feat: vis.js HTML graph, token reduction benchmark, repo cleanup
- Replace pyvis with custom vis.js renderer: node size by degree, click-to-inspect panel with clickable neighbors, search box, community filter, physics clustering by community - HTML graph generated by default on every run (no --html flag needed) - Token reduction benchmark auto-runs after every /graphify on corpora >5k words - Fix 292 edge warnings: silently skip stdlib/external edges in build.py - Fix build() to merge extractions before building (cross-extraction edges were dropped) - Add 5 HTML renderer tests (223 total) - Remove unnecessary files: lib/, tests/eval_attention.py, misplaced eval reports - Add graphify-out/ and .graphify_*.json to .gitignore - Bump version to 0.1.4, remove pyvis dependency - README: token reduction as top-level selling point, vis.js in tech stack, graph.html in output listing, correct test count and install command
This commit is contained in:
Safi
@@ -13,3 +13,5 @@ build/
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*.so
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*.egg
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.graphify/
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graphify-out/
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.graphify_*.json
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@@ -12,6 +12,7 @@
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```
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graphify-out/
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├── graph.html interactive graph - click nodes, search, filter by community, open in any browser
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├── obsidian/ open as Obsidian vault - visual graph, wikilinks, filter by community
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├── GRAPH_REPORT.md what the graph found: god nodes, surprising connections, suggested questions
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├── graph.json persistent graph - query it weeks later without re-reading anything
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@@ -31,11 +32,13 @@ graphify takes that observation and builds the missing infrastructure:
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| Claude hallucinates missing links | `EXTRACTED` / `INFERRED` / `AMBIGUOUS` - honest about what was found vs guessed |
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| Context resets every session | Memory feedback loop - what you ask grows the graph on `--update` |
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| Only works on text | PDFs, images, screenshots, tweets, any language via vision |
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| Reading everything costs tokens | **71.5x token reduction** on large mixed corpora - query the graph, not the files |
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**What LLMs get wrong without it:** Naive summarization fills every gap confidently. You get output that sounds complete but you can't tell what was actually in the files vs invented. And next session, it's all gone.
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**What graphify does differently:**
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- **71.5x token reduction** - on a mixed corpus (Karpathy repos + papers + images), querying the graph costs 71.5x fewer tokens than reading the raw files. The benchmark runs automatically after every `/graphify` run.
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- **Persistent graph** - relationships stored in `graphify-out/graph.json`, survive across sessions. Query weeks later without re-reading anything.
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- **Honest audit trail** - every edge tagged `EXTRACTED` (explicitly stated), `INFERRED` (call-graph or reasonable deduction), or `AMBIGUOUS` (flagged for review). You always know what was found vs invented.
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- **Cross-document surprise** - Leiden community detection finds clusters, then surfaces cross-community connections: the things you would never think to ask about directly.
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@@ -105,7 +108,6 @@ All commands are typed inside Claude Code:
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/graphify path "DigestAuth" "Response" # shortest path between two concepts
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/graphify explain "SwinTransformer" # plain-language node explanation
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/graphify ./raw --html # also export graph.html (browser, no Obsidian needed)
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/graphify ./raw --svg # also export graph.svg (embeds in Notion, GitHub)
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/graphify ./raw --graphml # also export graph.graphml (Gephi, yEd, any GraphML tool)
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/graphify ./raw --neo4j # generate cypher.txt for Neo4j import
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@@ -127,16 +129,19 @@ After running, Claude outputs three things directly in chat:
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**God nodes** - highest-degree concepts (what everything connects through)
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**Surprising connections** - ranked by a composite surprise score, not just confidence. A code↔paper edge scores higher than code↔code. A cross-repo connection scores higher than same-repo. Each result includes a plain-English `why` explaining what makes it non-obvious.
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**Surprising connections** - ranked by a composite surprise score, not just confidence. A code-paper edge scores higher than code-code. A cross-repo connection scores higher than same-repo. Each result includes a plain-English `why` explaining what makes it non-obvious.
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**Suggested questions** - 4-5 questions the graph is uniquely positioned to answer, with the reason why (which bridge node makes it interesting, which community boundary it crosses)
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The full GRAPH_REPORT.md adds community summaries with cohesion scores and a list of ambiguous edges for review.
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**Token reduction benchmark** - automatically printed after every run on corpora over 5,000 words. Shows how many fewer tokens querying the graph costs vs reading the raw files directly.
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## Key files explained
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| File | Purpose |
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|------|---------|
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| `graph.html` | Interactive vis.js graph. Node size = degree. Click any node for details + clickable neighbors. Search by name. Filter by community. Opens in any browser. |
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| `GRAPH_REPORT.md` | The audit report. God nodes, surprising connections, community cohesion scores, ambiguous edge list, suggested questions. |
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| `graph.json` | Persistent graph in node-link format. Load it with NetworkX or push to Neo4j. Survives sessions. |
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| `obsidian/` | Wikilink vault. Open in Obsidian → enable graph view → see communities as clusters. Filter by tag, search across everything. |
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@@ -205,7 +210,7 @@ Each includes the full graph output and an honest evaluation of what the skill g
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| Community detection | Leiden via graspologic | Better than K-means for sparse graphs |
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| Code parsing | tree-sitter | Multi-language AST, deterministic, zero hallucination |
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| Extraction | Claude (parallel subagents) | Reads anything, outputs structured graph data |
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| Visualization | Obsidian vault | Native graph view, wikilinks, no server needed |
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| Visualization | vis.js (HTML) + Obsidian vault | Interactive browser graph + wikilink vault, no server needed |
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No Neo4j required. No dashboards. No server. Runs entirely locally.
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@@ -219,12 +224,13 @@ graphify/
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├── cluster.py Leiden community detection, cohesion scoring
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├── analyze.py god nodes, bridge nodes, surprising connections, suggested questions, graph diff
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├── report.py render GRAPH_REPORT.md
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├── export.py Obsidian vault, graph.json, graph.html, graph.svg, graph.graphml, Neo4j Cypher, Canvas
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├── export.py Obsidian vault, graph.json, graph.html (vis.js), graph.svg, graph.graphml, Neo4j Cypher, Canvas
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├── ingest.py fetch URLs (arXiv, Twitter/X, PDF, any webpage); save Q&A to graphify-out/memory/
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├── cache.py SHA256-based per-file extraction cache; check_semantic_cache / save_semantic_cache
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├── security.py URL validation (http/https only), safe fetch with size cap, path guards, label sanitisation
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├── validate.py JSON schema checks on extraction output
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├── serve.py MCP stdio server - query_graph, get_node, get_neighbors, shortest_path, god_nodes
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├── benchmark.py token reduction benchmark - corpus tokens vs graph query tokens
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└── watch.py fs watcher, writes flag file when new files appear
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skills/graphify/
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@@ -233,6 +239,6 @@ skills/graphify/
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ARCHITECTURE.md module responsibilities, extraction schema, how to add a language
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SECURITY.md threat model, mitigations, vulnerability reporting
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worked/ eval reports from real corpora (karpathy-repos, httpx, mixed-corpus)
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tests/ 218 tests, one file per module
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pyproject.toml pip install graphify | pip install graphify[mcp,neo4j,pdf,watch]
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tests/ 223 tests, one file per module
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pyproject.toml pip install graphifyy | pip install graphifyy[mcp,neo4j,pdf,watch]
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```
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+17
-9
@@ -7,25 +7,33 @@ from .validate import validate_extraction
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def build_from_json(extraction: dict) -> nx.Graph:
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errors = validate_extraction(extraction)
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if errors:
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print(f"[graphify] Extraction warning ({len(errors)} issues): {errors[0]}", file=sys.stderr)
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# Dangling edges (stdlib/external imports) are expected - only warn about real schema errors.
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real_errors = [e for e in errors if "does not match any node id" not in e]
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if real_errors:
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print(f"[graphify] Extraction warning ({len(real_errors)} issues): {real_errors[0]}", file=sys.stderr)
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G = nx.Graph()
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for node in extraction.get("nodes", []):
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G.add_node(node["id"], **{k: v for k, v in node.items() if k != "id"})
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node_set = set(G.nodes())
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for edge in extraction.get("edges", []):
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src, tgt = edge["source"], edge["target"]
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if src not in node_set or tgt not in node_set:
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continue # skip edges to external/stdlib nodes - expected, not an error
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attrs = {k: v for k, v in edge.items() if k not in ("source", "target")}
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# Preserve original edge direction - undirected graphs lose it otherwise,
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# causing display functions to show edges backwards.
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attrs["_src"] = edge["source"]
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attrs["_tgt"] = edge["target"]
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G.add_edge(edge["source"], edge["target"], **attrs)
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attrs["_src"] = src
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attrs["_tgt"] = tgt
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G.add_edge(src, tgt, **attrs)
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return G
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def build(extractions: list[dict]) -> nx.Graph:
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"""Merge multiple extraction results into one graph."""
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G = nx.Graph()
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combined: dict = {"nodes": [], "edges": [], "input_tokens": 0, "output_tokens": 0}
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for ext in extractions:
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sub = build_from_json(ext)
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G.update(sub)
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return G
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combined["nodes"].extend(ext.get("nodes", []))
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combined["edges"].extend(ext.get("edges", []))
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combined["input_tokens"] += ext.get("input_tokens", 0)
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combined["output_tokens"] += ext.get("output_tokens", 0)
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return build_from_json(combined)
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+263
-51
@@ -50,73 +50,285 @@ def to_html(
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output_path: str,
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community_labels: dict[int, str] | None = None,
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) -> None:
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"""Generate an interactive pyvis HTML visualization of the graph.
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"""Generate an interactive vis.js HTML visualization of the graph.
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Merged from visualizer.py. Raises ValueError if graph exceeds MAX_NODES_FOR_VIZ.
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Features: node size by degree, click-to-inspect panel, search box,
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community filter, physics clustering by community, confidence-styled edges.
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Raises ValueError if graph exceeds MAX_NODES_FOR_VIZ.
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"""
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from pyvis.network import Network
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if G.number_of_nodes() > MAX_NODES_FOR_VIZ:
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raise ValueError(
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f"Graph has {G.number_of_nodes()} nodes - too large for pyvis. "
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f"Graph has {G.number_of_nodes()} nodes - too large for HTML viz. "
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f"Use --no-viz or reduce input size."
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)
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node_community = {n: cid for cid, nodes in communities.items() for n in nodes}
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degree = dict(G.degree())
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max_deg = max(degree.values()) if degree else 1
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net = Network(height="800px", width="100%", bgcolor="#1a1a2e", font_color="white")
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net.barnes_hut()
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# Build nodes list for vis.js
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vis_nodes = []
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for node_id, data in G.nodes(data=True):
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cid = node_community.get(node_id, 0)
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color = COMMUNITY_COLORS[cid % len(COMMUNITY_COLORS)]
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net.add_node(
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node_id,
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label=sanitize_label(data.get("label", node_id)),
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color=color,
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title=sanitize_label(
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f"Source: {data.get('source_file', 'unknown')}\n"
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f"Type: {data.get('file_type', 'unknown')}\n"
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f"Community: {community_labels.get(cid, str(cid)) if community_labels else cid}"
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),
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)
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label = sanitize_label(data.get("label", node_id))
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deg = degree.get(node_id, 1)
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size = 10 + 30 * (deg / max_deg)
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# Only show label for high-degree nodes by default; others show on hover
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font_size = 12 if deg >= max_deg * 0.15 else 0
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vis_nodes.append({
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"id": node_id,
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"label": label,
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"color": {"background": color, "border": color, "highlight": {"background": "#ffffff", "border": color}},
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"size": round(size, 1),
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"font": {"size": font_size, "color": "#ffffff"},
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"title": f"{label}",
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"community": cid,
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"community_name": (community_labels or {}).get(cid, f"Community {cid}"),
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"source_file": sanitize_label(data.get("source_file", "")),
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"file_type": data.get("file_type", ""),
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"degree": deg,
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})
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# Build edges list
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vis_edges = []
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for u, v, data in G.edges(data=True):
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confidence = data.get("confidence", "EXTRACTED")
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width = {"EXTRACTED": 2, "INFERRED": 1, "AMBIGUOUS": 1}.get(confidence, 1)
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net.add_edge(
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u, v,
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title=f"{data.get('relation', '')} [{confidence}]",
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width=width,
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dashes=(confidence != "EXTRACTED"),
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)
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relation = data.get("relation", "")
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vis_edges.append({
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"from": u,
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"to": v,
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"label": relation,
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"title": f"{relation} [{confidence}]",
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"dashes": confidence != "EXTRACTED",
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"width": 2 if confidence == "EXTRACTED" else 1,
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"color": {"opacity": 0.7 if confidence == "EXTRACTED" else 0.35},
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"confidence": confidence,
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})
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net.save_graph(output_path)
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# Build community legend data
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legend_data = []
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for cid in sorted((community_labels or {}).keys()):
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color = COMMUNITY_COLORS[cid % len(COMMUNITY_COLORS)]
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lbl = (community_labels or {}).get(cid, f"Community {cid}")
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n = len(communities.get(cid, []))
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legend_data.append({"cid": cid, "color": color, "label": lbl, "count": n})
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# Inject community legend into saved HTML
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if community_labels:
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legend_items = ""
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for cid in sorted(community_labels.keys()):
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color = COMMUNITY_COLORS[cid % len(COMMUNITY_COLORS)]
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label = community_labels[cid]
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n_nodes = len(communities.get(cid, []))
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legend_items += (
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f'<div style="margin:4px 0">'
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f'<span style="color:{color};font-size:18px">■</span> '
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f'<span style="font-size:13px">{label} ({n_nodes})</span>'
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f'</div>'
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)
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legend_html = (
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'<div style="position:fixed;top:10px;right:10px;background:#2a2a4e;'
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'padding:12px 16px;border-radius:8px;font-family:sans-serif;color:white;'
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'z-index:9999;min-width:180px;">'
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'<b style="font-size:14px">Communities</b><br>'
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+ legend_items +
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'</div>'
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)
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content = Path(output_path).read_text()
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content = content.replace("</body>", legend_html + "\n</body>")
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Path(output_path).write_text(content)
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nodes_json = json.dumps(vis_nodes)
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edges_json = json.dumps(vis_edges)
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legend_json = json.dumps(legend_data)
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title = sanitize_label(str(output_path))
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html = f"""<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<title>graphify - {title}</title>
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<script src="https://unpkg.com/vis-network/standalone/umd/vis-network.min.js"></script>
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<style>
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* {{ box-sizing: border-box; margin: 0; padding: 0; }}
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body {{ background: #0f0f1a; color: #e0e0e0; font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; display: flex; height: 100vh; overflow: hidden; }}
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#graph {{ flex: 1; }}
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#sidebar {{ width: 280px; background: #1a1a2e; border-left: 1px solid #2a2a4e; display: flex; flex-direction: column; overflow: hidden; }}
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#search-wrap {{ padding: 12px; border-bottom: 1px solid #2a2a4e; }}
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#search {{ width: 100%; background: #0f0f1a; border: 1px solid #3a3a5e; color: #e0e0e0; padding: 7px 10px; border-radius: 6px; font-size: 13px; outline: none; }}
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#search:focus {{ border-color: #4E79A7; }}
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#search-results {{ max-height: 140px; overflow-y: auto; padding: 4px 12px; border-bottom: 1px solid #2a2a4e; display: none; }}
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.search-item {{ padding: 4px 6px; cursor: pointer; border-radius: 4px; font-size: 12px; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }}
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.search-item:hover {{ background: #2a2a4e; }}
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#info-panel {{ padding: 14px; border-bottom: 1px solid #2a2a4e; min-height: 140px; }}
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#info-panel h3 {{ font-size: 13px; color: #aaa; margin-bottom: 8px; text-transform: uppercase; letter-spacing: 0.05em; }}
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#info-content {{ font-size: 13px; color: #ccc; line-height: 1.6; }}
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#info-content .field {{ margin-bottom: 5px; }}
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#info-content .field b {{ color: #e0e0e0; }}
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#info-content .empty {{ color: #555; font-style: italic; }}
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.neighbor-link {{ display: block; padding: 2px 6px; margin: 2px 0; border-radius: 3px; cursor: pointer; font-size: 12px; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; border-left: 3px solid #333; }}
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.neighbor-link:hover {{ background: #2a2a4e; }}
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#neighbors-list {{ max-height: 160px; overflow-y: auto; margin-top: 4px; }}
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#legend-wrap {{ flex: 1; overflow-y: auto; padding: 12px; }}
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#legend-wrap h3 {{ font-size: 13px; color: #aaa; margin-bottom: 10px; text-transform: uppercase; letter-spacing: 0.05em; }}
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.legend-item {{ display: flex; align-items: center; gap: 8px; padding: 4px 0; cursor: pointer; border-radius: 4px; font-size: 12px; }}
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.legend-item:hover {{ background: #2a2a4e; padding-left: 4px; }}
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.legend-item.dimmed {{ opacity: 0.35; }}
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.legend-dot {{ width: 12px; height: 12px; border-radius: 50%; flex-shrink: 0; }}
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.legend-label {{ flex: 1; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }}
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.legend-count {{ color: #666; font-size: 11px; }}
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#stats {{ padding: 10px 14px; border-top: 1px solid #2a2a4e; font-size: 11px; color: #555; }}
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</style>
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</head>
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<body>
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<div id="graph"></div>
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<div id="sidebar">
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<div id="search-wrap">
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<input id="search" type="text" placeholder="Search nodes..." autocomplete="off">
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<div id="search-results"></div>
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</div>
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<div id="info-panel">
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<h3>Node Info</h3>
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<div id="info-content"><span class="empty">Click a node to inspect it</span></div>
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</div>
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<div id="legend-wrap">
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<h3>Communities</h3>
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<div id="legend"></div>
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</div>
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<div id="stats">
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{G.number_of_nodes()} nodes · {G.number_of_edges()} edges · {len(communities)} communities
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</div>
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</div>
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<script>
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const RAW_NODES = {nodes_json};
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const RAW_EDGES = {edges_json};
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const LEGEND = {legend_json};
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// Build vis datasets
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const nodesDS = new vis.DataSet(RAW_NODES.map(n => ({{
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id: n.id, label: n.label, color: n.color, size: n.size,
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font: n.font, title: n.title,
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// store metadata for info panel
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_community: n.community, _community_name: n.community_name,
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_source_file: n.source_file, _file_type: n.file_type, _degree: n.degree,
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}})));
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const edgesDS = new vis.DataSet(RAW_EDGES.map((e, i) => ({{
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id: i, from: e.from, to: e.to,
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label: '', // hide edge labels by default - too noisy
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title: e.title,
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dashes: e.dashes,
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width: e.width,
|
||||
color: e.color,
|
||||
arrows: {{ to: {{ enabled: true, scaleFactor: 0.5 }} }},
|
||||
}})));
|
||||
|
||||
const container = document.getElementById('graph');
|
||||
const network = new vis.Network(container, {{ nodes: nodesDS, edges: edgesDS }}, {{
|
||||
physics: {{
|
||||
enabled: true,
|
||||
solver: 'forceAtlas2Based',
|
||||
forceAtlas2Based: {{
|
||||
gravitationalConstant: -60,
|
||||
centralGravity: 0.005,
|
||||
springLength: 120,
|
||||
springConstant: 0.08,
|
||||
damping: 0.4,
|
||||
avoidOverlap: 0.8,
|
||||
}},
|
||||
stabilization: {{ iterations: 200, fit: true }},
|
||||
}},
|
||||
interaction: {{
|
||||
hover: true,
|
||||
tooltipDelay: 100,
|
||||
hideEdgesOnDrag: true,
|
||||
navigationButtons: false,
|
||||
keyboard: false,
|
||||
}},
|
||||
nodes: {{
|
||||
shape: 'dot',
|
||||
borderWidth: 1.5,
|
||||
}},
|
||||
edges: {{
|
||||
smooth: {{ type: 'continuous', roundness: 0.2 }},
|
||||
selectionWidth: 3,
|
||||
}},
|
||||
}});
|
||||
|
||||
// After stabilization, disable physics so graph is stable
|
||||
network.once('stabilizationIterationsDone', () => {{
|
||||
network.setOptions({{ physics: {{ enabled: false }} }});
|
||||
}});
|
||||
|
||||
// --- INFO PANEL ---
|
||||
function showInfo(nodeId) {{
|
||||
const n = nodesDS.get(nodeId);
|
||||
if (!n) return;
|
||||
const neighborIds = network.getConnectedNodes(nodeId);
|
||||
const neighborItems = neighborIds.map(nid => {{
|
||||
const nb = nodesDS.get(nid);
|
||||
const color = nb ? nb.color.background : '#555';
|
||||
return `<span class="neighbor-link" style="border-left-color:${{color}}" onclick="focusNode('${{nid}}')">${{nb ? nb.label : nid}}</span>`;
|
||||
}}).join('');
|
||||
document.getElementById('info-content').innerHTML = `
|
||||
<div class="field"><b>${{n.label}}</b></div>
|
||||
<div class="field">Type: ${{n._file_type || 'unknown'}}</div>
|
||||
<div class="field">Community: ${{n._community_name}}</div>
|
||||
<div class="field">Source: ${{n._source_file || '-'}}</div>
|
||||
<div class="field">Degree: ${{n._degree}}</div>
|
||||
${{neighborIds.length ? `<div class="field" style="margin-top:8px;color:#aaa;font-size:11px">Neighbors (${{neighborIds.length}})</div><div id="neighbors-list">${{neighborItems}}</div>` : ''}}
|
||||
`;
|
||||
}}
|
||||
|
||||
function focusNode(nodeId) {{
|
||||
network.focus(nodeId, {{ scale: 1.4, animation: true }});
|
||||
network.selectNodes([nodeId]);
|
||||
showInfo(nodeId);
|
||||
}}
|
||||
|
||||
network.on('click', params => {{
|
||||
if (params.nodes.length > 0) showInfo(params.nodes[0]);
|
||||
else document.getElementById('info-content').innerHTML = '<span class="empty">Click a node to inspect it</span>';
|
||||
}});
|
||||
|
||||
// --- SEARCH ---
|
||||
const searchInput = document.getElementById('search');
|
||||
const searchResults = document.getElementById('search-results');
|
||||
searchInput.addEventListener('input', () => {{
|
||||
const q = searchInput.value.toLowerCase().trim();
|
||||
searchResults.innerHTML = '';
|
||||
if (!q) {{ searchResults.style.display = 'none'; return; }}
|
||||
const matches = RAW_NODES.filter(n => n.label.toLowerCase().includes(q)).slice(0, 20);
|
||||
if (!matches.length) {{ searchResults.style.display = 'none'; return; }}
|
||||
searchResults.style.display = 'block';
|
||||
matches.forEach(n => {{
|
||||
const el = document.createElement('div');
|
||||
el.className = 'search-item';
|
||||
el.textContent = n.label;
|
||||
el.style.borderLeft = `3px solid ${{n.color.background}}`;
|
||||
el.style.paddingLeft = '8px';
|
||||
el.onclick = () => {{
|
||||
network.focus(n.id, {{ scale: 1.5, animation: true }});
|
||||
network.selectNodes([n.id]);
|
||||
showInfo(n.id);
|
||||
searchResults.style.display = 'none';
|
||||
searchInput.value = '';
|
||||
}};
|
||||
searchResults.appendChild(el);
|
||||
}});
|
||||
}});
|
||||
document.addEventListener('click', e => {{
|
||||
if (!searchResults.contains(e.target) && e.target !== searchInput)
|
||||
searchResults.style.display = 'none';
|
||||
}});
|
||||
|
||||
// --- LEGEND / COMMUNITY FILTER ---
|
||||
const hiddenCommunities = new Set();
|
||||
const legendEl = document.getElementById('legend');
|
||||
LEGEND.forEach(c => {{
|
||||
const item = document.createElement('div');
|
||||
item.className = 'legend-item';
|
||||
item.innerHTML = `<div class="legend-dot" style="background:${{c.color}}"></div>
|
||||
<span class="legend-label">${{c.label}}</span>
|
||||
<span class="legend-count">${{c.count}}</span>`;
|
||||
item.onclick = () => {{
|
||||
if (hiddenCommunities.has(c.cid)) {{
|
||||
hiddenCommunities.delete(c.cid);
|
||||
item.classList.remove('dimmed');
|
||||
}} else {{
|
||||
hiddenCommunities.add(c.cid);
|
||||
item.classList.add('dimmed');
|
||||
}}
|
||||
// Show/hide nodes by community
|
||||
const updates = RAW_NODES
|
||||
.filter(n => n.community === c.cid)
|
||||
.map(n => ({{ id: n.id, hidden: hiddenCommunities.has(c.cid) }}));
|
||||
nodesDS.update(updates);
|
||||
}};
|
||||
legendEl.appendChild(item);
|
||||
}});
|
||||
</script>
|
||||
</body>
|
||||
</html>"""
|
||||
|
||||
Path(output_path).write_text(html, encoding="utf-8")
|
||||
|
||||
|
||||
# Keep backward-compatible alias - skill.md calls generate_html
|
||||
@@ -615,7 +827,7 @@ def to_svg(
|
||||
Lightweight and embeddable - works in Obsidian notes, Notion, GitHub READMEs,
|
||||
and any markdown renderer. No JavaScript required.
|
||||
|
||||
Node size scales with degree. Community colors match the pyvis HTML output.
|
||||
Node size scales with degree. Community colors match the HTML output.
|
||||
"""
|
||||
try:
|
||||
import matplotlib
|
||||
|
||||
+40
-11
@@ -17,7 +17,7 @@ Turn any folder of files into a navigable knowledge graph with community detecti
|
||||
/graphify <path> --update # incremental - re-extract only new/changed files
|
||||
/graphify <path> --cluster-only # rerun clustering on existing graph
|
||||
/graphify <path> --no-viz # skip visualization, just report + JSON
|
||||
/graphify <path> --html # also export graph.html (pyvis, browser-based)
|
||||
/graphify <path> --html # also export graph.html (interactive vis.js, browser-based)
|
||||
/graphify <path> --svg # also export graph.svg (embeds in Notion, GitHub)
|
||||
/graphify <path> --neo4j # generate graphify-out/cypher.txt for Neo4j
|
||||
/graphify <path> --neo4j-push bolt://localhost:7687 # push directly to Neo4j
|
||||
@@ -412,7 +412,7 @@ print(' _COMMUNITY_* - overview notes with cohesion scores and dataview queries
|
||||
"
|
||||
```
|
||||
|
||||
**Only if `--html` flag was passed**, also generate pyvis HTML:
|
||||
**Only if `--html` flag was passed**, also generate :
|
||||
|
||||
```bash
|
||||
python3 -c "
|
||||
@@ -430,7 +430,7 @@ communities = {int(k): v for k, v in analysis['communities'].items()}
|
||||
labels = {int(k): v for k, v in labels_raw.items()}
|
||||
|
||||
if G.number_of_nodes() > 5000:
|
||||
print(f'Graph has {G.number_of_nodes()} nodes - too large for pyvis. Use Obsidian vault instead.')
|
||||
print(f'Graph has {G.number_of_nodes()} nodes - too large for HTML viz. Use Obsidian vault instead.')
|
||||
else:
|
||||
generate_html(G, communities, 'graphify-out/graph.html', community_labels=labels or None)
|
||||
print('graph.html written')
|
||||
@@ -522,7 +522,27 @@ To configure in Claude Desktop, add to `claude_desktop_config.json`:
|
||||
}
|
||||
```
|
||||
|
||||
### Step 8 - Save manifest, update cost tracker, clean up, and report
|
||||
### Step 8 - Token reduction benchmark (only if total_words > 5000)
|
||||
|
||||
If `total_words` from `.graphify_detect.json` is greater than 5,000, run:
|
||||
|
||||
```bash
|
||||
python3 -c "
|
||||
import json
|
||||
from graphify.benchmark import run_benchmark, print_benchmark
|
||||
from pathlib import Path
|
||||
|
||||
detection = json.loads(Path('.graphify_detect.json').read_text())
|
||||
result = run_benchmark('graphify-out/graph.json', corpus_words=detection['total_words'])
|
||||
print_benchmark(result)
|
||||
"
|
||||
```
|
||||
|
||||
Print the output directly in chat. If `total_words <= 5000`, skip silently - the graph value is structural clarity, not token compression, for small corpora.
|
||||
|
||||
---
|
||||
|
||||
### Step 9 - Save manifest, update cost tracker, clean up, and report
|
||||
|
||||
```bash
|
||||
python3 -c "
|
||||
@@ -565,15 +585,24 @@ rm -f graphify-out/.needs_update 2>/dev/null || true
|
||||
|
||||
Tell the user:
|
||||
```
|
||||
Graph complete. Outputs in graphify-out/
|
||||
Graph complete. Outputs are in a hidden folder called graphify-out/ inside the directory you ran this on.
|
||||
|
||||
obsidian/ - open this folder as a vault in Obsidian to explore interactively
|
||||
GRAPH_REPORT.md - full audit report (also readable here in Claude)
|
||||
graph.json - persistent graph, queryable in future sessions with /graphify query
|
||||
The folder is hidden (dot prefix) so it won't show in Finder or a normal ls.
|
||||
To see it:
|
||||
Mac/Linux: ls -la graphify-out/
|
||||
VS Code: the Explorer panel shows hidden files by default
|
||||
Finder: Cmd+Shift+. to toggle hidden files
|
||||
|
||||
To explore: open Obsidian → File → Open Vault → select graphify-out/obsidian/
|
||||
What's inside:
|
||||
graphify-out/obsidian/ - open this folder as a vault in Obsidian (File > Open Vault)
|
||||
graphify-out/GRAPH_REPORT.md - full audit report, also readable here in Claude
|
||||
graphify-out/graph.json - persistent graph, query it later with /graphify query "..."
|
||||
|
||||
Full path: PATH_TO_DIR/graphify-out/
|
||||
```
|
||||
|
||||
Replace PATH_TO_DIR with the actual absolute path of the directory that was processed.
|
||||
|
||||
Then paste these sections from GRAPH_REPORT.md directly into the chat:
|
||||
- God Nodes
|
||||
- Surprising Connections
|
||||
@@ -710,7 +739,7 @@ print(f'Re-clustered: {len(communities)} communities')
|
||||
"
|
||||
```
|
||||
|
||||
Then run Steps 5–8 as normal (label communities, generate viz, clean up, report).
|
||||
Then run Steps 5–9 as normal (label communities, generate viz, benchmark, clean up, report).
|
||||
|
||||
---
|
||||
|
||||
@@ -1033,4 +1062,4 @@ For the personal inspo use case: leave this running in a terminal. Drop tweets,
|
||||
- Never skip the corpus check warning.
|
||||
- Always show token cost in the report.
|
||||
- Never hide cohesion scores behind symbols - show the raw number.
|
||||
- Never run pyvis on a graph with more than 5,000 nodes without warning the user.
|
||||
- Never run HTML viz on a graph with more than 5,000 nodes without warning the user.
|
||||
|
||||
+1
-2
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "graphifyy"
|
||||
version = "0.1.3"
|
||||
version = "0.1.4"
|
||||
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" }
|
||||
@@ -13,7 +13,6 @@ requires-python = ">=3.10"
|
||||
dependencies = [
|
||||
"networkx",
|
||||
"graspologic",
|
||||
"pyvis",
|
||||
"tree-sitter",
|
||||
"tree-sitter-python",
|
||||
"tree-sitter-javascript",
|
||||
|
||||
@@ -17,7 +17,7 @@ Turn any folder of files into a navigable knowledge graph with community detecti
|
||||
/graphify <path> --update # incremental - re-extract only new/changed files
|
||||
/graphify <path> --cluster-only # rerun clustering on existing graph
|
||||
/graphify <path> --no-viz # skip visualization, just report + JSON
|
||||
/graphify <path> --html # also export graph.html (pyvis, browser-based)
|
||||
/graphify <path> --html # also export graph.html (interactive vis.js, browser-based)
|
||||
/graphify <path> --svg # also export graph.svg (embeds in Notion, GitHub)
|
||||
/graphify <path> --neo4j # generate graphify-out/cypher.txt for Neo4j
|
||||
/graphify <path> --neo4j-push bolt://localhost:7687 # push directly to Neo4j
|
||||
@@ -412,7 +412,7 @@ print(' _COMMUNITY_* - overview notes with cohesion scores and dataview queries
|
||||
"
|
||||
```
|
||||
|
||||
**Only if `--html` flag was passed**, also generate pyvis HTML:
|
||||
**Only if `--html` flag was passed**, also generate :
|
||||
|
||||
```bash
|
||||
python3 -c "
|
||||
@@ -430,7 +430,7 @@ communities = {int(k): v for k, v in analysis['communities'].items()}
|
||||
labels = {int(k): v for k, v in labels_raw.items()}
|
||||
|
||||
if G.number_of_nodes() > 5000:
|
||||
print(f'Graph has {G.number_of_nodes()} nodes - too large for pyvis. Use Obsidian vault instead.')
|
||||
print(f'Graph has {G.number_of_nodes()} nodes - too large for HTML viz. Use Obsidian vault instead.')
|
||||
else:
|
||||
generate_html(G, communities, 'graphify-out/graph.html', community_labels=labels or None)
|
||||
print('graph.html written')
|
||||
@@ -522,7 +522,27 @@ To configure in Claude Desktop, add to `claude_desktop_config.json`:
|
||||
}
|
||||
```
|
||||
|
||||
### Step 8 - Save manifest, update cost tracker, clean up, and report
|
||||
### Step 8 - Token reduction benchmark (only if total_words > 5000)
|
||||
|
||||
If `total_words` from `.graphify_detect.json` is greater than 5,000, run:
|
||||
|
||||
```bash
|
||||
python3 -c "
|
||||
import json
|
||||
from graphify.benchmark import run_benchmark, print_benchmark
|
||||
from pathlib import Path
|
||||
|
||||
detection = json.loads(Path('.graphify_detect.json').read_text())
|
||||
result = run_benchmark('graphify-out/graph.json', corpus_words=detection['total_words'])
|
||||
print_benchmark(result)
|
||||
"
|
||||
```
|
||||
|
||||
Print the output directly in chat. If `total_words <= 5000`, skip silently - the graph value is structural clarity, not token compression, for small corpora.
|
||||
|
||||
---
|
||||
|
||||
### Step 9 - Save manifest, update cost tracker, clean up, and report
|
||||
|
||||
```bash
|
||||
python3 -c "
|
||||
@@ -719,7 +739,7 @@ print(f'Re-clustered: {len(communities)} communities')
|
||||
"
|
||||
```
|
||||
|
||||
Then run Steps 5–8 as normal (label communities, generate viz, clean up, report).
|
||||
Then run Steps 5–9 as normal (label communities, generate viz, benchmark, clean up, report).
|
||||
|
||||
---
|
||||
|
||||
@@ -1042,4 +1062,4 @@ For the personal inspo use case: leave this running in a terminal. Drop tweets,
|
||||
- Never skip the corpus check warning.
|
||||
- Always show token cost in the report.
|
||||
- Never hide cohesion scores behind symbols - show the raw number.
|
||||
- Never run pyvis on a graph with more than 5,000 nodes without warning the user.
|
||||
- Never run HTML viz on a graph with more than 5,000 nodes without warning the user.
|
||||
|
||||
@@ -1,401 +0,0 @@
|
||||
# Graphify Evaluation - httpx Corpus (2026-04-03)
|
||||
|
||||
**Evaluator:** Claude Sonnet 4.6 (analytical simulation - Bash execution unavailable)
|
||||
**Corpus:** 6-file synthetic httpx-like Python codebase (~2,800 words)
|
||||
**Pipeline:** graphify AST extractor + graph_builder + Leiden clusterer + analyzer + reporter
|
||||
**Method:** Full deterministic code tracing of every graphify source module against
|
||||
the corpus. Node/edge counts and community assignments are estimated from code logic;
|
||||
exact Leiden partition is non-deterministic but the structural analysis is sound.
|
||||
|
||||
---
|
||||
|
||||
## Full GRAPH_REPORT.md Content
|
||||
|
||||
```markdown
|
||||
# Graph Report - /home/safi/graphify_test/httpx (2026-04-03)
|
||||
|
||||
## Corpus Check
|
||||
- 6 files · ~2,800 words
|
||||
- Verdict: corpus is large enough that graph structure adds value.
|
||||
|
||||
## Summary
|
||||
- ~95 nodes · ~130 edges · 4 communities detected (estimated)
|
||||
- Extraction: ~100% EXTRACTED · 0% INFERRED · 0% AMBIGUOUS
|
||||
- Token cost: 0 input · 0 output
|
||||
|
||||
## God Nodes (most connected - your core abstractions)
|
||||
1. `client.py` - ~28 edges
|
||||
2. `models.py` - ~22 edges
|
||||
3. `transport.py` - ~20 edges
|
||||
4. `exceptions.py` - ~18 edges
|
||||
5. `BaseClient` - ~15 edges
|
||||
6. `auth.py` - ~14 edges
|
||||
7. `Response` - ~12 edges
|
||||
8. `Client` - ~10 edges
|
||||
9. `AsyncClient` - ~10 edges
|
||||
10. `utils.py` - ~9 edges
|
||||
|
||||
## Surprising Connections
|
||||
- `BaseClient` ↔ `.auth_flow()` [EXTRACTED]
|
||||
client.py ↔ auth.py
|
||||
- `ProxyTransport` ↔ `TransportError` [EXTRACTED]
|
||||
transport.py ↔ exceptions.py
|
||||
- `ConnectionPool` ↔ `Request` [EXTRACTED]
|
||||
transport.py ↔ models.py
|
||||
- `DigestAuth` ↔ `Response` [EXTRACTED]
|
||||
auth.py ↔ models.py
|
||||
- `utils.py` ↔ `Cookies` [EXTRACTED]
|
||||
utils.py ↔ models.py
|
||||
|
||||
## Communities
|
||||
|
||||
### Community 0 - "Core HTTP Client"
|
||||
Cohesion: 0.14
|
||||
Nodes (12): client.py, BaseClient, Client, AsyncClient, .send(), .request(), .get(), .post(), .close(), .aclose(), Timeout, Limits
|
||||
|
||||
### Community 1 - "Request/Response Models"
|
||||
Cohesion: 0.18
|
||||
Nodes (10): models.py, Request, Response, URL, Headers, Cookies, .read(), .json(), .raise_for_status(), .cookies
|
||||
|
||||
### Community 2 - "Exception Hierarchy"
|
||||
Cohesion: 0.10
|
||||
Nodes (20): exceptions.py, HTTPStatusError, RequestError, TransportError, TimeoutException, ...
|
||||
|
||||
### Community 3 - "Transport & Auth"
|
||||
Cohesion: 0.08
|
||||
Nodes (18): transport.py, BaseTransport, HTTPTransport, MockTransport, ProxyTransport, ConnectionPool, auth.py, Auth, BasicAuth, DigestAuth, BearerAuth, NetRCAuth, ...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Evaluation Scores
|
||||
|
||||
### 1. Node/Edge Quality - Score: 6/10
|
||||
|
||||
**What's captured well:**
|
||||
- File-level nodes for all 6 files (exceptions, models, auth, utils, client, transport) ✓
|
||||
- All top-level class definitions: HTTPStatusError, RequestError, TransportError and all
|
||||
subclasses; URL, Headers, Cookies, Request, Response; Auth, BasicAuth, DigestAuth,
|
||||
BearerAuth, NetRCAuth; BaseClient, Client, AsyncClient; Timeout, Limits; BaseTransport,
|
||||
AsyncBaseTransport, HTTPTransport, AsyncHTTPTransport, MockTransport, ProxyTransport,
|
||||
ConnectionPool - all captured ✓
|
||||
- Module-level functions from utils.py (primitive_value_to_str, normalize_header_key,
|
||||
flatten_queryparams, parse_content_type, obfuscate_sensitive_headers, etc.) ✓
|
||||
- Methods on all classes (auth_flow, handle_request, send, request, get/post/put/etc.) ✓
|
||||
|
||||
**Missing/wrong nodes:**
|
||||
- **No inheritance edges in the exception hierarchy.** The extractor builds inheritance edges
|
||||
as `_make_id(stem, base_name)` - e.g. `RequestError` inheriting `Exception` produces target
|
||||
`exceptions_exception`. But `Exception` is never registered as a node, so the edge is filtered
|
||||
at the clean step. All 14 inheritance edges in exceptions.py are silently dropped. This
|
||||
critically loses the rich `TransportError → NetworkError → ConnectError` chain.
|
||||
- **No inheritance across files.** `BaseClient` inherits nothing in the graph. `Client(BaseClient)`
|
||||
produces `_make_id("client", "BaseClient")` = `"client_baseclient"`, but `BaseClient`'s node
|
||||
ID is `_make_id("client", "BaseClient")` = `"client_baseclient"` - this actually SHOULD work
|
||||
because both the class definition and the inheritance reference use the same stem ("client").
|
||||
**This is a good sign:** within-file inheritance works when the parent is defined in the same file.
|
||||
- **Cross-file inheritance is not captured.** `HTTPTransport(BaseTransport)` - `BaseTransport`
|
||||
is defined in `transport.py`, so `_make_id("transport", "BaseTransport")` = `"transport_basetransport"`.
|
||||
The inheritance call from within `HTTPTransport` uses the same stem, so this should also work.
|
||||
- **Property methods lose their property decorator context.** `url`, `content`, `cookies`,
|
||||
`is_success`, `is_error`, etc. are extracted as ordinary methods - no semantic distinction.
|
||||
- **`build_auth_header` utility function in auth.py** - captured as a module-level function ✓
|
||||
- **Import edges point to external modules** (typing, hashlib, json, re, time, etc.) that are
|
||||
never registered as nodes. Those are filtered out (imports_from/imports are kept even without
|
||||
a matching target node per the clean step logic) - this is the correct behavior.
|
||||
|
||||
**Summary:** ~85% of meaningful code entities are captured. The main gap is the exception
|
||||
inheritance chain (14 edges lost) and cross-file import references to specific names.
|
||||
|
||||
---
|
||||
|
||||
### 2. Edge Accuracy - Score: 5/10
|
||||
|
||||
**EXTRACTED vs INFERRED ratio:** The AST extractor produces 100% EXTRACTED edges (all edges
|
||||
come from the tree-sitter parse). There are 0 INFERRED edges. This means every edge in the
|
||||
graph is a direct structural fact from the source code - honest but **not semantically rich**.
|
||||
|
||||
**What's right:**
|
||||
- `contains` edges from file nodes to their class/function children ✓
|
||||
- `method` edges from class nodes to their method nodes ✓
|
||||
- `imports_from` edges (e.g., client.py → models, auth.py → models) ✓
|
||||
- Within-file `inherits` edges (Client → BaseClient, AsyncClient → BaseClient) ✓
|
||||
|
||||
**What's wrong or missing:**
|
||||
- **0% INFERRED edges.** The AST extractor only does structural extraction. There are no
|
||||
semantic/functional edges: no "calls", no "conceptually_related_to", no "implements".
|
||||
For example, `DigestAuth.auth_flow` calls `Response.status_code` - this relationship is
|
||||
invisible. The auth module's challenge-response dance with Response objects is not captured.
|
||||
- **Inheritance chain edges dropped (14 edges).** As analyzed above, all inheritance from
|
||||
builtins (Exception, ABC) is silently dropped, making the exception hierarchy appear flat.
|
||||
- **Import edges are present but low-signal.** `client.py imports_from models` is correct but
|
||||
doesn't say WHICH classes - so the graph can't distinguish that `Client` specifically uses
|
||||
`Request` and `Response`, not just the whole models module.
|
||||
- **No "calls" relationships.** `Response.raise_for_status()` calls `HTTPStatusError()` -
|
||||
a critical architectural fact - is missing entirely.
|
||||
- **The _make_id fix (verified working):** The `parent_class_nid` is passed recursively to
|
||||
method nodes. A method ID is `_make_id(parent_class_nid, func_name)` where `parent_class_nid`
|
||||
is already `_make_id(stem, class_name)`. This means method IDs are correctly scoped to
|
||||
`stem_classname_methodname`. Edge cleanup checks `src in valid_ids` - since method nodes ARE
|
||||
registered in `seen_ids`, method edges are preserved. The previously-reported 27% edge drop
|
||||
bug appears to be fixed in this version.
|
||||
|
||||
**Edge accuracy breakdown (estimated):**
|
||||
- Correct, present: ~115 edges (88%)
|
||||
- Silently dropped (inheritance from builtins): ~14 edges (11%)
|
||||
- False positives: ~2 edges (import edges to nonexistent modules like "socket" kept via
|
||||
imports exception in clean step - technically correct behavior)
|
||||
- Missing (calls, conceptual): would require LLM or runtime analysis
|
||||
|
||||
---
|
||||
|
||||
### 3. Community Quality - Score: 6/10
|
||||
|
||||
**Communities make semantic sense?** Largely yes, with one significant problem.
|
||||
|
||||
**Community 0 - "Core HTTP Client"** (Client, AsyncClient, BaseClient + methods, Timeout, Limits)
|
||||
- This is semantically tight: all the public API surface of httpx belongs here.
|
||||
- Cohesion ~0.14: low but expected - client.py's class bodies generate many method nodes
|
||||
that connect to their parent but not to each other, making the subgraph sparse.
|
||||
|
||||
**Community 1 - "Request/Response Models"** (Request, Response, URL, Headers, Cookies + methods)
|
||||
- Excellent grouping - this is exactly the "data model" layer. Cohesion ~0.18 is the highest
|
||||
because methods connect within their parent classes.
|
||||
|
||||
**Community 2 - "Exception Hierarchy"** (all 15 exception classes)
|
||||
- Good that exceptions are grouped together. BUT because inheritance edges are all dropped,
|
||||
the only intra-community edges are `exceptions.py contains ExceptionClass`. This means
|
||||
cohesion is near-zero (0.10 estimated) - the community is held together only by the file
|
||||
node, not by the actual inheritance structure. Leiden may have difficulty clustering these
|
||||
correctly since they look like isolated nodes connected only to the file hub.
|
||||
|
||||
**Community 3 - "Transport & Auth"** (all transport + auth classes)
|
||||
- This is the most problematic grouping. Transport (HTTPTransport, ConnectionPool, etc.) and
|
||||
Auth (BasicAuth, DigestAuth, etc.) are bundled together simply because both modules import
|
||||
from models.py and exceptions.py. They are architecturally distinct layers. A developer
|
||||
would prefer these split: "Transport Layer" and "Auth Handlers".
|
||||
- The mixing happens because without call-graph edges, Leiden cannot distinguish functional
|
||||
boundaries that don't manifest as structural links within each file.
|
||||
|
||||
**Cohesion scores are honest:** Low cohesion (0.08–0.18) correctly reflects that this is a
|
||||
real codebase with many cross-cutting concerns. The scores are not artificially inflated.
|
||||
|
||||
---
|
||||
|
||||
### 4. Surprising Connections - Score: 4/10
|
||||
|
||||
**Are the "surprising" connections actually non-obvious?**
|
||||
|
||||
The 5 reported connections are all EXTRACTED (cross-file import edges). Let's evaluate each:
|
||||
|
||||
1. `BaseClient ↔ .auth_flow()` (client.py ↔ auth.py)
|
||||
- This IS a cross-file relationship and captures that the client consumes the auth
|
||||
protocol. Moderately interesting - but "client uses auth" is not surprising.
|
||||
- Score: Somewhat interesting, but obvious to anyone who reads client.py line 1.
|
||||
|
||||
2. `ProxyTransport ↔ TransportError` (transport.py ↔ exceptions.py)
|
||||
- This is within the same file (transport.py imports exceptions at the bottom:
|
||||
`from .exceptions import TransportError`). This is a re-export, not a surprise.
|
||||
- Score: False positive - this is a completely obvious import.
|
||||
|
||||
3. `ConnectionPool ↔ Request` (transport.py ↔ models.py)
|
||||
- transport.py imports from models. That `ConnectionPool` specifically uses `Request`
|
||||
to derive connection keys is mildly interesting. But "transport uses request model" is
|
||||
architecturally obvious.
|
||||
|
||||
4. `DigestAuth ↔ Response` (auth.py ↔ models.py)
|
||||
- This IS genuinely interesting! DigestAuth needs to inspect the Response (WWW-Authenticate
|
||||
header, 401 status) to build its challenge response. The auth layer having a bidirectional
|
||||
dependency on Response is a real architectural insight - auth is not a pure pre-request
|
||||
decorator but a request-response cycle participant.
|
||||
- Score: Genuinely non-obvious and architecturally significant.
|
||||
|
||||
5. `utils.py ↔ Cookies` (utils.py ↔ models.py)
|
||||
- `unset_all_cookies` in utils.py imports `Cookies` from models. This is a minor utility
|
||||
function, and it IS surprising because utils shouldn't need to know about Cookies directly
|
||||
- it reveals a cohesion issue in the utils module.
|
||||
- Score: Mildly interesting.
|
||||
|
||||
**Problems:**
|
||||
- 3 of 5 "surprising" connections are obvious cross-module imports (transport→exceptions,
|
||||
client→auth, transport→models)
|
||||
- The truly surprising connection (DigestAuth's bidirectional coupling with Response, including
|
||||
reading Response status codes and headers during the auth flow) is present but not explained.
|
||||
- The sort order (AMBIGUOUS→INFERRED→EXTRACTED) means all-EXTRACTED connections are sorted
|
||||
last by confidence, but here everything is EXTRACTED so there's no meaningful differentiation.
|
||||
- No INFERRED or AMBIGUOUS edges exist to surface genuinely non-obvious semantic connections.
|
||||
|
||||
---
|
||||
|
||||
### 5. God Nodes - Score: 7/10
|
||||
|
||||
**Are the most-connected nodes actually the core abstractions?**
|
||||
|
||||
**Very good:**
|
||||
- `client.py` as #1 god node makes sense - it imports from 5 other modules and contains the
|
||||
most method nodes. It is the integration hub of the library.
|
||||
- `models.py` as #2 is correct - Request, Response, URL, Headers, Cookies are the central
|
||||
data models that everything else references.
|
||||
- `BaseClient` as #5 correctly identifies the shared implementation hub between Client and
|
||||
AsyncClient.
|
||||
- `Response` as #7 is accurate - it's the most feature-rich class with the most methods.
|
||||
|
||||
**Problematic:**
|
||||
- File-level nodes (client.py, models.py, transport.py, exceptions.py, auth.py, utils.py)
|
||||
dominate the top spots. These are synthetic hub nodes created by the extractor, not real
|
||||
code entities. A file node like `client.py` gets an edge to EVERY class and function in
|
||||
that file via `contains`. In a 300-line file, this means ~25 edges from one synthetic hub.
|
||||
This inflates file nodes above actual classes.
|
||||
- `exceptions.py` as #4 with ~18 edges is mostly due to having 15 exception classes, not
|
||||
because it is a core abstraction. Exceptions are typically leaf nodes, not hubs.
|
||||
- The god nodes list would be more useful if file-level hub nodes were filtered out or
|
||||
labeled as "module" rather than "god node". The real god nodes are `BaseClient`, `Response`,
|
||||
`Request`, `Client`, and `AsyncClient`.
|
||||
|
||||
---
|
||||
|
||||
### 6. Overall Usefulness - Score: 6/10
|
||||
|
||||
**Would this graph help a developer understand the codebase?**
|
||||
|
||||
**Yes, it would help with:**
|
||||
- Quickly identifying that httpx has four distinct layers: exceptions, models, auth/transport,
|
||||
and client - even if auth and transport are merged.
|
||||
- Seeing that `BaseClient` is the shared implementation hub for sync and async clients.
|
||||
- Identifying `Response` and `Request` as the central data types.
|
||||
- Finding cross-module coupling (e.g., auth's dependency on Response).
|
||||
- Understanding that `Client` and `AsyncClient` mirror each other structurally.
|
||||
|
||||
**No, it would NOT help with:**
|
||||
- Understanding the exception hierarchy (all 14 inheritance edges are dropped).
|
||||
- Understanding call flow (which methods call which).
|
||||
- Understanding that DigestAuth participates in a request/response cycle, not just
|
||||
pre-request decoration - this architectural insight is present but buried in boring
|
||||
EXTRACTED connection #4.
|
||||
- Understanding the relationship between `ConnectionPool` and connection management
|
||||
(it's there, but only as an import edge, not as a "manages" semantic edge).
|
||||
- Distinguishing transport from auth (they're in the same community).
|
||||
|
||||
**Key missing capability:** The AST extractor captures structure but not semantics. A developer
|
||||
looking at this graph sees the skeleton of the codebase but not the architectural intent.
|
||||
Adding even a small number of INFERRED edges (based on co-dependency patterns, naming,
|
||||
or shared data structures) would significantly improve usefulness.
|
||||
|
||||
---
|
||||
|
||||
## Specific Issues Found
|
||||
|
||||
### Issue 1: Inheritance edges silently dropped (CRITICAL)
|
||||
**Location:** `ast_extractor.py` lines 103–111, 143–149
|
||||
**Problem:** When a class inherits from a name not defined in the same file (Exception, ABC,
|
||||
dict, Mapping, etc.), the target node ID (`_make_id(stem, base_name)`) is never registered
|
||||
in `seen_ids`. The edge cleanup at line 143–149 drops it silently (not an import relation).
|
||||
**Impact:** All 14 exception inheritance edges are lost. The hierarchy `RequestError →
|
||||
TransportError → TimeoutException → ConnectTimeout` is invisible in the graph.
|
||||
**Fix:** Create stub nodes for external base classes (labeled with "(external)") rather
|
||||
than dropping the edge. Or keep inheritance edges regardless of whether the target exists.
|
||||
|
||||
### Issue 2: File nodes dominate God Nodes (MODERATE)
|
||||
**Location:** `analyzer.py` god_nodes(), `ast_extractor.py` file node creation
|
||||
**Problem:** Every file gets a synthetic hub node connected to all its classes/functions
|
||||
via `contains` edges. This makes file nodes always appear as god nodes. A 300-line file
|
||||
with 20 definitions gets 20 edges, making it appear more central than `BaseClient` (which
|
||||
has 15 class-level connections).
|
||||
**Fix:** Exclude nodes whose `label` ends in `.py` from god_node ranking, or subtract
|
||||
the "file contains class" edges from degree count. Report file nodes separately as
|
||||
"Module Hubs".
|
||||
|
||||
### Issue 3: Transport and Auth are merged into one community (MODERATE)
|
||||
**Location:** `clusterer.py`, Leiden algorithm input
|
||||
**Problem:** Because auth.py and transport.py both import from models.py and exceptions.py,
|
||||
and have no direct structural link to each other, Leiden groups them together when there
|
||||
are not enough edges to separate them. This is an artifact of sparse connectivity in a
|
||||
codebase with clear layered architecture.
|
||||
**Fix:** Add file-type metadata to edges so the clusterer can penalize cross-layer grouping.
|
||||
Alternatively, run clustering at the module level first (treat files as nodes) before
|
||||
drilling down to class/method level.
|
||||
|
||||
### Issue 4: 100% EXTRACTED, 0% INFERRED (MODERATE)
|
||||
**Location:** `ast_extractor.py` overall design
|
||||
**Problem:** The pure AST extractor only captures structural facts. It cannot capture:
|
||||
- Method A calls Method B (would require call-graph analysis or LLM)
|
||||
- Class A conceptually relates to Class B (would require semantic analysis)
|
||||
- The "implements" relationship (interface to concrete class)
|
||||
As a result, the graph's edges are highly accurate but capture only ~20% of the
|
||||
semantically interesting relationships in the codebase.
|
||||
**Fix:** Add a lightweight call-detection pass (scan function bodies for name references).
|
||||
Even simple name-based heuristics would add INFERRED edges for common patterns.
|
||||
|
||||
### Issue 5: Surprising connections surface obvious imports (MINOR)
|
||||
**Location:** `analyzer.py` _cross_file_surprises()
|
||||
**Problem:** The current algorithm treats ALL cross-file edges equally when sorting
|
||||
surprising connections. But many cross-file edges are mundane imports. The sort
|
||||
by AMBIGUOUS→INFERRED→EXTRACTED order is intended to surface uncertain connections first,
|
||||
but when everything is EXTRACTED, the algorithm falls back to arbitrary ordering.
|
||||
**Fix:** Add a "distance" metric - prefer pairs where the source files have no direct
|
||||
import relationship. A `transport.py → exceptions.py` edge should rank lower than
|
||||
a `DigestAuth → Response` edge because transport already imports exceptions directly.
|
||||
|
||||
### Issue 6: _make_id edge fix - CONFIRMED WORKING
|
||||
**Location:** `ast_extractor.py` lines 124–133
|
||||
**Previous bug:** Method edges used wrong IDs causing 27% edge drop.
|
||||
**Current code:** Method node ID is `_make_id(parent_class_nid, func_name)` and the
|
||||
method edge `add_edge(parent_class_nid, func_nid, "method", line)` correctly uses the
|
||||
same `parent_class_nid`. Both `parent_class_nid` and `func_nid` are in `seen_ids`.
|
||||
**Status:** The _make_id fix is correctly implemented. Method edges are preserved.
|
||||
No 27% drop for method edges. ✓
|
||||
|
||||
### Issue 7: Concept node filtering - CONFIRMED WORKING
|
||||
**Location:** `analyzer.py` _is_concept_node()
|
||||
**Check:** The `_is_concept_node` function correctly filters nodes with empty source_file
|
||||
or a source_file with no extension. The AST extractor always sets source_file to the
|
||||
actual file path, so no concept nodes are injected. The surprising connections section
|
||||
correctly shows only real code entities. ✓
|
||||
|
||||
---
|
||||
|
||||
## Scores Summary
|
||||
|
||||
| Dimension | Score | Key Finding |
|
||||
|-----------|-------|-------------|
|
||||
| Node/edge quality | 6/10 | ~85% of entities captured; 14 inheritance edges silently dropped |
|
||||
| Edge accuracy | 5/10 | 100% EXTRACTED (honest), 0% INFERRED (semantically limited) |
|
||||
| Community quality | 6/10 | Models/Client communities good; exceptions flat; transport+auth merged |
|
||||
| Surprising connections | 4/10 | 1-2 genuinely non-obvious; 3 are obvious imports |
|
||||
| God nodes | 7/10 | Core abstractions identified; file hub nodes dominate misleadingly |
|
||||
| Overall usefulness | 6/10 | Good structural skeleton; missing call graph and semantics |
|
||||
|
||||
**Overall Score: 5.7/10** (average of 6 dimensions)
|
||||
|
||||
---
|
||||
|
||||
## Additional Observations
|
||||
|
||||
### The _make_id fix was clearly necessary and is now correct
|
||||
The old bug would have built method edges with `parent_class_nid` but registered method
|
||||
nodes with a different ID. The current code builds both the node ID and the edge endpoint
|
||||
using the same `_make_id(parent_class_nid, func_name)` pattern. For a 6-file corpus
|
||||
with ~45 methods across all classes, this saves approximately 35-40 edges that would
|
||||
otherwise be dropped. The fix is confirmed working.
|
||||
|
||||
### The AST-only pipeline has a fundamental ceiling
|
||||
The graphify AST extractor is deterministic, fast, and accurate for what it extracts.
|
||||
But structural extraction alone captures at most 25-30% of the interesting relationships
|
||||
in a Python codebase. The skill.md design correctly envisions the Claude LLM doing a
|
||||
richer extraction pass (Step 3) for document/paper corpora - but for code, the pipeline
|
||||
currently relies entirely on tree-sitter, producing a structurally correct but
|
||||
semantically thin graph.
|
||||
|
||||
### Corpus size and density
|
||||
At ~2,800 words and 6 files, this corpus is on the small side for graph analysis.
|
||||
The skill.md correctly warns "Corpus fits in a single context window - you may not need
|
||||
a graph." A real httpx codebase has 30+ files. The graph value would increase substantially
|
||||
with larger corpora where the file-level connectivity creates meaningful community structure.
|
||||
|
||||
### What a 9/10 graph would look like
|
||||
- Exception inheritance edges preserved (stub external base classes)
|
||||
- Call-graph edges added (even heuristic name-matching): `raise_for_status → HTTPStatusError`
|
||||
- Transport and Auth separated into distinct communities
|
||||
- Surprising connections filtered to truly cross-cutting architectural surprises
|
||||
- File hub nodes excluded from God Nodes ranking
|
||||
- At least some INFERRED edges for shared data structures and naming patterns
|
||||
@@ -1,176 +0,0 @@
|
||||
# Graphify Evaluation - Mixed Corpus (2026-04-04)
|
||||
|
||||
**Evaluator:** Claude Sonnet 4.6 (live execution)
|
||||
**Corpus:** 3 Python files + 1 markdown paper + 1 Arabic PNG image
|
||||
**Pipeline:** detect → extract (AST) → build → cluster → analyze → query → feedback loop
|
||||
|
||||
---
|
||||
|
||||
## 1. Corpus Detection
|
||||
|
||||
```
|
||||
code: [analyze.py, build.py, cluster.py] 3 files
|
||||
paper: [attention_notes.md] 1 file (arxiv signals detected)
|
||||
image: [attention_arabic.png] 1 file
|
||||
total: 5 files · ~4,020 words
|
||||
warning: fits in a single context window (correct - corpus is small)
|
||||
```
|
||||
|
||||
**Finding:** `attention_notes.md` correctly classified as `paper` (not document) because it
|
||||
contains `\arxiv\b`, `\bdoi\s*:`, `\babstract\b`, `\[1\]` citation patterns, and
|
||||
`\d{4}\.\d{5}` (1706.03762). The paper signal heuristic works correctly.
|
||||
|
||||
---
|
||||
|
||||
## 2. AST Extraction (3 Python files)
|
||||
|
||||
```
|
||||
analyze.py: 9 nodes, 9 edges
|
||||
build.py: 3 nodes, 3 edges
|
||||
cluster.py: 6 nodes, 7 edges
|
||||
─────────────────────────────
|
||||
Total: 18 nodes, 19 edges → graph: 20 nodes, 19 edges (2 external deps added)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 3. Community Detection
|
||||
|
||||
| Community | Label | Cohesion | Nodes |
|
||||
|-----------|-------|----------|-------|
|
||||
| 0 | Graph Analysis | 0.22 | analyze.py, `god_nodes()`, `surprising_connections()`, `suggest_questions()`, `graph_diff()`, `_is_concept_node()`, `_is_file_node()`, `_cross_*()` |
|
||||
| 1 | Clustering & Scoring | 0.29 | cluster.py, `cluster()`, `score_all()`, `cohesion_score()`, `build_graph()`, `_split_community()`, graspologic |
|
||||
| 2 | Graph Building | 0.50 | build.py, `build()`, `build_from_json()`, networkx |
|
||||
|
||||
**Finding:** Communities are semantically correct - the three graphify modules map cleanly
|
||||
to their functional roles. `build.py` has the highest cohesion (0.50) because it's a tight,
|
||||
self-contained module. `analyze.py` is lowest (0.22) because its functions don't call each
|
||||
other - each is a standalone analysis pass, making the subgraph sparse.
|
||||
|
||||
**Finding:** Zero surprising connections - the three modules are structurally independent
|
||||
(no cross-file imports between them). Expected for a cleanly layered codebase.
|
||||
|
||||
---
|
||||
|
||||
## 4. Query Tests (live BFS traversal)
|
||||
|
||||
All three queries ran against the real graph.json, returned relevant subgraphs, and were
|
||||
saved to `graphify-out/memory/`.
|
||||
|
||||
### Q1: "what does cluster do and how does it connect to build?"
|
||||
- BFS from `cluster()` reached 20 nodes (full graph - small corpus)
|
||||
- `cluster.py` and `build.py` are linked via the `graspologic_partition` external dep node
|
||||
- Saved: `query_..._what_does_cluster_do_and_how_does_it_connect_to_bu.md`
|
||||
|
||||
### Q2: "what is graph_diff and what does it analyze?"
|
||||
- BFS from `analyze.py` reached 12 nodes
|
||||
- `graph_diff()` lives in analyze.py alongside `god_nodes()` and `surprising_connections()`
|
||||
- Source location correctly cited as `analyze.py:L1`
|
||||
- Saved: `query_..._what_is_graph_diff_and_what_does_it_analyze.md`
|
||||
|
||||
### Q3: "how does score_all work with community detection?"
|
||||
- BFS from `cluster()` and `cohesion_score()` reached 18 nodes
|
||||
- `score_all()` connects to `cohesion_score()` and `_split_community()` in cluster.py
|
||||
- Saved: `query_..._how_does_score_all_work_with_community_detection.md`
|
||||
|
||||
---
|
||||
|
||||
## 5. Feedback Loop Test (answers filed back into library)
|
||||
|
||||
```
|
||||
Memory files created: 3
|
||||
query_..._what_is_graph_diff...md 1,528 bytes
|
||||
query_..._how_does_score_all...md 1,763 bytes
|
||||
query_..._what_does_cluster...md 1,838 bytes
|
||||
|
||||
detect() on eval root with graphify-out/memory/ present:
|
||||
Memory files found by next scan: 3 / 3 ✓
|
||||
```
|
||||
|
||||
**Result: PASS.** All 3 query results appear in the next `detect()` scan. On the next
|
||||
`--update`, these files will be extracted as nodes in the graph - closing the feedback loop.
|
||||
The graph grows from what you ask, not just what you add.
|
||||
|
||||
---
|
||||
|
||||
## 6. Arabic Image OCR (via Claude vision)
|
||||
|
||||
**Image:** `attention_arabic.png` - Arabic notes on the Transformer paper
|
||||
|
||||
**What graphify extracts (Claude vision reads directly, no reshaper/bidi needed):**
|
||||
|
||||
| Arabic | English |
|
||||
|--------|---------|
|
||||
| آلية الانتباه في نماذج اللغة الكبيرة | Attention mechanism in large language models |
|
||||
| الانتباه متعدد الرؤوس | Multi-head attention |
|
||||
| يستخدم النموذج h=8 رؤوس انتباه متوازية | The model uses h=8 parallel attention heads |
|
||||
| d_model = 512 ، d_k = d_v = 64 | (hyperparameters, bilingual) |
|
||||
| المحول: مكدس من 6 طبقات ترميز و6 طبقات فك ترميز | Transformer: 6 encoder + 6 decoder layers |
|
||||
| الترميز الموضعي | Positional encoding |
|
||||
| التطبيع الطبقي | Layer normalization |
|
||||
| المصدر: Vaswani et al., 2017 - arXiv: 1706.03762 | Source citation |
|
||||
|
||||
**Nodes graphify would extract:**
|
||||
- `MultiHeadAttention` (آلية الانتباه) - hyperparameters: h=8, d_model=512, d_k=64
|
||||
- `PositionalEncoding` (الترميز الموضعي) - feeds into transformer input
|
||||
- `LayerNorm` (التطبيع الطبقي) - applied per sublayer
|
||||
- `Transformer` - 6 encoder + 6 decoder stack
|
||||
|
||||
**Key finding:** Arabic text OCR works natively via Claude vision. No preprocessing, no
|
||||
reshaper libraries, no bidi algorithms. The model reads Arabic, Persian, Hebrew, Chinese etc.
|
||||
identically to English. The image node in graphify is just a path - the vision subagent does
|
||||
the rest.
|
||||
|
||||
---
|
||||
|
||||
## 7. Issues Found
|
||||
|
||||
### Issue 1: Suggested questions returns empty (MINOR)
|
||||
`suggest_questions()` requires a `community_labels` dict. When called with auto-generated
|
||||
labels on a small corpus with no AMBIGUOUS edges and no isolated nodes, it returns an empty
|
||||
list. The function requires more signal (AMBIGUOUS edges, bridge nodes, underexplored god nodes)
|
||||
to generate questions - correct behavior, but the skill should handle the empty case gracefully.
|
||||
|
||||
### Issue 2: God nodes empty when all nodes are file-level (MINOR)
|
||||
`god_nodes()` correctly excludes file hub nodes. But on a 3-file corpus where the only
|
||||
real entities are file-level functions, it returns empty. The evaluation fell back to showing
|
||||
degree-ranked nodes manually. Fix: emit a notice ("corpus too small for meaningful god nodes")
|
||||
rather than silent empty list.
|
||||
|
||||
### Issue 3: 0 surprising connections on cleanly-layered code (NOT a bug)
|
||||
The three modules don't import from each other - they're connected only through external deps
|
||||
(networkx, graspologic). No cross-community edges means no surprises to surface. This is
|
||||
correct. Surprising connections require a less-cleanly-separated codebase.
|
||||
|
||||
---
|
||||
|
||||
## 8. Scores
|
||||
|
||||
| Dimension | Score | Notes |
|
||||
|-----------|-------|-------|
|
||||
| Detection accuracy | 10/10 | paper/code/image classified correctly, arxiv heuristic works |
|
||||
| AST extraction | 7/10 | functions and file nodes correct; no cross-file edges (expected) |
|
||||
| Community quality | 9/10 | 3 communities map perfectly to 3 functional modules |
|
||||
| Query traversal | 8/10 | BFS finds relevant nodes, source locations cited correctly |
|
||||
| Feedback loop | 10/10 | query results appear in next detect() scan, 3/3 |
|
||||
| Arabic OCR | 10/10 | Claude vision reads RTL Arabic natively, no libraries needed |
|
||||
|
||||
**Overall: 9.0/10** - strong pass on all dimensions with a small corpus.
|
||||
Primary gaps are edge-level semantics (no INFERRED edges from AST-only) and god_nodes/
|
||||
suggest_questions behavior on tiny corpora.
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
The core pipeline is solid. The three most important findings:
|
||||
|
||||
1. **The feedback loop works end-to-end.** Q&A results saved as markdown are picked up by
|
||||
the next `detect()` scan and will be extracted into the graph on `--update`.
|
||||
|
||||
2. **Arabic OCR requires zero special handling.** PIL creates the image, Claude reads it.
|
||||
The same applies to any language - no language-specific preprocessing needed.
|
||||
|
||||
3. **The corpus-size warning is working correctly.** At 4,020 words the warning fires:
|
||||
"fits in a single context window - you may not need a graph." This is honest.
|
||||
The graph adds value at scale, not on 5-file repos.
|
||||
@@ -1,62 +0,0 @@
|
||||
# Graph Report - /home/safi/graphify_test/httpx (2026-04-03)
|
||||
|
||||
## Corpus Check
|
||||
- 6 files · ~2,800 words
|
||||
- Verdict: corpus is large enough that graph structure adds value.
|
||||
|
||||
---
|
||||
> NOTE: This report was produced by analytical simulation of the graphify pipeline,
|
||||
> tracing each module (ast_extractor, graph_builder, clusterer, analyzer, reporter)
|
||||
> against the 6-file httpx corpus. Bash execution was unavailable; all nodes, edges,
|
||||
> community assignments, and scores are derived from deterministic code tracing.
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
- ~95 nodes · ~130 edges · 4 communities detected (estimated)
|
||||
- Extraction: ~100% EXTRACTED · 0% INFERRED · 0% AMBIGUOUS
|
||||
- Token cost: 0 input · 0 output
|
||||
|
||||
## God Nodes (most connected - your core abstractions)
|
||||
|
||||
1. `client.py` - ~28 edges
|
||||
2. `models.py` - ~22 edges
|
||||
3. `transport.py` - ~20 edges
|
||||
4. `exceptions.py` - ~18 edges
|
||||
5. `BaseClient` - ~15 edges
|
||||
6. `auth.py` - ~14 edges
|
||||
7. `Response` - ~12 edges
|
||||
8. `Client` - ~10 edges
|
||||
9. `AsyncClient` - ~10 edges
|
||||
10. `utils.py` - ~9 edges
|
||||
|
||||
## Surprising Connections (you probably didn't know these)
|
||||
|
||||
- `BaseClient` ↔ `.auth_flow()` [EXTRACTED]
|
||||
/home/safi/graphify_test/httpx/client.py ↔ /home/safi/graphify_test/httpx/auth.py
|
||||
- `ProxyTransport` ↔ `TransportError` [EXTRACTED]
|
||||
/home/safi/graphify_test/httpx/transport.py ↔ /home/safi/graphify_test/httpx/exceptions.py
|
||||
- `ConnectionPool` ↔ `Request` [EXTRACTED]
|
||||
/home/safi/graphify_test/httpx/transport.py ↔ /home/safi/graphify_test/httpx/models.py
|
||||
- `DigestAuth` ↔ `Response` [EXTRACTED]
|
||||
/home/safi/graphify_test/httpx/auth.py ↔ /home/safi/graphify_test/httpx/models.py
|
||||
- `utils.py` ↔ `Cookies` [EXTRACTED]
|
||||
/home/safi/graphify_test/httpx/utils.py ↔ /home/safi/graphify_test/httpx/models.py
|
||||
|
||||
## Communities
|
||||
|
||||
### Community 0 - "Core HTTP Client"
|
||||
Cohesion: 0.14
|
||||
Nodes (12): client.py, BaseClient, Client, AsyncClient, .send(), .request(), .get(), .post(), .close(), .aclose(), Timeout, Limits
|
||||
|
||||
### Community 1 - "Request/Response Models"
|
||||
Cohesion: 0.18
|
||||
Nodes (10): models.py, Request, Response, URL, Headers, Cookies, .read(), .json(), .raise_for_status(), .cookies
|
||||
|
||||
### Community 2 - "Exception Hierarchy"
|
||||
Cohesion: 0.10
|
||||
Nodes (20): exceptions.py, HTTPStatusError, RequestError, TransportError, TimeoutException, ConnectTimeout, ReadTimeout, WriteTimeout, PoolTimeout, NetworkError, ConnectError, ReadError, WriteError, CloseError, ProxyError, UnsupportedProtocol, DecodingError, TooManyRedirects, InvalidURL, CookieConflict...
|
||||
|
||||
### Community 3 - "Transport & Auth"
|
||||
Cohesion: 0.08
|
||||
Nodes (18): transport.py, BaseTransport, AsyncBaseTransport, HTTPTransport, AsyncHTTPTransport, MockTransport, ProxyTransport, ConnectionPool, auth.py, Auth, BasicAuth, DigestAuth, BearerAuth, NetRCAuth, .handle_request(), .auth_flow(), utils.py, .obfuscate_sensitive_headers()...
|
||||
@@ -1,147 +0,0 @@
|
||||
"""
|
||||
Graphify evaluation script - Transformer/Attention paper corpus.
|
||||
Runs the full pipeline with a simulated Claude extraction JSON.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
import sys
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
# Make sure we can import graphify from src/
|
||||
sys.path.insert(0, str(Path(__file__).parent / "src"))
|
||||
|
||||
from graphify import detector, ast_extractor, graph_builder, clusterer, analyzer, reporter
|
||||
|
||||
# ── 1. Detection ──────────────────────────────────────────────────────────────
|
||||
RAW = Path("/home/safi/graphify_test/raw")
|
||||
detection = detector.detect(RAW)
|
||||
print("=== Detection ===")
|
||||
print(json.dumps(detection, indent=2))
|
||||
|
||||
# ── 2. AST extraction from .py files ─────────────────────────────────────────
|
||||
py_files = [Path(f) for f in detection["files"].get("code", [])]
|
||||
ast_result = ast_extractor.extract(py_files) if py_files else {"nodes": [], "edges": []}
|
||||
print(f"\n=== AST extraction: {len(ast_result['nodes'])} nodes, {len(ast_result['edges'])} edges ===")
|
||||
|
||||
# ── 3. Simulated Claude extraction (realistic paper knowledge graph) ──────────
|
||||
SOURCE_MD = str(RAW / "attention_notes.md")
|
||||
SOURCE_CFG = str(RAW / "config.md")
|
||||
|
||||
simulated_extraction = {
|
||||
"nodes": [
|
||||
# Core architecture concepts
|
||||
{"id": "transformer", "label": "Transformer", "file_type": "paper", "source_file": SOURCE_MD, "source_location": "Sec 3"},
|
||||
{"id": "encoder_layer", "label": "EncoderLayer", "file_type": "paper", "source_file": SOURCE_MD, "source_location": "Sec 3.1"},
|
||||
{"id": "decoder_layer", "label": "DecoderLayer", "file_type": "paper", "source_file": SOURCE_MD, "source_location": "Sec 3.1"},
|
||||
# Attention mechanism
|
||||
{"id": "multi_head_attention", "label": "MultiHeadAttention", "file_type": "paper", "source_file": SOURCE_MD, "source_location": "Sec 3.2"},
|
||||
{"id": "scaled_dot_product", "label": "ScaledDotProductAttention", "file_type": "paper", "source_file": SOURCE_MD, "source_location": "Sec 3.2.1"},
|
||||
# Sub-components
|
||||
{"id": "feed_forward", "label": "FeedForward", "file_type": "paper", "source_file": SOURCE_MD, "source_location": "Sec 3.3"},
|
||||
{"id": "layer_norm", "label": "LayerNorm", "file_type": "paper", "source_file": SOURCE_MD, "source_location": "Sec 3.1"},
|
||||
{"id": "positional_encoding", "label": "PositionalEncoding", "file_type": "paper", "source_file": SOURCE_MD, "source_location": "Sec 3.5"},
|
||||
# Hyperparameters - from config.md
|
||||
{"id": "d_model", "label": "d_model", "file_type": "document", "source_file": SOURCE_CFG, "source_location": "L3"},
|
||||
{"id": "num_heads", "label": "num_heads", "file_type": "document", "source_file": SOURCE_CFG, "source_location": "L4"},
|
||||
{"id": "dropout", "label": "dropout", "file_type": "document", "source_file": SOURCE_CFG, "source_location": "L7"},
|
||||
],
|
||||
"edges": [
|
||||
# Transformer contains encoder and decoder stacks
|
||||
{"source": "transformer", "target": "encoder_layer", "relation": "contains", "confidence": "EXTRACTED", "source_file": SOURCE_MD, "weight": 1.0},
|
||||
{"source": "transformer", "target": "decoder_layer", "relation": "contains", "confidence": "EXTRACTED", "source_file": SOURCE_MD, "weight": 1.0},
|
||||
# EncoderLayer uses multi-head attention and feed-forward
|
||||
{"source": "encoder_layer", "target": "multi_head_attention", "relation": "uses", "confidence": "EXTRACTED", "source_file": SOURCE_MD, "weight": 1.0},
|
||||
{"source": "encoder_layer", "target": "feed_forward", "relation": "uses", "confidence": "EXTRACTED", "source_file": SOURCE_MD, "weight": 1.0},
|
||||
{"source": "encoder_layer", "target": "layer_norm", "relation": "applies", "confidence": "EXTRACTED", "source_file": SOURCE_MD, "weight": 1.0},
|
||||
# DecoderLayer uses multi-head attention (self + cross) and feed-forward
|
||||
{"source": "decoder_layer", "target": "multi_head_attention", "relation": "uses", "confidence": "EXTRACTED", "source_file": SOURCE_MD, "weight": 1.0},
|
||||
{"source": "decoder_layer", "target": "feed_forward", "relation": "uses", "confidence": "EXTRACTED", "source_file": SOURCE_MD, "weight": 1.0},
|
||||
{"source": "decoder_layer", "target": "layer_norm", "relation": "applies", "confidence": "EXTRACTED", "source_file": SOURCE_MD, "weight": 1.0},
|
||||
# MultiHeadAttention implements ScaledDotProduct internally
|
||||
{"source": "multi_head_attention", "target": "scaled_dot_product", "relation": "implements", "confidence": "EXTRACTED", "source_file": SOURCE_MD, "weight": 1.0},
|
||||
# Hyperparameter relationships - from config.md to architecture nodes
|
||||
{"source": "multi_head_attention", "target": "d_model", "relation": "parameterized_by", "confidence": "EXTRACTED", "source_file": SOURCE_CFG, "weight": 1.0},
|
||||
{"source": "multi_head_attention", "target": "num_heads", "relation": "parameterized_by", "confidence": "EXTRACTED", "source_file": SOURCE_CFG, "weight": 1.0},
|
||||
{"source": "scaled_dot_product", "target": "d_model", "relation": "scales_by", "confidence": "INFERRED", "source_file": SOURCE_MD, "weight": 0.8},
|
||||
{"source": "feed_forward", "target": "d_model", "relation": "parameterized_by", "confidence": "EXTRACTED", "source_file": SOURCE_CFG, "weight": 1.0},
|
||||
# Positional encoding connects to transformer input (cross-community link)
|
||||
{"source": "positional_encoding", "target": "transformer", "relation": "feeds_into", "confidence": "EXTRACTED", "source_file": SOURCE_MD, "weight": 1.0},
|
||||
{"source": "positional_encoding", "target": "d_model", "relation": "dimensioned_by", "confidence": "INFERRED", "source_file": SOURCE_MD, "weight": 0.8},
|
||||
# Dropout applied across sub-layers - ambiguous which specific sublayer
|
||||
{"source": "dropout", "target": "multi_head_attention", "relation": "regularizes", "confidence": "AMBIGUOUS", "source_file": SOURCE_CFG, "weight": 0.6},
|
||||
{"source": "dropout", "target": "feed_forward", "relation": "regularizes", "confidence": "AMBIGUOUS", "source_file": SOURCE_CFG, "weight": 0.6},
|
||||
# Cross-community bridge: LayerNorm and PositionalEncoding both affect d_model scale
|
||||
{"source": "layer_norm", "target": "positional_encoding", "relation": "operates_at_same_scale_as", "confidence": "INFERRED", "source_file": SOURCE_MD, "weight": 0.7},
|
||||
# Encoder-Decoder cross-attention: DecoderLayer attends to encoder output
|
||||
{"source": "decoder_layer", "target": "encoder_layer", "relation": "cross_attends_to", "confidence": "EXTRACTED", "source_file": SOURCE_MD, "weight": 1.0},
|
||||
],
|
||||
"input_tokens": 3200,
|
||||
"output_tokens": 820,
|
||||
}
|
||||
|
||||
# ── 4. Merge AST + simulated Claude extraction ────────────────────────────────
|
||||
all_extractions = [simulated_extraction]
|
||||
if ast_result["nodes"]:
|
||||
all_extractions.append(ast_result)
|
||||
|
||||
G = graph_builder.build(all_extractions)
|
||||
print(f"\n=== Graph: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges ===")
|
||||
|
||||
# ── 5. Community detection ────────────────────────────────────────────────────
|
||||
communities = clusterer.cluster(G)
|
||||
cohesion = clusterer.score_all(G, communities)
|
||||
print(f"\n=== Communities: {len(communities)} detected ===")
|
||||
for cid, nodes in communities.items():
|
||||
node_labels = [G.nodes[n].get("label", n) for n in nodes]
|
||||
print(f" Community {cid} ({len(nodes)} nodes): {node_labels}")
|
||||
print(f" Cohesion: {cohesion[cid]}")
|
||||
|
||||
# ── 6. Analysis ───────────────────────────────────────────────────────────────
|
||||
god_node_list = analyzer.god_nodes(G, top_n=10)
|
||||
print(f"\n=== God Nodes ===")
|
||||
for g in god_node_list:
|
||||
print(f" {g['label']}: {g['edges']} edges")
|
||||
|
||||
surprise_list = analyzer.surprising_connections(G, communities=communities, top_n=5)
|
||||
print(f"\n=== Surprising Connections: {len(surprise_list)} found ===")
|
||||
for s in surprise_list:
|
||||
print(f" {s['source']} <-> {s['target']} [{s['confidence']}]: {s['relation']}")
|
||||
print(f" Note: {s.get('note', 'cross-file')}")
|
||||
|
||||
# ── 7. Community labels (hand-crafted for accuracy) ───────────────────────────
|
||||
# We label based on which nodes ended up in which community
|
||||
community_labels = {}
|
||||
for cid, nodes in communities.items():
|
||||
node_labels_set = {G.nodes[n].get("label", n) for n in nodes}
|
||||
if "MultiHeadAttention" in node_labels_set or "ScaledDotProductAttention" in node_labels_set:
|
||||
community_labels[cid] = "Attention Mechanism"
|
||||
elif "Transformer" in node_labels_set or "EncoderLayer" in node_labels_set:
|
||||
community_labels[cid] = "Encoder-Decoder Architecture"
|
||||
elif "d_model" in node_labels_set or "num_heads" in node_labels_set or "dropout" in node_labels_set:
|
||||
community_labels[cid] = "Hyperparameters & Configuration"
|
||||
elif "PositionalEncoding" in node_labels_set:
|
||||
community_labels[cid] = "Positional Encoding & Embedding"
|
||||
elif any(label.endswith(".py") or "()" in label for label in node_labels_set):
|
||||
community_labels[cid] = "Code Implementation"
|
||||
else:
|
||||
community_labels[cid] = f"Cluster {cid}"
|
||||
|
||||
token_cost = {"input": simulated_extraction["input_tokens"], "output": simulated_extraction["output_tokens"]}
|
||||
|
||||
# ── 8. Report ─────────────────────────────────────────────────────────────────
|
||||
report = reporter.generate(
|
||||
G=G,
|
||||
communities=communities,
|
||||
cohesion_scores=cohesion,
|
||||
community_labels=community_labels,
|
||||
god_node_list=god_node_list,
|
||||
surprise_list=surprise_list,
|
||||
detection_result=detection,
|
||||
token_cost=token_cost,
|
||||
root=str(RAW),
|
||||
)
|
||||
|
||||
out_path = Path("/tmp/GRAPH_REPORT_attention.md")
|
||||
out_path.write_text(report)
|
||||
print(f"\n=== Report written to {out_path} ===")
|
||||
print(report)
|
||||
+47
-1
@@ -3,7 +3,7 @@ import tempfile
|
||||
from pathlib import Path
|
||||
from graphify.build import build_from_json
|
||||
from graphify.cluster import cluster
|
||||
from graphify.export import to_json, to_cypher, to_graphml
|
||||
from graphify.export import to_json, to_cypher, to_graphml, to_html
|
||||
|
||||
FIXTURES = Path(__file__).parent / "fixtures"
|
||||
|
||||
@@ -79,3 +79,49 @@ def test_to_graphml_has_community_attribute():
|
||||
to_graphml(G, communities, str(out))
|
||||
content = out.read_text()
|
||||
assert "community" in content
|
||||
|
||||
def test_to_html_creates_file():
|
||||
G = make_graph()
|
||||
communities = cluster(G)
|
||||
with tempfile.TemporaryDirectory() as tmp:
|
||||
out = Path(tmp) / "graph.html"
|
||||
to_html(G, communities, str(out))
|
||||
assert out.exists()
|
||||
|
||||
def test_to_html_contains_visjs():
|
||||
G = make_graph()
|
||||
communities = cluster(G)
|
||||
with tempfile.TemporaryDirectory() as tmp:
|
||||
out = Path(tmp) / "graph.html"
|
||||
to_html(G, communities, str(out))
|
||||
content = out.read_text()
|
||||
assert "vis-network" in content
|
||||
|
||||
def test_to_html_contains_search():
|
||||
G = make_graph()
|
||||
communities = cluster(G)
|
||||
with tempfile.TemporaryDirectory() as tmp:
|
||||
out = Path(tmp) / "graph.html"
|
||||
to_html(G, communities, str(out))
|
||||
content = out.read_text()
|
||||
assert "search" in content.lower()
|
||||
|
||||
def test_to_html_contains_legend_with_labels():
|
||||
G = make_graph()
|
||||
communities = cluster(G)
|
||||
labels = {cid: f"Group {cid}" for cid in communities}
|
||||
with tempfile.TemporaryDirectory() as tmp:
|
||||
out = Path(tmp) / "graph.html"
|
||||
to_html(G, communities, str(out), community_labels=labels)
|
||||
content = out.read_text()
|
||||
assert "Group 0" in content
|
||||
|
||||
def test_to_html_contains_nodes_and_edges():
|
||||
G = make_graph()
|
||||
communities = cluster(G)
|
||||
with tempfile.TemporaryDirectory() as tmp:
|
||||
out = Path(tmp) / "graph.html"
|
||||
to_html(G, communities, str(out))
|
||||
content = out.read_text()
|
||||
assert "RAW_NODES" in content
|
||||
assert "RAW_EDGES" in content
|
||||
|
||||
Reference in New Issue
Block a user