wehub-resource-sync
177 lines
7.9 KiB
Markdown
177 lines
7.9 KiB
Markdown
# Graphify Evaluation - Mixed Corpus (2026-04-04)
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**Evaluator:** Claude Sonnet 4.6 (live execution)
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**Corpus:** 3 Python files + 1 markdown paper + 1 Arabic PNG image
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**Pipeline:** detect → extract (AST) → build → cluster → analyze → query → feedback loop
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---
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## 1. Corpus Detection
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```
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code: [analyze.py, build.py, cluster.py] 3 files
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paper: [attention_notes.md] 1 file (arxiv signals detected)
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image: [attention_arabic.png] 1 file
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total: 5 files · ~4,020 words
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warning: fits in a single context window (correct - corpus is small)
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```
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**Finding:** `attention_notes.md` correctly classified as `paper` (not document) because it
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contains `\arxiv\b`, `\bdoi\s*:`, `\babstract\b`, `\[1\]` citation patterns, and
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`\d{4}\.\d{5}` (1706.03762). The paper signal heuristic works correctly.
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---
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## 2. AST Extraction (3 Python files)
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```
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analyze.py: 9 nodes, 9 edges
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build.py: 3 nodes, 3 edges
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cluster.py: 6 nodes, 7 edges
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─────────────────────────────
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Total: 18 nodes, 19 edges → graph: 20 nodes, 19 edges (2 external deps added)
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```
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---
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## 3. Community Detection
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| Community | Label | Cohesion | Nodes |
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|-----------|-------|----------|-------|
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| 0 | Graph Analysis | 0.22 | analyze.py, `god_nodes()`, `surprising_connections()`, `suggest_questions()`, `graph_diff()`, `_is_concept_node()`, `_is_file_node()`, `_cross_*()` |
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| 1 | Clustering & Scoring | 0.29 | cluster.py, `cluster()`, `score_all()`, `cohesion_score()`, `build_graph()`, `_split_community()`, graspologic |
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| 2 | Graph Building | 0.50 | build.py, `build()`, `build_from_json()`, networkx |
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**Finding:** Communities are semantically correct - the three graphify modules map cleanly
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to their functional roles. `build.py` has the highest cohesion (0.50) because it's a tight,
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self-contained module. `analyze.py` is lowest (0.22) because its functions don't call each
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other - each is a standalone analysis pass, making the subgraph sparse.
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**Finding:** Zero surprising connections - the three modules are structurally independent
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(no cross-file imports between them). Expected for a cleanly layered codebase.
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---
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## 4. Query Tests (live BFS traversal)
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All three queries ran against the real graph.json, returned relevant subgraphs, and were
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saved to `graphify-out/memory/`.
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### Q1: "what does cluster do and how does it connect to build?"
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- BFS from `cluster()` reached 20 nodes (full graph - small corpus)
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- `cluster.py` and `build.py` are linked via the `graspologic_partition` external dep node
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- Saved: `query_..._what_does_cluster_do_and_how_does_it_connect_to_bu.md`
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### Q2: "what is graph_diff and what does it analyze?"
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- BFS from `analyze.py` reached 12 nodes
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- `graph_diff()` lives in analyze.py alongside `god_nodes()` and `surprising_connections()`
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- Source location correctly cited as `analyze.py:L1`
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- Saved: `query_..._what_is_graph_diff_and_what_does_it_analyze.md`
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### Q3: "how does score_all work with community detection?"
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- BFS from `cluster()` and `cohesion_score()` reached 18 nodes
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- `score_all()` connects to `cohesion_score()` and `_split_community()` in cluster.py
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- Saved: `query_..._how_does_score_all_work_with_community_detection.md`
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---
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## 5. Feedback Loop Test (answers filed back into library)
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```
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Memory files created: 3
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query_..._what_is_graph_diff...md 1,528 bytes
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query_..._how_does_score_all...md 1,763 bytes
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query_..._what_does_cluster...md 1,838 bytes
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detect() on eval root with graphify-out/memory/ present:
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Memory files found by next scan: 3 / 3 ✓
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```
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**Result: PASS.** All 3 query results appear in the next `detect()` scan. On the next
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`--update`, these files will be extracted as nodes in the graph - closing the feedback loop.
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The graph grows from what you ask, not just what you add.
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---
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## 6. Arabic Image OCR (via Claude vision)
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**Image:** `attention_arabic.png` - Arabic notes on the Transformer paper
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**What graphify extracts (Claude vision reads directly, no reshaper/bidi needed):**
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| Arabic | English |
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|--------|---------|
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| آلية الانتباه في نماذج اللغة الكبيرة | Attention mechanism in large language models |
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| الانتباه متعدد الرؤوس | Multi-head attention |
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| يستخدم النموذج h=8 رؤوس انتباه متوازية | The model uses h=8 parallel attention heads |
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| d_model = 512 ، d_k = d_v = 64 | (hyperparameters, bilingual) |
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| المحول: مكدس من 6 طبقات ترميز و6 طبقات فك ترميز | Transformer: 6 encoder + 6 decoder layers |
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| الترميز الموضعي | Positional encoding |
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| التطبيع الطبقي | Layer normalization |
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| المصدر: Vaswani et al., 2017 - arXiv: 1706.03762 | Source citation |
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**Nodes graphify would extract:**
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- `MultiHeadAttention` (آلية الانتباه) - hyperparameters: h=8, d_model=512, d_k=64
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- `PositionalEncoding` (الترميز الموضعي) - feeds into transformer input
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- `LayerNorm` (التطبيع الطبقي) - applied per sublayer
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- `Transformer` - 6 encoder + 6 decoder stack
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**Key finding:** Arabic text OCR works natively via Claude vision. No preprocessing, no
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reshaper libraries, no bidi algorithms. The model reads Arabic, Persian, Hebrew, Chinese etc.
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identically to English. The image node in graphify is just a path - the vision subagent does
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the rest.
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---
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## 7. Issues Found
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### Issue 1: Suggested questions returns empty (MINOR)
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`suggest_questions()` requires a `community_labels` dict. When called with auto-generated
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labels on a small corpus with no AMBIGUOUS edges and no isolated nodes, it returns an empty
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list. The function requires more signal (AMBIGUOUS edges, bridge nodes, underexplored god nodes)
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to generate questions - correct behavior, but the skill should handle the empty case gracefully.
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### Issue 2: God nodes empty when all nodes are file-level (MINOR)
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`god_nodes()` correctly excludes file hub nodes. But on a 3-file corpus where the only
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real entities are file-level functions, it returns empty. The evaluation fell back to showing
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degree-ranked nodes manually. Fix: emit a notice ("corpus too small for meaningful god nodes")
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rather than silent empty list.
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### Issue 3: 0 surprising connections on cleanly-layered code (NOT a bug)
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The three modules don't import from each other - they're connected only through external deps
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(networkx, graspologic). No cross-community edges means no surprises to surface. This is
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correct. Surprising connections require a less-cleanly-separated codebase.
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---
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## 8. Scores
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| Dimension | Score | Notes |
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|-----------|-------|-------|
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| Detection accuracy | 10/10 | paper/code/image classified correctly, arxiv heuristic works |
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| AST extraction | 7/10 | functions and file nodes correct; no cross-file edges (expected) |
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| Community quality | 9/10 | 3 communities map perfectly to 3 functional modules |
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| Query traversal | 8/10 | BFS finds relevant nodes, source locations cited correctly |
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| Feedback loop | 10/10 | query results appear in next detect() scan, 3/3 |
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| Arabic OCR | 10/10 | Claude vision reads RTL Arabic natively, no libraries needed |
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**Overall: 9.0/10** - strong pass on all dimensions with a small corpus.
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Primary gaps are edge-level semantics (no INFERRED edges from AST-only) and god_nodes/
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suggest_questions behavior on tiny corpora.
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---
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## Conclusion
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The core pipeline is solid. The three most important findings:
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1. **The feedback loop works end-to-end.** Q&A results saved as markdown are picked up by
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the next `detect()` scan and will be extracted into the graph on `--update`.
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2. **Arabic OCR requires zero special handling.** PIL creates the image, Claude reads it.
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The same applies to any language - no language-specific preprocessing needed.
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3. **The corpus-size warning is working correctly.** At 4,020 words the warning fires:
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"fits in a single context window - you may not need a graph." This is honest.
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The graph adds value at scale, not on 5-file repos.
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