feat: add qdrant vector adapter

harness/tht/vectorstore/records.py

@@ -17,6 +17,31 @@ class VectorRecord(BaseModel):
     metadata: dict = {}
 
 
+def qdrant_semantic_kind(kind: str) -> str:
+    if kind in {"schema_table", "schema_column"}:
+        return "schema"
+    if kind in {"memory", "solved_question"}:
+        return "memory"
+    if kind == "evidence":
+        return "evidence"
+    raise ValueError(f"Unsupported vector kind: {kind}")
+
+
+def qdrant_payload(record: VectorRecord, *, content_hash: str, workspace_id: str) -> dict:
+    semantic_kind = qdrant_semantic_kind(record.kind)
+    return {
+        "workspace_id": workspace_id,
+        "kind": semantic_kind,
+        "record_kind": record.kind,
+        "record_key": record.id,
+        "ref": record.ref,
+        "title": record.title,
+        "content": record.content,
+        "content_hash": content_hash,
+        **record.metadata,
+    }
+
+
 def split_markdown(text: str, max_chars: int) -> list[str]:
     """Spezza un markdown: intero se sta nel limite, altrimenti per heading '##',
     e in ultima istanza per accumulo greedy di righe."""

harness/tht/vectorstore/store.py

@@ -42,7 +42,7 @@ def hit_from_metadata(similarity: float, metadata: dict | None) -> VectorHit:
     md = metadata or {}
     return VectorHit(
         id=md.get("record_key", ""),
-        kind=md.get("kind", ""),
+        kind=md.get("record_kind", md.get("kind", "")),
         ref=md.get("ref", ""),
         title=md.get("title", ""),
         content=md.get("content", ""),