feat(pipeline): implement parliamentary embedding pipeline MVP

- Add 4 migration files: mp_votes, mp_metadata, svd_vectors, fused_embeddings
- Extend database.py with 5 new helper methods and table init
- Add pipeline/ package: extract_mp_votes, fetch_mp_metadata, text_pipeline,
  svd_pipeline (with Procrustes alignment), fusion
- Add full test suite (17 tests) covering all pipeline modules and migrations
- Fix Procrustes alignment bug: scipy scale is a norm value, not a multiplier
- Fix DuckDB date type handling in test assertions (datetime.date vs string)
- Remove duckdb.py shim; tests now run against real duckdb + scipy via uv

Ref: thoughts/shared/plans/2026-03-21-parliamentary-embedding-pipeline-plan.md

tests/integration/test_pipeline_end_to_end.py

@@ -0,0 +1,87 @@
+import json
+import os
+import numpy as np
+import pytest
+
+# duckdb is an optional dependency in some environments; skip test if not available
+duckdb = pytest.importorskip("duckdb")
+
+
+def test_pipeline_end_to_end(tmp_path, monkeypatch):
+    # ensure determinism for any random embedding generation
+    np.random.seed(0)
+
+    # prepare temp db
+    db_path = str(tmp_path / "motions.db")
+
+    # create the minimal MotionDatabase schema using existing code where possible
+    from database import MotionDatabase
+
+    db = MotionDatabase(db_path)
+
+    # create embeddings table (migration would normally do this)
+    conn = duckdb.connect(db.db_path)
+    conn.execute("CREATE SEQUENCE IF NOT EXISTS embeddings_id_seq START 1")
+    conn.execute(
+        "CREATE TABLE IF NOT EXISTS embeddings (id INTEGER PRIMARY KEY DEFAULT nextval('embeddings_id_seq'), motion_id INTEGER, model TEXT, vector JSON, created_at TIMESTAMP)"
+    )
+
+    # insert three motions
+    conn.execute(
+        "INSERT INTO motions (title, description, url, layman_explanation) VALUES (?, ?, ?, ?)",
+        ("t1", "d1", "u1", "ex1"),
+    )
+    conn.execute(
+        "INSERT INTO motions (title, description, url, layman_explanation) VALUES (?, ?, ?, ?)",
+        ("t2", "d2", "u2", "ex2"),
+    )
+    conn.execute(
+        "INSERT INTO motions (title, description, url, layman_explanation) VALUES (?, ?, ?, ?)",
+        ("t3", "d3", "u3", "ex3"),
+    )
+
+    # fetch ids
+    rows = conn.execute("SELECT id FROM motions ORDER BY id").fetchall()
+    ids = [r[0] for r in rows]
+
+    # insert existing embedding for first motion
+    vec = json.dumps([0.1] * 16)
+    conn.execute(
+        "INSERT INTO embeddings (motion_id, model, vector) VALUES (?, ?, ?)",
+        (ids[0], "test-model", vec),
+    )
+
+    conn.close()
+
+    # monkeypatch ai_provider.get_embedding to deterministic vector
+    import ai_provider
+
+    def fake_get_embedding(text, model=None):
+        # produce a deterministic vector based on seeded numpy
+        return list(np.random.rand(16))
+
+    monkeypatch.setattr("ai_provider.get_embedding", fake_get_embedding)
+
+    # run ensure_text_embeddings
+    from pipeline.text_pipeline import ensure_text_embeddings
+
+    stored, skipped_existing, skipped_no_text, errors = ensure_text_embeddings(
+        db_path=db_path, model="test-model"
+    )
+
+    assert stored == 2
+    assert skipped_existing == 1
+    assert skipped_no_text == 0
+    assert errors == 0
+
+    # verify stored vectors length
+    conn = duckdb.connect(db.db_path)
+    rows = conn.execute(
+        "SELECT vector FROM embeddings WHERE model = ? ORDER BY motion_id",
+        ("test-model",),
+    ).fetchall()
+    conn.close()
+    assert len(rows) == 3
+    for r in rows:
+        v = json.loads(r[0])
+        assert len(v) == 16