chore: import upstream snapshot with attribution

bench/baselines/python/coderankembed_runner.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+"""CodeRankEmbed Hybrid baseline runner.
+
+Wrapper that lets the Go-side bench/baselines harness invoke
+CodeRankEmbed Hybrid without per-baseline Go code growing model-
+download logic. Usage:
+
+    python3 bench/baselines/python/coderankembed_runner.py \\
+        --repo PATH --query "validateToken" --top-k 10
+
+Emits one repo-relative path per line on stdout. Errors go to
+stderr; non-zero exit when the model isn't available.
+
+Install: `pip install sentence-transformers transformers torch`.
+First run downloads the CodeRankEmbed model (~440 MB).
+"""
+
+import argparse
+import os
+import sys
+from pathlib import Path
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--repo", required=True, help="indexed corpus path")
+    ap.add_argument("--query", required=True, help="single query string")
+    ap.add_argument("--top-k", type=int, default=10)
+    args = ap.parse_args()
+
+    try:
+        from sentence_transformers import SentenceTransformer
+    except ImportError as e:
+        print(
+            f"coderankembed_runner: missing dependency ({e}). "
+            "pip install sentence-transformers transformers torch",
+            file=sys.stderr,
+        )
+        return 2
+
+    model = SentenceTransformer("nomic-ai/CodeRankEmbed", trust_remote_code=True)
+
+    # Index every file under repo (cheap for sub-million LoC; the
+    # ground-truth fixture is the gortex repo itself).
+    repo = Path(args.repo).resolve()
+    paths: list[Path] = []
+    texts: list[str] = []
+    for p in repo.rglob("*"):
+        if not p.is_file():
+            continue
+        if any(seg.startswith(".") for seg in p.relative_to(repo).parts):
+            continue
+        if p.suffix.lower() not in {
+            ".go", ".py", ".ts", ".tsx", ".js", ".jsx", ".rs",
+            ".java", ".kt", ".swift", ".rb", ".cs", ".cpp", ".c",
+            ".h", ".hpp", ".md", ".yaml", ".yml", ".json",
+        }:
+            continue
+        try:
+            text = p.read_text(errors="ignore")
+        except OSError:
+            continue
+        if not text.strip():
+            continue
+        paths.append(p)
+        texts.append(text[:8000])  # truncate to keep the embed cheap
+
+    if not texts:
+        print(
+            "coderankembed_runner: no indexable files under repo",
+            file=sys.stderr,
+        )
+        return 1
+
+    embeds = model.encode(texts, show_progress_bar=False, convert_to_numpy=True)
+    qe = model.encode([args.query], show_progress_bar=False, convert_to_numpy=True)[0]
+
+    # Cosine similarity → rank.
+    import numpy as np
+
+    sims = embeds @ qe / (
+        (np.linalg.norm(embeds, axis=1) * np.linalg.norm(qe)) + 1e-12
+    )
+    order = np.argsort(-sims)[: args.top_k]
+    for idx in order:
+        rel = paths[idx].relative_to(repo)
+        print(rel.as_posix())
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())