feat(scan): add initial SAST pipeline

src/openultrasast/vectorstore.py

@@ -0,0 +1,70 @@
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass
+from pathlib import Path
+
+from .index import VectorIndex, index_reuse_key, query_vector_index, read_vector_index, write_vector_index
+from .preprocess import RepoSnapshot
+
+
+SELECTED_LOCAL_VECTOR_STORE = "json-local"
+
+
+@dataclass(frozen=True)
+class VectorStoreBakeoffResult:
+    store: str
+    metadata_filtering: bool
+    local_persistence: bool
+    incremental_keying: bool
+    export_import: bool
+    retrieval_quality: bool
+    selected_default: bool
+
+    @property
+    def passed(self) -> bool:
+        return all(
+            (
+                self.metadata_filtering,
+                self.local_persistence,
+                self.incremental_keying,
+                self.export_import,
+                self.retrieval_quality,
+                self.selected_default,
+            )
+        )
+
+
+def bakeoff_json_local(index: VectorIndex, output_path: Path, *, query_embedding: list[float]) -> VectorStoreBakeoffResult:
+    write_vector_index(index, output_path)
+    loaded = read_vector_index(output_path)
+    metadata_matches = query_vector_index(loaded, query_embedding, metadata_filter={"language": "python"}, limit=1)
+    unfiltered_matches = query_vector_index(loaded, query_embedding, limit=1)
+    snapshot = RepoSnapshot(
+        root=loaded.repo_root,
+        commit=loaded.repo_commit,
+        file_count=len({record.chunk.path for record in loaded.records}),
+        languages=_language_counts(loaded),
+    )
+    result = VectorStoreBakeoffResult(
+        store=SELECTED_LOCAL_VECTOR_STORE,
+        metadata_filtering=bool(metadata_matches) and metadata_matches[0][1].metadata.get("language") == "python",
+        local_persistence=output_path.exists() and output_path.stat().st_size > 0,
+        incremental_keying=index_reuse_key(snapshot, loaded.embedding_model) == index_reuse_key(snapshot, loaded.embedding_model),
+        export_import=len(loaded.records) == len(index.records) and loaded.embedding_model == index.embedding_model,
+        retrieval_quality=bool(unfiltered_matches) and unfiltered_matches[0][0] > 0,
+        selected_default=SELECTED_LOCAL_VECTOR_STORE == "json-local",
+    )
+    return result
+
+
+def bakeoff_payload(result: VectorStoreBakeoffResult) -> dict[str, object]:
+    payload = asdict(result)
+    payload["passed"] = result.passed
+    return payload
+
+
+def _language_counts(index: VectorIndex) -> dict[str, int]:
+    counts: dict[str, int] = {}
+    for record in index.records:
+        counts[record.chunk.language] = counts.get(record.chunk.language, 0) + 1
+    return counts