feat: expand reference dataset with 25 new diagrams across 6 new domain categories

prompts/diagram/retriever.txt

@@ -18,7 +18,7 @@ You must select the **Top {num_examples} candidates** that would be most helpful
 Your goal is to find examples that match the Target in both **Domain** and **Diagram Type**.
 
 **1. Match Research Topic (Use Methodology & Caption):**
-* What is the domain? (e.g., Agent & Reasoning, Vision & Perception, Generative & Learning, Science & Applications).
+* What is the domain? (e.g., Agent & Reasoning, Vision & Perception, Generative & Learning, Science & Applications, Systems & Networking, NLP & Language, Robotics & Control, Multimodal Fusion, Optimization & Theory, Healthcare & Medical).
 * Select candidates that belong to the **same research domain**.
 * *Why?* Similar domains share similar terminology (e.g., "Actor-Critic" in RL).
 

tests/test_pipeline/test_reference_index.py

@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import pytest
+
+from paperbanana.core.types import ReferenceExample
+from paperbanana.reference.store import ReferenceStore
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+INDEX_DIR = REPO_ROOT / "data" / "reference_sets"
+INDEX_PATH = INDEX_DIR / "index.json"
+
+EXPECTED_TOTAL = 38
+EXPECTED_CATEGORIES = {
+    "agent_reasoning",
+    "generative_learning",
+    "healthcare_medical",
+    "multimodal_fusion",
+    "nlp_language",
+    "optimization_theory",
+    "robotics_control",
+    "science_applications",
+    "systems_networking",
+    "vision_perception",
+}
+
+
+@pytest.fixture()
+def index_data() -> dict:
+    with open(INDEX_PATH, encoding="utf-8") as f:
+        return json.load(f)
+
+
+class TestIndexJson:
+    def test_index_file_exists(self):
+        assert INDEX_PATH.exists(), f"{INDEX_PATH} is missing"
+
+    def test_total_examples_matches(self, index_data: dict):
+        examples = index_data["examples"]
+        declared = index_data["metadata"]["total_examples"]
+        assert len(examples) == declared
+        assert len(examples) == EXPECTED_TOTAL
+
+    def test_categories_complete(self, index_data: dict):
+        declared = set(index_data["metadata"]["categories"])
+        assert declared == EXPECTED_CATEGORIES
+
+    def test_no_duplicate_ids(self, index_data: dict):
+        ids = [e["id"] for e in index_data["examples"]]
+        assert len(ids) == len(set(ids)), f"Duplicate IDs: {[x for x in ids if ids.count(x) > 1]}"
+
+    def test_every_entry_has_source_paper(self, index_data: dict):
+        for e in index_data["examples"]:
+            assert "source_paper" in e and e["source_paper"], (
+                f"Entry {e['id']} missing source_paper"
+            )
+
+    def test_every_category_assigned(self, index_data: dict):
+        assigned = {e["category"] for e in index_data["examples"] if e.get("category")}
+        missing = EXPECTED_CATEGORIES - assigned
+        assert not missing, f"Categories with zero entries: {missing}"
+
+    def test_image_files_exist(self, index_data: dict):
+        missing = []
+        for e in index_data["examples"]:
+            img = INDEX_DIR / e["image_path"]
+            if not img.exists():
+                missing.append(e["id"])
+        assert not missing, f"Missing images for: {missing}"
+
+    def test_entries_parse_as_reference_example(self, index_data: dict):
+        for e in index_data["examples"]:
+            ReferenceExample(**e)
+
+    def test_reference_store_loads_all(self):
+        store = ReferenceStore(str(INDEX_DIR))
+        assert store.count == EXPECTED_TOTAL
+
+    def test_reference_store_category_counts(self):
+        store = ReferenceStore(str(INDEX_DIR))
+        for cat in EXPECTED_CATEGORIES:
+            entries = store.get_by_category(cat)
+            assert len(entries) > 0, f"No entries for category: {cat}"