v2: semantic similarity edges, scored higher in surprising connections

2026-07-15 03:47:05 +00:00 · 2026-04-05 23:12:47 +01:00
parent 0f11f6a32d
commit e6fefa8a2c
6 changed files with 216 additions and 4 deletions
@@ -166,6 +166,11 @@ def _surprise_score(
        score += 1
        reasons.append("bridges separate communities")

+    # 4b. Semantic similarity bonus - non-obvious conceptual links score higher
+    if data.get("relation") == "semantically_similar_to":
+        score = int(score * 1.5)
+        reasons.append("semantically similar concepts with no structural link")
+
    # 5. Peripheral→hub: a low-degree node connecting to a high-degree one
    deg_u = G.degree(u)
    deg_v = G.degree(v)
@@ -66,8 +66,9 @@ def generate(
                conf_tag = f"INFERRED {cscore:.2f}"
            else:
                conf_tag = conf
+            sem_tag = " [semantically similar]" if relation == "semantically_similar_to" else ""
            lines += [
-                f"- `{s['source']}` --{relation}--> `{s['target']}`  [{conf_tag}]",
+                f"- `{s['source']}` --{relation}--> `{s['target']}`  [{conf_tag}]{sem_tag}",
                f"  {files[0]} → {files[1]}" + (f"  _{note}_" if note else ""),
            ]
    else:
@@ -207,6 +207,12 @@ Image files: use vision to understand what the image IS - do not just OCR.
 DEEP_MODE (if --mode deep was given): be aggressive with INFERRED edges - indirect deps,
  shared assumptions, latent couplings. Mark uncertain ones AMBIGUOUS instead of omitting.

+Semantic similarity: if two concepts in this chunk solve the same problem or represent the same idea without any structural link (no import, no call, no citation), add a `semantically_similar_to` edge marked INFERRED with a confidence_score reflecting how similar they are (0.6-0.95). Examples:
+- Two functions that both validate user input but never call each other
+- A class in code and a concept in a paper that describe the same algorithm
+- Two error types that handle the same failure mode differently
+Only add these when the similarity is genuinely non-obvious and cross-cutting. Do not add them for trivially similar things.
+
 If a file has YAML frontmatter (--- ... ---), copy source_url, captured_at, author,
  contributor onto every node from that file.

@@ -218,7 +224,7 @@ confidence_score rules:
 - AMBIGUOUS edges: score 0.1-0.3

 Output exactly this JSON (no other text):
-{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"relative/path","source_location":null,"weight":1.0}],"input_tokens":0,"output_tokens":0}
+{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"relative/path","source_location":null,"weight":1.0}],"input_tokens":0,"output_tokens":0}
 ```

 **Step B3 - Collect, cache, and merge**
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "graphifyy"
-version = "0.1.12"
+version = "0.1.13"
 description = "Claude Code skill - turn any folder of code, docs, papers, images, or tweets into a queryable knowledge graph"
 readme = "README.md"
 license = { text = "MIT" }
@@ -207,6 +207,12 @@ Image files: use vision to understand what the image IS - do not just OCR.
 DEEP_MODE (if --mode deep was given): be aggressive with INFERRED edges - indirect deps,
  shared assumptions, latent couplings. Mark uncertain ones AMBIGUOUS instead of omitting.

+Semantic similarity: if two concepts in this chunk solve the same problem or represent the same idea without any structural link (no import, no call, no citation), add a `semantically_similar_to` edge marked INFERRED with a confidence_score reflecting how similar they are (0.6-0.95). Examples:
+- Two functions that both validate user input but never call each other
+- A class in code and a concept in a paper that describe the same algorithm
+- Two error types that handle the same failure mode differently
+Only add these when the similarity is genuinely non-obvious and cross-cutting. Do not add them for trivially similar things.
+
 If a file has YAML frontmatter (--- ... ---), copy source_url, captured_at, author,
  contributor onto every node from that file.

@@ -218,7 +224,7 @@ confidence_score rules:
 - AMBIGUOUS edges: score 0.1-0.3

 Output exactly this JSON (no other text):
-{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"relative/path","source_location":null,"weight":1.0}],"input_tokens":0,"output_tokens":0}
+{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"relative/path","source_location":null,"weight":1.0}],"input_tokens":0,"output_tokens":0}
 ```

 **Step B3 - Collect, cache, and merge**
@@ -0,0 +1,194 @@
+"""Tests for semantically_similar_to edge support."""
+import networkx as nx
+import pytest
+from graphify.build import build_from_json
+from graphify.analyze import surprising_connections, _surprise_score
+from graphify.report import generate
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _make_extraction_with_semantic_edge():
+    """Two nodes in separate files connected by a semantically_similar_to edge."""
+    return {
+        "nodes": [
+            {"id": "a_validate_input", "label": "validate_input", "file_type": "code",
+             "source_file": "auth/validators.py", "source_location": "L5"},
+            {"id": "b_check_input", "label": "check_input", "file_type": "code",
+             "source_file": "api/checks.py", "source_location": "L12"},
+        ],
+        "edges": [
+            {
+                "source": "a_validate_input",
+                "target": "b_check_input",
+                "relation": "semantically_similar_to",
+                "confidence": "INFERRED",
+                "confidence_score": 0.82,
+                "source_file": "auth/validators.py",
+                "source_location": None,
+                "weight": 0.82,
+            }
+        ],
+        "input_tokens": 100,
+        "output_tokens": 50,
+    }
+
+
+def _make_graph_with_semantic_edge():
+    return build_from_json(_make_extraction_with_semantic_edge())
+
+
+def _make_two_edge_graph():
+    """Graph with one semantically_similar_to edge and one references edge, both cross-file."""
+    G = nx.Graph()
+    for nid, label, src in [
+        ("a", "ValidateInput", "auth/validators.py"),
+        ("b", "CheckInput", "api/checks.py"),
+        ("c", "LoadConfig", "config/loader.py"),
+        ("d", "ReadConfig", "utils/reader.py"),
+    ]:
+        G.add_node(nid, label=label, source_file=src, file_type="code")
+    # semantically_similar_to edge
+    G.add_edge("a", "b", relation="semantically_similar_to", confidence="INFERRED",
+               confidence_score=0.82, source_file="auth/validators.py", weight=0.82,
+               _src="a", _tgt="b")
+    # plain references edge (same confidence tier)
+    G.add_edge("c", "d", relation="references", confidence="INFERRED",
+               confidence_score=0.7, source_file="config/loader.py", weight=0.7,
+               _src="c", _tgt="d")
+    return G
+
+
+# ---------------------------------------------------------------------------
+# Test 1: semantically_similar_to passes through build_from_json without being dropped
+# ---------------------------------------------------------------------------
+
+def test_semantic_edge_survives_build_from_json():
+    G = _make_graph_with_semantic_edge()
+    assert G.number_of_edges() == 1
+    u, v, data = next(iter(G.edges(data=True)))
+    assert data["relation"] == "semantically_similar_to"
+
+
+def test_semantic_edge_nodes_present():
+    G = _make_graph_with_semantic_edge()
+    assert "a_validate_input" in G.nodes
+    assert "b_check_input" in G.nodes
+
+
+# ---------------------------------------------------------------------------
+# Test 2: confidence_score is preserved for semantically_similar_to edges
+# ---------------------------------------------------------------------------
+
+def test_semantic_edge_confidence_score_preserved():
+    G = _make_graph_with_semantic_edge()
+    u, v, data = next(iter(G.edges(data=True)))
+    assert data.get("confidence_score") == pytest.approx(0.82)
+    assert data.get("confidence") == "INFERRED"
+
+
+# ---------------------------------------------------------------------------
+# Test 3: surprising_connections scores semantically_similar_to edges higher
+#         than references edges with the same community membership
+# ---------------------------------------------------------------------------
+
+def test_semantic_edge_scores_higher_than_references():
+    G = _make_two_edge_graph()
+    communities = {0: ["a", "b"], 1: ["c", "d"]}
+    node_community = {"a": 0, "b": 0, "c": 1, "d": 1}
+
+    score_sem, reasons_sem = _surprise_score(
+        G, "a", "b", G.edges["a", "b"], node_community,
+        "auth/validators.py", "api/checks.py"
+    )
+    score_ref, _ = _surprise_score(
+        G, "c", "d", G.edges["c", "d"], node_community,
+        "config/loader.py", "utils/reader.py"
+    )
+    assert score_sem > score_ref
+
+
+def test_semantic_edge_reason_mentions_similarity():
+    G = _make_two_edge_graph()
+    communities = {0: ["a", "b"], 1: ["c", "d"]}
+    node_community = {"a": 0, "b": 0, "c": 1, "d": 1}
+
+    _, reasons = _surprise_score(
+        G, "a", "b", G.edges["a", "b"], node_community,
+        "auth/validators.py", "api/checks.py"
+    )
+    assert any("similar" in r for r in reasons)
+
+
+# ---------------------------------------------------------------------------
+# Test 4: report renders [semantically similar] tag for these edges
+# ---------------------------------------------------------------------------
+
+def _make_report_with_semantic_surprise():
+    G = _make_graph_with_semantic_edge()
+    communities = {0: ["a_validate_input", "b_check_input"]}
+    cohesion = {0: 0.5}
+    labels = {0: "Validators"}
+    gods = []
+    surprises = [
+        {
+            "source": "validate_input",
+            "target": "check_input",
+            "relation": "semantically_similar_to",
+            "confidence": "INFERRED",
+            "confidence_score": 0.82,
+            "source_files": ["auth/validators.py", "api/checks.py"],
+            "why": "semantically similar concepts with no structural link",
+        }
+    ]
+    detection = {"total_files": 2, "total_words": 500, "needs_graph": True, "warning": None}
+    tokens = {"input": 100, "output": 50}
+    return generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, "./project")
+
+
+def test_report_renders_semantically_similar_tag():
+    report = _make_report_with_semantic_surprise()
+    assert "[semantically similar]" in report
+
+
+def test_report_semantic_tag_on_correct_line():
+    report = _make_report_with_semantic_surprise()
+    for line in report.splitlines():
+        if "semantically_similar_to" in line:
+            assert "[semantically similar]" in line
+            break
+    else:
+        pytest.fail("No line with semantically_similar_to found in report")
+
+
+def test_report_no_semantic_tag_for_other_relations():
+    """Non-semantic edges must not get the [semantically similar] tag."""
+    G = nx.Graph()
+    for nid, label, src in [
+        ("x", "Alpha", "repo1/a.py"),
+        ("y", "Beta", "repo2/b.py"),
+    ]:
+        G.add_node(nid, label=label, source_file=src, file_type="code")
+    G.add_edge("x", "y", relation="references", confidence="EXTRACTED",
+               confidence_score=1.0, source_file="repo1/a.py", weight=1.0)
+
+    communities = {0: ["x", "y"]}
+    cohesion = {0: 0.5}
+    labels = {0: "Misc"}
+    gods = []
+    surprises = [
+        {
+            "source": "Alpha",
+            "target": "Beta",
+            "relation": "references",
+            "confidence": "EXTRACTED",
+            "source_files": ["repo1/a.py", "repo2/b.py"],
+            "why": "cross-file connection",
+        }
+    ]
+    detection = {"total_files": 2, "total_words": 200, "needs_graph": True, "warning": None}
+    tokens = {"input": 50, "output": 25}
+    report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, "./project")
+    assert "[semantically similar]" not in report