feat(rag): expose vector listing API with backend filter support

src/langbot/pkg/plugin/handler.py

@@ -555,6 +555,20 @@ class RuntimeConnectionHandler(handler.Handler):
             except Exception as e:
                 return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id)
 
+        @self.action(PluginToRuntimeAction.VECTOR_LIST)
+        async def vector_list(data: dict[str, Any]) -> handler.ActionResponse:
+            collection_id = data['collection_id']
+            filters = data.get('filters')
+            limit = data.get('limit', 20)
+            offset = data.get('offset', 0)
+            try:
+                items, total = await self.ap.rag_runtime_service.vector_list(
+                    collection_id, filters, limit, offset
+                )
+                return handler.ActionResponse.success(data={'items': items, 'total': total})
+            except Exception as e:
+                return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id)
+
         @self.action(PluginToRuntimeAction.GET_KNOWLEDEGE_FILE_STREAM)
         async def get_knowledge_file_stream(data: dict[str, Any]) -> handler.ActionResponse:
             storage_path = data['storage_path']

src/langbot/pkg/rag/service/runtime.py

@@ -75,6 +75,31 @@ class RAGRuntimeService:
             count = await self.ap.vector_db_mgr.delete_by_filter(collection_name=collection_id, filter=filters)
         return count
 
+    async def vector_list(
+        self,
+        collection_id: str,
+        filters: dict[str, Any] | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[dict[str, Any]], int]:
+        """Handle VECTOR_LIST action.
+
+        Args:
+            collection_id: The collection to list from.
+            filters: Optional metadata filters.
+            limit: Maximum number of items to return.
+            offset: Number of items to skip.
+
+        Returns:
+            Tuple of (items, total).
+        """
+        return await self.ap.vector_db_mgr.list_by_filter(
+            collection_name=collection_id,
+            filter=filters,
+            limit=limit,
+            offset=offset,
+        )
+
     async def get_file_stream(self, storage_path: str) -> bytes:
         """Handle GET_KNOWLEDEGE_FILE_STREAM action.
 

src/langbot/pkg/vector/filter_utils.py

@@ -49,17 +49,25 @@ def normalize_filter(
 def strip_unsupported_fields(
     triples: list[tuple[str, str, Any]],
     supported_fields: set[str],
+    field_aliases: dict[str, str] | None = None,
 ) -> list[tuple[str, str, Any]]:
     """Return only triples whose field is in *supported_fields*.
 
+    If *field_aliases* is provided, aliased field names are mapped to the
+    canonical backend name before the support check.  For example,
+    ``{'uuid': 'chunk_uuid'}`` allows callers to use ``uuid`` which is
+    transparently rewritten to ``chunk_uuid``.
+
     Dropped fields are logged at WARNING level so the caller knows they were
     silently ignored (useful for Milvus / pgvector which only store a fixed
     schema).
     """
+    aliases = field_aliases or {}
     kept: list[tuple[str, str, Any]] = []
     for field, op, value in triples:
-        if field in supported_fields:
+        resolved = aliases.get(field, field)
-            kept.append((field, op, value))
+        if resolved in supported_fields:
+            kept.append((resolved, op, value))
         else:
             logger.warning(
                 'Filter field %r is not supported by this backend and will be ignored (supported: %s)',

src/langbot/pkg/vector/mgr.py

@@ -157,3 +157,17 @@ class VectorDBManager:
             Number of deleted vectors (best-effort; some backends return 0).
         """
         return await self.vector_db.delete_by_filter(collection_name, filter)
+
+    async def list_by_filter(
+        self,
+        collection_name: str,
+        filter: dict | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[dict], int]:
+        """Proxy: List vectors by metadata filter with pagination.
+
+        Returns:
+            Tuple of (items, total).
+        """
+        return await self.vector_db.list_by_filter(collection_name, filter, limit, offset)

src/langbot/pkg/vector/vdb.py

@@ -92,6 +92,28 @@ class VectorDatabase(abc.ABC):
         """
         pass
 
+    async def list_by_filter(
+        self,
+        collection: str,
+        filter: dict[str, Any] | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[dict[str, Any]], int]:
+        """List vectors matching the given metadata filter with pagination.
+
+        Args:
+            collection: Collection name.
+            filter: Optional metadata filter dict in canonical format.
+            limit: Maximum number of items to return.
+            offset: Number of items to skip.
+
+        Returns:
+            Tuple of (items, total) where items is a list of dicts with
+            keys 'id', 'document', 'metadata', and total is the best-effort
+            count of all matching vectors (-1 if unknown).
+        """
+        return [], -1
+
     @abc.abstractmethod
     async def get_or_create_collection(self, collection: str):
         """Get or create collection."""

src/langbot/pkg/vector/vdbs/chroma.py

@@ -221,6 +221,39 @@ class ChromaVectorDatabase(VectorDatabase):
         self.ap.logger.info(f"Deleted embeddings from Chroma collection '{collection}' by filter")
         return 0  # Chroma delete does not return a count
 
+    async def list_by_filter(
+        self,
+        collection: str,
+        filter: dict[str, Any] | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[dict[str, Any]], int]:
+        col = await self.get_or_create_collection(collection)
+        get_kwargs: dict[str, Any] = dict(
+            include=['metadatas', 'documents'],
+            limit=limit,
+            offset=offset,
+        )
+        if filter:
+            get_kwargs['where'] = filter
+        results = await asyncio.to_thread(col.get, **get_kwargs)
+
+        ids = results.get('ids', [])
+        metadatas = results.get('metadatas', []) or [None] * len(ids)
+        documents = results.get('documents', []) or [None] * len(ids)
+
+        items = []
+        for i, vid in enumerate(ids):
+            items.append({
+                'id': vid,
+                'document': documents[i] if i < len(documents) else None,
+                'metadata': metadatas[i] if i < len(metadatas) else {},
+            })
+
+        # Chroma col.count() gives total in collection; filtered count not available
+        total = await asyncio.to_thread(col.count) if not filter else -1
+        return items, total
+
     async def delete_collection(self, collection: str):
         if collection in self._collections:
             del self._collections[collection]

src/langbot/pkg/vector/vdbs/milvus.py

@@ -11,11 +11,14 @@ from langbot.pkg.core import app
 # silently dropped with a warning.
 _MILVUS_SUPPORTED_FIELDS = {'text', 'file_id', 'chunk_uuid'}
 
+# Callers use canonical metadata key 'uuid' but Milvus stores it as 'chunk_uuid'.
+_MILVUS_FIELD_ALIASES = {'uuid': 'chunk_uuid'}
+
 
 def _build_milvus_expr(filter_dict: dict[str, Any]) -> str:
     """Translate canonical filter dict into a Milvus boolean expression string."""
     triples = normalize_filter(filter_dict)
-    triples = strip_unsupported_fields(triples, _MILVUS_SUPPORTED_FIELDS)
+    triples = strip_unsupported_fields(triples, _MILVUS_SUPPORTED_FIELDS, _MILVUS_FIELD_ALIASES)
     if not triples:
         return ''
 
@@ -340,6 +343,60 @@ class MilvusVectorDatabase(VectorDatabase):
         self.ap.logger.info(f"Deleted embeddings from Milvus collection '{collection}' by filter")
         return 0  # Milvus delete does not return a count
 
+    async def list_by_filter(
+        self,
+        collection: str,
+        filter: dict[str, Any] | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[dict[str, Any]], int]:
+        collection = self._normalize_collection_name(collection)
+        await self.get_or_create_collection(collection)
+
+        query_kwargs: dict[str, Any] = dict(
+            collection_name=collection,
+            output_fields=['text', 'file_id', 'chunk_uuid'],
+            limit=limit,
+            offset=offset,
+        )
+        if filter:
+            expr = _build_milvus_expr(filter)
+            if expr:
+                query_kwargs['filter'] = expr
+
+        results = await asyncio.to_thread(self.client.query, **query_kwargs)
+
+        items = []
+        for row in results:
+            items.append({
+                'id': row.get('id', ''),
+                'document': row.get('text'),
+                'metadata': {
+                    'text': row.get('text', ''),
+                    'file_id': row.get('file_id', ''),
+                    'uuid': row.get('chunk_uuid', ''),
+                },
+            })
+
+        # Milvus query with count(*)
+        total = -1
+        try:
+            count_kwargs: dict[str, Any] = dict(
+                collection_name=collection,
+                output_fields=['count(*)'],
+            )
+            if filter:
+                expr = _build_milvus_expr(filter)
+                if expr:
+                    count_kwargs['filter'] = expr
+            count_result = await asyncio.to_thread(self.client.query, **count_kwargs)
+            if count_result:
+                total = count_result[0].get('count(*)', -1)
+        except Exception:
+            pass
+
+        return items, total
+
     async def delete_collection(self, collection: str):
         """Delete a Milvus collection
 

src/langbot/pkg/vector/vdbs/pgvector_db.py

@@ -13,6 +13,9 @@ Base = declarative_base()
 # pgvector schema only stores these metadata fields.
 _PG_SUPPORTED_FIELDS = {'text', 'file_id', 'chunk_uuid'}
 
+# Callers use canonical metadata key 'uuid' but pgvector stores it as 'chunk_uuid'.
+_PG_FIELD_ALIASES = {'uuid': 'chunk_uuid'}
+
 # Map schema field names to SQLAlchemy columns (resolved lazily from PgVectorEntry).
 _PG_COLUMN_MAP = {
     'text': 'text',
@@ -37,7 +40,7 @@ class PgVectorEntry(Base):
 def _build_pg_conditions(filter_dict: dict[str, Any]) -> list:
     """Translate canonical filter dict into a list of SQLAlchemy conditions."""
     triples = normalize_filter(filter_dict)
-    triples = strip_unsupported_fields(triples, _PG_SUPPORTED_FIELDS)
+    triples = strip_unsupported_fields(triples, _PG_SUPPORTED_FIELDS, _PG_FIELD_ALIASES)
 
     conditions = []
     for field, op, value in triples:
@@ -309,6 +312,65 @@ class PgVectorDatabase(VectorDatabase):
                 self.ap.logger.error(f'Error deleting from pgvector by filter: {e}')
                 raise
 
+    async def list_by_filter(
+        self,
+        collection: str,
+        filter: dict[str, Any] | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[dict[str, Any]], int]:
+        await self.get_or_create_collection(collection)
+
+        async with self.AsyncSessionLocal() as session:
+            try:
+                from sqlalchemy import select, func
+
+                stmt = (
+                    select(
+                        PgVectorEntry.id,
+                        PgVectorEntry.text,
+                        PgVectorEntry.file_id,
+                        PgVectorEntry.chunk_uuid,
+                    )
+                    .filter(PgVectorEntry.collection == collection)
+                    .offset(offset)
+                    .limit(limit)
+                )
+
+                count_stmt = (
+                    select(func.count())
+                    .select_from(PgVectorEntry)
+                    .filter(PgVectorEntry.collection == collection)
+                )
+
+                if filter:
+                    for cond in _build_pg_conditions(filter):
+                        stmt = stmt.filter(cond)
+                        count_stmt = count_stmt.filter(cond)
+
+                result = await session.execute(stmt)
+                rows = result.fetchall()
+
+                count_result = await session.execute(count_stmt)
+                total = count_result.scalar() or 0
+
+                items = []
+                for row in rows:
+                    items.append({
+                        'id': row.id,
+                        'document': row.text or '',
+                        'metadata': {
+                            'text': row.text or '',
+                            'file_id': row.file_id or '',
+                            'uuid': row.chunk_uuid or '',
+                        },
+                    })
+
+                return items, total
+            except Exception as e:
+                self.ap.logger.error(f'Error listing from pgvector: {e}')
+                raise
+
     async def delete_collection(self, collection: str):
         """Delete all vectors in a collection
 

src/langbot/pkg/vector/vdbs/qdrant.py

@@ -150,6 +150,95 @@ class QdrantVectorDatabase(VectorDatabase):
         self.ap.logger.info(f"Deleted embeddings from Qdrant collection '{collection}' by filter")
         return 0  # Qdrant delete does not return a count
 
+    async def list_by_filter(
+        self,
+        collection: str,
+        filter: dict[str, Any] | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[dict[str, Any]], int]:
+        exists = await self.client.collection_exists(collection)
+        if not exists:
+            return [], 0
+
+        qdrant_filter = _build_qdrant_filter(filter) if filter else None
+
+        # Qdrant scroll uses cursor-based pagination (offset = point ID),
+        # not numeric skip.  To support numeric offset we scroll through
+        # `offset + limit` items and discard the first `offset`.
+        remaining_to_skip = offset
+        remaining_to_collect = limit
+        cursor: int | str | None = None
+        collected: list[dict[str, Any]] = []
+
+        while remaining_to_skip > 0 or remaining_to_collect > 0:
+            batch_size = remaining_to_skip + remaining_to_collect if remaining_to_skip > 0 else remaining_to_collect
+            scroll_kwargs: dict[str, Any] = dict(
+                collection_name=collection,
+                limit=min(batch_size, 256),
+                with_payload=True if remaining_to_skip == 0 else False,
+                with_vectors=False,
+            )
+            if qdrant_filter:
+                scroll_kwargs['scroll_filter'] = qdrant_filter
+            if cursor is not None:
+                scroll_kwargs['offset'] = cursor
+
+            points, next_cursor = await self.client.scroll(**scroll_kwargs)
+            if not points:
+                break
+
+            for point in points:
+                if remaining_to_skip > 0:
+                    remaining_to_skip -= 1
+                    continue
+                if remaining_to_collect <= 0:
+                    break
+                # Re-fetch payload if we skipped it during the skip phase
+                payload = point.payload or {}
+                collected.append({
+                    'id': str(point.id),
+                    'document': payload.get('text') or payload.get('document'),
+                    'metadata': payload,
+                })
+                remaining_to_collect -= 1
+
+            if next_cursor is None:
+                break
+            cursor = next_cursor
+
+        # If we skipped without payload, re-fetch the collected items' payloads
+        # (only needed when offset > 0 and items were collected in a skip batch)
+        if offset > 0 and collected:
+            refetch_ids = [item['id'] for item in collected if not item.get('metadata')]
+            if refetch_ids:
+                fetched_points = await self.client.retrieve(
+                    collection_name=collection,
+                    ids=refetch_ids,
+                    with_payload=True,
+                    with_vectors=False,
+                )
+                payload_map = {str(p.id): p.payload or {} for p in fetched_points}
+                for item in collected:
+                    if item['id'] in payload_map:
+                        payload = payload_map[item['id']]
+                        item['metadata'] = payload
+                        item['document'] = payload.get('text') or payload.get('document')
+
+        # Use count() for accurate total (supports filter)
+        total = -1
+        try:
+            count_result = await self.client.count(
+                collection_name=collection,
+                count_filter=qdrant_filter,
+                exact=True,
+            )
+            total = count_result.count
+        except Exception:
+            pass
+
+        return collected, total
+
     async def delete_collection(self, collection: str):
         try:
             await self.client.delete_collection(collection)

src/langbot/pkg/vector/vdbs/seekdb.py

@@ -340,6 +340,48 @@ class SeekDBVectorDatabase(VectorDatabase):
         self.ap.logger.info(f"Deleted embeddings from SeekDB collection '{collection}' by filter")
         return 0  # SeekDB delete does not return a count
 
+    async def list_by_filter(
+        self,
+        collection: str,
+        filter: Dict[str, Any] | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[Dict[str, Any]], int]:
+        exists = await asyncio.to_thread(self.client.has_collection, collection)
+        if not exists:
+            return [], 0
+
+        if collection not in self._collections:
+            coll = await asyncio.to_thread(self.client.get_collection, collection, embedding_function=None)
+            self._collections[collection] = coll
+        else:
+            coll = self._collections[collection]
+
+        get_kwargs: Dict[str, Any] = dict(
+            include=['metadatas', 'documents'],
+            limit=limit,
+            offset=offset,
+        )
+        if filter:
+            get_kwargs['where'] = filter
+
+        results = await asyncio.to_thread(coll.get, **get_kwargs)
+
+        ids = results.get('ids', [])
+        metadatas = results.get('metadatas', []) or [None] * len(ids)
+        documents = results.get('documents', []) or [None] * len(ids)
+
+        items = []
+        for i, vid in enumerate(ids):
+            items.append({
+                'id': vid,
+                'document': documents[i] if i < len(documents) else None,
+                'metadata': metadatas[i] if i < len(metadatas) else {},
+            })
+
+        total = await asyncio.to_thread(coll.count) if not filter else -1
+        return items, total
+
     async def delete_collection(self, collection: str):
         """Delete the entire collection.