chore: import upstream snapshot with attribution

graphify/benchmark.py

@@ -0,0 +1,157 @@
+"""Token-reduction benchmark - measures how much context graphify saves vs naive full-corpus approach."""
+from __future__ import annotations
+import json
+import sys
+from pathlib import Path
+import networkx as nx
+from networkx.readwrite import json_graph
+
+from graphify.build import edge_data
+from graphify.serve import _query_terms
+from graphify.paths import default_graph_json as _default_graph_json
+
+
+_CHARS_PER_TOKEN = 4  # standard approximation
+
+
+def _safe(unicode_char: str, ascii_fallback: str) -> str:
+    """Return unicode_char if stdout can encode it, else ascii_fallback.
+
+    Windows consoles often default to cp1252 which cannot encode box-drawing
+    or arrow glyphs; printing them raises UnicodeEncodeError mid-output.
+    """
+    encoding = getattr(sys.stdout, "encoding", None) or ""
+    try:
+        unicode_char.encode(encoding)
+        return unicode_char
+    except (UnicodeEncodeError, LookupError):
+        return ascii_fallback
+
+
+def _hr(width: int = 50) -> str:
+    """Horizontal rule that survives non-UTF-8 stdout (e.g. Windows cp1252 console)."""
+    return _safe("─", "-") * width
+
+
+def _estimate_tokens(text: str) -> int:
+    return max(1, len(text) // _CHARS_PER_TOKEN)
+
+
+def _query_subgraph_tokens(G: nx.Graph, question: str, depth: int = 3) -> int:
+    """Run BFS from best-matching nodes and return estimated tokens in the subgraph context."""
+    terms = _query_terms(question)
+    scored = []
+    for nid, data in G.nodes(data=True):
+        label = data.get("label", "").lower()
+        score = sum(1 for t in terms if t in label)
+        if score > 0:
+            scored.append((score, nid))
+    scored.sort(reverse=True)
+    start_nodes = [nid for _, nid in scored[:3]]
+    if not start_nodes:
+        return 0
+
+    visited: set[str] = set(start_nodes)
+    frontier = set(start_nodes)
+    edges_seen: list[tuple] = []
+    for _ in range(depth):
+        next_frontier: set[str] = set()
+        for n in frontier:
+            for neighbor in G.neighbors(n):
+                if neighbor not in visited:
+                    next_frontier.add(neighbor)
+                    edges_seen.append((n, neighbor))
+        visited.update(next_frontier)
+        frontier = next_frontier
+
+    lines = []
+    for nid in visited:
+        d = G.nodes[nid]
+        lines.append(f"NODE {d.get('label', nid)} src={d.get('source_file', '')} loc={d.get('source_location', '')}")
+    for u, v in edges_seen:
+        if u in visited and v in visited:
+            d = edge_data(G, u, v)
+            lines.append(f"EDGE {G.nodes[u].get('label', u)} --{d.get('relation', '')}--> {G.nodes[v].get('label', v)}")
+
+    return _estimate_tokens("\n".join(lines))
+
+
+_SAMPLE_QUESTIONS = [
+    "how does authentication work",
+    "what is the main entry point",
+    "how are errors handled",
+    "what connects the data layer to the api",
+    "what are the core abstractions",
+]
+
+
+def run_benchmark(
+    graph_path: str | None = None,
+    corpus_words: int | None = None,
+    questions: list[str] | None = None,
+) -> dict:
+    """Measure token reduction: corpus tokens vs graphify query tokens.
+
+    Args:
+        graph_path: path to the built graph
+        corpus_words: total word count from detect() output; if None, estimated from graph
+        questions: list of questions to benchmark; defaults to _SAMPLE_QUESTIONS
+
+    Returns dict with: corpus_tokens, avg_query_tokens, reduction_ratio, per_question
+    """
+    graph_path = graph_path or _default_graph_json()
+    from graphify.security import check_graph_file_size_cap
+    check_graph_file_size_cap(Path(graph_path))
+    data = json.loads(Path(graph_path).read_text(encoding="utf-8"))
+    try:
+        G = json_graph.node_link_graph(data, edges="links")
+    except TypeError:
+        G = json_graph.node_link_graph(data)
+
+    if corpus_words is None:
+        # Rough estimate: each node label is ~3 words, plus source context
+        corpus_words = G.number_of_nodes() * 50
+
+    corpus_tokens = corpus_words * 100 // 75  # words → tokens (100 words ≈ 133 tokens)
+
+    qs = questions or _SAMPLE_QUESTIONS
+    per_question = []
+    for q in qs:
+        qt = _query_subgraph_tokens(G, q)
+        if qt > 0:
+            per_question.append({"question": q, "query_tokens": qt, "reduction": round(corpus_tokens / qt, 1)})
+
+    if not per_question:
+        return {"error": "No matching nodes found for sample questions. Build the graph first."}
+
+    avg_query_tokens = sum(p["query_tokens"] for p in per_question) // len(per_question)
+    reduction_ratio = round(corpus_tokens / avg_query_tokens, 1) if avg_query_tokens > 0 else 0
+
+    return {
+        "corpus_tokens": corpus_tokens,
+        "corpus_words": corpus_words,
+        "nodes": G.number_of_nodes(),
+        "edges": G.number_of_edges(),
+        "avg_query_tokens": avg_query_tokens,
+        "reduction_ratio": reduction_ratio,
+        "per_question": per_question,
+    }
+
+
+def print_benchmark(result: dict) -> None:
+    """Print a human-readable benchmark report."""
+    if "error" in result:
+        print(f"Benchmark error: {result['error']}")
+        return
+
+    print(f"\ngraphify token reduction benchmark")
+    print(_hr(50))
+    arrow = _safe("→", "->")
+    print(f"  Corpus:          {result['corpus_words']:,} words {arrow} ~{result['corpus_tokens']:,} tokens (naive)")
+    print(f"  Graph:           {result['nodes']:,} nodes, {result['edges']:,} edges")
+    print(f"  Avg query cost:  ~{result['avg_query_tokens']:,} tokens")
+    print(f"  Reduction:       {result['reduction_ratio']}x fewer tokens per query")
+    print(f"\n  Per question:")
+    for p in result["per_question"]:
+        print(f"    [{p['reduction']}x] {p['question'][:55]}")
+    print()