feat: index semantic records in qdrant

harness/tests/test_adapter_factory.py

@@ -1,8 +1,8 @@
 import pytest
 
 from tht.adapters.dwh import PostgresDwhAdapter, ThothRestDwhAdapter
-from tht.adapters.vector import PgVectorStore, ThothHttpVectorStore
 from tht.adapters.factory import build_dwh, build_vector_store
+from tht.adapters.vector import PgVectorStore, QdrantVectorStore, ThothHttpVectorStore
 from tht.config import Config, ConfigError
 
 
@@ -125,6 +125,50 @@ def test_factory_builds_writer_only_direct_vector_when_write_is_required():
     assert store.capabilities.upsert is True
 
 
+def test_factory_selects_qdrant_for_schema_v3_runtime():
+    config = Config.model_validate(
+        {
+            "dwh": {
+                "type": "postgres_direct",
+                "connection": {
+                    "host": "db",
+                    "database": "analytics",
+                    "schema": "mart",
+                    "user": "reader",
+                    "password": "secret",
+                },
+            },
+            "database": {
+                "host": "db",
+                "database": "analytics",
+                "schema": "mart",
+                "user": "reader",
+                "password": "secret",
+                "transport": "direct",
+            },
+            "vectors": {
+                "type": "qdrant",
+                "base_url": "http://qdrant:6333",
+                "collection": "psd-clinical",
+            },
+            "embeddings": {
+                "provider": "ollama_internal",
+                "base_url": "http://embedding:11434",
+                "model": "qwen3-embedding:0.6b",
+                "dim": 1024,
+            },
+        }
+    )
+    config._workspace_id = "psd-clinical"
+    config._workspace_revision = "a" * 40
+
+    store = build_vector_store(config, require_write=True)
+
+    assert isinstance(store, QdrantVectorStore)
+    assert store.capabilities.search is True
+    assert store.capabilities.upsert is True
+
+
 def test_factory_reuses_legacy_direct_connection_for_server_writes_only():
     server = _config(vector_type="pgvector_direct", writer=False)
     server.vectors.connection = server.vectors.reader

harness/tests/test_config_resources.py

@@ -6,6 +6,7 @@ from tht.config import (
     ConfigError,
     PgvectorDirectConfig,
     PostgresDwhConfig,
+    QdrantConfig,
     ThothRestDwhConfig,
     ThothVectorHttpConfig,
     load_config,
@@ -237,6 +238,33 @@ resources:
     assert cfg.embeddings.dim == 1024
 
 
+def test_accepts_internal_qdrant_resource_contract(tmp_path):
+    workspace = tmp_path / "workspace.yaml"
+    workspace.write_text(
+        """
+dwh:
+  type: postgres_direct
+  connection: {database: analytics, schema: mart, user: reader, password: secret}
+resources:
+  vector:
+    engine: qdrant
+    base_url: http://qdrant:6333
+    collection: psd-clinical
+  embeddings:
+    provider: ollama_internal
+    base_url: http://embedding:11434
+    model: qwen3-embedding:0.6b
+    dimensions: 1024
+"""
+    )
+
+    cfg = load_config(workspace)
+
+    assert isinstance(cfg.vectors, QdrantConfig)
+    assert cfg.vectors.base_url == "http://qdrant:6333"
+    assert cfg.vectors.collection == "psd-clinical"
+
+
 @pytest.mark.parametrize(
     ("snippet", "pattern"),
     [

harness/tests/test_memory_save_one.py

@@ -7,19 +7,28 @@ pgvector as a one-row upsert. This test pins the pure core of that behavior:
 - the writer.upsert_records is called once with a single row
 - writer.sync is NEVER called (that is the full-resync path)
 """
-from datetime import datetime
+from datetime import UTC, datetime
 from unittest.mock import MagicMock
 
+from tht.adapters.vector.qdrant import point_id
 from tht.memory import MemoryRecord, memory_vector_record_for_decision, save_one_memory
+from tht.vectorstore.records import qdrant_payload
 
 
 def _record(seq: int = 7, **kw) -> MemoryRecord:
-    base = dict(
-        id="mem-0007", ts=datetime(2025, 1, 1), session_id="s1", decision_seq=seq,
-        type="concept_clarified", subject="paziente attivo",
-        detail="flag_attivo = TRUE", rationale="r",
-        question_context="dammi i pazienti", tables=[], concepts=["paziente attivo"],
-    )
+    base = {
+        "id": "mem-0007",
+        "ts": datetime(2025, 1, 1, tzinfo=UTC),
+        "session_id": "s1",
+        "decision_seq": seq,
+        "type": "concept_clarified",
+        "subject": "paziente attivo",
+        "detail": "flag_attivo = TRUE",
+        "rationale": "r",
+        "question_context": "dammi i pazienti",
+        "tables": [],
+        "concepts": ["paziente attivo"],
+    }
     base.update(kw)
     return MemoryRecord(**base)
 
@@ -85,3 +94,29 @@ def test_save_one_uses_writer_key_for_upsert():
     save_one_memory(records, decision_seq=7, store=writer, embedder=embedder)
     # one upsert call, single row, table=memory
     assert writer.upsert.call_count == 1
+
+
+def test_save_one_preserves_semantic_point_identity_fields():
+    records = [_record(seq=7)]
+    writer = MagicMock()
+    writer.existing_hashes.return_value = {}
+    writer.upsert.return_value = 1
+    embedder = MagicMock()
+    embedder.embed_documents.return_value = [[0.0] * 4]
+
+    save_one_memory(records, decision_seq=7, store=writer, embedder=embedder)
+
+    row = writer.upsert.call_args.args[1][0]
+    payload = qdrant_payload(
+        row.record,
+        content_hash=row.content_hash,
+        workspace_id="psd-clinical",
+        workspace_revision="a" * 40,
+    )
+
+    assert point_id("psd-clinical", "memory", row.record.id) == point_id(
+        "psd-clinical", "memory", "memory:mem-0007"
+    )
+    assert payload["kind"] == "memory"
+    assert payload["workspace_id"] == "psd-clinical"
+    assert payload["workspace_revision"] == "a" * 40

harness/tests/test_qdrant_vector_store.py

@@ -166,6 +166,7 @@ def _store(fake: FakeQdrantHttp) -> QdrantVectorStore:
         base_url="http://qdrant:6333",
         collection="workspace-semantic",
         workspace_id="demo",
+        workspace_revision="a" * 40,
         expected_dimension=1024,
         request=fake.request,
     )
@@ -195,6 +196,7 @@ def test_upsert_creates_collection_and_keyword_indexes_idempotently():
         "record_kind",
         "vector_generation",
         "workspace_id",
+        "workspace_revision",
     }
 
 
@@ -239,6 +241,7 @@ def test_upsert_serializes_qdrant_point_payloads(record, semantic_kind):
     assert point["id"] == point_id("demo", semantic_kind, record.record.id)
     assert point["vector"] == record.embedding
     assert point["payload"]["workspace_id"] == "demo"
+    assert point["payload"]["workspace_revision"] == "a" * 40
     assert point["payload"]["kind"] == semantic_kind
     assert point["payload"]["record_kind"] == record.record.kind
     assert point["payload"]["record_key"] == record.record.id

harness/tests/test_search_pack.py

@@ -1,5 +1,5 @@
 import json
-from datetime import datetime
+from datetime import UTC, datetime
 from types import SimpleNamespace
 
 from typer.testing import CliRunner
@@ -7,8 +7,8 @@ from typer.testing import CliRunner
 from tht.cli import app
 from tht.config import load_config
 from tht.jobs.dwh_pipeline import DwhPreprocessPipeline, config_dwh_binding
-from tht.ports.vector import VectorReadUnavailable
 from tht.mschema.models import ColumnPhysical, PhysicalSchema, TablePhysical
+from tht.ports.vector import VectorReadUnavailable
 from tht.vectorstore.embeddings import EmbeddingsError
 
 
@@ -22,7 +22,11 @@ class _FakeEmbedder:
 
 
 class _FakeSearcher:
+    def __init__(self):
+        self.calls = []
+
     def search(self, vec, top_n, kinds=None):
+        self.calls.append({"top_n": top_n, "kinds": kinds})
         if kinds == ["solved_question"]:
             return [SimpleNamespace(
                 kind="memory", ref="s-1", id="m1", title="q solved",
@@ -47,7 +51,7 @@ class _FakeSearcher:
 
 def _workspace(tmp_path, with_session=None):
     physical = PhysicalSchema(
-        database="d", schema="s", introspected_at=datetime(2026, 1, 1),
+        database="d", schema="s", introspected_at=datetime(2026, 1, 1, tzinfo=UTC),
         tables={"fact_ablazione": TablePhysical(
             comment="Ablazioni", columns={"cod_paz": ColumnPhysical(type="bigint")})},
     )
@@ -55,7 +59,7 @@ def _workspace(tmp_path, with_session=None):
     cfg.write_text(
         "database: {database: d, schema: s, user: u, password: p, transport: direct}\n"
         "vector_db: {database: v, schema: public, user: u, password: p}\n"
-        "embeddings: {base_url: 'http://localhost:11434', model: nomic-embed-text, dim: 8}\n"
+        "embeddings: {base_url: 'http://localhost:11434', model: qwen3-embedding:0.6b, dim: 1024}\n"
         f"paths: {{artifacts: {tmp_path/'artifacts'}, indexes: {tmp_path/'i'}, "
         f"sessions: {tmp_path/'sessions'}}}\n"
     )
@@ -91,10 +95,15 @@ def _patch(monkeypatch, embedder, searcher):
 def test_pack_single_embed_and_sections(tmp_path, monkeypatch):
     cfg = _workspace(tmp_path)
     emb = _FakeEmbedder()
-    _patch(monkeypatch, emb, _FakeSearcher())
+    searcher = _FakeSearcher()
+    _patch(monkeypatch, emb, searcher)
     res = CliRunner().invoke(app, ["search", "pack", "quanti pazienti", "-c", str(cfg)])
     assert res.exit_code == 0, res.output
     assert emb.calls == 1  # UN solo embedding per le tre ricerche
+    assert [call["kinds"] for call in searcher.calls] == [
+        ["schema_table", "schema_column"],
+        ["solved_question"],
+    ]
     assert "fact_ablazione" in res.output and "Ablazioni" in res.output
     # Evidence is fail-closed until an ACTIVE corpus exists; legacy vector rows
     # must not leak into a new search pack.

harness/tests/test_semantic_kind_isolation.py

@@ -0,0 +1,222 @@
+from __future__ import annotations
+
+import hashlib
+from dataclasses import dataclass
+from datetime import UTC, datetime
+
+from tht.adapters.vector.qdrant import point_id
+from tht.cli.vector_cmd import sync_canonical_records
+from tht.corpus.chunk import ChunkPolicy
+from tht.corpus.models import CanonicalChunk
+from tht.corpus.pipeline import CorpusPipeline
+from tht.corpus.store import CorpusStore
+from tht.memory import MemoryRecord, save_one_memory
+from tht.mschema.models import (
+    Annotations,
+    ColumnPhysical,
+    PhysicalSchema,
+    TablePhysical,
+)
+from tht.ports.vector import VectorCapabilities, VectorHealth
+from tht.vectorstore.records import qdrant_payload, schema_records
+
+
+def _sha(content: str) -> str:
+    return f"sha256:{hashlib.sha256(content.encode('utf-8')).hexdigest()}"
+
+
+class _Embedder:
+    def embed_documents(self, documents):
+        return [[float(index + 1)] * 4 for index, _ in enumerate(documents)]
+
+
+@dataclass
+class _Point:
+    point_id: str
+    payload: dict
+    embedding: list[float]
+
+
+class FakeVectorStore:
+    def __init__(self, workspace_id="psd-clinical", workspace_revision=None):
+        self.workspace_id = workspace_id
+        self.workspace_revision = workspace_revision or "a" * 40
+        self.points: dict[str, _Point] = {}
+        self.search_calls: list[dict] = []
+
+    @property
+    def capabilities(self):
+        return VectorCapabilities(
+            search=True,
+            existing_hashes=True,
+            upsert=True,
+            metadata_filter=True,
+            delete_generation=True,
+            list_evidence_generations=True,
+        )
+
+    def health(self):
+        return VectorHealth(ok=True)
+
+    def search(self, collections, embedding, *, limit, kinds=None, metadata_filter=None):
+        self.search_calls.append(
+            {
+                "collections": collections,
+                "embedding": embedding,
+                "limit": limit,
+                "kinds": kinds,
+                "metadata_filter": metadata_filter,
+            }
+        )
+        return []
+
+    def existing_hashes(self, collection, kinds):
+        allowed = set(kinds)
+        return {
+            point.payload["record_key"]: point.payload["content_hash"]
+            for point in self.points.values()
+            if point.payload["record_kind"] in allowed
+        }
+
+    def upsert(self, collection, records):
+        for row in records:
+            semantic_kind = qdrant_payload(
+                row.record,
+                content_hash=row.content_hash,
+                workspace_id=self.workspace_id,
+                workspace_revision=self.workspace_revision,
+            )["kind"]
+            payload = qdrant_payload(
+                row.record,
+                content_hash=row.content_hash,
+                workspace_id=self.workspace_id,
+                workspace_revision=self.workspace_revision,
+            )
+            self.points[point_id(self.workspace_id, semantic_kind, row.record.id)] = _Point(
+                point_id=point_id(self.workspace_id, semantic_kind, row.record.id),
+                payload=payload,
+                embedding=row.embedding,
+            )
+        return len(records)
+
+    def delete_generation(self, collection, generation, workspace_id):
+        doomed = [
+            key
+            for key, point in self.points.items()
+            if point.payload.get("record_kind") == "evidence"
+            and point.payload.get("vector_generation") == generation
+            and point.payload.get("workspace_id") == workspace_id
+        ]
+        for key in doomed:
+            self.points.pop(key)
+        return len(doomed)
+
+    def list_evidence_generations(self, collection, workspace_id):
+        return sorted(
+            {
+                point.payload["vector_generation"]
+                for point in self.points.values()
+                if point.payload.get("record_kind") == "evidence"
+                and point.payload.get("workspace_id") == workspace_id
+            }
+        )
+
+
+def _schema_records():
+    return schema_records(
+        PhysicalSchema(
+            database="analytics",
+            schema="mart",
+            introspected_at=datetime.now(UTC),
+            tables={
+                "fact_patient": TablePhysical(
+                    comment="Patients",
+                    columns={"id": ColumnPhysical(type="bigint", comment="pk")},
+                )
+            },
+        ),
+        Annotations(),
+    )
+
+
+def _memory_records():
+    return [
+        MemoryRecord(
+            id="mem-0001",
+            ts=datetime(2026, 1, 1, tzinfo=UTC),
+            session_id="s1",
+            decision_seq=7,
+            type="concept_clarified",
+            subject="paziente attivo",
+            detail="flag_attivo = true",
+            rationale="r",
+            question_context="dammi i pazienti attivi",
+            tables=[],
+            concepts=["paziente attivo"],
+        )
+    ]
+
+
+def test_schema_and_memory_use_expected_semantic_kinds_and_shared_identity():
+    store = FakeVectorStore()
+    embedder = _Embedder()
+
+    schema_stats = sync_canonical_records(
+        "schema_records",
+        _schema_records(),
+        store=store,
+        embedder=embedder,
+    )
+    memory_count = save_one_memory(_memory_records(), 7, store=store, embedder=embedder)
+
+    assert schema_stats.added == 2
+    assert memory_count == 1
+    payloads = {point.payload["record_kind"]: point.payload for point in store.points.values()}
+    assert payloads["schema_table"]["kind"] == "schema"
+    assert payloads["schema_column"]["kind"] == "schema"
+    assert payloads["memory"]["kind"] == "memory"
+    assert {payload["workspace_id"] for payload in payloads.values()} == {"psd-clinical"}
+    assert {payload["workspace_revision"] for payload in payloads.values()} == {"a" * 40}
+
+
+def test_corpus_vector_records_keep_exact_generation_and_retry_is_idempotent(tmp_path):
+    store = FakeVectorStore()
+    pipeline = CorpusPipeline(
+        store=CorpusStore(tmp_path / "corpus"),
+        sources=[],
+        embedder=None,
+        vector_store=store,
+        embedding_model="qwen3-embedding:0.6b",
+        embedding_dimensions=1024,
+        chunk_policy=ChunkPolicy(version="chunk-v1", max_chars=4000),
+        pipeline_version="evidence-v1",
+        workspace_id="psd-clinical",
+    )
+    chunk = CanonicalChunk(
+        chunk_id="chunk:1",
+        document_id="doc:patient-guide",
+        ordinal=0,
+        content="Patient evidence",
+        content_hash=_sha("Patient evidence"),
+        source_uri="file:///tmp/patient-guide.md",
+        pipeline_version="evidence-v1",
+    )
+    row = pipeline._vector_record(
+        chunk,
+        [0.1, 0.2, 0.3, 0.4],
+        "gen:" + "1" * 32,
+        "psd-clinical",
+    )
+
+    assert row.record.kind == "evidence"
+    assert row.record.metadata["vector_generation"] == "gen:" + "1" * 32
+
+    store.upsert("evidence", [row])
+    store.upsert("evidence", [row])
+
+    assert len(store.points) == 1
+    point = next(iter(store.points.values()))
+    assert point.payload["kind"] == "evidence"
+    assert point.payload["vector_generation"] == "gen:" + "1" * 32
+    assert point.payload["workspace_id"] == "psd-clinical"
+    assert point.payload["workspace_revision"] == "a" * 40

harness/tht/adapters/factory.py

@@ -3,7 +3,7 @@
 from tht.adapters.dwh import PostgresDwhAdapter, ThothRestDwhAdapter
 from tht.adapters.evidence import FilesystemEvidenceSource, HttpManifestEvidenceSource
 from tht.adapters.evidence.s3 import S3EvidenceSource
-from tht.adapters.vector import PgVectorStore, ThothHttpVectorStore
+from tht.adapters.vector import PgVectorStore, QdrantVectorStore, ThothHttpVectorStore
 from tht.config import Config, ConfigError
 from tht.db.connection import make_engine
 from tht.ports.dwh import DwhAdapter
@@ -56,6 +56,14 @@ def build_vector_store(cfg: Config, *, require_write: bool = False) -> VectorSto
                 VectorRestClient(resource.writer) if resource.writer is not None else None,
                 expected_dimension=cfg.embeddings.dim if cfg.embeddings is not None else None,
             )
+        case "qdrant":
+            return QdrantVectorStore(
+                base_url=resource.base_url,
+                collection=resource.collection,
+                workspace_id=cfg._workspace_id,
+                workspace_revision=cfg._workspace_revision,
+                expected_dimension=cfg.embeddings.dim if cfg.embeddings is not None else None,
+            )
         case other:  # pragma: no cover - Pydantic's discriminator rejects this first.
             raise ConfigError(f"Adapter vector non supportato: {other}")
 

harness/tht/adapters/vector/qdrant.py

@@ -32,6 +32,7 @@ _KEYWORD_INDEXES = (
     "record_kind",
     "vector_generation",
     "workspace_id",
+    "workspace_revision",
 )
 
 
@@ -52,6 +53,7 @@ class QdrantVectorStore:
         base_url: str,
         collection: str,
         workspace_id: str,
+        workspace_revision: str | None = None,
         expected_dimension: int | None = None,
         request: Callable[..., object] | None = None,
         connect_timeout: float = 2.0,
@@ -60,6 +62,7 @@ class QdrantVectorStore:
         self._base_url = base_url.rstrip("/")
         self._collection = collection
         self._workspace_id = workspace_id
+        self._workspace_revision = workspace_revision
         self._expected_dimension = expected_dimension
         self._request = request or requests.request
         self._timeout = (connect_timeout, read_timeout)
@@ -198,6 +201,7 @@ class QdrantVectorStore:
                         write_record.record,
                         content_hash=write_record.content_hash,
                         workspace_id=self._workspace_id,
+                        workspace_revision=self._workspace_revision,
                     ),
                 }
             )

harness/tht/cli/memory_cmd.py

@@ -10,17 +10,18 @@ from pathlib import Path
 import typer
 from sqlalchemy.exc import OperationalError, ProgrammingError
 
-from tht.cli.config_cmd import CONFIG_OPT
-from tht.cli.schema_cmd import _load_config_or_exit
 from tht.cli._guards import (
     has_vector_write_rest,
     require_server_profile,
     require_vector_write_allowed,
 )
+from tht.cli.config_cmd import CONFIG_OPT
+from tht.cli.schema_cmd import _load_config_or_exit
 from tht.cli.session_cmd import load_snapshot_or_exit
 from tht.cli.vector_cmd import require_vector_cfg
 
 memory_app = typer.Typer(help="Review memory (registro canonico + indice pgvector)")
+DECISION_OPT = typer.Option(None, "--decision", help="Seq da promuovere (ripetibile).")
 
 
 def registry_path(cfg) -> Path:
@@ -29,18 +30,23 @@ def registry_path(cfg) -> Path:
 
 def _resync_memory(cfg):
     """Risincronizza l'indice pgvector col registro corrente (incrementale)."""
-    from tht.cli.vector_cmd import make_embedder, open_store
+    from tht.adapters.factory import build_vector_store
+    from tht.cli.vector_cmd import make_embedder, sync_canonical_records
     from tht.memory import load_registry, memory_vector_records
 
     records = memory_vector_records(load_registry(registry_path(cfg)))
-    store = open_store(cfg, "memory")
-    return store.sync(records, make_embedder(cfg.embeddings), kinds={"memory"})
+    return sync_canonical_records(
+        "memory",
+        records,
+        store=build_vector_store(cfg, require_write=True),
+        embedder=make_embedder(cfg.embeddings),
+    )
 
 
 @memory_app.command("promote")
 def promote_cmd(
     session: str = typer.Option(..., "--session"),
-    decision: list[int] = typer.Option(None, "--decision", help="Seq da promuovere (ripetibile)."),
+    decision: list[int] = DECISION_OPT,
     preview: bool = typer.Option(False, "--preview", help="Mostra i candidati in JSON, non scrive."),
     json_out: bool = typer.Option(False, "--json", help="Output JSON (per Pi)."),
     config: Path = CONFIG_OPT,
@@ -55,7 +61,9 @@ def promote_cmd(
 
     if preview:
         from tht.memory import (
-            MAX_PROMOTION_CANDIDATES, preview_promotions_snapshot, reusable_promotions_snapshot,
+            MAX_PROMOTION_CANDIDATES,
+            preview_promotions_snapshot,
+            reusable_promotions_snapshot,
         )
         cand = preview_promotions_snapshot(snapshot, registry_path(cfg))
         extra = len(reusable_promotions_snapshot(snapshot, registry_path(cfg))) - len(cand)
@@ -304,8 +312,8 @@ def update_cmd(
     """Modifica i campi di merito di una memoria (provenienza immutabile)."""
     from typing import get_args
 
-    from tht.memory import MemoryNotFound, update_record
     from tht.decisions import DecisionType
+    from tht.memory import MemoryNotFound, update_record
 
     cfg = _load_config_or_exit(config)
 

harness/tht/cli/vector_cmd.py

@@ -2,9 +2,15 @@ from pathlib import Path
 
 import typer
 
-from tht.cli._guards import has_vector_write_rest, require_server_profile, require_vector_write_allowed
+from tht.cli._guards import (
+    has_vector_write_rest,
+    require_server_profile,
+    require_vector_write_allowed,
+)
 from tht.cli.config_cmd import CONFIG_OPT
 from tht.cli.schema_cmd import _load_config_or_exit, annotations_path, physical_path
+from tht.ports.vector import VectorWriteRecord
+from tht.vectorstore.store import SyncStats, content_hash
 
 vector_app = typer.Typer(help="Indice semantico pgvector (derivato, rigenerabile)")
 
@@ -69,6 +75,31 @@ def open_searcher(cfg):
     return AdapterSearcher()
 
 
+def sync_canonical_records(collection, records, *, store, embedder):
+    kinds = sorted({record.kind for record in records})
+    existing = store.existing_hashes(collection, kinds)
+    pending = []
+    stats = SyncStats()
+    changed = []
+    for record in records:
+        hashed = content_hash(record.content)
+        current = existing.get(record.id)
+        if current == hashed:
+            stats.unchanged += 1
+            continue
+        changed.append((record, hashed, current is None))
+    if changed:
+        embeddings = embedder.embed_documents([record.content for record, *_ in changed])
+        for (record, hashed, is_added), embedding in zip(changed, embeddings, strict=True):
+            pending.append(VectorWriteRecord(record=record, embedding=embedding, content_hash=hashed))
+            if is_added:
+                stats.added += 1
+            else:
+                stats.updated += 1
+        store.upsert(collection, pending)
+    return stats
+
+
 def _print_stats(stats) -> None:
     typer.secho(
         f"OK: {stats.added} nuovi, {stats.updated} aggiornati, "
@@ -88,7 +119,6 @@ def init_cmd(
     from sqlalchemy.exc import OperationalError
 
     from tht.vectorstore.embeddings import EmbeddingsError
-
     from tht.vectorstore.reader import ALL_TABLES
 
     cfg = _load_config_or_exit(config)
@@ -131,8 +161,12 @@ def index_schema_cmd(config: Path = CONFIG_OPT) -> None:
     physical = PhysicalSchema.from_yaml(phys_file)
     annotations = Annotations.from_yaml(annotations_path(cfg))
     records = schema_records(physical, annotations)
-    store = open_store(cfg, "schema_records")
-    stats = store.sync(
-        records, make_embedder(cfg.embeddings), kinds={"schema_table", "schema_column"}
+    from tht.adapters.factory import build_vector_store
+
+    stats = sync_canonical_records(
+        "schema_records",
+        records,
+        store=build_vector_store(cfg, require_write=True),
+        embedder=make_embedder(cfg.embeddings),
     )
     _print_stats(stats)

harness/tht/config.py

@@ -152,8 +152,14 @@ class ThothVectorHttpConfig(BaseModel):
     direct: DatabaseConfig | None = None
 
 
+class QdrantConfig(BaseModel):
+    type: Literal["qdrant"]
+    base_url: str
+    collection: str = Field(min_length=1)
+
+
 VectorResourceConfig = Annotated[
-    PgvectorDirectConfig | ThothVectorHttpConfig,
+    PgvectorDirectConfig | ThothVectorHttpConfig | QdrantConfig,
     Field(discriminator="type"),
 ]
 
@@ -397,6 +403,7 @@ def load_config(path: Path) -> Config:
         raise ConfigError(f"Configurazione non valida (atteso un mapping YAML): {path}")
     expanded = _resolve_secret_files(_expand_env(raw))
     _validate_internal_embedding_contract(expanded, path)
+    _validate_internal_vector_contract(expanded, path)
     translated, used_legacy = translate_legacy_config(expanded)
     _populate_legacy_views(translated)
     try:
@@ -455,6 +462,7 @@ def load_config(path: Path) -> Config:
         else path.resolve().as_posix()
     )
     _validate_active_embeddings_config(cfg.embeddings, path)
+    _validate_active_vector_config(cfg.vectors, path)
     return cfg
 
 
@@ -500,6 +508,42 @@ def _validate_internal_embedding_contract(raw: dict[str, Any], path: Path) -> No
         )
 
 
+def _validate_internal_vector_contract(raw: dict[str, Any], path: Path) -> None:
+    resources = raw.get("resources")
+    if not isinstance(resources, dict):
+        return
+    vector = resources.get("vector")
+    if not isinstance(vector, dict):
+        return
+
+    engine = vector.get("engine")
+    base_url = vector.get("base_url")
+    collection = vector.get("collection")
+    allowed = {"engine", "base_url", "collection"}
+    unexpected = sorted(set(vector) - allowed)
+    if unexpected:
+        raise ConfigError(
+            f"Configurazione non valida in {path}:\n"
+            f"resources.vector non supporta: {', '.join(unexpected)}"
+        )
+    if engine != "qdrant":
+        raise ConfigError(
+            f"Configurazione non valida in {path}:\n"
+            "resources.vector.engine deve essere 'qdrant'"
+        )
+    if not isinstance(collection, str) or not collection:
+        raise ConfigError(
+            f"Configurazione non valida in {path}:\n"
+            "resources.vector.collection deve essere valorizzato"
+        )
+    if not _is_allowed_internal_qdrant_url(base_url):
+        raise ConfigError(
+            f"Configurazione non valida in {path}:\n"
+            "resources.vector.base_url deve usare http://qdrant:6333 "
+            "oppure un endpoint loopback di sviluppo su porta 6333"
+        )
+
+
 def _validate_active_embeddings_config(
     embeddings: "EmbeddingsConfig | None",
     path: Path,
@@ -529,6 +573,20 @@ def _validate_active_embeddings_config(
         )
 
 
+def _validate_active_vector_config(
+    vectors: "VectorResourceConfig | None",
+    path: Path,
+) -> None:
+    if vectors is None or vectors.type != "qdrant":
+        return
+    if not _is_allowed_internal_qdrant_url(vectors.base_url):
+        raise ConfigError(
+            f"Configurazione non valida in {path}:\n"
+            "vectors.base_url deve usare http://qdrant:6333 "
+            "oppure un endpoint loopback di sviluppo su porta 6333"
+        )
+
+
 def _is_allowed_internal_embedding_url(value: Any) -> bool:
     if not isinstance(value, str):
         return False
@@ -548,6 +606,25 @@ def _is_allowed_internal_embedding_url(value: Any) -> bool:
     return host.is_loopback
 
 
+def _is_allowed_internal_qdrant_url(value: Any) -> bool:
+    if not isinstance(value, str):
+        return False
+    parsed = urlparse(value)
+    if parsed.scheme != "http" or not parsed.hostname or parsed.port != 6333:
+        return False
+    if parsed.params or parsed.query or parsed.fragment:
+        return False
+    if parsed.path not in ("", "/"):
+        return False
+    if parsed.hostname == "qdrant":
+        return True
+    try:
+        host = ip_address(parsed.hostname)
+    except ValueError:
+        return parsed.hostname == "localhost"
+    return host.is_loopback
+
+
 def _populate_legacy_views(raw: dict[str, Any]) -> None:
     """Populate old Config attributes for command compatibility during migration."""
     dwh = raw.get("dwh")

harness/tht/config_compat.py

@@ -33,6 +33,14 @@ def translate_legacy_config(raw: dict[str, Any]) -> tuple[dict[str, Any], bool]:
             translated["embeddings"] = embedding
             if "dimensions" in translated["embeddings"] and "dim" not in translated["embeddings"]:
                 translated["embeddings"]["dim"] = translated["embeddings"].pop("dimensions")
+    if isinstance(resources, dict) and "vector" in resources and "vectors" not in translated:
+        vector = _as_mapping(resources.get("vector"))
+        if isinstance(vector, dict):
+            translated["vectors"] = {
+                "type": "qdrant",
+                "base_url": vector.get("base_url"),
+                "collection": vector.get("collection"),
+            }
     legacy = any(key in raw for key in _LEGACY_RESOURCE_KEYS)
     if not legacy:
         return translated, False

harness/tht/vectorstore/records.py

@@ -27,11 +27,18 @@ def qdrant_semantic_kind(kind: str) -> str:
     raise ValueError(f"Unsupported vector kind: {kind}")
 
 
-def qdrant_payload(record: VectorRecord, *, content_hash: str, workspace_id: str) -> dict:
+def qdrant_payload(
+    record: VectorRecord,
+    *,
+    content_hash: str,
+    workspace_id: str,
+    workspace_revision: str | None = None,
+) -> dict:
     semantic_kind = qdrant_semantic_kind(record.kind)
     return {
         **record.metadata,
         "workspace_id": workspace_id,
+        **({"workspace_revision": workspace_revision} if workspace_revision else {}),
         "kind": semantic_kind,
         "record_kind": record.kind,
         "record_key": record.id,