feat: complete parliamentary embedding pipeline with full historical coverage

- Add fused (SVD + text) embedding pipeline for annual windows 2016-2026
- Fix store_fused_embedding duplicate bug: DELETE before INSERT (idempotent)
- Add --text-batch-size CLI flag to run_pipeline.py (default 200)
- Add explicit --start-date/--end-date to download_past_year.py
- Backfill mp_votes for all motions (party-level votes, 111k new rows)
- Add similarity cache recompute: 212k rows across 9 annual windows
- Improve ai_provider retry logic, text_pipeline batching
- Improve analysis/political_axis PCA handling and visualizations
- Add diagnostic/utility scripts: compare_svd, generate_compass, inspect_axis, etc.
- Untrack data/motions.db (3.6GB binary), add to .gitignore with outputs/
- Update continuity ledger with full session state

tests/integration/test_pipeline_end_to_end.py

@@ -53,14 +53,14 @@ def test_pipeline_end_to_end(tmp_path, monkeypatch):
 
     conn.close()
 
-    # monkeypatch ai_provider.get_embedding to deterministic vector
+    # monkeypatch ai_provider.get_embeddings_batch to deterministic vectors
     import ai_provider
 
-    def fake_get_embedding(text, model=None):
-        # produce a deterministic vector based on seeded numpy
-        return list(np.random.rand(16))
+    def fake_get_embeddings_batch(texts, model=None, batch_size=50):
+        # produce a deterministic vector per text based on seeded numpy
+        return [list(np.random.rand(16)) for _ in texts]
 
-    monkeypatch.setattr("ai_provider.get_embedding", fake_get_embedding)
+    monkeypatch.setattr("ai_provider.get_embeddings_batch", fake_get_embeddings_batch)
 
     # run ensure_text_embeddings
     from pipeline.text_pipeline import ensure_text_embeddings

tests/test_text_pipeline.py

@@ -49,11 +49,11 @@ def test_ensure_text_embeddings_monkeypatch(tmp_path, monkeypatch):
 
     conn.close()
 
-    # monkeypatch ai_provider.get_embedding
-    def fake_get_embedding(text, model=None):
-        return [0.1] * 16
+    # monkeypatch ai_provider.get_embeddings_batch (used by batched pipeline)
+    def fake_get_embeddings_batch(texts, model=None, batch_size=50):
+        return [[0.1] * 16 for _ in texts]
 
-    monkeypatch.setattr("ai_provider.get_embedding", fake_get_embedding)
+    monkeypatch.setattr("ai_provider.get_embeddings_batch", fake_get_embeddings_batch)
 
     # run ensure_text_embeddings
     from pipeline.text_pipeline import ensure_text_embeddings