed:.1f}s ({elapsed / 60:.1f} min)")
+
+    # Log topic summary for quick inspection
+    topic_info = topic_model.get_topic_info()
+    logger.info(f"Topics found: {len(topic_info)}")
+    logger.info(f"\n{topic_info.to_string()}")
+
+    model_save_path = Path(config.model_save_path)
+    model_save_path.mkdir(parents=True, exist_ok=True)
+    logger.info(f"Saving model to {model_save_path}")
+
+    topic_model.save(
+        str(model_save_path),
+        serialization="safetensors",
+        save_ctfidf=True,
+        save_embedding_model=True,
+    )
+    logger.info("Model saved")
+
+
+if __name__ == "__main__":
+    main()

pipelines/config.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from os import getenv
+from datetime import date
 from pathlib import Path
 import tomllib
 
@@ -70,40 +70,47 @@ class BenchmarkConfig:
 
 
 @dataclass
-class OpenAIConfig:
+class BertTopicTrainConfig:
-    """OpenAI API configuration."""
+    """BERTopic training configuration loaded from TOML."""
 
-    api_key: str
+    sample_rate: float
-    openai_project_id: str
+    min_text_length: int
-    openai_chat_completions_url: str
+    n_topics: int
-    model: str
+    model_save_path: str
-    timeout_seconds: int
+    model_version: str | None = None
 
     @classmethod
-    def from_toml(cls, config_path: Path) -> OpenAIConfig:
+    def from_toml(cls, config_path: Path) -> BertTopicTrainConfig:
-        """Load OpenAI config from a TOML file."""
+        """Load BERTopic training config from a TOML file."""
-        raw = tomllib.loads(config_path.read_text()).get("openai", {})
+        raw = tomllib.loads(config_path.read_text())["bertopic"]["train"]
-        api_key = getenv("CLOSEDAI_TOKEN")
+
-        if not api_key:
+        today = date.today().isoformat()
-            message = "CLOSEDAI_TOKEN is required"
+        if raw.get("model_version") is None:
-            raise KeyError(message)
+            raw["model_version"] = (
-        return cls(
+                f"{today}-{raw['sample_rate']}-{raw['min_text_length']}-{raw['n_topics']}"
-            api_key=api_key,
+            )
-            openai_project_id=raw.get(
+        return cls(**raw)
-                "openai_project_id", "proj_fQBPEXFgnS87Fk6wZwploFwE"
+
-            ),
+
-            openai_chat_completions_url=raw.get(
+@dataclass
-                "openai_chat_completions_url",
+class BertTopicInferConfig:
-                "https://api.openai.com/v1/chat/completions",
+    """BERTopic inference configuration loaded from TOML."""
-            ),
+
-            model=raw.get("model", "gpt-5.4-mini"),
+    min_text_length: int
-            timeout_seconds=raw.get("timeout_seconds", 60),
+    poc_batch_size: int
-        )
+    model_version: str
+    model_save_path: str
+
+    @classmethod
+    def from_toml(cls, config_path: Path) -> BertTopicInferConfig:
+        """Load BERTopic inference config from a TOML file."""
+        raw = tomllib.loads(config_path.read_text())["bertopic"]["infer"]
+        return cls(**raw)
 
 
 def get_config_dir() -> Path:
-    """Get the path to the config directory."""
+    """Get the path to the config file."""
-    return Path(__file__).resolve().parents[2] / "config"
+    return Path(__file__).resolve().parent.parent.parent / "config"
 
 
 def default_config_path() -> Path:
@@ -111,12 +118,6 @@ def default_config_path() -> Path:
     return get_config_dir() / "config.toml"
 
 
-def get_openai_config(config_path: Path | None = None) -> OpenAIConfig:
-    if config_path is None:
-        config_path = default_config_path()
-    return OpenAIConfig.from_toml(config_path)
-
-
 def get_finetune_config(config_path: Path | None = None) -> FinetuneConfig:
     if config_path is None:
         config_path = default_config_path()
@@ -127,3 +128,19 @@ def get_benchmark_config(config_path: Path | None = None) -> BenchmarkConfig:
     if config_path is None:
         config_path = default_config_path()
     return BenchmarkConfig.from_toml(config_path)
+
+
+def get_bertopic_train_config(
+    config_path: Path | None = None,
+) -> BertTopicTrainConfig:
+    if config_path is None:
+        config_path = default_config_path()
+    return BertTopicTrainConfig.from_toml(config_path)
+
+
+def get_bertopic_infer_config(
+    config_path: Path | None = None,
+) -> BertTopicInferConfig:
+    if config_path is None:
+        config_path = default_config_path()
+    return BertTopicInferConfig.from_toml(config_path)

pipelines/containers/__init__.py

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

pipelines/containers/bert_topic.py

@@ -1,235 +0,0 @@
-"""Docker container lifecycle management for BERTopic jobs on Jeeves."""
-
-from __future__ import annotations
-
-import logging
-import os
-import subprocess
-from pathlib import Path
-from typing import Annotated, Literal
-
-import typer
-
-logger = logging.getLogger(__name__)
-
-JOBMode = Literal["train", "infer"]
-IMAGE_NAME = "bert-topic:latest"
-REPO_DIR = Path(__file__).resolve().parents[3]
-DEFAULT_CACHE_ROOT = Path("/zfs/storage/main/ds_thing/models/bert_topic")
-DEFAULT_POSTGRES_SOCKET_DIR = Path("/run/postgresql")
-DB_ENV_VARS = (
-    "DATA_SCIENCE_DEV_DB",
-    "DATA_SCIENCE_DEV_HOST",
-    "DATA_SCIENCE_DEV_PORT",
-    "DATA_SCIENCE_DEV_USER",
-    "DATA_SCIENCE_DEV_PASSWORD",
-)
-
-app = typer.Typer(help="BERTopic container management.")
-
-
-def _container_name(mode: JOBMode) -> str:
-    """Return the Docker container name for the selected BERTopic job."""
-    return f"bert-topic-{mode}"
-
-
-def _module_name(mode: JOBMode) -> str:
-    """Return the Python module to run inside the container."""
-    return f"pipelines.bert_topic.{mode}"
-
-
-def _env_args(*, use_postgres_socket: bool) -> list[str]:
-    """Pass through database environment variables from the host shell."""
-    required = [
-        "DATA_SCIENCE_DEV_DB",
-        "DATA_SCIENCE_DEV_PORT",
-        "DATA_SCIENCE_DEV_USER",
-    ]
-    if not use_postgres_socket:
-        required.append("DATA_SCIENCE_DEV_HOST")
-    missing = [name for name in required if not os.getenv(name)]
-    if missing:
-        message = "Missing required database environment variables: " + ", ".join(
-            missing
-        )
-        raise RuntimeError(message)
-    args: list[str] = []
-    if use_postgres_socket:
-        args.extend(["-e", f"DATA_SCIENCE_DEV_HOST={DEFAULT_POSTGRES_SOCKET_DIR}"])
-    for name in DB_ENV_VARS:
-        if use_postgres_socket and name == "DATA_SCIENCE_DEV_HOST":
-            continue
-        if os.getenv(name):
-            args.extend(["-e", name])
-    return args
-
-
-def build_image() -> None:
-    """Build the BERTopic Docker image."""
-    dockerfile = REPO_DIR / "pipelines/containers/docker_files/Dockerfile.bert_topic"
-    logger.info("Building BERTopic image: %s", IMAGE_NAME)
-    result = subprocess.run(
-        [
-            "docker",
-            "build",
-            "--network",
-            "host",
-            "-f",
-            str(dockerfile),
-            "-t",
-            IMAGE_NAME,
-            str(REPO_DIR),
-        ],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    if result.returncode != 0:
-        message = (
-            "Failed to build BERTopic image. "
-            f"docker build stderr:\n{result.stderr.strip()}"
-        )
-        raise RuntimeError(message)
-    logger.info("Image built: %s", IMAGE_NAME)
-
-
-def stop_job(*, mode: JOBMode) -> None:
-    """Stop and remove the BERTopic container for the selected mode."""
-    container_name = _container_name(mode)
-    logger.info("Stopping BERTopic container: %s", container_name)
-    subprocess.run(["docker", "stop", container_name], capture_output=True, check=False)
-    subprocess.run(
-        ["docker", "rm", "-f", container_name], capture_output=True, check=False
-    )
-
-
-def start_job(
-    *,
-    mode: JOBMode,
-    cache_root: Path = DEFAULT_CACHE_ROOT,
-    postgres_socket_dir: Path = DEFAULT_POSTGRES_SOCKET_DIR,
-    detach: bool = False,
-) -> None:
-    """Run BERTopic training or inference in Docker on Jeeves."""
-    cache_root = cache_root.resolve()
-    cache_root.mkdir(parents=True, exist_ok=True)
-    postgres_socket_dir = postgres_socket_dir.resolve()
-    stop_job(mode=mode)
-    use_postgres_socket = postgres_socket_dir.exists()
-
-    command = [
-        "docker",
-        "run",
-        "--name",
-        _container_name(mode),
-        "--ipc=host",
-        "-v",
-        f"{cache_root}:/cache",
-        *_env_args(use_postgres_socket=use_postgres_socket),
-        IMAGE_NAME,
-        _module_name(mode),
-    ]
-    if use_postgres_socket:
-        command[7:7] = ["-v", f"{postgres_socket_dir}:{DEFAULT_POSTGRES_SOCKET_DIR}"]
-    if detach:
-        command.insert(2, "-d")
-
-    logger.info("Starting BERTopic %s container", mode)
-    logger.info("  Cache root: %s", cache_root)
-    if use_postgres_socket:
-        logger.info("  Postgres socket: %s", postgres_socket_dir)
-    result = subprocess.run(command, text=True, capture_output=detach, check=False)
-    if result.returncode != 0:
-        detail = (
-            result.stderr.strip() if result.stderr else f"exit code {result.returncode}"
-        )
-        raise RuntimeError(f"BERTopic container failed to start: {detail}")
-    if detach:
-        logger.info("Container started: %s", result.stdout.strip()[:12])
-    else:
-        logger.info("BERTopic %s run complete", mode)
-
-
-def logs_job(*, mode: JOBMode) -> str | None:
-    """Return recent logs from the BERTopic container, or None if absent."""
-    result = subprocess.run(
-        ["docker", "logs", "--tail", "100", _container_name(mode)],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    if result.returncode != 0:
-        return None
-    return result.stdout + result.stderr
-
-
-@app.command()
-def build(
-    log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
-) -> None:
-    """Build the BERTopic Docker image."""
-    logging.basicConfig(
-        level=log_level,
-        format="%(asctime)s %(levelname)s %(name)s: %(message)s",
-    )
-    build_image()
-
-
-@app.command("run")
-def run_job_command(
-    mode: Annotated[JOBMode, typer.Option(help="Which BERTopic job to run")] = "train",
-    cache_root: Annotated[
-        Path, typer.Option(help="Host path mounted to /cache for model and HF cache")
-    ] = DEFAULT_CACHE_ROOT,
-    postgres_socket_dir: Annotated[
-        Path, typer.Option(help="Host Postgres socket directory to mount into the container")
-    ] = DEFAULT_POSTGRES_SOCKET_DIR,
-    detach: Annotated[
-        bool, typer.Option(help="Start the container in the background")
-    ] = False,
-    log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
-) -> None:
-    """Run BERTopic training or inference inside Docker."""
-    logging.basicConfig(
-        level=log_level,
-        format="%(asctime)s %(levelname)s %(name)s: %(message)s",
-    )
-    start_job(
-        mode=mode,
-        cache_root=cache_root,
-        postgres_socket_dir=postgres_socket_dir,
-        detach=detach,
-    )
-
-
-@app.command("stop")
-def stop_job_command(
-    mode: Annotated[
-        JOBMode, typer.Option(help="Which BERTopic container to stop")
-    ] = "train",
-) -> None:
-    """Stop and remove the BERTopic container."""
-    stop_job(mode=mode)
-
-
-@app.command("logs")
-def logs_job_command(
-    mode: Annotated[
-        JOBMode, typer.Option(help="Which BERTopic container logs to show")
-    ] = "train",
-) -> None:
-    """Show recent logs from the BERTopic container."""
-    output = logs_job(mode=mode)
-    if output is None:
-        typer.echo(f"No BERTopic container found for mode={mode}.")
-        raise typer.Exit(code=1)
-    typer.echo(output)
-
-
-def cli() -> None:
-    """Typer entry point."""
-    app()
-
-
-if __name__ == "__main__":
-    cli()

pipelines/containers/docker_files/Dockerfile.bert_topic

@@ -1,38 +0,0 @@
-FROM python:3.12-bookworm
-
-ENV DEBIAN_FRONTEND=noninteractive
-ENV PYTHONUNBUFFERED=1
-ENV PIP_NO_CACHE_DIR=1
-
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    gcc \
-    g++ \
-    git \
-    libgomp1 \
-    libpq-dev \
-    && rm -rf /var/lib/apt/lists/*
-
-WORKDIR /app
-
-COPY pipelines ./pipelines
-
-RUN python -m pip install --upgrade pip setuptools wheel && \
-    python -m pip install \
-    torch \
-    --index-url https://download.pytorch.org/whl/cpu && \
-    python -m pip install \
-    typer \
-    sqlalchemy \
-    bertopic \
-    sentence-transformers \
-    scikit-learn \
-    pandas \
-    numpy \
-    "psycopg[binary]"
-
-ENV HF_HOME=/cache/huggingface
-ENV TRANSFORMERS_CACHE=/cache/huggingface
-
-ENTRYPOINT ["python", "-m"]
-CMD ["pipelines.bert_topic.train"]

pipelines/containers/docker_files/Dockerfile.finetune

@@ -1,11 +0,0 @@
-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 config/prompts/summarization_prompts.toml config/prompts/summarization_prompts.toml
-COPY python/prompt_bench/__init__.py python/prompt_bench/__init__.py
-COPY python/__init__.py python/__init__.py
-
-ENTRYPOINT ["python", "-m", "pipelines.prompt_bench.finetune"]

pipelines/jobs/calculate_legislator_scores.py

@@ -1,574 +0,0 @@
-"""Calculate legislator topic scores from bill topics and roll-call votes."""
-
-from __future__ import annotations
-
-import logging
-from dataclasses import dataclass
-from typing import Annotated, Sequence
-
-import typer
-from sqlalchemy import (
-    ColumnElement,
-    Integer,
-    Select,
-    and_,
-    case,
-    cast,
-    delete,
-    extract,
-    func,
-    or_,
-    select,
-    tuple_,
-)
-from sqlalchemy.orm import Session
-
-from pipelines.congress_vote_context import create_score_run, finalize_score_run
-from pipelines.orm.common import get_postgres_engine
-from pipelines.orm.data_science_dev.congress import (
-    BillTopic,
-    BillTopicPosition,
-    LegislatorScore,
-    SubjectType,
-    Vote,
-    VoteClassification,
-    VoteEffect,
-    VoteMeasureLink,
-    VoteMeasureRole,
-    VotePositionMeaning,
-    VoteRelationship,
-    VoteRecord,
-)
-from pipelines.pipelines.jobs.extract_bill_topics import normalize_topic_label
-from pipelines.web.scoring import (
-    OPPOSE_POSITIONS,
-    SUPPORT_POSITIONS,
-    normalized_position_expression,
-)
-
-logger = logging.getLogger(__name__)
-
-DELETE_BATCH_SIZE = 5_000
-
-
-@dataclass(frozen=True)
-class ScoreDiagnostics:
-    """Counts for the input stages required to calculate legislator scores."""
-
-    bill_topic_rows: int
-    linked_vote_rows: int
-    vote_record_rows: int
-    topic_vote_links: int
-    scorable_vote_records: int
-
-
-@dataclass(frozen=True)
-class LegislatorScoreInput:
-    """One aggregated score ready to store in legislator_score."""
-
-    legislator_id: int
-    year: int
-    topic: str
-    score: float
-    supportive: int
-    opposed: int
-
-
-def create_legislator_score_query(
-    *,
-    congress: int | None = None,
-    bill_ids: Sequence[int] | None = None,
-    topics: Sequence[str] | None = None,
-) -> Select:
-    """Build the aggregate score query from extracted bill topics and vote records."""
-    normalized_vote = normalized_position_expression(VoteRecord.position)
-    supportive_vote = _supportive_vote_expression(normalized_vote)
-    opposed_vote = _opposed_vote_expression(normalized_vote)
-    supportive_count = func.sum(supportive_vote)
-    opposed_count = func.sum(opposed_vote)
-    total_count = supportive_count + opposed_count
-    vote_year = cast(extract("year", Vote.vote_date), Integer)
-    score = (100.0 * supportive_count / func.nullif(total_count, 0)).label("score")
-
-    stmt = (
-        select(
-            VoteRecord.legislator_id.label("legislator_id"),
-            vote_year.label("year"),
-            BillTopic.topic.label("topic"),
-            score,
-            supportive_count.label("supportive"),
-            opposed_count.label("opposed"),
-            total_count.label("total"),
-        )
-        .select_from(VoteRecord)
-        .join(Vote, Vote.id == VoteRecord.vote_id)
-        .join(VoteClassification, VoteClassification.vote_id == Vote.id)
-        .join(VotePositionMeaning, VotePositionMeaning.vote_id == Vote.id)
-        .join(
-            VoteMeasureLink,
-            and_(
-                VoteMeasureLink.vote_id == Vote.id,
-                VoteMeasureLink.role == VoteMeasureRole.VOTED_ON,
-            ),
-        )
-        .join(BillTopic, BillTopic.bill_id == VoteMeasureLink.measure_id)
-        .where(
-            *_eligible_vote_filters(),
-            _is_scorable_position(normalized_vote),
-        )
-        .group_by(VoteRecord.legislator_id, vote_year, BillTopic.topic)
-        .having(total_count > 0)
-        .order_by(VoteRecord.legislator_id, vote_year, BillTopic.topic)
-    )
-    if congress is not None:
-        stmt = stmt.where(Vote.congress == congress)
-    if bill_ids:
-        stmt = stmt.where(VoteMeasureLink.measure_id.in_(list(bill_ids)))
-
-    normalized_topics = _normalize_topics(topics)
-    if normalized_topics:
-        stmt = stmt.where(BillTopic.topic.in_(normalized_topics))
-
-    return stmt
-
-
-def collect_legislator_scores(
-    session: Session,
-    *,
-    congress: int | None = None,
-    bill_ids: Sequence[int] | None = None,
-    topics: Sequence[str] | None = None,
-) -> list[LegislatorScoreInput]:
-    """Run the aggregate query and return score rows."""
-    rows = session.execute(
-        create_legislator_score_query(
-            congress=congress,
-            bill_ids=bill_ids,
-            topics=topics,
-        )
-    )
-    return [
-        LegislatorScoreInput(
-            legislator_id=int(row.legislator_id),
-            year=int(row.year),
-            topic=str(row.topic),
-            score=float(row.score),
-            supportive=int(row.supportive),
-            opposed=int(row.opposed),
-        )
-        for row in rows
-        if row.score is not None
-    ]
-
-
-def collect_score_diagnostics(
-    session: Session,
-    *,
-    congress: int | None = None,
-    bill_ids: Sequence[int] | None = None,
-    topics: Sequence[str] | None = None,
-) -> ScoreDiagnostics:
-    """Count score pipeline inputs for explaining empty score runs."""
-    normalized_topics = _normalize_topics(topics)
-    vote_filters = _vote_scope_filters(congress=congress, bill_ids=bill_ids)
-    topic_filters = _topic_scope_filters(bill_ids=bill_ids, topics=normalized_topics)
-    normalized_vote = normalized_position_expression(VoteRecord.position)
-    eligible_vote_filters = _eligible_vote_filters()
-
-    bill_topic_rows = session.scalar(
-        select(func.count(BillTopic.id)).where(*topic_filters)
-    )
-    linked_vote_rows = session.scalar(
-        select(func.count(func.distinct(Vote.id)))
-        .select_from(Vote)
-        .join(VoteClassification, VoteClassification.vote_id == Vote.id)
-        .join(
-            VoteMeasureLink,
-            and_(
-                VoteMeasureLink.vote_id == Vote.id,
-                VoteMeasureLink.role == VoteMeasureRole.VOTED_ON,
-            ),
-        )
-        .where(*vote_filters, *eligible_vote_filters)
-    )
-    vote_record_rows = session.scalar(
-        select(func.count())
-        .select_from(VoteRecord)
-        .join(Vote, Vote.id == VoteRecord.vote_id)
-        .join(VoteClassification, VoteClassification.vote_id == Vote.id)
-        .where(*vote_filters, *eligible_vote_filters)
-    )
-    topic_vote_links = session.scalar(
-        select(func.count())
-        .select_from(VoteRecord)
-        .join(Vote, Vote.id == VoteRecord.vote_id)
-        .join(VoteClassification, VoteClassification.vote_id == Vote.id)
-        .join(VotePositionMeaning, VotePositionMeaning.vote_id == Vote.id)
-        .join(
-            VoteMeasureLink,
-            and_(
-                VoteMeasureLink.vote_id == Vote.id,
-                VoteMeasureLink.role == VoteMeasureRole.VOTED_ON,
-            ),
-        )
-        .join(BillTopic, BillTopic.bill_id == VoteMeasureLink.measure_id)
-        .where(*vote_filters, *topic_filters, *eligible_vote_filters)
-    )
-    scorable_vote_records = session.scalar(
-        select(func.count())
-        .select_from(VoteRecord)
-        .join(Vote, Vote.id == VoteRecord.vote_id)
-        .join(VoteClassification, VoteClassification.vote_id == Vote.id)
-        .join(VotePositionMeaning, VotePositionMeaning.vote_id == Vote.id)
-        .join(
-            VoteMeasureLink,
-            and_(
-                VoteMeasureLink.vote_id == Vote.id,
-                VoteMeasureLink.role == VoteMeasureRole.VOTED_ON,
-            ),
-        )
-        .join(BillTopic, BillTopic.bill_id == VoteMeasureLink.measure_id)
-        .where(
-            *vote_filters,
-            *topic_filters,
-            *eligible_vote_filters,
-            _is_scorable_position(normalized_vote),
-        )
-    )
-
-    return ScoreDiagnostics(
-        bill_topic_rows=bill_topic_rows or 0,
-        linked_vote_rows=linked_vote_rows or 0,
-        vote_record_rows=vote_record_rows or 0,
-        topic_vote_links=topic_vote_links or 0,
-        scorable_vote_records=scorable_vote_records or 0,
-    )
-
-
-def store_legislator_scores(
-    session: Session,
-    rows: Sequence[LegislatorScoreInput],
-    *,
-    score_run_id: int | None,
-    replace_all: bool = False,
-) -> int:
-    """Replace matching score rows and insert the newly calculated scores."""
-    if replace_all:
-        session.execute(delete(LegislatorScore))
-    elif rows:
-        keys = [
-            (row.legislator_id, row.year, row.topic)
-            for row in rows
-        ]
-        for key_batch in _batched(keys, DELETE_BATCH_SIZE):
-            session.execute(
-                delete(LegislatorScore).where(
-                    tuple_(
-                        LegislatorScore.legislator_id,
-                        LegislatorScore.year,
-                        LegislatorScore.topic,
-                    ).in_(key_batch)
-                )
-            )
-
-    session.add_all(
-        [
-            LegislatorScore(
-                legislator_id=row.legislator_id,
-                year=row.year,
-                topic=row.topic,
-                score=row.score,
-                score_run_id=score_run_id,
-            )
-            for row in rows
-        ]
-    )
-    return len(rows)
-
-
-def _supportive_vote_expression(
-    normalized_vote: ColumnElement[str | None],
-) -> ColumnElement[int]:
-    supports_text = _position_matches_effect(normalized_vote, VoteEffect.SUPPORTS_TEXT)
-    opposes_text = _position_matches_effect(normalized_vote, VoteEffect.OPPOSES_TEXT)
-    return case(
-        (
-            and_(
-                BillTopic.support_position == BillTopicPosition.FOR,
-                supports_text,
-            ),
-            1,
-        ),
-        (
-            and_(
-                BillTopic.support_position == BillTopicPosition.AGAINST,
-                opposes_text,
-            ),
-            1,
-        ),
-        else_=0,
-    )
-
-
-def _opposed_vote_expression(
-    normalized_vote: ColumnElement[str | None],
-) -> ColumnElement[int]:
-    supports_text = _position_matches_effect(normalized_vote, VoteEffect.SUPPORTS_TEXT)
-    opposes_text = _position_matches_effect(normalized_vote, VoteEffect.OPPOSES_TEXT)
-    return case(
-        (
-            and_(
-                BillTopic.support_position == BillTopicPosition.FOR,
-                opposes_text,
-            ),
-            1,
-        ),
-        (
-            and_(
-                BillTopic.support_position == BillTopicPosition.AGAINST,
-                supports_text,
-            ),
-            1,
-        ),
-        else_=0,
-    )
-
-
-def _position_matches_effect(
-    normalized_vote: ColumnElement[str | None],
-    effect: VoteEffect,
-) -> ColumnElement[bool]:
-    return or_(
-        and_(
-            normalized_vote.in_(sorted(SUPPORT_POSITIONS)),
-            VotePositionMeaning.yea_effect == effect,
-        ),
-        and_(
-            normalized_vote.in_(sorted(OPPOSE_POSITIONS)),
-            VotePositionMeaning.nay_effect == effect,
-        ),
-        and_(
-            normalized_vote == "present",
-            VotePositionMeaning.present_effect == effect,
-        ),
-    )
-
-
-def _is_scorable_position(normalized_vote: ColumnElement[str | None]) -> ColumnElement[bool]:
-    return or_(
-        _position_matches_effect(normalized_vote, VoteEffect.SUPPORTS_TEXT),
-        _position_matches_effect(normalized_vote, VoteEffect.OPPOSES_TEXT),
-    )
-
-
-def _normalize_topics(topics: Sequence[str] | None) -> list[str]:
-    normalized: list[str] = []
-    seen: set[str] = set()
-    for topic in topics or []:
-        value = normalize_topic_label(topic)
-        if value and value not in seen:
-            normalized.append(value)
-            seen.add(value)
-    return normalized
-
-
-def _batched[T](items: Sequence[T], batch_size: int) -> list[Sequence[T]]:
-    return [
-        items[index : index + batch_size]
-        for index in range(0, len(items), batch_size)
-    ]
-
-
-def _vote_scope_filters(
-    *,
-    congress: int | None,
-    bill_ids: Sequence[int] | None,
-) -> list[ColumnElement[bool]]:
-    filters: list[ColumnElement[bool]] = []
-    if congress is not None:
-        filters.append(Vote.congress == congress)
-    if bill_ids:
-        filters.append(VoteMeasureLink.measure_id.in_(list(bill_ids)))
-    return filters
-
-
-def _topic_scope_filters(
-    *,
-    bill_ids: Sequence[int] | None,
-    topics: Sequence[str],
-) -> list[ColumnElement[bool]]:
-    filters: list[ColumnElement[bool]] = []
-    if bill_ids:
-        filters.append(BillTopic.bill_id.in_(list(bill_ids)))
-    if topics:
-        filters.append(BillTopic.topic.in_(list(topics)))
-    return filters
-
-
-def _has_score_scope(
-    *,
-    congress: int | None,
-    bill_ids: Sequence[int] | None,
-    topics: Sequence[str] | None,
-) -> bool:
-    return congress is not None or bool(bill_ids) or bool(topics)
-
-
-def _eligible_vote_filters() -> list[ColumnElement[bool]]:
-    return [
-        VoteClassification.subject_type == SubjectType.MEASURE,
-        VoteClassification.vote_relationship == VoteRelationship.DIRECT_TEXT_VOTE,
-        VoteClassification.is_direct_vote_on_legislative_text.is_(True),
-        VoteClassification.is_substantive_policy_vote.is_(True),
-        VoteClassification.is_special_rule.is_(False),
-    ]
-
-
-def main(
-    congress: Annotated[
-        int | None,
-        typer.Option(help="Only score votes from one Congress."),
-    ] = None,
-    bill_ids: Annotated[
-        list[int] | None,
-        typer.Option(
-            "--bill-id",
-            help="Only score votes linked to one internal bill.id. Repeatable.",
-        ),
-    ] = None,
-    topics: Annotated[
-        list[str] | None,
-        typer.Option("--topic", help="Only score one normalized topic. Repeatable."),
-    ] = None,
-    replace_all: Annotated[
-        bool,
-        typer.Option(
-            help="Delete every existing legislator score before inserting. "
-            "Unfiltered runs do this automatically."
-        ),
-    ] = False,
-    dry_run: Annotated[
-        bool,
-        typer.Option(help="Calculate scores without writing to the database."),
-    ] = False,
-    log_level: Annotated[str, typer.Option(help="Log level.")] = "INFO",
-    diagnose: Annotated[
-        bool,
-        typer.Option(help="Log input-stage counts even when rows are calculated."),
-    ] = False,
-) -> None:
-    """CLI entrypoint for calculating and storing legislator topic scores."""
-    logging.basicConfig(
-        level=log_level,
-        format="%(asctime)s %(levelname)s %(name)s: %(message)s",
-    )
-
-    engine = get_postgres_engine(name="DATA_SCIENCE_DEV")
-    with Session(engine) as session:
-        rows = collect_legislator_scores(
-            session,
-            congress=congress,
-            bill_ids=bill_ids,
-            topics=topics,
-        )
-        logger.info("Calculated %d legislator topic score rows", len(rows))
-        if diagnose or not rows:
-            diagnostics = collect_score_diagnostics(
-                session,
-                congress=congress,
-                bill_ids=bill_ids,
-                topics=topics,
-            )
-            _log_diagnostics(diagnostics)
-
-        if dry_run:
-            session.rollback()
-            return
-
-        score_run = create_score_run(session)
-        should_replace_all = replace_all or not _has_score_scope(
-            congress=congress,
-            bill_ids=bill_ids,
-            topics=topics,
-        )
-        written = store_legislator_scores(
-            session,
-            rows,
-            score_run_id=score_run.id,
-            replace_all=should_replace_all,
-        )
-        included_vote_count = session.scalar(
-            select(func.count(func.distinct(Vote.id)))
-            .select_from(VoteRecord)
-            .join(Vote, Vote.id == VoteRecord.vote_id)
-            .join(VoteClassification, VoteClassification.vote_id == Vote.id)
-            .join(VotePositionMeaning, VotePositionMeaning.vote_id == Vote.id)
-            .join(
-                VoteMeasureLink,
-                and_(
-                    VoteMeasureLink.vote_id == Vote.id,
-                    VoteMeasureLink.role == VoteMeasureRole.VOTED_ON,
-                ),
-            )
-            .join(BillTopic, BillTopic.bill_id == VoteMeasureLink.measure_id)
-            .where(
-                *_vote_scope_filters(congress=congress, bill_ids=bill_ids),
-                *_topic_scope_filters(bill_ids=bill_ids, topics=_normalize_topics(topics)),
-                *_eligible_vote_filters(),
-                _is_scorable_position(normalized_position_expression(VoteRecord.position)),
-            )
-        ) or 0
-        total_scoped_votes = session.scalar(
-            select(func.count(func.distinct(Vote.id)))
-            .select_from(Vote)
-            .join(VoteClassification, VoteClassification.vote_id == Vote.id)
-            .join(
-                VoteMeasureLink,
-                and_(
-                    VoteMeasureLink.vote_id == Vote.id,
-                    VoteMeasureLink.role == VoteMeasureRole.VOTED_ON,
-                ),
-            )
-            .where(*_vote_scope_filters(congress=congress, bill_ids=bill_ids))
-        ) or 0
-        finalize_score_run(
-            session,
-            score_run=score_run,
-            included_vote_count=included_vote_count,
-            excluded_vote_count=max(total_scoped_votes - included_vote_count, 0),
-        )
-        session.commit()
-        logger.info("Stored %d legislator topic score rows", written)
-
-
-def _log_diagnostics(diagnostics: ScoreDiagnostics) -> None:
-    logger.info(
-        "Score input diagnostics: bill_topic_rows=%d linked_vote_rows=%d "
-        "vote_record_rows=%d topic_vote_links=%d scorable_vote_records=%d",
-        diagnostics.bill_topic_rows,
-        diagnostics.linked_vote_rows,
-        diagnostics.vote_record_rows,
-        diagnostics.topic_vote_links,
-        diagnostics.scorable_vote_records,
-    )
-    if diagnostics.bill_topic_rows == 0:
-        logger.warning(
-            "No extracted bill topics matched the score scope. Run "
-            "pipelines.tools.extract_bill_topics after bill summarization."
-        )
-    elif diagnostics.linked_vote_rows == 0:
-        logger.warning("No direct substantive text votes matched the score scope.")
-    elif diagnostics.vote_record_rows == 0:
-        logger.warning("No individual vote records matched the score scope.")
-    elif diagnostics.topic_vote_links == 0:
-        logger.warning(
-            "Bill topics exist, but none are attached to bills that have eligible scored votes."
-        )
-    elif diagnostics.scorable_vote_records == 0:
-        logger.warning(
-            "Topic-vote links exist, but no joined vote records had Yea/Aye/Yes/Nay/No positions."
-        )
-
-
-if __name__ == "__main__":
-    typer.run(main)

pipelines/jobs/extract_bill_topics.py

@@ -1,682 +0,0 @@
-"""Extract bill topics from bill text using a configurable topic catalog."""
-
-from __future__ import annotations
-
-import json
-import logging
-import re
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Annotated, Any, Sequence
-
-import httpx
-import typer
-from sqlalchemy import ColumnElement, Select, delete, exists, func, select
-from sqlalchemy.orm import Session, selectinload
-
-from pipelines.config import OpenAIConfig, get_config_dir, get_openai_config
-from pipelines.orm.common import get_postgres_engine
-from pipelines.orm.data_science_dev.congress import (
-    Bill,
-    BillText,
-    BillTopic,
-    BillTopicPosition,
-    SubjectType,
-    VoteClassification,
-    VoteRelationship,
-    VoteTextTarget,
-)
-
-logger = logging.getLogger(__name__)
-
-OPENAI_PROJECT_ID = "proj_fQBPEXFgnS87Fk6wZwploFwE"
-OPENAI_CHAT_COMPLETIONS_URL = "https://api.openai.com/v1/chat/completions"
-REQUEST_TIMEOUT_SECONDS = 60
-DEFAULT_TOPICS_PATH = get_config_dir() / "congressional_issues_comprehensive.json"
-
-
-class TopicExtractionError(RuntimeError):
-    """Raised when a topic extraction request or response is invalid."""
-
-
-@dataclass(frozen=True)
-class TopicCatalog:
-    """Loaded topic catalog with categories for prompting and flat candidates."""
-
-    topics_by_category: dict[str, list[str]]
-    candidate_topics: list[str]
-
-
-@dataclass(frozen=True)
-class TopicExtractionDiagnostics:
-    """Counts for the bill summary inputs needed by topic extraction."""
-
-    bill_rows: int
-    bill_text_rows: int
-    summarized_bill_text_rows: int
-    bills_with_summaries: int
-    bill_topic_rows: int
-    selected_bills: int
-
-
-@dataclass(frozen=True)
-class ExtractedBillTopic:
-    """One extracted bill topic and yes-vote stance."""
-
-    topic: str
-    support_position: BillTopicPosition
-    confidence: float | None = None
-    evidence: str | None = None
-
-
-def _select_bill_text_for_topic_extraction(bill: Bill) -> BillText | None:
-    """Pick one summarized bill_text row from the already-loaded relationship."""
-    for bill_text in bill.bill_texts:
-        if bill_text.summary and bill_text.summary.strip():
-            return bill_text
-    return None
-
-
-def normalize_topic_label(value: str) -> str:
-    """Normalize a topic label for storage, comparison, and de-duping."""
-    normalized = value.strip().strip("\"'")
-    normalized = normalized.strip().rstrip(".").strip()
-    return re.sub(r"\s+", " ", normalized).lower()
-
-
-def load_topic_catalog(path: Path | None = None) -> TopicCatalog:
-    """Load, validate, normalize, and flatten the bill topic catalog."""
-    topics_path = path or DEFAULT_TOPICS_PATH
-    try:
-        raw = json.loads(topics_path.read_text())
-    except FileNotFoundError as exc:
-        msg = f"Topic catalog not found: {topics_path}"
-        raise TopicExtractionError(msg) from exc
-    except json.JSONDecodeError as exc:
-        msg = f"Topic catalog is not valid JSON: {topics_path}: {exc}"
-        raise TopicExtractionError(msg) from exc
-
-    if not isinstance(raw, dict):
-        msg = "Topic catalog root must be an object mapping category names to lists"
-        raise TopicExtractionError(msg)
-
-    topics_by_category: dict[str, list[str]] = {}
-    candidate_topics: list[str] = []
-    seen_topics: set[str] = set()
-
-    for category, topics in raw.items():
-        if not isinstance(category, str) or not category.strip():
-            msg = "Topic catalog category names must be non-empty strings"
-            raise TopicExtractionError(msg)
-        if not isinstance(topics, list):
-            msg = f"Topic catalog category {category!r} must contain a list"
-            raise TopicExtractionError(msg)
-
-        normalized_topics: list[str] = []
-        for topic in topics:
-            if not isinstance(topic, str):
-                msg = f"Topic catalog category {category!r} contains a non-string topic"
-                raise TopicExtractionError(msg)
-            normalized_topic = normalize_topic_label(topic)
-            if not normalized_topic:
-                msg = f"Topic catalog category {category!r} contains a blank topic"
-                raise TopicExtractionError(msg)
-            if normalized_topic in seen_topics:
-                continue
-            seen_topics.add(normalized_topic)
-            normalized_topics.append(normalized_topic)
-            candidate_topics.append(normalized_topic)
-
-        topics_by_category[category.strip()] = normalized_topics
-
-    return TopicCatalog(
-        topics_by_category=topics_by_category,
-        candidate_topics=candidate_topics,
-    )
-
-
-def build_topic_extraction_messages(
-    *,
-    bill: Bill,
-    bill_text: str,
-    candidate_topics: Sequence[str],
-) -> list[dict[str, str]]:
-    """Build GPT messages for extracting a bill's scored topics."""
-    normalized_candidates = [normalize_topic_label(topic) for topic in candidate_topics]
-    candidate_list = "\n".join(f"- {topic}" for topic in normalized_candidates)
-    metadata = "\n".join(
-        (
-            f"Congress: {bill.congress}",
-            f"Bill: {bill.bill_type} {bill.number}",
-            f"Title: {bill.title_short or bill.title or bill.official_title or ''}",
-            f"Top subject term: {bill.subjects_top_term or ''}",
-        )
-    )
-
-    system_prompt = (
-        "You extract policy topics from U.S. congressional bills.\n"
-        'For each selected topic, decide whether a Yes/Yea vote on the bill is "for" or "against" that topic.\n'
-        'Use "support_position": "for" when a Yes/Yea vote advances or supports the topic.\n'
-        'Use "support_position": "against" when a Yes/Yea vote restricts, repeals, blocks, or opposes the topic.\n'
-        "Select only topics from the provided candidate topic list.\n"
-        "Omit topics that are not materially addressed by the bill.\n"
-        "Return strict JSON only, with this shape:\n"
-        '{"topics":[{"topic":"candidate topic","support_position":"for","confidence":0.0,"evidence":"short reason"}]}'
-    )
-    user_prompt = "\n\n".join(
-        (
-            "BILL METADATA:",
-            metadata,
-            "CANDIDATE TOPICS:",
-            candidate_list,
-            "BILL TEXT:",
-            bill_text,
-        )
-    )
-    return [
-        {"role": "system", "content": system_prompt},
-        {"role": "user", "content": user_prompt},
-    ]
-
-
-def call_openai_topic_extraction(
-    *,
-    openai_config: OpenAIConfig,
-    messages: list[dict[str, str]],
-) -> str:
-    """Call GPT and return the assistant message content."""
-
-    response = httpx.post(
-        openai_config.openai_chat_completions_url,
-        headers={
-            "Authorization": f"Bearer {openai_config.api_key}",
-            "OpenAI-Project": openai_config.openai_project_id,
-            "Content-Type": "application/json",
-        },
-        json={
-            "model": "gpt-5.4-mini",
-            "messages": messages,
-        },
-        timeout=openai_config.timeout_seconds,
-    )
-    response.raise_for_status()
-    return extract_message_content(response.json())
-
-
-def extract_message_content(data: dict[str, Any]) -> str:
-    """Extract message content from a chat-completions response body."""
-    choices = data.get("choices")
-    if not isinstance(choices, list) or not choices:
-        msg = "Chat completion response did not contain choices"
-        raise TopicExtractionError(msg)
-
-    first = choices[0]
-    if not isinstance(first, dict):
-        msg = "Chat completion choice must be an object"
-        raise TopicExtractionError(msg)
-
-    message = first.get("message")
-    if isinstance(message, dict) and isinstance(message.get("content"), str):
-        return message["content"]
-    if isinstance(first.get("text"), str):
-        return first["text"]
-
-    msg = "Chat completion response did not contain message content"
-    raise TopicExtractionError(msg)
-
-
-def parse_topic_extraction_response(response_text: str) -> list[ExtractedBillTopic]:
-    """Parse, normalize, validate, and de-dupe a topic extraction response."""
-    payload = _load_json_response(response_text)
-    topics = payload.get("topics")
-    if not isinstance(topics, list):
-        msg = "Topic extraction response must contain a topics list"
-        raise TopicExtractionError(msg)
-
-    deduped: dict[tuple[str, BillTopicPosition], ExtractedBillTopic] = {}
-    for item in topics:
-        if not isinstance(item, dict):
-            msg = "Topic extraction response topics must be objects"
-            raise TopicExtractionError(msg)
-
-        raw_topic = _extract_topic_label(item)
-        topic = normalize_topic_label(raw_topic)
-        if not topic:
-            msg = "Topic extraction response topic must not be blank"
-            raise TopicExtractionError(msg)
-
-        raw_position = item.get("support_position")
-        try:
-            support_position = BillTopicPosition(raw_position)
-        except ValueError as exc:
-            msg = f"Invalid support_position: {raw_position!r}"
-            raise TopicExtractionError(msg) from exc
-
-        confidence = _parse_confidence(item.get("confidence"))
-        evidence = item.get("evidence")
-        if evidence is not None and not isinstance(evidence, str):
-            evidence = str(evidence)
-
-        extracted = ExtractedBillTopic(
-            topic=topic,
-            support_position=support_position,
-            confidence=confidence,
-            evidence=evidence,
-        )
-        key = (topic, support_position)
-        existing = deduped.get(key)
-        if existing is None or _confidence_rank(extracted) > _confidence_rank(existing):
-            deduped[key] = extracted
-
-    return list(deduped.values())
-
-
-def extract_topics_for_bill_text(
-    *,
-    openai_config: OpenAIConfig,
-    bill: Bill,
-    text: str,
-    candidate_topics: Sequence[str],
-) -> list[ExtractedBillTopic]:
-    """Extract accepted catalog topics for a bill text string."""
-    normalized_candidates = {normalize_topic_label(topic) for topic in candidate_topics}
-    messages = build_topic_extraction_messages(
-        bill=bill,
-        bill_text=text,
-        candidate_topics=sorted(normalized_candidates),
-    )
-    response_text = call_openai_topic_extraction(
-        openai_config=openai_config,
-        messages=messages,
-    )
-    extracted_topics = parse_topic_extraction_response(response_text)
-    return [topic for topic in extracted_topics if topic.topic in normalized_candidates]
-
-
-def store_bill_topic_result(
-    *,
-    session: Session,
-    bill: Bill,
-    topics: Sequence[ExtractedBillTopic],
-    replace_existing: bool = True,
-) -> None:
-    """Store extracted topics for one bill."""
-    if replace_existing:
-        session.execute(delete(BillTopic).where(BillTopic.bill_id == bill.id))
-
-    for topic in topics:
-        session.add(
-            BillTopic(
-                bill_id=bill.id,
-                topic=normalize_topic_label(topic.topic),
-                support_position=topic.support_position,
-            )
-        )
-
-
-def create_select_bills_for_topic_extraction(
-    congress: int | None = None,
-    bill_ids: list[int] | None = None,
-    bill_text_ids: list[int] | None = None,
-    with_votes_only: bool = False,
-    force: bool = False,
-    limit: int | None = None,
-) -> Select[tuple[Bill]]:
-    """Select bill rows that have summarized bill_text rows for topic extraction."""
-    has_summary = (BillText.summary.is_not(None), BillText.summary != "")
-    summarized_text_filters: list[ColumnElement[bool]] = [
-        BillText.bill_id == Bill.id,
-        *has_summary,
-    ]
-    if with_votes_only:
-        summarized_text_filters.append(
-            exists(
-                select(VoteTextTarget.vote_id)
-                .join(
-                    VoteClassification,
-                    VoteClassification.vote_id == VoteTextTarget.vote_id,
-                )
-                .where(
-                    VoteTextTarget.voted_text_version_id == BillText.id,
-                    VoteClassification.subject_type == SubjectType.MEASURE,
-                    VoteClassification.vote_relationship
-                    == VoteRelationship.DIRECT_TEXT_VOTE,
-                    VoteClassification.is_direct_vote_on_legislative_text.is_(True),
-                    VoteClassification.is_substantive_policy_vote.is_(True),
-                    VoteClassification.is_special_rule.is_(False),
-                )
-            )
-        )
-    summarized_text_exists = exists(select(BillText.id).where(*summarized_text_filters))
-    stmt = (
-        select(Bill)
-        .where(summarized_text_exists)
-        .options(selectinload(Bill.bill_texts.and_(*summarized_text_filters[1:])))
-        .order_by(Bill.id)
-    )
-    if congress is not None:
-        stmt = stmt.where(Bill.congress == congress)
-    if bill_ids:
-        stmt = stmt.where(Bill.id.in_(bill_ids))
-    if bill_text_ids:
-        selected_text_exists = exists(
-            select(BillText.id).where(
-                BillText.bill_id == Bill.id,
-                BillText.id.in_(bill_text_ids),
-                *summarized_text_filters[1:],
-            )
-        )
-        stmt = stmt.where(selected_text_exists)
-    if not force:
-        stmt = stmt.where(
-            ~exists(select(BillTopic.id).where(BillTopic.bill_id == Bill.id))
-        )
-    if limit is not None:
-        stmt = stmt.limit(limit)
-    return stmt
-
-
-def collect_topic_extraction_diagnostics(
-    session: Session,
-    *,
-    congress: int | None = None,
-    bill_ids: list[int] | None = None,
-    bill_text_ids: list[int] | None = None,
-    with_votes_only: bool = False,
-    force: bool = False,
-    limit: int | None = None,
-) -> TopicExtractionDiagnostics:
-    """Count topic extraction inputs for explaining empty selections."""
-    bill_filters = []
-    bill_text_filters: list[ColumnElement[bool]] = []
-    if congress is not None:
-        bill_filters.append(Bill.congress == congress)
-    if bill_ids:
-        bill_filters.append(Bill.id.in_(bill_ids))
-        bill_text_filters.append(BillText.bill_id.in_(bill_ids))
-    if bill_text_ids:
-        bill_text_filters.append(BillText.id.in_(bill_text_ids))
-    if with_votes_only:
-        bill_text_filters.append(
-            exists(
-                select(VoteTextTarget.vote_id)
-                .join(
-                    VoteClassification,
-                    VoteClassification.vote_id == VoteTextTarget.vote_id,
-                )
-                .where(
-                    VoteTextTarget.voted_text_version_id == BillText.id,
-                    VoteClassification.subject_type == SubjectType.MEASURE,
-                    VoteClassification.vote_relationship
-                    == VoteRelationship.DIRECT_TEXT_VOTE,
-                    VoteClassification.is_direct_vote_on_legislative_text.is_(True),
-                    VoteClassification.is_substantive_policy_vote.is_(True),
-                    VoteClassification.is_special_rule.is_(False),
-                )
-            )
-        )
-
-    has_summary = (BillText.summary.is_not(None), BillText.summary != "")
-    summary_filters = [*bill_text_filters, *has_summary]
-
-    bills_with_summaries = session.scalar(
-        select(func.count(func.distinct(Bill.id)))
-        .select_from(Bill)
-        .join(BillText, BillText.bill_id == Bill.id)
-        .where(*bill_filters, *summary_filters)
-    )
-    selected_bills = session.scalar(
-        select(func.count()).select_from(
-            create_select_bills_for_topic_extraction(
-                congress=congress,
-                bill_ids=bill_ids,
-                bill_text_ids=bill_text_ids,
-                with_votes_only=with_votes_only,
-                force=force,
-                limit=limit,
-            ).subquery()
-        )
-    )
-
-    return TopicExtractionDiagnostics(
-        bill_rows=session.scalar(select(func.count(Bill.id)).where(*bill_filters)) or 0,
-        bill_text_rows=_count_bill_texts(
-            session,
-            bill_filters=bill_filters,
-            bill_text_filters=bill_text_filters,
-        ),
-        summarized_bill_text_rows=_count_bill_texts(
-            session,
-            bill_filters=bill_filters,
-            bill_text_filters=summary_filters,
-        ),
-        bills_with_summaries=bills_with_summaries or 0,
-        bill_topic_rows=session.scalar(select(func.count(BillTopic.id))) or 0,
-        selected_bills=selected_bills or 0,
-    )
-
-
-def _load_json_response(response_text: str) -> dict[str, Any]:
-    text = response_text.strip()
-    fenced = re.fullmatch(r"```(?:json)?\s*(.*?)\s*```", text, flags=re.DOTALL)
-    if fenced:
-        text = fenced.group(1).strip()
-
-    try:
-        payload = json.loads(text)
-    except json.JSONDecodeError as exc:
-        msg = f"Topic extraction response is not valid JSON: {exc}"
-        raise TopicExtractionError(msg) from exc
-    if not isinstance(payload, dict):
-        msg = "Topic extraction response must be a JSON object"
-        raise TopicExtractionError(msg)
-    return payload
-
-
-def _parse_confidence(raw: Any) -> float | None:
-    if raw is None:
-        return None
-    try:
-        return float(raw)
-    except (TypeError, ValueError) as exc:
-        msg = f"Invalid confidence: {raw!r}"
-        raise TopicExtractionError(msg) from exc
-
-
-def _confidence_rank(topic: ExtractedBillTopic) -> tuple[int, float]:
-    if topic.confidence is None:
-        return (0, 0.0)
-    return (1, topic.confidence)
-
-
-def _extract_topic_label(item: dict[str, Any]) -> str:
-    raw_topic = item.get("topic")
-    if isinstance(raw_topic, str):
-        return raw_topic
-    if isinstance(raw_topic, dict):
-        for key in ("topic", "label", "name", "title"):
-            value = raw_topic.get(key)
-            if isinstance(value, str):
-                return value
-
-    msg = "Topic extraction response topic must be a string"
-    raise TopicExtractionError(msg)
-
-
-def _count_bill_texts(
-    session: Session,
-    *,
-    bill_filters: Sequence[ColumnElement[bool]],
-    bill_text_filters: Sequence[ColumnElement[bool]],
-) -> int:
-    stmt = select(func.count(BillText.id))
-    if bill_filters:
-        stmt = stmt.join(Bill, Bill.id == BillText.bill_id).where(*bill_filters)
-    return session.scalar(stmt.where(*bill_text_filters)) or 0
-
-
-def main(
-    topics_path: Annotated[
-        Path, typer.Option(help="Path to congressional issue topic JSON.")
-    ] = DEFAULT_TOPICS_PATH,
-    congress: Annotated[
-        int | None, typer.Option(help="Only process one Congress.")
-    ] = None,
-    bill_ids: Annotated[
-        list[int] | None,
-        typer.Option(
-            "--bill-id",
-            help="Only process one internal bill.id. Repeat for multiple bills.",
-        ),
-    ] = None,
-    bill_text_ids: Annotated[
-        list[int] | None,
-        typer.Option(
-            "--bill-text-id",
-            help="Only process one internal bill_text.id. Repeat for multiple rows.",
-        ),
-    ] = None,
-    with_votes_only: Annotated[
-        bool,
-        typer.Option(
-            "--with-votes-only",
-            help="Only process summarized bill_text rows linked to at least one vote.",
-        ),
-    ] = True,
-    limit: Annotated[int | None, typer.Option(help="Maximum rows to process.")] = None,
-    force: Annotated[
-        bool,
-        typer.Option(help="Regenerate topics for bills that already have topics."),
-    ] = False,
-    dry_run: Annotated[
-        bool,
-        typer.Option(help="Select bills and print diagnostics without calling OpenAI."),
-    ] = False,
-    diagnose: Annotated[
-        bool,
-        typer.Option(help="Log input-stage counts before processing."),
-    ] = False,
-    log_level: Annotated[str, typer.Option(help="Log level.")] = "INFO",
-) -> None:
-    """CLI entrypoint for generating and storing bill topics."""
-    logging.basicConfig(
-        level=log_level,
-        format="%(asctime)s %(levelname)s %(name)s: %(message)s",
-    )
-
-    topic_catalog = load_topic_catalog(topics_path)
-    logger.info(
-        "Loaded %d candidate topics from %s",
-        len(topic_catalog.candidate_topics),
-        topics_path,
-    )
-
-    engine = get_postgres_engine(name="DATA_SCIENCE_DEV")
-    with Session(engine) as session:
-        if diagnose or dry_run:
-            diagnostics = collect_topic_extraction_diagnostics(
-                session,
-                congress=congress,
-                bill_ids=bill_ids,
-                bill_text_ids=bill_text_ids,
-                with_votes_only=with_votes_only,
-                force=force,
-                limit=limit,
-            )
-            _log_topic_extraction_diagnostics(diagnostics)
-            if dry_run:
-                return
-
-        openai_config = get_openai_config()
-
-        stmt = create_select_bills_for_topic_extraction(
-            congress=congress,
-            bill_ids=bill_ids,
-            bill_text_ids=bill_text_ids,
-            with_votes_only=with_votes_only,
-            force=force,
-            limit=limit,
-        )
-        bills = session.scalars(stmt).all()
-        logger.info("Selected %d bills for topic extraction", len(bills))
-
-        written = 0
-        failed = 0
-        for index, bill in enumerate(bills, 1):
-            bill_text = _select_bill_text_for_topic_extraction(bill)
-            if bill_text is None:
-                logger.warning("Skipping bill id=%s: no usable summary", bill.id)
-                continue
-            summary = bill_text.summary.strip()
-
-            try:
-                extracted_topics = extract_topics_for_bill_text(
-                    openai_config=openai_config,
-                    bill=bill,
-                    text=summary,
-                    candidate_topics=topic_catalog.candidate_topics,
-                )
-            except (httpx.HTTPError, TopicExtractionError):
-                failed += 1
-                logger.exception(
-                    "Skipping bill id=%s after topic extraction failure", bill.id
-                )
-                continue
-
-            store_bill_topic_result(
-                session=session,
-                bill=bill,
-                topics=extracted_topics,
-                replace_existing=True,
-            )
-            written += 1
-            if index % 100 == 0:
-                session.commit()
-            logger.info(
-                "Stored %d topics for bill id=%s",
-                len(extracted_topics),
-                bill.id,
-            )
-
-        session.commit()
-        logger.info(
-            "Done: stored topic results for %d bills; failed %d bills",
-            written,
-            failed,
-        )
-
-
-def _log_topic_extraction_diagnostics(
-    diagnostics: TopicExtractionDiagnostics,
-) -> None:
-    logger.info(
-        "Topic extraction diagnostics: bill_rows=%d bill_text_rows=%d "
-        "summarized_bill_text_rows=%d bills_with_summaries=%d "
-        "bill_topic_rows=%d selected_bills=%d",
-        diagnostics.bill_rows,
-        diagnostics.bill_text_rows,
-        diagnostics.summarized_bill_text_rows,
-        diagnostics.bills_with_summaries,
-        diagnostics.bill_topic_rows,
-        diagnostics.selected_bills,
-    )
-    if diagnostics.bill_rows == 0:
-        logger.warning("No bills matched the topic extraction scope.")
-    elif diagnostics.bill_text_rows == 0:
-        logger.warning("No bill_text rows matched the topic extraction scope.")
-    elif diagnostics.summarized_bill_text_rows == 0:
-        logger.warning(
-            "No summarized bill_text rows matched the topic extraction scope. "
-            "Run pipelines.tools.summarize_bills first."
-        )
-    elif diagnostics.selected_bills == 0 and diagnostics.bill_topic_rows > 0:
-        logger.warning(
-            "No bills selected because matching bills already have topics. "
-            "Use --force to regenerate them."
-        )
-    elif diagnostics.selected_bills == 0:
-        logger.warning("No bills selected for topic extraction.")
-
-
-if __name__ == "__main__":
-    typer.run(main)

pipelines/jobs/summarize_bills.py

@@ -1,309 +0,0 @@
-"""Summarize bill_text rows with GPT-5 and store results in the database."""
-
-from __future__ import annotations
-
-import logging
-import tomllib
-from os import getenv
-from typing import Annotated, Any
-
-import httpx
-import typer
-from sqlalchemy import Select, exists, or_, select
-from sqlalchemy.orm import Session, selectinload
-
-from tiktoken import get_encoding
-
-
-from pipelines.config import get_config_dir
-from pipelines.orm.common import get_postgres_engine
-from pipelines.orm.data_science_dev.congress import (
-    Bill,
-    BillText,
-    SubjectType,
-    VoteClassification,
-    VoteRelationship,
-    VoteTextTarget,
-)
-from pipelines.tools.bill_token_compression import compress_bill_text
-
-logger = logging.getLogger(__name__)
-
-OPENAI_CHAT_COMPLETIONS_URL = "https://api.openai.com/v1/chat/completions"
-OPENAI_PROJECT_ID = "proj_fQBPEXFgnS87Fk6wZwploFwE"
-REQUEST_TIMEOUT_SECONDS = 60
-
-
-def load_summarization_prompts(
-    section: str = "summarization",
-) -> dict[str, str]:
-    summarization_prompts = get_config_dir() / "prompts" / "summarization_prompts.toml"
-
-    return tomllib.loads(summarization_prompts.read_text())[section]
-
-
-class BillSummaryError(RuntimeError):
-    """Raised when a bill summary request or response is invalid."""
-
-
-def call_openai_summary(
-    *,
-    model: str,
-    messages: list[dict[str, str]],
-) -> str:
-    """Call GPT and return the assistant message content."""
-    api_key = getenv("CLOSEDAI_TOKEN")
-    if not api_key:
-        msg = "CLOSEDAI_TOKEN is required"
-        raise BillSummaryError(msg)
-
-    response = httpx.post(
-        OPENAI_CHAT_COMPLETIONS_URL,
-        headers={
-            "Authorization": f"Bearer {api_key}",
-            "OpenAI-Project": OPENAI_PROJECT_ID,
-            "Content-Type": "application/json",
-        },
-        json={
-            "model": model,
-            "messages": messages,
-        },
-        timeout=REQUEST_TIMEOUT_SECONDS,
-    )
-    logger.info(f"{response.text=}")
-    response.raise_for_status()
-    return extract_message_content(response.json())
-
-
-def build_bill_summary_messages(
-    *,
-    bill_text: BillText,
-    summarization_prompts: dict[str, str],
-) -> list[dict[str, str]]:
-    """Build the GPT prompt messages plus compressed text and user prompt."""
-    if not bill_text.text_content:
-        msg = f"bill_text id={bill_text.id} has no text_content"
-        raise BillSummaryError(msg)
-
-    compressed_text = compress_bill_text(bill_text.text_content)
-    if not compressed_text:
-        msg = f"bill_text id={bill_text.id} has no summarizable text_content"
-        raise BillSummaryError(msg)
-
-    user_prompt = summarization_prompts["user_template"].format(
-        text_content=compressed_text
-    )
-
-    user_prompt_tokens = len(get_encoding("o200k_base").encode(user_prompt))
-    logger.info(f"{user_prompt_tokens=}")
-
-    messages = [
-        {"role": "system", "content": summarization_prompts["system_prompt"]},
-        {
-            "role": "user",
-            "content": user_prompt,
-        },
-    ]
-    return messages, user_prompt_tokens
-
-
-def summarize_bill_text(
-    *,
-    model: str,
-    bill_text: BillText,
-    summarization_prompts: dict[str, str],
-) -> str:
-    """Generate and return a summary for one bill_text row."""
-    messages, user_prompt_tokens = build_bill_summary_messages(
-        bill_text=bill_text,
-        summarization_prompts=summarization_prompts,
-    )
-    # This may only be for gpt-5.4 mini I need to read the docs
-    if user_prompt_tokens > 272000:
-        msg = f"Compressed bill_text id={bill_text.id} is too long for summarization ({user_prompt_tokens} tokens)"
-        logger.warning(msg)
-        return None
-
-    summary = call_openai_summary(
-        model=model,
-        messages=messages,
-    ).strip()
-    if not summary:
-        msg = f"Model returned an empty summary for bill_text id={bill_text.id}"
-        raise BillSummaryError(msg)
-    return summary
-
-
-def store_bill_summary_result(
-    *,
-    bill_text: BillText,
-    summary: str,
-    model: str,
-) -> None:
-    """Store a generated summary and the prompt/model metadata that produced it."""
-    bill_text.summary = summary
-    bill_text.summarization_model = model
-    bill_text.summarization_system_prompt_version = "v1.2"
-    bill_text.summarization_user_prompt_version = "v1"
-
-
-def create_select_bill_texts_for_summarization(
-    congress: int | None = None,
-    bill_ids: list[int] | None = None,
-    bill_text_ids: list[int] | None = None,
-    with_votes_only: bool = False,
-    force: bool = False,
-    limit: int | None = None,
-) -> Select:
-    """Select bill_text rows that have source text and need summaries."""
-    stmt = (
-        select(BillText)
-        .join(Bill, Bill.id == BillText.bill_id)
-        .where(BillText.text_content.is_not(None), BillText.text_content != "")
-        .options(selectinload(BillText.bill))
-        .order_by(BillText.id)
-    )
-    if congress is not None:
-        stmt = stmt.where(Bill.congress == congress)
-    if bill_ids:
-        stmt = stmt.where(BillText.bill_id.in_(bill_ids))
-    if bill_text_ids:
-        stmt = stmt.where(BillText.id.in_(bill_text_ids))
-    if with_votes_only:
-        stmt = stmt.where(
-            exists(
-                select(VoteTextTarget.vote_id)
-                .join(
-                    VoteClassification,
-                    VoteClassification.vote_id == VoteTextTarget.vote_id,
-                )
-                .where(
-                    VoteTextTarget.voted_text_version_id == BillText.id,
-                    VoteClassification.subject_type == SubjectType.MEASURE,
-                    VoteClassification.vote_relationship
-                    == VoteRelationship.DIRECT_TEXT_VOTE,
-                    VoteClassification.is_direct_vote_on_legislative_text.is_(True),
-                    VoteClassification.is_substantive_policy_vote.is_(True),
-                    VoteClassification.is_special_rule.is_(False),
-                )
-            )
-        )
-    if not force:
-        stmt = stmt.where(or_(BillText.summary.is_(None), BillText.summary == ""))
-    if limit is not None:
-        stmt = stmt.limit(limit)
-    return stmt
-
-
-def extract_message_content(data: dict[str, Any]) -> str:
-    """Extract message content from a chat-completions response body."""
-    choices = data.get("choices")
-    if not isinstance(choices, list) or not choices:
-        msg = "Chat completion response did not contain choices"
-        raise BillSummaryError(msg)
-
-    first = choices[0]
-    if not isinstance(first, dict):
-        msg = "Chat completion choice must be an object"
-        raise BillSummaryError(msg)
-
-    message = first.get("message")
-    if isinstance(message, dict) and isinstance(message.get("content"), str):
-        return message["content"]
-    if isinstance(first.get("text"), str):
-        return first["text"]
-
-    msg = "Chat completion response did not contain message content"
-    raise BillSummaryError(msg)
-
-
-def main(
-    model: Annotated[str, typer.Option(help="OpenAI model id.")] = "gpt-5.4-mini",
-    congress: Annotated[
-        int | None, typer.Option(help="Only process one Congress.")
-    ] = None,
-    bill_ids: Annotated[
-        list[int] | None,
-        typer.Option(
-            "--bill-id",
-            help="Only process one internal bill.id. Repeat for multiple bills.",
-        ),
-    ] = None,
-    bill_text_ids: Annotated[
-        list[int] | None,
-        typer.Option(
-            "--bill-text-id",
-            help="Only process one internal bill_text.id. Repeat for multiple rows.",
-        ),
-    ] = None,
-    with_votes_only: Annotated[
-        bool,
-        typer.Option(
-            "--with-votes-only",
-            help="Only process bill_text rows linked to at least one vote.",
-        ),
-    ] = False,
-    limit: Annotated[int | None, typer.Option(help="Maximum rows to process.")] = None,
-    force: Annotated[
-        bool,
-        typer.Option(help="Regenerate summaries for rows that already have a summary."),
-    ] = False,
-    dry_run: Annotated[
-        bool, typer.Option(help="Print summaries without writing them to the database.")
-    ] = False,
-    log_level: Annotated[str, typer.Option(help="Log level.")] = "INFO",
-) -> None:
-    """CLI entrypoint for generating and storing bill summaries."""
-    logging.basicConfig(
-        level=log_level,
-        format="%(asctime)s %(levelname)s %(name)s: %(message)s",
-    )
-    if not getenv("CLOSEDAI_TOKEN"):
-        message = "CLOSEDAI_TOKEN is required"
-        raise typer.BadParameter(message)
-
-    summarization_prompts = load_summarization_prompts()
-    engine = get_postgres_engine(name="DATA_SCIENCE_DEV")
-    with Session(engine) as session:
-        stmt = create_select_bill_texts_for_summarization(
-            congress=congress,
-            bill_ids=bill_ids,
-            bill_text_ids=bill_text_ids,
-            with_votes_only=with_votes_only,
-            force=force,
-            limit=limit,
-        )
-        bill_texts = session.scalars(stmt).all()
-        logger.info("Selected %d bill_text rows for summarization", len(bill_texts))
-
-        written = 0
-        for index, bill_text in enumerate(bill_texts, 1):
-            summary = summarize_bill_text(
-                model=model,
-                bill_text=bill_text,
-                summarization_prompts=summarization_prompts,
-            )
-            if summary is None:
-                logger.warning("Skipping bill_text id=%s", bill_text.id)
-                continue
-            store_bill_summary_result(
-                bill_text=bill_text,
-                summary=summary,
-                model=model,
-            )
-            if index % 100 == 0:
-                session.commit()
-            written += 1
-            session.commit()
-            logger.info("Stored summary for bill_text id=%s", bill_text.id)
-
-        logger.info("Done: stored %d summaries", written)
-
-
-def cli() -> None:
-    """Typer entry point."""
-    typer.run(main)
-
-
-if __name__ == "__main__":
-    cli()

pipelines/tools/Dockerfile.finetune

@@ -0,0 +1,25 @@
+# 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 config/prompts/summarization_prompts.toml config/prompts/summarization_prompts.toml
+COPY python/prompt_bench/__init__.py python/prompt_bench/__init__.py
+COPY python/__init__.py python/__init__.py
+
+ENTRYPOINT ["python", "-m", "pipelines.prompt_bench.finetune"]

pipelines/tools/batch_bill_summarizer.py

@@ -23,10 +23,14 @@ import httpx
 import typer
 from tiktoken import Encoding, get_encoding
 
-from pipelines.config import get_config_dir
 from pipelines.tools.bill_token_compression import compress_bill_text
 
-_PROMPTS_PATH = get_config_dir() / "prompts" / "summarization_prompts.toml"
+_PROMPTS_PATH = (
+    Path(__file__).resolve().parents[2]
+    / "config"
+    / "prompts"
+    / "summarization_prompts.toml"
+)
 _PROMPTS = tomllib.loads(_PROMPTS_PATH.read_text())["summarization"]
 SUMMARIZATION_SYSTEM_PROMPT: str = _PROMPTS["system_prompt"]
 SUMMARIZATION_USER_TEMPLATE: str = _PROMPTS["user_template"]

pipelines/tools/compresion_test.py

@@ -24,10 +24,14 @@ from typing import Annotated
 import httpx
 import typer
 
-from pipelines.config import get_config_dir
 from pipelines.tools.bill_token_compression import compress_bill_text
 
-_PROMPTS_PATH = get_config_dir() / "prompts" / "summarization_prompts.toml"
+_PROMPTS_PATH = (
+    Path(__file__).resolve().parents[2]
+    / "config"
+    / "prompts"
+    / "summarization_prompts.toml"
+)
 _PROMPTS = tomllib.loads(_PROMPTS_PATH.read_text())["summarization"]
 SUMMARIZATION_SYSTEM_PROMPT: str = _PROMPTS["system_prompt"]
 SUMMARIZATION_USER_TEMPLATE: str = _PROMPTS["user_template"]

pipelines/tools/containers/finetune.py

@@ -9,7 +9,7 @@ from typing import Annotated
 
 import typer
 
-from pipelines.pipelines.containers.lib import check_gpu_free
+from pipelines.tools.containers.lib import check_gpu_free
 
 logger = logging.getLogger(__name__)
 
@@ -27,7 +27,7 @@ def build_image() -> None:
             "docker",
             "build",
             "-f",
-            str(REPO_DIR / "pipelines/containers/docker_files/Dockerfile.finetune"),
+            str(REPO_DIR / "python/prompt_bench/Dockerfile.finetune"),
             "-t",
             FINETUNE_IMAGE,
             ".",

pipelines/tools/finetune.py

@@ -25,8 +25,6 @@ from datasets import Dataset
 from transformers import TrainingArguments
 from trl import SFTTrainer
 
-from pipelines.config import default_config_path
-
 logger = logging.getLogger(__name__)
 
 
@@ -125,7 +123,7 @@ def main(
     config_path: Annotated[
         Path,
         typer.Option("--config", help="TOML config file"),
-    ] = default_config_path(),
+    ] = Path(__file__).parent / "config.toml",
     save_gguf: Annotated[
         bool, typer.Option("--save-gguf/--no-save-gguf", help="Also save GGUF")
     ] = False,

pipelines/tools/prompt_bench.py

@@ -11,8 +11,8 @@ from typing import Annotated
 
 import typer
 
-from pipelines.containers.lib import check_gpu_free
+from pipelines.tools.containers.lib import check_gpu_free
-from pipelines.containers.vllm import start_vllm, stop_vllm
+from pipelines.tools.containers.vllm import start_vllm, stop_vllm
 from pipelines.tools.downloader import is_model_present
 from pipelines.tools.models import BenchmarkConfig
 from pipelines.tools.vllm_client import VLLMClient

pipelines/web/__init__.py

@@ -1 +0,0 @@
-"""FastAPI HTMX front end for the legislative database."""

pipelines/web/db.py

@@ -1,31 +0,0 @@
-"""Database access for the FastAPI web app."""
-
-from __future__ import annotations
-
-from collections.abc import Iterator
-from contextlib import contextmanager
-from functools import lru_cache
-
-from sqlalchemy.engine import Engine
-from sqlalchemy.orm import Session
-
-from pipelines.orm.common import get_postgres_engine
-
-
-@lru_cache(maxsize=1)
-def get_engine() -> Engine:
-    """Return the lazily-created DATA_SCIENCE_DEV SQLAlchemy engine."""
-    return get_postgres_engine(name="DATA_SCIENCE_DEV")
-
-
-def validate_database_connection() -> None:
-    """Fail fast if the configured DATA_SCIENCE_DEV database is unavailable."""
-    with get_engine().connect():
-        pass
-
-
-@contextmanager
-def session_scope() -> Iterator[Session]:
-    """Yield a SQLAlchemy session for a read-only request."""
-    with Session(get_engine()) as session:
-        yield session

pipelines/web/main.py

@@ -1,589 +0,0 @@
-"""FastAPI app for the HTMX legislative dashboard."""
-
-from __future__ import annotations
-
-from contextlib import asynccontextmanager
-from dataclasses import dataclass
-import hashlib
-import hmac
-from os import getenv
-from pathlib import Path
-import secrets
-from typing import Any
-from urllib.parse import parse_qs
-
-from fastapi import Depends, FastAPI, HTTPException, Request, Response, status
-from fastapi.responses import HTMLResponse, PlainTextResponse, RedirectResponse
-from fastapi.staticfiles import StaticFiles
-from fastapi.templating import Jinja2Templates
-
-from pipelines.web import repository
-from pipelines.web.db import session_scope, validate_database_connection
-from pipelines.web.repository import Chamber, RankingResult
-from pipelines.web.scoring import normalize_issues
-from pipelines.web.svg import render_compare_radar_svg, render_score_history_svg
-
-BASE_DIR = Path(__file__).resolve().parent
-TEMPLATES_DIR = BASE_DIR / "templates"
-STATIC_DIR = BASE_DIR / "static"
-
-templates = Jinja2Templates(directory=TEMPLATES_DIR)
-ADMIN_USERNAME = "admin"
-ADMIN_PASSWORD = "admin"
-SESSION_COOKIE = "nornsight_admin"
-SESSION_SECRET = "nornsight-local-dev-session-secret"
-
-
-@asynccontextmanager
-async def lifespan(_: FastAPI):
-    """Validate database access when the CLI starts the web server."""
-    if getenv("PYTEST_CURRENT_TEST") is None:
-        validate_database_connection()
-    yield
-
-
-app = FastAPI(title="Nornsight Legislative Dashboard", lifespan=lifespan)
-app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
-
-
-@dataclass(frozen=True)
-class DashboardState:
-    """Dashboard query-string state."""
-
-    issues: list[str]
-    chamber: Chamber
-    congress: int | None
-    compare: list[int]
-
-
-@app.get("/healthz", response_class=PlainTextResponse)
-def healthz() -> str:
-    """Return a simple liveness response."""
-    return "ok"
-
-
-@app.get("/login", response_class=HTMLResponse)
-def login(request: Request) -> Response:
-    """Render the integrated login page."""
-    next_path = _safe_next_path(request.query_params.get("next"))
-    if _authenticated_user(request) is not None:
-        return RedirectResponse(next_path, status_code=status.HTTP_303_SEE_OTHER)
-    return templates.TemplateResponse(
-        request,
-        "login.html",
-        {
-            "error": "",
-            "is_authenticated": False,
-            "show_primary_nav": False,
-            "next_path": next_path,
-            "username": "",
-        },
-    )
-
-
-@app.post("/login", response_class=HTMLResponse)
-async def login_submit(request: Request) -> Response:
-    """Authenticate the hard-coded admin user and set a session cookie."""
-    form = parse_qs((await request.body()).decode())
-    username = form.get("username", [""])[0]
-    password = form.get("password", [""])[0]
-    next_path = _safe_next_path(form.get("next", [request.query_params.get("next")])[0])
-
-    username_ok = secrets.compare_digest(username, ADMIN_USERNAME)
-    password_ok = secrets.compare_digest(password, ADMIN_PASSWORD)
-    if not (username_ok and password_ok):
-        return templates.TemplateResponse(
-            request,
-            "login.html",
-            {
-                "error": "Invalid username or password.",
-                "is_authenticated": False,
-                "show_primary_nav": False,
-                "next_path": next_path,
-                "username": username,
-            },
-            status_code=status.HTTP_401_UNAUTHORIZED,
-        )
-
-    response = RedirectResponse(next_path, status_code=status.HTTP_303_SEE_OTHER)
-    response.set_cookie(
-        SESSION_COOKIE,
-        _sign_session(username),
-        httponly=True,
-        samesite="lax",
-    )
-    return response
-
-
-@app.get("/logout")
-def logout() -> Response:
-    """Clear the local admin session."""
-    response = RedirectResponse("/login", status_code=status.HTTP_303_SEE_OTHER)
-    response.delete_cookie(SESSION_COOKIE)
-    return response
-
-
-def require_admin(request: Request) -> str:
-    """Redirect unauthenticated users to the in-site login page."""
-    username = _authenticated_user(request)
-    if username is not None:
-        return username
-    next_path = request.url.path
-    if request.url.query:
-        next_path = f"{next_path}?{request.url.query}"
-    login_url = request.url_for("login").include_query_params(next=next_path)
-    raise HTTPException(
-        status_code=status.HTTP_303_SEE_OTHER,
-        headers={"Location": str(login_url)},
-    )
-
-
-def _authenticated_user(request: Request) -> str | None:
-    token = request.cookies.get(SESSION_COOKIE)
-    if token is None:
-        return None
-    try:
-        username, signature = token.split(":", 1)
-    except ValueError:
-        return None
-    if username != ADMIN_USERNAME:
-        return None
-    expected = _session_signature(username)
-    if secrets.compare_digest(signature, expected):
-        return username
-    return None
-
-
-def _sign_session(username: str) -> str:
-    return f"{username}:{_session_signature(username)}"
-
-
-def _session_signature(username: str) -> str:
-    return hmac.new(
-        SESSION_SECRET.encode(),
-        username.encode(),
-        hashlib.sha256,
-    ).hexdigest()
-
-
-def _safe_next_path(value: str | None) -> str:
-    if value and value.startswith("/") and not value.startswith("//"):
-        return value
-    return "/"
-
-
-@app.get("/", response_class=HTMLResponse)
-def dashboard(request: Request, _: str = Depends(require_admin)) -> Response:
-    """Render the full dashboard page."""
-    context = _dashboard_context(request)
-    if request.headers.get("hx-request") == "true":
-        return templates.TemplateResponse(request, "partials/_dashboard.html", context)
-    return templates.TemplateResponse(request, "dashboard.html", context)
-
-
-@app.get("/partials/dashboard", response_class=HTMLResponse)
-def dashboard_partial(request: Request, _: str = Depends(require_admin)) -> Response:
-    """Render the filter-dependent dashboard body."""
-    context = _dashboard_context(request)
-    return templates.TemplateResponse(request, "partials/_dashboard.html", context)
-
-
-@app.get("/partials/issues", response_class=HTMLResponse)
-def issues_partial(request: Request, _: str = Depends(require_admin)) -> Response:
-    """Render only issue filters."""
-    context = _dashboard_context(request)
-    return templates.TemplateResponse(request, "partials/_issue_filters.html", context)
-
-
-@app.get("/partials/rankings", response_class=HTMLResponse)
-def rankings_partial(request: Request, _: str = Depends(require_admin)) -> Response:
-    """Render only ranking panels."""
-    context = _dashboard_context(request)
-    return templates.TemplateResponse(request, "partials/_rankings.html", context)
-
-
-@app.get("/partials/chart", response_class=HTMLResponse)
-def chart_partial(request: Request, _: str = Depends(require_admin)) -> Response:
-    """Render only the SVG chart panel."""
-    context = _dashboard_context(request)
-    return templates.TemplateResponse(request, "partials/_chart.html", context)
-
-
-@app.get("/legislators", response_class=HTMLResponse)
-def legislators(request: Request, _: str = Depends(require_admin)) -> Response:
-    """Render the legislator profile/search page."""
-    context = _legislators_context(request)
-    return templates.TemplateResponse(request, "legislators.html", context)
-
-
-@app.get("/partials/legislator-suggestions", response_class=HTMLResponse)
-def legislator_suggestions_partial(
-    request: Request, _: str = Depends(require_admin)
-) -> Response:
-    """Render legislator search suggestions for the HTMX typeahead."""
-    query = request.query_params.get("q", "").strip()
-    context: dict[str, Any] = {
-        "q": query if len(query) >= 2 else "",
-        "matches": [],
-        "build_legislator_url": _build_legislator_url,
-    }
-    if len(query) >= 2:
-        with session_scope() as session:
-            context["matches"] = repository.search_legislators(
-                session, query=query, limit=8
-            )
-    return templates.TemplateResponse(
-        request, "partials/_legislator_suggestions.html", context
-    )
-
-
-@app.get("/compare", response_class=HTMLResponse)
-def compare(request: Request, _: str = Depends(require_admin)) -> Response:
-    """Render the legislator radar comparison page."""
-    context = _compare_context(request)
-    return templates.TemplateResponse(request, "compare.html", context)
-
-
-def _dashboard_context(request: Request) -> dict[str, Any]:
-    state = _parse_state(request)
-    base_context: dict[str, Any] = {
-        "state": state,
-        "issues": state.issues,
-        "selected_issue_label": " + ".join(state.issues) if state.issues else "",
-        "chamber": state.chamber,
-        "congress": state.congress,
-        "latest_score_year": None,
-        "last_updated": None,
-        "suggestions": [],
-        "rankings": RankingResult(supportive=[], opposed=[]),
-        "compare": [],
-        "chart_svg": render_score_history_svg([]),
-        "chart_series": [],
-        "has_votes": False,
-        "has_scores": False,
-        "empty_message": "",
-        "build_url": _build_url,
-        "toggle_compare": _toggle_compare,
-    }
-    with session_scope() as session:
-        congress = state.congress or repository.latest_congress(session)
-        base_context["congress"] = congress
-        base_context["has_scores"] = repository.has_scores(session)
-        base_context["latest_score_year"] = repository.latest_score_year(session)
-        base_context["last_updated"] = repository.latest_vote_date(session, congress)
-        base_context["suggestions"] = repository.issue_suggestions(
-            session, congress=congress
-        )
-
-        if not base_context["has_scores"]:
-            base_context["empty_message"] = (
-                "No legislator scores are loaded yet. Run the score calculator first."
-            )
-            return base_context
-
-        if congress is None:
-            base_context["congress"] = "Computed"
-
-        if not state.issues:
-            base_context["empty_message"] = (
-                "Choose one or more issue areas to calculate roll-call support scores."
-            )
-            return base_context
-
-        rankings = repository.get_rankings(
-            session,
-            issues=state.issues,
-            chamber=state.chamber,
-            congress=congress,
-        )
-        base_context["rankings"] = rankings
-        compare = state.compare or [row.legislator_id for row in rankings.supportive[:2]]
-        base_context["compare"] = compare
-        if not rankings.supportive and not rankings.opposed:
-            base_context["empty_message"] = "No matching roll-call votes."
-            return base_context
-
-        history = repository.get_score_history(
-            session,
-            issues=state.issues,
-            chamber=state.chamber,
-            congress=congress,
-            legislator_ids=compare,
-        )
-        base_context["chart_series"] = history
-        base_context["chart_svg"] = render_score_history_svg(history)
-    return base_context
-
-
-def _parse_state(request: Request) -> DashboardState:
-    query = request.query_params
-    chamber = query.get("chamber", "senate").lower()
-    if chamber not in {"house", "senate", "all"}:
-        chamber = "senate"
-    congress = _parse_int(query.get("congress"))
-    compare = [
-        value
-        for value in (_parse_int(raw) for raw in query.getlist("compare"))
-        if value is not None
-    ]
-    return DashboardState(
-        issues=normalize_issues(query.getlist("issues")),
-        chamber=chamber,  # type: ignore[arg-type]
-        congress=congress,
-        compare=compare,
-    )
-
-
-def _legislators_context(request: Request) -> dict[str, Any]:
-    query = request.query_params.get("q", "").strip()
-    legislator_id = _parse_int(request.query_params.get("legislator_id"))
-    selected_topic = request.query_params.get("topic", "").strip()
-    per_page = _parse_per_page(request.query_params.get("per_page"))
-    page = max(_parse_int(request.query_params.get("page")) or 1, 1)
-    base_context: dict[str, Any] = {
-        "q": query,
-        "profile": None,
-        "matches": [],
-        "result_count": 0,
-        "page": page,
-        "per_page": per_page,
-        "per_page_options": [10, 25, 50],
-        "total_pages": 1,
-        "previous_page": None,
-        "next_page": None,
-        "selected_topic": selected_topic,
-        "history_svg": render_score_history_svg([]),
-        "history_series": [],
-        "build_legislator_url": _build_legislator_url,
-        "build_legislator_search_url": _build_legislator_search_url,
-    }
-    with session_scope() as session:
-        result_count = repository.count_legislators(session, query=query) if query else 0
-        total_pages = max((result_count + per_page - 1) // per_page, 1)
-        if page > total_pages:
-            page = total_pages
-            base_context["page"] = page
-        matches = (
-            repository.search_legislators(
-                session,
-                query=query,
-                limit=per_page,
-                offset=(page - 1) * per_page,
-            )
-            if query
-            else []
-        )
-        profile = repository.get_legislator_profile(
-            session, legislator_id=legislator_id, query=None
-        )
-        base_context["profile"] = profile
-        base_context["matches"] = matches
-        base_context["result_count"] = result_count
-        base_context["total_pages"] = total_pages
-        base_context["previous_page"] = page - 1 if page > 1 else None
-        base_context["next_page"] = page + 1 if page < total_pages else None
-        if profile is None:
-            return base_context
-        if not selected_topic:
-            if profile.bottom_topics:
-                selected_topic = profile.bottom_topics[0].topic
-            elif profile.top_topics:
-                selected_topic = profile.top_topics[0].topic
-        base_context["selected_topic"] = selected_topic
-        if selected_topic:
-            history = repository.get_single_legislator_history(
-                session,
-                legislator_id=profile.legislator.legislator_id,
-                topic=selected_topic,
-            )
-            base_context["history_series"] = history
-            base_context["history_svg"] = render_score_history_svg(history)
-    return base_context
-
-
-def _compare_context(request: Request) -> dict[str, Any]:
-    selected_legislators = _parse_int_list(
-        request.query_params.getlist("legislator_id")
-        or request.query_params.getlist("compare")
-    )[:4]
-    topics = normalize_issues(
-        request.query_params.getlist("topic") or request.query_params.getlist("issues")
-    )[:8]
-    query = request.query_params.get("q", "").strip()
-    base_context: dict[str, Any] = {
-        "selected_legislators": selected_legislators,
-        "selected_legislator_options": [],
-        "topics": topics,
-        "q": query,
-        "series": [],
-        "radar_svg": render_compare_radar_svg([], []),
-        "legislator_options": [],
-        "topic_options": [],
-        "build_compare_url": _build_compare_url,
-    }
-    with session_scope() as session:
-        default_legislators, default_topics = repository.get_compare_defaults(session)
-        if not selected_legislators and not query:
-            selected_legislators = default_legislators[:3]
-        if not topics:
-            topics = default_topics[:6]
-        selected_legislator_options = repository.get_legislator_options(
-            session, selected_legislators
-        )
-        series = repository.get_compare_radar_series(
-            session, legislator_ids=selected_legislators, topics=topics
-        )
-        base_context.update(
-            {
-                "selected_legislators": selected_legislators,
-                "selected_legislator_options": selected_legislator_options,
-                "topics": topics,
-                "q": query,
-                "series": series,
-                "radar_svg": render_compare_radar_svg(topics, series),
-                "legislator_options": repository.search_legislators(
-                    session, query=query or None, limit=12
-                ),
-                "topic_options": repository.issue_suggestions(
-                    session, congress=None, limit=12
-                ),
-            }
-        )
-    return base_context
-
-
-def _parse_int(value: str | None) -> int | None:
-    if value is None or value == "":
-        return None
-    try:
-        return int(value)
-    except ValueError:
-        return None
-
-
-def _parse_int_list(values: list[str]) -> list[int]:
-    parsed: list[int] = []
-    seen: set[int] = set()
-    for value in values:
-        integer = _parse_int(value)
-        if integer is not None and integer not in seen:
-            parsed.append(integer)
-            seen.add(integer)
-    return parsed
-
-
-def _parse_per_page(value: str | None) -> int:
-    parsed = _parse_int(value)
-    return parsed if parsed in {10, 25, 50} else 10
-
-
-def _build_url(
-    request: Request,
-    *,
-    issues: list[str] | None = None,
-    chamber: str | None = None,
-    congress: int | None = None,
-    compare: list[int] | None = None,
-) -> str:
-    params: list[tuple[str, str]] = []
-    chosen_issues = (
-        issues
-        if issues is not None
-        else normalize_issues(request.query_params.getlist("issues"))
-    )
-    chosen_chamber = (
-        chamber
-        if chamber is not None
-        else request.query_params.get("chamber", "senate")
-    )
-    chosen_congress = (
-        congress
-        if congress is not None
-        else _parse_int(request.query_params.get("congress"))
-    )
-    chosen_compare = (
-        compare
-        if compare is not None
-        else [
-            value
-            for value in (
-                _parse_int(raw) for raw in request.query_params.getlist("compare")
-            )
-            if value is not None
-        ]
-    )
-    for issue in chosen_issues:
-        params.append(("issues", issue))
-    params.append(("chamber", chosen_chamber))
-    if chosen_congress is not None:
-        params.append(("congress", str(chosen_congress)))
-    for legislator_id in chosen_compare:
-        params.append(("compare", str(legislator_id)))
-    if not params:
-        return "/"
-    from urllib.parse import urlencode
-
-    return f"/?{urlencode(params, doseq=True)}"
-
-
-def _toggle_compare(compare: list[int], legislator_id: int) -> list[int]:
-    """Return compare IDs with the legislator added or removed."""
-    if legislator_id in compare:
-        return [value for value in compare if value != legislator_id]
-    return [*compare, legislator_id]
-
-
-def _build_legislator_url(
-    *,
-    legislator_id: int | None = None,
-    q: str | None = None,
-    topic: str | None = None,
-    per_page: int | None = None,
-) -> str:
-    from urllib.parse import urlencode
-
-    params: list[tuple[str, str]] = []
-    if legislator_id is not None:
-        params.append(("legislator_id", str(legislator_id)))
-    if q:
-        params.append(("q", q))
-    if topic:
-        params.append(("topic", topic))
-    if per_page in {10, 25, 50} and per_page != 10:
-        params.append(("per_page", str(per_page)))
-    return f"/legislators?{urlencode(params)}" if params else "/legislators"
-
-
-def _build_legislator_search_url(
-    *,
-    q: str,
-    per_page: int,
-    page: int = 1,
-) -> str:
-    from urllib.parse import urlencode
-
-    params: list[tuple[str, str]] = []
-    if q:
-        params.append(("q", q))
-    params.append(("per_page", str(per_page)))
-    if page > 1:
-        params.append(("page", str(page)))
-    return f"/legislators?{urlencode(params)}" if params else "/legislators"
-
-
-def _build_compare_url(
-    *,
-    legislator_ids: list[int],
-    topics: list[str],
-    q: str | None = None,
-) -> str:
-    from urllib.parse import urlencode
-
-    params: list[tuple[str, str]] = []
-    for legislator_id in legislator_ids[:4]:
-        params.append(("legislator_id", str(legislator_id)))
-    for topic in topics[:8]:
-        params.append(("topic", topic))
-    if q:
-        params.append(("q", q))
-    return f"/compare?{urlencode(params, doseq=True)}" if params else "/compare"

pipelines/web/repository.py

@@ -1,670 +0,0 @@
-"""Congress database queries for the web dashboard."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from datetime import date
-from typing import Literal
-
-from sqlalchemy import ColumnElement, Select, case, desc, false, func, or_, select, true
-from sqlalchemy.orm import Session
-
-from pipelines.orm.data_science_dev.congress import (
-    BillTopic,
-    Legislator,
-    LegislatorScore,
-    Vote,
-)
-from pipelines.web.scoring import normalize_issues
-
-Chamber = Literal["house", "senate", "all"]
-
-STATE_ALIASES = {
-    "alabama": "AL",
-    "alaska": "AK",
-    "arizona": "AZ",
-    "arkansas": "AR",
-    "california": "CA",
-    "colorado": "CO",
-    "connecticut": "CT",
-    "delaware": "DE",
-    "florida": "FL",
-    "georgia": "GA",
-    "hawaii": "HI",
-    "idaho": "ID",
-    "illinois": "IL",
-    "indiana": "IN",
-    "iowa": "IA",
-    "kansas": "KS",
-    "kentucky": "KY",
-    "louisiana": "LA",
-    "maine": "ME",
-    "maryland": "MD",
-    "massachusetts": "MA",
-    "michigan": "MI",
-    "minnesota": "MN",
-    "mississippi": "MS",
-    "missouri": "MO",
-    "montana": "MT",
-    "nebraska": "NE",
-    "nevada": "NV",
-    "new hampshire": "NH",
-    "new jersey": "NJ",
-    "new mexico": "NM",
-    "new york": "NY",
-    "north carolina": "NC",
-    "north dakota": "ND",
-    "ohio": "OH",
-    "oklahoma": "OK",
-    "oregon": "OR",
-    "pennsylvania": "PA",
-    "rhode island": "RI",
-    "south carolina": "SC",
-    "south dakota": "SD",
-    "tennessee": "TN",
-    "texas": "TX",
-    "utah": "UT",
-    "vermont": "VT",
-    "virginia": "VA",
-    "washington": "WA",
-    "west virginia": "WV",
-    "wisconsin": "WI",
-    "wyoming": "WY",
-    "district of columbia": "DC",
-}
-
-
-@dataclass(frozen=True)
-class RankingRow:
-    """A legislator support score row."""
-
-    legislator_id: int
-    display_name: str
-    party: str | None
-    state: str | None
-    chamber: str | None
-    score: float | None
-    supportive: int
-    opposed: int
-
-    @property
-    def total(self) -> int:
-        return self.supportive + self.opposed
-
-
-@dataclass(frozen=True)
-class RankingResult:
-    """Supportive and opposed ranking lists."""
-
-    supportive: list[RankingRow]
-    opposed: list[RankingRow]
-
-
-@dataclass(frozen=True)
-class TimePoint:
-    """One yearly chart point."""
-
-    year: int
-    score: float
-
-
-@dataclass(frozen=True)
-class ChartSeries:
-    """One legislator score-history series."""
-
-    legislator_id: int
-    label: str
-    party: str | None
-    state: str | None
-    points: list[TimePoint]
-
-
-@dataclass(frozen=True)
-class TopicScore:
-    """Average score for one topic."""
-
-    topic: str
-    score: float
-    count: int
-
-
-@dataclass(frozen=True)
-class LegislatorOption:
-    """Compact legislator metadata for search and comparison controls."""
-
-    legislator_id: int
-    display_name: str
-    party: str | None
-    state: str | None
-    chamber: str | None
-
-
-@dataclass(frozen=True)
-class LegislatorProfile:
-    """Legislator metadata plus issue score summary."""
-
-    legislator: LegislatorOption
-    overall_score: float | None
-    serving_since: int | None
-    top_topics: list[TopicScore]
-    bottom_topics: list[TopicScore]
-
-
-@dataclass(frozen=True)
-class RadarSeries:
-    """One legislator polygon for the compare radar chart."""
-
-    legislator: LegislatorOption
-    average_score: float | None
-    scores_by_topic: dict[str, float]
-
-
-def latest_congress(session: Session) -> int | None:
-    """Return the latest congress number in the vote table."""
-    return session.scalar(select(func.max(Vote.congress)))
-
-
-def latest_vote_date(session: Session, congress: int | None = None) -> date | None:
-    """Return the most recent vote date, optionally scoped to a congress."""
-    stmt = select(func.max(Vote.vote_date))
-    if congress is not None:
-        stmt = stmt.where(Vote.congress == congress)
-    return session.scalar(stmt)
-
-
-def latest_score_year(session: Session) -> int | None:
-    """Return the latest year in the precomputed legislator score table."""
-    return session.scalar(select(func.max(LegislatorScore.year)))
-
-
-def has_scores(session: Session) -> bool:
-    """Return True when the database has at least one precomputed score."""
-    return session.scalar(select(LegislatorScore.id).limit(1)) is not None
-
-
-def issue_suggestions(
-    session: Session,
-    *,
-    congress: int | None,
-    limit: int = 12,
-) -> list[str]:
-    """Return common precomputed score topics for issue filter suggestions."""
-    stmt = (
-        select(LegislatorScore.topic, func.count(LegislatorScore.id).label("score_count"))
-        .where(LegislatorScore.topic != "")
-        .group_by(LegislatorScore.topic)
-        .order_by(desc("score_count"), LegislatorScore.topic)
-        .limit(limit)
-    )
-    suggestions = [row[0] for row in session.execute(stmt).all()]
-    if suggestions:
-        return suggestions
-
-    fallback = (
-        select(BillTopic.topic, func.count(BillTopic.id).label("topic_count"))
-        .where(BillTopic.topic != "")
-        .group_by(BillTopic.topic)
-        .order_by(desc("topic_count"), BillTopic.topic)
-        .limit(limit)
-    )
-    return [row[0] for row in session.execute(fallback).all()]
-
-
-def ranking_query(
-    *,
-    issues: list[str],
-    chamber: Chamber,
-    congress: int,
-) -> Select:
-    """Build the aggregate ranking query from precomputed scores."""
-    average_score = func.avg(LegislatorScore.score).label("score")
-    supportive = func.sum(case((LegislatorScore.score >= 50, 1), else_=0)).label(
-        "supportive"
-    )
-    opposed = func.sum(case((LegislatorScore.score < 50, 1), else_=0)).label("opposed")
-
-    stmt = (
-        select(
-            Legislator.id,
-            Legislator.official_full_name,
-            Legislator.last_name,
-            Legislator.current_party,
-            Legislator.current_state,
-            Legislator.current_chamber,
-            average_score,
-            supportive,
-            opposed,
-        )
-        .join(LegislatorScore, LegislatorScore.legislator_id == Legislator.id)
-        .where(_score_topic_match_condition(issues))
-        .group_by(
-            Legislator.id,
-            Legislator.official_full_name,
-            Legislator.last_name,
-            Legislator.current_party,
-            Legislator.current_state,
-            Legislator.current_chamber,
-        )
-    )
-    if chamber != "all":
-        stmt = stmt.where(Legislator.current_chamber == _db_chamber(chamber))
-    return stmt
-
-
-def get_rankings(
-    session: Session,
-    *,
-    issues: list[str],
-    chamber: Chamber,
-    congress: int,
-    limit: int = 10,
-) -> RankingResult:
-    """Return top supportive and opposed legislators from precomputed scores."""
-    rows = [
-        _ranking_row(row)
-        for row in session.execute(
-            ranking_query(issues=issues, chamber=chamber, congress=congress)
-        )
-    ]
-    scored = [row for row in rows if row.score is not None]
-    supportive = sorted(
-        scored, key=lambda row: (-float(row.score), -row.total, row.display_name)
-    )[:limit]
-    opposed = sorted(
-        scored, key=lambda row: (float(row.score), -row.total, row.display_name)
-    )[:limit]
-    return RankingResult(supportive=supportive, opposed=opposed)
-
-
-def get_score_history(
-    session: Session,
-    *,
-    issues: list[str],
-    chamber: Chamber,
-    congress: int,
-    legislator_ids: list[int],
-) -> list[ChartSeries]:
-    """Return yearly score history from precomputed scores."""
-    if not legislator_ids:
-        return []
-
-    average_score = func.avg(LegislatorScore.score).label("score")
-    stmt = (
-        select(
-            Legislator.id,
-            Legislator.official_full_name,
-            Legislator.last_name,
-            Legislator.current_party,
-            Legislator.current_state,
-            LegislatorScore.year,
-            average_score,
-        )
-        .join(LegislatorScore, LegislatorScore.legislator_id == Legislator.id)
-        .where(
-            Legislator.id.in_(legislator_ids),
-            _score_topic_match_condition(issues),
-        )
-        .group_by(
-            Legislator.id,
-            Legislator.official_full_name,
-            Legislator.last_name,
-            Legislator.current_party,
-            Legislator.current_state,
-            LegislatorScore.year,
-        )
-        .order_by(Legislator.id, LegislatorScore.year)
-    )
-    if chamber != "all":
-        stmt = stmt.where(Legislator.current_chamber == _db_chamber(chamber))
-
-    by_legislator: dict[int, ChartSeries] = {}
-    for row in session.execute(stmt):
-        if row.score is None:
-            continue
-        series = by_legislator.setdefault(
-            row.id,
-            ChartSeries(
-                legislator_id=row.id,
-                label=_display_name(row.official_full_name, row.last_name),
-                party=row.current_party,
-                state=row.current_state,
-                points=[],
-            ),
-        )
-        series.points.append(TimePoint(year=int(row.year), score=float(row.score)))
-    return list(by_legislator.values())
-
-
-def _ranking_row(row: object) -> RankingRow:
-    return RankingRow(
-        legislator_id=row.id,
-        display_name=_display_name(row.official_full_name, row.last_name),
-        party=row.current_party,
-        state=row.current_state,
-        chamber=row.current_chamber,
-        score=float(row.score) if row.score is not None else None,
-        supportive=row.supportive or 0,
-        opposed=row.opposed or 0,
-    )
-
-
-def _score_topic_match_condition(
-    issues: list[str] | tuple[str, ...],
-) -> ColumnElement[bool]:
-    normalized = normalize_issues(list(issues))
-    if not normalized:
-        return false()
-    return or_(*(LegislatorScore.topic.ilike(f"%{issue}%") for issue in normalized))
-
-
-def search_legislators(
-    session: Session,
-    *,
-    query: str | None = None,
-    limit: int = 12,
-    offset: int = 0,
-) -> list[LegislatorOption]:
-    """Search ingested legislators, preferring those with computed scores."""
-    return [
-        _legislator_option(row)
-        for row in session.execute(
-            legislator_search_query(query=query, limit=limit, offset=offset)
-        )
-    ]
-
-
-def count_legislators(session: Session, *, query: str | None = None) -> int:
-    """Return the total number of legislators matching a search query."""
-    return int(
-        session.scalar(
-            select(func.count(Legislator.id)).where(_legislator_search_condition(query))
-        )
-        or 0
-    )
-
-
-def get_legislator_options(
-    session: Session, legislator_ids: list[int]
-) -> list[LegislatorOption]:
-    """Return legislator options in the same order as the selected IDs."""
-    options = {
-        option.legislator_id: option
-        for option in (
-            _get_legislator_option(session, legislator_id)
-            for legislator_id in legislator_ids
-        )
-        if option is not None
-    }
-    return [
-        options[legislator_id]
-        for legislator_id in legislator_ids
-        if legislator_id in options
-    ]
-
-
-def legislator_search_query(
-    *,
-    query: str | None = None,
-    limit: int = 12,
-    offset: int = 0,
-) -> Select:
-    """Build the legislator search query used by profile and compare controls."""
-    score_count = func.count(LegislatorScore.id).label("score_count")
-    stmt = (
-        select(
-            Legislator.id,
-            Legislator.official_full_name,
-            Legislator.last_name,
-            Legislator.current_party,
-            Legislator.current_state,
-            Legislator.current_chamber,
-            score_count,
-        )
-        .outerjoin(LegislatorScore, LegislatorScore.legislator_id == Legislator.id)
-        .group_by(
-            Legislator.id,
-            Legislator.official_full_name,
-            Legislator.first_name,
-            Legislator.last_name,
-            Legislator.current_party,
-            Legislator.current_state,
-            Legislator.current_chamber,
-            Legislator.bioguide_id,
-        )
-        .order_by(desc("score_count"), Legislator.last_name, Legislator.first_name)
-        .limit(limit)
-        .offset(offset)
-    )
-    return stmt.where(_legislator_search_condition(query))
-
-
-def _legislator_search_condition(query: str | None) -> ColumnElement[bool]:
-    cleaned_query = query.strip() if query else ""
-    if not cleaned_query:
-        return true()
-
-    pattern = f"%{cleaned_query}%"
-    state_alias = _state_alias(cleaned_query)
-    conditions: list[ColumnElement[bool]] = [
-        Legislator.official_full_name.ilike(pattern),
-        Legislator.first_name.ilike(pattern),
-        Legislator.last_name.ilike(pattern),
-        Legislator.current_state.ilike(pattern),
-        Legislator.bioguide_id.ilike(pattern),
-    ]
-    if state_alias is not None:
-        conditions.append(Legislator.current_state == state_alias)
-    return or_(*conditions)
-
-
-def _state_alias(query: str) -> str | None:
-    normalized = " ".join(query.lower().replace(".", "").split())
-    if len(normalized) == 2 and normalized.isalpha():
-        return normalized.upper()
-    return STATE_ALIASES.get(normalized)
-
-
-def get_legislator_profile(
-    session: Session,
-    *,
-    legislator_id: int | None = None,
-    query: str | None = None,
-) -> LegislatorProfile | None:
-    """Return the selected legislator profile and top/bottom topic scores."""
-    selected = _get_legislator_option(session, legislator_id)
-    cleaned_query = query.strip() if query else ""
-    if selected is None and cleaned_query:
-        matches = search_legislators(session, query=query, limit=1)
-        selected = matches[0] if matches else None
-    if selected is None:
-        return None
-
-    topic_scores = get_legislator_topic_scores(
-        session, legislator_id=selected.legislator_id
-    )
-    top_topics = sorted(topic_scores, key=lambda item: (-item.score, item.topic))[:3]
-    bottom_topics = sorted(topic_scores, key=lambda item: (item.score, item.topic))[:3]
-    overall_score = session.scalar(
-        select(func.avg(LegislatorScore.score)).where(
-            LegislatorScore.legislator_id == selected.legislator_id
-        )
-    )
-    serving_since = session.scalar(
-        select(func.min(LegislatorScore.year)).where(
-            LegislatorScore.legislator_id == selected.legislator_id
-        )
-    )
-    return LegislatorProfile(
-        legislator=selected,
-        overall_score=float(overall_score) if overall_score is not None else None,
-        serving_since=int(serving_since) if serving_since is not None else None,
-        top_topics=top_topics,
-        bottom_topics=bottom_topics,
-    )
-
-
-def get_legislator_topic_scores(
-    session: Session,
-    *,
-    legislator_id: int,
-) -> list[TopicScore]:
-    """Return all average topic scores for one legislator."""
-    rows = session.execute(
-        select(
-            LegislatorScore.topic,
-            func.avg(LegislatorScore.score).label("score"),
-            func.count(LegislatorScore.id).label("count"),
-        )
-        .where(LegislatorScore.legislator_id == legislator_id)
-        .group_by(LegislatorScore.topic)
-        .order_by(LegislatorScore.topic)
-    )
-    return [
-        TopicScore(topic=row.topic, score=float(row.score), count=row.count)
-        for row in rows
-        if row.score is not None
-    ]
-
-
-def get_single_legislator_history(
-    session: Session,
-    *,
-    legislator_id: int,
-    topic: str,
-) -> list[ChartSeries]:
-    """Return score history for one legislator/topic pair."""
-    option = _get_legislator_option(session, legislator_id)
-    if option is None:
-        return []
-
-    rows = session.execute(
-        select(
-            LegislatorScore.year,
-            func.avg(LegislatorScore.score).label("score"),
-        )
-        .where(
-            LegislatorScore.legislator_id == legislator_id,
-            LegislatorScore.topic == topic,
-        )
-        .group_by(LegislatorScore.year)
-        .order_by(LegislatorScore.year)
-    )
-    points = [
-        TimePoint(year=int(row.year), score=float(row.score))
-        for row in rows
-        if row.score is not None
-    ]
-    return [
-        ChartSeries(
-            legislator_id=option.legislator_id,
-            label=option.display_name,
-            party=option.party,
-            state=option.state,
-            points=points,
-        )
-    ]
-
-
-def get_compare_defaults(session: Session) -> tuple[list[int], list[str]]:
-    """Return default compare legislators and topics."""
-    legislators = search_legislators(session, limit=3)
-    topics = issue_suggestions(session, congress=None, limit=6)
-    return [item.legislator_id for item in legislators], topics
-
-
-def get_compare_radar_series(
-    session: Session,
-    *,
-    legislator_ids: list[int],
-    topics: list[str],
-) -> list[RadarSeries]:
-    """Return radar chart scores for selected legislators and topics."""
-    if not legislator_ids:
-        return []
-
-    options = {
-        option.legislator_id: option
-        for option in (
-            _get_legislator_option(session, legislator_id)
-            for legislator_id in legislator_ids
-        )
-        if option is not None
-    }
-    if not options:
-        return []
-
-    scores: dict[int, dict[str, float]] = {
-        legislator_id: {} for legislator_id in options
-    }
-    if topics:
-        rows = session.execute(
-            select(
-                LegislatorScore.legislator_id,
-                LegislatorScore.topic,
-                func.avg(LegislatorScore.score).label("score"),
-            )
-            .where(
-                LegislatorScore.legislator_id.in_(list(options)),
-                LegislatorScore.topic.in_(topics),
-            )
-            .group_by(LegislatorScore.legislator_id, LegislatorScore.topic)
-        )
-        for row in rows:
-            scores[row.legislator_id][row.topic] = float(row.score)
-
-    series: list[RadarSeries] = []
-    for legislator_id in legislator_ids:
-        option = options.get(legislator_id)
-        if option is None:
-            continue
-        topic_scores = scores.get(legislator_id, {})
-        values = list(topic_scores.values())
-        series.append(
-            RadarSeries(
-                legislator=option,
-                average_score=sum(values) / len(values) if values else None,
-                scores_by_topic=topic_scores,
-            )
-        )
-    return series
-
-
-def _display_name(official_full_name: str | None, last_name: str | None) -> str:
-    if official_full_name:
-        parts = official_full_name.split()
-        if len(parts) > 1:
-            return f"{parts[-1]}, {' '.join(parts[:-1])}"
-        return official_full_name
-    return last_name or "Unknown"
-
-
-def _legislator_option(row: object) -> LegislatorOption:
-    return LegislatorOption(
-        legislator_id=row.id,
-        display_name=_display_name(row.official_full_name, row.last_name),
-        party=row.current_party,
-        state=row.current_state,
-        chamber=row.current_chamber,
-    )
-
-
-def _get_legislator_option(
-    session: Session, legislator_id: int | None
-) -> LegislatorOption | None:
-    if legislator_id is None:
-        return None
-    row = session.execute(
-        select(
-            Legislator.id,
-            Legislator.official_full_name,
-            Legislator.last_name,
-            Legislator.current_party,
-            Legislator.current_state,
-            Legislator.current_chamber,
-        ).where(Legislator.id == legislator_id)
-    ).first()
-    return _legislator_option(row) if row is not None else None
-
-
-def _db_chamber(chamber: Chamber) -> str:
-    return {"house": "House", "senate": "Senate", "all": "all"}[chamber]

pipelines/web/scoring.py

@@ -1,100 +0,0 @@
-"""Issue matching and voting score helpers."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-
-from sqlalchemy import ColumnElement, false, func, or_
-from sqlalchemy.sql.elements import BinaryExpression
-
-from pipelines.orm.data_science_dev.congress import Bill, BillTopicPosition, Vote
-
-SUPPORT_POSITIONS = frozenset({"yea", "aye", "yes"})
-OPPOSE_POSITIONS = frozenset({"nay", "no"})
-
-
-@dataclass(frozen=True)
-class ScoreCounts:
-    """Support/opposition counts for one legislator or time bucket."""
-
-    supportive: int
-    opposed: int
-
-    @property
-    def total(self) -> int:
-        return self.supportive + self.opposed
-
-
-def normalize_position(position: str | None) -> str | None:
-    """Normalize a raw roll-call position into support/oppose/ignore buckets."""
-    if position is None:
-        return None
-    value = position.strip().lower()
-    if value in SUPPORT_POSITIONS:
-        return "support"
-    if value in OPPOSE_POSITIONS:
-        return "oppose"
-    return None
-
-
-def score_vote_position(
-    position: str | None,
-    support_position: BillTopicPosition | str,
-) -> str | None:
-    """Score a raw vote as support/opposition for an extracted bill topic."""
-    normalized_vote = normalize_position(position)
-    if normalized_vote is None:
-        return None
-
-    topic_position = BillTopicPosition(support_position)
-    if topic_position is BillTopicPosition.FOR:
-        return normalized_vote
-    if normalized_vote == "support":
-        return "oppose"
-    return "support"
-
-
-def calculate_score(counts: ScoreCounts) -> int | None:
-    """Calculate the 0-100 support score, or None when there are no scored votes."""
-    if counts.total == 0:
-        return None
-    return round(100 * counts.supportive / counts.total)
-
-
-def normalize_issues(issues: list[str] | tuple[str, ...]) -> list[str]:
-    """Trim, de-duplicate, and preserve issue order for display and queries."""
-    normalized: list[str] = []
-    seen: set[str] = set()
-    for issue in issues:
-        value = issue.strip()
-        key = value.casefold()
-        if value and key not in seen:
-            normalized.append(value)
-            seen.add(key)
-    return normalized
-
-
-def issue_match_condition(issues: list[str] | tuple[str, ...]) -> ColumnElement[bool]:
-    """Build the SQLAlchemy condition for issue text matching."""
-    normalized = normalize_issues(list(issues))
-    if not normalized:
-        return false()
-
-    fields: tuple[ColumnElement[str | None], ...] = (
-        Bill.subjects_top_term,
-        Bill.title,
-        Bill.title_short,
-        Bill.official_title,
-        Vote.question,
-        Vote.result_text,
-    )
-    terms: list[BinaryExpression[bool]] = []
-    for issue in normalized:
-        pattern = f"%{issue}%"
-        terms.extend(field.ilike(pattern) for field in fields)
-    return or_(*terms)
-
-
-def normalized_position_expression(column: ColumnElement[str]) -> ColumnElement[str | None]:
-    """Lowercase and trim a SQL column containing raw vote positions."""
-    return func.lower(func.trim(column))

pipelines/web/svg.py

@@ -1,231 +0,0 @@
-"""Inline SVG rendering helpers."""
-
-from __future__ import annotations
-
-from html import escape
-from math import cos, pi, sin
-
-from pipelines.web.repository import ChartSeries, RadarSeries
-
-SERIES_STYLES = (
-    {
-        "color": "#009e73",
-        "dasharray": None,
-        "marker": "circle",
-    },
-    {
-        "color": "#0072b2",
-        "dasharray": "10 6",
-        "marker": "square",
-    },
-    {
-        "color": "#e69f00",
-        "dasharray": "4 5",
-        "marker": "diamond",
-    },
-    {
-        "color": "#cc79a7",
-        "dasharray": "14 5 3 5",
-        "marker": "triangle",
-    },
-)
-
-
-def render_score_history_svg(series: list[ChartSeries]) -> str:
-    """Render a responsive inline SVG score history chart."""
-    width = 880
-    height = 300
-    margin_left = 70
-    margin_right = 28
-    margin_top = 24
-    margin_bottom = 48
-    plot_width = width - margin_left - margin_right
-    plot_height = height - margin_top - margin_bottom
-
-    all_years = sorted({point.year for item in series for point in item.points})
-    if not all_years:
-        return _empty_svg(width, height, "No score history for this selection")
-
-    min_year = min(all_years)
-    max_year = max(all_years)
-    year_span = max(max_year - min_year, 1)
-
-    def x_for(year: int) -> float:
-        return margin_left + ((year - min_year) / year_span) * plot_width
-
-    def y_for(score: int) -> float:
-        return margin_top + ((100 - score) / 100) * plot_height
-
-    parts: list[str] = [
-        f'<svg viewBox="0 0 {width} {height}" role="img" aria-label="Score history chart" class="score-chart">',
-        '<rect width="100%" height="100%" fill="transparent" />',
-    ]
-
-    for score in (0, 25, 50, 75, 100):
-        y = y_for(score)
-        parts.append(
-            f'<line x1="{margin_left}" y1="{y:.2f}" x2="{width - margin_right}" y2="{y:.2f}" class="chart-grid" />'
-        )
-        parts.append(
-            f'<text x="{margin_left - 16}" y="{y + 4:.2f}" text-anchor="end" class="chart-axis-label">{score}</text>'
-        )
-
-    tick_years = _tick_years(all_years)
-    for year in tick_years:
-        x = x_for(year)
-        parts.append(
-            f'<line x1="{x:.2f}" y1="{margin_top}" x2="{x:.2f}" y2="{height - margin_bottom}" class="chart-year-line" />'
-        )
-        parts.append(
-            f'<text x="{x:.2f}" y="{height - 18}" text-anchor="middle" class="chart-axis-label">{year}</text>'
-        )
-
-    parts.append(
-        f'<line x1="{margin_left}" y1="{height - margin_bottom}" x2="{width - margin_right}" y2="{height - margin_bottom}" class="chart-axis" />'
-    )
-    parts.append(
-        f'<line x1="{margin_left}" y1="{margin_top}" x2="{margin_left}" y2="{height - margin_bottom}" class="chart-axis" />'
-    )
-
-    for index, item in enumerate(series):
-        points = sorted(item.points, key=lambda point: point.year)
-        if not points:
-            continue
-        style = SERIES_STYLES[index % len(SERIES_STYLES)]
-        color = style["color"]
-        path = " ".join(
-            f"{'M' if point_index == 0 else 'L'} {x_for(point.year):.2f} {y_for(point.score):.2f}"
-            for point_index, point in enumerate(points)
-        )
-        label = escape(item.label)
-        dash_attr = (
-            f' stroke-dasharray="{style["dasharray"]}"'
-            if style["dasharray"]
-            else ""
-        )
-        parts.append(
-            f'<path d="{path}" fill="none" stroke="{color}" stroke-width="3.5" stroke-linecap="round" stroke-linejoin="round"{dash_attr}>'
-            f"<title>{label}</title></path>"
-        )
-        for point in points:
-            parts.append(
-                _point_marker(
-                    marker=style["marker"],
-                    x=x_for(point.year),
-                    y=y_for(point.score),
-                    color=color,
-                    label=f"{label}: {point.score:.0f} in {point.year}",
-                )
-            )
-        last = points[-1]
-        parts.append(
-            f'<text x="{x_for(last.year) - 10:.2f}" y="{y_for(last.score) + 4:.2f}" text-anchor="end" class="chart-series-label" fill="{color}">'
-            f"{last.score:.0f}</text>"
-        )
-
-    parts.append("</svg>")
-    return "".join(parts)
-
-
-def _empty_svg(width: int, height: int, message: str) -> str:
-    return (
-        f'<svg viewBox="0 0 {width} {height}" role="img" aria-label="{escape(message)}" class="score-chart">'
-        '<rect width="100%" height="100%" fill="transparent" />'
-        f'<text x="{width / 2}" y="{height / 2}" text-anchor="middle" class="chart-empty">{escape(message)}</text>'
-        "</svg>"
-    )
-
-
-def _tick_years(years: list[int]) -> list[int]:
-    first = years[0]
-    last = years[-1]
-    start = first - (first % 5)
-    tick_years = {year for year in range(start, last + 1, 5) if first <= year <= last}
-    tick_years.add(first)
-    tick_years.add(last)
-    return sorted(tick_years)
-
-
-def render_compare_radar_svg(topics: list[str], series: list[RadarSeries]) -> str:
-    """Render a server-side radar chart for legislator comparison."""
-    width = 720
-    height = 560
-    center_x = 285
-    center_y = 280
-    radius = 200
-    if len(topics) < 3 or not series:
-        return _empty_svg(width, height, "Choose at least 3 axes and 1 legislator")
-
-    axis_count = len(topics)
-
-    def point_for(index: int, score: float) -> tuple[float, float]:
-        angle = -pi / 2 + (2 * pi * index / axis_count)
-        distance = radius * max(0, min(score, 100)) / 100
-        return center_x + cos(angle) * distance, center_y + sin(angle) * distance
-
-    def ring_points(score: float) -> str:
-        return " ".join(
-            f"{point_for(index, score)[0]:.2f},{point_for(index, score)[1]:.2f}"
-            for index in range(axis_count)
-        )
-
-    parts: list[str] = [
-        f'<svg viewBox="0 0 {width} {height}" role="img" aria-label="Compare legislators radar chart" class="radar-chart">',
-        '<rect width="100%" height="100%" fill="transparent" />',
-    ]
-    for ring in (25, 50, 75, 100):
-        parts.append(f'<polygon points="{ring_points(ring)}" class="radar-ring" />')
-    for index, topic in enumerate(topics):
-        outer_x, outer_y = point_for(index, 100)
-        label_x, label_y = point_for(index, 113)
-        parts.append(
-            f'<line x1="{center_x}" y1="{center_y}" x2="{outer_x:.2f}" y2="{outer_y:.2f}" class="radar-axis" />'
-        )
-        anchor = "middle"
-        if label_x < center_x - 24:
-            anchor = "end"
-        elif label_x > center_x + 24:
-            anchor = "start"
-        parts.append(
-            f'<text x="{label_x:.2f}" y="{label_y:.2f}" text-anchor="{anchor}" class="radar-label">{escape(topic)}</text>'
-        )
-
-    for index, item in enumerate(series):
-        color = SERIES_STYLES[index % len(SERIES_STYLES)]["color"]
-        points = " ".join(
-            f"{point_for(topic_index, item.scores_by_topic.get(topic, 50.0))[0]:.2f},"
-            f"{point_for(topic_index, item.scores_by_topic.get(topic, 50.0))[1]:.2f}"
-            for topic_index, topic in enumerate(topics)
-        )
-        label = escape(item.legislator.display_name)
-        parts.append(
-            f'<polygon points="{points}" fill="{color}" fill-opacity="0.14" stroke="{color}" stroke-width="3" class="radar-series">'
-            f"<title>{label}</title></polygon>"
-        )
-    parts.append("</svg>")
-    return "".join(parts)
-
-
-def _point_marker(*, marker: str, x: float, y: float, color: str, label: str) -> str:
-    title = f"<title>{escape(label)}</title>"
-    if marker == "square":
-        return (
-            f'<rect x="{x - 4.25:.2f}" y="{y - 4.25:.2f}" width="8.5" height="8.5" '
-            f'fill="{color}" rx="1.5" ry="1.5">{title}</rect>'
-        )
-    if marker == "diamond":
-        points = (
-            f"{x:.2f},{y - 5.2:.2f} "
-            f"{x + 5.2:.2f},{y:.2f} "
-            f"{x:.2f},{y + 5.2:.2f} "
-            f"{x - 5.2:.2f},{y:.2f}"
-        )
-        return f'<polygon points="{points}" fill="{color}">{title}</polygon>'
-    if marker == "triangle":
-        points = (
-            f"{x:.2f},{y - 5.5:.2f} "
-            f"{x + 5.5:.2f},{y + 4.5:.2f} "
-            f"{x - 5.5:.2f},{y + 4.5:.2f}"
-        )
-        return f'<polygon points="{points}" fill="{color}">{title}</polygon>'
-    return f'<circle cx="{x:.2f}" cy="{y:.2f}" r="4.5" fill="{color}">{title}</circle>'

pipelines/web/templates/base.html

@@ -1,40 +0,0 @@
-<!doctype html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1">
-    <title>{% block title %}Nornsight{% endblock %}</title>
-    <link rel="stylesheet" href="{{ url_for('static', path='styles.css') }}">
-    <script src="{{ url_for('static', path='vendor/htmx.min.js') }}" defer></script>
-  </head>
-  <body>
-    <header class="topbar">
-      <a class="brand" href="/">
-        <span class="brand-mark">N</span>
-        <span>Nornsight</span>
-      </a>
-      {% if show_primary_nav|default(true) %}
-      <nav class="primary-nav" aria-label="Primary">
-        <a href="/">Issues</a>
-        <a href="/legislators">Legislators</a>
-        <a href="/compare">Compare</a>
-      </nav>
-      {% endif %}
-      <nav class="account-nav" aria-label="Account">
-        <a href="#" aria-disabled="true">Help</a>
-        {% if is_authenticated|default(true) %}
-        <details class="account-menu">
-          <summary>My account</summary>
-          <div class="account-menu-panel">
-            <a href="#" aria-disabled="true">Account settings</a>
-            <a class="sign-out" href="/logout">Sign out</a>
-          </div>
-        </details>
-        {% else %}
-        <a class="sign-in" href="/login">Sign in</a>
-        {% endif %}
-      </nav>
-    </header>
-    {% block body %}{% endblock %}
-  </body>
-</html>

pipelines/web/templates/compare.html

@@ -1,87 +0,0 @@
-{% extends "base.html" %}
-
-{% block title %}Compare Legislators{% endblock %}
-
-{% block body %}
-  <main class="shell">
-    <section class="page-heading stacked-heading">
-      <div>
-        <h1>Compare legislators</h1>
-        <p>Up to 4 legislators · up to 8 issue axes · each polygon = one legislator's full issue profile</p>
-      </div>
-    </section>
-
-    <section class="compare-controls">
-      <form class="wide-search compare-search" action="/compare" method="get">
-        <label class="sr-only" for="compare-legislator-search">Search legislators</label>
-        {% for legislator_id in selected_legislators %}
-          <input type="hidden" name="legislator_id" value="{{ legislator_id }}">
-        {% endfor %}
-        {% for topic in topics %}
-          <input type="hidden" name="topic" value="{{ topic }}">
-        {% endfor %}
-        <input
-          id="compare-legislator-search"
-          type="search"
-          name="q"
-          value="{{ q }}"
-          placeholder="Search legislators to add"
-          autocomplete="off">
-        <button type="submit">Search</button>
-      </form>
-
-      <h2>Legislators ({{ selected_legislator_options|length }} / 4)</h2>
-      <div class="result-chips">
-        {% for legislator in selected_legislator_options %}
-          {% set without = selected_legislators | reject('equalto', legislator.legislator_id) | list %}
-          <a href="{{ build_compare_url(legislator_ids=without, topics=topics, q=q) }}"><span class="legend-dot dot-{{ loop.index0 }}"></span>{{ legislator.display_name }}{% if legislator.state %} — {{ legislator.state }}{% endif %} ×</a>
-        {% endfor %}
-        {% if selected_legislator_options|length < 4 %}
-          {% for option in legislator_options %}
-            {% if option.legislator_id not in selected_legislators %}
-              <a class="dashed-chip" href="{{ build_compare_url(legislator_ids=selected_legislators + [option.legislator_id], topics=topics, q=q) }}">+ {{ option.display_name }}</a>
-            {% endif %}
-          {% endfor %}
-        {% endif %}
-      </div>
-
-      <h2>Issue axes ({{ topics|length }} / 8)</h2>
-      <div class="axis-chips">
-        {% for topic in topics %}
-          {% set without_topic = topics[:loop.index0] + topics[loop.index:] %}
-          <a href="{{ build_compare_url(legislator_ids=selected_legislators, topics=without_topic, q=q) }}">{{ topic }} ×</a>
-        {% endfor %}
-        {% if topics|length < 8 %}
-          {% for topic in topic_options %}
-            {% if topic not in topics %}
-              <a href="{{ build_compare_url(legislator_ids=selected_legislators, topics=topics + [topic], q=q) }}">{{ topic }}</a>
-            {% endif %}
-          {% endfor %}
-        {% endif %}
-      </div>
-    </section>
-
-    <section class="compare-card">
-      <div class="radar-frame">{{ radar_svg | safe }}</div>
-      <aside class="compare-legend">
-        <h2>Legend</h2>
-        {% for item in series %}
-          <div class="legend-row">
-            <span class="legend-line line-{{ loop.index0 }}"></span>
-            <div>
-              <strong>{{ item.legislator.display_name }}</strong>
-              <small>{{ item.legislator.state or "US" }} · {{ item.legislator.party or "—" }} · avg {{ item.average_score|round(0) if item.average_score is not none else "—" }}</small>
-            </div>
-          </div>
-        {% endfor %}
-        <p>Outer ring = 100% support. Each axis is scored independently against full roll-call record.</p>
-        <p><em>Max 4 legislators · max 8 axes</em></p>
-      </aside>
-    </section>
-  </main>
-  <footer class="footer">
-    <span>Actual record, not rhetoric</span>
-    <span>Source: congressional roll-call votes</span>
-    <span>Not affiliated with any political party or organization</span>
-  </footer>
-{% endblock %}

pipelines/web/templates/dashboard.html

@@ -1,30 +0,0 @@
-{% extends "base.html" %}
-
-{% block title %}Legislative Accountability{% endblock %}
-
-{% block body %}
-  <main class="shell">
-    <section class="page-heading">
-      <div>
-        <h1>Legislative accountability</h1>
-        <p>US legislative accountability · precomputed legislator topic scores{% if latest_score_year %} through {{ latest_score_year }}{% endif %}</p>
-      </div>
-      <div class="heading-actions">
-        <a href="#" aria-disabled="true">Methodology</a>
-        <a href="#" aria-disabled="true">Data sources</a>
-        <span>Last updated: {{ last_updated.strftime("%b %Y") if last_updated else "Unavailable" }}</span>
-      </div>
-    </section>
-
-    <div class="notice">Choose one or more score topics, then select lawmakers to compare computed records over time.</div>
-
-    <div id="dashboard-body">
-      {% include "partials/_dashboard.html" %}
-    </div>
-  </main>
-  <footer class="footer">
-    <span>Actual record, not rhetoric</span>
-    <span>Source: congressional roll-call votes</span>
-    <span>Not affiliated with any political party or organization</span>
-  </footer>
-{% endblock %}

pipelines/web/templates/legislators.html

@@ -1,148 +0,0 @@
-{% extends "base.html" %}
-
-{% block title %}Legislator Profiles{% endblock %}
-
-{% block body %}
-  <main class="shell">
-    <section class="page-heading stacked-heading">
-      <div>
-        <h1>Legislator profiles</h1>
-        <p>Full issue taxonomy · search any current legislator</p>
-      </div>
-    </section>
-
-    <form class="wide-search legislator-search-form" action="/legislators" method="get">
-      <label class="sr-only" for="legislator-search">Search legislators</label>
-      <input
-        id="legislator-search"
-        type="search"
-        name="q"
-        value="{{ q }}"
-        placeholder="Search by name or state"
-        autocomplete="off"
-        hx-get="/partials/legislator-suggestions"
-        hx-trigger="input changed delay:200ms, search"
-        hx-target="#legislator-suggestions"
-        hx-swap="innerHTML">
-      <label class="sr-only" for="legislator-page-size">Results per page</label>
-      <select id="legislator-page-size" name="per_page" aria-label="Results per page">
-        {% for option in per_page_options %}
-          <option value="{{ option }}" {{ "selected" if option == per_page else "" }}>{{ option }}</option>
-        {% endfor %}
-      </select>
-      <button type="submit">Search</button>
-    </form>
-
-    <div id="legislator-suggestions" aria-live="polite"></div>
-
-    {% if q %}
-      <section class="phonebook-results" aria-label="Matching legislators">
-        <header>
-          <h2>Matching legislators</h2>
-          <span>{{ result_count }} result{{ "" if result_count == 1 else "s" }}</span>
-        </header>
-        {% if matches %}
-          <ol class="phonebook-list" start="{{ ((page - 1) * per_page) + 1 }}">
-            {% for option in matches %}
-              <li>
-                <a href="{{ build_legislator_url(legislator_id=option.legislator_id, q=q, per_page=per_page) }}">
-                  <span class="phonebook-name">{{ option.display_name }}</span>
-                  <span class="phonebook-meta">
-                    {{ option.state or "US" }}{% if option.party %} · {{ option.party }}{% endif %}{% if option.chamber %} · {{ option.chamber }}{% endif %}
-                  </span>
-                </a>
-              </li>
-            {% endfor %}
-          </ol>
-          <nav class="pagination" aria-label="Legislator results pages">
-            {% if previous_page %}
-              <a href="{{ build_legislator_search_url(q=q, per_page=per_page, page=previous_page) }}">Previous</a>
-            {% else %}
-              <span>Previous</span>
-            {% endif %}
-            <strong>Page {{ page }} of {{ total_pages }}</strong>
-            {% if next_page %}
-              <a href="{{ build_legislator_search_url(q=q, per_page=per_page, page=next_page) }}">Next</a>
-            {% else %}
-              <span>Next</span>
-            {% endif %}
-          </nav>
-        {% else %}
-          <p class="empty-state">No legislators matched this search.</p>
-        {% endif %}
-      </section>
-    {% endif %}
-
-    {% if profile %}
-      <section class="profile-card">
-        <header class="profile-header">
-          <div class="profile-identity">
-            <span class="avatar">{{ profile.legislator.display_name.split(',')[0][:1] }}{{ profile.legislator.display_name.split(',')[-1].strip()[:1] }}</span>
-            <div>
-              <h2>{{ profile.legislator.display_name }} <span class="party-pill">{{ profile.legislator.chamber or "LEG" }}</span></h2>
-              <p>{{ profile.legislator.state or "US" }} · {{ profile.legislator.party or "Unaffiliated" }}{% if profile.serving_since %} · Serving since {{ profile.serving_since }}{% endif %}</p>
-            </div>
-          </div>
-          <div class="overall-score">
-            <span>Overall avg</span>
-            <strong>{{ profile.overall_score|round(0) if profile.overall_score is not none else "—" }}</strong>
-            <small>/ 100</small>
-          </div>
-        </header>
-
-        {% if profile.top_topics or profile.bottom_topics %}
-          <div class="topic-panels">
-            <article>
-              <h3>Most important issues for</h3>
-              {% for item in profile.top_topics %}
-                <a class="topic-row" href="{{ build_legislator_url(legislator_id=profile.legislator.legislator_id, topic=item.topic) }}">
-                  <strong class="score positive">{{ item.score|round(0) }}</strong>
-                  <span>{{ item.topic }}</span>
-                  <i style="width: {{ item.score }}%"></i>
-                </a>
-              {% endfor %}
-            </article>
-            <article>
-              <h3 class="opposed-heading">Most important issues against</h3>
-              {% for item in profile.bottom_topics %}
-                <a class="topic-row {{ 'active' if item.topic == selected_topic else '' }}" href="{{ build_legislator_url(legislator_id=profile.legislator.legislator_id, topic=item.topic) }}">
-                  <strong class="score negative">{{ item.score|round(0) }}</strong>
-                  <span>{{ item.topic }}</span>
-                  <i style="width: {{ item.score }}%"></i>
-                </a>
-              {% endfor %}
-            </article>
-          </div>
-
-          <section class="profile-history">
-            <h3>{{ selected_topic or "Topic" }} — score history</h3>
-            <div class="chart-frame">{{ history_svg | safe }}</div>
-            {% if history_series %}
-              <div class="chart-legend compact" aria-label="Chart legend">
-                {% for item in history_series %}
-                  <div class="chart-legend-row">
-                    <span class="chart-legend-line line-0"></span>
-                    <span class="chart-legend-marker marker-0"></span>
-                    <div class="chart-legend-copy">
-                      <span class="chart-legend-label">{{ item.label }}</span>
-                      <span class="chart-legend-meta">
-                        {% if item.party %}{{ item.party }}{% endif %}{% if item.party and item.state %} · {% endif %}{% if item.state %}{{ item.state }}{% endif %}
-                      </span>
-                    </div>
-                  </div>
-                {% endfor %}
-              </div>
-            {% endif %}
-          </section>
-        {% else %}
-          <p class="empty-state">No issue scores are available for this legislator yet.</p>
-        {% endif %}
-      </section>
-    {% endif %}
-  </main>
-  <footer class="footer">
-    <span>Actual record, not rhetoric</span>
-    <span>Source: congressional roll-call votes</span>
-    <span>Not affiliated with any political party or organization</span>
-  </footer>
-{% endblock %}

pipelines/web/templates/login.html

@@ -1,45 +0,0 @@
-{% extends "base.html" %}
-
-{% block title %}Sign in | Nornsight{% endblock %}
-
-{% block body %}
-<main class="login-shell">
-  <section class="login-panel" aria-labelledby="login-title">
-    <div class="login-copy">
-      <p class="eyebrow">Admin access</p>
-      <h1 id="login-title">Sign in to Nornsight</h1>
-      <p>Use the dashboard account to review rankings, profiles, and legislator comparisons.</p>
-    </div>
-
-    <form class="login-form" action="/login" method="post">
-      <input type="hidden" name="next" value="{{ next_path }}">
-
-      {% if error %}
-      <p class="form-error" role="alert">{{ error }}</p>
-      {% endif %}
-
-      <label for="username">Username</label>
-      <input
-        id="username"
-        name="username"
-        type="text"
-        autocomplete="username"
-        value="{{ username }}"
-        required
-        autofocus
-      >
-
-      <label for="password">Password</label>
-      <input
-        id="password"
-        name="password"
-        type="password"
-        autocomplete="current-password"
-        required
-      >
-
-      <button type="submit">Sign in</button>
-    </form>
-  </section>
-</main>
-{% endblock %}

pipelines/web/templates/partials/_chart.html

@@ -1,30 +0,0 @@
-<section class="chart-card">
-  <header>
-    <h2>Score history{% if selected_issue_label %} — {{ selected_issue_label }}{% endif %}</h2>
-    <a href="{{ build_url(request, compare=[]) }}"
-       hx-get="/partials/dashboard{{ build_url(request, compare=[])|replace('/', '', 1) }}"
-       hx-target="#dashboard-body"
-       hx-push-url="{{ build_url(request, compare=[]) }}">Clear comparison</a>
-  </header>
-  <div class="chart-frame">
-    {{ chart_svg | safe }}
-  </div>
-  {% if chart_series %}
-    <div class="chart-legend" aria-label="Chart legend">
-      {% for item in chart_series %}
-        {% set style_index = loop.index0 % 4 %}
-        <div class="chart-legend-row">
-          <span class="chart-legend-line line-{{ style_index }}"></span>
-          <span class="chart-legend-marker marker-{{ style_index }}"></span>
-          <div class="chart-legend-copy">
-            <span class="chart-legend-label">{{ item.label }}</span>
-            <span class="chart-legend-meta">
-              {% if item.party %}{{ item.party }}{% endif %}{% if item.party and item.state %} · {% endif %}{% if item.state %}{{ item.state }}{% endif %}
-            </span>
-          </div>
-        </div>
-      {% endfor %}
-    </div>
-  {% endif %}
-  <p class="score-note">Scores reflect averaged precomputed topic rows per year. Sparse years are omitted rather than plotted as zero.</p>
-</section>

pipelines/web/templates/partials/_dashboard.html

@@ -1,25 +0,0 @@
-<section class="controls-grid">
-  {% include "partials/_issue_filters.html" %}
-  <div class="chamber-card">
-    <a class="segment {{ 'active' if chamber == 'house' else '' }}"
-       href="{{ build_url(request, chamber='house') }}"
-       hx-get="/partials/dashboard{{ build_url(request, chamber='house')|replace('/', '', 1) }}"
-       hx-target="#dashboard-body"
-       hx-push-url="{{ build_url(request, chamber='house') }}">House</a>
-    <a class="segment {{ 'active' if chamber == 'senate' else '' }}"
-       href="{{ build_url(request, chamber='senate') }}"
-       hx-get="/partials/dashboard{{ build_url(request, chamber='senate')|replace('/', '', 1) }}"
-       hx-target="#dashboard-body"
-       hx-push-url="{{ build_url(request, chamber='senate') }}">Senate</a>
-    <a class="segment {{ 'active' if chamber == 'all' else '' }}"
-       href="{{ build_url(request, chamber='all') }}"
-       hx-get="/partials/dashboard{{ build_url(request, chamber='all')|replace('/', '', 1) }}"
-       hx-target="#dashboard-body"
-       hx-push-url="{{ build_url(request, chamber='all') }}">All</a>
-  </div>
-</section>
-
-<p class="score-note">Support score: 1-100 precomputed from bill topic stance and roll-call votes. Higher means more aligned with the topic.</p>
-
-{% include "partials/_rankings.html" %}
-{% include "partials/_chart.html" %}

pipelines/web/templates/partials/_issue_filters.html

@@ -1,46 +0,0 @@
-<section class="filter-card">
-  <h2>Issue filters</h2>
-  <form class="issue-form"
-        method="get"
-        action="/"
-        hx-get="/"
-        hx-target="#dashboard-body"
-        hx-push-url="true">
-    <input type="hidden" name="chamber" value="{{ chamber }}">
-    {% if congress %}
-      <input type="hidden" name="congress" value="{{ congress }}">
-    {% endif %}
-    {% for legislator_id in compare %}
-      <input type="hidden" name="compare" value="{{ legislator_id }}">
-    {% endfor %}
-    {% for issue in issues %}
-      <span class="chip">
-        {{ issue }}
-        <a href="{{ build_url(request, issues=issues[:loop.index0] + issues[loop.index:]) }}"
-           hx-get="/partials/dashboard{{ build_url(request, issues=issues[:loop.index0] + issues[loop.index:])|replace('/', '', 1) }}"
-           hx-target="#dashboard-body"
-           hx-push-url="{{ build_url(request, issues=issues[:loop.index0] + issues[loop.index:]) }}"
-           aria-label="Remove {{ issue }}">×</a>
-      </span>
-      <input type="hidden" name="issues" value="{{ issue }}">
-    {% endfor %}
-    <label class="search-box">
-      <span class="sr-only">Search issue areas</span>
-      <input type="search" name="issues" placeholder="Search issue areas" autocomplete="off">
-    </label>
-    <button type="submit">Apply</button>
-  </form>
-
-  {% if suggestions %}
-    <div class="suggestions" aria-label="Issue suggestions">
-      {% for suggestion in suggestions %}
-        {% if suggestion not in issues %}
-          <a href="{{ build_url(request, issues=issues + [suggestion]) }}"
-             hx-get="/partials/dashboard{{ build_url(request, issues=issues + [suggestion])|replace('/', '', 1) }}"
-             hx-target="#dashboard-body"
-             hx-push-url="{{ build_url(request, issues=issues + [suggestion]) }}">{{ suggestion }}</a>
-        {% endif %}
-      {% endfor %}
-    </div>
-  {% endif %}
-</section>

pipelines/web/templates/partials/_legislator_suggestions.html

@@ -1,11 +0,0 @@
-{% if matches %}
-  <div class="result-chips" aria-label="Search suggestions">
-    {% for option in matches %}
-      <a href="{{ build_legislator_url(legislator_id=option.legislator_id) }}">
-        {{ option.display_name }}{% if option.state %} · {{ option.state }}{% endif %}
-      </a>
-    {% endfor %}
-  </div>
-{% elif q %}
-  <p class="suggestion-empty">No matches</p>
-{% endif %}

pipelines/web/templates/partials/_rankings.html

@@ -1,61 +0,0 @@
-<section class="rankings-grid">
-  <article class="ranking-card">
-    <header>
-      <h2>Most supportive</h2>
-      <span>Top 10</span>
-    </header>
-    {% if rankings.supportive %}
-      <ol class="ranking-list">
-        {% for row in rankings.supportive %}
-          {% set next_compare = toggle_compare(compare, row.legislator_id) %}
-          <li class="{{ 'selected' if row.legislator_id in compare else '' }}">
-            <a href="{{ build_url(request, compare=next_compare) }}"
-               hx-get="/partials/dashboard{{ build_url(request, compare=next_compare)|replace('/', '', 1) }}"
-               hx-target="#dashboard-body"
-               hx-push-url="{{ build_url(request, compare=next_compare) }}">
-              <span class="rank">{{ loop.index }}</span>
-              <strong class="score positive">{{ row.score|round(1) }}</strong>
-              <span class="member">
-                <strong>{{ row.display_name }}</strong>
-                <small>{{ row.state or "US" }}{% if row.party %} · {{ row.party[:1] }}{% endif %}</small>
-              </span>
-              <span class="votes">{{ row.total }} rows</span>
-            </a>
-          </li>
-        {% endfor %}
-      </ol>
-    {% else %}
-      <p class="empty-state">{{ empty_message }}</p>
-    {% endif %}
-  </article>
-
-  <article class="ranking-card">
-    <header>
-      <h2>Most opposed</h2>
-      <span>Bottom 10</span>
-    </header>
-    {% if rankings.opposed %}
-      <ol class="ranking-list">
-        {% for row in rankings.opposed %}
-          {% set next_compare = toggle_compare(compare, row.legislator_id) %}
-          <li class="{{ 'selected' if row.legislator_id in compare else '' }}">
-            <a href="{{ build_url(request, compare=next_compare) }}"
-               hx-get="/partials/dashboard{{ build_url(request, compare=next_compare)|replace('/', '', 1) }}"
-               hx-target="#dashboard-body"
-               hx-push-url="{{ build_url(request, compare=next_compare) }}">
-              <span class="rank">{{ loop.index }}</span>
-              <strong class="score negative">{{ row.score|round(1) }}</strong>
-              <span class="member">
-                <strong>{{ row.display_name }}</strong>
-                <small>{{ row.state or "US" }}{% if row.party %} · {{ row.party[:1] }}{% endif %}</small>
-              </span>
-              <span class="votes">{{ row.total }} rows</span>
-            </a>
-          </li>
-        {% endfor %}
-      </ol>
-    {% else %}
-      <p class="empty-state">{{ empty_message }}</p>
-    {% endif %}
-  </article>
-</section>

pipelines/web/templates/setup_error.html

@@ -1,15 +0,0 @@
-{% extends "base.html" %}
-
-{% block title %}Database Setup Required{% endblock %}
-
-{% block body %}
-  <main class="shell">
-    <section class="page-heading stacked-heading">
-      <div>
-        <h1>Database setup required</h1>
-        <p>Configure DATA_SCIENCE_DEV before opening the dashboard.</p>
-      </div>
-    </section>
-    <pre class="setup-error">{{ error }}</pre>
-  </main>
-{% endblock %}