feat(kg): add markdown export format for knowledge graph

- Add kg_ocr/export/markdown_exporter.py with export_markdown()
- Cluster entity co-occurrences using Louvain community detection
- Wire -f markdown into `ocr-pipeline kg export` CLI command with KG_REPORT.md default
- Add unit and CLI tests in tests/test_graph.py
- Document markdown export in README.md
- Untrack pycache binaries and ignore graphify-out and KG_REPORT.md

tests/test_graph.py

@@ -6,7 +6,7 @@ import pytest
 
 nx = pytest.importorskip("networkx", reason="kg extra not installed")
 
-from kg_ocr.export import export_graphml, export_json, load_json  # noqa: E402
+from kg_ocr.export import export_graphml, export_json, export_markdown, load_json  # noqa: E402
 from kg_ocr.graph import (  # noqa: E402
     build_from_directory,
     build_graph,
@@ -202,6 +202,50 @@ def test_kg_cli_build_and_stats(chunk_dir: Path, tmp_path: Path) -> None:
     assert result.exit_code == 0, result.output
     assert out_graphml.is_file()
 
+    out_md = tmp_path / "g_report.md"
+    result = runner.invoke(app, ["kg", "export", str(save), "-f", "markdown", "-o", str(out_md)])
+    assert result.exit_code == 0, result.output
+    assert out_md.is_file()
+    content = out_md.read_text(encoding="utf-8")
+    assert "## Top Entities" in content
+    assert "## Top Co-occurrences" in content
+    assert "## Top Citations" in content
+    assert "## Concept Clusters" in content
+    assert "## Anomalies" in content
+    assert "BRCA1" in content
+    assert "10.1038/nature12345" in content
+
+
+def test_export_markdown(chunk_dir: Path, tmp_path: Path) -> None:
+    graph = build_from_directory(chunk_dir)
+    save_json = tmp_path / "graph.json"
+    export_json(graph, save_json)
+
+    out_md = tmp_path / "report.md"
+    # Test with string paths as per signature requirement
+    written = export_markdown(str(save_json), str(out_md))
+    assert written == out_md
+    assert out_md.is_file()
+
+    text = out_md.read_text(encoding="utf-8")
+    assert "# Knowledge Graph Report" in text
+    assert "## Summary" in text
+    assert "## Top Entities" in text
+    assert "## Top Co-occurrences" in text
+    assert "## Top Citations" in text
+    assert "## Concept Clusters" in text
+    assert "## Anomalies" in text
+    assert "BRCA1" in text
+    assert "PARP" in text
+    assert "10.1038/nature12345" in text
+    assert "low_confidence" in text
+
+    # Also test passing in-memory graph directly
+    out_direct = tmp_path / "report_direct.md"
+    export_markdown(graph, out_direct)
+    assert out_direct.is_file()
+    assert "## Top Entities" in out_direct.read_text(encoding="utf-8")
+
 
 def test_chunks_for_entity(chunk_dir: Path) -> None:
     from kg_ocr.graph import chunks_for_entity